Data value added service comprehensive evaluation method based on energy digital platform

Abstract

The invention provides a data value added service comprehensive evaluation method based on an energy digital platform, and belongs to the field of data processing. The method comprises the following steps: obtaining a value added service dimension index of the power industry; determining a plurality of data value-added service comprehensive evaluation alternative schemes based on the energy digital platform according to the value-added service dimension indexes, and constructing an index system; for any alternative scheme, constructing a decision matrix according to the alternative scheme and the index system; determining a positive ideal value vector, a negative ideal value vector and an incomplete weight extreme point by adopting an improved multi-criterion compromise solution sorting method VIKOR and a decision matrix; based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme point, determining the group benefit and the individual regret of the alternative scheme; and based on the group benefit and the individual regret of each alternative scheme, obtaining an optimal scheme for comprehensive evaluation of the data value-added service based on the energy digital platform. According to the invention, scientific planning and transformation can be provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a comprehensive evaluation method for data value-added services based on an energy digital platform. Background Art

[0002] With the rapid development of my country's social economy, the energy industry has also ushered in a critical period of transformation and upgrading. Energy digital economy, as a product of the deep integration of digital technology, energy economy and digital economy, is becoming an important force to promote the innovation of the traditional energy industry, heralding a new direction for the revolution of the power industry. As the core of this process, the energy digital platform not only plays a key role in the upgrade of the power grid to the energy Internet, but also shows great development potential in building a high-quality new power system, promoting the digital transformation of the power industry chain, and supporting the construction of a smart dispatching and operation system for the development of new energy.

[0003] In the energy sector, big data value-added services are an emerging industry, and the study of their business models and evaluation systems is gradually becoming a research hotspot. These studies have systematically explored the business model of the energy Internet from four dimensions: value creation, information value-added, business restructuring, and deep mining of benefits, with the goal of maximizing the value of data resources. At the same time, scholars have also conducted in-depth business model construction in the field of integrated energy services, focusing on target customer groups, service scope, and profit structure. In addition, there has been extensive research on the specific operation of the energy data business model, including product strategy, revenue model, pricing strategy, market expansion path, and identification and response to operational risks, jointly building a business operation model system that matches the characteristics of energy data.

[0004] By reviewing the current research status, the inventors found that most technical methods are based on the perspective of balancing the interests of multiple parties in the energy system, and construct an evaluation system to comprehensively evaluate the comprehensive performance of various energy projects. However, there is a certain degree of dispersion in the selection of evaluation indicators. There is a lack of a special research framework that takes energy big data value-added services as the core and considers internal and external environmental factors to systematically evaluate the business model and effectiveness of the service. Therefore, in-depth evaluation research on energy big data value-added services and their business models still needs further exploration and improvement. Summary of the invention

[0005] The embodiment of the present invention provides a comprehensive evaluation method for data value-added services based on an energy digital platform to fill the gap in the current in-depth evaluation research on energy big data value-added services and their business models.

[0006] In a first aspect, an embodiment of the present invention provides a comprehensive evaluation method for data value-added services based on an energy digital platform, including:

[0007] Obtain value-added business dimension indicators for the power industry;

[0008] Determine multiple data value-added service comprehensive evaluation alternatives based on the energy digital platform according to the value-added service dimension indicators and build an indicator system;

[0009] For any alternative plan, a decision matrix is ​​constructed according to the alternative plan and the indicator system; the improved multi-criteria compromise solution sorting method VIKOR and the decision matrix are used to determine the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point; and the group benefit and individual regret of the alternative plan are determined based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point;

[0010] Based on the group benefits and individual regrets of each alternative plan, the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform is obtained.

[0011] In one possible implementation, the positive ideal value vector is calculated by the following formula:

[0012]

[0013] j=1,…,n

[0014] Among them, f * is a positive ideal value vector, is the optimized value of the nth indicator in the indicator set; It is used to characterize the maximum optimization value of each indicator; i and j are the indicator sets related to the benefit and cost standards respectively;

[0015] The negative ideal value vector is calculated by the following formula:

[0016]

[0017] j=1,…,n;

[0018] Among them, f - is a negative ideal value vector; is the optimized value of the nth indicator in the indicator set; Used to characterize the minimum optimization value of each indicator.

[0019] In a possible implementation, the improved multi-criteria compromise solution ranking method VIKOR is improved by introducing incomplete criterion weights into the VIKOR algorithm;

[0020] The incomplete weight extreme point is calculated by the following formula:

[0021] E=(λ 1 ,λ 2 ,…,λ n )

[0022]

[0023] Among them, i∈n; λ i is the i-th column vector whose elements from the first position to the i-th position are 1 / i and all others are zero.

[0024] In one possible implementation, group benefits include low-level group benefits and high-level group benefits;

[0025] The low-level group benefits are:

[0026]

[0027] The advanced group benefits are:

[0028]

[0029] in:

[0030]

[0031] In the formula, and is the normalized upper and lower result of the ith option; and They respectively represent the minimum optimization value of the nth indicator and the maximum optimization value of the nth indicator.

[0032] In one possible implementation, personal regret includes low-level personal regret and high-level personal regret;

[0033] Low-level personal regrets are:

[0034]

[0035] Senior personal regrets are:

[0036]

[0037] in, λ kj is the indicator weight.

