Enterprise emission verification, model building methods, devices, equipment and storage media

Abstract

The present invention relates to the field of pollutant emission verification technology, and in particular to an enterprise emission verification, model building method, device, equipment and storage medium. The method of the present invention first obtains an enterprise emission data queue and a material consumption data set; then extracts frequency domain features from the enterprise emission data queue, and constructs the obtained multiple frequency domain features and the material consumption data set into a first verification vector; then the first verification vector is respectively input into multiple verification models, and multiple first results are determined according to the outputs of the multiple verification models; finally, a target result is selected from the multiple first results as the first verified emission, and the enterprise emission monitoring amount is verified according to the first verified emission. The present invention predicts emissions based on the material consumption data and emission data sequence that affect the enterprise's emissions. Therefore, the verification results are more reliable and credible, and the efficiency of verification is improved.

Description

Technical Field

[0001] The present invention relates to the field of pollutant emission verification, and in particular to a method, device, equipment and storage medium for enterprise emission verification and model building. Background Art

[0002] Pollutant monitoring refers to the use of modern scientific and technological methods such as physics, chemistry, and biology to intermittently or continuously monitor and measure environmental chemical pollutants and physical and biological pollution factors on site, and make accurate environmental quality evaluations.

[0003] Pollutant monitoring data is affected by a variety of factors during the collection process, resulting in data deviations. Before periodic pollutant monitoring data is reported, it is usually necessary to verify it, and if necessary, it is necessary to go to the enterprise site for verification. The traditional method is manual verification, which is labor-intensive and prone to omissions. Even if the enterprise discharges secretly, it is difficult to find it through visits to the enterprise. In addition, since the data aggregation is periodic, when the data abnormality is found, a long period of time has often passed, which increases the difficulty of investigation.

[0004] Based on this, it is necessary to develop and design a method for verifying corporate emissions. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, equipment and storage medium for verifying and modeling enterprise emissions, which are used to solve the problem of inaccurate results of manual verification of enterprise emissions in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for verifying enterprise emissions, comprising: Acquire an enterprise emission data queue and a material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; Extracting frequency domain features from the enterprise emission data queue, and constructing a first verification vector with the obtained multiple frequency domain features and the material consumption data set; Inputting the first verification vector into a plurality of verification models respectively, and determining a plurality of first results according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model; A target result is selected from the multiple first results as the first verified emission amount, and the enterprise emission monitoring amount is verified based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

[0007] In a possible implementation, the extracting frequency domain features from the enterprise emission data queue and constructing a first verification vector using the obtained multiple frequency domain features and the material consumption data set includes: A plurality of frequency domain features of the enterprise emission data queue are extracted according to a first formula, wherein the first formula is:

[0008] In the formula, For the Sub-frequency domain features, For the first data, The total amount of data in the enterprise emission data queue, is a natural constant, is the circumference of a circle, is the fundamental frequency, is an imaginary unit, The total number of enterprise emission data obtained during the base wave period; The plurality of frequency domain features and the data in the material consumption data set are arranged according to a predetermined frequency feature order and a predetermined material order to obtain a first verification vector.

[0009] In a possible implementation, determining a plurality of first results according to outputs of the plurality of verification models includes: The outputs of the multiple verification models are respectively input into a second formula to obtain multiple first results, wherein the second formula is:

[0010] In the formula, For the first result, To verify the output of the model, To verify the upper limit of the emission verification range of the model, To verify the lower limit of the emission verification range of the model, is the scaling factor, is the first bias coefficient.

[0011] In a second aspect, an embodiment of the present invention provides a method for constructing an enterprise emission verification model, which is used to construct the enterprise emission verification method as described in the first aspect or any possible implementation of the first aspect, and the enterprise emission verification model construction method includes: Obtain a verification basic model, a plurality of second verification vectors, and a plurality of second verification emissions, wherein each second verification vector corresponds to a second verification emission, and the second verification vector is constructed based on a historical material consumption data set and a plurality of frequency domain features extracted based on a historical enterprise emission data queue, and the historical material consumption data set includes historical energy consumption data and / or historical material consumption data; dividing the plurality of second verification vectors into a plurality of first vector groups according to the intervals where the verified emission amounts are located, clustering the plurality of second verification vectors in each first vector group, reducing the plurality of second verification vectors in the first vector group according to the clustering result, and using the remaining second verification vectors as third verification vectors; According to the corresponding second verified emissions, multiple third verification vectors are divided into multiple second vector groups, and multiple coefficients of the verification basic model are determined according to each second vector group and the multiple second verified emissions corresponding to the second vector group to obtain the enterprise emission verification model.

