CN114462891A - Carbon emission detection method and device - Google Patents
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Abstract
The application discloses a carbon emission detection method and a device thereof, and relates to the field of carbon emission detection. The specific implementation scheme is as follows: determining a sample enterprise according to the industry composition of the detection industry; acquiring a carbon emission index system weight of the sample enterprise based on the carbon emission data of the sample enterprise; acquiring a carbon emission comprehensive score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise; and generating a carbon emission reduction scheme according with the sample enterprise condition based on a preset carbon emission comprehensive score threshold and the carbon emission comprehensive score. The method and the device can realize energy-saving and emission-reduction operation aiming at different enterprises, and effectively detect and reduce the emission of carbon in various industries.
Description
Technical Field
The present disclosure relates to carbon emission detection, and particularly to a method and an apparatus for detecting carbon emission.
Background
In recent years, as people burn fossil fuels such as petroleum, coal and the like, or fell down a forest and burn it, a large amount of carbon dioxide, namely greenhouse gas, is generated; and due to the continuous accumulation of greenhouse gases, the energy absorbed and emitted by a ground gas system is unbalanced, and the energy is continuously accumulated in the ground gas system, so that the temperature is increased, and the global warming is caused. In order to effectively control the emission of greenhouse gases, the carbon emission in various industries needs to be effectively detected and reduced.
Disclosure of Invention
The application provides a carbon emission detection method and a device thereof, which can be applied to a scene of detecting carbon emission conditions of enterprises.
According to a first aspect of embodiments of the present application, there is provided a carbon emission detection method, including:
determining a sample enterprise according to the industry composition of the detection industry;
acquiring a carbon emission index system weight of the sample enterprise based on the carbon emission data of the sample enterprise;
acquiring a carbon emission comprehensive score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise;
and generating a carbon emission reduction scheme according with the sample enterprise condition based on a preset carbon emission comprehensive score threshold and the carbon emission comprehensive score.
According to a second aspect of embodiments of the present application, there is provided a carbon emission detection apparatus including:
the determining module is used for determining a sample enterprise according to the industry composition of the detection industry;
the acquisition module is used for acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise;
the grading module is used for obtaining the comprehensive carbon emission grade of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise;
and the generating module is used for generating a carbon emission reduction scheme according with the sample enterprise condition based on a preset carbon emission comprehensive score threshold and the carbon emission comprehensive score.
According to a third aspect of the present application, there is provided a terminal device comprising:
a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being capable of executing the method of detecting carbon emissions as described in the preceding first aspect when the program is executed.
According to a fourth aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the aforementioned first aspect.
According to the technical scheme, the weight of the carbon emission index system of the sample enterprise is obtained and used as a basis for subsequently generating the comprehensive carbon emission score of the sample enterprise, so that the accuracy of the comprehensive carbon emission score of the sample enterprise is guaranteed, and a basis is provided for subsequently generating a carbon emission reduction scheme. By generating the carbon emission comprehensive score and generating the carbon emission reduction scheme according with the sample enterprise condition according to the carbon emission comprehensive score, the energy conservation and emission reduction operation aiming at different enterprises can be realized, and the carbon emission of all trades can be effectively detected and reduced.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a flowchart of a carbon emission detection method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of another method for carbon emission detection provided by an embodiment of the present application;
FIG. 3 is a flow chart of another method for carbon emission detection provided by an embodiment of the present application;
FIG. 4 is a flow chart of another method for carbon emission detection provided by an embodiment of the present application;
fig. 5 is a block diagram illustrating a structure of a carbon emission detection apparatus according to an embodiment of the present disclosure;
fig. 6 is a block diagram illustrating a structure of another carbon emission detection apparatus according to an embodiment of the present disclosure;
fig. 7 is a block diagram of another carbon emission detection apparatus according to an embodiment of the present disclosure;
fig. 8 is a block diagram of a terminal device of a carbon emission detection method according to an embodiment of the present disclosure.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It should be noted that, in recent years, since people burn fossil fuels such as petroleum, coal and the like, or fell down forests and burn them, a large amount of carbon dioxide, i.e., greenhouse gas, is generated; and due to the continuous accumulation of greenhouse gases, the energy absorbed and emitted by the ground gas system is unbalanced, and the energy is continuously accumulated in the ground gas system, so that the temperature is increased, and the global warming is caused. In order to effectively control the emission of greenhouse gases, the carbon emission in various industries needs to be effectively detected and reduced.
Based on the problems, the application discloses a carbon emission detection method and a device thereof, wherein the weight of a carbon emission index system is determined by acquiring carbon emission data of a sample enterprise; acquiring a carbon emission comprehensive score of a sample enterprise according to the weight of the carbon emission index system; and generating a carbon emission reduction scheme according with the conditions of the sample enterprises based on a preset carbon emission comprehensive score threshold and a preset carbon emission comprehensive score.
