CN116629694A

CN116629694A - ESG index system algorithm model determining method and device based on power grid engineering

Info

Publication number: CN116629694A
Application number: CN202310630885.2A
Authority: CN
Inventors: 曾文龙; 邹贵林; 王俊刚; 余海翔; 袁太平; 罗旭升; 李凡; 何飞鹏; 冷祥彪; 彭飞
Original assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Current assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Priority date: 2023-05-30
Filing date: 2023-05-30
Publication date: 2023-08-22

Abstract

The application relates to an ESG index system algorithm model determining method and device based on power grid engineering. The method comprises the following steps: responding to a power grid project influence scoring task corresponding to the power grid project, and acquiring full life cycle data of the power grid project; determining the index information and index information importance mapping relation of each power grid according to the whole life cycle data of the power grid engineering; according to the index information importance mapping relation, mapping the evaluation index information of each power grid into corresponding importance information to be adjusted; according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain the importance information after adjustment; and constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information. By adopting the method, the accuracy of describing the resource interaction condition generated between the operation and the environment interaction of the whole life cycle of the power grid engineering can be improved.

Description

ESG index system algorithm model determining method and device based on power grid engineering

Technical Field

The application relates to the technical field of carbon emission of power grid engineering, in particular to a method and a device for determining an ESG (electronic control unit) index system algorithm model based on power grid engineering.

Background

Along with the development of power grid construction, a large number of power grid engineering construction tasks are required to be carried out, and the recognition of carbon emission of power grid construction is limited to a narrow power transmission process for a long time, and specifically comprises line loss, power grid engineering ecological environment influence, solid waste, office facility environment influence and the like. In recent years, the above carbon emissions have been greatly improved, however, under the requirements of ecological civilization and carbon neutralization, the requirements for reducing the carbon emissions in the process of power grid construction are higher and higher.

In the traditional technology, the construction of the power grid engineering takes investment and operation economy as the dominant, and the problem of operation safety resource allocation is considered, but carbon emission data generated in the construction and operation of the power grid engineering is ignored, the power grid engineering cannot be accurately described, and a planning conclusion cannot be effectively adapted to a double-carbon target. Due to the lack of description of carbon emission data, the description of resource interaction conditions generated between full life cycle operation and environment interaction of power grid engineering is inaccurate.

Disclosure of Invention

Based on this, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for determining an algorithm model of an ESG-index system based on a power grid project, which can improve the accuracy of describing the resource interaction conditions generated by the power grid project between the operation and the environment interaction in the whole life cycle.

In a first aspect, the application provides a method for determining an ESG index system algorithm model based on power grid engineering. The method comprises the following steps: responding to a power grid project influence scoring task corresponding to a power grid project, and acquiring power grid project full life cycle data of the power grid project; determining each power grid evaluation index information and index information importance mapping relation corresponding to the power grid engineering according to the power grid engineering full life cycle data; mapping the evaluation index information of each power grid into corresponding importance information to be adjusted according to the index information importance mapping relation; according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information; constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining power resource interaction conditions in the power grid engineering.

In a second aspect, the application provides a power grid engineering index system algorithm model application method. The method comprises the following steps: responding to a power resource interaction condition monitoring task of power grid engineering, and acquiring a power grid engineering index system algorithm model, wherein the power grid engineering index system algorithm model is constructed according to an ESG index system algorithm model determining method based on power grid engineering; adding power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain a power interactive power grid engineering index system algorithm model; inputting the real-time description data of the power grid project into the power interactive power grid project index system algorithm model to obtain power resource interaction condition information; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.

In a third aspect, the application also provides a device for determining the algorithm model of the ESG index system based on the power grid engineering. The device comprises: the power grid data acquisition module is used for responding to a power grid project influence scoring task corresponding to the power grid project and acquiring power grid project full life cycle data of the power grid project; the mapping relation determining module is used for determining each power grid evaluation index information corresponding to the power grid project and an index information importance mapping relation according to the power grid project full life cycle data; the importance information calculation module is used for mapping each power grid evaluation index information into corresponding importance information to be adjusted according to the index information importance mapping relation; the importance information adjustment module is used for adjusting the importance information to be adjusted according to an importance information adjustment model corresponding to the power grid engineering influence scoring task to obtain adjusted importance information corresponding to the power grid evaluation index information; the power grid model construction module is used for constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining power resource interaction conditions in the power grid engineering.

In a fourth aspect, the application also provides an application device of the power grid engineering index system algorithm model. The device comprises: the system comprises a power grid model acquisition module, a power grid engineering index system algorithm module and a power grid model analysis module, wherein the power grid model acquisition module is used for responding to a power resource interaction condition monitoring task of power grid engineering to acquire a power grid engineering index system algorithm model, and the power grid engineering index system algorithm model is constructed according to an ESG index system algorithm model determination method based on power grid engineering; the interactive model obtaining module is used for adding power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain a power interactive power grid engineering index system algorithm model; the interactive information calculation module is used for inputting the real-time description data of the power grid project into the power interactive power grid project index system algorithm model to obtain power resource interactive condition information; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.

According to the method, the device, the computer equipment, the storage medium and the computer program product for determining the ESG index system algorithm model based on the power grid engineering, the full life cycle data of the power grid engineering are obtained by responding to the power grid engineering influence scoring task corresponding to the power grid engineering; determining the importance mapping relation of each power grid evaluation index information and index information corresponding to the power grid engineering according to the full life cycle data of the power grid engineering; according to the index information importance mapping relation, mapping the evaluation index information of each power grid into corresponding importance information to be adjusted; according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information; constructing a power grid engineering index system algorithm model corresponding to power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining the power resource interaction condition in power grid engineering.

