CN116843101A - Performance evaluation method, device, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a performance evaluation method, a device, an electronic device and a storage medium, comprising: receiving a first operation of a user; responding to the first operation, displaying a target evaluation interface corresponding to the target evaluation project, wherein the target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identifier is used for indicating a target index system tree corresponding to a target evaluation project, responding to a second operation of a user on the target evaluation instance identifier, and completing calculation of each evaluation index of the target evaluation instance based on information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize performance evaluation of the target evaluation project and obtain a target performance evaluation result.
Description
Technical Field
The present application relates to the field of weapon system performance evaluation, and in particular, to a performance evaluation method, apparatus, electronic device, and storage medium.
Background
Currently, weapon assessment is mostly performed by manually assessing data generated by a test or data (assessment data) of a deduction simulation based on expert experience, or developing a special performance assessment program based on expert experience, and then automatically assessing the data generated by the test or the data (assessment data) of the deduction simulation through the special performance assessment program.
However, both manual evaluation based on expert experience and automatic evaluation based on a dedicated performance evaluation program can only be satisfied for performance evaluation of a specific type of weapon equipment, and when evaluation is required for different types of weapon equipment, different experts are required for manual evaluation, or professional personnel perform automatic evaluation by modifying the dedicated performance evaluation program, so that the efficiency of performance evaluation is low.
Disclosure of Invention
The application provides a performance evaluation method, a performance evaluation device, electronic equipment and a storage medium, which can improve performance evaluation efficiency.
In a first aspect, the present application provides a performance evaluation method, including: receiving a first operation of a user, wherein the first operation is used for creating a target evaluation project; responding to the first operation, displaying a target evaluation interface corresponding to the target evaluation project, wherein the target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identifier is used for indicating a target index system tree corresponding to the target evaluation project, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation project, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identification is used for indicating an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data; and responding to a second operation of the user on the target evaluation instance identifier, and completing calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize performance evaluation of the target evaluation engineering and obtain a target performance evaluation result.
In a second aspect, the present application provides a performance evaluation apparatus, comprising: the receiving module is used for receiving a first operation of a user, wherein the first operation is used for creating a target evaluation project; the display module is used for responding to the first operation and displaying a target evaluation interface corresponding to the target evaluation project, wherein the target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identifier is used for indicating a target index system tree corresponding to the target evaluation project, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation project, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identification is used for indicating an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data; the evaluation module is used for responding to the second operation of the user on the target evaluation instance identifier, completing the calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize the efficiency evaluation of the target evaluation engineering and obtain a target efficiency evaluation result.
In a third aspect, the present application provides an electronic device, comprising: a processor for executing a computer program stored in a memory, which when executed by the processor implements the steps of any one of the performance assessment methods provided in the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of any of the performance assessment methods provided in the first aspect.
A fifth aspect of an embodiment of the present application provides a computer program product, wherein the computer program product comprises a computer program or instructions which, when run on a processor, cause the processor to execute the computer program or instructions for carrying out the steps of the performance assessment method as described in the first aspect.
In a sixth aspect of the embodiments of the present application, there is provided a chip comprising a processor, a memory and a communication interface, the communication interface being coupled to the processor, the memory being for storing a program or instructions executable on the processor, the processor being for executing the program or instructions to implement the steps of the performance assessment method as described in the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: in the embodiment of the application, through setting a visual evaluation interface, a user can set an evaluation index system according to requirements, set a weight algorithm and an evaluation algorithm corresponding to each layer of evaluation indexes, set an evaluation template, set a corresponding evaluation example based on evaluation data and an evaluation template needing performance evaluation, and then trigger to complete calculation of each evaluation index of the target evaluation example through operation of the evaluation example so as to realize performance evaluation of the target evaluation project and obtain a target performance evaluation result. Therefore, through the visual evaluation interface, a user can evaluate the efficiency of the evaluation engineering which needs to be evaluated according to the requirements, no professional knowledge is needed, no special efficiency evaluation program is needed, and according to the requirements, the user can evaluate the efficiency of various types of weapons through the visual evaluation interface, and the extensible general efficiency evaluation analysis method for the multi-field weapons is provided, so that the efficiency of efficiency evaluation can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a performance evaluation method according to the present application;
FIG. 2 is a schematic diagram of a target evaluation interface according to the present application;
FIG. 3 is a schematic diagram of another objective assessment interface provided by the present application;
FIG. 4 is a schematic diagram of a weight algorithm setting of an index subtree according to the present application;
FIG. 5 is a schematic diagram of another objective assessment interface provided by the present application;
FIG. 6 is a schematic diagram of an evaluation algorithm for an index subtree according to the present application;
FIG. 7 is a schematic diagram of a further object assessment interface provided by the present application;
FIG. 8 is a schematic diagram of a performance evaluation system according to the present application;
FIG. 9 is a flowchart of another performance evaluation method according to the present application;
FIG. 10 is a schematic diagram of a performance evaluation apparatus according to the present application;
fig. 11 is a schematic diagram of a hardware structure of an electronic device according to the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be more clearly understood, a further description of the application will be made. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more.
