An enterprise green electricity consumption data processing and analysis system

Abstract

The present invention discloses an enterprise green electricity data processing and analysis system, belonging to the technical field of green electricity, which includes a platform end and an enterprise end; the platform end includes a registration module, an enterprise analysis module, and a plan publicity module; the registration module is used for enterprises to conduct information registration, obtain the registration information of each registered enterprise, and send each registration information to the enterprise information database for storage; and open the enterprise end usage permission for the corresponding registered enterprises; the enterprise analysis module is used for analyzing the registration information of each registered enterprise to obtain each enterprise unit category; the plan publicity module is used for publicizing each green electricity improvement plan to each enterprise end and attaching the corresponding enterprise unit category label to each green electricity improvement plan; the enterprise end includes an electricity collection module and a green electricity analysis module; the electricity collection module is used for collecting the electricity consumption data of the enterprise and establishing an electricity consumption curve graph based on the electricity consumption data; the green electricity analysis module is used for conducting green electricity analysis based on the electricity consumption curve graph to obtain plan evaluation data.

Description

Technical Field

[0001] The invention belongs to the technical field of green power utilization, and specifically relates to a system for processing and analyzing enterprise green power consumption data. Background Art

[0002] In the current energy consumption structure, green power, as a key force in promoting energy conservation, emission reduction, and sustainable development, has become increasingly important. Green power, such as electricity generated from renewable energy sources like solar, wind, and hydropower, has gradually become an important choice for industrial enterprises' energy consumption due to its low emissions and high energy efficiency. However, despite the many advantages of green power, in the actual application process, various industrial enterprises generally do not carry out green power improvements for various reasons, resulting in the underutilization of green power in some enterprises of certain scales. For example, most industrial enterprises lack sufficient understanding of information such as the sources, prices, and environmental benefits of green power, making it difficult to make reasonable energy consumption decisions; the utilization of green power involves complex power system transformations and intelligent management, requiring high technical capabilities, and small and medium-sized enterprises often find it difficult to bear the costs themselves; industrial enterprises lack professional energy management knowledge and technical support, making it difficult to develop effective green power utilization plans based on their actual situations.

[0003] Based on this, to solve the above problems, the invention provides a system for processing and analyzing enterprise green power consumption data. Summary of the Invention

[0004] To solve the problems existing in the above solutions, the invention provides a system for processing and analyzing enterprise green power consumption data.

[0005] The object of the invention can be achieved through the following technical solutions:

[0006] A system for processing and analyzing enterprise green power consumption data, comprising a platform terminal and an enterprise terminal;

[0007] The platform terminal includes a registration module, an enterprise analysis module, and a solution publicity module;

[0008] The registration module is used for enterprises to register information, obtain the registration information of each registered enterprise, establish an enterprise information database, and send the registration information of each registered enterprise to the enterprise information database for storage; according to the registration information in the enterprise information database, the enterprise terminal usage permission is granted to the corresponding registered enterprises.

[0009] The enterprise analysis module is used to analyze the registration information of each registered enterprise to obtain each enterprise unit category.

[0010] The said plan publicity module is used to publicize each green power consumption improvement plan to each enterprise terminal, match each green power consumption improvement plan with each enterprise unit category, determine the green power consumption improvement plan suitable for each enterprise unit category, and label the corresponding enterprise unit category for each green power consumption improvement plan.

[0011] Furthermore, the method for analyzing the registration information of each registered enterprise includes:

[0012] Identify each registration information in the enterprise information database, extract features from each of the said registration information to obtain each enterprise feature; integrate each of the said enterprise features into the feature positioning data of the corresponding registered enterprise;

[0013] Based on each feature positioning data, conduct an initial classification of the registered enterprises to obtain each benchmark classification;

[0014] Set test simulation data, test each benchmark classification through the said test simulation data to obtain a set of test results corresponding to each benchmark classification, and the set of test results consists of each single test value;

[0015] Mark the single test value in the set of test results as DFi, where i = 1, 2,..., n, and n is a positive integer;

[0016] According to the formula Calculate the merging value between the corresponding two benchmark classifications;

[0017] In the formula: WA is the merging value; DFi1 and DFi2 are the corresponding single test values in the two sets of test results respectively;

[0018] Merge the benchmark classifications with the merging value less than the threshold X1 to obtain new benchmark classifications; and so on until there are no benchmark classifications that meet the merging requirements, and mark the remaining benchmark classifications as enterprise unit categories.