[0012] In a possible implementation, the multiple second verification vectors are divided into multiple first vector groups according to the interval where the verified emission amount is located, the multiple second verification vectors in each first vector group are clustered, the multiple second verification vectors in the first vector group are reduced according to the clustering result, and the remaining second verification vectors are used as third verification vectors, including: Arranging the plurality of second verified emissions according to the magnitude of the values; According to the intervals corresponding to the arrangement, the plurality of second verified emissions are divided into equal interval spans to obtain a plurality of emission groups, and according to the correspondence between the second verified emissions and the second verification vectors and the plurality of emission groups, the plurality of second verification vectors are divided into a plurality of first vector groups, wherein each first vector group corresponds to an emission group; For each first vector group, perform the following steps respectively: Get the number of clusters; According to the number of clusters, randomly selecting second verification vectors of the number of clusters from the first vector group as multiple temporary cluster centers; For each second verification vector in the first vector group, add the cluster where the temporary cluster center with the closest Euclidean distance is located; Obtaining class centers of multiple classes, wherein the class center is a second verification vector that is closest to a class mean vector, and the class mean vector is a mean vector of multiple vectors in the class; If there is a situation where the multiple temporary clustering centers are not class centers, the class centers of the multiple classes are used as multiple temporary clustering centers, and the process jumps to the step of adding the class where the temporary clustering center with the closest Euclidean distance is located for each second verification vector in the first vector group; Otherwise, the second verification vectors of the small-proportion class are eliminated, and the second verification vectors in the remaining classes are used as the third verification vectors, wherein the small-proportion class is a class in which the ratio of the number of second verification vectors in the class to the number of second verification vectors in the first vector group is less than the ratio threshold.

[0013] In a possible implementation, the verification basic model is:

[0014] In the formula, To verify the output of the model, is a natural constant, is a joint variable, For the The first coefficient, is the first elements, To verify the total number of vector elements, is the second bias coefficient, is the total number of times.

[0015] In a possible implementation, the method of dividing the plurality of third verification vectors into a plurality of second vector groups according to the corresponding second verification emissions, determining a plurality of coefficients of the verification basic model according to each second vector group and the plurality of second verification emissions corresponding to the second vector group, and obtaining the enterprise emission verification model includes: Arranging a plurality of third verification vectors according to values ​​corresponding to the second verification emission amounts; Sequentially taking out a preset number of verification vectors from the third verification vector arrangement to form a second vector group; For each second vector group, perform the following steps respectively: Configure the number of coefficients in the verification basic model according to the number of verification vectors in the second vector group; traversally taking out a third verification vector from the second vector group as a vector to be processed; According to the third formula, the verification value is determined, wherein the third formula is:

[0016] In the formula, To verify the value, is the second verified emission corresponding to the vector to be processed, is the scaling factor, is the upper limit of the verified emission range corresponding to the second vector group, is the lower limit of the verified emission range corresponding to the second vector group, is the first bias coefficient; Input the vector to be processed into the verification basic model, determine the model deviation according to the output of the verification basic model and the verification value, and add the model deviation to the deviation queue; If the values ​​of the last digits in the deviation queue are all smaller than the deviation threshold, the verification basic model is used as the enterprise emission verification model corresponding to the second vector group; Otherwise, multiple coefficients of the basic model are adjusted and verified according to the model deviation.

[0017] In a third aspect, an embodiment of the present invention provides an enterprise emission verification device, which is used to implement the enterprise emission verification method described in the first aspect or any possible implementation of the first aspect, and the enterprise emission verification device includes: A data acquisition module, used to acquire an enterprise emission data queue and a material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; A frequency domain analysis module, used for extracting frequency domain features from the enterprise emission data queue, and constructing a first verification vector with the obtained multiple frequency domain features and the material consumption data set; a verification result acquisition module, configured to input the first verification vector into a plurality of verification models respectively, and determine a plurality of first results according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model; as well as, A verification module is used to select a target result from the multiple first results as the first verified emission amount, and verify the enterprise emission monitoring amount based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

