Efficiency evaluation method for power supply area and related equipment

Abstract

The invention provides an efficiency evaluation method for a power supply area and related equipment. The method comprises the following steps: acquiring power data of the power supply area; wherein the power data comprises at least one of equipment operation data, line operation data, operation economic data or electric charge income data; the index weight of the efficiency index of the power supply area is determined based on a preset hierarchical model, and the preset hierarchical model is obtained based on the efficiency index and the power supply area; comparing the importance degree of each power supply area relative to the efficiency index based on the power data to obtain a target judgment matrix of the power supply area relative to the efficiency index; and performing efficiency evaluation on the power supply area based on the target judgment matrix and the index weight to obtain an efficiency evaluation result of the power supply area.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a method for evaluating the performance of a power supply area and related equipment. Background Art

[0002] In the power supply system, power supply enterprises divide and manage power users in the form of substations, making the substation an important management unit in the power supply system. The power supply substation portrait is based on the substation distribution and transformation data, and extracts substation feature labels through various methods to form a substation label system. It is constructed through clustering, classification, association and other algorithms according to decision-making needs. The substation portrait is a virtual abstraction of the real substation situation, which can help the power department quickly grasp the health status of the substation, power consumption mode and other conditions, assist managers in making power decisions, optimize resource allocation, and maximize the efficiency and benefits of the substation. In the existing technology, the evaluation of substation efficiency usually adopts simple financial indicator analysis. Although this method is intuitive and easy to understand, it often cannot fully reflect the complexity and multidimensionality of substation efficiency, and the accuracy of efficiency evaluation is not high. Summary of the invention

[0003] The present disclosure proposes a method and related equipment for evaluating the performance of a power supply station, so as to solve the technical problem of low accuracy of the performance evaluation of the power supply station to a certain extent.

[0004] In a first aspect, the present disclosure provides a method for evaluating the effectiveness of a power supply area, comprising:

[0005] Acquire power data of the power supply area; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity fee income data;

[0006] Determining an indicator weight of the performance indicator of the power supply area based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance indicator and the power supply area;

[0007] Comparing the importance of each of the power supply areas relative to the performance index based on the power data, and obtaining a target judgment matrix of the power supply areas relative to the performance index;

[0008] The performance of the power supply area is evaluated based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

[0009] In a second aspect of the present disclosure, a device for evaluating the effectiveness of a power supply area is provided, comprising:

[0010] An acquisition module, used to acquire power data of the power supply area; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity fee income data;

[0011] A weight module, used to determine the index weight of the performance index of the power supply area based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance index and the power supply area;

[0012] A matrix module, for comparing the importance of each of the power supply areas relative to the performance index based on the power data, and obtaining a target judgment matrix of the power supply areas relative to the performance index;

[0013] An evaluation module is used to perform performance evaluation on the power supply area based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

[0014] In a third aspect of the present disclosure, an electronic device is provided, comprising one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the programs include instructions for executing the method described in the first aspect.

[0015] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium containing a computer program is provided. When the computer program is executed by one or more processors, the processors execute the method described in the first aspect.

[0016] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0017] From the above, it can be seen that the efficiency evaluation method and related equipment of the power supply area provided by the present disclosure can accurately evaluate the efficiency of the power supply area by performing data analysis and data mining on the power data of the power supply area. Scientific methodology and advanced technical means are used to accurately analyze the power supply capacity and efficiency of the area, thereby optimizing resource allocation and promoting the improvement of the quality and efficiency of the area operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 Schematic diagram of a performance evaluation framework for a power supply area according to an embodiment of the present disclosure.

[0020] Figure 2 The figure is a schematic diagram of the hardware structure of an exemplary electronic device according to an embodiment of the present disclosure.

[0021] Figure 3 Schematic diagram of the flow of the method for evaluating the efficiency of a power supply area according to an embodiment of the present disclosure.

[0022] Figure 4 A schematic diagram of a preset hierarchical model of an embodiment of the present disclosure.

[0023] Figure 5 A schematic diagram of a method according to an embodiment of the present disclosure.

