Multi-dimensional rolling correction method and system for source-load-storage coordinated optimization effect of industrial park

Abstract

The invention relates to the field of industrial park source-load-storage coordinated optimization effect evaluation, in particular to an industrial park source-load-storage coordinated optimization effect multi-dimensional rolling correction method and system, and the method comprises the steps: inputting simulation data of a source-load-storage scheduling plan in a historical scene of an industrial park into a pre-constructed operation benefit general evaluation model, and obtaining a pre-evaluation value; inputting the actual operation data into the operation benefit general evaluation model to obtain a check evaluation value; performing difference analysis on the pre-evaluation value and the check evaluation value to obtain an evaluation difference adjustment coefficient; combining the evaluation difference adjustment coefficient with a historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient; and inputting the simulation data of the to-be-evaluated scheduling plan into the operation benefit general evaluation model to obtain a pre-evaluation value of the to-be-evaluated scheduling plan, and correcting the pre-evaluation value of the to-be-evaluated scheduling plan by adopting the target evaluation difference adjustment coefficient to obtain a final evaluation value of the to-be-evaluated scheduling plan. According to the invention, the evaluation value of the to-be-evaluated scheduling plan can be accurately corrected.

Description

Technical Field

[0001] The present invention relates to the field of evaluating the collaborative optimization effect of source, load and storage in industrial parks, and specifically relates to a multi-dimensional rolling correction method and system for coordinating and optimizing the effect of source, load and storage in industrial parks. Background Art

[0002] With the continuous advancement of the goals of low-carbon environmental protection and sustainable development, industrial parks can achieve integrated energy supply of source, load and storage by introducing new energy power generation, energy storage systems, and their own industrial adjustable loads, etc., greatly improving the demand response participation ability and promoting the local consumption of new energy power generation in the park. However, the collaborative optimization of source, load and storage in industrial parks needs to balance multiple goals such as economy, environment and technology. There are various energy sources (such as electric energy, thermal energy, gas energy) in the park, as well as various energy-consuming devices and storage devices, and problems such as coordinating the interaction and flow between different systems need to be solved. How to formulate a reasonable collaborative optimization strategy for source, load and storage in industrial parks, improve the energy management level of industrial parks, and also support the better integration and application of renewable energy, and contribute to the realization of the goals of low-carbon environmental protection and sustainable development is particularly important.

[0003] The collaborative optimization effect of source, load and storage in industrial parks involves multiple aspects, including economic benefits, technical safety, environmental impact and energy structure evaluation, etc. At present, the evaluation is based on the industrial park model, and the error between the simulation data and the actual operation data is not considered enough, so it is difficult to accurately evaluate the actual operation effect of the optimization strategy. There is a lack of scientific and reasonable multi-dimensional rolling evaluation indicators and correction methods for the collaborative optimization effect of source, load and storage in industrial parks, and it is urgent to carry out research on the construction of a systematic evaluation index system and energy efficiency evaluation. Summary of the Invention

[0004] To solve the problem that the collaborative optimization effect of source, load and storage in industrial parks in the prior art cannot be reasonably corrected, the first aspect of the present invention proposes a multi-dimensional rolling correction method for the collaborative optimization effect of source, load and storage in industrial parks, including:

[0005] Input the simulation data of the source, load and storage scheduling plan in the historical scenarios of the industrial park into a pre-constructed general evaluation model of operation benefits to obtain a pre-evaluation value of the operation benefits of the industrial park, and input the actual operation data of the source, load and storage scheduling plan in the historical scenarios of the industrial park into the general evaluation model of operation benefits to obtain a verification evaluation value of the operation benefits of the industrial park;

[0006] Perform difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient;

[0007] Combine the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient;

[0008] Input the simulation data of the to-be-evaluated scheduling plan of the source-load-storage in the industrial park into the general operation benefit evaluation model to obtain the pre-evaluation value of the to-be-evaluated scheduling plan, and use the target evaluation difference adjustment coefficient to correct the pre-evaluation value of the to-be-evaluated scheduling plan to obtain the final evaluation value of the to-be-evaluated scheduling plan;

[0009] Among them, the general operation benefit evaluation model is constructed by performing multi-dimensional evaluation and analysis on the operation data under the scheduling plan of the source-load-storage in the industrial park.

[0010] Optionally, the construction steps of the general operation benefit evaluation model include:

[0011] Conduct dimensional analysis on the operation data under the scheduling plan of the source-load-storage in the industrial park to obtain evaluation indicators in multiple dimensions;

[0012] Quantify the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores of the operation data in each dimension;

[0013] Based on the evaluation indicator values, use the combination weighting method based on minimum deviation to weight each evaluation indicator;

[0014] Based on the evaluation indicator scores and weights, use the matter-element extension method to evaluate each evaluation indicator to obtain a comprehensive evaluation value.

[0015] Optionally, the specific method for quantifying the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension is as follows:

[0016] Use the mathematical modeling method or the Delphi method to quantify the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension.

[0017] Optionally, the specific steps for analyzing the difference between the pre-evaluation value and the verification evaluation value to obtain the evaluation difference adjustment coefficient include:

[0018] Construct an original index data matrix with the pre-evaluation value and the verification evaluation value of the operation benefit of the industrial park as elements;

[0019] Analyze the variability and correlation of the elements in the original index data matrix to obtain the variation degree and index conflict of each evaluation indicator;

[0020] Multiply the variation degree and index conflict of each evaluation indicator to obtain the difference adjustment amount of the corresponding evaluation indicator;

[0021] Based on the ratio of the difference adjustment amount of each evaluation indicator to the sum of the difference adjustment amounts of all evaluation indicators, obtain the evaluation difference adjustment coefficient.

