Performance model optimization method of power grid company and related device

Abstract

The invention provides a performance model optimization method of a power grid company and a related device, and the method comprises the steps: obtaining a first index set and a first data set of a target company, and obtaining a second index set and a second data set of an external company; determining a difference index based on a statistical method, the first index set, the second index set, the first data set and the second data set, wherein the difference index represents different indexes of the first index set and the second index set; a disadvantage index and a path index are determined according to the difference index, the path index represents a reason for occurrence of the disadvantage index, and the disadvantage index is an index enabling the target company to have disadvantage; and performing index optimization based on the disadvantage index and the path index to obtain an optimization index, and optimizing the performance model of the target company according to the optimization index. Therefore, analysis can be carried out by integrating own and external indexes and data, own shortages are determined, and the performance index model is optimized in a targeted manner, so that the overall competitiveness is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a performance model optimization method and related devices for a power grid company. Background Art

[0002] With the continuous growth of electricity demand and the increasing complexity of power systems, power grid companies have increasingly higher requirements for project management and design quality. The quality of power grid project design is directly related to the safety, reliability and economic benefits of the power system. Therefore, how to effectively improve the design quality, project management level and customer satisfaction of power grid companies has become an important issue that all power grid companies need to solve.

[0003] However, when formulating their own performance indicators, many power grid companies often rely on internal experience and historical data, while ignoring benchmarking with other projects in the industry. This makes it impossible for power grid companies to accurately identify their own shortcomings, and thus unable to effectively improve their overall competitiveness. Summary of the invention

[0004] The embodiments of the present application provide a performance model optimization method and related devices for a power grid company, which can identify the shortcomings of the current performance model and help improve overall competitiveness.

[0005] In a first aspect, an embodiment of the present application provides a performance model optimization method for a power grid company, which is applied to electronic equipment, and the method includes:

[0006] Acquire a first indicator set and a first data set of the target company, and acquire a second indicator set and a second data set of the external company, wherein the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set, and the first indicator set and the second indicator set are indicators corresponding to the target project;

[0007] Determine a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, wherein the difference index represents a different index between the first index set and the second index set;

[0008] Determine the disadvantage indicator and path indicator based on the difference indicator. The path indicator represents the cause of the disadvantage indicator, and the disadvantage indicator is the indicator that makes the target company have a disadvantage.

[0009] The indicators are optimized based on the disadvantage indicators and path indicators to obtain the optimized indicators, and the performance model of the target company is optimized according to the optimized indicators.

[0010] In a possible embodiment, the first indicator set includes a plurality of first indicators corresponding to a plurality of indicator types, the second indicator set includes a plurality of second indicators corresponding to a plurality of indicator types, and determining the difference indicator based on the statistical method, the first indicator set, the second indicator set, the first data set, and the second data set includes:

[0011] Calculating statistics for each first indicator based on the first data set to obtain multiple first statistical values;

[0012] Calculating statistics for each second indicator based on the second data set to obtain multiple second statistical values;

[0013] A significance check is performed on the plurality of first statistical values ​​and the plurality of second statistical values ​​to obtain a difference index.

[0014] In a possible embodiment, determining the disadvantage indicator and the path indicator according to the difference indicator includes:

[0015] Construct prediction models and structural equation models based on difference indicators;

[0016] The disadvantage index is obtained based on the prediction model and the difference index;

[0017] Path analysis is performed based on the structural equation model and disadvantage indicators to obtain path indicators, which are used to characterize the causes of the disadvantage indicators.

[0018] In a possible embodiment, obtaining the disadvantage indicator according to the prediction model and the difference indicator includes:

[0019] According to the prediction model and the difference index, multiple prediction coefficients are obtained;

[0020] Based on multiple prediction coefficients, a significance analysis is performed to obtain a significant coefficient, which is used to characterize the degree of influence of the difference index;

[0021] The disadvantage index is obtained based on the significant coefficient.

[0022] In a possible embodiment, performing index optimization based on the disadvantage index and the path index to obtain the optimized index includes:

[0023] The optimization priority is obtained according to the path indicator and the priority determination strategy. Each path indicator corresponds to an optimization priority, and the priority determination strategy is a preset strategy.

[0024] Path optimization is performed according to the optimization priority and path index to obtain the optimization index.

[0025] In a possible embodiment, optimizing the performance model of the target company according to the optimization index includes:

[0026] A relationship model is constructed according to the optimization index, the first index and the prediction model, wherein the relationship model represents the correlation between the performance of the target company and the first index and the optimization index;

[0027] The relational model is reconstructed based on the relational model to obtain the performance model.