Fig. 1 is a flowchart of a carbon emission detection method according to an embodiment of the present disclosure, and as shown in fig. 1, the carbon emission detection method may include the following steps:
It is noted that the industry composition may include a first index system and a second index system. Wherein, the first index system is a carbon emission key industry; the second index system is an energy power enterprise.
In some embodiments of the present application, the detection industry is a pre-selected industry, and sample enterprises to be detected are determined according to industry composition to which the detection industry belongs, wherein the industry composition to which the detection industry belongs is different, and the selection methods of the sample enterprises are also different. It should be noted that, if the detection industry is the first index system, a listed company and a central management company that disclose periodic reports and social responsibility reports at a certain frequency in the detection industry are selected as sample enterprises. If the detection industry is the second index system, listed companies and central management enterprises which disclose regular reports and social responsibility reports at a certain frequency or related enterprises which have already developed data asset management are selected as sample enterprises.
And 102, acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise.
It should be noted that the carbon emission data of the sample enterprise may include, but is not limited to: carbon emissions from industrial electricity, carbon emissions from industrial water, carbon emissions from industrial natural gas and coal, and the like.
In some embodiments of the present application, the amount of carbon emissions generated by industrial electricity is calculated by: the carbon emission amount generated by the industrial electricity of the sample enterprise is calculated based on the conversion coefficient between the industrial electricity and the carbon emission amount by presetting the conversion coefficient between the industrial electricity and the carbon emission amount in the sample enterprise. For example, after a sample enterprise is determined, the industrial power consumption of the sample enterprise is obtained, a conversion coefficient between the industrial power consumption and the industrial carbon emission of the sample enterprise is preset, and a multiplication operation is performed on the industrial power consumption of the sample enterprise and the conversion coefficient between the industrial power consumption and the industrial carbon emission of the sample enterprise, so that the carbon emission generated by the industrial power consumption of the sample enterprise is obtained.
The conversion coefficient determining method of the sample enterprise comprises the following steps: and pre-formulating a conversion coefficient corresponding table, wherein different conversion coefficients can be determined according to the enterprise industry and the scale of the sample enterprise by the conversion coefficient corresponding table.
In some embodiments of the present application, the carbon emissions from industrial water are calculated by: and calculating the carbon emission amount of the industrial water of the sample enterprise based on the conversion coefficient between the industrial water and the carbon emission amount by presetting the conversion coefficient between the industrial water and the carbon emission amount in the sample enterprise. For example, after a sample enterprise is determined, the industrial water consumption of the sample enterprise is obtained, a conversion coefficient between the industrial water consumption of the sample enterprise and the industrial water carbon emission is preset, and the conversion coefficient between the industrial water consumption of the sample enterprise and the industrial water carbon emission of the sample enterprise is multiplied, so that the carbon emission generated by the industrial water of the sample enterprise is obtained.
It should be noted that the method for calculating the carbon emission generated by the industrial natural gas and the coal is the same as the method for calculating the carbon emission generated by the industrial electricity, and the details are not repeated herein.
In some embodiments of the present application, the carbon emissions index system weight refers to: the carbon emission data of the sample enterprise affects the importance of the total carbon emission of the sample enterprise. For example, the carbon emission of enterprise a is affected by industrial electricity and industrial water, and the carbon emission index system weight of enterprise a is: the carbon emission of enterprise a is affected by the industrial electricity and the carbon emission of enterprise a is affected by the industrial water.
It should be noted that, the determination method of the carbon emission index system weight may include, but is not limited to: subjective weighting and objective weighting.
The subjective weighting method can comprise an expert survey method, an analytic hierarchy process, a ring scale scoring method and the like, wherein the expert survey method is a method for judging the weight of the carbon emission index system of the sample enterprise according to the suggestions and experiences of relevant experts or authoritative persons; the analytic hierarchy process is a judgment method for obtaining the weight of the carbon emission index system of the sample enterprise by decomposing the carbon emission data of the sample enterprise into a plurality of targets or criteria and then carrying out method operation on the plurality of targets or criteria; the ring ratio scoring method is a judgment method for predetermining the importance parameters in the carbon emission data of the sample enterprises and then calculating the weight of the carbon emission index system of the sample enterprises according to the importance parameters. The objective weighting method can comprise a principal component analysis method, an entropy value method and the like, wherein the principal component analysis method is a judgment method for determining principal components in the carbon emission data of the sample enterprise according to variables in the carbon emission data of the sample enterprise so as to obtain the weight of the carbon emission index system of the sample enterprise according to the principal components; the entropy method is a judgment method for obtaining the weight of the carbon emission index system of the sample enterprise by calculating the dispersion of the carbon emission data of the sample enterprise.
And 103, acquiring a comprehensive carbon emission score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise.