[0022] Optionally, the variation degree is characterized by the standard deviation.

[0023] Optionally, the expression for the degree of variation is:

[0024]

[0025] where S j is the degree of variation of the j-th evaluation index, i is the dimension of the original index data matrix, p is the total number of dimensions of the original index data matrix, and x ij is the j-th evaluation index of the i-th dimension in the original index data matrix. is the average value of all dimensions of the j-th evaluation index in the original index data matrix.

[0026] Optionally, the index conflict is obtained through the following steps:

[0027] Obtain the correlation matrix of each evaluation index;

[0028] Obtain the index conflict based on the elements in the correlation matrix.

[0029] Optionally, the expression for the index conflict is:

[0030]

[0031] where R j is the index conflict of the j-th evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, and n is the total number of dimensions of the evaluation index.

[0032] Optionally, the expression for the evaluation difference adjustment coefficient is:

[0033] K γ = [k γ 1, k γ 2,..., k γ j,..., k γ n]

[0034]

[0035] where K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, C j is the difference adjustment amount of the j-th evaluation index, and n is the total number of dimensions of the evaluation index.

[0036] Optionally, the expression for the target evaluation difference adjustment coefficient is:

[0037] minK θ = (k θ 1, kθ 2, …, k θ j, …, k θ n)

[0038] Among them, minK θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the j-th evaluation index. Among them, mink θ j = α(k θ j - k γ j) 2 + β(k θ j - k η j) 2 , where 0 < α < 1, 0 < β < 1, α + β = 1, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, k η j is the historical difference adjustment coefficient of the j-th evaluation index, and n is the total dimension number of the evaluation indexes.

[0039] Optionally, the calculation formulas for α and β are:

[0040]

[0041] Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the j-th evaluation index, and β j is the relative importance coefficient of the historical difference adjustment coefficient of the j-th evaluation index.

[0042] Optionally, α j and β j are obtained based on the moment estimation method by calculating the expected values of the difference adjustment coefficients of different evaluation indexes.

[0043] Optionally, the calculation formulas for α j and β j are:

[0044] α j = k γ j / (k γ j + k η j), β j = k η j / (k γ j + k η j).

[0045] Optionally, the calculation formula for the final evaluation value is:

[0046] E = E' × K θ

[0047] Among them, E is the final evaluation value, and E' is the pre-evaluation value of the scheduling plan to be evaluated.

[0048] Optionally, the dimensions include multiple sub - dimensions under four dimensions of economic benefit, energy structure, environmental impact, and technical safety.

[0049] In the second aspect of the present invention, a multi - dimensional rolling correction system for the coordinated optimization effect of source - load - storage in an industrial park is provided. The system includes a processing module, and the processing module includes a data processing unit and an evaluation unit, where:

[0050] The data processing unit is used for:

[0051] Inputting the simulation data of the source - load - storage scheduling plan in the historical scenarios of the industrial park into a pre - constructed general evaluation model of operating benefits to obtain a pre - evaluation value of the operating benefits of the industrial park, and inputting the actual operating data of the source - load - storage scheduling plan in the historical scenarios of the industrial park into the general evaluation model of operating benefits to obtain a verified evaluation value of the operating benefits of the industrial park;

[0052] And performing a difference analysis on the pre - evaluation value and the verified evaluation value to obtain an evaluation difference adjustment coefficient;

[0053] The evaluation unit is used for:

[0054] Combining the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient;

[0055] Inputting the simulation data of the to - be - evaluated scheduling plan of the source - load - storage in the industrial park into the general evaluation model of operating benefits to obtain a pre - evaluation value of the to - be - evaluated scheduling plan, and using the target evaluation difference adjustment coefficient to correct the pre - evaluation value of the to - be - evaluated scheduling plan to obtain the final evaluation value of the to - be - evaluated scheduling plan.

[0056] Optionally, the construction steps of the general evaluation model of operating benefits in the data processing unit include:

[0057] Performing a dimension analysis on the operating data under the source - load - storage scheduling plan of the industrial park to obtain evaluation indicators of the operating data in multiple dimensions;

[0058] Quantifying the evaluation indicators under each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores of the operating data under each dimension;

[0059] Based on the evaluation indicator values, using the combination weighting method based on the minimum deviation to weight each evaluation indicator;

[0060] Based on the evaluation indicator scores and weights, using the matter - element extension method to evaluate each evaluation indicator to obtain a comprehensive evaluation value.

[0061] Optionally, the data processing unit quantifies the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension, specifically as follows:

[0062] The evaluation indicators in each dimension are quantified by using the mathematical modeling method or the Delphi method to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension.

[0063] Optionally, the data processing unit performs a difference analysis on the preliminary evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient. The specific steps include:

[0064] Construct an original index data matrix with the preliminary evaluation value and the verification evaluation value of the operation benefit of the industrial park as elements;

[0065] Perform variability and correlation analysis on the elements in the original index data matrix to obtain the variability degree and index conflict of each evaluation indicator;

[0066] Multiply the variability degree and index conflict of each evaluation indicator to obtain the difference adjustment amount of the corresponding evaluation indicator;

[0067] Obtain the evaluation difference adjustment coefficient based on the ratio of the difference adjustment amount of each evaluation indicator to the sum of the difference adjustment amounts of all evaluation indicators.

[0068] Optionally, the variability degree in the data processing unit is characterized by the standard deviation.

[0069] Optionally, the expression of the variability degree in the data processing unit is:

[0070]

[0071] where S j is the variability degree of the jth evaluation indicator, i is the dimension of the original index data matrix, p is the total number of dimensions of the original index data matrix, x ij is the jth evaluation indicator in the ith dimension of the original index data matrix, is the average value of all dimensions of the jth evaluation indicator in the original index data matrix.