The method comprises the steps of determining an index information importance mapping relation for calculating evaluation index information of each power grid by using the whole life cycle data of the power grid engineering, adjusting each importance information according to an importance information adjustment model based on the whole life cycle data of the power grid engineering, and finally constructing a power grid engineering index system algorithm model for describing the interaction condition of power resources by using the adjusted importance information. The method can improve the accuracy of describing the resource interaction condition generated between the running and environment interaction of the full life cycle of the power grid project, and effectively prevent ESG risks for the power grid project.

Drawings

Fig. 1 is an application environment diagram of an ESG-index system algorithm model determination method based on power grid engineering in one embodiment;

fig. 2 is a flow chart of a method for determining an algorithm model of an ESG-index system based on power grid engineering in one embodiment;

FIG. 3 is a flowchart of a method for determining importance information to be adjusted according to an embodiment;

FIG. 4 is a flowchart of a first importance information obtaining method according to an embodiment;

FIG. 5 is a flowchart of a second method for obtaining importance information according to one embodiment;

FIG. 6 is a flowchart of a method for obtaining adjusted importance information according to one embodiment;

FIG. 7 is a flowchart of a method for obtaining adjusted importance information according to another embodiment;

FIG. 8 is a flow chart of a method for constructing an algorithm model of a power grid engineering index system in one embodiment;

FIG. 9 is a flowchart of a method for applying an algorithm model of a power grid engineering index system according to an embodiment;

FIG. 10 is a flow chart of a method of developing full lifecycle data for power grid engineering in one embodiment;

FIG. 11 is a schematic diagram of an algorithm model analysis logic of a power grid engineering index system in one embodiment;

fig. 12 is a structural block diagram of an ESG-index system algorithm model determining device based on power grid engineering in one embodiment;

FIG. 13 is a block diagram of an apparatus for applying an algorithm model of a power grid engineering index system in one embodiment;

fig. 14 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The ESG index system algorithm model determining method based on the power grid engineering provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 responds to the power grid project influence scoring task corresponding to the power grid project of the terminal 102, and the server 104 acquires the power grid project full life cycle data of the power grid project from the terminal 102; determining the importance mapping relation of each power grid evaluation index information and index information corresponding to the power grid engineering according to the full life cycle data of the power grid engineering; according to the index information importance mapping relation, mapping the evaluation index information of each power grid into corresponding importance information to be adjusted; according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information; constructing a power grid engineering index system algorithm model corresponding to power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining the power resource interaction condition in power grid engineering. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, an ESG-index system algorithm model determining method based on power grid engineering is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

step 202, acquiring full life cycle data of the power grid project in response to the power grid project influence scoring task corresponding to the power grid project.

The power grid engineering may be the engineering of the whole life cycle of the power grid system, including design, purchase, construction, operation and rejection of the power grid system.

The power grid engineering influence scoring task can be a mutual influence among various indexes of the power grid engineering and a degree evaluation task of influence of the various indexes on the outside.

The power grid engineering full life cycle data can be description data of a full life cycle from construction to scrapping of the power grid engineering, wherein the power grid engineering full life cycle data comprises power grid engineering design data, power grid engineering resource interaction data, power grid engineering construction data, power grid engineering operation data and power grid engineering loss data which are data generated by design, purchase, construction, operation and scrapping of a power grid system respectively; the power grid engineering design data corresponds to data generated in the power grid system design process, the power grid engineering resource interaction data corresponds to data generated in the power grid system equipment purchasing process, the power grid engineering construction data corresponds to data generated in the power grid system construction process, the power grid engineering operation data corresponds to data generated in the power grid system construction operation, and the power grid engineering loss data corresponds to data generated after the power grid system is scrapped.

Specifically, the server 104 responds to the instruction of the terminal 102 about the scoring task of the grid project effect corresponding to the grid project, acquires the grid project full life cycle data of the grid project from the terminal 102, stores the acquired grid project full life cycle data in the storage unit, and when the server needs to process any data record in the grid project full life cycle data, retrieves volatile storage resources from the storage unit for the central processing unit to calculate. Any data record may be a single data input to the central processing unit, or may be a plurality of data input to the central processing unit at the same time. Before acquiring the full life cycle data of the power grid project, performing data preprocessing on the acquired data as shown in the step of the flow chart of the full life cycle data development method of the power grid project in one embodiment of fig. 10.

And 204, determining each power grid evaluation index information corresponding to the power grid engineering and an index information importance mapping relation according to the power grid engineering full life cycle data.

The grid evaluation index information may be a set of individual evaluation indexes for evaluating the grid engineering.

The index information importance mapping relationship may be a weight assignment algorithm for calculating each evaluation index of the power grid engineering. The index information importance mapping relation comprises an analytic hierarchy mapping relation and an entropy value mapping relation.

Specifically, according to the power grid engineering design data, the power grid engineering resource interaction data, the power grid engineering construction data, the power grid engineering operation data and the power grid engineering loss data in the power grid engineering full life cycle data, and combining the actual power grid engineering influence scoring task requirements, determining each power grid evaluation index information scoring the interaction influence of the power grid engineering; and selecting a plurality of proper weight assignment algorithms from the plurality of weight assignment algorithms as index information importance mapping relations for each power grid evaluation index information according to the data and the conditions.

And step 206, mapping the evaluation index information of each power grid into corresponding importance information to be adjusted according to the index information importance mapping relation.