In the field of military industry such as aerospace and army, in order to exert the maximization effect of equipment in a combat system, the efficiency of the system and the equipment needs to be evaluated based on data generated by a test or data of deduction simulation in the development process of the equipment.
With the development of modern science and technology, the popularization of information technology, the modern war environment becomes more complex, the connection between weapon systems is more compact, and the development of the fight between weapon equipment systems is gradually realized. In the countermeasure process, the system efficiency is realized by utilizing the cooperative linkage among all the components of the weapon equipment according to the structural composition and the application mode of different equipment systems, and the action of the weapon equipment is played to the greatest extent to master the battlefield situation.
The electronic device in the embodiment of the application can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), a personal computer (personal computer, PC), a Television (TV), a teller machine or a self-service machine, and the like, and can be specifically determined according to practical situations, but the application is not limited thereto.
The technical scheme of the present application is explained in detail below by means of several specific examples.
Fig. 1 is a flow chart of a performance evaluation method according to the present application, as shown in fig. 1, the performance evaluation method may include the following steps 101 to 103.
101. A first operation of a user is received, the first operation being used to create a target evaluation project.
102. And responding to the first operation, and displaying a target evaluation interface corresponding to the target evaluation project.
The target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identifier is used for indicating a target index system tree corresponding to the target evaluation project, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation project, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identification is used to indicate an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data.
In some embodiments of the present application, the evaluation project management interface provides a new evaluation project function for the user, where the first operation includes an operation of the user on the evaluation project management interface for identifying a new target evaluation project, an operation of editing a target index system tree indicated by the target index system identification, an operation of editing each weight algorithm indicated by the target weight scheme identification, an operation of editing each evaluation algorithm indicated by the target evaluation scheme identification, an operation of editing a target evaluation template indicated by the target evaluation template identification, and an operation of editing target evaluation data indicated by the target evaluation instance identification.
In some embodiments of the present application, before the step 101, the performance evaluation method provided in the embodiment of the present application further includes: displaying a first evaluation interface corresponding to a first evaluation project, wherein the first evaluation interface comprises the target index system identifier, the target weight scheme identifier, the target evaluation template identifier and the target evaluation instance identifier; the first operation includes an operation for copying the first evaluation project to generate the target evaluation project.
In the embodiment of the application, the target evaluation project can be created through the newly built evaluation project function provided by the evaluation project management interface, and the target evaluation function can be created through copying the existing evaluation project function, so that different ways of creating the target evaluation function are provided for users, and the user experience can be improved; and by copying the existing evaluation engineering function and creating the target evaluation function, the target evaluation function can be quickly created, and the efficiency of creating the target evaluation function is improved.
In some embodiments of the present application, the assessment engineering management interface further provides at least one of the following functions for the user: querying the evaluation engineering function, loading the evaluation engineering function, modifying the evaluation engineering function, deleting the evaluation engineering function, importing the evaluation engineering function, and exporting the evaluation engineering function. Therefore, the evaluation project management interface provides the user with a plurality of functions for editing the evaluation project, so that the operability of the target evaluation project can be improved, and the user experience can be improved.