[0072] Optionally, the index conflict in the data processing unit is obtained through the following steps:

[0073] Obtain the correlation matrix of each evaluation indicator;

[0074] Obtain the index conflict based on the elements in the correlation matrix.

[0075] Optionally, the expression of the index conflict in the data processing unit is:

[0076]

[0077] wherein, R j is the index conflict of the j-th evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, and n is the total dimension number of evaluation indexes.

[0078] Optionally, the expression of the evaluation difference adjustment coefficient in the data processing unit is:

[0079] K γ =[k γ 1, k γ 2,..., k γ j,..., k γ n]

[0080]

[0081] wherein, K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, C j is the difference adjustment amount of the j-th evaluation index, and n is the total dimension number of evaluation indexes.

[0082] Optionally, the expression of the target evaluation difference adjustment coefficient in the evaluation unit is:

[0083] minK θ =(k θ 1, k θ 2,..., k θ j,..., k θ n)

[0084] wherein, minK θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the j-th evaluation index, wherein, mink θ j = α(k θ j - k γ j) 2 + β(k θ j - k η j) 2 , wherein, 0 < α < 1, 0 < β < 1, α + β = 1, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, k η j is the historical difference adjustment coefficient of the j-th evaluation index, and n is the total dimension number of evaluation indexes.

[0085] Optionally, the calculation formulas of α and β in the evaluation unit are:

[0086]

[0087] Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the j-th evaluation index, and β j is the relative importance coefficient of the historical difference adjustment coefficient of the j-th evaluation index.

[0088] Optionally, in the evaluation unit, α j and β j are obtained by calculating the expected value of the difference adjustment coefficient of different evaluation indexes based on the moment estimation method.

[0089] Optionally, the calculation formulas of α j and β j in the evaluation unit are:

[0090] α j = k γ j / (k γ j + k η j), β j = k η j / (k γ j + k η j).

[0091] Optionally, the calculation formula of the final evaluation value in the evaluation unit is:

[0092] E = E' × K θ

[0093] Among them, E is the final evaluation value, and E' is the pre-evaluation value of the scheduling plan to be evaluated.

[0094] Optionally, the dimensions in the data processing unit include multiple sub-dimensions under four dimensions of economic benefits, energy structure, environmental impact, and technical safety.

[0095] Optionally, the system further includes a main module, a communication module, and a display module, where:

[0096] The main module is connected to the communication module, the processing module, and the display module;

[0097] The communication module is used for network connection and data reception, and transmits the received data to the main module, so that the main module transmits the data to the processing module for data processing;

[0098] The display module is used to receive the data from the main module and perform visual display.

[0099] On the other hand, the present invention also provides a computing device, including: at least one processor and a memory;

[0100] The memory is used to store one or more programs;

[0101] When the one or more programs are executed by the one or more processors, a multi-dimensional rolling correction method for the coordinated optimization effect of source-load-storage in an industrial park as described above is implemented.

[0102] On the other hand, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, a multi-dimensional rolling correction method for the coordinated optimization effect of source-load-storage in an industrial park as described above is implemented.

[0103] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0104] The present invention provides a multi-dimensional rolling correction method and system for the coordinated optimization effect of source-load-storage in an industrial park, including inputting simulation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into a pre-constructed general evaluation model of operation benefits to obtain a pre-evaluation value of the operation benefits of the industrial park, and inputting the actual operation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into the general evaluation model of operation benefits to obtain a verification evaluation value of the operation benefits of the industrial park; performing difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient; combining the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient; inputting the simulation data of the to-be-evaluated scheduling plan of the source-load-storage in the industrial park into the general evaluation model of operation benefits to obtain a pre-evaluation value of the to-be-evaluated scheduling plan, and using the target evaluation difference adjustment coefficient to correct the pre-evaluation value of the to-be-evaluated scheduling plan to obtain a final evaluation value of the to-be-evaluated scheduling plan; the present invention obtains an evaluation difference adjustment coefficient in the evaluation process through the pre-evaluation value and the verification evaluation value, combines the evaluation difference adjustment coefficient with the historical evaluation difference adjustment coefficient, realizes the correction and optimization of the historical evaluation difference adjustment coefficient, obtains a target evaluation difference adjustment coefficient, and uses the target evaluation difference adjustment coefficient to correct the simulation data to obtain a predicted actual evaluation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 It is a detailed step schematic diagram of the multi-dimensional rolling correction method for the coordinated optimization effect of source-load-storage in an industrial park proposed by the present invention;

[0106] Figure 2 It is a subdivision schematic diagram of four dimensions proposed by the present invention;

[0107] Figure 3 Proposed by the present invention Figure 1 It is a detailed step schematic diagram of step S2 in

[0108] Figure 4 It is an overall flow schematic diagram of the multi-dimensional rolling correction method for the coordinated optimization effect of source-load-storage in an industrial park proposed by the present invention;

[0109] Figure 5 It is a schematic structural diagram of a multi-dimensional rolling correction system for the coordinated optimization effect of the source, load and storage in an industrial park proposed by the present invention;

[0110] Figure 6 It is a schematic structural diagram of an electronic device proposed by the present invention. Specific embodiments

[0111] The present invention proposes a multi-dimensional rolling correction method and system for the coordinated optimization effect of the source, load and storage in an industrial park. The purpose is to break through the limitations of the existing evaluation model, estimate the difference adjustment coefficient of the simulation data based on the evaluation values of the simulation data and the actual data of the historical data, and then correct the simulation data of the scheduling plan to be run to achieve accurate prediction of the actual operation data.