[0038] In a possible implementation, based on the group benefits and individual regrets of each alternative solution, the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform is obtained, including:

[0039] According to the group benefits and individual regrets of each alternative plan, calculate the comprehensive performance value or comprehensive performance range of each alternative plan;

[0040] According to the comprehensive performance value or comprehensive performance range of each alternative plan, the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform is obtained.

[0041] In a possible implementation, the comprehensive performance value interval includes a low-level comprehensive performance value and a high-level comprehensive performance value;

[0042] The low-level comprehensive performance value is calculated by the following formula:

[0043]

[0044] The Advanced Composite Performance Value is calculated using the following formula:

[0045]

[0046] in,

[0047]

[0048] Where v is a constant, which is a weight introduced to support the group utility maximization strategy; (1-v) is used to weigh personal regret.

[0049] In a possible implementation, according to the comprehensive performance range of each alternative solution, the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform is obtained, including:

[0050] For any two alternatives, perform the following steps:

[0051] If the comprehensive performance ranges of the two alternatives are the same, the two alternatives are used as sub-alternatives until a sub-alternative is finally obtained; or if the comprehensive performance ranges of the sub-alternatives finally obtained are the same, the sub-alternative finally obtained is the optimal solution based on the comprehensive evaluation of the data value-added services of the energy digital platform;

[0052] If the high-level comprehensive performance value of one of the alternatives is less than or equal to the low-level comprehensive performance value of another alternative, the alternative with the smaller high-level comprehensive performance value will be used as a sub-alternative, and the final sub-alternative will be used as the optimal solution for the comprehensive evaluation of the data value-added service based on the energy digital platform;

[0053] If there is an overlap in the comprehensive performance intervals of the two alternative plans, or if one of the comprehensive performance intervals is within the other comprehensive performance interval, the grey correlation method is used to obtain the proportion of the overlapping parts of the two alternative plans and the midpoint proximity. According to the proportion of the overlapping parts and the midpoint proximity, the correlation is determined, and the alternative with a large correlation is taken as a sub-alternative plan, and the final sub-alternative plan is selected as the optimal plan based on the comprehensive evaluation of the data value-added services of the energy digital platform.

[0054] In a possible implementation, according to the comprehensive performance values ​​of the alternative solutions, the optimal solution for comprehensive evaluation of the data value-added service based on the energy digital platform is obtained, including:

[0055] The alternative solution with the smallest comprehensive performance value is selected as the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform.

[0056] In a second aspect, an embodiment of the present invention provides a data value-added service comprehensive evaluation device based on an energy digital platform, comprising:

[0057] The acquisition module is used to obtain the value-added business dimension indicators of the power industry;

[0058] A construction module is used to determine multiple data value-added service comprehensive evaluation alternatives based on the energy digital platform according to the value-added service dimension indicators and to construct an indicator system;

[0059] A calculation module is used to construct a decision matrix for any alternative plan according to the alternative plan and the indicator system; use the improved multi-criteria compromise solution sorting method VIKOR and the decision matrix to determine the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point; and determine the group benefit and individual regret of the alternative plan based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point;

[0060] The selection module is used to obtain the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform based on the group benefits and individual regrets of each alternative solution.

[0061] The embodiment of the present invention provides a comprehensive evaluation method for data value-added services based on an energy digital platform. In this method, multiple alternative comprehensive evaluation schemes for data value-added services based on an energy digital platform are pre-constructed, and an optimal scheme is selected from these schemes. The data value-added services of the power industry are comprehensively evaluated through the selected scheme, which can provide scientific planning and transformation for enterprises. At present, it is usually assumed that the exact values ​​in the decision-making model are known or can be obtained from the decision maker. However, in most cases, this assumption is not applicable. To solve this problem, in the process of selecting the optimal scheme, the embodiment of the present invention introduces incomplete criterion weights in the multi-attribute decision-making method VIKOR to calculate incomplete weight extreme value points. Furthermore, through incomplete weight extreme value points, combined with positive ideal value vectors and negative ideal value vectors, decision makers can weigh group benefits and personal regrets. In the case of incomplete standard weights, incomplete standard weights are used to provide decision makers with opportunities to enhance choice and standard freedom. On this basis, the accuracy of judgment and selection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 This is a flowchart of a comprehensive evaluation method for value-added data services based on an energy digital platform provided by an embodiment of the present invention;

[0064] Figure 2 It is a structural schematic diagram of a data value-added service comprehensive evaluation device based on an energy digital platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0067] Example 1:

[0068] Figure 1 is a flowchart of a comprehensive evaluation method for value-added data services based on an energy digital platform provided by an embodiment of the present invention. Figure 1 As shown, the method may include:

[0069] Step 110: Obtain value-added business dimension indicators of the power industry.

[0070] In this embodiment, energy data and market demand data can be collected in real time from power grids, power plants, industrial enterprises and user terminals through various sensors and intelligent devices. Among them, energy data can include energy supply and demand information, equipment operation status, power load, clean energy utilization rate, etc. Pre-processing steps such as cleaning, analysis, and classification are performed on each energy data to ensure the accuracy and integrity of the data. Then, the pre-processed energy data is analyzed and processed to obtain value-added business dimension indicators for the power industry.