In some embodiments of the present application, the evaluation project management interface supports management of evaluation projects of various kinds of weaponry, and specifically includes functions of creating, deleting, editing, querying the evaluation projects, and importing and exporting the evaluation projects. Providing a quick man-machine interaction interface, and quickly and conveniently completing various evaluation engineering management operations; the system provides an evaluation engineering library, provides a function of loading the created evaluation engineering, inquires according to the type or name of the weapon equipment, opens the created evaluation engineering, can refer to a related weapon equipment index system, weight, evaluation setting and the like for quick operation, and can quickly generate new evaluation engineering for operation through storage; after the new evaluation project is built, the system automatically loads the index system view, and a friendly evaluation project editing operation mode is provided.
In some embodiments of the present application, the target index system tree is a preset index system tree, or the target index system tree is a user-defined index system tree, or the target index system tree is generated based on at least one of the following user operations on the basis of the preset index system tree: adding index nodes, editing index nodes and deleting index nodes. Therefore, the method provides multiple functions for constructing the index system tree for the user, improves the operability of the user and improves the user experience.
In some embodiments of the present application, the tree structure of the target index system tree is constructed in a graphical manner, so that a user can understand the structure of the index system tree, and the construction of the index system tree is facilitated.
In some embodiments of the present application, the user may trigger the display of the index system interface by performing an operation on the target index system identifier.
In some embodiments of the present application, the index system tree is supported to be customized at an index system interface, and the index system interface has a function of constructing a multi-level index system tree. Specifically, the index hierarchy interface may provide functionality to add, edit, and delete index hierarchy nodes.
In some embodiments of the present application, a function is provided for selecting an index system tree from an assessment index system library at an index system interface. Aiming at all capability characteristics and parameters of the weapon equipment, the system provides a comprehensive assessment index system library corresponding to the weapon equipment, and simultaneously supports the modification and editing functions of the index system tree selected from the assessment index system library on an index system interface.
In some embodiments of the present application, the tree structure of the index system tree is constructed in a graphical manner, and index nodes can be added and deleted by right-hand mouse button, and the index nodes can be edited (including operations of dragging, scaling, renaming, etc. the index nodes).
Wherein, add index node: after selecting a certain index node, clicking a right button of a mouse, selecting an 'add index node', inputting information such as index names, index types and the like, and adding one index node; editing index nodes: clicking a right button of a mouse after selecting a certain index node, editing an index name in a popped index attribute information frame, finishing attribute editing by a point 'determining' button, and not modifying an index attribute value by clicking a 'cancel' button; deleting index nodes: after selecting a certain index node, clicking the right button of the mouse, and selecting the "delete index node", so as to delete one index node.
In some embodiments of the present application, the user may trigger the display of the weight scheme interface by manipulating the target weight scheme identifier. The user can trigger a weight algorithm for setting a layer-by-layer aggregation calculation process of the multi-layer evaluation indexes from the bottom evaluation index to the top evaluation index through the operation of the weight scheme interface. In the polymerization process, specific functional requirements are as follows:
(1) Providing weight algorithms such as an analytic hierarchy process, a direct weighting method, a fuzzy analytic hierarchy process, a cyclic ratio coefficient method, an expert scoring method, an entropy weighting method, a dispersion maximum method and the like in the weight scheme; (2) supporting a user-defined weighting algorithm; (3) support selection or setting of weight algorithms; (4) The weight algorithm can set the weight method by other nodes (nodes with child nodes) except the leaf nodes which cannot be set; (5) The upward aggregation of the indexes of each layer can be completed according to the configured comprehensive index weight algorithm.
Illustratively, the main weighting algorithm is introduced as follows:
(1) Hierarchical analysis (Analytic Hierarchy Process, AHP) refers to a decision method that decomposes elements that are always relevant to decision making into levels of targets, criteria, schemes, etc., on the basis of which qualitative and quantitative analysis is performed. Decomposing the target required to be achieved into a plurality of component factors according to the properties of the study object, combining the factors according to non-hierarchical aggregation according to the mutual correlation influence and membership between the factors to form a hierarchical structure model, and finally obtaining the importance weight of the lowest factor to the highest-level index and sequencing the merits. The hierarchical analysis is mainly used for calculating the weight of each index. The AHP method calculates the weight of each index according to the comparison of the importance degrees among the indexes. Inputting parameters: 1. an index system; 2. the ratio of importance levels between peer indices. Output parameters: weights of the respective indexes.