It should be noted that the comprehensive carbon emission score of the sample enterprise is a comprehensive score generated by considering the carbon emission amount of the sample enterprise, the emission reduction effect of the sample enterprise, the energy consumption amount of the sample enterprise, and the like at a certain stage. The higher the carbon emission comprehensive score of the sample enterprise, the better the carbon emission condition of the sample enterprise, the lower the carbon emission comprehensive score of the sample enterprise, and the worse the carbon emission condition of the sample enterprise. Wherein a stage may include, but is not limited to: one year, one quarter or one month.
In some embodiments of the present application, a good carbon emission condition means that a sample enterprise has a low carbon emission amount, or a sample enterprise has a low difficulty in carbon emission control; the poor carbon emission condition means that the carbon emission amount of a sample enterprise is large, or the carbon emission control difficulty of the sample enterprise is large.
It should be noted that, different industries form different methods for calculating the comprehensive carbon emission score; for example, if the industry constitutes a first index system, determining a specific quantitative index under the first index system; and establishing a data standard system according to the specific quantitative indexes, acquiring sample enterprise standard data according to the carbon emission data of the sample enterprise, and scoring the sample enterprise standard data based on the sample enterprise standard data and the weight of the carbon emission index system to obtain the comprehensive carbon emission score of the sample enterprise. If the industry is formed into a second index system, establishing a knowledge graph node; generating a factor base and a divisor based on the knowledge graph nodes, and establishing an influence graph according to the divisor and the factor base; and calculating to obtain the comprehensive score of the sample enterprise according to the influence map.
And 104, generating a carbon emission reduction scheme according with the conditions of the sample enterprises based on the preset carbon emission comprehensive score threshold and the preset carbon emission comprehensive score.
In some embodiments of the present application, whether the carbon emission comprehensive score of the sample enterprise is qualified is determined by determining a size relationship between the carbon emission comprehensive score threshold and the carbon emission comprehensive score, and if the carbon emission comprehensive score is greater than or equal to the carbon emission comprehensive score threshold, the carbon emission comprehensive score of the sample enterprise is qualified; and if the carbon emission comprehensive score is smaller than the carbon emission comprehensive score threshold value, the carbon emission comprehensive score of the sample enterprise is proved to be unqualified, and a carbon emission reduction scheme aiming at the sample enterprise is generated because the carbon emission comprehensive score of the sample enterprise is unqualified.
According to the carbon emission detection method, the weight of the carbon emission index system of the sample enterprise is obtained and used as a basis for subsequently generating the comprehensive carbon emission score of the sample enterprise, so that the accuracy of the comprehensive carbon emission score of the sample enterprise is guaranteed, and a basis is provided for subsequently generating a carbon emission reduction scheme. By generating the carbon emission comprehensive score and generating the carbon emission reduction scheme according with the conditions of the sample enterprises according to the carbon emission comprehensive score, the energy-saving and emission-reduction operation aiming at different enterprises can be realized, and the effective detection and emission reduction of the carbon emission of various industries are realized.
It should be noted that, when the industry of the detection industry is configured as the first index system, the sample enterprise may be subjected to the comprehensive carbon emission scoring based on the first index system and the weight of the carbon emission index system of the sample enterprise. As shown in fig. 2, fig. 2 is a flowchart of another carbon emission detection method provided in an embodiment of the present application, and the method may include the following steps:
In some embodiments of the present application, step 201 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described herein again.
And step 202, acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise.
In some embodiments of the present application, step 202 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described herein again.
And 203, determining a specific quantization index under the first index system according to the first index system.
It should be noted that the first index system is a carbon emission emphasis industry, wherein the carbon emission emphasis industry may include, but is not limited to: petrochemical industry, chemical production industry, building material production industry, steel production industry, non-ferrous metal smelting and calendering processing industry, paper and paper product production industry, power generation and civil aviation industry.
In some embodiments of the present application, the specific quantitative index refers to normal values of each item of data in a production operation process of an enterprise belonging to the first index system, and it should be noted that normal values of each item of data of enterprises in different production value intervals are different; the normal value of each item of data of the enterprise with higher output value is higher, the normal value of each item of data of the enterprise with lower output value is lower, and the normal value can be set according to the average value of each item of data of all enterprises in the output value interval, or the normal value can be set according to the variance of each item of data of all enterprises in the output value interval. The items of data may include, but are not limited to: industrial power consumption, industrial water consumption, and industrial natural gas and coal consumption.
And step 204, establishing a data standard system according to the specific quantization index.
In some embodiments of the present application, normal values of various data of a sample enterprise are determined according to a production value of the sample enterprise, and a data standard system is established based on the normal values of the various data of the sample enterprise, which can be understood as: the data standard system of the sample enterprise is established on the basis of normal values of various data of the sample enterprise and is used for calculating a comprehensive scoring system of the sample enterprise.