The importance information may be weights corresponding to respective evaluation indexes for evaluating the power grid engineering; the importance information that is not adjusted according to the actual situation is called importance information to be adjusted, and the importance information that is already adjusted according to the actual situation is called importance information that is already adjusted.

Specifically, based on an Analytic Hierarchy Process (AHP) mapping relation, a table according to the constructed evaluation indexes is generated according to the evaluation index information of each power grid and a 1-9 grade grading method, and then the evaluation index information of each power grid in the table is graded according to the whole life cycle data of the power grid engineering, so that an index information quantization judgment matrix is obtained. Further, a method is utilized to solve the matrix maximum eigenvalue (lambda max for short) and each matrix eigenvector (Wi for short) of the index information quantization judgment matrix, and consistency test is carried out on the matrix maximum eigenvalue and each matrix eigenvector. And finally, carrying out first weight assignment on the evaluation index information of each power grid according to the maximum eigenvalue of the matrix and the eigenvector of each matrix to obtain first importance information. Table 1 is a scale description of the hierarchical mapping relationship.

TABLE 1-9 scale description of analytic hierarchy mapping relationship

Based on the entropy mapping relation (entropy method), constructing an index information decision matrix corresponding to the entropy mapping relation according to each power grid evaluation index information, wherein the index information decision matrix comprises corresponding decision matrix information quantity generated by combining each power grid evaluation index information with power grid engineering full life cycle data. And solving the index information decision matrix by using a probability distribution method to obtain each piece of second importance information. In informatics, entropy is an indicator of uncertainty, represented by a probability distribution, which considers a broad distribution to be more uncertain than a distribution with distinct peaks. The specific expression method is as follows:

Wherein, the liquid crystal display device comprises a liquid crystal display device,k is a positive constant. When all P _i When all are equal, i.e. P _i =1/n, the entropy value is maximum. The smaller the difference in index values, the lower the evaluation effect on the scheme, and the weight should be reduced.

And finally, carrying out weighted summation on the first importance information and the second importance information of the same power grid evaluation index information to obtain importance information to be adjusted of the power grid evaluation index information, and similarly obtaining the importance information to be adjusted of each power grid evaluation index information.

And step 208, according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information.

The importance information adjustment model can be a modularized algorithm model, and an importance matrix model can be selected.

Specifically, according to the importance information to be adjusted and the importance information adjustment model, the importance data point of each importance information to be adjusted in the importance information adjustment model can be determined; and counting the importance information to be adjusted, and determining the importance median of all the importance information to be adjusted. Further, the importance data points corresponding to the importance information to be adjusted are respectively compared with the importance median, and an importance information comparison result is obtained, wherein the importance information comparison result comprises importance data points larger than the importance median, the importance data points are equal to the importance median, and the importance data points are smaller than the importance median. And grading the importance information to be adjusted according to the importance information comparison result, namely grading the importance information to be adjusted according to the difference value between the importance digital data point and the importance median, and sequencing the importance information to be adjusted according to the principle of ten equal divisions to obtain the importance information to be graded. And finally, determining the weight adjustment proportion allocated to each importance information to be adjusted according to each divided importance information, and multiplying each importance information to be adjusted by the corresponding weight adjustment proportion to obtain each adjusted importance information.

And 210, constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information.

The power grid engineering index system algorithm model can be a model for determining the power resource interaction condition in power grid engineering.

Specifically, according to the evaluation index information of each power grid and the influence scoring task of the power grid engineering, each target index evaluation module matched with the power grid engineering is selected from a plurality of index evaluation modules. For example: and combining an overall index framework, carrying out overall design on an index algorithm according to a modularized thought, and determining and forming three major modules of 'high-quality engineering evaluation', 'new index evaluation', 'comprehensive inspection and other evaluation'. According to the evaluation index information of each power grid and the adjusted importance information, the weight of each index evaluation module is calculated to obtain the importance information of the evaluation module corresponding to each index evaluation module, for example: the weight of each module in the corresponding report is set according to the duty ratio of each module in the second-level index and the third-level index, the power grid engineering ESG evaluation rating report (total report) is used, the weight of the high-quality engineering evaluation module is 0.4-0.55, the weight of the new index evaluation is 0.15-0.25, and the weight of other evaluation such as comprehensive inspection is 0.2-0.45. And finally, constructing and constructing a power grid engineering index system algorithm model according to the importance information of each evaluation module, wherein if the power grid engineering index system algorithm model is not developed in the current period, the latest period latest range of the inspection evaluation result is selected as the score of the module. FIG. 11 is a schematic diagram of an algorithm model analysis logic of a power grid engineering index system in one embodiment.

In the method for determining the ESG index system algorithm model based on the power grid engineering, the full life cycle data of the power grid engineering is obtained by responding to the power grid engineering influence scoring task corresponding to the power grid engineering; determining the importance mapping relation of each power grid evaluation index information and index information corresponding to the power grid engineering according to the full life cycle data of the power grid engineering; according to the index information importance mapping relation, mapping the evaluation index information of each power grid into corresponding importance information to be adjusted; according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information; constructing a power grid engineering index system algorithm model corresponding to power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining the power resource interaction condition in power grid engineering.

In one embodiment, as shown in fig. 3, according to the index information importance mapping relationship, mapping each power grid evaluation index information into corresponding importance information to be adjusted includes:

step 302, mapping the evaluation index information of each power grid into corresponding first importance information according to the analytic hierarchy process mapping relationship.

Wherein, the analytic hierarchy process mapping relationship may be an AHP analytic hierarchy process.

The first importance information may be a weight obtained by performing weight assignment calculation on the grid evaluation index information through an AHP analytic hierarchy process.

