Method, system, computer device and storage medium for dynamically constructing performance evaluation system

Abstract

A method, device, computer equipment and storage medium for dynamically constructing a performance evaluation system. The method includes: receiving an indicator system establishment instruction, creating a new indicator system architecture, the indicator system including a system performance indicator system; creating indicators at each level of the indicator system, editing the relationship between indicators at each level, and forming a hierarchical structure of the indicator system; editing the indicator attributes of each indicator to form an edited indicator system, and obtaining the created final indicator system after verification; based on the evaluation method selected and imported from a dynamic library and the hierarchical structure of the final indicator system, setting the weights of indicators at each level in the final indicator system, and selecting indicator aggregation operators for setting indicators at each level from the dynamic library; calculating and obtaining the performance value of the top-level indicator according to the weights of the indicators at each level and the calculation rules of the indicator aggregation operator, and obtaining the performance evaluation result according to the performance value of the top-level indicator. The method can be used to construct a performance evaluation system with high availability and high efficiency.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for dynamically constructing a performance evaluation system, a system for dynamically constructing a performance evaluation system, a computer device, and a computer-readable storage medium. Background Art

[0002] Performance evaluation is a complex statistical activity process, and it is also a quantitative thinking process, with a basic evaluation operation system. As a statistical activity process, system performance evaluation is a dialectical process of "qualitative-quantitative-qualitative", and its operation process is mainly: clarifying the evaluation objectives, building an evaluation index system, building a simulation model, solving evaluation indicators, establishing an evaluation model, conducting system performance evaluation, confidence level analysis, and giving evaluation results and suggestions for improvement. In actual business, when building an indicator system for performance evaluation, how to determine the evaluation indicators, the relationship between indicators, the normalization method, the empowerment method, and the aggregation method are all complex processes. Generally, relevant experts are required to make the final determination based on relevant experience and experimental demonstration, which has a certain degree of subjectivity. It is precisely because of this subjectivity that the determination of the indicator system is an iterative process, and each experimental demonstration and expert discussion will change the indicator system. In the traditional way, the indicator system is solidified in the code, and the software indicators and model data and evaluation operators are fixedly associated, which leads to frequent changes in software requirements and software codes, resulting in low software availability and low efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide a method for dynamically constructing a performance evaluation system, a system for dynamically constructing a performance evaluation system, a computer device, and a computer-readable storage medium to address the above technical issues.

[0004] A method for dynamically constructing an effectiveness evaluation system, the method comprising:

[0005] Receiving an instruction to establish an indicator system, and establishing a new indicator system structure, wherein the indicator system includes a system performance indicator system;

[0006] Based on the evaluation scheme, create indicators at each level of the indicator system, and edit the relationship between the indicators at each level to form a hierarchical structure of the indicator system;

[0007] Edit the indicator attributes of each of the indicators to form an edited indicator system, and obtain the created final indicator system after verification;

[0008] Based on the evaluation method selected and imported from the dynamic library and the hierarchical structure of the final indicator system, the weights of the indicators at each level in the final indicator system are set, and the indicator aggregation operator for setting the indicators at each level is selected from the dynamic library;

[0009] According to the weights of the indicators at each layer and the calculation rules of the indicator aggregation operator, the performance value of the top-level indicator is calculated, and the performance evaluation result is obtained according to the performance value of the top-level indicator.

[0010] A system for dynamically constructing an effectiveness evaluation system, the system comprising:

[0011] A system architecture creation module, used to receive an indicator system establishment instruction and create an indicator system architecture, wherein the indicator system includes a system performance indicator system;

[0012] An indicator creation module, used to create indicators of each level of the indicator system based on the evaluation scheme, and edit the relationship between the indicators of each level to form a hierarchical structure of the indicator system;

[0013] The indicator editing module is used to edit the indicator attributes of each of the indicators to form an edited indicator system, and obtain the created final indicator system after verification;

[0014] A system indicator setting module, which is used to set the weights of indicators at each level in the final indicator system based on the evaluation method selected and imported from the dynamic library and the hierarchical structure of the final indicator system, and select an indicator aggregation operator for setting indicators at each level from the dynamic library;

[0015] The performance calculation module is used to calculate the performance value of the top-level indicator according to the weights of the indicators at each level and the calculation rules of the indicator aggregation operator, and obtain the performance evaluation result according to the performance value of the top-level indicator.

[0016] A computer device comprises a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the above method when executing the computer program.

[0017] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described above when executed by a processor.

[0018] Based on the scheme of the embodiment of the present application as described above, by presetting relevant evaluation methods and indicator aggregation operators, etc., and storing them in a dynamic library, when constructing a performance evaluation system, based on the created indicator system, the required evaluation methods and indicator aggregation operators, etc. can be directly selected and imported from the dynamic library, and the performance value can be calculated on this basis to obtain the performance evaluation result. Therefore, there is no need for a close association between the performance indicators and model data, evaluation operators, etc., and they can be selected from the dynamic library when the performance evaluation system needs to be constructed. Therefore, there is no need to frequently change software requirements and software codes, the software availability is high, and the efficiency of building a performance evaluation system is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of a method for dynamically constructing a performance evaluation system in one embodiment;

[0020] Figure 2 A schematic diagram of an interface of a storage normalization operator in one embodiment;

[0021] Figure 3 A schematic diagram of an interface for selecting a normalization operator to be stored in a library in one embodiment;

[0022] Figure 4 A schematic diagram of an interface for editing a normalization operator in one embodiment;

[0023] Figure 5 A schematic diagram of an interface of an indicator system architecture constructed in an embodiment;

[0024] Figure 6 A schematic diagram of a property display interface of a bottom-level indicator in an embodiment;

[0025] Figure 7 A schematic diagram of an interface for editing function matching properties of underlying indicators in one embodiment;

[0026] Figure 8 A schematic diagram of an interface for editing associated model attributes of underlying indicators in one embodiment;

[0027] Fig. 9 A schematic diagram of an interface for editing normalized operator properties of underlying indicators in one embodiment;

[0028] Fig.10 A schematic diagram of an attribute display interface of an upper-level indicator in an embodiment;

[0029] Fig.11 A schematic diagram of an interface for editing weighting operator attributes of an upper-level indicator in an embodiment;

[0030] Fig.12 A schematic diagram of an interface for editing the aggregation operator properties of an upper-level indicator in one embodiment;

[0031] Fig.13 A schematic diagram of an interface of analysis results in one embodiment;

[0032] Fig.14 A structural block diagram of a system for dynamically constructing a performance evaluation system in one embodiment;

[0033] Fig.15 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] The solution of the embodiment of the present application can be applied to various application scenarios that require the construction of a performance evaluation system and the performance evaluation based on the constructed performance evaluation system. For example, a user creates a dynamic library through a terminal and stores the dynamic library locally in the terminal or on a server. In the process of building a performance indicator evaluation system, by accessing the dynamic library locally in the terminal or stored in the server, the required evaluation method, related operators, etc. are selected and imported to create an actual performance evaluation indicator system.