[0018] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0019] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The embodiment of the present invention discloses a method for verifying enterprise emissions, which first obtains an enterprise emission data queue and a material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; then extracts frequency domain features from the enterprise emission data queue, and constructs the obtained multiple frequency domain features and the material consumption data set into a first verification vector; then inputs the first verification vector into multiple verification models respectively, and determines multiple first results according to the outputs of the multiple verification models, wherein each first result corresponds to a verification model; finally, selects a target result from the multiple first results as the first verified emission, and verifies the enterprise emission monitoring amount according to the first verified emission, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval. The embodiment of the present invention predicts emissions based on the material consumption data and emission data sequence that affect the enterprise's emissions, and verifies the enterprise's emissions according to the prediction results. Since both factor and sequence data are integrated, the verification results are more reliable and credible, and the efficiency of verification is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0022] Figure 1 It is a flow chart of the enterprise emission verification method provided by the embodiment of the present invention; Figure 2 It is a flow chart of a method for constructing an enterprise emission verification model provided by an embodiment of the present invention; Figure 3 It is a principle diagram of the process of eliminating verification vectors provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the process of constructing an enterprise emission verification model through a second vector group provided by an embodiment of the present invention; Figure 5 It is a functional block diagram of an enterprise emission verification device provided by an embodiment of the present invention; Figure 6 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0024] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0025] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0026] Figure 1 A flow chart of a method for verifying enterprise emissions provided in an embodiment of the present invention.

[0027] like Figure 1 As shown, it shows a flowchart of the implementation method of the enterprise emission verification method provided by the first aspect of the embodiment of the present invention, which is described in detail as follows: In step 101, an enterprise emission data queue and a material consumption data set are obtained, wherein the material consumption data set includes: energy consumption data and / or material consumption data.

[0028] In step 102, frequency domain features are extracted from the enterprise emission data queue, and the obtained multiple frequency domain features and the material consumption data set are constructed into a first verification vector.

[0029] In some implementations, the extracting frequency domain features from the enterprise emission data queue and constructing the obtained multiple frequency domain features and the material consumption data set into a first verification vector includes: A plurality of frequency domain features of the enterprise emission data queue are extracted according to a first formula, wherein the first formula is:

[0030] In the formula, For the Sub-frequency domain features, For the first data, The total amount of data in the enterprise emission data queue, is a natural constant, is the circumference of a circle, is the fundamental frequency, is an imaginary unit, The total number of enterprise emission data obtained during the base wave period; The plurality of frequency domain features and the data in the material consumption data set are arranged according to a predetermined frequency feature order and a predetermined material order to obtain a first verification vector.

[0031] Exemplarily, the implementation method of the present invention is to obtain the previous enterprise emission data and the material consumption data of the same period, and determine whether the enterprise emission monitoring data is credible by comparing the emission data sequence and the material consumption of the same period. For example, for some pollutant indicators, when the monitoring value is obtained, the pollutant emission data of multiple time nodes in the previous period are also obtained, and arranged in the order of time nodes to obtain the enterprise emission data queue. Then, based on the time node of the pollutant monitoring value, the material consumption of the node relative to the previous time node is obtained, such as electricity consumption, water consumption, raw material consumption, etc.

[0032] The embodiment of the present invention aims to comprehensively verify whether the emission data is credible from the time series dimension and the dimension of factors affecting the emission data through the emission data sequence. In terms of the analysis of the time series dimension, the embodiment of the present invention converts the time domain data into the frequency domain data to facilitate analysis and reduce the dimension of the data. In one application scenario, the first formula is applied to extract the frequency domain characteristics of the enterprise emission data queue:

[0033] In the formula, For the Sub-frequency domain features, For the first data, The total amount of data in the enterprise emission data queue, is a natural constant, is the circumference of a circle, is the fundamental frequency, is an imaginary unit, It is the total number of enterprise emission data obtained during the base wave period.

[0034] The multiple frequency domain features obtained through the above formula are arranged in a preset order with the material consumption data to obtain a verification vector.

[0035] In step 103, the first verification vector is input into a plurality of verification models respectively, and a plurality of first results are determined according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model.

[0036] In some embodiments, determining a plurality of first results according to the outputs of the plurality of verification models comprises: The outputs of the multiple verification models are respectively input into a second formula to obtain multiple first results, wherein the second formula is:

[0037] In the formula, For the first result, To verify the output of the model, To verify the upper limit of the emission verification range of the model, To verify the lower limit of the emission verification range of the model, is the scaling factor, is the first bias coefficient.