Specifically, based on an Analytic Hierarchy Process (AHP) mapping relation, a table according to the constructed evaluation indexes is generated according to the evaluation index information of each power grid and a 1-9 grade grading method, and then the evaluation index information of each power grid in the table is graded according to the whole life cycle data of the power grid engineering, so that an index information quantization judgment matrix is obtained. Further, a method is utilized to solve the matrix maximum eigenvalue (lambda max for short) and each matrix eigenvector (Wi for short) of the index information quantization judgment matrix, and consistency test is carried out on the matrix maximum eigenvalue and each matrix eigenvector. And finally, carrying out first weight assignment on the evaluation index information of each power grid according to the maximum eigenvalue of the matrix and the eigenvector of each matrix to obtain first importance information.

And step 304, mapping the evaluation index information of each power grid into corresponding second importance information according to the entropy mapping relation.

The entropy mapping relationship may be an entropy method.

And the second importance information carries out weight assignment calculation on the power grid evaluation index information through an entropy method to obtain weight.

Specifically, based on the entropy mapping relation (entropy method), an index information decision matrix corresponding to the entropy mapping relation is constructed according to each power grid evaluation index information, wherein the index information decision matrix comprises corresponding decision matrix information quantity generated by combining each power grid evaluation index information with power grid engineering full life cycle data. And solving the index information decision matrix by using a probability distribution method to obtain each piece of second importance information. In informatics, entropy is an indicator of uncertainty, represented by a probability distribution, which considers a broad distribution to be more uncertain than a distribution with distinct peaks. The specific expression method is as follows:

Step 306, determining importance information to be adjusted according to the first importance information and the second importance information.

Specifically, the first importance information and the second importance information of the same power grid evaluation index information are weighted and summed, so that the importance information to be adjusted of the power grid evaluation index information can be obtained, and the importance information to be adjusted of each power grid evaluation index information can be obtained in the same way.

In this embodiment, the importance information of the power grid evaluation index information under different mapping relations is calculated by using different mapping relations, and the importance information calculated by the two mapping relations is integrated for the same power grid evaluation index information, so that the final importance information corresponding to the power grid evaluation index information can reflect the actual bias more and the matching degree of the index scoring of the power grid project with the reality can be improved by combining the characteristics of different mapping relations.

In one embodiment, as shown in fig. 4, mapping each grid evaluation index information into corresponding first importance information according to a hierarchical analysis mapping relationship includes:

and step 402, constructing an index information quantization judgment matrix according to the evaluation index information of each power grid based on the analytic hierarchy process mapping relationship.

The index information quantization judgment matrix can be a matrix in the weight assignment calculation process of the AHP analytic hierarchy process on the power grid evaluation index information, the matrix is the judgment given to the relative importance of each power grid evaluation index information of each hierarchy, the judgment is represented by a numerical value, and the judgment is written into a matrix form result.

Specifically, based on an Analytic Hierarchy Process (AHP) mapping relation, a table according to the constructed evaluation indexes is generated according to the evaluation index information of each power grid and a 1-9 grade grading method, and then the evaluation index information of each power grid in the table is graded according to the whole life cycle data of the power grid engineering, so that an index information quantization judgment matrix is obtained.

And step 404, solving the index information quantization judgment matrix according to a method root to obtain a matrix maximum eigenvalue and each matrix eigenvector.

The maximum eigenvalue of the matrix may be the eigenvalue with the largest eigenvalue value among the eigenvalues calculated by the index information quantization judgment matrix.

The matrix eigenvector may be an eigenvector obtained after the index information quantization judgment matrix is solved.

Specifically, a method of solving the maximum eigenvalue (lambda max for short) and each matrix eigenvector (Wi for short) of the index information quantization judgment matrix is utilized, and consistency test is carried out on the maximum eigenvalue and each matrix eigenvector.

And step 406, obtaining the first importance information according to the matrix maximum eigenvalue and the matrix eigenvectors.

Specifically, first weight assignment is carried out on each power grid evaluation index information according to the matrix maximum eigenvalue and each matrix eigenvector, and each first importance information is obtained.

In this embodiment, the index information quantization judgment matrix constructed according to the power grid evaluation index information is solved to obtain the maximum eigenvalue of the matrix and the eigenvectors of each matrix, and the importance information of the power grid evaluation index information is calculated by using the solving result, so that the elements related to decision can be decomposed into the levels of targets, criteria, schemes and the like, and the targets of qualitative analysis and quantitative analysis can be satisfied on the basis, so that the first importance information can be highly matched with the corresponding calculated power grid evaluation index information.

In one embodiment, as shown in fig. 5, mapping each grid evaluation index information into corresponding second importance information according to the entropy mapping relationship includes:

step 502, constructing an index information decision matrix according to the evaluation index information of each power grid based on the entropy mapping relation.

The index information decision matrix can be a matrix form which represents the correlation between a decision scheme and grid evaluation index information, and is commonly used for quantitative decision analysis.

Specifically, based on the entropy mapping relation (entropy method), an index information decision matrix corresponding to the entropy mapping relation is constructed according to each power grid evaluation index information, wherein the index information decision matrix comprises corresponding decision matrix information quantity generated by combining each power grid evaluation index information with power grid engineering full life cycle data.

And step 504, solving an index information decision matrix to obtain each piece of second importance information.