[0036] like Figure 1 As shown, a method for dynamically constructing a performance evaluation system in an embodiment is applied to a user terminal and includes the following steps S10 to S50.

[0037] Step S10: receiving an indicator system establishment instruction and creating a new indicator system architecture, wherein the indicator system includes a system performance indicator system.

[0038] Among them, the indicator system establishment instruction is an instruction to instruct the establishment of a new indicator system, which is used to establish an indicator system for a new evaluation scheme. After receiving the indicator system establishment instruction, a new indicator system architecture is created. The indicator system is the core of the performance evaluation and analysis system. The indicator is a characteristic basis that can be determined based on the object and purpose of the research to reflect a certain situation of the research object.

[0039] The indicator system in the architecture includes system effectiveness performance indicators. System effectiveness performance indicators are measures of the degree to which the system completes a given task and are specific measures for evaluating and comparing system effectiveness. In one embodiment, the indicator system in the architecture may also include combat effectiveness indicators, which are quantitative descriptions of the results of the interaction between the two hostile parties.

[0040] In one embodiment, before the above step S10, the following steps may be further included:

[0041] Prepare various evaluation operators, including indicator normalization operator, indicator weighting operator, indicator aggregation operator, and design the interface of each evaluation operator, and then store each evaluation operator in the dynamic library.

[0042] By designing the interface of each evaluation operator and storing it in a dynamic library, each evaluation operator can be made into a plug-in. In the process of building a performance evaluation system, it is convenient to add and delete evaluation operators in the form of plug-ins without solidifying them in the code, which facilitates the expansion of operators.

[0043] In one embodiment, before the above step S10, the following steps may be further included:

[0044] After preparing each evaluation method and designing the interface of each evaluation method, each evaluation method is stored in a dynamic library; in one embodiment, the evaluation methods involved may include: one or more of the following methods: hierarchical analysis method, fuzzy comprehensive evaluation method, grey hierarchical evaluation method, SCA-based exploratory evaluation method, etc., and may also include other evaluation methods.

[0045] By designing the interface of each evaluation method and storing it in a dynamic library, each evaluation method can be made into a plug-in. In the process of building a performance evaluation system, it is convenient to dynamically select the evaluation method to be used by means of a plug-in instead of solidifying it in the code, which facilitates the expansion of the evaluation method.

[0046] Step S20: Based on the evaluation scheme, create indicators at each level of the indicator system, and edit the relationship between the indicators at each level to form a hierarchical structure of the indicator system.

[0047] Specifically, according to the specific evaluation scheme, for each indicator system, such as system effectiveness performance indicators and combat effectiveness indicators, indicators at each level can be created respectively, and the relationship between indicators can be determined, so as to form a hierarchical structure of the indicator system. In a specific example, the hierarchical structure of the indicator system can be a tree structure, that is, a one-to-many structure, and each indicator will have corresponding information on which level it is at.

[0048] Step S30: Edit the indicator attributes of each of the indicators to form an edited indicator system, and obtain the created final indicator system after verification.

[0049] When editing the indicator attributes of each indicator, operations such as indicator normalization, performance indicator weighting, and performance indicator aggregation can be performed on the indicator to define the relevant attributes of each indicator and form a final indicator system.

[0050] Based on the above hierarchical structure of the indicator system, the indicators in the hierarchical structure of the indicator system may be bottom-level indicators or upper-level indicators. Bottom-level indicators are basic indicators that directly bear attributes, while upper-level indicators are aggregate indicators that need to aggregate and analyze and calculate multiple lower-level indicators to obtain the attributes they bear.

[0051] In one embodiment, editing the indicator attributes of each of the indicators to form an edited indicator system includes:

[0052] When the indicator is a bottom-level indicator, editing the indicator attribute of the indicator includes: editing the basic attribute of the indicator, the indicator calculation attribute, the model association, and the normalization operator;

[0053] When the indicator is an upper-level indicator, the indicator attributes for editing the indicator include: basic attributes, relationship attributes, weighting operators, and aggregation operators.

[0054] Specifically, when the indicator is a bottom-level indicator, editing the indicator attribute of the indicator includes:

[0055] In the indicator system workspace, select the indicator system tab page, select the target underlying indicator to be modified in the indicator system tab page, and display the underlying indicator attribute display interface, which displays the basic attributes, relationship attributes, indicator calculation, model association, normalization operator and appearance of the target underlying indicator.

[0056] When it is necessary to edit the basic attributes, receive the basic attribute editing instruction, edit at least one of the indicator name, indicator type, indicator description and indicator unit of the target underlying indicator, and when editing the indicator name, adjust the indicator graph corresponding to the target underlying indicator in real time after the editing is completed.

[0057] When the indicator calculation needs to be edited, an indicator calculation editing instruction is received, a matching function associated with the target underlying indicator is selected from the imported functions, and parameter information of the selected matching function is edited.

[0058] When the model association needs to be edited, a model association editing instruction is received, and the input / output fields of the stored model are selected, and the number of fields of the selected stored model is consistent with the input number of the matching function.

[0059] When the normalization operator needs to be edited, a normalization operator editing instruction is received, a normalization operator associated with the target underlying indicator is selected from a dynamic library, and parameter information of the selected normalization operator is edited.

[0060] In one embodiment, when the indicator is an upper-level indicator, editing the indicator attribute of the indicator includes:

[0061] Select the indicator system tab page in the indicator system workspace, select the target upper-level indicator to be modified in the indicator system tab page, and display the upper-level indicator attribute display interface, which displays the basic attributes, relationship attributes and appearance of the target upper-level indicator.

[0062] When the basic attributes need to be edited, a basic attribute editing instruction is received, and at least one of the indicator name, indicator description and indicator unit of the target upper-level indicator is edited. When editing the indicator name, the indicator graph corresponding to the target upper-level indicator is adjusted in real time after the editing is completed.

[0063] In one embodiment, when the evaluation has been performed, the upper-level indicator attribute display interface can also display the weighting operator and aggregation operator of the target upper-level indicator. At this time, when the indicator is an upper-level indicator, the indicator attribute of the indicator can be edited, and at least one of the weighting operator and the aggregation operator can be further edited.

[0064] When the weighting operator needs to be edited, the weighting operator editing instruction is received, the weighting operator associated with the target upper-level indicator is selected from the dynamic library, the parameter information of the selected weighting operator is input, and calculation is performed based on the parameter information of the selected weighting operator. When the calculated CR value meets the matrix consistency verification condition, the parameter information of the edited selected weighting operator is obtained.

[0065] When the aggregation operator needs to be edited, an aggregation operator editing instruction is received, an aggregation operator associated with the target upper-layer indicator is selected from a dynamic library, and parameter information of the selected aggregation operator is edited.

[0066] After the editing is completed and the edited indicator system is formed, the edited indicator system can be verified, specifically, integrity verification and consistency verification can be performed. After the verification passes, the final indicator system created can be obtained.