(2) Fuzzy Analytic Hierarchy Process (FAHP) refers to a decision method for performing qualitative and quantitative analysis based on the concept of fuzzy judgment introduced to decompose elements related to decision into levels of targets, criteria, schemes and the like. Decomposing the target to be achieved into a plurality of component factors according to the properties of the study object, aggregating and combining the factors according to different levels according to the mutual correlation influence and membership between the factors to form a hierarchical structure model, and finally obtaining the importance weight of the lowest level factor to the highest level index or sorting the merits according to the level analysis. The fuzzy analytic hierarchy process is mainly used for calculating the weight of each index. The FAHP method calculates the weight of each index according to the comparison of the importance degrees among the indexes. Inputting parameters: 1. an index system; 2. the ratio of importance levels between peer indices. Output parameters: weights of the respective indexes.
(3) The cyclic ratio coefficient method, also called DARE method, is a method for evaluating and selecting an innovative scheme by determining importance coefficients of various factors, and is simpler than the analytic hierarchy process, but less accurate than the analytic hierarchy process. The cyclic ratio coefficient method is a method of evaluating and selecting an innovative scheme by determining importance coefficients of various factors. The method comprises the steps of sequentially comparing importance degrees of two adjacent indexes from top to bottom to give a functional importance value, then enabling the importance value of the last compared index to be 1 (serving as a base), sequentially correcting the importance ratio, and multiplying the corrected importance ratio of the index arranged below by the importance ratio of the last index adjacent to the lower index to obtain the corrected importance ratio of the last index. Dividing the index correction importance ratio by the sum of the functional correction values to obtain the index weights. The method is suitable for the situation that the evaluation objects have obvious comparability, can be directly compared and can accurately evaluate the functional importance ratio. Inputting parameters: 1. an index system; 2. the ratio of importance levels between peer indices. Output parameters: weights of the respective indexes.
(4) The entropy weight method is an objective weight giving method and is used for carrying out thief weight according to the provided actual data. Entropy is the amount of uncertainty in the information theory that measures one system. The entropy weight method is mainly used for determining the index weight by utilizing the entropy value of the index according to the information quantity contained in each index value. Inputting parameters: 1. an index system; 2. the values of the respective indices. Output parameters: weights of the respective indexes.
(5) The dispersion maximization method is an objective weighting method and is used for objectively evaluating the weight of an index according to the difference of the change of the judgment index value to the values of all schemes. Inputting parameters: 1. an index system; 2. the values of the respective indices. Output parameters: weights of the respective indexes.
In some embodiments of the present application, a user may trigger to display an evaluation scheme interface through an operation on a target evaluation scheme identifier, and may trigger an evaluation algorithm for setting a layer-by-layer aggregation calculation process of multiple layers of evaluation indexes from a bottom layer evaluation index to a top layer evaluation index through an operation on the evaluation scheme interface. In the polymerization process, specific functional requirements are as follows:
(1) The evaluation scheme comprises a support vector machine method, a neural network method, an ideal point method, a weighted arithmetic average method, a weighted geometric average method, a Delphi method, a gray correlation degree method, an SEA method, an ADC method, a PAU method, an ARINC method, an AHP method, a TOPSIS method, a principal component analysis method, a factor analysis method, a gray whitening weight function clustering method, a fuzzy comprehensive analysis method, a self-defining method and the like; (2) Supporting user-defined evaluation algorithms corresponding to the evaluation indexes of each layer; (3) supporting selection or setting of an evaluation algorithm; (4) The evaluation algorithm can be set by other nodes except the leaf node which cannot be set; (5) The upward aggregation of the indexes of each layer can be completed according to the configured comprehensive index evaluation algorithm.