[0028] In a possible embodiment, reconstructing the relationship model based on the relationship model to obtain the performance model includes:

[0029] Calculate the relationship regression coefficient of the relationship model;

[0030] Calculate indicator weights based on relationship regression coefficients;

[0031] Reconstruction is performed based on the indicator weights, the optimization indicator and the first indicator to obtain a performance model.

[0032] In a second aspect, an embodiment of the present application provides a performance model optimization device for a power grid company, which is applied to an electronic device, and the device includes:

[0033] An acquisition module is used to acquire a first indicator set and a first data set of a target company, and acquire a second indicator set and a second data set of an external company, wherein the first indicator set and the second indicator set correspond to indicators of a target project, the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set;

[0034] An analysis module, configured to determine a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, wherein the difference index represents a different index between the first index set and the second index set;

[0035] A determination module is used to determine disadvantage indicators and path indicators according to the difference indicators. The path indicators represent the reasons for the disadvantage indicators. The disadvantage indicators are indicators that make the target company have disadvantages.

[0036] The reconstruction module is used to optimize the indicators based on the disadvantage indicators and path indicators to obtain the optimized indicators, and optimize the performance model of the target company according to the optimized indicators.

[0037] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing some or all of the steps described in the first aspect of the embodiment of the present application.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes some or all of the steps described in the first aspect.

[0039] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0040] By implementing the embodiment of the present application, the first indicator set and the first data set of the target company are obtained, and the second indicator set and the second data set of the external company are obtained. The first indicator set and the second indicator set correspond to the indicators of the target project, the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set; based on the statistical method, the first indicator set, the second indicator set, the first data set and the second data set, the difference indicator is determined, and the difference indicator represents the difference between the first indicator set and the second indicator set; according to the difference indicator, the disadvantage indicator and the path indicator are determined, and the path indicator represents the reason for the disadvantage indicator, and the disadvantage indicator is the indicator that makes the target company have a disadvantage; based on the disadvantage indicator and the path indicator, the indicator is optimized to obtain the optimized indicator, and the performance model of the target company is optimized according to the optimized indicator. In this way, it is possible to analyze the indicators and data of the company and the outside world comprehensively, clarify its own shortcomings, and optimize the performance indicator model in a targeted manner, thereby effectively improving the overall competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0042] Figure 1 It is a flow chart of a performance model optimization method of a power grid company provided in an embodiment of the present application;

[0043] Figure 2 It is a flow chart of another performance model optimization method of a power grid company provided in an embodiment of the present application;

[0044] Figure 3 It is a structural schematic diagram of a performance model optimization device for a power grid company provided in an embodiment of the present application;

[0045] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0048] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0049] See also Figure 1 , Figure 1 is a flow chart of a performance model optimization method for a power grid company provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0050] S110, obtaining a first indicator set and a first data set of the target company, and obtaining a second indicator set and a second data set of the external company, wherein the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set, and the first indicator set and the second indicator set are indicators corresponding to the target project.

[0051] Among them, the target company is the power grid company, that is, the company to be optimized, the external company is other companies except the target company, the first indicator set and the first data set are relevant data sets of the target company's historical projects, and the historical projects are projects of the target company's indicators to be optimized. The second indicator set and the second data set of the external company are the relevant data sets of the projects benchmarked by the historical projects.

[0052] Among them, the first indicator focus is the historical performance indicators of historical projects, and the second indicator focus is the performance indicators of benchmark projects. The first data set includes the number of multiple data points of historical projects, and the second data set includes the number of multiple data points of benchmark projects. The data points are specific observation values ​​or measurement results of performance indicators.

[0053] S120, determining a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, where the difference index represents a different index between the first index set and the second index set.

[0054] Among them, statistical methods include calculating the mean, geometric mean, harmonic mean, square mean, median, etc. The difference index is an index that is different from that in the first index set and the second index set. For example, assuming that the first index set includes A, B, and C indicators, and the second index set includes A, B, C, D, and E, then, after calculating the first data set and the second data set using statistical methods, the difference index obtained may be D and / or E.

[0055] In a possible embodiment, the first indicator set includes multiple first indicators corresponding to multiple indicator types, the second indicator set includes multiple second indicators corresponding to multiple indicator types, and determining the difference indicator based on the statistical method, the first indicator set, the second indicator set, the first data set and the second data set includes: calculating statistics for each first indicator based on the first data set to obtain multiple first statistical values; calculating statistics for each second indicator based on the second data set to obtain multiple second statistical values; and performing significance verification on the multiple first statistical values ​​and the multiple second statistical values ​​to obtain the difference indicator.