[0067] Step S40: Based on the evaluation method selected and imported from the dynamic library and the hierarchical structure of the final indicator system, the weights of the indicators at each level in the final indicator system are set, and an indicator aggregation operator for setting the indicators at each level is selected from the dynamic library.

[0068] Among them, various evaluation methods and interfaces of various evaluation methods are pre-implemented, and various evaluation methods carrying interfaces are stored in a dynamic library to realize plug-in of each evaluation method.

[0069] Step S50: Calculate the performance value of the top-level indicator according to the set weights of the indicators at each level and the calculation rules of the indicator aggregation operator, and obtain the performance evaluation result according to the performance value of the top-level indicator.

[0070] In one embodiment, after obtaining the performance evaluation result, the following steps may be further included:

[0071] The indicator value of the target indicator is set to different values, and corresponding calculations are performed to obtain the corresponding performance value of the top-level indicator. According to the changes in the indicator value of the target indicator and the performance value corresponding to the top-level indicator, the influence of the target indicator on the top-level indicator is obtained;

[0072] The evaluation scheme is adjusted according to the impact of multiple target indicators on the top-level indicators.

[0073] Based on the above-mentioned embodiments, a detailed description is given below in conjunction with a specific example.

[0074] Effectiveness evaluation is a complex statistical activity process, and it is also a quantitative thinking process, with its basic evaluation operation system. As a statistical activity process, system effectiveness evaluation is a dialectical process of "qualitative-quantitative-qualitative". In the embodiment of this application, its operation process is mainly: clarifying the evaluation objectives, building an evaluation index system, building a simulation model, solving the evaluation index, establishing an evaluation model, conducting system effectiveness evaluation, confidence level analysis, giving evaluation results, and proposing improvement suggestions.

[0075] Among them, clarifying the evaluation objectives mainly involves gathering and clarifying the most basic and important target requirements of the evaluation implementation unit for effectiveness evaluation; and converting this requirement into index requirements for the physical composition, functions and operation results of the effectiveness simulation and evaluation system to be built. Clarifying the evaluation objectives helps to determine the composition structure and functions of the evaluation system and the status and role of each subsystem in completing the task, modeling, calculating and analyzing the system, and coordinating the objectives with knowledge (information), time and cost. In the case of effectiveness evaluation of combat systems, clarifying the evaluation objectives also helps to provide a basis for the selection of weapon system effectiveness evaluation indicators and clarify the main input and output of the evaluation system.

[0076] Constructing an evaluation index system is the primary prerequisite for conducting effectiveness evaluation. System analysis for effectiveness evaluation includes system composition analysis, operation process analysis and determination of effectiveness measurement. System composition analysis requires clarifying the composition, function and combat technical indicators of each element in the system; operation process analysis refers to clarifying the information flow and data flow of the system operation, as well as how each element in the system implements combat tasks and how to achieve goals. The indicators of effectiveness measurement vary with the methods used for different effectiveness evaluation problems, different components, different combat phases and different mission tasks. Constructing an index system is the primary prerequisite for conducting effectiveness evaluation, including clarifying the evaluation object, defining evaluation indicators and stipulating evaluation conditions. After the evaluation index system is constructed, the rationality and feasibility of the evaluation index system can be determined through the operation and demonstration of the simulation model, and the evaluation index system can be modified and improved.

[0077] Constructing simulation models and solving evaluation indicators refers to using simulation methods to evaluate system effectiveness. Taking the effectiveness evaluation of combat systems as an example, it is necessary to build simulation models of weapon systems and a large number of related models. The accuracy and credibility of simulation models directly affect the correctness of simulation results. The basic process of solving evaluation indicators mainly includes: loading the indicator simulation calculation model from the indicator model library; loading simulation initialization data from experimental results and combat scenarios; implementing simulation operations according to the actual system operation process; and storing the evaluation indicator values ​​in the indicator result database.

[0078] When establishing an evaluation model and conducting system performance evaluation, the system performance evaluation is conducted in combination with the established evaluation model. System performance evaluation is the ultimate goal of simulation-based evaluation activities. Establishing an evaluation model mainly includes clarifying the evaluation method, selecting the model software of the evaluation method, and establishing the system performance evaluation model. The basic process of performance evaluation is as follows: load the indicator data from the evaluation indicator result database; select the evaluation model from the evaluation model library; perform evaluation calculations according to the comprehensive evaluation method; and record the comprehensive evaluation result data.

[0079] Confidence level analysis is used to analyze credibility. Since the evaluation system cannot accurately reflect all the details of the task process, some necessary assumptions will be made on certain links in the actual system to simplify the design and development of the evaluation system without affecting the system operation results. Confidence level analysis includes the confidence level of indicator calculation and the confidence level of evaluation method. The confidence level of indicator calculation depends on the authenticity of the simulation model; the confidence level of the evaluation method depends on the mathematical logic of the method.

[0080] Providing evaluation results and making improvement suggestions are the ultimate goals of system effectiveness, and the content covers the main aspects of the above-mentioned execution process. Improvement suggestions include: the rationality and comprehensiveness of evaluation indicators; the rationality of the association between evaluation implementation plans and evaluation goals; the rationality of key indicators; and the correlation between evaluation activities and simulation models and calculation results.

[0081] Based on the evaluation process described above, in the execution scheme of the embodiment of the present application, it is necessary to prepare the evaluation operator first.

[0082] The application system provides a unified evaluation operator interface, such as an indicator normalization interface, an indicator weighting interface, and an indicator aggregation interface. Correspondingly, the relevant indicator normalization operator, indicator weighting operator, and indicator aggregation operator implement the corresponding interface and use plug-ins to add and delete models without being solidified in the code, which facilitates the expansion of the operator.

[0083] Taking the normalization operator as an example, in one embodiment, when storing the normalization operator in the library, the following steps may be included. After selecting the operator tab in the resource area, open the Figure 2 The list of normalization operators is shown, and click the normalization operator you select in the list of normalization operators (such as Figure 2 The newly added normalization operator control (such as Figure 2 The right side of the benefit-based indicator normalization operator shown button, select the normalization operator dynamic library (DLL file) file path (such as Figure 3 In this path, you can select one or more normalization operators by clicking Figure 3The "Open" button and other controls shown in the figure issue a selection instruction. The selected normalization operator can preview the information of the imported normalization operator to allow the user to clearly understand the calculation type and calculation method of the normalization operator, such as Figure 4 After previewing and verifying that everything is correct, you can click Figure 4 The "OK" button shown in the figure issues a confirmation selection instruction, and the selected normalized operator is successfully stored in the database. After the storage is successful, the newly stored no case can be displayed in the operator list.

[0084] For other evaluation operators, such as aggregation operators and weighting operators, their corresponding storage methods can be the same as the storage methods of the above-mentioned normalization operators.