Illustratively, the main evaluation algorithm is introduced as follows:
(1) The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. According to the comprehensive evaluation method, qualitative evaluation is converted into quantitative evaluation according to membership theory of fuzzy mathematics, namely the problem of qualitative is solved by using the fuzzy mathematics. The main idea of the modular comprehensive evaluation method is that a group of evaluation (evaluation grade) sets such as excellent, good, medium, general, poor and the like are defined firstly, then evaluation matrixes of all evaluation indexes are obtained through scoring by a plurality of experts, then the evaluation values of all indexes are converted into membership degrees and membership degree weights by using a set membership function, finally a corresponding membership degree weight matrix is generated, finally an index weight vector is introduced, and a specific evaluation result is finally obtained through fuzzy change operation. The input of the fuzzy comprehensive evaluation method is an index weight matrix, a scoring short matrix, the number of grades and the membership degree of each target on each evaluation grade. Inputting parameters: 1. a rating list; 2. a list of evaluation targets; 3. the expert evaluates all the bottom evaluation indexes; 4. weights of the respective indexes. Output parameters: membership of all targets with respect to each rating level.
(2) The Support Vector Machine (SVM) is a novel machine learning method developed on the basis of statistical theory. The support vector machine method adopts a structural risk minimization criterion (SRM) to train a learning machine, is established on the basis of a strict theory, well solves the problems of nonlinearity, high dimensionality, local minimum points and the like, and is a new research hotspot in the machine learning field after a neural network. The basic idea is: an optimal classification hyperplane meeting classification requirements is searched, so that the blank areas on two sides of the hyperplane can be maximized while the classification accuracy is ensured, and optimal classification of the linear separable data is realized. Vector machine type, SW. Kernel function type Radial Basis Function (RBF). Inputting parameters 1 and an index system; 2. training samples, namely a bottom layer evaluation index value and a next layer index predicted value. And outputting parameters, namely a neural network training result, namely an aggregation algorithm from the bottom evaluation index to the next layer of index.
(3) In the neural network approach, an Artificial Neural Network (ANN) is based on brain processing mechanisms to develop algorithms for creating hetero-patterns and predicting problems. BP (Back Fropagation) algorithm is also called error back propagation algorithm, and is a supervised learning algorithm in artificial neural network. The BP neural network algorithm can approach any function in theory, and the basic structure consists of nonlinear change units, so that the BP neural network algorithm has strong nonlinear mapping capability. The parameters such as the number of intermediate layers of the network, the number of processing units of each layer and the learning coefficient of the network can be set according to specific conditions, the flexibility is high, and the method has wide application prospects in various fields such as optimization, signal processing and pattern recognition, intelligent control, fault diagnosis and the like. Neural networks are an algorithmic model that simulates the structure of the human brain. The principle is to store information in a distributed manner and to co-process the information in parallel. Although the function of each unit is very simple, a network system formed by a large-scale sheet can realize very complex data calculation, and is also a highly complex nonlinear power learning system. Inputting parameters: 1. an index system; 2. training samples: the bottom layer evaluation index value and the next layer index value are predicted. Output parameters: neural network training results: and (3) an aggregation algorithm from the bottom layer evaluation index to the next layer index.
(4) Weighted arithmetic averaging, which is the basic method of comprehensive performance assessment. A trend prediction method for predicting the predicted value of the variable in the future period uses the number of past observations of the same variable arranged in time sequence and weighted arithmetic mean of the observations as weight. The arithmetic mean prediction method assumes that the actual observations of the previous periods have an equal effect on the future predictions. However, in real economic activities, the influence of the observed values of different periods of the previous periods on the future predicted object is different, i.e. the observed values of some periods have a large scenery on the future predicted object, while the observed values of other periods have a small influence on the future predicted value. To account for this difference in effect, we give the difference. Observations of epochs are weighted differently. For large impact, the weight given is large, whereas the weight given is small. The weighted arithmetic average method is that in the I observation data, each observation value gives different weights according to different degrees of influence on future predicted values, the observation value in each period is multiplied by own weight, and then the sum of the observation values is divided by the sum of the weights, so that the obtained quotient is the future predicted value. Inputting parameters: 1. multi-index data; 2. weight coefficient. Output parameters: performance evaluation value of the upper level index.