Specifically, the index information decision matrix is solved by using a probability distribution method, so that each piece of second importance information can be obtained. In informatics, entropy is an indicator of uncertainty, represented by a probability distribution, which considers a broad distribution to be more uncertain than a distribution with distinct peaks. The specific expression method is as follows:

In this embodiment, the importance information of each power grid evaluation index information is obtained by solving the index information decision matrix constructed according to the power grid evaluation index information, so that the objectivity of the second importance information can be stronger according to the objective weighting legal analysis and quantitative analysis targets.

In one embodiment, as shown in fig. 6, according to an importance information adjustment model corresponding to a power grid engineering influence scoring task, adjusting importance information to be adjusted to obtain adjusted importance information corresponding to power grid evaluation index information, including:

step 602, determining importance digital data points and importance median corresponding to the importance information to be adjusted according to the importance information to be adjusted.

The importance degree data points can be represented by the importance degree information to be adjusted in the form of data points.

The importance median may be a median of each importance information to be adjusted.

Specifically, according to the importance information to be adjusted and the importance information adjustment model, the importance data point of each importance information to be adjusted in the importance information adjustment model can be determined; and counting the importance information to be adjusted, and determining the importance median of all the importance information to be adjusted.

Step 604, based on the importance information adjustment model, each importance information to be adjusted is adjusted according to each importance digital data point and the importance median, so as to obtain each adjusted importance information.

Specifically, the importance digital data points corresponding to the importance information to be adjusted are respectively compared with the importance median to obtain an importance information comparison result, wherein the importance information comparison result comprises that the importance digital data points are larger than the importance median, the importance digital data points are equal to the importance median, and the importance digital data points are smaller than the importance median. And grading the importance information to be adjusted according to the importance information comparison result, namely grading the importance information to be adjusted according to the difference value between the importance digital data point and the importance median, and sequencing the importance information to be adjusted according to the principle of ten equal divisions to obtain the importance information to be graded. And finally, determining the weight adjustment proportion allocated to each importance information to be adjusted according to each divided importance information, and multiplying each importance information to be adjusted by the corresponding weight adjustment proportion to obtain each adjusted importance information.

In this embodiment, the importance information to be adjusted is input to the importance information adjustment model to determine the importance data points and the importance median of each importance degree, so as to be used for adjusting the corresponding importance information to be adjusted respectively, so that the power grid engineering can be assisted to analyze internal and external factors of various indexes in the internal interaction and external interaction process, and the accuracy of the power resource interaction condition is improved.

In one embodiment, as shown in fig. 7, based on the importance information adjustment model, according to each importance digital data point and the importance median, each importance information to be adjusted is adjusted, so as to obtain each adjusted importance information, which includes:

and step 702, comparing each importance degree data point with the importance degree median to obtain an importance degree information comparison result.

The importance information comparison result may be a comparison result obtained by comparing the importance data point with the importance median, and represents the magnitude relationship between the importance data point and the importance median.

Specifically, the importance digital data points corresponding to the importance information to be adjusted are respectively compared with the importance median to obtain an importance information comparison result, wherein the importance information comparison result comprises that the importance digital data points are larger than the importance median, the importance digital data points are equal to the importance median, and the importance digital data points are smaller than the importance median.

And step 704, grading the importance information to be adjusted according to the importance information comparison result to obtain the importance information.

Wherein the partitioned importance information may be

Specifically, the importance information to be adjusted is classified according to the importance information comparison result, namely, the importance information to be adjusted is classified according to the difference value between the importance data point and the importance median, and then is ranked according to the principle of ten equal divisions, so that the importance information to be adjusted is obtained.

Step 706, adjusting each piece of divided importance information to obtain each piece of adjusted importance information.

Specifically, after weight adjustment ratios assigned to the importance information to be adjusted are determined according to the divided importance information, the importance information to be adjusted is multiplied by the corresponding weight adjustment ratio to obtain the adjusted importance information.

In this embodiment, each importance degree data point is compared with the importance degree median respectively, and is used for performing weighting calculation after grading the importance degree information to be adjusted, so that the weight assigned to the data point can be determined according to the grading ranking, and the subsequent construction of the power grid engineering index system algorithm model is more in line with the actual situation.

In one embodiment, as shown in fig. 8, according to each power grid evaluation index information and each adjusted importance information, a power grid engineering index system algorithm model corresponding to a power grid engineering is constructed, including:

Step 802, determining each index evaluation module of the power grid project according to each power grid evaluation index information and the power grid project influence scoring task.

The index evaluation module may be a module obtained by integrating a plurality of pieces of grid evaluation index information of the same type meeting the grid engineering influence scoring task, and is used for scoring the grid engineering.

Specifically, according to the evaluation index information of each power grid and the influence scoring task of the power grid engineering, each target index evaluation module matched with the power grid engineering is selected from a plurality of index evaluation modules. For example: and combining an overall index framework, carrying out overall design on an index algorithm according to a modularized thought, and determining and forming three major modules of 'high-quality engineering evaluation', 'new index evaluation', 'comprehensive inspection and other evaluation'.

Step 804, determining importance information of the evaluation module corresponding to each index evaluation module according to the evaluation index information of each power grid and the adjusted importance information.

The importance information of the evaluation module may be a weight corresponding to the index evaluation module.

Specifically, according to the evaluation index information of each power grid and the adjusted importance information, the weight of each index evaluation module is calculated to obtain the importance information of the evaluation module corresponding to each index evaluation module, for example: the weight of each module in the corresponding report is set according to the duty ratio of each module in the second-level index and the third-level index, the power grid engineering ESG evaluation rating report (total report) is used, the weight of the high-quality engineering evaluation module is 0.4-0.55, the weight of the new index evaluation is 0.15-0.25, and the weight of other evaluation such as comprehensive inspection is 0.2-0.45.