In some embodiments of the present application, the normalization process refers to processing data by a standard range method, a logarithmic range method, and an interval membership method; removing the maximum value and the minimum value in each item of carbon emission data of the sample enterprise by a standard range method and a logarithmic range method so as to observe the interval span of the carbon emission data of the sample enterprise; and (4) carrying out quantitative analysis on the carbon emission data of the sample enterprise by an interval membership method, and finally obtaining the standard data of the sample enterprise.
And step 206, grading and scoring the standard data of the sample enterprise based on the weight of the carbon emission index system and the data standard system to obtain the grading score of the sample enterprise.
The grading score is a summary of the scoring conditions of each item of data of the sample enterprise, and it needs to be noted that the degree of scoring of one item of data of the sample enterprise reflects the degree of carbon emission generated by the item of data of the sample enterprise and the difficulty level of carbon emission control of the item of data. It is understood that the grading score of the enterprise a includes the scoring condition of the enterprise a industrial electricity data, the scoring condition of the enterprise a industrial water data and the like.
In an embodiment of the application, standard data of a sample enterprise is judged according to a data standard system, whether the data of the sample enterprise exceeds a normal value is judged, so that the data of the sample enterprise is obtained, the grading score of the sample enterprise is judged based on the weight of a carbon emission index system and the data of the sample enterprise, and the grading score of the sample enterprise is generated according to the data of the enterprise exceeding the normal value and the importance degree of the data of the enterprise on the total carbon emission of the enterprise. It should be noted that, if one item of data in the sample enterprise exceeds the normal value, the grading score of the item of data in the sample enterprise is lower; and the higher the importance of the data of the sample enterprise exceeding the normal value to the total carbon emission of the sample enterprise, the lower the grading score of the data of the sample enterprise.
And step 207, calculating to obtain a comprehensive score of the sample enterprise according to the grading score of the sample enterprise.
It should be noted that there are many methods for calculating the composite score of the sample enterprise according to the grading score of the sample enterprise, which may include but are not limited to: and carrying out mean operation on the grading scores of the sample enterprises to obtain the comprehensive scores of the sample enterprises, or carrying out mode operation on the grading scores of the sample enterprises to obtain the comprehensive scores of the sample enterprises. The following is an example for two cases:
as an example, the ranking scores of a sample business are respectively: the score of the industrial electricity data was 80, the score of the industrial water data was 60, and the score of the industrial gas and coal data was 80, and the average value of the classification scores of the sample enterprises was calculated, whereby the total score of the sample enterprises was 73.3 points (one point after the decimal point).
As another example, the ranking scores for a sample business are respectively: the score of the industrial electricity data was 80, the score of the industrial water data was 60, and the score of the industrial natural gas and coal data was 80, and the overall score of the sample enterprise was 80 by performing a mode operation on the classification scores of the sample enterprises.
And 208, generating a carbon emission reduction scheme according with the conditions of the sample enterprises based on the preset carbon emission comprehensive score threshold and the preset carbon emission comprehensive score.
In some embodiments of the present application, step 208 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
According to the carbon emission detection method, when the industry of the detection industry is formed into the first index system, the specific quantitative indexes are determined to provide a basis for subsequently establishing a data standard system, and guarantee is provided for subsequently obtaining the enterprise grading score of the sample. The carbon emission data of the sample enterprises are subjected to standardized processing, so that the carbon emission data of the sample enterprises can better complete the subsequent grading operation, and the accuracy of grading results is ensured. By acquiring the grading score of the sample enterprise and generating the comprehensive score of the sample enterprise according to the grading score of the sample enterprise, the comprehensive score of the sample enterprise can be ensured to be more consistent with the actual carbon emission condition of the sample enterprise.
It should be noted that, when the industry of the detection industry is configured as the second index system, the sample enterprise may be comprehensively scored for carbon emission based on the second index system and the weight of the carbon emission index system of the sample enterprise. As shown in fig. 3, fig. 3 is a flowchart of another carbon emission detection method provided in an embodiment of the present application, where the method may include the following steps:
In some embodiments of the present application, step 301 may be implemented by using any one of the embodiments of the present application, and this is not limited in this embodiment of the present application and is not described again.
And 302, acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise.
In some embodiments of the present application, step 302 may be implemented by using any one of the embodiments of the present application, and this is not limited in this embodiment of the present application and is not described again.
It should be noted that the second index system is an energy power enterprise, wherein the energy power enterprise may include, but is not limited to, a wind power enterprise, a hydraulic power enterprise, and the like.
It should be noted that the instruction file of the second instruction system includes the normal value range of each item of data of the enterprise in the production process in the second instruction system, wherein the normal value range of each item of data of the enterprise in the production process is influenced by the production value of the enterprise and changes; for example, if the output value of the enterprise is higher, it is specified in the guidance file that the normal value range of each item of data of the enterprise in the production process is higher, and if the output value of the enterprise is lower, it is specified in the guidance file that the normal value range of each item of data of the enterprise in the production process is lower.