[0024] Figure 6 Schematic diagram of a power supply area performance evaluation device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0026] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0027] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0028] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0029] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0030] Figure 1 FIG. 1 is a schematic diagram showing an efficiency evaluation framework of a power supply area according to an embodiment of the present disclosure. Figure 1 The performance evaluation architecture 100 of the power supply area may include a server 110, a terminal 120, and a network 130 that provides a communication link. The server 110 and the terminal 120 may be connected via a wired or wireless network 130. The server 110 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and CDN.

[0031] The terminal 120 may be implemented in hardware or software. For example, when the terminal 120 is implemented in hardware, it may be various electronic devices having a display screen and supporting page display, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. When the terminal 120 device is implemented in software, it may be installed in the electronic devices listed above; it may be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0032] It should be noted that the power supply area efficiency evaluation method provided in the embodiment of the present application can be executed by the terminal 120 or by the server 110. It should be understood that Figure 1 The number of terminals, networks and servers in the embodiment is only for illustration and is not intended to limit the number of terminals, networks and servers. Any number of terminals, networks and servers may be provided as required.

[0033] Figure 2 FIG. 2 shows a schematic diagram of the hardware structure of an exemplary electronic device 200 provided in an embodiment of the present disclosure. Figure 2 As shown, the electronic device 200 may include: a processor 202, a memory 204, a network module 206, a peripheral interface 208 and a bus 210. The processor 202, the memory 204, the network module 206 and the peripheral interface 208 are connected to each other in communication within the electronic device 200 through the bus 210.

[0034] Processor 202 may be a central processing unit (CPU), a data processor, a neural network processor (NPU), a microcontroller (MCU), a programmable logic device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or one or more integrated circuits. Processor 202 may be used to perform functions related to the technology described in this disclosure. In some embodiments, processor 202 may also include multiple processors integrated into a single logical component. For example, Figure 2 As shown, processor 202 may include a plurality of processors 202a, 202b, and 202c.

[0035] The memory 204 may be configured to store data (eg, instructions, computer code, etc.). Figure 2 As shown, the data stored in the memory 204 may include program instructions (for example, program instructions for implementing the efficiency evaluation method of the power supply area of ​​the embodiment of the present disclosure) and data to be processed (for example, the memory may store configuration files of other modules, etc.). The processor 202 may also access the program instructions and data stored in the memory 204, and execute the program instructions to operate on the data to be processed. The memory 204 may include a volatile storage device or a non-volatile storage device. In some embodiments, the memory 204 may include a random access memory (RAM), a read-only memory (ROM), an optical disk, a magnetic disk, a hard disk, a solid-state drive (SSD), a flash memory, a memory stick, etc.

[0036] The network module 206 can be configured to provide communication with other external devices to the electronic device 200 via a network. The network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, near field communication (NFC) etc.), a cellular network, the Internet or a combination thereof. It is understood that the type of network is not limited to the above specific examples. In some embodiments, the network module 306 can include any number of network interface controllers (NICs), radio frequency modules, transceivers, modems, routers, gateways, adapters, cellular network chips, etc., in any combination.

[0037] The peripheral interface 208 can be configured to connect the electronic device 200 to one or more peripheral devices to achieve information input and output. For example, the peripheral devices can include input devices such as a keyboard, a mouse, a touch pad, a touch screen, a microphone, and various sensors, and output devices such as a display, a speaker, a vibrator, and an indicator light.

[0038] The bus 210 can be configured to transmit information between various components of the electronic device 200 (e.g., the processor 202, the memory 204, the network module 206, and the peripheral interface 208), such as an internal bus (e.g., a processor-memory bus), an external bus (USB port, PCI-E bus), etc.

[0039] It should be noted that, although the architecture of the electronic device 200 only shows the processor 202, the memory 204, the network module 206, the peripheral interface 208 and the bus 210, in the specific implementation process, the architecture of the electronic device 200 may also include other components necessary for normal execution. In addition, it can be understood by those skilled in the art that the architecture of the electronic device 200 may also only include the components necessary for implementing the embodiments of the present disclosure, and does not necessarily include all the components shown in the figure.