[0019] Furthermore, the method for conducting an initial classification of the registered enterprises based on each feature positioning data includes:

[0020] Step SA1: Establish a difference judgment model;

[0021] Step SA2: Analyze each feature positioning data through the difference judgment model to obtain the difference judgment values between each feature positioning data; the difference judgment values include 1 and 0;

[0022] Step SA3: Classify the registered enterprises corresponding to the two feature positioning data with the difference judgment value of 1 into one category to obtain a benchmark classification, and take the intermediate value of each feature positioning data corresponding to the benchmark classification as the benchmark feature positioning data corresponding to the benchmark classification;

[0023] Step SA4: Calculate the difference judgment value between the reference feature location data of the reference classification and each feature location data or the reference feature location data of other reference classifications; perform corresponding merging according to the difference judgment value to obtain a new reference classification.

[0024] Step SA5: Loop step SA4 until there is no difference judgment value equal to 1, which means analyzing based on the reference classification and there is no case where the difference judgment value is 1; return to step SA2 until there is no difference judgment value equal to 1, which means that the difference judgment values between the remaining feature location data are not equal to 1, or there is no feature location data to compare; obtain each reference classification, and the registered enterprises corresponding to the remaining feature location data become reference classifications independently.

[0025] Furthermore, the expression of the difference judgment model is:

[0026]

[0027] In the formula: s is the input data, and the input data is the feature location data for comparison; the output data is the difference judgment value CR(s).

[0028] Furthermore, data protection is carried out between the platform side and each enterprise side through preset data protection measures.

[0029] The enterprise side includes an electricity collection module and a green electricity analysis module;

[0030] The electricity collection module is used to collect the electricity consumption data of the enterprise, establish an electricity consumption curve graph based on the electricity consumption data, and display the electricity consumption curve graph to the enterprise management personnel in real time.

[0031] The green electricity analysis module is used to conduct green electricity consumption analysis based on the electricity consumption curve graph, determine each evaluation plan according to the enterprise information; conduct application evaluation on each evaluation plan based on the electricity consumption curve graph to obtain the corresponding plan evaluation data, and display the plan evaluation data of each evaluation plan to the enterprise management personnel. The enterprise management personnel make green electricity consumption decisions based on the displayed plan evaluation data and evaluation plans.

[0032] Furthermore, the method for establishing the electricity consumption curve graph includes:

[0033] Real-time collect the electricity consumption data of the enterprise, identify the electricity attributes corresponding to each time of the electricity consumption data, and the electricity attributes include self-produced green electricity, public green electricity, and conventional electricity;

[0034] Classify and identify power consumption data according to power consumption attributes to obtain self-generated power consumption, public power consumption, and conventional power consumption; calculate the comprehensive power consumption corresponding to each time according to the self-generated power consumption, public power consumption, and conventional power consumption; generate a power consumption curve graph based on the comprehensive power consumption at each time, with the horizontal axis of the power consumption curve graph being time and the vertical axis being the comprehensive power consumption, and mark the self-generated power consumption, public power consumption, and conventional power consumption corresponding to each time in the power consumption curve graph.

[0035] Furthermore, the method for applying and evaluating each evaluation scheme based on the power consumption curve graph includes:

[0036] Conduct simulation analysis according to the evaluation scheme to obtain a simulation curve graph;

[0037] Set up a power optimization graph according to the simulation curve graph and the power consumption curve graph, and identify the green subsidies and implementation costs corresponding to each evaluation scheme;

[0038] Fit the power optimization graph to obtain the corresponding power optimization function, and mark the power optimization function as GX(t);

[0039] According to the formula Calculate the scheme evaluation value of the evaluation scheme;

[0040] In the formula: PX is the scheme evaluation value; FB is the implementation cost; BY is the green subsidy;

[0041] Integrate the scheme evaluation value, the simulation curve graph, and the power optimization graph into scheme evaluation data.