[0038] In step 104, a target result is selected from the multiple first results as the first verified emission amount, and the enterprise emission monitoring amount is verified based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

[0039] Exemplarily, the embodiment of the present invention inputs the verification vectors obtained in the above steps into multiple verification models respectively, and each model will output a result. The verification model of the embodiment of the present invention is segmented according to the emission amount. In other words, different pollutant emissions correspond to different verification models. The advantage of doing so is to fit nonlinear relationships with multiple relatively linear models. The nonlinearity here refers to the nonlinear relationship between emissions and time domain characteristics and material consumption data.

[0040] Since there are multiple models, when we get the verification vector, we may not know which model to input to determine the verified emissions. In fact, all verification models map the pollutant emissions data to the interval [0-1] and scale and offset them, for example, [0.1-0.9] after scaling. If the verification vector is not applicable to a verification model, after the verification vector is input to the verification model, the data output by the verification model will exceed the interval. For example, when the verification model is input to an inapplicable model, the output value of the model is greater than 0.9 or less than 0.1.

[0041] And because the model is mapped to the interval, we need to perform a secondary conversion after the model output. The embodiment of the present invention applies the second formula for conversion to restore the emission estimation value. The second formula is:

[0042] In the formula, For the first result, To verify the output of the model, To verify the upper limit of the emission verification range of the model, To verify the lower limit of the emission verification range of the model, is the scaling factor, is the first bias coefficient.

[0043] Once the emissions estimate is obtained based on the model, it can be compared with the measured value to determine whether the company's emission monitoring is correct.

[0044] In terms of constructing an enterprise emission verification model, the present invention has been discussed in detail in the second aspect of the implementation method.

[0045] Figure 2 A flowchart of a method for constructing a verification model for enterprise emissions verification provided in an embodiment of the present invention.

[0046] like Figure 2 As shown, it shows a flowchart of the implementation method of the enterprise emission verification model construction method provided by the second aspect of the embodiment of the present invention, which is described in detail as follows: In step 201, a verification basic model, a plurality of second verification vectors, and a plurality of second verification emissions are obtained, wherein each second verification vector corresponds to a second verification emission, and the second verification vector is constructed based on a historical material consumption data set and a plurality of frequency domain features extracted based on a historical enterprise emission data queue, and the historical material consumption data set includes historical energy consumption data and / or historical material consumption data; In some embodiments, the verification basic model is:

[0047] In the formula, To verify the output of the model, is a natural constant, is a joint variable, For the The first coefficient, is the first elements, To verify the total number of vector elements, is the second bias coefficient, is the total number of times.

[0048] Exemplarily, the embodiment of the present invention completes model construction by determining multiple parameters of the verification basic model based on multiple verification vectors and multiple emission amounts corresponding to these verification vectors.

[0049] The basic verification model is:

[0050] In the formula, To verify the output of the model, is a natural constant, is a joint variable, For the The first coefficient, is the first elements, To verify the total number of vector elements, is the second bias coefficient, is the total number of times.

[0051] In terms of the verification vector (to distinguish it from the verification model of the first aspect, the verification vector of the second aspect is the second verification vector), each second verification vector corresponds to a second verified emission amount, which is the same as the relationship and acquisition method between the verification vector and the verified emission amount in the first aspect. The second verification vector is based on the historical enterprise emission data queue constructed based on the emission data of multiple enterprises in the period before the second verified emission amount, and extracts frequency domain features. The vector is constructed by combining the multiple frequency domain features obtained and the historical material consumption data. The historical material consumption data here is concurrent with the second verified emission amount.

[0052] In step 202, the multiple second verification vectors are divided into multiple first vector groups according to the interval where the verified emission is located, the multiple second verification vectors in each first vector group are clustered, and the multiple second verification vectors in the first vector group are reduced according to the clustering result, and the remaining second verification vectors are used as third verification vectors.