[0040] With the rapid development of the power industry, the substation is an important part of the power system. Its substation performance evaluation and portrait construction are of great significance to improving the overall operation efficiency of the power system and optimizing resource allocation. In the power supply system, power supply enterprises divide and manage power users in the form of substations, making the substation an important management unit in the power supply system. The power supply substation portrait is based on the substation distribution and transformation data, and extracts substation feature labels through various methods to form a substation label system. It is constructed through clustering, classification, association and other algorithms according to decision-making needs. The substation portrait is a virtual abstraction of the real substation situation, which can help the power department quickly grasp the health status of the substation, power consumption mode, etc., assist management personnel in making power decisions, optimize resource allocation, and maximize the efficiency and benefits of the substation.

[0041] In the prior art, simple financial indicator analysis is usually used for the evaluation of the performance of the substation. Although this method is intuitive and easy to understand, it often cannot fully reflect the complexity and multidimensionality of the economic benefits of the substation. In addition, the construction of the portrait of the economic benefits of the substation in the prior art also lacks systematicity and scientificity, and it is difficult to provide effective decision support for decision makers. At present, the existing implementation scheme mainly relies on methods such as cluster analysis and expert scoring. For example, the cluster analysis method often uses big data technology to profile the substation from multiple dimensions, quantify the priority of each substation, and describe the characteristics of the substation through cluster analysis, providing a reference for the calculation of the economic benefits of the substation. Another example is the expert scoring method, which generally constructs the substation portrait by selecting appropriate experts, determining evaluation indicators, providing background information, anonymously soliciting expert opinions, analyzing and summarizing and feedback, and multiple rounds of consultation and feedback. Among them, the cluster analysis method is easily affected by the initial value (i.e., the initial cluster center). Different initial values ​​may lead to different clustering results, and the number of clusters is difficult to determine. It is sensitive to abnormal points and is not suitable for nonlinear data. In the indicators of portrait construction, there are many nonlinear data. Expert scoring methods often rely on qualitative analysis and empirical judgment, and have strict requirements on the selection of experts, evaluation indicators, and scoring rules, making it difficult to fully and accurately reflect the true situation of the economic benefits of the substation. This type of method often has the problems of strong subjectivity and low accuracy in the evaluation process. Therefore, how to reasonably evaluate the effectiveness of the power supply substation and improve the accuracy of the evaluation has become a technical problem that needs to be solved urgently.

[0042] In view of this, the embodiments of the present disclosure provide a method and related equipment for evaluating the efficiency of a power supply area. By performing data analysis and data mining on the power data of the power supply area, an accurate evaluation of the efficiency of the power supply area is achieved. By using scientific methodology and advanced technical means, the power supply capacity and efficiency of the area are accurately analyzed, thereby optimizing resource allocation and promoting the improvement of the quality and efficiency of the area's operation.

[0043] The performance evaluation of the substation mainly focuses on the extraction of indicators such as the dynamic equipment and line operation characteristics, development trends and economic benefits of the substation, which mainly includes data mining, data processing, statistical analysis, artificial intelligence and visualization technology. Among them, data mining technology can refer to the collection, collation, analysis and mining of relevant indicators that affect the economic benefits of the substation through data mining technology, discovering the hidden patterns, associations and trends in the substation data, and providing strong support for the construction of an accurate economic benefit portrait of the substation. Data processing technology can refer to the use of big data technology to clean, collate, convert and standardize the data related to the substation to ensure the quality and accuracy of the data. Statistical analysis technology can refer to the use of statistical methods to conduct statistical analysis of indicators within the substation to reveal the laws and trends behind the data. Artificial intelligence technology can refer to the use of artificial intelligence technology to intelligently analyze and predict the data within the substation to improve the accuracy and efficiency of portrait construction. Visualization technology can refer to the intuitive display of data analysis results in the form of graphics, images, etc., so that decision makers can quickly understand and grasp the operation status and development trends of the substation. To sum up, the construction of the district performance portrait is a process involving multiple technical fields. It requires the comprehensive use of multiple technical means such as data mining, data processing, statistical analysis, artificial intelligence and visualization technology to provide strong technical support for the construction of the district performance portrait and achieve the goal of comprehensively and accurately reflecting the actual benefits of the district economy.