[0112] Example 1:

[0113] A multi-dimensional rolling correction method for the coordinated optimization effect of the source, load and storage in an industrial park, as Figure 1 shown, includes the following steps S1 to S4.

[0114] S1: Input the simulation data of the source, load and storage scheduling plan in the historical scenario of the industrial park into a pre-constructed general evaluation model of operation benefits to obtain a pre-evaluation value of the operation benefits of the industrial park, and input the actual operation data of the source, load and storage scheduling plan in the historical scenario of the industrial park into the general evaluation model of operation benefits to obtain a verified evaluation value of the operation benefits of the industrial park;

[0115] S2: Conduct a difference analysis on the pre-evaluation value and the verified evaluation value to obtain an evaluation difference adjustment coefficient;

[0116] S3: Combine the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient;

[0117] S4: Input the simulation data of the scheduling plan to be evaluated of the source, load and storage in the industrial park into the general evaluation model of operation benefits to obtain a pre-evaluation value of the scheduling plan to be evaluated, and use the target evaluation difference adjustment coefficient to correct the pre-evaluation value of the scheduling plan to be evaluated to obtain a final evaluation value of the scheduling plan to be evaluated;

[0118] Among them, the general evaluation model of operation benefits is constructed by conducting multi-dimensional evaluation and analysis on the operation data under the source, load and storage scheduling plan of the industrial park.

[0119] Comprehensively considering the operation elements of the industrial park, generally including four dimensions: economic benefits, energy structure, environmental impact, and technical safety, where:

[0120] Economic benefits: By optimizing electricity demand and achieving peak-valley balance, electricity costs can be reduced and economic returns can be increased, reducing the dependence on peak electricity demand, thereby lowering production and operation costs.

[0121] Energy structure: Improving electricity efficiency may increase corporate profits, and thus have the ability to create more job opportunities. Improved power management can support a better working environment, enhancing employee satisfaction and productivity.

[0122] Environmental impact: By optimizing electricity consumption patterns and reducing peak-hour demands, carbon emissions and pollution emissions can be reduced, energy utilization efficiency can be improved, resource consumption and waste can be minimized, making it more environmentally friendly.

[0123] Technical safety: Reducing power load fluctuations, improving grid stability and reliability, and lowering the risk of power outages. Through the application of real-time monitoring and automation technologies, technical capabilities and safety management levels can be enhanced.

[0124] However, these four dimensions are difficult to accurately characterize the actual characteristics of operation data. Therefore, the present invention is further subdivided, as Figure 2 shown. Among them, the economic benefits dimension is subdivided into three dimensions: dimension cost, unit energy supply benefit, and regulation cost; the energy structure dimension is subdivided into four dimensions: energy storage configuration rate, relative energy saving rate, comprehensive energy utilization rate, and renewable energy utilization rate; the environmental impact dimension is subdivided into four dimensions: carbon storage benefit, pollutant emissions, resource consumption, and carbon utilization rate; the technical safety dimension is subdivided into three dimensions: technical maturity, system average power supply reliability rate, and load failure rate. Therefore, the embodiments of the present invention altogether include 14 dimensions, as shown in Table 1 below, that is, there are 14 evaluation indicators.

[0125] Table 1

[0126]

[0127]

[0128] In a further preferred embodiment, the steps for constructing the general evaluation model for operation benefits include the following steps 1 to 4:

[0129] Step 1: Conduct a dimensional analysis of the operation data under the source-load-storage scheduling plan of the industrial park to obtain evaluation indicators for multiple dimensions.

[0130] Based on the above analysis, the present invention adopts 14-dimensional evaluation indicators: dimension cost, unit energy supply benefit, regulation cost, energy storage configuration rate, relative energy saving rate, comprehensive energy utilization rate, renewable energy utilization rate, carbon storage benefit, pollutant emissions, resource consumption, carbon utilization rate, technical maturity, system average power supply reliability rate, and load failure rate.

[0131] Step 2: Quantify the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension.

[0132] Specifically:

[0133] Use mathematical modeling method or Delphi method to quantify the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores in each dimension.

[0134] Step 3: Based on the evaluation indicator values, use the combined weighting method based on minimum deviation to weight each evaluation indicator;

[0135] Step 4: Based on the evaluation indicator scores and weights, use the matter-element extension method to evaluate each evaluation indicator to obtain a comprehensive evaluation value. In step S1, the simulation data of the source-load-storage scheduling plan in the historical scenario of the industrial park is input into the pre-constructed general evaluation model of operation benefits to obtain the preliminary evaluation value E1 of the operation benefits of the industrial park:

[0136]

[0137] Among them, E1j is the preliminary evaluation value of the jth evaluation indicator.

[0138] The simulation data can be obtained by inputting the scheduling plan into the simulation software.

[0139] Collect the actual operation data of the industrial park under the scheduling plan described in step S1, and input the actual operation data into the pre-constructed general evaluation model of operation benefits to obtain the verification evaluation value E2 of the operation benefits of the industrial park:

[0140]

[0141] In a further optimized solution, as Figure 3 shown, the specific steps of step S2 include steps S21 to S24, where:

[0142] S21: Construct an original index data matrix X with the preliminary evaluation value and verification evaluation value as elements.

[0143]

[0144] Furthermore, to eliminate the influence of different dimensions on the evaluation results, the evaluation indicators are normalized or reversed.

[0145] S22: Analyze the variability and correlation of the elements in the original index data matrix to obtain the variability degree and index conflict of each evaluation indicator.

[0146] In a further optimized solution, the variability degree is characterized by the standard deviation, so the expression of the variability degree is:

[0147]

[0148] Among them, S j is the degree of variation of the j-th evaluation index, i is the dimension of the original index data matrix, p is the total number of dimensions of the original index data matrix, and in the embodiment of the present invention, P = 2, x ij is the j-th evaluation index of the i-th dimension in the original index data matrix, is the average value of all dimensions of the j-th evaluation index in the original index data matrix.