[0053] In some embodiments, the method of dividing the plurality of second verification vectors into a plurality of first vector groups according to the interval in which the verified emission amount is located, clustering the plurality of second verification vectors in each first vector group, reducing the plurality of second verification vectors in the first vector group according to the clustering result, and using the remaining second verification vectors as the third verification vector includes: Arranging the plurality of second verified emissions according to the magnitude of the values; According to the intervals corresponding to the arrangement, the plurality of second verified emissions are divided into equal interval spans to obtain a plurality of emission groups, and according to the correspondence between the second verified emissions and the second verification vectors and the plurality of emission groups, the plurality of second verification vectors are divided into a plurality of first vector groups, wherein each first vector group corresponds to an emission group; For each first vector group, perform the following steps respectively: Get the number of clusters; According to the number of clusters, randomly selecting second verification vectors of the number of clusters from the first vector group as multiple temporary cluster centers; For each second verification vector in the first vector group, add the cluster where the temporary cluster center with the closest Euclidean distance is located; Obtaining class centers of multiple classes, wherein the class center is a second verification vector that is closest to a class mean vector, and the class mean vector is a mean vector of multiple vectors in the class; If there is a situation where the multiple temporary clustering centers are not class centers, the class centers of the multiple classes are used as multiple temporary clustering centers, and the process jumps to the step of adding the class where the temporary clustering center with the closest Euclidean distance is located for each second verification vector in the first vector group; Otherwise, the second verification vectors of the small-proportion class are eliminated, and the second verification vectors in the remaining classes are used as the third verification vectors, wherein the small-proportion class is a class in which the ratio of the number of second verification vectors in the class to the number of second verification vectors in the first vector group is less than the ratio threshold.

[0054] For example, Figure 3 As shown, after obtaining multiple second verification vectors 301, the embodiment of the present invention eliminates some of the vectors therein, with the purpose of retaining more reliable and trustworthy vectors and avoiding the problem of model overfitting due to data noise.

[0055] The implementation mode of the present invention is to reduce the second verification vectors 301 by groups. The grouping method is to arrange them according to the second verification emission 302, for example, from small to large. After arrangement, they are divided into multiple first vector groups 303 according to the equal intervals of the second verification emission 302. For example, after the second verification emission 302 is arranged, its value is N100-N8100, and the equal interval division interval is N800, so it can be divided into 10 intervals, of which the first interval is N100-N900. Note that the number of vectors in the first vector group 303 obtained by this division method is different. Generally, the vector group closer to the median of the emission has more vectors, and the vector group closer to the extreme value has fewer vectors.

[0056] Then, clustering is performed for each vector group, usually by specifying the number of clusters, and clustering is performed according to the number of clusters. After clustering is completed and multiple classes 304 are obtained, the clustering results are analyzed, and the classes 304 with fewer vectors in the vector groups are eliminated. Generally, a ratio threshold is set, for example, the ratio threshold is 5%, that is, if the number of vectors in a class 304 is less than 5% of the number of vectors in the vector group, all vectors in the class 304 are deleted. In this way, vectors with strong noise are deleted, preparing for the accuracy of model construction.

[0057] In step 203, multiple third verification vectors are divided into multiple second vector groups according to the corresponding second verification emissions, and multiple coefficients of the verification basic model are determined according to each second vector group and the multiple second verification emissions corresponding to the second vector group to obtain the enterprise emission verification model.

[0058] In some embodiments, the method of dividing the plurality of third verification vectors into a plurality of second vector groups according to the corresponding second verified emissions, determining a plurality of coefficients of the verification basic model according to each second vector group and the plurality of second verified emissions corresponding to the second vector group, and obtaining the enterprise emission verification model includes: Arranging a plurality of third verification vectors according to values ​​corresponding to the second verification emission amounts; Sequentially taking out a preset number of verification vectors from the third verification vector arrangement to construct a second vector group; For each second vector group, perform the following steps respectively: Configure the number of coefficients in the verification basic model according to the number of verification vectors in the second vector group; traversally taking out a third verification vector from the second vector group as a vector to be processed; According to the third formula, the verification value is determined, wherein the third formula is:

[0059] In the formula, To verify the value, is the second verified emission corresponding to the vector to be processed, is the scaling factor, is the upper limit of the verified emission range corresponding to the second vector group, is the lower limit of the verified emission range corresponding to the second vector group, is the first bias coefficient; Input the vector to be processed into the verification basic model, determine the model deviation according to the output of the verification basic model and the verification value, and add the model deviation to the deviation queue; If the values ​​of the last digits in the deviation queue are all smaller than the deviation threshold, the verification basic model is used as the enterprise emission verification model corresponding to the second vector group; Otherwise, multiple coefficients of the basic model are adjusted and verified according to the model deviation.