In some embodiments of the present application, each item of data of the sample enterprise is divided and stored in a corresponding topic in the knowledge-graph node according to each level of topic in the instruction file of the second instruction system as each level of topic of the knowledge-graph node, so as to obtain a complete knowledge-graph node. The data items of the sample enterprise may include, but are not limited to: system configuration, scientific and technological project management, scientific and technological achievement management and the like.
It should be noted that the nodes of the knowledge graph can be corrected and supplemented according to the file number of each item of data of the sample enterprise and the division result of each item of data.
And step 304, performing index extraction on the text file of the second index system to generate a knowledge graph sub-node.
In some embodiments of the present application, the second index system may include structured data and unstructured data, and the extraction result is used as a knowledge graph child node by performing index extraction on the structured data and the unstructured data.
And 305, generating a factor library according to the knowledge graph nodes and the knowledge graph sub-nodes.
In some embodiments of the present application, a formation factor library is built by aggregating knowledge-graph nodes and knowledge-graph sub-nodes.
And step 306, generating a divisor according to the historical data and the public data source of the nodes of the knowledge graph.
In some embodiments of the present application, index extraction is performed on nodes of a knowledge graph, so as to obtain historical data of each node in the nodes of the knowledge graph, and data acquisition is performed based on a public data source, so as to supplement the historical data, and the supplemented historical data is subjected to algorithm analysis, so as to generate a divisor.
It should be noted that the divisor may perform a fixed frequency update operation based on the historical data and the update of the public data source, where the update frequency may include, but is not limited to: hours, daily frequency, monthly frequency, etc.
And 307, establishing an influence map according to the factor library and the divisor.
It should be noted that the influence map is established based on the factor library and the divisor, so that the influence map has timely updating property, and the influence map can perform deep learning to generate data prediction analysis aiming at the nodes of the knowledge map.
And 308, calculating to obtain the comprehensive score of the sample enterprise based on the influence map.
In some embodiments of the application, the carbon emission related data of the sample enterprise is obtained according to the influence diagram spectrum, and the comprehensive score of the sample enterprise is calculated according to the carbon emission related data of the sample enterprise; since the influence map spectrum is updatable, when acquiring the carbon emission related data of the sample enterprise based on the influence map spectrum, it is necessary to perform data acquisition a plurality of times, and calculate an average value of the carbon emission related data of the sample enterprise from the carbon emission related data of the sample enterprise acquired a plurality of times.
And 309, generating a carbon emission reduction scheme according with the condition of the sample enterprise based on a preset carbon emission comprehensive score threshold and a preset carbon emission comprehensive score.
In some embodiments of the present application, step 309 may be implemented by using any one of the embodiments of the present application, and this is not limited by the embodiments of the present application and is not described herein again.
According to the carbon emission detection method, when the industry of the detection industry is formed into the second index system, the acquisition and classification of the data of the sample enterprises are realized by establishing the knowledge graph nodes and the knowledge graph sub-nodes, the integrity of the data is ensured, and basic guarantee is provided for the subsequent calculation of the comprehensive scores of the sample enterprises. The influence map is established by generating the factor library and the divisor, so that the gathering and the arrangement of the related data of the carbon emission of the sample enterprise are realized, the influence map is updated in time, the influence map data are continuously perfected and updated, the comprehensive score of the sample enterprise, which is calculated subsequently, can better accord with the actual situation of the sample enterprise, and the accuracy of the comprehensive score of the sample enterprise is improved.
It should be noted that, whether the carbon emission composite score is qualified is judged by setting a carbon emission composite score threshold. As shown in fig. 4, fig. 4 is a flowchart of another carbon emission detection method provided in an embodiment of the present application, and the method may include the following steps:
In some embodiments of the present application, step 401 may be implemented by using any one of the embodiments of the present application, and this is not limited in this embodiment of the present application and is not described again.
And step 402, acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise.
In some embodiments of the present application, step 402 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
And 403, acquiring a comprehensive carbon emission score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise.
In some embodiments of the present application, step 403 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described herein again.
And step 404, judging whether the carbon emission comprehensive score is qualified according to the carbon emission comprehensive score threshold value.
It should be noted that the setting conditions of the carbon emission composite score threshold may include, but are not limited to: when the comprehensive score of the sample enterprise is equal to the emission comprehensive score threshold value, the carbon emission of the sample enterprise cannot exceed the average carbon emission value of enterprises with the same output value; when the comprehensive score of the sample enterprise is equal to the emission comprehensive score threshold, the energy-saving and emission-reducing effect of the sample enterprise needs to be greater than that of the enterprise with the same output value (the judgment can be carried out according to the emission reduction amount).
In some embodiments of the present application, the carbon emission comprehensive score thresholds corresponding to the enterprises with different output values are different, the higher the output value of the enterprise is, the higher the carbon emission comprehensive score threshold corresponding to the enterprise with the higher output value is, the lower the output value of the enterprise is, the lower the carbon emission comprehensive score threshold corresponding to the enterprise with the lower output value is; assuming that the carbon emission comprehensive score threshold is A, the carbon emission comprehensive score of the sample enterprise is B, and if A is smaller than B, the carbon emission comprehensive score of the sample enterprise is proved to be unqualified; and if A is larger than or equal to B, the comprehensive carbon emission score of the sample enterprise is qualified.