[0085] After preparing each evaluation operator and storing it in the library, each evaluation method can be prepared and stored in the library. The evaluation method is the step, method, etc. of the evaluation. In some embodiments, the prepared evaluation method may include a method based on range analysis and a method based on variance analysis, such as a hierarchical analysis method, a fuzzy comprehensive evaluation method, a gray hierarchical evaluation method, an exploratory evaluation method based on SCA, etc. After preparing each evaluation method, each evaluation method can be stored in the library.

[0086] It is understandable that the above-mentioned process of preparing the evaluation operator and the evaluation method can be carried out in any order. In addition, the evaluation operator and the evaluation method can be added during the process of building the performance evaluation system, or after the process of building the performance evaluation system is completed, the evaluation operator and the evaluation method can be stored in the dynamic library to be applied to the next construction of the performance evaluation system.

[0087] After the above preparations are completed, the performance evaluation system can be constructed at any time based on the solution of the embodiment of the present application.

[0088] Before constructing the performance evaluation system, it is necessary to construct an evaluation scheme. The evaluation scheme relates to the target of the evaluation, the supporting method (e.g., evaluation method to be used) adopted for the target of the evaluation, etc., to construct a specific performance evaluation system based on the evaluation scheme. It is understandable that the evaluation scheme constructed can be stored in the system involved in the present application scheme in the form of an electronic file, or it can exist in other ways, as long as the user knows his or her own evaluation scheme or the evaluation scheme based on it when the present application scheme is used to construct the performance evaluation system.

[0089] Specifically, when using the embodiment of the present application to construct a performance evaluation system, it is necessary to first construct an indicator system.

[0090] In an embodiment of the present application, the indicators in the constructed index system include system effectiveness indicators. Taking the application in combat scenarios as an example, the indicators in the constructed index system also include combat effectiveness indicators. Among them, the system effectiveness indicator is a measure of the degree to which the system completes a given task, and is a specific scale for evaluating and comparing system effectiveness. The combat effectiveness indicator is a quantitative description of the results of the interaction between the two hostile parties. It can not only be used to illustrate the relationship between the effectiveness of the weapon equipment system and the combat outcome, but also to evaluate the combat effectiveness of the weapon equipment system and determine the combat effectiveness of one force relative to another. A schematic diagram of the index system of the components in one embodiment is shown in FIG. Figure 5 shown.

[0091] The construction of the indicator system is responsible for combining a series of indicators according to their interrelationships, which can comprehensively reflect the situation of all aspects of the evaluation object. Among them, the implementation methods of system effectiveness indicators and combat effectiveness indicators are the same, and the main difference is the evaluation angle. The following is an example of combat effectiveness indicators.

[0092] For combat effectiveness indicators, a combat effectiveness indicator system with a tree structure can be established, providing general indicator system editing functions such as adding, deleting, modifying, cutting, and pasting to achieve dynamic construction of the indicator system. At the same time, a variety of indicator normalization models are provided to support users to select normalization models and set model parameters according to the characteristics of effectiveness indicators, and calculate the normalized score values ​​of basic indicators. At the same time, subjective and objective weighting methods can be provided to assist users in assigning weights to all indicators of the effectiveness indicator system; and commonly used indicator aggregation methods are provided to achieve bottom-up aggregation calculation of combat effectiveness indicators and realize combat effectiveness evaluation.

[0093] Specifically, the following steps can be included when constructing an indicator system.

[0094] First, when an instruction to establish an indicator system is received, for example, an instruction to establish an indicator system is received by a user by clicking a new button on an interface related to the indicator system, a corresponding indicator system is newly created according to the object and purpose of the research in the evaluation plan.

[0095] Secondly, according to the specific evaluation application, create indicators at each level and determine the relationship between indicators to form an indicator system hierarchical structure. Among them, the indicator hierarchical structure refers to the relationship between indicators. In one embodiment of the present application, a tree structure (i.e., a one-to-many structure) can be used to reflect the relationship between indicators. Therefore, after the indicator system is determined, each indicator will have a corresponding indicator level.

[0096] Next, edit the indicator attributes of the indicators, and perform operations such as indicator normalization, performance indicator weighting, and performance indicator aggregation on the indicators to define the relevant attributes of each indicator and form the final indicator system.

[0097] The editing of the indicator attributes of an indicator can include editing the attributes of the underlying indicators and editing the attributes of the upper-level indicators. Indicator normalization refers to the process of calculating different model data through relevant algorithms to obtain normalized values ​​within a certain range (usually 1-100). Performance indicator weighting is to quantify the indicators according to the hierarchical relationship of the indicators and the importance of the indicators. The sum of the weights of all subordinate indicators of a certain indicator is equal to 1. Performance indicator aggregation refers to the algorithm for calculating the upper-level indicator values ​​from the lower-level indicator values.

[0098] Specifically, when editing the indicator attributes of an indicator, considering the division by composition structure, the indicators of the indicator system can be divided into bottom-level indicators (basic indicators) and upper-level indicators (aggregate indicators). Therefore, the editing of bottom-level indicators and upper-level indicators can be described as follows respectively.

[0099] In a specific example, the editing of the underlying indicators can be performed in the following manner.

[0100] First, select the indicator system tab in the indicator system workspace and select the underlying indicator to be modified (also referred to as the target underlying indicator in the relevant embodiments of the present application). The underlying indicator to be modified can be selected in various possible ways, such as by left-clicking. It is understood that in other embodiments, selection can also be made in other ways.

[0101] After selecting the target underlying indicator, the attribute area automatically switches to display the attributes of the selected underlying indicator, including the basic attributes, relationship attributes, indicator calculation, model association, normalization operator, appearance, etc. of the target underlying indicator. Figure 6 Among them, the basic attributes are the basic attributes of the underlying indicators, such as indicator name, indicator type, indicator description, indicator unit, etc.

[0102] The relationship attributes are attributes about the level of the indicator and the upper and lower indicators associated with it, that is, they can include the indicator level, the upper indicator list and the lower indicator list. It should be understood that since the indicator is a bottom-level indicator, its lower indicator list is empty. When the hierarchical structure of the indicator system is changed so that the indicator is changed from a bottom-level indicator to an upper-level indicator, its lower indicator list will contain the information of the list corresponding to the corresponding indicator to be aggregated.

[0103] The attribute of the indicator calculation is the information about the function matching corresponding to the underlying indicator of the target, that is, the information of the function matching the indicator, such as Figure 6 Function 3 is shown.

[0104] The attributes associated with the model are information about the model associated with the underlying indicator of the target, such as information about the associated long-layer indicator, such as Figure 6 The upper model associated with the underlying indicators shown is historical.

[0105] The attributes of the normalization operator are information about the normalization operator used by the underlying indicator of the target.

[0106] When editing the basic properties of the target underlying indicators, you can edit the indicator name, indicator type, indicator description, indicator unit, etc. in the basic properties. When editing the indicator name and indicator type, after the indicator name or indicator type is changed, the graph of the underlying indicator displayed in the indicator system workspace will change in real time.