[0071] In this embodiment, the value-added business dimension indicators of the power industry may include energy data processing capabilities, market development level, data security, user demand level, project investment return rate, technological progress benefits, etc.

[0072] Step 120: Determine multiple comprehensive evaluation alternatives for data value-added services based on the energy digital platform based on the value-added service dimension indicators and construct an indicator system.

[0073] In this embodiment, a comprehensive evaluation index system for data value-added services based on the energy digital platform can be constructed from the perspective of power companies in the power industry. The evaluation system covers seven dimensions: external policy environment, energy data technology support, legal compliance framework, value-added service supply and demand situation, economic results, social impact, and environmental contribution. The index system is led by the target layer, refined by the criteria layer, and supported by the basic layer, which together constitute a comprehensive and in-depth evaluation framework for the energy big data value-added service business model. The details are shown in Table 1.

[0074] Table 1 Comprehensive and in-depth evaluation framework for energy big data value-added service business models

[0075]

[0076] In Table 1, a comprehensive evaluation method for data value-added services based on energy digital platform is established with the target layer as the basis. The criteria layer includes data technology dimension, environmental dimension, data value-added service supply and demand dimension, data value-added service economic benefit dimension, data value-added service social benefit dimension and data value-added service environmental benefit dimension.

[0077] The basic layer corresponding to the data technology dimension and the environmental dimension includes the degree of data market development, the strength of data policy support, the scale of data access, data processing capabilities, data acquisition and update speed, data resource sharing capabilities and data security; the basic layer corresponding to the data value-added service supply and demand dimension includes the user demand level, supply and demand dimensions and user demand level; the basic layer corresponding to the data value-added service economic benefit dimension includes the investment cost per unit service capacity, data platform operation and management expenses, the investment rate of return of value-added service projects and the investment payback period of value-added service projects; the basic layer corresponding to the data value-added service social benefit dimension includes the benefits of data technology progress, the contribution of industrial economic development and the promotability of value-added service projects; the basic layer corresponding to the data value-added service environmental benefit dimension includes energy intensity and clean energy utilization rate.

[0078] Through the basic layer, different dimensional evaluation criteria are given, and higher values ​​are required for beneficial criteria. Based on these dimensions and corresponding evaluation criteria, multiple comprehensive evaluation alternatives for data value-added services based on the energy digital platform are obtained. Among them, in this embodiment, in order to ensure that the evaluation process is objective and accurate, the entropy weight method can be used to assign weights to each indicator.

[0079] Step 130: For any alternative plan, a decision matrix is ​​constructed according to the alternative plan and the indicator system; the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point are determined by using the improved multi-criteria compromise solution ranking method VIKOR and the decision matrix; and the group benefit and individual regret of the alternative plan are determined based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point.

[0080] In this embodiment, for different alternative plans, the basic layer standards in different benefit dimensions corresponding to the indicator system have different emphases, thereby constructing a decision matrix for the energy digital platform data value-added service business problem.

[0081] After obtaining the decision matrix, the decision matrix is ​​converted into an interval decision matrix and an extreme point matrix, wherein the extreme point decision matrix is ​​determined by the following method:

[0082] When the decision information is unclear or the specific weight value cannot be given, this information becomes incomplete information. According to the five dimensions divided in the criterion layer, its linear inequalities can be divided into five categories, and the corresponding WI linear equation can be expressed as:

[0083] W WI ={w i -w j ≥α i}

[0084] Among them, w i is the weight of the i-th indicator; w j is the weight of the i-th indicator; α i is a non-negative constant.

[0085] Introduce a new variable μ so that w n =μ n , then:

[0086] w n-1 =μ n-1 +μ n ,w n-2 =μ n-2 +μ n-1 +μ n ,...,w 1 =μ 1 +μ 2 +...+μn

[0087] By replacing n The equivalent set of :

[0088]

[0089] Next, we introduce the variable γ i =i·μ i ,i=1...,n will M WI Convert to E WI :

[0090]

[0091] Find E WI The extreme point of is taken as an identity matrix I;

[0092] I=(e 1 ,e 2 ,...,e i );

[0093] where e i is a unit column vector with i elements being 1 and the rest being zero. i Multiply by a scalar 1 / j to obtain the extreme point matrix E of the standard weight set.

[0094] In this embodiment, an improved multi-criteria compromise solution ranking method (VlseKriterijumska Optimizacija Kompromisno Resenje, VIKOR) is obtained by introducing incomplete criterion weights into the VIKOR algorithm.

[0095] Using improved VIKOR and decision matrix, determining positive ideal value vector, negative ideal value vector, and incomplete weight extreme value points can be divided into two parts. One part is to use improved VIKOR and decision matrix to convert the decision matrix into an interval decision matrix, and based on the interval decision matrix, obtain the positive ideal value vector and the negative ideal value vector; the other part is to use improved VIKOR and decision matrix to obtain the extreme value point matrix, and based on the extreme value point matrix, obtain the incomplete weight extreme value points.

[0096] In this embodiment, the positive ideal value vector is calculated by the following formula:

[0097]

[0098] j=1,…,n

[0099] Among them, f * is a positive ideal value vector, is the optimized value of the nth indicator in the indicator set; It is used to characterize the maximum optimization value of each indicator; i and j are the indicator sets related to the benefit and cost standards respectively;

[0100] The negative ideal value vector is calculated by the following formula:

[0101]

[0102] j=1,…,n.