[0044] See also Figure 3 , Figure 3 The schematic flow chart of the method for evaluating the effectiveness of a power supply station according to an embodiment of the present disclosure is shown. The method for evaluating the effectiveness of a power supply station according to an embodiment of the present disclosure can be deployed on a terminal or a server. Figure 3 In the embodiment, the power supply area efficiency evaluation method 300 may further include the following steps.

[0045] In step S310, power data of the power supply area is acquired; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity cost revenue data.

[0046] Among them, equipment operation data may refer to the data generated by various equipment in the power system during their operation, which may include but is not limited to at least one of the following: current and voltage, measuring the current and voltage levels of the input and output of the equipment. Power factor, which represents the ratio of effective power to apparent power in the power system. Temperature, monitoring the temperature of the equipment, especially the temperature of key components such as generators and transformers. Load, reflecting the actual load condition of the equipment, that is, the workload of the equipment. Fault records, recording the number, type and duration of equipment faults, etc. Maintenance records, recording the maintenance history of the equipment, including maintenance date, maintenance items, etc.

[0047] Line operation data may refer to data generated during the operation of power transmission and distribution lines, which may include but are not limited to at least one of the following: current and voltage, measuring the current and voltage levels at both ends of the line. Power, measuring the active power and reactive power transmitted by the line. Loss, evaluating the energy loss of the line, including heat loss and other forms of loss. Load, reflecting the actual load of the line. Fault record, recording the number, type and duration of faults on the line. Temperature, monitoring the temperature of the line, especially the cable.

[0048] Operational economic data refers to economic benefit data related to the operation of the power system, which may include but is not limited to at least one of the following: Generation cost, which calculates the cost of generating each unit of electricity by the power plant. Transmission cost, which calculates the cost of transmitting electricity from the power plant to the user. Distribution cost, which calculates the cost of distributing electricity to the end user. Maintenance cost, which reflects the cost required for equipment and line maintenance. Energy efficiency, which evaluates the efficiency of the power system in the process of energy conversion and transmission. Return on investment, which calculates the return on investment of power facilities.

[0049] The electricity revenue data may refer to the electricity revenue data collected by the power company from the user, which may include but is not limited to at least one of the following: electricity consumption, the amount of electricity consumed by the user in a certain period. Electricity fee, the amount of electricity fee that the user should pay. Arrears record, recording the situation of not paying the electricity fee on time. Payment record: recording the time and amount of the user's payment.

[0050] In step S320, the indicator weight of the performance indicator of the power supply station is determined based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance indicator and the power supply station.

[0051] Among them, the target layer can be determined to calculate the performance evaluation score of the substation, and evaluate the performance of the substation in terms of power grid technical indicators, economic factors, line losses, electricity charges, etc. The criterion layer is the constructed indicator category for evaluating the economic benefits of the substation, and the sub-criterion layer is the sub-indicator in the indicator category. The solution layer is the substation to be evaluated. The economic benefits of the substation are analyzed around reducing substation losses, energy-saving transformation potential, operation management, etc., and the corresponding indicators are extracted.

[0052] In some embodiments, the preset hierarchical model includes a first layer, a second layer, a third layer and a fourth layer; wherein,

[0053] The first layer includes the performance evaluation results of the power supply area;

[0054] The second layer includes the first category indicators, the second category indicators, the third category indicators and the fourth category indicators;

[0055] The third layer includes the first type of parameters in the first type of indicators, the second type of parameters in the second type of indicators, the third type of parameters in the third type of indicators, and the fourth type of parameters in the fourth type of indicators;

[0056] The fourth layer includes the power supply platform area.

[0057] Among them, see Figure 4 , Figure 4 A schematic diagram of a preset hierarchical model according to an embodiment of the present disclosure is shown. Figure 4 In the preset hierarchical model, the target layer, criterion layer, sub-criterion layer and solution layer are included. The target layer is the performance score of the substation; the criterion layer and sub-criterion layer extract the evaluation indicators that affect the performance of the substation from the perspectives of reducing substation losses, energy-saving transformation potential, and operation management. The criterion layer mainly includes the technical indicators of the power grid, line loss, economic factors, electricity charges and other indicators; the sub-criterion layer includes the technical indicators of the power grid such as operating capacity, operating efficiency, and transformer use time, line loss indicators such as line loss level and line loss sequence change trend, the proportion of industrial and commercial / agricultural production / residential electricity consumption in the total electricity consumption, the economic development trend of the industry, the photovoltaic access rate, the growth rate of special transformer sales, the growth rate of public transformer sales, and other economic factors, and the electricity fee recovery cycle, electricity fee recovery rate and other electricity fee indicators.