[0042] Furthermore, the method for setting up a power optimization graph according to the simulation curve graph and the power consumption curve graph includes:

[0043] Calculate the power consumption saving cost and the green power consumption change amount corresponding to each time according to the simulation curve graph and the power consumption curve graph;

[0044] Calculate the power optimization value corresponding to the corresponding time according to the formula QAt = YBt + η × LKt;

[0045] In the formula: QAt is the power optimization value; t is time; YBt is the power consumption saving cost; η is the green power conversion coefficient; LKt is the green power consumption change amount;

[0046] Generate a power optimization graph based on the obtained power optimization value, with the horizontal axis being time and the vertical axis being the power optimization value.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] Through the present invention, aiming at the problem that existing industrial enterprises are not proactive and difficult to analyze and utilize green electricity by themselves, the platform party has realized the dynamic display of the real-time electricity consumption of enterprises, the dynamic announcement and simulation of green electricity improvement plans, and intelligent recommendation and evaluation by constructing a unified green electricity service platform; it not only helps to improve the awareness and utilization rate of green electricity by industrial enterprises, but also can effectively reduce the electricity consumption cost of enterprises, and promote the low-carbon and sustainable development of energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is a block diagram of the principle of the present invention;

[0051] Figure 2 It is an example diagram of the electricity consumption curve of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0053] As Figures 1 to 2 shown, an enterprise green electricity data processing and analysis system includes a platform end and an enterprise end;

[0054] The platform end is generally organized and established by the corresponding competent department, or can also be established by the corresponding enterprise; it includes a registration module, an enterprise analysis module, and a plan publicity module;

[0055] The registration module is used for enterprises to register information. The registration information includes relevant information such as enterprise name, address, scale, business scope, electricity consumption method, etc. Obtain the registration information of each registered enterprise, establish an enterprise information database, and send the obtained registration information to the enterprise information database for storage; according to the registration information in the enterprise information database, open the corresponding enterprise end usage rights for the corresponding enterprises.

[0056] The enterprise analysis module is used to analyze the registration information of each registered enterprise to obtain each enterprise unit class.

[0057] The methods for analyzing the registration information of each registered enterprise include:

[0058] Dock with the enterprise information database, identify each registration information in the enterprise information database, extract features from each registration information to obtain each enterprise feature. The enterprise features are set according to the factors that affect green electricity consumption, such as environmental conditions, enterprise types, electricity consumption methods, electricity consumption scales, etc. Specifically, the platform party establishes an enterprise feature template, and subsequent feature identification and extraction are carried out according to the enterprise feature template; integrate the enterprise features corresponding to the registered enterprises into feature positioning data;

[0059] Compare the feature positioning data with each other, and merge the registered enterprises corresponding to the feature positioning data with smaller differences to obtain each benchmark classification; smaller differences mean that the corresponding differences can directly determine that they do not affect the green electricity consumption improvement plan, which is used to quickly screen and reduce the subsequent data processing volume; specifically, a difference judgment model can be established, and the expression of the difference judgment model is In the formula: s is the input data, and the input data is the feature positioning data for comparison; the output data is the difference judgment value CR(s); where meeting the difference requirements means meeting the smaller differences, that is, according to the historical green electricity consumption improvement data, it can be accurately ensured that the two can apply the same green electricity consumption improvement plan. Adapting to the same green electricity consumption improvement plan does not mean that the plans are exactly the same, but that based on the same plan framework, subsequent supplements are made to the framework according to the actual situation and needs of the enterprise; analyze the feature positioning data through the difference judgment model to obtain the difference judgment values between the feature positioning data, and classify the registered enterprises corresponding to the feature positioning data with a difference judgment value of 1 into one category; and so on, to obtain each benchmark classification.

[0060] Set test simulation data, and the test simulation data is each historical green electricity consumption improvement plan collected; test each benchmark classification through the test simulation data, that is, analyze whether the corresponding test simulation data can be applied according to the benchmark feature positioning data corresponding to each benchmark classification. If it cannot be applied, output a single test value of 1, otherwise it is 0, that is, the test evaluation result of a historical green electricity consumption improvement plan in the test simulation data; test each benchmark classification through the test simulation data, and a test result set corresponding to each benchmark classification will be obtained, which is composed of each single test value;

[0061] Mark the single test value in the test result set as DFi, i = 1, 2,..., n, and n is a positive integer;

[0062] According to the formula Calculate the merger value between the corresponding two benchmark classifications;

[0063] In the formula: WA is the merger value; DFi1 and DFi2 are the corresponding single test values in the two test result sets respectively;

[0064] Merge the benchmark classifications whose combined values are less than the threshold X1 to obtain new benchmark classifications; and so on until there are no benchmark classifications that meet the merging requirements. For the merged evaluation of the benchmark classifications, it is necessary to calculate the combined values between their corresponding original benchmark classifications one by one. If they are all less than the threshold X1, it is regarded as meeting the merging requirements; mark the remaining benchmark classifications as enterprise unit classes.