[0103] Among them, f - is a negative ideal value vector; is the optimized value of the nth indicator in the indicator set; Used to characterize the minimum optimization value of each indicator.

[0104] In this embodiment, first, the extreme points of the criterion weight set are calculated according to the extreme point matrix:

[0105]

[0106] In general, incomplete weights will lead to a region of standard weights, from which multiple extreme points can be found. If it is not empty, the extreme points of its set WI can be rewritten as the extreme point matrix E.

[0107] The extreme point matrix E is expressed as:

[0108] E=(λ 1 ,λ 2 ,…,λ n ), where λ i is the i-th column vector, whose elements from the first position to the i-th position are 1 / i, and the others are zero. Correspondingly, the extreme point matrix E is transformed into:

[0109]

[0110] In the formula, i∈n; each point is an incomplete weight extreme point.

[0111] In this embodiment, when comparing different alternatives, it is necessary to calculate group utility and individual regret. After introducing the incomplete criterion weight, the improved VIKOR method needs to modify the original single-level group utility to a group benefit that includes low-level group benefits and high-level group benefits, and modify the single-pole individual regret in the original formula to an individual regret that includes low-level individual regret and high-level individual regret, so as to take into account the interval results. Applicable to all i and j.

[0112] Optional, low-level group benefits are:

[0113]

[0114] The advanced group benefits are:

[0115]

[0116] in:

[0117]

[0118] In the formula, and is the normalized upper and lower result of the ith option; and They respectively represent the minimum optimization value of the nth indicator and the maximum optimization value of the nth indicator.

[0119] In the traditional method, low-level personal regret and high-level personal regret are expressed as:

[0120]

[0121] The embodiment of the present invention proposes an improvement of unweighted individual regret, that is, the individual regret or synergy effect does not need to consider the indicator weight. Accordingly, the modified low-level individual regret and high-level individual regret are respectively expressed as:

[0122] Low-level personal regrets are:

[0123]

[0124] Senior personal regrets are:

[0125]

[0126] in, λ kj is the indicator weight.

[0127] Step 140: Based on the group benefits and individual regrets of each alternative plan, the optimal plan for comprehensive evaluation of the data value-added service based on the energy digital platform is obtained.

[0128] In this embodiment, the comprehensive performance value or comprehensive performance range corresponding to each alternative plan can be calculated based on the group benefits and personal regrets of each alternative plan, and the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform can be determined based on the comprehensive performance value or comprehensive performance range corresponding to each alternative plan.

[0129] In this embodiment, the comprehensive performance value interval includes a low-level comprehensive performance value and a high-level comprehensive performance value;

[0130] The low-level comprehensive performance value is calculated by the following formula:

[0131]

[0132] The advanced comprehensive performance value is calculated by the following formula:

[0133]

[0134] in,

[0135]

[0136] Where v is a constant, which is a weight introduced to support the group utility maximization strategy; (1-v) is used to weigh personal regret.

[0137] In this embodiment, the comprehensive performance value corresponding to each alternative solution may be an average value of a low-level comprehensive performance value and a high-level comprehensive performance value in a comprehensive performance interval corresponding to each alternative solution.

[0138] In an optional embodiment, according to the comprehensive performance values ​​of the alternative solutions, the optimal solution for comprehensive evaluation of the data value-added service based on the energy digital platform is obtained, which may include:

[0139] The alternative solution with the smallest comprehensive performance value is selected as the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform.

[0140] In this embodiment, the smaller the comprehensive performance value is, the more corresponding alternative solutions there are. Therefore, the alternative solution with the smallest comprehensive performance value can be regarded as the optimal solution for comprehensive evaluation of value-added data services based on the energy digital platform.

[0141] In an optional embodiment, according to the comprehensive performance range of each alternative solution, the optimal solution for comprehensive evaluation of the data value-added service based on the energy digital platform is obtained, which may include:

[0142] For any two alternatives, perform the following steps:

[0143] If the comprehensive performance ranges of the two alternatives are the same, the two alternatives will be used as sub-alternatives until a sub-alternative is finally obtained; or if the comprehensive performance ranges of the sub-alternatives finally obtained are the same, the sub-alternative finally obtained will be the optimal solution based on the comprehensive evaluation of the data value-added services of the energy digital platform.

[0144] If the high-level comprehensive performance value of one of the alternatives is less than or equal to the low-level comprehensive performance value of another alternative, the alternative with the smaller high-level comprehensive performance value will be used as a sub-alternative, and the final sub-alternative will be used as the optimal solution for the comprehensive evaluation of data value-added services based on the energy digital platform.

[0145] If there is an overlap in the comprehensive performance intervals of the two alternative plans, or if one of the comprehensive performance intervals is within the other comprehensive performance interval, the grey correlation method is used to obtain the proportion of the overlapping parts of the two alternative plans and the midpoint proximity. According to the proportion of the overlapping parts and the midpoint proximity, the correlation is determined, and the alternative with a large correlation is taken as a sub-alternative plan, and the final sub-alternative plan is selected as the optimal plan based on the comprehensive evaluation of the data value-added services of the energy digital platform.