[0060] For example, Figure 4As shown, the embodiment of the present invention is based on the verification vector after the reduction for group modeling. Specifically, in order to facilitate the distinction, the verification vector obtained by the reduction in the previous step is used as the third verification vector 401, and is sorted again according to the value of the corresponding verification emission 302, and then grouped with equal vector quantity to obtain the second vector group 402. Then, according to the number of vectors in the second vector group 402, the number of coefficients of the verification basic model 403 is configured, for example, by adjusting the total number of times, so that the number of coefficients of the verification basic model 403 is not more than the number of vectors in the second vector group 402. In this way, there will be no problem of multiple solutions or no solutions to the model coefficients, and the overfitting and underfitting problems caused by unreasonable coefficient settings will be prevented.

[0061] Then, the vectors in the second vector group 402 are ergodically extracted and input into the verification basic model 403 (the coefficients of the verification basic model 403 are generally randomly initialized and randomly set to some non-zero values). The result 404 output by the model is compared with the verification value determined according to the verification emission amount corresponding to the vector to obtain the model deviation 405. The verification value is determined according to the third formula:

[0062] In the formula, To verify the value, is the second verified emission corresponding to the vector to be processed, is the scaling factor, is the upper limit of the verified emission range corresponding to the second vector group, is the lower limit of the verified emission range corresponding to the second vector group, is the first bias coefficient.

[0063] Generally, the scaling factor takes a value less than 1, such as 0.8, and the first bias factor is a value less than 1 and the difference between the scaling factor and the scaling factor. For example, when the scaling factor takes 0.8, the first bias factor takes 0.1.

[0064] The model deviation 405 is added to the deviation queue 406, and the last few digits of the deviation queue 406 are used to determine whether the conditions for terminating the iteration are met (for example, five consecutive digits). For example, if the absolute values ​​of the last few digits of the model deviation 405 are all less than a threshold value (for example, 5%), the iteration is terminated and the verification basic model 403 can be used as the enterprise emission verification model.

[0065] If the conditions for terminating the iteration are not met, an optimization algorithm, such as gradient descent method, particle swarm method, and genetic algorithm, is used to optimize the model coefficients.

[0066] For example, the gradient descent method is an iterative algorithm for optimizing an objective function. Its main purpose is to find the minimum value of the objective function (in some cases, it can also be used to find the maximum value, and the embodiment of the present invention is to find the minimum value of the absolute value of the model deviation).

[0067] The gradient descent method uses the gradient of the objective function to determine the update direction of the parameters. The objective function of the present invention is very clear, which is the difference between the model output and the verification value. The gradient is a vector, each of which is the partial derivative of the objective function with respect to the corresponding parameter. The direction of the gradient indicates the direction in which the function rises fastest at that point, and the opposite direction is the direction in which it falls fastest.

[0068] At each iteration, the gradient of the objective function is calculated with respect to the current values ​​of the parameters.

[0069] Then, update the parameters according to the following formula:

[0070] in, is the current parameter value, is the updated parameter value, is the learning rate, The objective function is The gradient at .

[0071] The learning rate controls the step size of each parameter update. If the learning rate is too large, the algorithm may not converge or oscillate around the minimum value; if the learning rate is too small, the algorithm will converge very slowly.

[0072] Through iterative optimization of the optimization algorithm, the coefficients will eventually converge and a more accurate model will be obtained.

[0073] The implementation method of the enterprise emission verification method of the present invention first obtains the enterprise emission data queue and the material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; then the frequency domain feature extraction is performed on the enterprise emission data queue, and the obtained multiple frequency domain features and the material consumption data set are constructed as a first verification vector; then the first verification vector is respectively input into multiple verification models, and multiple first results are determined according to the outputs of the multiple verification models, wherein each first result corresponds to a verification model; finally, the target result is selected from the multiple first results as the first verification emission, and the enterprise emission monitoring amount is verified according to the first verification emission, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval. The implementation method of the present invention predicts the emission based on the material consumption data and emission data sequence that affect the enterprise emission, and verifies the enterprise emission according to the prediction result. Since the two data of factors and sequences are integrated, the verification result is more reliable and credible, and the efficiency of verification is improved.