[0058] Specifically, the technical indicators of the power grid may include: operating capacity indicators, because the size of the transformer capacity has a significant impact on whether the system operates normally and whether overload causes power outages. Load rate, which reflects the load-bearing capacity of the transformer, constructs a load rate indicator, and its calculation formula is: load rate = actual load borne by the transformer / operating capacity. Transformer usage time, because the transformer usage time affects the operation and maintenance costs of the substation to a certain extent, these costs directly affect the economy of the substation. System efficiency, an efficient distribution system can effectively reduce energy consumption and loss, and its calculation formula is: system efficiency = power output provided during operation / actual power consumed.

[0059] Line loss management index is an important technical indicator for improving the economic benefits of the substation. The change trend of the line loss sequence can reflect whether there is electricity theft, whether there is a high management line loss caused by electricians not reading meters properly, meter failure, etc. The present disclosure extracts indicators related to line loss, including statistics of line loss sequences, change trends of line loss sequences, etc. Among them, the statistics of line loss sequences include: at least one of the mean, variance, and the difference between the maximum and minimum values. The change trend of the line loss sequence may include: The calculation method of the change trend of the line loss rate in the ith month is: in represents the average line loss in the first half of the ith month, Represents the average line loss in the second half of the i-th month.

[0060] Economic factor indicators may include: grid profitability, which can directly reflect its economic effect, and its calculation formula is: grid profitability = income of the substation - cost of the substation. Return on investment, which can be an important indicator for evaluating investment benefits, and its calculation formula is: return on investment = income obtained after substation investment / original investment. Analysis of industry economic development trends, which can predict in advance the impact of economic development on users' production and operation conditions, and directly affect the economic benefits of the substation. Extracting industry economic development trends through industry electricity sales, the calculation steps are: First, decompose the industry electricity sales into three subsequences: trend term, seasonal term and random term: Q(i) = Q t (i)+Q s (i)+Q r (i), where {Q(i)|i∈1,2,...,n} represents the historical electricity sales data of the industry, {Q t (i)|i∈1,2,...,n} is the trend term of electricity sales, {Q s (i)|i∈1,2,...,n} is the seasonal term of electricity sales, {Q r (i)|i∈1,2,…,n} is a random item of electricity sales, and n is a positive integer, which can represent the number of time units, such as n months, n quarters, n years, etc.

[0061] By using the correlation analysis between the power sales trend items of each industry and the indicators related to economic development, we can obtain the influencing factors that need to be considered when predicting the development trend of power sales in each industry. The influencing factors are divided into immediate indicators and leading indicators. Immediate indicators refer to the current influencing factors that affect the current power sales trend development, and leading indicators refer to the current influencing factors that affect the future power sales trend development. The dynamic time warping (DTW) algorithm is used to analyze the leading relationship between leading indicators and power sales trend development. The monthly power sales trend items are mainly affected by economic factors. The steps for predicting the trend items of each industry are as follows:

[0062] Step 1: The data sequence of the trend item of electricity sales is {Q t (i)|i∈1,2,…,N}.

[0063] Step 2: Obtain the immediate factors corresponding to the industry, recorded as {E(i)|i∈1,2,...,N}. If there are two influencing factors, then E(i)={E 1 (i), E 2 (i)|i∈1,2,…,N}.

[0064] Step 3: Obtain the leading factors corresponding to the industry, recorded as {G(i)|i∈1,2,...,N}, then G(i)={G 1 (i), G 2 (i)|i∈1,2,…,N}.

[0065] The fourth step is indicator decorrelation. The PCA principal component analysis algorithm is used to obtain the decorrelation results of the immediate indicator and the leading indicator, for example, P(i) = pca(E(i), G(i)), i = 1, 2, ..., N.

[0066] The fifth step is to establish a trend item prediction model and use the SVM algorithm for prediction. The following trend item prediction model is established: in It is the forecast value of industry electricity sales trend in the i-th month, that is, the forecast result of industry development trend.