[0056] The first data set includes multiple first data points, each of which corresponds to a first indicator, and one first indicator can correspond to multiple first data points; the second data set includes multiple second data points, each of which corresponds to a second indicator, and one second indicator can correspond to multiple second data points. The data point corresponding to an indicator can be understood as the specific observation value or measurement result of the indicator, which can be used for indicator differentiation calculation.

[0057] Among them, the first statistical value is obtained by calculating the statistics for one or more first data points corresponding to each first indicator, and the second statistical value is obtained by calculating the statistics for one or more second data points corresponding to each second indicator. Each first indicator can obtain a first statistical value, and each second indicator can obtain a second statistical value. By performing a significance check on the first statistical value and the second statistical value, a difference indicator with difference can be obtained. The difference indicator is an indicator that is different from the first indicator set and the second indicator set, and can be an indicator that is different from the first indicator set and the second indicator set.

[0058] Specifically, the statistical method may include: calculating the means of the first data point and the second data point of the target company and the external company on each first indicator and the second indicator:

[0059]

[0060] in, is the average of the first data point of the first indicator of the target company; is the average value of the second data point of the second indicator of the external company; di is the first data point corresponding to the first indicator of the target company, X bi is the second data point corresponding to a second indicator of an external company, N d is the data volume of the first data point corresponding to the first indicator, N b is the data volume of the second data point corresponding to the second indicator;

[0061] Specifically, the significant verification process may include: calculating a first variance of a first data point of a first indicator of a target company Find the second variance for the second data point of the second indicator of the external company The significant verification is performed according to the following formula to obtain the significant verification result:

[0062]

[0063] Wherein, t is the significant verification result. By judging the relationship between the t value and the significant verification threshold, it can be obtained whether the indicator has a significant impact. The above-mentioned significant verification threshold is a preset value, which can be preset according to an empirical value or mathematical calculation. For example, if the t value is greater than the significant verification threshold, the indicator has a significant influence, and the indicator with a significant influence is used as a difference indicator.

[0064] It can be seen that in this embodiment, statistical calculations are performed on the data points corresponding to each first indicator and the second indicator, and then a comparison is made to obtain the first indicator and the second indicator with differences, and then a significance check is performed to obtain a difference indicator with significant influence. This avoids the deviation of subjective judgment, obtains a difference indicator with a scientific basis, and is conducive to the optimization of the indicator.

[0065] S130, determining a disadvantage indicator and a path indicator according to the difference indicator, wherein the path indicator represents the cause of the disadvantage indicator, and the disadvantage indicator is an indicator that makes the target company have a disadvantage.

[0066] Among them, the disadvantage indicator is a difference indicator that makes the target company have a disadvantage compared with the external company, which is obtained by quantitatively analyzing the difference indicator. The path indicator is the reason why the difference indicator has a disadvantage. The path indicator can be a coefficient of an indicator that makes the difference indicator have a disadvantage.

[0067] The method of determining the disadvantage indicator according to the difference indicator may be by constructing a prediction model, and the prediction model may be a factor regression model, a principal component regression model, a machine learning model, a multivariate regression model, etc., which is not limited here. The method of obtaining the path indicator may be to obtain the path indicator by analyzing the disadvantage indicator, specifically, by calculating the path coefficient, etc.

[0068] In a possible embodiment, Figure 2 As shown, Figure 2 is a flow chart of another performance model optimization method for a power grid company provided in an embodiment of the present application, such as Figure 2 As shown in the figure, the disadvantage indicators and path indicators determined based on the difference indicators include:

[0069] S131, construct prediction models and structural equation models based on difference indicators;

[0070] S132, obtaining a disadvantage indicator according to the prediction model and the difference indicator;

[0071] S133. Path analysis is performed based on the structural equation model and disadvantage indicators to obtain path indicators, which are used to characterize the causes of the disadvantage indicators.

[0072] Among them, the difference index also includes the difference data points corresponding to the difference index. The difference index or the difference data points corresponding to the difference index are used as independent variables to build a prediction model. According to the parameters obtained after the prediction model analysis, it is determined which difference indexes are disadvantage indicators; the structural equation model is built according to the difference data points or difference indicators corresponding to the difference index. The disadvantage index also includes the disadvantage index data points corresponding to the disadvantage index. The disadvantage index is obtained by analyzing and calculating the difference data points or disadvantage index corresponding to the prediction model and the difference index. The path index is obtained by analyzing and calculating the difference data points corresponding to the difference index and the disadvantage index data points corresponding to the disadvantage index according to the structural equation model and the difference data points corresponding to the difference index and the disadvantage index data points corresponding to the disadvantage index.