[0146] In this embodiment, the optimal solution is determined by the comprehensive performance interval of each alternative solution. Compared with determining the optimal solution by the comprehensive performance value of each alternative solution, it has higher accuracy, but at the same time, its calculation amount is larger.

[0147] In this embodiment, when determining the optimal solution through the comprehensive performance intervals of each alternative solution, a pairwise comparison method can be used. Through comparison, the better alternative solution is seated as a sub-alternative solution until only one sub-alternative solution remains, or when the comprehensive performance intervals of the remaining sub-alternative solutions are the same, the obtained sub-alternative solution is taken as the optimal solution.

[0148] In this embodiment, it is assumed that the comprehensive performance intervals of any two alternative solutions are:

[0149]

[0150] There are three possible situations for the comprehensive performance range of any two alternatives. The first is that the two comprehensive performance ranges are the same, that is, The second type is that the minimum value in one comprehensive performance interval is greater than or equal to the maximum value in another comprehensive performance interval, that is, The third type is that the two comprehensive performance intervals overlap, that is, or

[0151] These three situations can be handled in the following ways:

[0152] For the first case, both alternatives can be used as sub-alternatives and compared with the comprehensive performance range of other alternatives. In order to reduce the amount of calculation, only one of the sub-alternatives can be used in the comparison process.

[0153] For the second case, if Then we think Q i is the minimum interval, then the i-th alternative is taken as the sub-alternative.

[0154] For the third case, the grey correlation method can be introduced to determine the priority of the two comprehensive performance intervals by comparing the proportion of the overlapping parts of the two comprehensive performance intervals and the proximity of the midpoints between the two comprehensive performance intervals and constructing the grey correlation degree after dimensionless conversion.

[0155] Correspondingly, the midpoint proximity, that is, the degree of proximity between the midpoints of two comprehensive performance intervals, can be expressed as:

[0156]

[0157] Among them, l i is the midpoint proximity of the ith alternative; l j is the midpoint proximity of the jth alternative.

[0158] The proportion of the overlap between the two comprehensive performance intervals can be expressed as:

[0159]

[0160] Among them, p ij is the proportion of the overlapping parts of the comprehensive performance interval of the ith alternative relative to the jth alternative; p ji It is the ratio of the overlapping parts of the comprehensive performance interval of the j-th alternative relative to the ith alternative.

[0161] According to the proportion of the overlapping parts and the proximity of the midpoints, the dimensionless method is used to calculate the correlation coefficient of the i-th alternative:

[0162]

[0163] Among them, ρ is the resolution coefficient, which ranges from 0≤ρ≤1 (usually 0.5), and is used to adjust the sensitivity of the correlation coefficient; ξ i (j) is the correlation coefficient of the base layer indicator j of the backup plan i; p 0j It is the ratio of the overlapping part of the first basic layer indicator in the ith alternative plan relative to the comprehensive performance range of the ith alternative plan.

[0164] The relevance of the ith alternative is:

[0165]

[0166] The alternative with greater correlation is closer to the ideal solution. Therefore, the above-mentioned related formula can be referred to to calculate the correlation of each alternative, and the alternative with the greatest correlation is taken as the sub-alternative. The final sub-alternative is the optimal solution based on the comprehensive evaluation of the data value-added service of the energy digital platform.

[0167] To verify the effectiveness of the embodiment of the present invention, in an optional example, this embodiment provides the following algorithm example:

[0168] In this embodiment, the data technology dimension and environment dimension in the comprehensive evaluation method of the energy digital platform data value-added service business model are taken as an example, and different evaluation strategies are adopted to obtain multiple alternative evaluation indicators A 1 , A 2 , A 3 , A 4 , A 5 , A 6 , A 7 . The data market development level of the standard layer in the benefit dimension is C 1 , Data policy support strength C 2 , Data access scale C 3 , data processing capability C 4 , Data resource sharing capability C 5 、Data security C 6 And data acquisition and update speed C 7 As an indicator system, the decision matrix obtained is shown in Table 2:

[0169] Table 2 Decision matrix example table

[0170] C1 C2 C3 C4 C5 C6 C7 A1 3200 451 3475 756 17 4.15 18 A2 2400 690 4975 1324 98 3 60 A3 5000 850 6900 1532 13 4.5 864 A4 3000 400 3800 879 30 4 152 A5 8000 953 6700 4688 1200 8.6 1300 A6 2550 440 4600 480 200 3.1 10 A7 2800 460 1721 600 90 2.5 50 A8 1200 160 1750 620 2.2 8.2 45

[0171] To facilitate the demonstration of calculations, the original decision matrix is ​​converted into an interval decision matrix to represent the fuzzy interval decision matrix obtained in practice, in which all standard values ​​are expressed in the form of intervals considering a 10% change in the data, as shown in Table 3.