[0067] The photovoltaic access rate, which uses clean energy and low-carbon technology, can reduce environmental pollution and measure the potential economic benefits of the station area.

[0068] The growth rate of electricity sales of enterprise-specific transformers and public transformers can reflect the economic situation to a certain extent. The calculation formula is: Growth rate = (electricity sales - electricity sales in the same period last year) / electricity sales in the same period last year * 100%.

[0069] Electricity bill indicators may include electricity bill recovery period, electricity bill recovery rate, proportion of electricity usage in breach of contract and regulations, etc.

[0070] In some embodiments, determining the indicator weight of the performance indicator of the power supply area based on a preset hierarchical model includes:

[0071] Determine a first weight of the second layer relative to the first layer based on a pairwise comparison of the first category of indicators, the second category of indicators, the third category of indicators, and the fourth category of indicators;

[0072] Determine a second weight of the third layer relative to the second layer based on a pairwise comparison between the first type of parameters, the second type of parameters, the third type of parameters, and the fourth type of parameters;

[0073] The indicator weight of the third layer relative to the first layer is obtained based on the product of the first weight and the second weight.

[0074] For the indicators of the second layer, a judgment matrix between each indicator can be constructed, and the first weight can be calculated using the square root method. For the parameters of the third layer, for each indicator, a judgment matrix between the parameters under the indicator is constructed, and the second weight can be calculated using the square root method.

[0075] In some embodiments, determining a first weight of the second layer relative to the first layer based on pairwise comparisons of the first category of indicators, the second category of indicators, the third category of indicators, and the fourth category of indicators includes:

[0076] Among them, Aij Indicator A i A j The importance of A ii =1, A ij >0, A ij =A ji , n is the number of the first category indicators, the second category indicators, the third category indicators and the fourth category indicators;

[0077] The importance is calculated and consistency checked based on a square root solution method to obtain the first weight.

[0078] Specifically, the 1-9 scale method is used to compare the indicators with each other, as shown in Table 1, to determine the weight of the criterion layer to the target layer. For example:

[0079] grade Scale The former is more important than the latter 1 9 / 9 Equally important 3 9 / 7 Slightly important 5 9 / 5 Relatively important 7 9 / 3 Clearly important 8 9 / 1 Absolutely important 2,4,6,8 9 / (10-k) The middle value between adjacent judgments

[0080] Table 1

[0081] Assume that the indicators in the criterion layer are {A1, A2, A i ,...,An}, the constructed judgment matrix is: Among them A ij Indicator A ij Indicator A i A j The importance of A ii =1, A ij >0, A ij =A ji .

[0082] Perform hierarchical single sorting on the judgment matrix A and use the square root method to solve the index {A1, A2, A n}, that is, first calculate the nth power of the product of each row of the judgment matrix A to obtain an n-dimensional vector in

[0083] Then the vector is normalized to get the weight of each indicator. The i-th indicator A i The weight w i The calculation formula is:

[0084] Get the weight matrix W = [w1,...,w i ,...,w n ], calculate the maximum characteristic root, the calculation formula is as follows:

[0085] Finally, the consistency index CI is calculated by the following formula:

[0086] After obtaining the n-order consistency index RI by looking up the table, calculate the CR value. The formula is as follows:

[0087] If CR<0, it indicates that the consistency of the judgment matrix A is within the allowable range and can be used to calculate the weight vector; if CR≥0.1, the judgment matrix A is modified.

[0088] In some embodiments, determining the second weight of the third layer relative to the second layer based on pairwise comparisons among the first type of parameters, the second type of parameters, the third type of parameters, and the fourth type of parameters includes:

[0089] Based on the comparison between the first type of parameters, the second type of parameters, the third type of parameters and the fourth type of parameters, the importance of the first type of parameters, the second type of parameters, the third type of parameters and the fourth type of parameters relative to each of the first type of indicators, the second type of indicators, the third type of indicators and the fourth type of indicators is determined to obtain an initial judgment matrix;

[0090] The initial judgment matrix is ​​calculated and consistency checked based on a square root solution method to obtain the second weight.