[0073] It can be seen that in this embodiment, by constructing a prediction model and a structural equation model, the difference index is analyzed to obtain a disadvantage index with significant influence, thereby avoiding the deviation of subjective judgment and obtaining a difference index with a scientific basis, which is conducive to the optimization of the index.

[0074] In a possible embodiment, obtaining the disadvantage indicator according to the prediction model and the difference indicator includes:

[0075] S1321. Obtain multiple prediction coefficients according to the prediction model and the difference index;

[0076] S1322. Performing a significance analysis based on multiple prediction coefficients to obtain a significant coefficient, where the significant coefficient is used to characterize the influence of the difference index;

[0077] S1323. Obtain the disadvantage index based on the significant coefficient.

[0078] When performing the analysis and calculation of the prediction model, it is also necessary to obtain the current performance results, that is, the performance results generated by the target company when using the first indicator set and the current performance model. The prediction model can be a multiple regression model, which takes the difference indicator as the independent variable and the current performance result of the target company as the dependent variable to construct the prediction model:

[0079] Y=β0+β1X1+β2X2+β3(X1×X2)+…+β n X n +∈;

[0080] Among them, X1, X2…X n is the independent variable, β3(X1×X2) represents the interaction term between the independent variables X1 and X2. The interaction term represents the interaction of two independent variables. β1, β2, β3, …, β n represents the prediction coefficient, ∈ represents the error term, β0 represents the intercept, and Y represents the dependent variable.

[0081] The prediction coefficient is calculated by least square method, matrix calculation method, software calculation and other methods. The specific method is not limited here. One or more difference indicators are judged as disadvantage indicators according to the value of the prediction coefficient.

[0082] Specifically, whether the difference indicator is an inferior indicator can be determined by the sign of the prediction coefficient or by a significance test. If the t-test analysis is chosen, then:

[0083]

[0084] Among them, t i Represents the independent variable X i The significant verification results of β i Represents the independent variable X i The prediction coefficient of i ) represents the standard error of the prediction coefficient; if the t value of an independent variable is less than the significant test threshold, it is considered that the independent variable has a significant impact on the dependent variable. According to the t value, each difference indicator is judged to have a significant impact and obtain a poor quality indicator.

[0085] It can be seen that in this embodiment, the difference index is analyzed by regression analysis and significance calculation method to obtain the disadvantage index with significant influence, which avoids the deviation of subjective judgment and obtains the difference index with scientific basis, which is conducive to the optimization of the index.

[0086] In a possible embodiment, the prediction model is a ridge regression model, and obtaining the disadvantage index according to the prediction model and the difference index includes: obtaining multiple ridge regression prediction coefficients and regularization parameters according to the ridge regression model and the difference index; performing significance analysis based on the multiple ridge regression prediction coefficients to obtain the ridge regression significant coefficient, and the ridge regression significant coefficient is used to characterize the influence degree of the difference index; and obtaining the disadvantage index according to the ridge regression significant coefficient.

[0087] Among them, when performing the analysis and calculation of the ridge regression model, it is also necessary to obtain the current performance results, that is, the performance results generated by the target company when using the first indicator set and the current performance model. The ridge regression model can be a multivariate regression model, taking the difference indicator as the independent variable and the current performance result of the target company as the dependent variable to construct the ridge regression model:

[0088]

[0089] Among them, λ represents the regularization parameter, which is used to control the penalty intensity; represents the penalty term, which is used to suppress the excessive prediction coefficient of ridge regression; X i represents the ith independent variable; n represents the number of independent variables; β i represents the ridge regression prediction coefficient corresponding to the i-th independent variable.

[0090] The ridge regression prediction coefficient is calculated by least square method, matrix calculation method, software calculation and other methods. The specific method is not limited here. One or more difference indicators are judged as inferior indicators according to the value of the ridge regression prediction coefficient.

[0091] Specifically, whether the difference index is an inferiority index can be determined by the sign of the ridge regression prediction coefficient or by a significance test. If a t-test analysis is chosen, the specific process has been described in the above embodiment and will not be repeated here.

[0092] It can be seen that in this embodiment, the complexity of the multivariate regression model is controlled by the regularization parameter, the ridge regression method is used for model fitting, and the difference index is analyzed by the ridge regression analysis and significance calculation method to obtain the disadvantage index with significant influence. This in-depth analysis can help the design unit identify the core factors affecting performance and understand how these factors act on the disadvantage index through a complex relationship network. In this way, the deviation of subjective judgment is avoided, and the difference index with scientific basis is obtained, which is conducive to the optimization of the index.