[0107] When editing the properties of indicator calculation, you can click the option of indicator calculation function matching, select the function associated with the underlying indicator from the imported functions, and edit the parameter information of the function, such as Figure 7 As shown, after editing is completed, click the "OK" button to complete the editing of the properties calculated for this indicator.

[0108] When editing the properties associated with a model, you can select the input / output fields of the stored model. The number of fields in the associated model must be consistent with the number of inputs of the matching function, such as Figure 8 As shown, after the editing is completed, click the "OK" button to complete the editing of the attributes associated with the model. After clicking the OK button, the calculation result of the associated function changes in real time.

[0109] When editing a normalization operator, you can click the "Normalization Operator" option, select the normalization operator associated with the indicator, and edit the normalization operator parameter information, such as Fig. 9 As shown, click the "OK" button to complete the editing process of the normalization operator. After clicking the OK button to complete the editing process, the normalization calculation result of the normalization operator changes in real time.

[0110] The editing of upper-level indicators can be carried out in the following way.

[0111] First, select the indicator system tab in the indicator system workspace and select the upper-level indicator to be modified (also referred to as the target upper-level indicator in the relevant embodiments of the present application). The upper-level indicator to be modified can be selected in various possible ways, such as by left-clicking. It is understood that in other embodiments, selection can also be made in other ways.

[0112] After selecting the target upper-level indicator, the attribute area automatically switches to display the attributes of the selected upper-level indicator, including the basic attributes, relationship attributes and appearance of the target upper-level indicator. If the indicator system has been evaluated, the attributes of the target upper-level indicator displayed also include the attributes of the weighting operator and aggregation operator of the upper-level indicator.

[0113] Among them, the basic attributes are the basic attributes of the upper-level indicators, such as indicator name, indicator type, indicator unit, indicator size, etc.

[0114] The relationship attribute is the attribute of the level of the target upper-level indicator and the upper-level indicators and lower-level indicators associated with it, that is, it can include the indicator level, the upper-level indicator list and the lower-level indicator list. Since the hierarchical structure of each indicator and the hierarchical relationship between indicators have been determined before, the relationship attribute can be set to a non-editable state. It is understandable that if the hierarchical relationship between indicators needs to be adjusted in real time, the relationship attribute can also be set to an editable state.

[0115] The attributes of the weighting operator are information about the weighting operator used by the upper-level indicator of the target, and the attributes of the aggregation operator are information about the aggregation operator used by the upper-level indicator of the target.

[0116] When editing the basic properties of the target upper-level indicators, you can edit the indicator name, indicator description, indicator unit, etc. in the basic properties. Among them, when editing the indicator name, after the indicator name is changed, the graph of the target upper-level indicator displayed in the indicator system workspace will change in real time.

[0117] When editing the properties of a weighted operator, you can select the weighted operator associated with the upper-level indicator of the target and open the editing interface of the weighted operator, such as Fig.11 As shown, and input the parameter information of the weighting operator, you can click the "Calculate" button in the interface to trigger the calculation of the weighting operator and display it, for example, the weight calculation result and consistency verification result of the weighting operator are calculated. When the obtained CR value (consistency verification result) is less than 0.1, the matrix consistency verification passes, click the "OK" button to complete the editing of the properties of the weighting operator, where after clicking the OK button, the calculation result of the associated indicator size changes in real time.

[0118] When editing the properties of an aggregation operator, you can select the weighting operator associated with the target upper-level indicator and open the editing interface of the weighting operator, such as Fig.12As shown, edit the parameter information of the aggregation operator associated with the target upper-level indicator, such as the operator name and operator description, and click the "OK" button to complete the editing of the properties of the aggregation operator. After clicking the OK button, the calculation result of the indicator size associated with the aggregation operator changes in real time.

[0119] After editing is completed, the created indicator system can be verified. The verification of the indicator system can include integrity verification and consistency verification. The specific verification method can be any possible verification method. After the integrity verification and consistency verification are passed, the indicator system is built. The interface of the indicator system built in a specific example is as follows Figure 5 shown.

[0120] After the construction of the indicator system is completed, the evaluation process can be executed. The specific execution steps of the evaluation process are determined by the evaluation method adopted. Taking the evaluation method using the hierarchical analysis method as an example, the evaluation can be carried out in two steps. First, according to the indicator hierarchy structure, the weight of each layer of indicators is set from the bottom to the top, and then according to the indicator hierarchy structure, the aggregation operator of each layer of indicators is set from the bottom to the top. After the setting is completed, the system will calculate the performance value of each indicator from the bottom to the top according to the weight distribution and aggregation operator calculation rules, obtain the top-level indicators, and judge according to the performance value rules combined with the top-level indicators to obtain the final evaluation. Taking the simple rule as an example, it can be that when the top-level indicator obtained is less than 60 points, it means poor, 60-70 means poor, 70-80 means medium, 80-90 means good, and 90-100 means excellent.

[0121] After obtaining the above evaluation results, you can analyze the evaluation results and adjust the evaluation plan based on the analysis results.

[0122] Among them, the analysis of the evaluation results can be performed on indicators. The analysis of indicators mainly calculates the degree of influence of different influencing factors on each indicator in the evaluation scheme. In the embodiments of the present application, methods such as range analysis and variance analysis can be used for analysis. Among them, range analysis is to quantitatively calculate the degree of influence of each influencing factor on the indicator without considering the experimental error. Variance analysis introduces experimental errors and uses the F distribution test to determine the significance of the influence of each influencing factor on the indicator. The sensitivity analysis results are displayed in two ways: analysis charts and natural language descriptions.

[0123] by Figure 5Taking the created indicator system as an example, it is necessary to analyze which indicator has the most obvious impact on the quality of the combat plan. For example, the indicator value of the "concentrate superior forces" indicator can be calculated 100 times from 0 to 100, and then the changes in the effectiveness value of the combat plan's quality indicator can be observed, and then the same operation can be performed on other indicator values. Then, based on the difference in the change in the effectiveness value of the combat plan's quality indicator, it is analyzed which indicator has the greatest impact on the final result, so as to determine which aspect of the improvement plan can achieve the best effect. The analysis result obtained in one embodiment can be as follows: Fig.13 , Fig.13 The figure shows that the radar detection distance index has the greatest impact on helicopter maneuver operations, while the maximum range has the least impact on helicopter maneuver operations.

[0124] Among them, variance analysis methods mainly include one-way analysis of variance, multi-factor analysis of variance and covariance analysis.

