CN113869660A - Enterprise carbon data comprehensive intelligent management and control system based on big data analysis - Google Patents
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Abstract
The invention discloses an enterprise carbon data comprehensive intelligent management and control system based on big data analysis, which relates to the technical field of enterprise carbon data management and control, solves the technical problem that the trend of the corresponding carbon emission of an enterprise cannot be accurately predicted in the prior art, and can accurately predict the trend of the corresponding carbon emission of the enterprise through the average value and the stable value of the carbon emission; the energy data of each enterprise are classified, the operation state of each enterprise can be clearly collected, different standards are adopted for each enterprise, and the problem that data collection is wrong due to the fact that high benefit and low benefit enterprise standards are the same is avoided; the energy types which are consumed most correspondingly by each enterprise are collected, so that the control direction of each enterprise is defined, the control efficiency of each enterprise on the energy consumption is improved, and the waste of manpower and material resource costs caused by errors in control is prevented; and the energy consumption and the carbon emission are simultaneously subjected to data analysis, so that the acquisition times are reduced.
Description
Technical Field
The invention relates to the technical field of enterprise carbon number data management and control, in particular to an enterprise carbon data comprehensive intelligent management and control system based on big data analysis.
Background
At present, global warming has become a global hotspot problem, and the influence on human life is more serious, for example, disastrous climate such as typhoon and natural disasters such as earthquake and tsunami occur frequently, carbon dioxide discharged in human production activities is a main cause of global warming, in order to reduce the influence of climate warming on environment, a low-carbon development mode of low energy consumption, low pollution and low emission is realized, and enterprise carbon data has become an important standard for enterprise evaluation;
an enterprise carbon data or carbon asset intelligent management and control platform based on big data analysis is disclosed in a patent with the application number of CN2019107320164, and can be used for uniformly managing and storing carbon data on a data level, timely accounting carbon emission data and further mining the potential value of the data; accounting is carried out on the carbon emission condition of an enterprise in a business layer, a set of complete enterprise carbon emission management system is formed, related contents of carbon emission and carbon asset management are comprehensively covered, the carbon emission condition of the enterprise is comprehensively reflected, business points such as carbon assets, carbon number data statistical analysis, carbon emission prediction and the like are involved, and the central management of the carbon assets of the enterprise is realized;
in the patent, however, although the technical level can be popularized in different industries, the technology has universality, practicability and reproducibility; mining and analyzing historical data, and predicting the carbon emission of enterprises based on algorithms such as a neural network algorithm, regression analysis and the like; however, the energy consumption corresponding to each enterprise and the influence area of carbon emission in the enterprise cannot be accurately screened out for each enterprise, and meanwhile, the association between the energy consumption and the carbon emission of each enterprise is not analyzed, so that the carbon emission cannot be accurately reduced while the energy consumption is reduced, the working intensity is increased by secondary screening, the data is not unique, and the influence of error increase is caused by data acquisition for many times.
Disclosure of Invention
The invention aims to provide an enterprise carbon data comprehensive intelligent control system based on big data analysis, which can accurately predict the trend of the corresponding carbon emission of an enterprise through the average value and the stable value of the carbon emission; the energy data of each enterprise are classified, the operation state of each enterprise can be clearly collected, different standards are adopted for each enterprise, and the problem that data collection is wrong due to the fact that high benefit and low benefit enterprise standards are the same is avoided; the energy types which are consumed most correspondingly by each enterprise are collected, so that the control direction of each enterprise is defined, the control efficiency of each enterprise on the energy consumption is improved, and the waste of manpower and material resource costs caused by errors in control is prevented; and the energy consumption and the carbon emission are simultaneously subjected to data analysis, so that the acquisition times are reduced.