[0074] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0075] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0076] Figure 5 This is a functional block diagram of the enterprise emission verification device provided by the embodiment of the present invention, referring to Figure 5 The enterprise emission verification device includes: a data acquisition module 501, a frequency domain analysis module 502, a verification result acquisition module 503 and a verification module 504, wherein: The data acquisition module 501 is used to acquire the enterprise emission data queue and the material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; A frequency domain analysis module 502 is used to extract frequency domain features from the enterprise emission data queue, and construct a first verification vector using the obtained multiple frequency domain features and the material consumption data set; A verification result acquisition module 503, configured to input the first verification vector into a plurality of verification models respectively, and determine a plurality of first results according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model; The verification module 504 is used to select a target result from the multiple first results as the first verified emission amount, and verify the enterprise emission monitoring amount based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

[0077] Figure 6 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 6 As shown, the electronic device 6 of this embodiment includes: a processor 600 and a memory 601, wherein the memory 601 stores a computer program 602 that can be run on the processor 600. When the processor 600 executes the computer program 602, the steps in the above-mentioned enterprise emission verification method and implementation method are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0078] Exemplarily, the computer program 602 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 601 and executed by the processor 600 to implement the present invention.

[0079] The electronic device 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 6 may include, but is not limited to, a processor 600 and a memory 601. Those skilled in the art will appreciate that Figure 6 It is only an example of the electronic device 6 and does not constitute a limitation of the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 6 may also include input and output devices, network access devices, buses, etc.

[0080] The processor 600 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0081] The memory 601 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. The memory 601 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 6. Further, the memory 601 may also include both an internal storage unit and an external storage device of the electronic device 6. The memory 601 is used to store the computer program 602 and other programs and data required by the electronic device 6. The memory 601 may also be used to temporarily store data that has been output or is to be output.

[0082] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0083] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0085] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0087] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0088] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various methods and device implementation methods can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0089] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for verifying enterprise emissions, characterized in that: include: Acquire an enterprise emission data queue and a material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; Extracting frequency domain features from the enterprise emission data queue, and constructing a first verification vector with the obtained multiple frequency domain features and the material consumption data set; Inputting the first verification vector into a plurality of verification models respectively, and determining a plurality of first results according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model; A target result is selected from the multiple first results as the first verified emission amount, and the enterprise emission monitoring amount is verified based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

2. The method for verifying enterprise emissions according to claim 1, characterized in that: The extracting frequency domain features from the enterprise emission data queue and constructing a first verification vector using the obtained multiple frequency domain features and the material consumption data set includes: A plurality of frequency domain features of the enterprise emission data queue are extracted according to a first formula, wherein the first formula is: In the formula, For the Sub-frequency domain features, For the first data, The total amount of data in the enterprise emission data queue, is a natural constant, is the circumference of a circle, is the fundamental frequency, is an imaginary unit, The total number of enterprise emission data obtained during the base wave period; The plurality of frequency domain features and the data in the material consumption data set are arranged according to a predetermined frequency feature order and a predetermined material order to obtain a first verification vector.

3. The enterprise emission verification method according to any one of claims 1-2, characterized in that: The determining a plurality of first results according to the outputs of the plurality of verification models comprises: The outputs of the multiple verification models are respectively input into a second formula to obtain multiple first results, wherein the second formula is: In the formula, For the first result, To verify the output of the model, To verify the upper limit of the emission verification range of the model, To verify the lower limit of the emission verification range of the model, is the scaling factor, is the first bias coefficient.

4. A method for constructing an enterprise emission verification model, characterized in that: Used to construct a verification model used in the enterprise emission verification method as claimed in any one of claims 1 to 3, the enterprise emission verification model construction method comprises: Obtain a verification basic model, a plurality of second verification vectors, and a plurality of second verification emissions, wherein each second verification vector corresponds to a second verification emission, and the second verification vector is constructed based on a historical material consumption data set and a plurality of frequency domain features extracted based on a historical enterprise emission data queue, and the historical material consumption data set includes historical energy consumption data and / or historical material consumption data; dividing the plurality of second verification vectors into a plurality of first vector groups according to the intervals where the verified emission amounts are located, clustering the plurality of second verification vectors in each first vector group, reducing the plurality of second verification vectors in the first vector group according to the clustering result, and using the remaining second verification vectors as third verification vectors; According to the corresponding second verified emissions, multiple third verification vectors are divided into multiple second vector groups, and multiple coefficients of the verification basic model are determined according to each second vector group and the multiple second verified emissions corresponding to the second vector group to obtain the enterprise emission verification model.