[0065] The solution publicity module is used to publicize each green electricity improvement solution, which is set by the platform side and generally formulated based on the policies of relevant departments to form green electricity improvement solutions suitable for different types of enterprises; it is equivalent to a case solution summarized according to experience, resources, etc.; and match each green electricity improvement solution with each enterprise unit class to determine the green electricity improvement solution suitable for each enterprise unit class and label the corresponding enterprise unit class for each green electricity improvement solution.

[0066] The matching of the green electricity improvement solution can generally be determined by the corresponding staff when setting the green electricity improvement solution, that is, for what kind of enterprises it is set, or specified by the platform side staff.

[0067] The enterprise side is a user side established by the platform side for enterprises to register and use, and has data security protection measures, that is, the enterprise private data such as the enterprise electricity consumption data displayed in real time in the enterprise side cannot be accessed or viewed by the platform side; that is, corresponding data protection measures are set by the platform side according to the data protection requirements, and data protection is carried out between the platform side and each enterprise side through the preset data protection measures.

[0068] The enterprise side includes a power collection module and a green electricity analysis module;

[0069] The power collection module is used to collect the electricity consumption data of the enterprise. Through devices such as smart meters and sensors, it collects the electricity consumption data of each link of the enterprise power grid in real time, including voltage, current, power, electricity consumption, etc.; to obtain the electricity consumption data under normal conditions, which is representative; determine the power attributes of the electricity consumption data, and the power attributes include self-produced green electricity, public green electricity and conventional electricity. Self-produced green electricity refers to the power supply by the enterprise's self-built green power generation equipment, public green electricity refers to the power supply through external green power, and conventional electricity refers to the electricity consumption corresponding to non-green power supply; the electricity consumption attributes can be determined according to its electricity consumption data;

[0070] The electricity consumption data is classified and identified according to the electricity consumption attributes to obtain self-generated electricity consumption, public electricity consumption and conventional electricity consumption; the comprehensive electricity consumption corresponding to each time is calculated based on the self-generated electricity consumption, public electricity consumption and conventional electricity consumption; an electricity consumption curve is generated based on the comprehensive electricity consumption at each time, the horizontal axis of the electricity consumption curve is time, the vertical axis is comprehensive electricity consumption, and the self-generated electricity consumption, public electricity consumption and conventional electricity consumption corresponding to each time are marked in the electricity consumption curve, such as Figure 2 As shown, the electricity consumption curve chart consists of three parts, which can intuitively understand the composition of electricity consumption at the corresponding time; the electricity consumption curve chart is displayed to enterprise managers in real time.

[0071] The green electricity analysis module is used to perform green electricity analysis based on the electricity curve, identify the enterprise unit class corresponding to the enterprise, determine the green electricity improvement plan that meets the requirements according to the enterprise unit class, and mark it as an evaluation plan; perform application evaluation on each evaluation plan based on the electricity curve, obtain the corresponding plan evaluation data, and display the plan evaluation data of each evaluation plan to the enterprise managers, who make green electricity decisions based on the displayed plan evaluation data and the evaluation plan. That is, decide whether to apply it, or which evaluation plan to apply, and then make green electricity improvements based on the assistance of the platform.