The purpose of the invention can be realized by the following technical scheme:
an enterprise carbon data comprehensive intelligent management and control system based on big data analysis comprises a sensing layer, a network layer and an application layer; a server, an enterprise end, a management end and a data transmission unit are arranged in the sensing layer; the enterprise end comprises an enterprise analysis unit and a trend analysis unit; the management terminal comprises an energy analysis unit and a region analysis unit; the network layer comprises a data receiving unit, a processor and an equipment detection unit;
the sensing layer is used for detecting carbon number data of an enterprise, performing data acquisition through a server in the sensing layer, acquiring data signals through a sensor and respectively sending the data signals to an enterprise end and a management end; analyzing each enterprise through an enterprise analysis unit and classifying each enterprise through analysis; analyzing the carbon emission of each enterprise through a trend analysis unit so as to judge the development trend of the carbon emission of the enterprises; sequencing the consumed energy in each enterprise through an energy analysis unit, and acquiring the energy type which is consumed most correspondingly by each enterprise; analyzing consumption areas of main consumption energy and secondary consumption energy in each enterprise through a region analysis unit, and judging the influence of the main consumption energy and the secondary consumption energy on carbon emission of the enterprise;
the network layer is used for analyzing the data detected by the sensing layer; the network layer data receiving unit is used for receiving the data sent by the sensing layer and transmitting the data to the processor; the processor generates a device detection signal and sends the device detection signal to the device detection unit; the equipment detection unit is used for detecting the quality of the data acquisition equipment of the sensing layer;
the application layer is used for regulating and controlling carbon emission or equipment in an enterprise.
Further, the analysis process of the enterprise analysis unit is as follows:
acquiring a regional perimeter, marking the region in the regional perimeter as a detection region, acquiring all enterprises in the detection region, marking the enterprises as i, acquiring the consumption energy source variety number of each enterprise in the detection region, acquiring the total carbon emission amount of each enterprise in three months in history by taking three months in history as detection time, acquiring the total benefit amount of each enterprise in three months in history, and acquiring the energy consumption coefficient of each enterprise through analysis;
if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is larger than or equal to the benefit amount threshold value, marking the corresponding enterprise as a high-consumption high-benefit enterprise; if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is smaller than the benefit amount threshold value, marking the corresponding enterprise as a high-consumption low-benefit enterprise; if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is more than or equal to the benefit amount threshold value, marking the corresponding enterprise as a low-consumption high-benefit enterprise; and if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is less than the benefit amount threshold value, marking the corresponding enterprise as a low-consumption low-benefit enterprise.
Further, the analysis process of the trend analysis unit is as follows:
taking historical one week as analysis time, setting t time nodes in the analysis time, collecting the real-time carbon emission of each time node, marking the real-time carbon emission of each time node as Pt, constructing a carbon emission set { P1, P2, …, Pt } according to the t time nodes, and obtaining the carbon emission set by using a formula
Obtaining a mean value Gt of the carbon emission set, and comparing each subset in the carbon emission set with the mean value Gt:
dividing the carbon emission set into an upper set and a lower set by taking a middle subset in the carbon emission set as a boundary, and if eighty percent of subsets in the upper set are smaller than the mean value Gt and eighty percent of subsets in the lower set are larger than the mean value Gt, judging that the carbon emission in the corresponding enterprise is in an increasing trend; if eighty percent of the subsets in the upper set are larger than the mean value Gt and eighty percent of the subsets in the lower set are smaller than the mean value Gt, determining that the carbon emission in the corresponding enterprise is in a decreasing trend;
if the subsets in the upper set and the lower set which are larger than the average value exceed the corresponding quantity threshold value, comparing the subset with the first ordering of the upper set with the subset with the last ordering of the lower set:
if the emission corresponding to the subset sorted at the first end of the upper set is larger than the emission corresponding to the subset sorted at the last end of the lower set, the carbon emission is judged to be in a reduction trend; if the emission corresponding to the subset with the first sorting of the upper set is smaller than the emission corresponding to the subset with the last sorting of the lower set, judging that the carbon emission is in an ascending trend;
by the formula
Obtaining a stability coefficient Wt of the carbon emission set, and comparing the stability coefficient Wt of the carbon emission set with a stability coefficient threshold value: if the stability coefficient Wt of the carbon emission set is larger than or equal to the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is stable; if the stability coefficient Wt of the carbon emission set is smaller than the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is unstable;
and sending the carbon emission trend and the stability coefficient corresponding to each type of enterprise and enterprise to a network layer and an application layer through a data acquisition unit.