And 405, responding to unqualified carbon emission comprehensive scores, and determining influence factors influencing the carbon emission comprehensive scores in the sample enterprises according to the carbon emission comprehensive score threshold value.
It should be noted that the carbon emission composite score threshold may include a carbon emission score threshold and an emission reduction capability score threshold; and the carbon emission comprehensive score can comprise a carbon emission comprehensive score and an emission reduction capability comprehensive score, a carbon emission score threshold value is compared with the carbon emission comprehensive score, and an emission reduction capability score threshold value is compared with the emission reduction capability comprehensive score, so that influence factors influencing the carbon emission comprehensive score are determined. If the carbon emission scoring threshold is greater than the carbon emission comprehensive score and the emission reduction capability scoring threshold is less than the emission reduction capability comprehensive score, the problem that a sample enterprise needs to improve on the carbon emission problem is proved; if the carbon emission score threshold is smaller than the carbon emission comprehensive score and the emission reduction capability score threshold is larger than the emission reduction capability comprehensive score, the problem that a sample enterprise needs to improve on the emission reduction capability problem is proved; if the carbon emission scoring threshold is larger than the carbon emission comprehensive score and the emission reduction capability scoring threshold is larger than the emission reduction capability comprehensive score, the problem that the sample enterprise needs to improve in terms of both carbon emission and emission reduction capability is proved.
And step 406, generating a carbon emission reduction scheme according with the sample enterprise condition according to the influence factors.
In some embodiments of the application, if the influence factor is that the emission reduction capability of the sample enterprise is insufficient, a corresponding carbon emission reduction scheme is generated for the emission reduction capability of the sample enterprise, so that the problem existing in the sample enterprise is remedied. And if the influence factor is that the carbon emission amount of the sample enterprise is too high, generating a corresponding carbon emission reduction scheme aiming at the carbon emission amount of the sample enterprise, so as to realize the compensation of the problems of the sample enterprise.
According to the carbon emission detection method, the comprehensive carbon emission score is judged through the comprehensive carbon emission score threshold, whether the comprehensive carbon emission score is qualified or not is determined, factors influencing the comprehensive carbon emission score are further determined, the carbon emission condition of a sample enterprise is known and diagnosed, and in response to the fact that the comprehensive carbon emission score is unqualified, a corresponding carbon emission reduction scheme is generated according to the influencing factors influencing the comprehensive carbon emission score, so that the generated carbon emission reduction scheme is guaranteed to meet the actual condition of the sample enterprise.
In order to realize the embodiment, the application also provides a carbon emission detection device.
Fig. 5 is a block diagram of a carbon emission detection apparatus according to an embodiment of the present disclosure, and as shown in fig. 5, the carbon emission detection apparatus may include: the determination module 5100, the acquisition module 5200, the scoring module 5300 and the generation module 5400.
The determining module 5100 is configured to determine a sample enterprise according to an industry composition of a detection industry.
The obtaining module 5200 is configured to obtain the carbon emission index system weight of the sample enterprise based on the carbon emission data of the sample enterprise.
The scoring module 5300 is configured to obtain a comprehensive carbon emission score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise.
The generating module 5400 is configured to generate a carbon emission reduction scheme meeting the conditions of the sample enterprise based on a preset carbon emission comprehensive score threshold and a preset carbon emission comprehensive score.
Whether the carbon emission comprehensive score is qualified is judged according to the carbon emission comprehensive score threshold; responding to disqualification of the carbon emission comprehensive score, and determining influence factors influencing the carbon emission comprehensive score in the sample enterprise according to the carbon emission comprehensive score threshold; and generating a carbon emission reduction scheme according with the conditions of the sample enterprises according to the influence factors.
According to the carbon emission detection device, the weight of the carbon emission index system of the sample enterprise is obtained and used as the basis for subsequently generating the comprehensive carbon emission score of the sample enterprise, so that the accuracy of the comprehensive carbon emission score of the sample enterprise is guaranteed, and a basis is provided for subsequently generating a carbon emission reduction scheme. By generating the carbon emission comprehensive score and generating the carbon emission reduction scheme according with the conditions of the sample enterprises according to the carbon emission comprehensive score, the energy-saving and emission-reduction operation aiming at different enterprises can be realized, and the effective detection and emission reduction of the carbon emission of various industries are realized.
In some embodiments of the present application, an industry of the detection industry constitutes a first index system, as shown in fig. 6, fig. 6 is a block diagram of another carbon emission detection apparatus provided in the embodiments of the present application, and the scoring module 6300 of the carbon emission detection apparatus may include: a determination unit 6301, a setup unit 6302, a processing unit 6303, a scoring unit 6304, and a calculation unit 6305.