[0093] S140, optimizing the indicators based on the disadvantage indicators and the path indicators to obtain the optimized indicators, and optimizing the performance model of the target company according to the optimized indicators.

[0094] Among them, according to the obtained disadvantageous indicators and the reasons why the inferior indicators are disadvantageous indicators, that is, path indicators, the disadvantageous indicators can be optimized to obtain optimized indicators. And according to the optimized indicators, the performance model of the target company is reconstructed to obtain a new performance model with more competitiveness.

[0095] When optimizing, the importance or influence of the path indicators can also be ranked to perform more targeted optimization.

[0096] In a possible embodiment, optimizing the indicators based on the disadvantage indicators and the path indicators to obtain the optimization indicators includes: obtaining the optimization priority according to the path indicators and the priority determination strategy, each path indicator corresponds to an optimization priority, and the priority determination strategy is a preset strategy; performing path optimization according to the optimization priority and the path indicator to obtain the optimization indicator.

[0097] Among them, the priority determination strategy can be a preset calculation strategy, which calculates the path indicators according to the priority determination strategy to obtain a priority corresponding to each path indicator, and then sorts them according to the optimization priority, starting from the highest optimization priority to obtain the optimization indicator.

[0098] Specifically, the priority determination strategy can be to determine the size of the optimization priority according to the size of the prediction coefficient or the ridge regression prediction coefficient. For example, the larger the prediction coefficient or the ridge regression prediction coefficient, the higher the optimization priority. Then, the optimization priority is screened to obtain the path index corresponding to the optimization priority above the preset priority threshold. For example, the maximum value of the optimization priority can be taken, and the one with the largest optimization priority is selected as the main influencing factor for optimization.

[0099] For example, assuming that the determined disadvantage indicator is the design quality score (Y quality ), and through path analysis, it is found that the main influencing factor of this indicator is project cost control (L cost ) and project schedule management (L schedule ); and for Y quality The prediction coefficients of or ridge regression prediction coefficients are L cost and L schedule ; According to the size of the prediction coefficient or ridge regression prediction coefficient, determine the optimization priority; if the prediction coefficient or ridge regression prediction coefficient L cost is large, indicating that project cost control has a greater impact on the design quality score, and it is necessary to prioritize project cost control; similarly, if L schedule If the project duration management is large, it is necessary to optimize the project duration management; the details are as follows: M = max (L cost ,L schedule), where M represents the path indicator that needs to be optimized first.

[0100] Among them, path optimization is performed according to optimization priorities and path indicators. The process of obtaining the optimization indicators can be to formulate specific optimization measures for the path indicators. The formulation can be manual or obtained through strategy libraries, large models, machine learning, etc.

[0101] For example, specific optimization measures are formulated for the identified major influencing factors, where the optimization measures can be based on the following aspects: Optimization of project cost control, further subdividing the project cost, identifying the main cost sources of each stage or each resource, and making targeted budget adjustments. Project duration management optimization, identifying key figures in the duration through the critical path method, and accelerating or optimizing resource allocation: Among them, G represents the critical path duration, g j represents the duration of the critical task, and Δt represents the time saved after resource optimization or task parallel adjustment.

[0102] After optimizing the path indicator, it is determined whether the optimized indicator has a positive effect. If it has a positive effect, the indicator is used as the optimization indicator.

[0103] For example, the index obtained after optimization is named Y quality,opt , the path index before optimization is Y quality , calculate ΔY quality =Y quality,opt -Y quality , if ΔY quality If it is a positive number, it means that the optimization measures have achieved the expected results. If it is negative, it is necessary to further adjust the optimization strategy until ΔY quality Is a positive number.

[0104] It can be seen that in this embodiment, through path indicator calculation and path optimization, the main influencing factors of disadvantages can be identified based on scientific data and causal relationships, and specific optimization measures can be formulated. These measures are based on a detailed analysis of the key path, ensuring the effectiveness and accuracy of the optimization, thereby improving the overall competitiveness of the unit.

[0105] In a possible embodiment, optimizing the performance model of the target company according to the optimization index includes: constructing a relationship model according to the optimization index, the first index and the prediction model, the relationship model characterizing the correlation between the performance of the target company and the first index and the optimization index; reconstructing the relationship model based on the relationship model to obtain a performance model.