[0072] Methods for evaluating the application of various evaluation schemes based on the electricity consumption curve diagram include:

[0073] Perform simulation analysis according to the evaluation plan to obtain a simulation curve graph; that is, assume that the evaluation plan is implemented, and analyze it according to the evaluation plan to determine its impact on the existing enterprise power application, specifically the impact on self-generated electricity consumption, public electricity consumption and conventional electricity consumption, and form a power consumption curve graph after the application change, marked as a simulation curve graph, and analyze based on the effect of the evaluation plan; exemplarily, a simulation evaluation model is established based on a neural network such as a DNN network, and a corresponding training set is established manually for training, and the training set includes input data and output data, the input data is the evaluation plan and enterprise electricity consumption related data; the output data is the self-generated electricity consumption, public electricity consumption and conventional electricity consumption obtained by simulation, and then a simulation curve graph is generated;

[0074] Set up a power optimization diagram based on the simulation curve diagram and the power consumption curve diagram to identify the green subsidies and implementation costs corresponding to each evaluation plan. The green subsidy is the economic subsidy given to the enterprise in accordance with the relevant regulations; the implementation cost is the expenditure cost corresponding to the implementation of the evaluation plan;

[0075] Fit the power optimization graph to obtain the corresponding power optimization function, and mark the power optimization function as GX(t);

[0076] According to the formula Calculate the scheme evaluation value of the evaluation scheme;

[0077] Where: PX is the scheme evaluation value; FB is the implementation cost; BY is the green subsidy;

[0078] Integrate the scheme evaluation value, the simulation curve graph and the power optimization graph into the scheme evaluation data.

[0079] The method for setting the power optimization graph according to the simulation curve graph and the power consumption curve graph includes:

[0080] Calculate the power consumption saving cost and the corresponding green power consumption change amount at each time according to the simulation curve graph and the power consumption curve graph. When applying green power such as self-built solar power, the power consumption cost will be reduced. Calculate by combining the power consumption change and the power consumption price at the corresponding time; the green power consumption change amount is obtained by comparing the self-generated power consumption and the public power consumption; the platform party sets the green power conversion coefficient, that is, converts the corresponding green power consumption into the corresponding economic value, which is set by the policies announced by the corresponding management department, etc., and can be adjusted in combination with the requirements of the enterprise. If the impact is not considered in the actual situation, the enterprise can set the green power conversion coefficient to 0;

[0081] Calculate the power optimization value at the corresponding time according to the formula QAt = YBt + η × LKt;

[0082] Where: QAt is the power optimization value; t is the time; YBt is the power consumption saving cost; η is the green power conversion coefficient; LKt is the green power consumption change amount;

[0083] Generate a power optimization graph according to the obtained power optimization value, with the horizontal axis being the time and the vertical axis being the power optimization value.

[0084] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0085] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An enterprise green electricity consumption data processing and analysis system, characterized in that It includes a platform side and an enterprise side; The platform side includes a registration module, an enterprise analysis module, and a solution publicity module; The registration module is used for enterprises to register information, obtain the registration information of each registered enterprise, establish an enterprise information database, and send the registration information of each registered enterprise to the enterprise information database for storage; According to the registration information in the enterprise information database, the enterprise side usage permission is enabled for the corresponding registered enterprises; The enterprise analysis module is used to analyze the registration information of each registered enterprise to obtain each enterprise unit category; The solution publicity module is used to publicize each green electricity improvement solution to each enterprise side, match each green electricity improvement solution with each enterprise unit category, determine the green electricity improvement solutions suitable for each enterprise unit category, and label each green electricity improvement solution with the corresponding enterprise unit category label; The enterprise side includes a power collection module and a green electricity analysis module; The power collection module is used to collect the electricity consumption data of the enterprise, establish an electricity consumption curve graph based on the electricity consumption data, and display the electricity consumption curve graph to the enterprise management personnel in real time; The green electricity analysis module is used to conduct green electricity analysis based on the electricity consumption curve graph, determine each evaluation plan according to the enterprise information; conduct application evaluation on each evaluation plan based on the electricity consumption curve graph to obtain the corresponding plan evaluation data, display the plan evaluation data of each evaluation plan to the enterprise management personnel, and the enterprise management personnel make green electricity decisions according to the displayed plan evaluation data and evaluation plans; The method for analyzing the registration information of each registered enterprise includes: Identify the registration information in the enterprise information database, extract the features of each registration information to obtain each enterprise feature; integrate each enterprise feature into the feature positioning data of the corresponding registered enterprise; Based on each feature positioning data, conduct initial classification on the registered enterprises to obtain each benchmark classification; Set test simulation data, and test each benchmark classification through the test simulation data to obtain a set of test results corresponding to each benchmark classification, and the set of test results consists of each single test value; Mark the single test value in the set of test results as DFi, i = 1, 2,..., n, where n is a positive integer; According to the formula calculate the combined value between the corresponding two reference classifications; In the formula: WA is the combined value; DFi1 and DFi2 are the corresponding single test values in two sets of test results respectively; Merge the benchmark classifications with the combined value less than the threshold X1 to obtain new benchmark classifications; and so on until there are no benchmark classifications that meet the merging requirements, and mark the remaining benchmark classifications as enterprise unit categories.