[0172] Table 3 Interval decision matrix example table

[0173]

[0174] Based on the data provided in Table 3, the positive ideal value vector and the negative ideal value vector are calculated:

[0175]

[0176] E=(λ 1 ,λ 2 ,…,λ 7 ), where the weight can be expressed as:

[0177]

[0178] Corresponding:

[0179] λ 1 =(0,0,1,0,0,0) T ,

[0180]

[0181]

[0182] The low-level group benefits and high-level group benefits are:

[0183]

[0184] Low-level personal regret and high-level personal regret are:

[0185]

[0186] in,

[0187]

[0188] Based on other options, the group benefits and individual regrets of each alternative are calculated as shown in Table 4:

[0189] Table 4 Group benefits and individual regrets calculated for other schemes

[0190]

[0191]

[0192] Correspondingly, the comprehensive performance values ​​and comprehensive performance value ranges corresponding to each alternative are shown in Table 5:

[0193] Table 5 Comprehensive performance values ​​and comprehensive performance value ranges corresponding to each alternative solution

[0194]

[0195] According to the method provided in this embodiment, the final ranking result of each alternative solution is:

[0196] A 5 >A 3 >A 2 >A 6 >A 4 >A 1 >A 7 >A 8

[0197] Based on this result, it can be determined that Alternative A 5 is the best solution.

[0198] It can be seen that the value-added service evaluation system based on the energy big data platform provided by the embodiment of the present invention can effectively support the digital transformation of the power industry and provide scientific planning and implementation basis for enterprises. Considering that it is usually assumed that the exact value in the decision-making model is known or can be obtained from the decision maker, however, in most cases, this assumption is not applicable, and incomplete information may even become an advantage. For example, using incomplete information in the MCDM problem helps to reduce the workload of collecting weights. Therefore, this embodiment proposes the most appropriate solution for the case where the decision maker's weight coefficient information is incompletely determined and the scheme value is described through the Fuzzy extended VIKOR method and the gray correlation method of incomplete criterion weights. It combines the expected opportunity loss and the maximum and minimum regrets, so that the decision maker can weigh the maximum group benefit and the minimum personal regret. In the case of incomplete standard weights, the incomplete standard weights are used to provide decision makers with the opportunity to enhance the freedom of choice and norms. At the same time, the gray correlation method is introduced. In the case of interval results, incomplete weights and interval overlaps, the possibility of the sorting interval is converted into the gray correlation degree of different scheme sequences, and the different schemes are comprehensively sorted to select the optimal scheme.

[0199] In addition, the method provided in this embodiment is applicable to a variety of value-added service businesses, including data credit investigation, energy trading decision support, environmental monitoring, user energy consumption trusteeship, multi-energy complementary optimization and equipment performance optimization, etc., providing a reliable evaluation tool for the promotion and implementation of these businesses. At the same time, under the background of the "dual carbon" strategic goal, the embodiment of the present invention helps to promote energy conservation and carbon reduction, especially in improving the utilization rate of clean energy, optimizing energy efficiency and reducing carbon emissions. It has significant economic and social benefits. Through this evaluation system, power grid companies and related enterprises can better develop and optimize value-added services, enhance energy management capabilities, promote the sustainable development of the power market, and help achieve the national strategic goals of carbon peak and carbon neutrality.

[0200] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0201] Example 2:

[0202] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0203] Figure 2 The structure diagram of a data value-added service comprehensive evaluation device based on an energy digital platform provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0204] like Figure 2As shown, a data value-added service comprehensive evaluation device 2 based on an energy digital platform includes:

[0205] An acquisition module 21 is used to acquire value-added service dimension indicators of the power industry;

[0206] A construction module 22 is used to determine a plurality of data value-added service comprehensive evaluation alternatives based on the energy digital platform according to the value-added service dimension indicators and to construct an indicator system;

[0207] The calculation module 23 is used to construct a decision matrix for any alternative plan according to the alternative plan and the indicator system; use the improved multi-criteria compromise solution sorting method VIKOR and the decision matrix to determine the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point; and determine the group benefit and individual regret of the alternative plan based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point;

[0208] The selection module 24 is used to obtain the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform based on the group benefits and personal regrets of each alternative solution.

[0209] In one possible implementation, the positive ideal value vector is calculated by the following formula:

[0210]

[0211] j=1,…,n

[0212] Among them, i and j are the indicator sets related to benefit and cost criteria, respectively;

[0213] The negative ideal value vector is calculated by the following formula:

[0214]

[0215] j=1,…,n.

[0216] In a possible implementation, the improved multi-criteria compromise solution ranking method VIKOR is improved by introducing incomplete criterion weights into the VIKOR algorithm;

[0217] The incomplete weight extreme point is calculated by the following formula:

[0218] E=(λ 1 ,λ 2 ,…,λ n )

[0219]

[0220] Among them, i∈n; λ iis the i-th column vector whose elements from the first position to the i-th position are 1 / i and all others are zero.

[0221] In one possible implementation, group benefits include low-level group benefits and high-level group benefits;

[0222] The low-level group benefits are:

[0223]

[0224] The advanced group benefits are:

[0225]

[0226] In one possible implementation, personal regret includes low-level personal regret and high-level personal regret;

[0227] Low-level personal regrets are:

[0228]

[0229] Senior personal regrets are:

[0230]

[0231] in:

[0232]

[0233] In the formula, and is the normalized upper and lower result of the ith option.

[0234] In a possible implementation, the selection module 24 is specifically configured to:

[0235] According to the group benefits and individual regrets of each alternative plan, calculate the comprehensive performance value or comprehensive performance range of each alternative plan;

[0236] According to the comprehensive performance value or comprehensive performance range of each alternative plan, the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform is obtained.