[0106] Among them, the prediction model can be a multivariate regression model, which is constructed based on the optimization index and the first index, that is, the original first index and the optimized optimization index are used as independent variables, and the overall performance of the target company for the target project is used as the dependent variable to construct the prediction model.

[0107] For example, assuming that the first indicator includes Y quality , Y schedule and Y cost , added optimization index Y customer , construct Z = α0 + α1Y quality +α2Y cost +α3Y schedule +α4Y customer +∈; where Z represents the overall performance of the target company for the target project, α1, α2, α3, and α4 represent the regression coefficients of the relationship between each indicator and the overall performance, ∈ represents the error term, and Y quality represents the design quality score, Y cost Indicates project cost control, Y schedule represents project duration management, Y customer Indicates customer satisfaction. The regression coefficient can be calculated by the least squares method, matrix calculation method, software calculation method, etc. to obtain the specific value of the relationship regression coefficient. Then, the performance model is reconstructed based on the constructed relationship model.

[0108] It can be seen that in this embodiment, by introducing the indicators in the benchmarking project into the evaluation system of the target company's target project, it can ensure that the evaluation system is more scientific and objective, so that the evaluation results can more accurately reflect the overall performance of the design unit, and can also give priority to indicators that have a greater impact on the overall performance, thereby further improving its market competitiveness.

[0109] In a possible embodiment, reconstructing the relational model based on the relational model to obtain the performance model includes: calculating the relational regression coefficient of the relational model; calculating the indicator weight according to the relational regression coefficient; and reconstructing according to the indicator weight, the optimization indicator and the first indicator to obtain the performance model.

[0110] When reconstructing the performance model, further calculation of the regression coefficient of the relationship model may be performed, such as weight calculation to obtain indicator weights, and then the performance model may be reconstructed based on the indicator weights, optimization indicators and the first indicator.

[0111] Specifically, the weight of each relationship regression coefficient is calculated according to the relationship regression coefficient: Among them, w i ′ Represents the new weight of the i-th indicator; α iRepresents the relationship regression coefficient of the corresponding indicator; according to the new weight distribution, reconstruct the overall performance score of the design unit: Z'=w'1Y quality +w'2Y cost +w'3Y schedule +w'4Y customer ; Wherein, Z′ is the overall performance of the target company after reconstruction for the target project.

[0112] It can be seen that in this embodiment, by introducing the indicators in the benchmarking project into the evaluation system of the target company's target project and adjusting the weight distribution according to the results of the multiple regression analysis, it can ensure that the evaluation system is more scientific and objective, so that the evaluation results can more accurately reflect the overall performance of the design unit, and can also give priority to indicators that have a greater impact on the overall performance, thereby further improving its market competitiveness.

[0113] It can be seen that by implementing the embodiment of the present application, the first indicator set and the first data set of the target company are obtained, and the second indicator set and the second data set of the external company are obtained. The first indicator set and the second indicator set correspond to the indicators of the target project, the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set; based on the statistical method, the first indicator set, the second indicator set, the first data set and the second data set, the difference indicator is determined, and the difference indicator represents the difference between the first indicator set and the second indicator set; according to the difference indicator, the disadvantage indicator and the path indicator are determined, and the path indicator represents the reason for the disadvantage indicator, and the disadvantage indicator is the indicator that makes the target company have a disadvantage; based on the disadvantage indicator and the path indicator, the indicator is optimized to obtain the optimized indicator, and the performance model of the target company is optimized according to the optimized indicator. In this way, it is possible to analyze the indicators and data of the company and the outside world comprehensively, clarify its own shortcomings, and optimize the performance indicator model in a targeted manner, thereby effectively improving the overall competitiveness.

[0114] See also Figure 3 , Figure 3 is a schematic diagram of a performance model optimization device for a power grid company proposed in an embodiment of the present application, and the device is applied to electronic equipment, such as Figure 3 As shown, the performance model optimization device 300 of the power grid company includes: an acquisition module 310, an analysis module 320, a determination module 330, and a reconstruction module 340, wherein:

[0115] An acquisition module 310 is used to acquire a first indicator set and a first data set of a target company, and acquire a second indicator set and a second data set of an external company, wherein the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set, and the first indicator set and the second indicator set are indicators corresponding to the target project;

[0116] An analysis module 320, configured to determine a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, wherein the difference index represents a different index between the first index set and the second index set;

[0117] A determination module 330 is used to determine a disadvantage indicator and a path indicator according to the difference indicator, wherein the path indicator represents the cause of the disadvantage indicator, and the disadvantage indicator is an indicator that makes the target company have a disadvantage;

[0118] The reconstruction module 340 is used to optimize the indicators based on the disadvantage indicators and the path indicators to obtain the optimized indicators, and optimize the performance model of the target company according to the optimized indicators.