2. The enterprise green electricity consumption data processing and analysis system according to claim 1, wherein The method for conducting initial classification on the registered enterprises based on each feature positioning data includes: Step SA1: Establish a difference judgment model; Step SA2: Analyze each feature positioning data through the difference judgment model to obtain the difference judgment values between each feature positioning data; the difference judgment values include 1 and 0; Step SA3: Classify the registered enterprises corresponding to the two feature positioning data with the difference judgment value of 1 into one category to obtain a benchmark classification, and take the intermediate value of each feature positioning data corresponding to the benchmark classification as the benchmark feature positioning data corresponding to the benchmark classification; Step SA4: Calculate the difference judgment value between the reference feature location data of the reference classification and each feature location data or the reference feature location data of other reference classifications; perform corresponding merging according to the difference judgment value to obtain a new reference classification. Step SA5: Loop Step SA4 until there is no difference judgment value of 1; in the case where there is no difference judgment value of 1, return to Step SA2 until there is no difference judgment value of 1 to obtain each reference classification.

3. The enterprise green electricity consumption data processing and analysis system according to claim 2, wherein The expression of the difference judgment model is: ; In the formula: s is the input data, and the input data is the feature location data for comparison; the output data is the difference judgment value CR(s).

4. The enterprise green power consumption data processing and analysis system according to claim 1, characterized in that Data protection is carried out between the platform side and each enterprise side through preset data protection measures.

5. The enterprise green electricity consumption data processing and analysis system according to claim 1, characterized in that The method for establishing an electricity consumption curve graph includes: Real-time collect the electricity consumption data of the enterprise, and identify the electricity attributes corresponding to each time of the electricity consumption data. The electricity attributes include self-produced green electricity, public green electricity, and conventional electricity. Classify and identify the electricity consumption data according to the electricity consumption attributes to obtain the self-produced electricity consumption, public electricity consumption, and conventional electricity consumption; calculate the comprehensive electricity consumption corresponding to each time according to the self-produced electricity consumption, public electricity consumption, and conventional electricity consumption; generate an electricity consumption curve graph based on the comprehensive electricity consumption at each time. The horizontal axis of the electricity consumption curve graph is time, and the vertical axis is the comprehensive electricity consumption. And mark the self-produced electricity consumption, public electricity consumption, and conventional electricity consumption corresponding to each time in the electricity consumption curve graph.

6. The enterprise green electricity consumption data processing and analysis system according to claim 1, wherein The method for applying and evaluating each evaluation plan based on the electricity consumption curve graph includes: Conduct simulation analysis according to the evaluation plan to obtain a simulation curve graph. Set up a power optimization graph according to the simulation curve graph and the electricity consumption curve graph, and identify the green subsidies and implementation costs corresponding to each evaluation plan. Fit the power optimization graph to obtain the corresponding power optimization function, and mark the power optimization function as GX(t). According to the formula calculate the evaluation value of the evaluation plan; In the formula: PX is the plan evaluation value; FB is the implementation cost; BY is the green subsidy. Integrate the plan evaluation value, the simulation curve graph, and the power optimization graph into plan evaluation data.

7. The enterprise green power consumption data processing and analysis system according to claim 6, wherein The method for setting up a power optimization graph according to the simulation curve graph and the electricity consumption curve graph includes: Calculate the electricity saving cost and the change in green electricity consumption corresponding to each time according to the simulation curve graph and the electricity consumption curve graph. Calculate the power optimization value for the corresponding time according to the formula QAt = YBt + η × LKt. In the formula: QAt is the power optimization value; t is time; YBt is the electricity saving cost; η is the green electricity conversion coefficient; LKt is the change in green electricity consumption. Generate a power optimization graph based on the obtained power optimization value. The horizontal axis is time, and the vertical axis is the power optimization value.

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