[0125] Among them, one-way ANOVA is used to study whether different levels of a control variable have a significant impact on the observed variable. Here, since only the impact of a single factor on the observed variable is studied, it is called one-way ANOVA. Taking the combat system as an example, it can be, for example, analyzing the impact of different bomb loads on the strike effect, and the impact of different meteorological environments on the tank's mobility speed. These problems can be answered through one-way ANOVA. When conducting one-way ANOVA, first of all, the observed variables and control variables can be clarified. For example, the observed variables in the above question are strike effect and mobility speed; the control variables are order quantity and meteorological environment. Secondly, analyze the variance of the observed variables. Variance analysis believes that the change in the value of the observed variable will be affected by both the control variable and the random variable. Based on this, one-way ANOVA decomposes the total deviation sum of squares of the observed variable into two parts: the sum of squares of the deviation between groups and the sum of squares of the deviation within groups, which is expressed in mathematical form as: SST = SSA + SSE. Then, by comparing the proportion of each part of the total deviation sum of squares of the observed variable, it is inferred whether the control variable has a significant impact on the observed variable. Based on one-way analysis of variance, in the total sum of squares of the observed variables, if the proportion of the sum of squares of the between-group deviations is large, it means that the changes in the observed variables are mainly caused by the control variables, can be mainly explained by the control variables, and the control variables have a significant impact on the observed variables; conversely, if the proportion of the sum of squares of the between-group deviations is small, it means that the changes in the observed variables are not mainly caused by the control variables, cannot be mainly explained by the control variables, different levels of the control variables do not have a significant impact on the observed variables, and the changes in the values ​​of the observed variables are caused by random variable factors.

[0126] Specifically, in a specific example, when using the one-way ANOVA method for analysis, the following steps can be included. First, the null hypothesis is proposed: H0 - no difference; H1 - there is a significant difference. Secondly, the test statistic is selected. Specifically, the F statistic can be used as the F value test. Then, the observed value of the test statistic and the corresponding probability P value are calculated. Finally, the significance level is given and a decision is made.

[0127] After completing the basic analysis of the above one-way ANOVA, we can draw a conclusion on whether the control variable has a significant impact on the observed variable. We can further conduct variance homogeneity test and multiple comparison test, and we can also perform other checks.

[0128] Among them, the variance homogeneity test is to test whether the variance of each observed variable population is equal under different levels of the control variable. As mentioned above, it is a prerequisite for variance analysis that the variance of the observed variables population under different levels is not significantly different when the control variables are different. If this prerequisite is not met, it cannot be considered that the distributions of each population are the same. Therefore, it is necessary to test whether the variance is homogeneous. In some embodiments, the variance homogeneity test method can be used to perform the variance homogeneity test, and its original hypothesis is: there is no significant difference in the variance of the observed variables population under each level.

[0129] The multiple comparison test uses all the observed variable values ​​to achieve a pair-by-pair comparison of the overall mean of the observed variables at each level. As mentioned above, the basic analysis of the one-way ANOVA can only determine whether the control variable has a significant effect on the observed variable. If the control variable does have a significant effect on the observed variable, it is further necessary to determine the degree of influence of different levels of the control variable on the observed variable, which level has a significantly different effect from other levels, which level has an insignificant effect, and so on. For example, if it is determined that rain has a significant effect on tank mobility, then it is also necessary to understand whether light rain (24h rainfall less than 10mm), moderate rain (24h rainfall 10-25mm), heavy rain (or 24h rainfall 25-50mm), torrential rain 24h rainfall 50-100mm) and other different precipitation amounts have different effects on tank mobility, which type of rain has an insignificant effect on tank mobility speed, and which type of rain is most unfavorable to tank mobility operations. Mastering these important information will help commanders formulate reasonable combat methods and improve combat effectiveness.

[0130] Since the multiple comparison test problem is also a hypothesis test problem, it also follows the basic steps of hypothesis testing. Specifically, it can be done in the following way. First, construct a test statistic. You can use the LSD method, SNK method and other methods to construct the test statistic. The LSD method is called the Least Significant Difference method. It reflects the high sensitivity of its test, that is, as long as there is a certain degree of slight difference in the means between levels, it may be tested. It uses all observed variable values, rather than just the data of two groups, and is used when the variances of each population are equal. The SNK method is an effective method for dividing similarity subsets. This method is suitable for situations where the number of observations at each level is equal.

[0131] Other tests performed may include a priori contrast tests and trend checks. In some embodiments, when in a multiple comparison test, if it is found that the mean difference between some levels and other levels is significant, such as there are five levels, where the means of x1, x2, x3 and x4, x5 are significantly different, it is possible to further analyze and compare whether there is a significant difference in the total means of the two groups, that is, whether 1 / 3 (x1 + x2 + x3) and 1 / 2 (x4 + x5) are significantly different. This analysis method of specifying the coefficients of each mean in advance and then testing its linear combination is called a priori contrast test. Through a priori contrast tests, the degree of difference in the means between each level or each similarity subset can be more accurately grasped. Trend test is to analyze the overall trend of the change of the observed variable value with the change of the control variable level when the control variable is an ordinal variable, whether it presents a linear change trend, or a quadratic, cubic or other polynomial change. Through trend test, people can grasp the degree of the overall effect of different levels of control variables on the observed variable from another perspective.

[0132] Multi-factor ANOVA is used to study whether two or more control variables have a significant impact on the observed variables. Since it studies the impact of multiple factors on the observed variables, it is called multi-factor ANOVA. Multi-factor ANOVA can not only analyze the independent impact of multiple factors on the observed variables, but also analyze whether the interaction of multiple control factors can have a significant impact on the distribution of the observed variables, and finally find the optimal combination that is beneficial to the observed variables. For example: when analyzing the impact of different meteorological conditions (rain, snow, fog, etc.) and different weather types on tank mobility, the tank mobility speed can be used as an observed variable. Meteorological conditions and weather types are used as control variables, and the multi-factor ANOVA method is used to study how different meteorological conditions and different weather types affect tank mobility operations, and further study what kind of weather conditions and what kind of weather types are most conducive to tank operations.

[0133] In some embodiments, the multi-factor variance analysis function can also be used to compare whether there is a significant difference in the mean of the observed variables under different levels of each control variable. There are two implementation methods, namely multiple comparison test and contrast test. The method of multiple comparison test is similar to single-factor variance analysis. The contrast test can adopt the method of single sample t test, which regards the observed variable values ​​under different levels of the control variable as samples from different populations, and successively checks whether the mean of these populations is significantly different from a certain specified test value. Among them, the test value can be specified as the following: the mean (Deviation) of the observed variable, the mean (Simple) of the observed variable on the first level or the last level, the mean (Difference) of the observed variable on the previous level, and the mean (Helmert) of the observed variable on the latter level. In addition, during the multi-factor variance analysis, the interaction of the control variables can be analyzed intuitively by graphics.

[0134] Whether it is a one-way ANOVA or a multi-way ANOVA, the control factors are controllable, and their various levels can be controlled and determined through human efforts. However, in many practical problems, some control factors are difficult to control artificially, but their different levels do have a more significant impact on the observed variables. For example, when studying tank mobile operations, if only the impact of meteorological conditions and meteorological types on tank mobility is considered, and variance analysis is conducted without considering factors such as different terrains and different damage states, it is obviously incomplete. Because in fact, tanks are fast in dry land and rocky land, and slow in dense forests, muddy land, and residential areas. Failure to consider these factors in analysis may result in: tanks may be faster in heavy rain than in light rain.