[0237] In a possible implementation, the comprehensive performance value interval includes a low-level comprehensive performance value and a high-level comprehensive performance value;

[0238] The low-level comprehensive performance value is calculated by the following formula:

[0239]

[0240] The advanced comprehensive performance value is calculated by the following formula:

[0241]

[0242] in,

[0243]

[0244] Where v is a constant, which is a weight introduced to support the group utility maximization strategy; (1-v) is used to weigh personal regret.

[0245] In a possible implementation, the selection module 24 is specifically configured to:

[0246] For any two alternatives, perform the following steps:

[0247] If the comprehensive performance ranges of the two alternatives are the same, the two alternatives are used as sub-alternatives until a sub-alternative is finally obtained; or if the comprehensive performance ranges of the sub-alternatives finally obtained are the same, the sub-alternative finally obtained is the optimal solution based on the comprehensive evaluation of the data value-added services of the energy digital platform;

[0248] If the high-level comprehensive performance value of one of the alternatives is less than or equal to the low-level comprehensive performance value of another alternative, the alternative with the smaller high-level comprehensive performance value will be used as a sub-alternative, and the final sub-alternative will be used as the optimal solution for the comprehensive evaluation of the data value-added service based on the energy digital platform;

[0249] If there is an overlap in the comprehensive performance intervals of the two alternative plans, or if one of the comprehensive performance intervals is within the other comprehensive performance interval, the grey correlation method is used to obtain the proportion of the overlapping parts of the two alternative plans and the midpoint proximity. According to the proportion of the overlapping parts and the midpoint proximity, the correlation is determined, and the alternative with a large correlation is taken as a sub-alternative plan, and the final sub-alternative plan is selected as the optimal plan based on the comprehensive evaluation of the data value-added services of the energy digital platform.

[0250] In a possible implementation, the selection module 24 is specifically configured to:

[0251] The alternative solution with the smallest comprehensive performance value is selected as the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform.

[0252] Example 3:

[0253] An embodiment of the present invention also provides a comprehensive evaluation platform for data value-added services based on an energy digital platform, which includes multiple sub-platforms such as an energy big data center, a new energy cloud platform, an online State Grid platform, and a smart energy service platform. These platforms are connected through a network to form a system for data collection, processing, and analysis covering the entire process of energy production, transmission, and consumption.

[0254] Each platform in the system has different functions. The Energy Big Data Center is responsible for storing and managing data from other sub-platforms, and determining the optimal solution based on the data. The new energy cloud platform can collect data on new energy power generation through various sensors, and monitor and optimize according to the solution made by the Energy Big Data Center. The online State Grid platform collects trading market data and user data, and is responsible for energy trading and data exchange based on the solution made by the Energy Big Data Center. The smart energy service platform provides users with energy efficiency optimization services according to the solution made by the Energy Big Data Center.

[0255] In an optional embodiment, the method provided in Example 1 can be applied to an energy big data center in a data value-added service comprehensive evaluation platform based on an energy digital platform.

[0256] Based on the method in Example 1, the platform provided in this embodiment can support services such as data credit reporting, energy trading decision support, environmental monitoring, user energy consumption hosting, multi-energy complementary optimization, and equipment performance optimization and improvement.

[0257] Among them, data credit reporting services can be used to conduct credit assessments on electricity and energy-related data based on big data technology, provide support for the financialization of electricity trading and electricity-using equipment, help financial institutions conduct comprehensive assessments of corporate energy usage, and provide accurate credit rating services.

[0258] Energy trading decision-making support services can use data analysis to optimize electricity market trading strategies. By analyzing real-time market supply and demand information through big data, they can help energy suppliers and users formulate optimal trading strategies, improve trading flexibility and efficiency, and especially optimize energy buying and selling decisions in the electricity spot market.

[0259] Energy environmental monitoring services can ensure compliance with energy conservation and emission reduction requirements by monitoring and analyzing environmental data on energy consumption, helping companies and governments implement optimized environmental policies.

[0260] User energy management services help businesses and individuals optimize energy use and reduce costs by providing energy consumption management services.

[0261] Multi-energy complementary optimization services support the coordinated management and optimization of multiple energy types (such as electricity, gas, heat, etc.), and achieve efficient allocation and scheduling of energy through big data analysis, especially in industrial parks and urban energy management, to achieve optimal utilization of energy resources.

[0262] Equipment performance optimization and improvement services optimize equipment efficiency, extend service life and reduce energy consumption by monitoring equipment operation data. These value-added services are supported by the big data platform, combined with the expanded VIKOR method and grey correlation analysis method to comprehensively evaluate the economic, social and environmental benefits of various services, helping enterprises optimize business decisions.

[0263] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0264] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0265] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned embodiments of the comprehensive evaluation method of data value-added services based on the energy digital platform. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0266] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for data value-added services based on an energy digital platform, characterized in that: include: Obtain value-added business dimension indicators for the power industry; Determine multiple data value-added service comprehensive evaluation alternatives based on the energy digital platform according to the value-added service dimension indicators and construct an indicator system; For any alternative plan, a decision matrix is ​​constructed based on the alternative plan and the indicator system; Using the improved multi-criteria compromise solution ranking method VIKOR and the decision matrix, determine the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point; and determine the group benefit and individual regret of the alternative plan based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point; Based on the group benefits and individual regrets of each alternative plan, the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform is obtained.