5. The method for constructing an enterprise emission verification model according to claim 4, characterized in that: The method of dividing the plurality of second verification vectors into a plurality of first vector groups according to the interval where the verified emission amount is located, clustering the plurality of second verification vectors in each first vector group, reducing the plurality of second verification vectors in the first vector group according to the clustering result, and using the remaining second verification vectors as third verification vectors includes: Arranging the plurality of second verified emissions according to the magnitude of the values; According to the intervals corresponding to the arrangement, the plurality of second verified emissions are divided into equal interval spans to obtain a plurality of emission groups, and according to the correspondence between the second verified emissions and the second verification vectors and the plurality of emission groups, the plurality of second verification vectors are divided into a plurality of first vector groups, wherein each first vector group corresponds to an emission group; For each first vector group, perform the following steps respectively: Get the number of clusters; According to the number of clusters, randomly selecting second verification vectors of the number of clusters from the first vector group as multiple temporary cluster centers; For each second verification vector in the first vector group, add the cluster where the temporary cluster center with the closest Euclidean distance is located; Obtaining class centers of multiple classes, wherein the class center is a second verification vector that is closest to a class mean vector, and the class mean vector is a mean vector of multiple vectors in the class; If there is a situation where the multiple temporary clustering centers are not class centers, the class centers of the multiple classes are used as multiple temporary clustering centers, and the process jumps to the step of adding the class where the temporary clustering center with the closest Euclidean distance is located for each second verification vector in the first vector group; Otherwise, the second verification vectors of the small-proportion class are eliminated, and the second verification vectors in the remaining classes are used as the third verification vectors, wherein the small-proportion class is a class in which the ratio of the number of second verification vectors in the class to the number of second verification vectors in the first vector group is less than the ratio threshold.

6. The method for constructing an enterprise emission verification model according to claim 4, characterized in that: The basic verification model is: In the formula, To verify the output of the model, is a natural constant, is a joint variable, For the The first coefficient, is the first elements, To verify the total number of vector elements, is the second bias coefficient, is the total number of times.

7. The method for constructing an enterprise emission verification model according to any one of claims 4 to 6, characterized in that: The method of dividing the plurality of third verification vectors into a plurality of second vector groups according to the corresponding second verification emission amounts, determining a plurality of coefficients of the verification basic model according to each second vector group and the plurality of second verification emission amounts corresponding to the second vector group, and obtaining the enterprise emission verification model includes: Arranging a plurality of third verification vectors according to values ​​corresponding to the second verification emission amounts; Sequentially taking out a preset number of verification vectors from the third verification vector arrangement to form a second vector group; For each second vector group, perform the following steps respectively: Configure the number of coefficients in the verification basic model according to the number of verification vectors in the second vector group; traversally taking out a third verification vector from the second vector group as a vector to be processed; According to the third formula, the verification value is determined, wherein the third formula is: In the formula, To verify the value, is the second verified emission corresponding to the vector to be processed, is the scaling factor, is the upper limit of the verified emission range corresponding to the second vector group, is the lower limit of the verified emission range corresponding to the second vector group, is the first bias coefficient; Input the vector to be processed into the verification basic model, determine the model deviation according to the output of the verification basic model and the verification value, and add the model deviation to the deviation queue; If the values ​​of the last digits in the deviation queue are all smaller than the deviation threshold, the verification basic model is used as the enterprise emission verification model corresponding to the second vector group; Otherwise, multiple coefficients of the basic model are adjusted and verified according to the model deviation.

8. An enterprise emission verification device, characterized in that: Used to implement the enterprise emission verification method according to any one of claims 1 to 3, the enterprise emission verification device comprises: A data acquisition module, used to acquire an enterprise emission data queue and a material consumption data set, wherein the material consumption data set includes: energy consumption data and / or material consumption data; A frequency domain analysis module, used for extracting frequency domain features from the enterprise emission data queue, and constructing a first verification vector with the obtained multiple frequency domain features and the material consumption data set; a verification result acquisition module, configured to input the first verification vector into a plurality of verification models respectively, and determine a plurality of first results according to outputs of the plurality of verification models, wherein each first result corresponds to a verification model; as well as, A verification module is used to select a target result from the multiple first results as the first verified emission amount, and verify the enterprise emission monitoring amount based on the first verified emission amount, wherein the target result is the first result located in the emission verification interval, and each verification model corresponds to an emission verification interval.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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