The determining unit 6301 is configured to determine a specific quantization index in the first index system according to the first index system.
The establishing unit 6302 is configured to establish a data standard system according to the specific quantization index.
And the processing unit 6303 is configured to perform standardization processing on the carbon emission data of the sample enterprise, and obtain standard data of the sample enterprise.
And the grading unit 6304 is used for grading and grading the standard data of the sample enterprise based on the weight of the carbon emission index system and the data standard system to obtain the grading score of the sample enterprise.
And the calculating unit 6305 is configured to calculate a comprehensive score of the sample enterprise according to the hierarchical score of the sample enterprise.
Wherein 6100, 6200 and 6400 in fig. 6 and 5100, 5200 and 5400 in fig. 5 have the same function and structure.
According to the carbon emission detection device, when the industry of the detection industry is formed into the first index system, the specific quantitative indexes are determined to provide a basis for subsequently establishing a data standard system, and guarantee is provided for subsequently obtaining the enterprise grading score of the sample. The carbon emission data of the sample enterprises are subjected to standardized processing, so that the subsequent grading and scoring operations of the carbon emission data of the sample enterprises can be better completed, and the accuracy of grading and scoring results is guaranteed. By acquiring the grading score of the sample enterprise and generating the comprehensive score of the sample enterprise according to the grading score of the sample enterprise, the comprehensive score of the sample enterprise can be ensured to be more consistent with the actual carbon emission condition of the sample enterprise.
In some embodiments of the present application, the industry of the detection industry constitutes a second index system, as shown in fig. 7, fig. 7 is a block diagram of another carbon emission detection apparatus provided in the embodiments of the present application, and the scoring module in the carbon emission detection apparatus may further include: a first node unit 7306, a second node unit 7307, a first generation unit 7308, a second generation unit 7309, a map generation unit 7310, and an acquisition unit 7311.
A first node unit 7306, configured to establish a knowledge-graph node based on the guideline file of the second guideline system.
A second node unit 7307, configured to generate a knowledge graph sub-node by performing index extraction on the text file of the second index system.
A first generating unit 7308, configured to generate a factor library according to the knowledge graph nodes and the knowledge graph sub-nodes.
A second generating unit 7309, configured to generate a divisor according to the historical data of the knowledge-graph node and the public data source.
A map generating unit 7310, configured to create an influence map according to the factor library and the divisor.
An obtaining unit 7311, configured to obtain a composite score of the sample enterprise based on the influence map.
Wherein 7100, 7200 and 7400 in fig. 7 and 6100, 6200 and 6400 in fig. 6 have the same functions and structures.
According to the carbon emission detection device, when the industry of the detection industry is formed into the second index system, the acquisition and classification of the data of the sample enterprises are realized by establishing the knowledge graph nodes and the knowledge graph sub-nodes, the integrity of the data is ensured, and basic guarantee is provided for the subsequent calculation of the comprehensive scores of the sample enterprises. The influence map is established by generating the factor library and the divisor, so that the gathering and the arrangement of the related data of the carbon emission of the sample enterprise are realized, the influence map is updated in time, the influence map data are continuously perfected and updated, the comprehensive score of the sample enterprise, which is calculated subsequently, can better accord with the actual situation of the sample enterprise, and the accuracy of the comprehensive score of the sample enterprise is improved.
Fig. 8 is a block diagram illustrating a terminal device 800 according to an example embodiment. For example, the terminal device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like.
Referring to fig. 8, terminal device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 88, input/output (I/O) interface 812, sensor component 814, and communication component 818.
The processing component 802 generally controls overall operation of the terminal device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the terminal device 800. Examples of such data include instructions for any application or method operating on terminal device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a touch-sensitive display screen that provides an output interface between the terminal device 800 and the user. In some embodiments, the touch display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. When the terminal device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 88 is configured to output and/or input audio signals. For example, the audio component 88 includes a Microphone (MIC) configured to receive external audio signals when the terminal device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 804 or transmitted via the communication component 818. In some embodiments, audio assembly 88 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The communication component 818 is configured to facilitate communications between the terminal device 800 and other devices in a wired or wireless manner. The terminal device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 818 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 818 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the terminal device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described carbon emission detection method.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the terminal device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
To achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium, which when executed by a processor of the terminal device 800, enables the terminal device 800 to perform the carbon emission detection method according to any of the above embodiments of the present application.