[0135] Therefore, in some embodiments, the covariance analysis method can also be combined for analysis. Covariance analysis uses control factors that are difficult to control artificially as covariates, and analyzes the effect of control variables (controllable) on observed variables under the condition of excluding the effect of covariates on observed variables, so as to more accurately evaluate control factors. Covariance analysis follows the basic idea of ​​variance analysis. When analyzing the variation of observed variables, the influence of covariates is considered. The change of artificially observed variables is affected by four aspects: the independent effect of control variables, the interaction of control variables, the effect of covariates and the effect of random factors. After deducting the influence of covariates, the influence of control variables is analyzed.

[0136] The range analysis method is referred to as the R method. It includes two steps: calculation and judgment. The specific principle is the same as that of variance analysis, but the analysis algorithm is different. Among them, the range is the difference between the maximum and minimum values ​​in the average effect. With the range, we can find the main factors that affect the final combat effectiveness, and can help us find the best factor level combination. For example, based on the size of the range, we can judge the priority of the factors. Figure 6 The analysis result interface shown shows that the radar detection distance index has the greatest impact on helicopter maneuver operations, while the maximum range has the least impact on helicopter maneuver operations.

[0137] Based on the solution of the embodiment of the present application as described above, the problem of combat equipment effectiveness analysis can be solved.

[0138] For example, the indicator normalization processing technology is used to realize the normalization of different value spaces of different indicators. Operational evaluation indicators can be divided into four categories: benefit type, cost type, intermediate type, and deviation type. The larger the indicator value of the benefit type indicator, the better, the smaller the indicator value of the cost type indicator, the better, the indicator value of the intermediate type indicator is best within a certain range, and the indicator value of the deviation type indicator is as far away from a certain range as possible.

[0139] For another example, subjective weighting techniques and objective weighting techniques are used to determine the mutual importance of each evaluation indicator. The indicator weight refers to the contribution of each indicator to the realization of the overall goal, which reflects the value status coefficient of each indicator in the evaluation object. According to the different sources of the original data when calculating the weight, the weight can be divided into subjective weighting method and objective weighting method. The specific methods used in the subjective weighting method can include expert consultation method, relative comparison method, AHP method, Delphi method, chain ratio method and other methods. The specific methods used in the objective weighting method can mainly include entropy method, grade separation method, ideal point approach method, principal component analysis method and other methods.

[0140] For another example, when conducting effectiveness evaluation, analytical methods, statistical methods, combat simulation methods, and multi-index comprehensive evaluation methods can usually be used for evaluation. For complex combat evaluation, its effectiveness presents a more complex hierarchical structure. Some high-level effectiveness indicators and their lower-level indicators directly affect each other, but there is no flaw in the functional relationship. At this time, only after comprehensive processing of its lower-level indicators can its effectiveness indicators be evaluated, such as using hierarchical analysis method, expert evaluation method, gray hierarchical evaluation method, fuzzy comprehensive evaluation method, etc.

[0141] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0142] In one embodiment, Fig.14 As shown, a system for dynamically constructing an effectiveness evaluation system is provided. The system can use software modules or hardware modules, or a combination of the two to form a part of a computer device. The system specifically includes:

[0143] A system architecture creation module 10 is used to receive an indicator system establishment instruction and create an indicator system architecture, wherein the indicator system includes a system performance indicator system;

[0144] An indicator creation module 20 is used to create indicators of each level of the indicator system based on the evaluation scheme, and edit the relationship between the indicators of each level to form a hierarchical structure of the indicator system;

[0145] The indicator editing module 30 is used to edit the indicator attributes of each of the indicators to form an edited indicator system, and obtain the created final indicator system after verification;

[0146] A system indicator setting module 40 is used to set the weight of each layer of indicators in the final indicator system based on the evaluation method selected and imported from the dynamic library and the hierarchical structure of the final indicator system, and select an indicator aggregation operator for setting each layer of indicators from the dynamic library;

[0147] The performance calculation module 50 is used to calculate the performance value of the top-level indicator according to the weights of the indicators at each level and the calculation rules of the indicator aggregation operator, and obtain the performance evaluation result according to the performance value of the top-level indicator.

[0148] In one embodiment, the index system also includes a combat effectiveness index, which is a quantitative description of the results of the interaction between the two hostile parties.

[0149] In one embodiment, the indicator editing module 30 includes:

[0150] A bottom-level indicator editing module, used for editing the indicator attributes of the bottom-level indicator when the indicator is a bottom-level indicator, the indicator attributes of the bottom-level indicator including: basic attributes of the indicator, indicator calculation attributes, model association, and normalization operator;

[0151] The upper-layer indicator editing module is used to edit the indicator attributes of the upper-layer indicator when the indicator is an upper-layer indicator. The indicator attributes of the upper-layer indicator include: basic attributes, relationship attributes, weighting operators, and aggregation operators.

[0152] In one embodiment, the underlying indicator editing module is used to receive an instruction to select an indicator system tab page in the indicator system workspace, display the indicator system tab page, and receive an instruction to select a target underlying indicator to be modified on the indicator system tab page, and display an underlying indicator attribute display interface, wherein the underlying indicator attribute display interface displays the basic attributes, relationship attributes, indicator calculation, model association, normalization operator and appearance of the target underlying indicator.

[0153] In one embodiment, the underlying indicator editing module is used to receive basic attribute editing instructions, edit at least one of the indicator name, indicator type, indicator description and indicator unit of the target underlying indicator, and when editing the indicator name, adjust the indicator graph corresponding to the target underlying indicator in real time after the editing is completed.

[0154] In one embodiment, the underlying indicator editing module is used to receive an indicator calculation editing instruction, select a matching function associated with the target underlying indicator from the imported functions, and edit parameter information of the selected matching function.

[0155] In one embodiment, the bottom-level indicator editing module is used to receive a model association editing instruction, select input / output fields of the stored model, and the number of fields of the selected stored model is consistent with the number of inputs of the matching function.

[0156] In one embodiment, the underlying indicator editing module is used to receive a normalization operator editing instruction, select a normalization operator associated with the target underlying indicator from a dynamic library, and edit parameter information of the selected normalization operator.

[0157] In one embodiment, the upper-level indicator editing module receives an instruction to select an indicator system tab page in the indicator system workspace, displays the indicator system tab page, and when receiving an instruction to select a target upper-level indicator to be modified on the indicator system tab page, displays an upper-level indicator attribute display interface, wherein the upper-level indicator attribute display interface displays the basic attributes, relationship attributes and appearance of the target upper-level indicator.

[0158] In one embodiment, the upper-level indicator editing module receives a basic attribute editing instruction, edits at least one of the indicator name, indicator description and indicator unit of the target upper-level indicator, and when editing the indicator name, adjusts the indicator graph corresponding to the target upper-level indicator in real time after the editing is completed.