Further, the analysis sequencing process of the energy analysis unit is as follows:
marking high-consumption high-benefit enterprises and high-consumption low-benefit enterprises as energy detection enterprises, and collecting energy types o in the energy detection enterprises; acquiring the energy consumption occupation ratio and the energy consumption base number corresponding to each energy type in the energy detection enterprise, acquiring the consumption coefficient Ko of each energy type in the corresponding enterprise through analysis, and comparing the consumption coefficient Ko of each energy type with a consumption coefficient threshold value: if the consumption coefficient of the corresponding type of energy is larger than or equal to the consumption coefficient threshold value, marking the corresponding type of energy as main consumption energy; and if the consumption coefficient of the corresponding type of energy is less than the consumption coefficient threshold value, marking the corresponding type of energy as secondary consumption energy.
Further, the analysis process of the area analysis unit is as follows:
acquiring main consumption energy and secondary consumption energy in a corresponding enterprise, setting a detection time threshold, acquiring the total carbon emission amount and the carbon emission trend of the corresponding enterprise in the detection time threshold, analyzing the main consumption energy and the secondary consumption energy when the total carbon emission amount is greater than the corresponding threshold and the carbon emission trend is increased, and judging the main consumption energy as a carbon emission influence factor if the total main consumption energy amount is greater than the main consumption energy threshold and the main consumption energy is increased; if the total amount of the sub-consumption energy is larger than the threshold value of the sub-consumption energy and the sub-consumption energy is in an ascending trend, judging the sub-consumption energy as a carbon emission influence factor;
dividing the production plants in the corresponding enterprises into areas, analyzing the consumed energy of the corresponding areas, and if the consumed energy of the corresponding areas is a carbon emission influence factor, marking the corresponding areas as carbon emission influence areas; if the consumed energy of the corresponding area is not a carbon emission influence factor, marking the corresponding area as an energy consumption influence area; and transmitting the main consumption energy, the secondary consumption energy, the carbon emission influence area and the energy consumption influence area of each enterprise to a network layer and an application layer through a data transmission unit.
Further, the detection process of the device detection unit is as follows:
acquiring the total operation time length of the data acquisition equipment and the error frequency in the total operation time length, acquiring a detection coefficient Z of the data acquisition equipment through analysis, and comparing the detection coefficient of the data acquisition equipment with a detection coefficient threshold value of the data acquisition equipment: if the detection coefficient of the data acquisition equipment is larger than or equal to the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment is abnormal in operation, generating an equipment abnormal signal and sending the equipment abnormal signal to the processor, and after receiving the equipment abnormal signal, the processor generates a replacement signal and sends the replacement signal and the corresponding equipment to the application layer; and if the detection coefficient of the data acquisition equipment is less than the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment normally operates, generating an equipment normal signal and sending the equipment normal signal to the application layer.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the energy data of each enterprise are classified, the operation state of each enterprise can be clearly collected, different standards are adopted for each enterprise, and the problem that data collection is wrong due to the fact that high benefits are the same as low benefits of the enterprise standards is solved; the emission trend analysis is carried out on various enterprises, the management and control efficiency of the carbon data of the enterprises is improved, and the enterprise benefits are improved while the emission standards and the profit maximization are met; the trend of corresponding carbon emission of an enterprise can be accurately predicted through the average value and the stable value of the carbon emission;
gather each enterprise and correspond the most energy kind of consumption to definitely to the management and control direction of each enterprise helps improving the management and control efficiency of each enterprise to energy consumption, prevents that the management and control error from appearing and leading to the manpower and materials cost extravagant.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic block diagram of the present invention as a whole;