And step 806, constructing an algorithm model of the power grid engineering index system according to the importance information of each evaluation module.

Specifically, building and constructing a power grid engineering index system algorithm model according to importance information of each evaluation module, wherein if the power grid engineering index system algorithm model is not developed in the current period, the latest period latest range of inspection evaluation results are selected as the scores of the modules.

In this embodiment, by combining multiple pieces of grid evaluation index information of the same type into one index evaluation module, and subsequently using multiple index evaluation modules to construct a grid engineering index system algorithm model, the complexity of mutual interaction between the models can be reduced when the grid engineering index system algorithm model is constructed, the models can be more easily debugged to an optimal state when the models are debugged, and the calculation accuracy of the grid engineering index system algorithm model is improved.

In one embodiment, as shown in fig. 9, a method for applying an algorithm model of a power grid engineering index system is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

and step 902, responding to the power resource interaction condition monitoring task of the power grid engineering, and acquiring a power grid engineering index system algorithm model.

The power resource interaction condition monitoring task can be a degree monitoring task of interaction among various indexes of the power grid engineering and influence of the various indexes on the outside.

Specifically, the server 104 responds to the instruction of the terminal 102 about the power resource interaction condition monitoring task corresponding to the power grid project, acquires the power grid project index system algorithm model of the power grid project from the terminal 102, stores the acquired power grid project index system algorithm model in the storage unit, and when the server needs to utilize the power grid project index system algorithm model for calculation, retrieves volatile storage resources from the storage unit for calculation by the central processing unit. The power grid engineering index system algorithm model can be obtained by inputting single data into a central processing unit for serial calculation, or can be obtained by inputting a plurality of data into the central processing unit for parallel calculation.

And step 904, adding the power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain the power interactive power grid engineering index system algorithm model.

The power grid basic importance information can be preset according to the power resource interaction condition monitoring task, and the weight of each power grid evaluation index information is set.

The power interactive power grid engineering index system algorithm model can be a model with basic weight added to each power grid evaluation index information in the power grid engineering index system algorithm model.

Specifically, based on the basic importance information of the power grid and the adjusted importance information of the module algorithm, the ESG newly-added index evaluation sequentially carries out summarization and measurement on basic evaluation data according to the sequence of three-level indexes, two-level indexes and one-level indexes, and importance information setting rules adopted by each level index during summarization and measurement are in a mode of 'basic weight setting + weight dynamic adjustment', so that an algorithm model of the power interactive power grid engineering index system is obtained. And according to the power grid basic importance information in the model algorithm design part, the new index evaluation sum is 15% -25%. Before summarizing and metering, coding is set for each level of index for data processing. The index item codes of each level are as follows:

the codes in the first-level index corresponding index system are E1 and E2 … (first-level index codes for the environment), S1 and S2 … (first-level index codes for interaction) and G1 and G2 … (first-level index codes for monitoring) respectively;

the codes in the index system corresponding to the second-level indexes are E1.1 and E2.1 … (the second-level index codes for the environment), S1.1 and S2.1 … (the second-level index codes for the interaction), and G1.1 and G2.1 … (the second-level index codes for the monitoring) respectively;

The codes in the three-level index corresponding index system are E1.1.1 and E2.1.1 … (three-level index codes for the environment), S1.1.1 and S2.1.1 … (three-level index codes for interaction), G1.1.1 and G2.1.1 … (three-level index codes for monitoring);

the ESG final evaluation score of each evaluation subject is obtained by weighting and summarizing scores of the parts E, S and G, wherein the ESG final score is:

ESG score total = E environment total score × E environment weight + S interaction responsibility total score × S interaction responsibility weight + G monitoring total score × G monitoring weight (full 100 points)

E. The score of each part of S and G consists of three levels of index items arranged under each part, and the total metering process of the score of each part is divided into three stages:

firstly, setting a full score and a score/deduction rule for a three-level index item, wherein the full score accords with the score of index description evaluation content, the partial score accords with the index content but relates to the deduction rule, and the actual score (100 score) of the three-level index item is obtained when the partial score does not accord with the score 0 of the index description evaluation content;

secondly, based on the scores, carrying out layer-by-layer weighted summarization according to the weights of the set three-level index items and the set two-level index items to obtain the actual scores (100 points of full score) of the first-level index items;

Thirdly, based on the scores, weighting and summarizing according to the weights of all the set first-level index items, and finally obtaining the total score (100 points of full score) of the part of evaluation results.

And step 906, inputting the real-time description data of the power grid project into an algorithm model of the power interactive power grid project index system to obtain the power resource interaction condition information.

The power resource interaction condition information can be information representing specific conditions of interaction between the power grid engineering and the outside at any time in the whole life cycle process.

The power grid real-time description data can be data which is generated by the power grid engineering at any time in the whole life cycle process and is used for describing the condition of the power grid engineering.

Specifically, real-time description data of the power grid project is obtained, the data comprise implementation-generated power grid project design data, power grid project resource interaction data, power grid project construction data, power grid project operation data and power grid project loss data, the implementation-generated power grid project design data, power grid project resource interaction data, power grid project construction data, power grid project operation data and power grid project loss data are input into an electric power interaction power grid project index system algorithm model, electric power resource interaction condition information is obtained, and electric power resource interaction conditions of the power grid project are monitored.

In the embodiment, a power grid engineering index system algorithm model is obtained by responding to a power resource interaction condition monitoring task of power grid engineering, wherein the power grid engineering index system algorithm model is constructed according to a power grid engineering-based ESG index system algorithm model determining method; adding power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain a power interactive power grid engineering index system algorithm model; inputting the real-time description data of the power grid engineering into an algorithm model of the power interactive power grid engineering index system to obtain information of power resource interaction conditions; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.