[0119] In a possible implementation, the analysis module 320 is specifically configured to determine the difference index based on the statistical method, the first index set, the second index set, the first data set, and the second data set:

[0120] Calculating statistics for each first indicator based on the first data set to obtain multiple first statistical values;

[0121] Calculating statistics for each second indicator based on the second data set to obtain multiple second statistical values;

[0122] A significance check is performed on the plurality of first statistical values ​​and the plurality of second statistical values ​​to obtain a difference index.

[0123] In a possible implementation, the determination module 330 is specifically configured to:

[0124] Construct prediction models and structural equation models based on difference indicators;

[0125] The disadvantage index is obtained based on the prediction model and the difference index;

[0126] Path analysis is performed based on the structural equation model and disadvantage indicators to obtain path indicators, which are used to characterize the causes of the disadvantage indicators.

[0127] In a possible implementation, the determination module 330 is specifically configured to obtain the disadvantage indicator according to the prediction model and the difference indicator:

[0128] According to the prediction model and the difference index, multiple prediction coefficients are obtained;

[0129] Based on multiple prediction coefficients, a significance analysis is performed to obtain a significant coefficient, which is used to characterize the degree of influence of the difference index;

[0130] The disadvantage index is obtained based on the significant coefficient.

[0131] In a possible implementation, the reconstruction module 340 is specifically used to optimize the indicator based on the disadvantage indicator and the path indicator to obtain the optimized indicator:

[0132] The optimization priority is obtained according to the path indicator and the priority determination strategy. Each path indicator corresponds to an optimization priority, and the priority determination strategy is a preset strategy.

[0133] Path optimization is performed according to the optimization priority and path index to obtain the optimization index.

[0134] In a possible implementation, the reconstruction module 340 is specifically used to reconstruct the relationship model based on the relationship model to obtain the performance model:

[0135] A relationship model is constructed according to the optimization index, the first index and the prediction model, wherein the relationship model represents the correlation between the performance of the target company and the first index and the optimization index;

[0136] The relational model is reconstructed based on the relational model to obtain the performance model.

[0137] In a possible implementation, the reconstruction module 340 is specifically used to optimize the performance model of the target company according to the optimization index:

[0138] Calculate the relationship regression coefficient of the relationship model;

[0139] Calculate indicator weights based on relationship regression coefficients;

[0140] Reconstruction is performed based on the indicator weights, the optimization indicator and the first indicator to obtain a performance model.

[0141] It is worth noting that the specific functional implementation of the performance model optimization device 300 of the power grid company can be found in the above Figure 1 The description of the performance model optimization method of the power grid company shown in the figure, for example, the acquisition module 310 is used to implement the relevant content of executing S110. The various units or modules in the performance model optimization device 300 of the power grid company can be separately or completely combined into one or several other units or modules to constitute, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0142] It can be seen that the performance model optimization device of the power grid company described in the embodiment of the present application obtains the first indicator set and the first data set of the target company, obtains the second indicator set and the second data set of the external company, the first indicator set and the second indicator set correspond to the indicators of the target project, the first data set is the data set associated with the first indicator set, and the second data set is the data set associated with the second indicator set; based on the statistical method, the first indicator set, the second indicator set, the first data set and the second data set, the difference indicator is determined, and the difference indicator represents the difference between the first indicator set and the second indicator set; according to the difference indicator, the disadvantage indicator and the path indicator are determined, and the path indicator represents the reason for the disadvantage indicator, and the disadvantage indicator is the indicator that makes the target company have a disadvantage; based on the disadvantage indicator and the path indicator, the indicator is optimized to obtain the optimized indicator, and the performance model of the target company is optimized according to the optimized indicator. In this way, it is possible to analyze the indicators and data of itself and the outside, clarify its own shortcomings, and optimize the performance indicator model in a targeted manner, thereby effectively improving the overall competitiveness.

[0143] See also Figure 4 , Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. As shown in the figure, the electronic device 400 includes a processor 410, a memory 420, a communication interface 430 and one or more programs 421. The one or more programs 421 are stored in the memory 420 and are configured to be executed by the processor 410.

[0144] The processor 410, the memory 420, and the communication interface 430 are connected to each other and perform communication work with each other;

[0145] The memory 420 may be a volatile memory such as a dynamic random access memory DRAM, or a non-volatile memory such as a mechanical hard disk. The memory 420 is used to store a set of executable program codes, and the processor 410 is used to call one or more programs 421 stored in the memory 420, and can execute the above-mentioned Figure 1 Part or all of the steps of any power grid company's performance model optimization method recorded in the embodiments.