According to the technical scheme of the embodiment of the application, the weight of the carbon emission index system of the sample enterprise is obtained and used as a basis for subsequently generating the comprehensive carbon emission score of the sample enterprise, so that the accuracy of the comprehensive carbon emission score of the sample enterprise is ensured, and a basis is provided for subsequently generating a carbon emission reduction scheme. By generating the carbon emission comprehensive score and generating the carbon emission reduction scheme according with the sample enterprise condition according to the carbon emission comprehensive score, the energy saving and emission reduction operation aiming at different enterprises can be realized, and the carbon emission of various industries can be effectively detected and reduced.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of carbon emission detection, comprising:
determining a sample enterprise according to the industry composition of the detection industry;
acquiring a carbon emission index system weight of the sample enterprise based on the carbon emission data of the sample enterprise;
acquiring a carbon emission comprehensive score of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise;
and generating a carbon emission reduction scheme according with the sample enterprise condition based on a preset carbon emission comprehensive score threshold and the carbon emission comprehensive score.
2. The method of claim 1, wherein the industry composition of the detection industry is a first index system, and the step of performing the comprehensive carbon emission scoring on the sample enterprise based on the industry composition of the detection industry and the carbon emission index system weight of the sample enterprise comprises:
determining specific quantization indexes under a first index system according to the first index system;
establishing a data standard system according to the specific quantization index;
carrying out standardization processing on the carbon emission data of the sample enterprise to obtain standard data of the sample enterprise;
grading and scoring the sample enterprise standard data based on the carbon emission index system weight and the data standard system to obtain the grading score of the sample enterprise;
and calculating to obtain the comprehensive score of the sample enterprise according to the grading score of the sample enterprise.
3. The method of claim 1, wherein the industry composition of the detection industry is a second index system, and wherein the step of comprehensively scoring the sample enterprise based on the industry composition of the detection industry and the index system weight of the sample enterprise comprises:
establishing a knowledge graph node based on the guide file of the second index system;
generating a knowledge graph sub-node by performing index extraction on the text file of the second index system;
generating a factor library according to the knowledge graph nodes and the knowledge graph sub-nodes;
generating a divisor according to historical data and public data sources of the knowledge graph nodes;
establishing an influence map according to the factor library and the divisor;
and calculating to obtain the comprehensive score of the sample enterprise based on the influence map.
4. The method of claim 1, wherein generating a carbon emission reduction plan that meets the sample business situation based on a pre-set carbon emission composite score threshold and the carbon emission composite score comprises:
judging whether the carbon emission comprehensive score is qualified or not according to the carbon emission comprehensive score threshold;
in response to the carbon emission composite score being disqualified, determining influencing factors influencing the carbon emission composite score in the sample enterprise according to the carbon emission composite score threshold value;
and generating a carbon emission reduction scheme according with the sample enterprise condition according to the influence factors.
5. A carbon emission detection apparatus, comprising:
the determining module is used for determining a sample enterprise according to the industry composition of the detection industry;
the acquisition module is used for acquiring the weight of the carbon emission index system of the sample enterprise based on the carbon emission data of the sample enterprise;
the grading module is used for obtaining the comprehensive carbon emission grade of the sample enterprise according to the industry composition of the detection industry and the weight of the carbon emission index system of the sample enterprise;
and the generating module is used for generating a carbon emission reduction scheme according with the sample enterprise condition based on a preset carbon emission comprehensive score threshold and the carbon emission comprehensive score.
6. The apparatus of claim 5, wherein the industry of the detection industry constitutes a first index system, and the scoring module comprises:
the determining unit is used for determining a specific quantization index under a first index system according to the first index system;
the establishing unit is used for establishing a data standard system according to the specific quantization index;
the processing unit is used for carrying out standardization processing on the carbon emission data of the sample enterprise to obtain sample enterprise standard data;
the grading unit is used for grading and grading the sample enterprise standard data based on the carbon emission index system weight and the data standard system to obtain a grading score of the sample enterprise;
and the calculating unit is used for calculating the comprehensive score of the sample enterprise according to the grading score of the sample enterprise.
7. The apparatus of claim 5, wherein the industry of the detection industry constitutes a second system of criteria, and wherein the scoring module further comprises:
the first node unit is used for establishing a knowledge graph node based on the instruction file of the second instruction system;
the second node unit is used for generating a knowledge graph sub-node by performing index extraction on the text file of the second index system;
the first generation unit is used for generating a factor library according to the knowledge graph nodes and the knowledge graph sub-nodes;
the second generation unit is used for generating a divisor according to the historical data and the public data source of the knowledge graph nodes;
the map generation unit is used for establishing an influence map according to the factor library and the divisor;
and the acquisition unit is used for acquiring the comprehensive score of the sample enterprise based on the influence map.
8. The apparatus according to claim 5, wherein the generating module is specifically:
judging whether the carbon emission comprehensive score is qualified or not according to the carbon emission comprehensive score threshold;
in response to the carbon emission composite score being disqualified, determining influencing factors influencing the carbon emission composite score in the sample enterprise according to the carbon emission composite score threshold value;
and generating a carbon emission reduction scheme according with the sample enterprise condition according to the influence factors.
9. A terminal device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the carbon emission detection method of any one of claims 1 to 4.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the carbon emission detection method according to any one of claims 1 to 4.
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