[0091] Specifically, similar to the calculation process of the first weight, the initial judgment matrix can be obtained based on the pairwise comparison between the importance of the third layer parameters relative to the second layer. For each initial judgment matrix, the maximum characteristic root λmax is calculated. The consistency index CI and the random consistency index RI are calculated, and the consistency ratio CR is calculated. If CR<0.1, the judgment matrix is ​​considered to be consistent; otherwise, it needs to be re-evaluated. Thus, the initial judgment matrix that passes the consistency test is obtained, and the square root solution method is used to calculate the initial judgment matrix to obtain the second weight.

[0092] In step S330 , the importance of each of the power supply areas relative to the performance index is compared based on the power data to obtain a target judgment matrix of the power supply areas relative to the performance index.

[0093] Specifically, see Figure 5 , Figure 5 A schematic diagram of a method according to an embodiment of the present disclosure is shown. The scheme layer is scored by a single sorting method of type hierarchy to obtain the performance score of the substation. Assuming that there are m substations that need to be scored for performance, the target judgment matrix is ​​constructed based on the importance, including:

[0094] Among them, B k is the target judgment matrix for the kth performance indicator, k = 1, 2, ..., n, n is the number of performance indicators, m is the number of power supply areas, B11 …B 1m is the importance of the first power supply area relative to the first to mth power supply areas, ..., B m1 …B mm is the importance of the mth power supply area relative to the 1st power supply area to the mth power supply area. For example, for indicator A1, according to the 1-9 scaling method, an m*m judgment matrix is ​​constructed, that is,

[0095] In step S340, the performance of the power supply area is evaluated based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

[0096] In some embodiments, the performance evaluation of the power supply area is performed based on the target judgment matrix and the indicator weight to obtain the performance evaluation result of the power supply area, including:

[0097] Calculating the weight of the power supply area relative to the performance index for the target judgment matrix based on a square root solution method;

[0098] The sum of the products of the area weight and the indicator weight is calculated to obtain the performance evaluation result.

[0099] Specifically, the weight w of the area to the indicator is calculated according to the square root method mentioned above: i , for example, the weight of indicator A1 [T 11 ,...,T 1m ], for indicator A n The weight [T n1 ,...,T nm ]. Similarly, the consistency check may be performed according to the above method. Further, for each substation, the sum of the product of the indicator weight of each indicator and the substation weight may be calculated to obtain the performance score. For example, the performance score S1 of indicator substation 1 = T 11 *w 1+ . . . +T n1 *w n , the performance score of station 2 is S2 = T 12 *w 1+ . . . +T n2 *w n If you want to judge the score of a single indicator in the area, you can use only the weight of a certain indicator.

[0100] It can be seen that the present disclosure uses the hierarchical analysis method to evaluate and construct a portrait of the power supply area, and performs data analysis and data mining on the power data of the power supply area to achieve an accurate evaluation of the power supply area's efficiency. In addition, the evaluation results are more scientific and accurate, and can comprehensively evaluate the economic benefits of the power supply area and provide effective decision-making support for decision makers. The portrait construction is more intuitive and vivid, which makes it easier for managers and decision makers to quickly understand the status and trends of the economic benefits of the power supply area. It is conducive to improving the operational efficiency and management level of the power industry and promoting the optimal allocation of resources and sustainable development.

[0101] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0102] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an efficiency evaluation device for a power supply area, see Figure 6 , the efficiency evaluation device of the power supply area includes:

[0104] An acquisition module, used to acquire power data of the power supply area; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity fee income data;

[0105] A weight module, used to determine the index weight of the performance index of the power supply area based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance index and the power supply area;

[0106] A matrix module, for comparing the importance of each of the power supply areas relative to the performance index based on the power data, and obtaining a target judgment matrix of the power supply areas relative to the performance index;

[0107] An evaluation module is used to perform performance evaluation on the power supply area based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

[0108] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0109] The device of the above embodiment is used to implement the efficiency evaluation method of the corresponding power supply area in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0110] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the power supply area efficiency evaluation method described in any of the above embodiments.