fig. 2 is a schematic block diagram of an application layer in the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, an enterprise carbon data comprehensive intelligent management and control system based on big data analysis includes a sensing layer, a network layer and an application layer; the sensing layer is used for detecting carbon number data of an enterprise, and a server, an enterprise end, a management end and a data transmission unit are arranged in the sensing layer; the enterprise end comprises an enterprise analysis unit and a trend analysis unit; the management terminal comprises an energy analysis unit and a region analysis unit; the network layer is used for analyzing the data detected by the sensing layer and comprises a data receiving unit, a processor and an equipment detection unit; the processor is in bidirectional communication connection with the data receiving unit and the equipment detection unit; the application layer is used for regulating and controlling carbon emission or equipment in an enterprise;
data acquisition is carried out to the enterprise in to the server, through sensor acquisition data signal and with data signal respectively send to enterprise end and management end, include all kinds of energy data sensor in the server in this application, if: a carbon dioxide sensor and an electric quantity sensor;
the enterprise analysis unit is used for analyzing each enterprise and classifying each enterprise through the analysis, classifies according to the energy data of each enterprise, can clearly gather the running state of each enterprise, takes different standards to each enterprise, prevents that the high benefit is the same with the enterprise standard of low benefit, leads to data acquisition wrong, and concrete analytic process is as follows:
acquiring a region perimeter, marking the region in the region perimeter as a detection region, acquiring all enterprises in the detection region, and marking the enterprises as i, wherein i is a natural number greater than 1; collecting the number of the types of the consumed energy of each enterprise in the detection area, and marking the number of the types of the consumed energy of each enterprise in the detection area as SLi; collecting the total carbon emission amount of each enterprise in the historical three months as detection time, and marking the total carbon emission amount as PFi; collecting the total benefit amount of each enterprise in three months of history, and marking the total benefit amount of each enterprise in three months of history as EDi;
by the formula
To enterprisesThe energy consumption coefficient Xi, wherein a1 and a2 are proportional coefficients, a1 is greater than a2 is greater than 0, and the energy consumption coefficient is a numerical value for evaluating the energy consumption of the enterprise obtained by normalizing the characteristic parameters of each enterprise; the energy consumption coefficient is larger when the number of the consumed energy types and the total carbon emission are larger, and the larger the energy consumption coefficient is, the larger the energy consumption of an enterprise is;
if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is larger than or equal to the benefit amount threshold value, marking the corresponding enterprise as a high-consumption high-benefit enterprise; if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is smaller than the benefit amount threshold value, marking the corresponding enterprise as a high-consumption low-benefit enterprise; if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is more than or equal to the benefit amount threshold value, marking the corresponding enterprise as a low-consumption high-benefit enterprise; if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is less than the benefit amount threshold value, marking the corresponding enterprise as a low-consumption low-benefit enterprise;
the trend analysis unit is used for carrying out carbon emission analysis to each enterprise to judge the carbon emission development trend of enterprise, discharge trend analysis is carried out to various types of enterprises, has improved the management and control efficiency of enterprise carbon data, improves the performance of enterprises when according with emission standard and profit maximize, and concrete analytic process is as follows:
taking historical one week as analysis time, setting t time nodes in the analysis time, wherein t =1, 2, …, n and n are positive integers, collecting the real-time carbon emission of each time node, marking the real-time carbon emission of each time node as Pt, and constructing a carbon emission set { P1, P2, … and Pt } according to the t time nodes, wherein the subsets in the carbon emission set are sorted according to the corresponding time sequence of the t time nodes;
by the formula