Wherein the weight scheme identifies a weight algorithm for indicating each layer of evaluation indexes except the bottom layer of evaluation indexes, namely the weight scheme identifies a weight algorithm for setting each index subtree, and each index subtree comprises a father node (evaluation index) and a plurality of child nodes (evaluation indexes). The evaluation scheme identification is used for indicating the evaluation algorithm of each layer of evaluation indexes except for the bottom layer of evaluation indexes, namely the evaluation scheme identification is used for setting the evaluation algorithm corresponding to each index subtree.
In some embodiments of the present application, a user may trigger to display an evaluation template interface by performing an operation on a target evaluation template identifier, where the evaluation template interface includes a selection evaluation data type identifier, a data source and bottom evaluation index mapping identifier, and a data preprocessing identifier. The user can trigger setting the type of the evaluation data through the operation of selecting the evaluation data type identifier of the evaluation template interface. The user can trigger and set the mapping relation between each data source in the evaluation data corresponding to the evaluation engineering and one evaluation index in the bottom evaluation index through the operation of mapping identification of the data source of the evaluation template interface and the bottom evaluation index. Wherein, the data source corresponds to the evaluation index one by one. The user can trigger and set the data preprocessing algorithm corresponding to each data source through the operation of the data preprocessing identification.
Among them, data preprocessing algorithms include, but are not limited to: variance, normalization, maximum and minimum values. The data preprocessing algorithm is used for preprocessing the data source corresponding to each evaluation index in the bottom evaluation indexes.
Setting the type of evaluation data, including supporting the list head in a CSV file and a MySQL file, uploading the file and analyzing the file by a system as the field name of a data source; after the database connection is tested, the fields in the data table are screened according to the service requirement to conduct data import by configuring database parameters including a database address, a user name, a password and a database name. The system provides the function of mapping the data source and the bottom evaluation index, selects the field names in the data source, corresponds to the index fields in the constructed index system tree, and is used for data editing preprocessing calculation. Data preprocessing, which supports the selection of simulation data and the data selection function of a plurality of data tables; the data screening is supported, the screening of basic data is supported, and the data retrieval conditions and relationships can be configured, so that a required data set is extracted, and the screening can be performed through SQL sentences in a database; supporting preview of simulation data; supporting editing pretreatment of data, realizing custom data pretreatment between basic data and bottom evaluation indexes (bottom evaluation index configuration, namely, realizing association between the data and the bottom evaluation indexes); aiming at the evaluation operator, the copy, paste and drag of the operator are supported in the data editing preprocessing process; supporting one-key default addition between simulation data and a bottom evaluation index; and the preservation and cancellation of the data source processing result are supported.
In some embodiments of the present application, a user may trigger the importation of target assessment data (importation of multiple data sources) by selecting an assessment data path through manipulation of target assessment instance identification.
Illustratively, as shown in fig. 2, the left side of the target evaluation interface is an identification column, which includes a target index system identifier, a target weight scheme identifier, a target evaluation template identifier, and a target evaluation instance identifier, where the identification column currently selects the target index system identifier, and the right side of the target evaluation interface displays a target index system tree indicated by the target index system identifier. The target index system tree comprises 6 index subtrees, which are respectively: taking XX combat efficacy as a father node, and taking a first-level index 1, a first-level index 2 and a first-level index 3 as index subtrees of child nodes; taking the first-level index 1 as a father node, and taking the second-level index 1 and the second-level index 2 as index subtrees of child nodes; taking the first-level index 2 as a father node, and taking the second-level index 3 and the second-level index 4 as index subtrees of child nodes; taking the first-level index 3 as a father node, and taking the second-level index 5 and the second-level index 6 as index subtrees of child nodes; taking the second-level index 5 as a father node, and taking the third-level index 1 and the third-level index 2 as index subtrees of child nodes; an index subtree with the secondary index 6 as a parent node, the tertiary index 3 and the tertiary index 4 as child nodes. Child nodes may be added by operations on any one node of the target metric hierarchy tree.