[0111] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0112] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the efficiency evaluation method of the power supply area as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0113] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0114] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0115] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0116] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for evaluating the efficiency of a power supply area, comprising: Acquire power data of the power supply area; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity fee income data; Determining an indicator weight of the performance indicator of the power supply area based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance indicator and the power supply area; Comparing the importance of each of the power supply areas relative to the performance index based on the power data, and obtaining a target judgment matrix of the power supply areas relative to the performance index; The performance of the power supply area is evaluated based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

2. The method according to claim 1, wherein: The preset hierarchical model includes a first layer, a second layer, a third layer and a fourth layer; wherein, The first layer includes the performance evaluation results of the power supply area; The second layer includes the first category indicators, the second category indicators, the third category indicators and the fourth category indicators; The third layer includes the first type of parameters in the first type of indicators, the second type of parameters in the second type of indicators, the third type of parameters in the third type of indicators, and the fourth type of parameters in the fourth type of indicators; The fourth layer includes the power supply platform area.

3. The method according to claim 2, wherein: Determining the index weight of the efficiency index of the power supply area based on a preset hierarchical model includes: Determine a first weight of the second layer relative to the first layer based on a pairwise comparison of the first category of indicators, the second category of indicators, the third category of indicators, and the fourth category of indicators; Determine a second weight of the third layer relative to the second layer based on a pairwise comparison between the first type of parameters, the second type of parameters, the third type of parameters, and the fourth type of parameters; The indicator weight of the third layer relative to the first layer is obtained based on the product of the first weight and the second weight.

4. The method according to claim 3, wherein: Determining a first weight of the second layer relative to the first layer based on pairwise comparison of the first category indicators, the second category indicators, the third category indicators, and the fourth category indicators includes: Among them, A ij Indicator A i A j The importance of A ii =1, A ij >0, A ij =A ji , n is the number of the first category indicators, the second category indicators, the third category indicators and the fourth category indicators; The importance is calculated and consistency checked based on a square root solution method to obtain the first weight.

5. The method according to claim 4, wherein: Determining a second weight of the third layer relative to the second layer based on pairwise comparisons among the first type of parameters, the second type of parameters, the third type of parameters, and the fourth type of parameters includes: Based on the comparison between the first type of parameters, the second type of parameters, the third type of parameters and the fourth type of parameters, the importance of the first type of parameters, the second type of parameters, the third type of parameters and the fourth type of parameters relative to each of the first type of indicators, the second type of indicators, the third type of indicators and the fourth type of indicators is determined to obtain an initial judgment matrix; The initial judgment matrix is ​​calculated and consistency checked based on a square root solution method to obtain the second weight.

6. The method according to claim 2, wherein: The importance of each of the power supply areas relative to the performance index is compared based on the power data to obtain a target judgment matrix of the power supply areas relative to the performance index, including: Comparing the power supply areas in pairs based on the power data of the power supply areas and a preset scaling method to determine the importance of the power supply areas relative to each of the performance indicators; Constructing the target judgment matrix based on the importance includes: Among them, B k is the target judgment matrix for the kth performance indicator, k = 1, 2, ..., n, n is the number of performance indicators, m is the number of power supply areas, B 11 .··B 1m is the importance of the first power supply area relative to the first to mth power supply areas, ..., B ml …B mm are the importance of the m-th power supply area relative to the 1st to m-th power supply areas respectively.

7. The method according to claim 1, wherein: The performance evaluation of the power supply area is performed based on the target judgment matrix and the indicator weights to obtain the performance evaluation result of the power supply area, including: Calculating the weight of the power supply area relative to the performance index for the target judgment matrix based on a square root solution method; The sum of the products of the area weight and the indicator weight is calculated to obtain the performance evaluation result.

8. An efficiency evaluation device for a power supply area, comprising: An acquisition module, used to acquire power data of the power supply area; wherein the power data includes at least one of equipment operation data, line operation data, operation economic data or electricity fee income data; A weight module, used to determine the index weight of the performance index of the power supply area based on a preset hierarchical model, wherein the preset hierarchical model is obtained based on the performance index and the power supply area; A matrix module, for comparing the importance of each of the power supply areas relative to the performance index based on the power data, and obtaining a target judgment matrix of the power supply areas relative to the performance index; An evaluation module is used to perform performance evaluation on the power supply area based on the target judgment matrix and the indicator weights to obtain a performance evaluation result of the power supply area.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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