Obtaining the average value Gt of the carbon emission set, and entering each subset in the carbon emission set with the average value GtPerforming comparison, dividing the carbon emission set into an upper set and a lower set by taking a middle subset in the carbon emission set as a boundary, and if eighty percent of subsets in the upper set are smaller than the mean value Gt and eighty percent of subsets in the lower set are larger than the mean value Gt, judging that the carbon emission in the corresponding enterprise is in an increasing trend; if eighty percent of the subsets in the upper set are larger than the mean value Gt and eighty percent of the subsets in the lower set are smaller than the mean value Gt, determining that the carbon emission in the corresponding enterprise is in a decreasing trend; if the subsets in the upper set and the lower set which are larger than the average value exceed the corresponding quantity threshold value, comparing the subset with the first ordering of the upper set with the subset with the last ordering of the lower set: if the emission corresponding to the subset sorted at the first end of the upper set is larger than the emission corresponding to the subset sorted at the last end of the lower set, the carbon emission is judged to be in a reduction trend; if the emission corresponding to the subset with the first sorting of the upper set is smaller than the emission corresponding to the subset with the last sorting of the lower set, judging that the carbon emission is in an ascending trend;
by the formula
Obtaining a stability factor Wt for the set of carbon emissions, wherein P is obtained when t =1t-1The value is 0; comparing the stability factor Wt of the carbon emissions set to a stability factor threshold: if the stability coefficient Wt of the carbon emission set is larger than or equal to the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is stable; if the stability coefficient Wt of the carbon emission set is smaller than the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is unstable;
the data acquisition unit sends the enterprises of various types, the carbon emission trend and the stability coefficient corresponding to the enterprises to the network layer and the application layer;
the energy analysis unit is used for sequencing the energy consumed in each enterprise, and the energy types which are consumed most correspondingly in each enterprise are collected, so that the management and control directions of each enterprise are definitely determined, the management and control efficiency of each enterprise on energy consumption is improved, the management and control errors are prevented from causing the waste of manpower and material resources, and the specific analysis sequencing process is as follows:
marking high-consumption high-benefit enterprises and high-consumption low-benefit enterprises as energy detection enterprises, and collecting the type o of the energy sources in the energy detection enterprises, wherein o is a natural number more than 1; acquiring energy consumption ratios and energy consumption cardinalities corresponding to various energy types in the energy detection enterprises, and respectively marking the energy consumption ratios and the energy consumption cardinalities corresponding to the various energy types in the energy detection enterprises as XHo and JSo; wherein, when the energy type is electricity, the energy consumption base number is the consumed wattage; when the energy type is water, the energy consumption base number is the consumed tonnage;
by the formula
Acquiring consumption coefficients Ko of various types of energy sources in corresponding enterprises, wherein b1 and b2 are proportional coefficients, b1 is larger than b2 is larger than 0, and beta is an error correction factor and takes a value of 1.25; comparing the consumption coefficient Ko of each kind of energy with a consumption coefficient threshold value: if the consumption coefficient of the corresponding type of energy is larger than or equal to the consumption coefficient threshold value, marking the corresponding type of energy as main consumption energy; if the consumption coefficient of the corresponding type of energy is less than the consumption coefficient threshold value, marking the corresponding type of energy as secondary consumption energy;
the regional analysis unit is used for analyzing the consumption region of the main consumption energy and the time consumption energy in each enterprise, and judges the influence of the main consumption energy and the time consumption energy on the carbon emission of the enterprise, thereby reducing the energy consumption of the enterprise and reducing the carbon emission, preventing the enterprise from limiting the consumption energy source, reducing the enterprise benefit and not reducing the carbon emission, and the specific analysis process is as follows:
acquiring main consumption energy and secondary consumption energy in a corresponding enterprise, setting a detection time threshold, acquiring the total carbon emission amount and the carbon emission trend of the corresponding enterprise in the detection time threshold, analyzing the main consumption energy and the secondary consumption energy when the total carbon emission amount is greater than the corresponding threshold and the carbon emission trend is increased, and judging the main consumption energy as a carbon emission influence factor if the total main consumption energy amount is greater than the main consumption energy threshold and the main consumption energy is increased; if the total amount of the sub-consumption energy is larger than the threshold value of the sub-consumption energy and the sub-consumption energy is in an ascending trend, judging the sub-consumption energy as a carbon emission influence factor;