[0149] The standard deviation is used to represent the difference fluctuation of the internal values of each evaluation index. The larger the standard deviation, the greater the numerical difference of the index and the greater the difference adjustment coefficient.

[0150] The index conflict is obtained in the following manner:

[0151] Obtain the correlation matrix of each evaluation index;

[0152] Obtain the index conflict based on the elements in the correlation matrix.

[0153] Among them, the correlation matrix R is:

[0154]

[0155] The expression of the index conflict is:

[0156]

[0157] Among them, R j is the index conflict of the j-th evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, which is an element in the correlation matrix R, and n is the total number of dimensions of the evaluation index.

[0158] S23: Multiply the degree of variation and the index conflict of each evaluation index to obtain the difference adjustment amount of the corresponding evaluation index.

[0159] The difference adjustment amount C j The expression is:

[0160]

[0161] S24: Obtain the evaluation difference adjustment coefficient based on the ratio of the difference adjustment amount of each evaluation index to the sum of the difference adjustment amounts of all evaluation indexes.

[0162] The expression of the evaluation difference adjustment coefficient is:

[0163] K γ =[k γ1, k γ 2, …, k γ j, …, k γ n]

[0164]

[0165] where K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, C j is the difference adjustment amount of the j-th evaluation index, and n is the total dimension number of the evaluation indexes.

[0166] In a further optimized solution, in step S3, the above difference adjustment coefficient K γ = [k γ 1, k γ 2, …, k γ n] is combined with the historical difference adjustment coefficient K η = [k η 1, k η 2, …, k η n] to obtain the final target evaluation difference adjustment coefficient minK θ = (k θ 1, k θ 2, …, k θ n). The relative importance degrees of the two for the target evaluation difference adjustment coefficient are α and β, and they satisfy that the deviation between the difference adjustment coefficient and the historical difference adjustment coefficient is the smallest. Here, the historical difference adjustment coefficient is obtained from the previous rolling evaluation. If it is the initial evaluation, the historical difference adjustment coefficient is 0.

[0167] The expression of the target evaluation difference adjustment coefficient is:

[0168] minK θ = (k θ 1, k θ 2, …, k θ j, …, k θ n)

[0169] where minK θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the j-th evaluation index, where mink θ j = α(k θ j - k γ j) 2 + β(k θ j - k η j) 2 , where 0 < α < 1, 0 < β < 1, α + β = 1, k γj is the evaluation difference adjustment coefficient of the jth evaluation index, k η j is the historical difference adjustment coefficient of the jth evaluation index, and n is the total dimension number of the evaluation indexes.

[0170] In a further optimized solution, the calculation formulas for α and β are as follows:

[0171]

[0172] Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the jth evaluation index, and β j is the relative importance coefficient of the historical difference adjustment coefficient of the jth evaluation index.

[0173] In a further optimized solution, according to the basic idea of moment estimation, the expected value of the difference adjustment coefficient of different indexes is calculated, and the relative importance coefficient of the difference adjustment coefficient of a single evaluation index can be calculated.

[0174] α j and β j The calculation formulas are as follows:

[0175] α j = k γ j / (k γ j + k η j), β j = k η j / (k γ j + k η j).

[0176] For each evaluation index, taking the minimum as the best, it can be transformed into:

[0177]

[0178] In step S4, the simulation data of the to-be-evaluated scheduling plan (such as the scheduling plan for the next day) of the industrial park's source-load-storage is input into the general evaluation model of the operating benefit to obtain the pre-evaluation value of the to-be-evaluated scheduling plan, and the pre-evaluation value of the to-be-evaluated scheduling plan is corrected by using the target evaluation difference adjustment coefficient to obtain the final evaluation value of the to-be-evaluated scheduling plan. The calculation formula for the final evaluation value is:

[0179] E = E' × K θ

[0180] Among them, E is the final evaluation value, and E' is the pre-evaluation value of the to-be-evaluated scheduling plan.

[0181] To sum up, as Figure 4As shown in the figure, the multi-dimensional rolling correction method for the coordinated optimization effect of the source-load-storage in the industrial park of the present invention first obtains evaluation indicators to establish an indicator system and calculate indicator weights, then calculates an evaluation difference adjustment coefficient, and finally uses an evaluation method to perform quantitative calculation of the evaluation value of the indicators, and uses the pre-evaluation value to correct the pre-evaluation value of the to-be-evaluated dispatching plan to obtain the final evaluation value of the to-be-evaluated dispatching plan.

[0182] Embodiment 2:

[0183] Based on the same inventive concept, the present invention also provides a multi-dimensional rolling correction system for the coordinated optimization effect of the source-load-storage in the industrial park, including a processing module, and the processing module includes a data processing unit and an evaluation unit, wherein:

[0184] The data processing unit is used for:

[0185] Inputting the simulation data of the source-load-storage dispatching plan in the historical scenario of the industrial park into a pre-constructed general evaluation model for operating benefits to obtain a pre-evaluation value of the operating benefits of the industrial park, and inputting the actual operating data of the source-load-storage dispatching plan in the historical scenario of the industrial park into the general evaluation model for operating benefits to obtain a verified evaluation value of the operating benefits of the industrial park;

[0186] And performing difference analysis on the pre-evaluation value and the verified evaluation value to obtain an evaluation difference adjustment coefficient;

[0187] The evaluation unit is used for:

[0188] Combining the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient;

[0189] Inputting the simulation data of the to-be-evaluated dispatching plan of the source-load-storage in the industrial park into the general evaluation model for operating benefits to obtain a pre-evaluation value of the to-be-evaluated dispatching plan, and using the target evaluation difference adjustment coefficient to correct the pre-evaluation value of the to-be-evaluated dispatching plan to obtain the final evaluation value of the to-be-evaluated dispatching plan.