By adding the power grid basic importance information into the power grid engineering index system algorithm model, the power interaction power grid engineering index system algorithm model meets the mode of basic weight setting and weight dynamic adjustment, the monitoring description of the interaction condition of the power resource interaction of the power engineering can be closer to the actual condition, and the ESG risk can be effectively prevented by the power grid engineering project.

It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a device for determining the ESG index system algorithm model based on the power grid engineering, which is used for realizing the method for determining the ESG index system algorithm model based on the power grid engineering. The implementation scheme of the device for solving the problems is similar to the implementation scheme recorded in the method, so that one or more ESG index system algorithm model determining devices based on power grid engineering are provided in the followingSpecific limitations in the embodiments can be referred to above for a limitation of an ESG-index system algorithm model determination method based on grid engineering, which is not described herein again _。

In one embodiment, as shown in fig. 12, there is provided an ESG-index system algorithm model determining apparatus based on power grid engineering, including: a power grid data acquisition module 1202, a mapping relationship determination module 1204, an importance information calculation module 1206, an importance information adjustment module 1208, and a power grid model construction module 1210, wherein:

the power grid data acquisition module 1202 is used for responding to a power grid project influence scoring task corresponding to the power grid project to acquire power grid project full life cycle data of the power grid project;

The mapping relation determining module 1204 is configured to determine, according to the full life cycle data of the power grid project, each power grid evaluation index information corresponding to the power grid project and an index information importance mapping relation;

the importance information calculating module 1206 is configured to map each power grid evaluation index information to corresponding importance information to be adjusted according to the index information importance mapping relationship;

the importance information adjustment module 1208 is configured to adjust importance information to be adjusted according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, so as to obtain adjusted importance information corresponding to the power grid evaluation index information;

the power grid model construction module 1210 is configured to construct a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining the power resource interaction condition in power grid engineering.

In one embodiment, the importance information calculating module 1206 is further configured to map each power grid evaluation index information to corresponding first importance information according to the analytic hierarchy process mapping relationship; mapping the evaluation index information of each power grid into corresponding second importance information according to the entropy mapping relation; and determining importance information to be adjusted according to the first importance information and the second importance information.

In one embodiment, the importance information calculating module 1206 is further configured to construct an index information quantization judgment matrix according to each power grid evaluation index information based on the hierarchical analysis mapping relationship; solving an index information quantization judgment matrix according to a method root to obtain a matrix maximum characteristic value and each matrix characteristic vector; and obtaining first importance information according to the matrix maximum eigenvalue and each matrix eigenvector.

In one embodiment, the importance information calculating module 1206 is further configured to construct an index information decision matrix according to the evaluation index information of each power grid based on the entropy mapping relationship; and solving an index information decision matrix to obtain each piece of second importance information.

In one embodiment, the importance information adjustment module 1208 is further configured to determine an importance data point and an importance median corresponding to each importance information to be adjusted according to each importance information to be adjusted; based on the importance information adjustment model, according to the importance digital data points and the importance median, the importance information to be adjusted is adjusted, and the adjusted importance information is obtained.

In one embodiment, the importance information adjustment module 1208 is further configured to compare each importance digital data point with an importance median to obtain an importance information comparison result; grading the importance information to be adjusted according to the importance information comparison result to obtain the importance information after grading; and adjusting the divided importance information to obtain the adjusted importance information.

In one embodiment, the power grid model construction module 1210 is further configured to determine each index evaluation module of the power grid project according to each power grid evaluation index information and the power grid project impact scoring task; determining importance information of an evaluation module corresponding to each index evaluation module according to the evaluation index information of each power grid and the adjusted importance information; and constructing an algorithm model of the power grid engineering index system according to the importance information of each evaluation module.

Based on the same inventive concept, the embodiment of the application also provides a method for using the sameThe power grid engineering index system algorithm model application device is used for realizing the power grid engineering index system algorithm model application method. The implementation scheme of the device for solving the problem is similar to the implementation scheme recorded in the method, so the specific limitation of the embodiment of the device for applying the algorithm model of the power grid engineering index system provided below can be referred to the limitation of the application method of the algorithm model of the power grid engineering index system, and the description is omitted herein _。

In one embodiment, as shown in fig. 13, there is provided a power grid engineering index system algorithm model application device, including: a power grid model acquisition module 1302, an interaction model obtaining module 1304, and an interaction information calculation module 1306, wherein:

The power grid model obtaining module 1302 is configured to obtain a power grid engineering index system algorithm model in response to a power resource interaction condition monitoring task of a power grid engineering, where the power grid engineering index system algorithm model is constructed according to a power grid engineering-based ESG index system algorithm model determining method;

the interaction model obtaining module 1304 is configured to add power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model, so as to obtain a power interaction power grid engineering index system algorithm model;

the interaction information calculation module 1306 is configured to input real-time description data of a power grid of the power grid project into an algorithm model of an index system of the power interaction power grid project, so as to obtain information of interaction conditions of power resources; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.

The above-mentioned ESG index system algorithm model determining device based on the power grid engineering and each module in the power grid engineering index system algorithm model applying device can be realized completely or partially by software, hardware and the combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 14. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing an ESG index system algorithm model determining method based on power grid engineering and an application method of the ESG index system algorithm model.

It will be appreciated by those skilled in the art that the structure shown in fig. 14 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements are applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-described method embodiments.