2. According to claim 1, a comprehensive evaluation method for data value-added services based on an energy digital platform is characterized in that: The positive ideal value vector is calculated by the following formula: Among them, f * is a positive ideal value vector, is the optimized value of the nth indicator in the indicator set; It is used to characterize the maximum optimization value of each indicator; i and j are the indicator sets related to the benefit and cost standards respectively; The negative ideal value vector is calculated by the following formula: Among them, f - is a negative ideal value vector; is the optimized value of the nth indicator in the indicator set; Used to characterize the minimum optimization value of each indicator.

3. According to claim 1, a comprehensive evaluation method for data value-added services based on an energy digital platform is characterized in that: The improved multi-criteria compromise solution ranking method VIKOR is improved by introducing incomplete criterion weights into the VIKOR algorithm; The incomplete weight extreme point is calculated by the following formula: E=(λ1,λ2,…,λ n ) Among them, i∈n; λ i is the i-th column vector whose elements from the first position to the i-th position are 1 / i and all others are zero.

4. According to claim 1, a comprehensive evaluation method for data value-added services based on an energy digital platform is characterized in that: The group benefits include low-level group benefits and high-level group benefits; The low-level group benefits are: The advanced group benefits are: in: In the formula, and is the normalized upper and lower result of the ith option; and They respectively represent the minimum optimization value of the nth indicator and the maximum optimization value of the nth indicator.

5. According to claim 1, a comprehensive evaluation method for data value-added services based on an energy digital platform is characterized in that: The personal regrets include low-level personal regrets and high-level personal regrets; The low-level personal regrets are: The senior personal regrets stated are: in, λ kj is the indicator weight.

6. According to claim 1, a comprehensive evaluation method for data value-added services based on an energy digital platform is characterized in that: The optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform is obtained based on the group benefits and individual regrets of each alternative solution, including: According to the group benefits and individual regrets of each alternative plan, calculate the comprehensive performance value or comprehensive performance range of each alternative plan; According to the comprehensive performance value or comprehensive performance range of each alternative plan, the optimal plan for comprehensive evaluation of data value-added services based on the energy digital platform is obtained.

7. A comprehensive evaluation method for data value-added services based on an energy digital platform according to claim 6, characterized in that: The comprehensive performance value interval includes a low-level comprehensive performance value and a high-level comprehensive performance value; The low-level comprehensive performance value is calculated by the following formula: The advanced comprehensive performance value is calculated by the following formula: in, Where v is a constant, which is a weight introduced to support the group utility maximization strategy; (1-v) is used to weigh personal regret.

8. A comprehensive evaluation method for data value-added services based on an energy digital platform according to claim 7, characterized in that: The optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform is obtained according to the comprehensive performance range of each alternative solution, including: For any two alternatives, perform the following steps: If the comprehensive performance ranges of the two alternatives are the same, the two alternatives are used as sub-alternatives until a sub-alternative is finally obtained; or if the comprehensive performance ranges of the sub-alternatives finally obtained are the same, the sub-alternative finally obtained is the optimal solution based on the comprehensive evaluation of the data value-added services of the energy digital platform; If the high-level comprehensive performance value of one of the alternatives is less than or equal to the low-level comprehensive performance value of another alternative, the alternative with the smaller high-level comprehensive performance value will be used as a sub-alternative, and the final sub-alternative will be used as the optimal solution for the comprehensive evaluation of the data value-added service based on the energy digital platform; If there is an overlap in the comprehensive performance intervals of the two alternative plans, or one of the comprehensive performance intervals is within the other comprehensive performance interval, the grey correlation method is used to obtain the proportion of the overlapping parts of the two alternative plans and the midpoint proximity; based on the proportion of the overlapping parts and the midpoint proximity, the correlation is determined, and the alternative with a large correlation is used as a sub-alternative plan, and the final sub-alternative plan is selected as the optimal plan based on the comprehensive evaluation of the data value-added services of the energy digital platform.

9. A comprehensive evaluation method for data value-added services based on an energy digital platform according to claim 6, characterized in that: The optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform is obtained based on the comprehensive performance values ​​of the alternative solutions, including: The alternative solution with the smallest comprehensive performance value is selected as the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform.

10. A data value-added service comprehensive evaluation device based on an energy digital platform, characterized in that: include: The acquisition module is used to obtain the value-added business dimension indicators of the power industry; A construction module, used to determine a plurality of data value-added service comprehensive evaluation alternatives based on the energy digital platform according to the value-added service dimension indicators and to construct an indicator system; A calculation module is used to construct a decision matrix for any alternative plan according to the alternative plan and the indicator system; Using the improved multi-criteria compromise solution ranking method VIKOR and the decision matrix, determine the positive ideal value vector, the negative ideal value vector, and the incomplete weight extreme value point; and determine the group benefit and individual regret of the alternative plan based on the positive ideal value vector, the negative ideal value vector and the incomplete weight extreme value point; The selection module is used to obtain the optimal solution for comprehensive evaluation of data value-added services based on the energy digital platform based on the group benefits and individual regrets of each alternative solution.

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