dividing the production plants in the corresponding enterprises into areas, analyzing the consumed energy of the corresponding areas, and if the consumed energy of the corresponding areas is a carbon emission influence factor, marking the corresponding areas as carbon emission influence areas; if the consumed energy of the corresponding area is not a carbon emission influence factor, marking the corresponding area as an energy consumption influence area;
the data transmission unit sends the main consumption energy, the secondary consumption energy, the carbon emission influence area and the energy consumption influence area of each enterprise to a network layer and an application layer;
the network layer data receiving unit is used for receiving the data sent by the sensing layer and transmitting the data to the processor; the processor generates a device detection signal and sends the device detection signal to the device detection unit; the equipment detecting element is used for carrying out quality detection to the data acquisition equipment on perception layer, ensures the accuracy that perception layer data gathered, has improved the efficiency of enterprise carbon emission management and control, prevents to have the mistake to lead to enterprise carbon emission management and control efficiency to reduce because of data, has wasted unnecessary manufacturing cost, and data acquisition equipment is all kinds of energy data sensor in the server in this application, and concrete testing process is as follows:
acquiring the total operation duration and the error frequency in the total operation duration of the data acquisition equipment, and respectively marking the total operation duration and the error frequency in the total operation duration of the data acquisition equipment as SC and PL; by the formula
Acquiring a detection coefficient Z of the data acquisition equipment, wherein v1 and v2 are proportionality coefficients, v1 is greater than v2 is greater than 0, and e is a natural constant;
comparing the detection coefficient of the data acquisition equipment with the detection coefficient threshold of the data acquisition equipment: if the detection coefficient of the data acquisition equipment is larger than or equal to the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment is abnormal in operation, generating an equipment abnormal signal and sending the equipment abnormal signal to the processor, and after receiving the equipment abnormal signal, the processor generates a replacement signal and sends the replacement signal and the corresponding equipment to the application layer; if the detection coefficient of the data acquisition equipment is smaller than the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment normally operates, generating an equipment normal signal and sending the equipment normal signal to an application layer;
example 2
As shown in fig. 2, an enterprise carbon data comprehensive intelligent management and control system based on big data analysis includes an application layer, where the application layer is configured to receive an equipment abnormal signal and an equipment normal signal sent by a network layer, and send corresponding signals to a controller;
after receiving the equipment regulating signal, the equipment regulating unit acquires the position of the corresponding abnormal equipment and replaces or maintains the corresponding abnormal equipment;
after receiving the normal equipment signal, the controller generates an enterprise regulation and control signal and a management regulation and control signal and respectively sends the enterprise regulation and control signal and the management regulation and control signal to an enterprise regulation and control unit and a management regulation and control unit;
after the enterprise regulation and control unit receives the enterprise regulation and control signal, energy consumption control is carried out on high-consumption high-benefit enterprises and high-consumption low-benefit enterprises, and energy consumption is set; energy consumption is reduced fundamentally, and the utilization rate of energy is improved;
after receiving the management regulation and control signal, the management regulation and control unit sets corresponding consumption thresholds for the main consumption energy and the secondary consumption energy of each enterprise respectively, wherein the corresponding consumption threshold for the main consumption energy is larger than the corresponding consumption threshold for the secondary consumption energy; and managing and controlling the carbon emission influence area, and controlling the carbon emission of the carbon emission influence area or increasing the waste gas treatment process of the carbon emission influence area.