[0190] In a further preferred solution, the construction steps of the general evaluation model for operating benefits in the data processing unit include:

[0191] Performing dimensional analysis on the operating data under the source-load-storage dispatching plan in the industrial park to obtain evaluation indicators of the operating data in multiple dimensions;

[0192] Quantifying the evaluation indicators in each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores of the operating data in each dimension;

[0193] Based on the evaluation indicator values, using the combination weighting method based on the minimum deviation to weight each evaluation indicator;

[0194] Based on the scores and weights of the evaluation indicators, the matter-element extension method is used to evaluate each evaluation indicator to obtain a comprehensive evaluation value.

[0195] In a further optimized solution, the data processing unit quantifies the evaluation indicators under each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores under each dimension, specifically as follows:

[0196] The mathematical modeling method or the Delphi method is used to quantify the evaluation indicators under each dimension to obtain the evaluation indicator values and corresponding evaluation indicator scores under each dimension.

[0197] In a further optimized solution, the data processing unit performs a difference analysis on the preliminary evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient. The specific steps include:

[0198] Construct an original index data matrix with the preliminary evaluation value and the verification evaluation value of the operation benefit of the industrial park as elements;

[0199] Analyze the variability and correlation of the elements in the original index data matrix to obtain the variation degree and index conflict of each evaluation indicator;

[0200] Multiply the variation degree and index conflict of each evaluation indicator to obtain the difference adjustment amount corresponding to the evaluation indicator;

[0201] Based on the ratio of the difference adjustment amount of each evaluation indicator to the sum of the difference adjustment amounts of all evaluation indicators, obtain the evaluation difference adjustment coefficient.

[0202] In a further optimized solution, the variation degree in the data processing unit is characterized by the standard deviation.

[0203] In a further optimized solution, the expression of the variation degree in the data processing unit is:

[0204]

[0205] where S j is the variation degree of the jth evaluation indicator, i is the dimension of the original index data matrix, p is the total dimension number of the original index data matrix, x ij is the jth evaluation indicator in the ith dimension of the original index data matrix, and x j is the average value of all dimensions of the jth evaluation indicator in the original index data matrix.

[0206] In a further optimized solution, the index conflict in the data processing unit is obtained through the following steps:

[0207] Obtain the correlation matrix of each evaluation indicator;

[0208] Obtain the index conflict based on the elements in the correlation matrix.

[0209] In a further preferred solution, the expression for the index conflict in the data processing unit is:

[0210]

[0211] where, R j is the index conflict of the j-th evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, and n is the total dimension number of the evaluation indexes.

[0212] In a further preferred solution, the expression for the evaluation difference adjustment coefficient in the data processing unit is:

[0213]

[0214] where, K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, C j is the difference adjustment amount of the j-th evaluation index, and n is the total dimension number of the evaluation indexes.

[0215] In a further preferred solution, the expression for the target evaluation difference adjustment coefficient in the evaluation unit is:

[0216] minK θ =(k θ 1,k θ 2,…,k θ j,…,k θ n)

[0217] where, minK θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the j-th evaluation index, where, mink θ j = α(k θ j - k γ j) 2 +β(k θ j - k η j) 2 , where, 0 < α < 1, 0 < β < 1, α + β = 1, k γ j is the evaluation difference adjustment coefficient of the j-th evaluation index, k η j is the historical difference adjustment coefficient of the j-th evaluation index, and n is the total dimension number of the evaluation indexes.

[0218] In a further preferred solution, the calculation formulas for α and β in the evaluation unit are:

[0219]

[0220] Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the j-th evaluation index, and β j is the relative importance coefficient of the historical difference adjustment coefficient of the j-th evaluation index.

[0221] In a further optimized solution, in the evaluation unit, α j and β j are obtained based on the moment estimation method by calculating the expected values of the difference adjustment coefficients of different evaluation indexes.

[0222] In a further optimized solution, in the evaluation unit, the calculation formulas of α j and β j are as follows:

[0223] α j = k γ j / (k γ j + k η j), β j = k η j / (k γ j + k η j).

[0224] In a further optimized solution, the calculation formula of the final evaluation value in the evaluation unit is:

[0225] E = E' × K θ

[0226] Among them, E is the final evaluation value, and E' is the pre-evaluation value of the scheduling plan to be evaluated.

[0227] In a further optimized solution, the dimensions in the data processing unit include multiple sub-dimensions under four dimensions of economic benefits, energy structure, environmental impact, and technical safety.

[0228] In a further optimized solution, as Figure 5 shown, the system further includes a main module, a communication module, and a display module, where:

[0229] The main module is connected to the communication module, the processing module, and the display module;

[0230] The communication module is used for network connection and data reception, and transmits the received data to the main module, so that the main module transmits the data to the processing module for data processing;

[0231] The display module is used for receiving the data from the main module and performing visual display.

[0232] Embodiment 3

[0233] As Figure 6 shown, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected through a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0234] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a multi-dimensional rolling correction method for the coordinated optimization effect of source, load, and storage in an industrial park in the above embodiment.

[0235] Embodiment 4

[0236] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device and is used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of a multi-dimensional rolling correction method for the coordinated optimization effect of source, load, and storage in an industrial park in the above embodiment can be implemented.