[0146] Among them, the electronic device 400 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0147] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0148] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0149] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0150] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0152] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0154] If the above-mentioned integrated 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 memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0155] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0156] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A performance model optimization method for a power grid company, characterized in that: Applied to electronic equipment, the method comprises: Acquire a first indicator set and a first data set of the target company, and acquire a second indicator set and a second data set of the external company, wherein the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set, and the first indicator set and the second indicator set are indicators corresponding to the target project; Determine a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, wherein the difference index represents different indicators between the first index set and the second index set; Determining a disadvantage indicator and a path indicator according to the difference indicator, wherein the path indicator represents the cause of the disadvantage indicator, and the disadvantage indicator is an indicator that makes the target company have a disadvantage; Based on the disadvantage indicator and the path indicator, the indicator is optimized to obtain the optimized indicator, and the performance model of the target company is optimized according to the optimized indicator.

2. The method according to claim 1, characterized in that: The first indicator set includes a plurality of first indicators corresponding to a plurality of indicator types, the second indicator set includes a plurality of second indicators corresponding to the plurality of indicator types, and the determining of the difference indicator based on the statistical method, the first indicator set, the second indicator set, the first data set, and the second data set includes: Calculating statistics for each of the first indicators based on the first data set to obtain a plurality of first statistical values; Calculating statistics for each of the second indicators based on the second data set to obtain multiple second statistical values; A significance check is performed on the plurality of first statistical values ​​and the plurality of second statistical values ​​to obtain a difference index.

3. The method according to claim 1, characterized in that Determining the disadvantage indicator and the path indicator according to the difference indicator includes: Constructing a prediction model and a structural equation model based on the difference indicators; Obtaining the disadvantage indicator according to the prediction model and the difference indicator; The path indicator is obtained by performing path analysis based on the structural equation model and the disadvantage indicator, and the path indicator is used to characterize the formation cause of the disadvantage indicator.

4. The method according to claim 3, characterized in that: The obtaining of the disadvantage indicator according to the prediction model and the difference indicator comprises: Obtaining a plurality of prediction coefficients according to the prediction model and the difference index; Performing a significance analysis based on the multiple prediction coefficients to obtain a significant coefficient, wherein the significant coefficient is used to characterize the influence degree of the difference index; The disadvantage indicator is obtained according to the significant coefficient.

5. The method according to claim 3, characterized in that: The optimization index obtained by performing index optimization based on the disadvantage index and the path index includes: Obtaining an optimization priority according to the path indicator and the priority determination strategy, each path indicator corresponds to one optimization priority, and the priority determination strategy is a preset strategy; Path optimization is performed according to the optimization priority and the path index to obtain the optimization index.

6. The method according to claim 2, characterized in that Optimizing the performance model of the target company according to the optimization index includes: Constructing a relationship model according to the optimization index, the first index and the prediction model, wherein the relationship model represents the correlation between the performance of the target company and the first index and the optimization index; The relationship model is reconstructed based on the relationship model to obtain the performance model.

7. The method according to claim 6, characterized in that The reconstructing the relationship model based on the relationship model to obtain the performance model includes: Calculating the relationship regression coefficient of the relationship model; Calculate the indicator weight according to the relationship regression coefficient; The performance model is obtained by reconstructing according to the indicator weight, the optimization indicator and the first indicator.

8. A performance model optimization device for a power grid company, characterized in that: Applied to electronic equipment, the device comprises: An acquisition module, used to acquire a first indicator set and a first data set of a target company, and acquire a second indicator set and a second data set of an external company, wherein the first data set is a data set associated with the first indicator set, and the second data set is a data set associated with the second indicator set, and the first indicator set and the second indicator set are indicators corresponding to a target project; an analysis module, configured to determine a difference index based on a statistical method, the first index set, the second index set, the first data set, and the second data set, wherein the difference index represents a different index between the first index set and the second index set; A determination module, configured to determine a disadvantage indicator and a path indicator according to the difference indicator, wherein the path indicator represents the cause of the disadvantage indicator, and the disadvantage indicator is an indicator that causes the target company to have a disadvantage; A reconstruction module is used to optimize the indicators based on the disadvantage indicators and the path indicators to obtain the optimized indicators, and optimize the performance model of the target company according to the optimized indicators.

9. A computer-readable storage medium, characterized in that: A performance model optimization program of a power grid company is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

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