An enterprise carbon data comprehensive intelligent management and control system based on big data analysis is characterized in that during work, carbon data of an enterprise are detected through a sensing layer, data are collected through a server in the sensing layer, data signals are collected through a sensor and are respectively sent to an enterprise end and a management end; analyzing each enterprise through an enterprise analysis unit and classifying each enterprise through analysis; analyzing the carbon emission of each enterprise through a trend analysis unit so as to judge the development trend of the carbon emission of the enterprises; sequencing the consumed energy in each enterprise through an energy analysis unit, and acquiring the energy type which is consumed most correspondingly by each enterprise; analyzing consumption areas of main consumption energy and secondary consumption energy in each enterprise through a region analysis unit, and judging the influence of the main consumption energy and the secondary consumption energy on carbon emission of the enterprise;
analyzing the data detected by the sensing layer through the network layer; receiving data sent by a sensing layer through a data receiving unit in a network layer, and transmitting the data to a processor; the processor generates a device detection signal and sends the device detection signal to the device detection unit; performing quality detection on the data acquisition equipment of the sensing layer through an equipment detection unit; and regulating and controlling carbon emission or equipment in an enterprise through an application layer.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. An enterprise carbon data comprehensive intelligent management and control system based on big data analysis is characterized by comprising a sensing layer, a network layer and an application layer; a server, an enterprise end, a management end and a data transmission unit are arranged in the sensing layer; the enterprise end comprises an enterprise analysis unit and a trend analysis unit; the management terminal comprises an energy analysis unit and a region analysis unit; the network layer comprises a data receiving unit, a processor and an equipment detection unit;
the sensing layer is used for detecting carbon number data of an enterprise, performing data acquisition through a server in the sensing layer, acquiring data signals through a sensor and respectively sending the data signals to an enterprise end and a management end; analyzing each enterprise through an enterprise analysis unit and classifying each enterprise through analysis; analyzing the carbon emission of each enterprise through a trend analysis unit so as to judge the development trend of the carbon emission of the enterprises; sequencing the consumed energy in each enterprise through an energy analysis unit, and acquiring the energy type which is consumed most correspondingly by each enterprise; analyzing consumption areas of main consumption energy and secondary consumption energy in each enterprise through a region analysis unit, and judging the influence of the main consumption energy and the secondary consumption energy on carbon emission of the enterprise;
the network layer is used for analyzing the data detected by the sensing layer; the network layer data receiving unit is used for receiving the data sent by the sensing layer and transmitting the data to the processor; the processor generates a device detection signal and sends the device detection signal to the device detection unit; the equipment detection unit is used for detecting the quality of the data acquisition equipment of the sensing layer;
the application layer is used for regulating and controlling carbon emission or equipment in an enterprise.
2. The enterprise carbon data comprehensive intelligent management and control system based on big data analysis according to claim 1, wherein the analysis process of the enterprise analysis unit is as follows:
acquiring a regional perimeter, marking the region in the regional perimeter as a detection region, acquiring all enterprises in the detection region, marking the enterprises as i, acquiring the consumption energy source variety number of each enterprise in the detection region, acquiring the total carbon emission amount of each enterprise in three months in history by taking three months in history as detection time, acquiring the total benefit amount of each enterprise in three months in history, and acquiring the energy consumption coefficient of each enterprise through analysis;
if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is larger than or equal to the benefit amount threshold value, marking the corresponding enterprise as a high-consumption high-benefit enterprise; if the energy consumption coefficient of the enterprise is larger than or equal to the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is smaller than the benefit amount threshold value, marking the corresponding enterprise as a high-consumption low-benefit enterprise; if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is more than or equal to the benefit amount threshold value, marking the corresponding enterprise as a low-consumption high-benefit enterprise; and if the energy consumption coefficient of the enterprise is less than the energy consumption coefficient threshold value and the total benefit amount of the corresponding enterprise is less than the benefit amount threshold value, marking the corresponding enterprise as a low-consumption low-benefit enterprise.
3. The enterprise carbon data comprehensive intelligent management and control system based on big data analysis according to claim 1, wherein the analysis process of the trend analysis unit is as follows:
taking historical one week as analysis time, setting t time nodes in the analysis time, collecting the real-time carbon emission of each time node, marking the real-time carbon emission of each time node as Pt, constructing a carbon emission set { P1, P2, …, Pt } according to the t time nodes, and obtaining the carbon emission set by using a formula
Obtaining a mean value Gt of the carbon emission set, and comparing each subset in the carbon emission set with the mean value Gt:
dividing the carbon emission set into an upper set and a lower set by taking a middle subset in the carbon emission set as a boundary, and if eighty percent of subsets in the upper set are smaller than the mean value Gt and eighty percent of subsets in the lower set are larger than the mean value Gt, judging that the carbon emission in the corresponding enterprise is in an increasing trend; if eighty percent of the subsets in the upper set are larger than the mean value Gt and eighty percent of the subsets in the lower set are smaller than the mean value Gt, determining that the carbon emission in the corresponding enterprise is in a decreasing trend;
if the subsets in the upper set and the lower set which are larger than the average value exceed the corresponding quantity threshold value, comparing the subset with the first ordering of the upper set with the subset with the last ordering of the lower set:
if the emission corresponding to the subset sorted at the first end of the upper set is larger than the emission corresponding to the subset sorted at the last end of the lower set, the carbon emission is judged to be in a reduction trend; if the emission corresponding to the subset with the first sorting of the upper set is smaller than the emission corresponding to the subset with the last sorting of the lower set, judging that the carbon emission is in an ascending trend;
by the formula
Obtaining a stability coefficient Wt of the carbon emission set, and comparing the stability coefficient Wt of the carbon emission set with a stability coefficient threshold value: if the stability coefficient Wt of the carbon emission set is larger than or equal to the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is stable; if the stability coefficient Wt of the carbon emission set is smaller than the stability coefficient threshold value, determining that the carbon emission data of the corresponding enterprise is unstable;
and sending the carbon emission trend and the stability coefficient corresponding to each type of enterprise and enterprise to a network layer and an application layer through a data acquisition unit.