[0159] In one embodiment, when the upper-level indicator editing module has performed an evaluation, the upper-level indicator attribute display interface also displays the weighting operator and aggregation operator of the target upper-level indicator.

[0160] In one embodiment, the upper-level indicator editing module also receives a weighting operator editing instruction, selects a weighting operator associated with the target upper-level indicator from a dynamic library, inputs parameter information of the selected weighting operator, and performs calculations based on the parameter information of the selected weighting operator. When the calculated CR value meets the matrix consistency verification condition, the edited parameter information of the selected weighting operator is obtained.

[0161] In one embodiment, the upper-level indicator editing module further receives an aggregation operator editing instruction, selects an aggregation operator associated with the target upper-level indicator from a dynamic library, and edits parameter information of the selected aggregation operator.

[0162] In one embodiment, the system further comprises:

[0163] The performance calculation module is used to set the index value of the target index to different values, perform corresponding calculations respectively, obtain the corresponding performance value of the top-level index, and obtain the influence of the target index on the top-level index according to the changes of the index value of the target index and the performance value corresponding to the top-level index;

[0164] The scheme adjustment module is used to adjust the evaluation scheme according to the impact of multiple target indicators on the top-level indicators.

[0165] For the specific definition of the system for dynamically constructing the performance evaluation system, please refer to the definition of the method for dynamically constructing the performance evaluation system above, which will not be repeated here. Each module in the system for dynamically constructing the performance evaluation system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.15As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for dynamically constructing an efficiency evaluation system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0167] Those skilled in the art will understand that Fig.15 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0169] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0170] Therefore, in one embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0171] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0172] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for dynamically constructing an effectiveness evaluation system, the method comprising: Receiving an instruction to establish an indicator system, and establishing a new indicator system structure, wherein the indicator system includes a system performance indicator system; Based on the evaluation scheme, create indicators at each level of the indicator system, and edit the relationship between the indicators at each level to form a hierarchical structure of the indicator system; Edit the indicator attributes of each of the indicators, and perform indicator normalization, performance indicator weighting, and performance indicator aggregation operations on the indicators to form an edited indicator system, and obtain the created final indicator system after verification; Based on the evaluation method selected and imported from the dynamic library and the hierarchical structure of the final indicator system, the weights of the indicators at each level in the final indicator system are set, and the indicator aggregation operator for setting the indicators at each level is selected from the dynamic library; According to the weights of the indicators at each layer and the calculation rules of the indicator aggregation operator, the performance value of the top-level indicator is calculated, and the performance evaluation result is obtained according to the performance value of the top-level indicator; The editing of the indicator attributes of each of the indicators to form an edited indicator system includes: when the indicator is an upper-level indicator, the indicator attributes of the indicator to be edited include: basic attributes, relationship attributes, weighting operators, and aggregation operators; When the indicator is an upper-level indicator, editing the indicator attribute of the indicator includes: Select the indicator system tab page in the indicator system work area, select the target upper-level indicator to be modified on the indicator system tab page, and display the upper-level indicator attribute display interface, which displays the basic attributes, relationship attributes and appearance of the target upper-level indicator; receiving a basic attribute editing instruction, editing at least one of an indicator name, an indicator description, and an indicator unit of the target upper-level indicator, and when editing the indicator name, adjusting an indicator graph corresponding to the target upper-level indicator in real time after the editing is completed; When the evaluation has been performed, the upper-level indicator attribute display interface also displays the weighting operator and aggregation operator of the target upper-level indicator; When the indicator is an upper-level indicator, editing the indicator attribute of the indicator further includes: Receive a weighting operator editing instruction, select a weighting operator associated with the target upper-level indicator from a dynamic library, input parameter information of the selected weighting operator, and perform calculation based on the parameter information of the selected weighting operator. When the calculated CR value meets the matrix consistency verification condition, obtain the parameter information of the edited selected weighting operator; An aggregation operator editing instruction is received, an aggregation operator associated with the target upper-layer indicator is selected from a dynamic library, and parameter information of the selected aggregation operator is edited.

2. The method according to claim 1, characterized in that The index system also includes combat effectiveness indicators, which are quantitative descriptions of the results of the interaction between the two hostile parties.

3. The method according to claim 1 or 2, characterized in that: Edit the indicator attributes of each of the indicators to form an edited indicator system, including: When the indicator is a bottom-level indicator, editing the indicator attribute of the indicator includes: editing the basic attribute of the indicator, the indicator calculation attribute, the model association, and the normalization operator.

4. The method according to claim 3, characterized in that When the indicator is a bottom-level indicator, editing the indicator attribute of the indicator includes: Select the indicator system tab page in the indicator system work area, select the target underlying indicator to be modified on the indicator system tab page, and display the underlying indicator attribute display interface, which displays the basic attributes, relationship attributes, indicator calculation, model association, normalization operator and appearance of the target underlying indicator; and also includes at least one of the following items: receiving a basic attribute editing instruction, editing at least one of the indicator name, indicator type, indicator description and indicator unit of the target underlying indicator, and when editing the indicator name, adjusting the indicator graph corresponding to the target underlying indicator in real time after the editing is completed; receiving an indicator calculation editing instruction, selecting a matching function associated with the target underlying indicator from the imported functions, and editing parameter information of the selected matching function; Receive a model association editing instruction, select input / output fields of the stored model, and the number of fields of the selected stored model is consistent with the number of inputs of the matching function; A normalization operator editing instruction is received, a normalization operator associated with the target underlying indicator is selected from a dynamic library, and parameter information of the selected normalization operator is edited.

5. The method according to claim 3, characterized in that: The index system hierarchical structure is a tree structure, and each index has a corresponding index level.

6. The method according to claim 4, characterized in that The relationship attributes include indicator levels, upper indicator lists and lower indicator lists, wherein the lower indicator list of the bottom indicator is empty.

7. The method according to claim 1 or 2, characterized in that: Also includes the steps: The indicator value of the target indicator is set to different values, and corresponding calculations are performed to obtain the corresponding performance value of the top-level indicator. According to the changes in the indicator value of the target indicator and the performance value corresponding to the top-level indicator, the influence of the target indicator on the top-level indicator is obtained; The evaluation scheme is adjusted according to the impact of multiple target indicators on the top-level indicators.

8. The method according to claim 1 or 2, characterized in that: Before receiving the instruction to establish the indicator system, the following steps are also included: Prepare various evaluation operators, including indicator normalization operator, indicator weighting operator, indicator aggregation operator, and design the interface of each evaluation operator, and then store each evaluation operator in the dynamic library; After preparing each evaluation method and designing the interface of each evaluation method, each evaluation method is stored in the dynamic library; the evaluation methods include: hierarchical analysis method, fuzzy comprehensive evaluation method, grey hierarchical evaluation method, and exploratory evaluation method based on SCA.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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