The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. An ESG index system algorithm model determining method based on power grid engineering is characterized by comprising the following steps:

responding to a power grid project influence scoring task corresponding to a power grid project, and acquiring power grid project full life cycle data of the power grid project;

determining each power grid evaluation index information and index information importance mapping relation corresponding to the power grid engineering according to the power grid engineering full life cycle data;

Mapping the evaluation index information of each power grid into corresponding importance information to be adjusted according to the index information importance mapping relation;

according to an importance information adjustment model corresponding to the power grid engineering influence scoring task, adjusting the importance information to be adjusted to obtain adjusted importance information corresponding to the power grid evaluation index information;

constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining power resource interaction conditions in the power grid engineering.

2. The method according to claim 1, wherein the index information importance map includes a hierarchical analysis map and an entropy value map; mapping the power grid evaluation index information into corresponding importance information to be adjusted according to the index information importance mapping relation, wherein the mapping comprises the following steps:

mapping each piece of power grid evaluation index information into corresponding first importance information according to the analytic hierarchy process mapping relationship;

mapping each power grid evaluation index information into corresponding second importance information according to the entropy mapping relation;

And determining each importance information to be adjusted according to each first importance information and each second importance information.

3. The method according to claim 2, wherein mapping each of the grid evaluation index information into corresponding first importance information according to the hierarchical analysis mapping relationship includes:

based on the analytic hierarchy process mapping relation, constructing an index information quantization judgment matrix according to each power grid evaluation index information;

solving the index information quantization judgment matrix according to a method root to obtain a matrix maximum eigenvalue and each matrix eigenvector;

and obtaining each piece of first importance information according to the matrix maximum eigenvalue and each matrix eigenvector.

4. The method according to claim 2, wherein mapping each of the grid evaluation index information into corresponding second importance information according to the entropy mapping relationship includes:

based on the entropy mapping relation, constructing an index information decision matrix according to each power grid evaluation index information;

and solving the index information decision matrix to obtain each piece of second importance information.

5. The method according to claim 1, wherein the adjusting each piece of importance information to be adjusted according to the importance information adjustment model corresponding to the power grid project influence scoring task to obtain adjusted importance information corresponding to each piece of power grid evaluation index information includes:

according to the importance information to be adjusted, determining importance digital data points and importance median corresponding to the importance information to be adjusted;

and based on the importance information adjustment model, adjusting each piece of importance information to be adjusted according to each importance digital data point and the importance median to obtain each piece of adjusted importance information.

6. The method of claim 5, wherein adjusting each of the importance information to be adjusted based on the importance information adjustment model according to each of the importance digital data points and the importance median to obtain each of the adjusted importance information comprises:

comparing each importance digital data point with the importance median to obtain an importance information comparison result;

Grading the importance information to be adjusted according to the importance information comparison result to obtain the importance information after grading;

and adjusting each piece of divided importance information to obtain each piece of adjusted importance information.

7. The method according to claim 1, wherein the constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to each power grid evaluation index information and each adjusted importance information includes:

determining each index evaluation module of the power grid project according to each power grid evaluation index information and the power grid project influence scoring task;

determining importance information of an evaluation module corresponding to each index evaluation module according to each power grid evaluation index information and each adjusted importance information;

and constructing the power grid engineering index system algorithm model according to the importance information of each evaluation module.

8. An application method of an algorithm model of a power grid engineering index system is characterized by comprising the following steps:

responding to a power resource interaction condition monitoring task of power grid engineering, and acquiring a power grid engineering index system algorithm model, wherein the power grid engineering index system algorithm model is constructed according to the ESG index system algorithm model determining method based on the power grid engineering as set forth in any one of claims 1 to 7;

Adding power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain a power interactive power grid engineering index system algorithm model;

inputting the real-time description data of the power grid project into the power interactive power grid project index system algorithm model to obtain power resource interaction condition information; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.

9. An ESG (electronic service guide) index system algorithm model determining device based on power grid engineering, which is characterized by comprising:

the power grid data acquisition module is used for responding to a power grid project influence scoring task corresponding to the power grid project and acquiring power grid project full life cycle data of the power grid project;

the mapping relation determining module is used for determining each power grid evaluation index information corresponding to the power grid project and an index information importance mapping relation according to the power grid project full life cycle data;

the importance information calculation module is used for mapping each power grid evaluation index information into corresponding importance information to be adjusted according to the index information importance mapping relation;

The importance information adjustment module is used for adjusting the importance information to be adjusted according to an importance information adjustment model corresponding to the power grid engineering influence scoring task to obtain adjusted importance information corresponding to the power grid evaluation index information;

the power grid model construction module is used for constructing a power grid engineering index system algorithm model corresponding to the power grid engineering according to the power grid evaluation index information and the adjusted importance information; the power grid engineering index system algorithm model is used for determining power resource interaction conditions in the power grid engineering.

10. An application device of an algorithm model of a power grid engineering index system, which is characterized by comprising:

the power grid model acquisition module is used for responding to a power resource interaction condition monitoring task of power grid engineering to acquire a power grid engineering index system algorithm model, wherein the power grid engineering index system algorithm model is constructed according to the ESG index system algorithm model determination method based on the power grid engineering as set forth in any one of claims 1 to 7;

the interactive model obtaining module is used for adding power grid basic importance information to each power grid evaluation index information in the power grid engineering index system algorithm model to obtain a power interactive power grid engineering index system algorithm model;

The interactive information calculation module is used for inputting the real-time description data of the power grid project into the power interactive power grid project index system algorithm model to obtain power resource interactive condition information; the power resource interaction condition information is used for monitoring the power resource interaction condition of the power grid engineering.