4. The enterprise carbon data comprehensive intelligent management and control system based on big data analysis according to claim 1, wherein the analysis sequencing process of the energy analysis unit is as follows:
marking high-consumption high-benefit enterprises and high-consumption low-benefit enterprises as energy detection enterprises, and collecting energy types o in the energy detection enterprises; acquiring the energy consumption occupation ratio and the energy consumption base number corresponding to each energy type in the energy detection enterprise, acquiring the consumption coefficient Ko of each energy type in the corresponding enterprise through analysis, and comparing the consumption coefficient Ko of each energy type with a consumption coefficient threshold value: if the consumption coefficient of the corresponding type of energy is larger than or equal to the consumption coefficient threshold value, marking the corresponding type of energy as main consumption energy; and if the consumption coefficient of the corresponding type of energy is less than the consumption coefficient threshold value, marking the corresponding type of energy as secondary consumption energy.
5. The enterprise carbon data comprehensive intelligent management and control system based on big data analysis according to claim 1, wherein the analysis process of the area analysis unit is as follows:
acquiring main consumption energy and secondary consumption energy in a corresponding enterprise, setting a detection time threshold, acquiring the total carbon emission amount and the carbon emission trend of the corresponding enterprise in the detection time threshold, analyzing the main consumption energy and the secondary consumption energy when the total carbon emission amount is greater than the corresponding threshold and the carbon emission trend is increased, and judging the main consumption energy as a carbon emission influence factor if the total main consumption energy amount is greater than the main consumption energy threshold and the main consumption energy is increased; if the total amount of the sub-consumption energy is larger than the threshold value of the sub-consumption energy and the sub-consumption energy is in an ascending trend, judging the sub-consumption energy as a carbon emission influence factor;
dividing the production plants in the corresponding enterprises into areas, analyzing the consumed energy of the corresponding areas, and if the consumed energy of the corresponding areas is a carbon emission influence factor, marking the corresponding areas as carbon emission influence areas; if the consumed energy of the corresponding area is not a carbon emission influence factor, marking the corresponding area as an energy consumption influence area; and transmitting the main consumption energy, the secondary consumption energy, the carbon emission influence area and the energy consumption influence area of each enterprise to a network layer and an application layer through a data transmission unit.
6. The enterprise carbon data comprehensive intelligent management and control system based on big data analysis according to claim 1, wherein the detection process of the equipment detection unit is as follows:
acquiring the total operation time length of the data acquisition equipment and the error frequency in the total operation time length, acquiring a detection coefficient Z of the data acquisition equipment through analysis, and comparing the detection coefficient of the data acquisition equipment with a detection coefficient threshold value of the data acquisition equipment: if the detection coefficient of the data acquisition equipment is larger than or equal to the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment is abnormal in operation, generating an equipment abnormal signal and sending the equipment abnormal signal to the processor, and after receiving the equipment abnormal signal, the processor generates a replacement signal and sends the replacement signal and the corresponding equipment to the application layer; and if the detection coefficient of the data acquisition equipment is less than the detection coefficient threshold of the data acquisition equipment, judging that the corresponding data acquisition equipment normally operates, generating an equipment normal signal and sending the equipment normal signal to the application layer.
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