[0237] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0238] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0239] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0240] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0241] The above are only the embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park, characterized in that: include: Inputting the simulation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into the pre-built general evaluation model of operation benefit to obtain the preliminary evaluation value of the operation benefit of the industrial park, and inputting the actual operation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into the general evaluation model of operation benefit to obtain the verification evaluation value of the operation benefit of the industrial park; Performing a difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient; Combining the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient; Inputting the simulation data of the to-be-evaluated scheduling plan of the industrial park source-load-storage into the general evaluation model of operation benefits to obtain the preliminary evaluation value of the to-be-evaluated scheduling plan, and using the target evaluation difference adjustment coefficient to correct the preliminary evaluation value of the to-be-evaluated scheduling plan to obtain the final evaluation value of the to-be-evaluated scheduling plan; Among them, the general evaluation model of operating benefits is constructed by conducting multi-dimensional evaluation and analysis on the operating data under the source-load-storage scheduling plan of the industrial park.

2. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 1 is characterized in that: The steps of constructing the general evaluation model of operation benefits include: Perform dimensional analysis on the operating data under the source-load-storage scheduling plan of the industrial park to obtain evaluation indicators of the operating data in multiple dimensions; Quantify the evaluation indicators under each dimension to obtain the evaluation indicator value of the operating data under each dimension and the corresponding evaluation indicator score; Based on the evaluation index value, weighting each evaluation index is performed using a combined weighting method based on minimum deviation; Based on the evaluation index scores and weights, each evaluation index is evaluated using the matter-element extension method to obtain a comprehensive evaluation value.

3. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 2 is characterized in that: The evaluation index under each dimension is quantified to obtain the evaluation index value under each dimension and the corresponding evaluation index score, specifically: Mathematical modeling or Delphi method is used to quantify the evaluation indicators under each dimension to obtain the evaluation indicator value and corresponding evaluation indicator score under each dimension.

4. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 1 is characterized in that: The step of performing a difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient comprises: An original indicator data matrix is ​​constructed with the pre-evaluation value and the verification evaluation value of the operation benefit of the industrial park as elements; Performing variability and correlation analysis on the elements in the original indicator data matrix to obtain the degree of variation and indicator conflict of each evaluation indicator; The difference adjustment amount of the corresponding evaluation index is obtained by multiplying the variation degree of each evaluation index and the index conflict; The evaluation difference adjustment coefficient is obtained based on the ratio of the difference adjustment amount of each evaluation indicator to the sum of the difference adjustment amounts of all evaluation indicators.

5. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 4 is characterized in that: The degree of variation is characterized by standard deviation.

6. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 4 or 5, characterized in that: The expression of the degree of variation is: Among them, S j is the variation degree of the jth evaluation index, i is the dimension of the original index data matrix, p is the total number of dimensions of the original index data matrix, x ij is the jth evaluation index of the i-th dimension in the original index data matrix, is the average value of all dimensions of the jth evaluation indicator in the original indicator data matrix.

7. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 4 is characterized in that: The index conflict is obtained by the following steps: Obtain the correlation matrix of each evaluation index; Indicator conflicts are obtained based on the elements in the correlation matrix.

8. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 4 or 7, characterized in that: The expression of the index conflict is: Among them, R j is the index conflict of the jth evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, and n is the total number of dimensions of the evaluation index.

9. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 4 is characterized in that: The expression of the evaluation difference adjustment coefficient is: K γ =[k γ 1,k γ 2,…,k γ j,…,k γ n] Among them, K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the jth evaluation index, C j is the difference adjustment of the jth evaluation index, and n is the total number of dimensions of the evaluation index.

10. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 1, characterized in that: The expression of the target evaluation difference adjustment coefficient is: my K θ =(k θ 1,k θ 2,…,k θ j,…,k θ n) Among them, min K θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the jth evaluation index, where mink θ j = α(k θ jk γ j) 2 +β(k θ jk η j) 2 , where, 0<α<1, 0<β<1, α+β=1, k γ j is the evaluation difference adjustment coefficient of the jth evaluation index, k η j is the historical difference adjustment coefficient of the jth evaluation indicator, and n is the total number of dimensions of the evaluation indicator.

11. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 10, characterized in that: The calculation formulas for α and β are: Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the jth evaluation index, β j is the relative importance coefficient of the historical difference adjustment coefficient of the j-th evaluation indicator.

12. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 11, characterized in that: α j and β j The expected value of the difference adjustment coefficient of different evaluation indicators is obtained by calculating the moment estimation method.

13. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 11 or 12, characterized in that: α j and β j The calculation formula is: α j =k γ j / (k γ j+k η j),β j =k η j / (k γ j+k η j)。 14. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 1 or 10, characterized in that: The calculation formula of the final evaluation value is: E=E'×K θ Among them, E is the final evaluation value, and E' is the preliminary evaluation value of the scheduling plan to be evaluated.

15. The multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park according to claim 2 is characterized in that: The dimensions include multiple sub-dimensions under the four dimensions of economic benefits, energy structure, environmental impact and technical safety.

16. A multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park, characterized in that: The system comprises a processing module, wherein the processing module comprises a data processing unit and an evaluation unit, wherein: The data processing unit is used for: Inputting the simulation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into the pre-built general evaluation model of operation benefit to obtain the preliminary evaluation value of the operation benefit of the industrial park, and inputting the actual operation data of the source-load-storage scheduling plan in the historical scenario of the industrial park into the general evaluation model of operation benefit to obtain the verification evaluation value of the operation benefit of the industrial park; and performing a difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient; The evaluation unit is used to: Combining the evaluation difference adjustment coefficient with the historical difference adjustment coefficient to obtain a target evaluation difference adjustment coefficient; The simulation data of the scheduling plan to be evaluated of the industrial park source, load and storage is input into the general evaluation model of operating benefits to obtain the preliminary evaluation value of the scheduling plan to be evaluated, and the preliminary evaluation value of the scheduling plan to be evaluated is corrected by using the target evaluation difference adjustment coefficient to obtain the final evaluation value of the scheduling plan to be evaluated.

17. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 16 is characterized in that: The steps of constructing the general evaluation model of operation benefits in the data processing unit include: Perform dimensional analysis on the operating data under the source-load-storage scheduling plan of the industrial park to obtain evaluation indicators of the operating data in multiple dimensions; Quantify the evaluation indicators under each dimension to obtain the evaluation indicator value of the operating data under each dimension and the corresponding evaluation indicator score; Based on the evaluation index value, weighting each evaluation index is performed using a combined weighting method based on minimum deviation; Based on the evaluation index scores and weights, each evaluation index is evaluated using the matter-element extension method to obtain a comprehensive evaluation value.

18. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 17 is characterized in that: The data processing unit quantifies the evaluation index under each dimension to obtain the evaluation index value under each dimension and the corresponding evaluation index score, specifically: Mathematical modeling or Delphi method is used to quantify the evaluation indicators under each dimension to obtain the evaluation indicator value and corresponding evaluation indicator score under each dimension.

19. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 16 is characterized in that: The data processing unit performs a difference analysis on the pre-evaluation value and the verification evaluation value to obtain an evaluation difference adjustment coefficient, and the specific steps include: An original indicator data matrix is ​​constructed with the pre-evaluation value and the verification evaluation value of the operation benefit of the industrial park as elements; Performing variability and correlation analysis on the elements in the original indicator data matrix to obtain the degree of variation and indicator conflict of each evaluation indicator; The difference adjustment amount of the corresponding evaluation index is obtained by multiplying the variation degree of each evaluation index and the index conflict; The evaluation difference adjustment coefficient is obtained based on the ratio of the difference adjustment amount of each evaluation indicator to the sum of the difference adjustment amounts of all evaluation indicators.

20. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 19, characterized in that: The degree of variation in the data processing unit is characterized by standard deviation.

21. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 19 or 20, characterized in that: The expression of the degree of variation in the data processing unit is: Among them, S j is the variation degree of the jth evaluation index, i is the dimension of the original index data matrix, p is the total number of dimensions of the original index data matrix, x ij is the jth evaluation index of the i-th dimension in the original index data matrix, is the average value of all dimensions of the jth evaluation indicator in the original indicator data matrix.

22. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 19, characterized in that: The index conflict in the data processing unit is obtained by the following steps: Obtain the correlation matrix of each evaluation index; Indicator conflicts are obtained based on the elements in the correlation matrix.

23. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 19 or 22, characterized in that: The expression of index conflict in the data processing unit is: Among them, R j is the index conflict of the jth evaluation index, r aj is the correlation coefficient between evaluation index a and evaluation index j, and n is the total number of dimensions of the evaluation index.

24. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 19, characterized in that: The expression of the evaluation difference adjustment coefficient in the data processing unit is: K γ =[k γ 1,k γ 2,…,k γ j,…,k γ n] Among them, K γ is the evaluation difference adjustment coefficient, k γ j is the evaluation difference adjustment coefficient of the jth evaluation index, C j is the difference adjustment of the jth evaluation index, and n is the total number of dimensions of the evaluation index.

25. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 16 is characterized in that: The expression of the target evaluation difference adjustment coefficient in the evaluation unit is: my K θ =(k θ 1,k θ 2,…,k θ j,…,k θ n) Among them, min K θ is the target evaluation difference adjustment coefficient, k θ j is the target evaluation difference adjustment coefficient of the jth evaluation index, where min k θ j = α(k θ jk γ j) 2 +β(k θ jk η j) 2 , where, 0<α<1, 0<β<1, α+β=1, k γ j is the evaluation difference adjustment coefficient of the jth evaluation index, k η j is the historical difference adjustment coefficient of the jth evaluation indicator, and n is the total number of dimensions of the evaluation indicator.

26. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 25, characterized in that: The calculation formulas for α and β in the evaluation unit are: Among them, α j is the relative importance coefficient of the difference adjustment coefficient of the jth evaluation index, β j is the relative importance coefficient of the historical difference adjustment coefficient of the j-th evaluation indicator.

27. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 26, characterized in that: The evaluation unit α j and β j The expected value of the difference adjustment coefficient of different evaluation indicators is obtained by calculating the moment estimation method.

28. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 26 or 27, characterized in that: The evaluation unit α j and β j The calculation formula is: α j =k γ j / (k γ j+k η j),β j =k η j / (k γ j+k η j)。 29. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in an industrial park according to claim 16 or 25, characterized in that: The calculation formula of the final evaluation value in the evaluation unit is: E=E'×K θ Among them, E is the final evaluation value, and E' is the preliminary evaluation value of the scheduling plan to be evaluated.

30. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 17 is characterized in that: The dimensions in the data processing unit include multiple subdimensions under the four dimensions of economic benefits, energy structure, environmental impact and technical safety.

31. The multi-dimensional rolling correction system for the coordinated optimization effect of source, load and storage in industrial parks according to claim 16 is characterized in that: The system also includes a main module, a communication module and a display module, wherein: The main module is connected to the communication module, the processing module and the display module; The communication module is used for network connection and data reception, and transmits the received data to the main module, so that the main module transmits the data to the processing module for data processing; The display module is used to receive data from the main module and perform visual display.

32. A computer device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage in an industrial park as described in any one of claims 1 to 15 is implemented.

33. A computer-readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-dimensional rolling correction method for the coordinated optimization effect of source, load and storage of an industrial park as described in any one of claims 1 to 15 is implemented.

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