Illustratively, in connection with FIG. 2, as shown in FIG. 3, when the target weight scheme identifier is currently selected by the identifier column, a corresponding weight scheme interface may be displayed by an operation on any one node of the target metric system tree. If the operation on the first-level index 1 is performed currently, a weight scheme control is popped up, the weight scheme control is clicked, the first-level index 1 shown in fig. 4 is displayed as a father node, the second-level index 1 and the second-level index 2 are displayed as weight schemes corresponding to index subtrees of child nodes, and a weight algorithm is an expert scoring method.
Illustratively, in connection with FIG. 2, as shown in FIG. 5, when the target evaluation scheme identifier is currently selected by the identifier column, a corresponding evaluation scheme interface may be displayed by an operation on any one node of the target index system tree. If the operation on the primary index 1 is performed currently, the evaluation scheme control is popped up, the evaluation scheme control is clicked, the primary index 1 shown in fig. 6 is displayed as a father node, the secondary indexes 1 and 2 are displayed as evaluation schemes corresponding to index subtrees of child nodes, and the evaluation algorithm is a weighted arithmetic average method.
For example, in conjunction with fig. 2, when the target evaluation template identifier is currently selected by the identifier bar, as shown in fig. 7, an evaluation template interface corresponding to the target evaluation template identifier is displayed on the right side of the target evaluation interface, where the evaluation template interface includes a "type of evaluation data" control, a "mapping relationship" control and a "data preprocessing algorithm" control, and as shown in fig. 7, the "mapping relationship" control is currently selected, and the evaluation template interface displays a mapping relationship between each data source in the evaluation data corresponding to the evaluation engineering and one evaluation index in the bottom evaluation index.
103. And responding to a second operation of the user on the target evaluation instance identifier, and completing calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize performance evaluation of the target evaluation engineering and obtain a target performance evaluation result.
In some embodiments of the present application, before performance evaluation, the method further includes checking a data preprocessing algorithm, a weighting algorithm, an evaluation algorithm, etc. of each layer of indexes between each data source and the bottom layer evaluation index in the target evaluation data, and giving a prompt of the detection result. If the examination is correct, evaluation calculation can be performed, and if the examination has a problem, the problem is pointed out, and the evaluation calculation cannot be performed.
And completing calculation of each evaluation index according to a data preprocessing algorithm among the configured bottom evaluation indexes, a weight algorithm of each layer of index and an evaluation algorithm, and storing all intermediate data in the calculation process to obtain a target efficiency evaluation result.
In the embodiment of the application, through setting a visual evaluation interface, a user can set an evaluation index system according to requirements, set a weight algorithm and an evaluation algorithm corresponding to each layer of evaluation indexes, set an evaluation template, set a corresponding evaluation example based on evaluation data and an evaluation template needing performance evaluation, and then trigger to complete calculation of each evaluation index of the target evaluation example through operation of the evaluation example so as to realize performance evaluation of the target evaluation project and obtain a target performance evaluation result. Therefore, through the visual evaluation interface, a user can evaluate the efficiency of the evaluation engineering which needs to be evaluated according to the requirements, no professional knowledge is needed, no special efficiency evaluation program is needed, and according to the requirements, the user can evaluate the efficiency of various types of weapons through the visual evaluation interface, and the extensible general efficiency evaluation analysis method for the multi-field weapons is provided, so that the efficiency of efficiency evaluation can be improved.
In some embodiments of the present application, after the step 103, the performance evaluation method provided in the embodiment of the present application may further include the following step 104.
104. Displaying the target performance evaluation result, wherein the target performance evaluation result comprises: each layer of evaluation indexes in the multi-layer evaluation indexes corresponds to an evaluation result of each evaluation index, and weight information corresponding to each layer of evaluation indexes.
In some embodiments of the present application, weight information corresponding to at least one layer of evaluation indexes in the multi-layer evaluation indexes may be displayed by default (for example, weight information corresponding to a top layer of evaluation indexes and weight information corresponding to a second layer of evaluation indexes are displayed by default), weight information corresponding to the other layers of evaluation indexes is hidden in a tree form, and weight information corresponding to a part of the hidden and displayed layers of evaluation indexes may be triggered and displayed by operation. Therefore, the display mode of the weight information corresponding to the evaluation indexes of each level is increased, and the user experience is improved.
In some embodiments of the present application, the index value and the weight value of each child node (evaluation index) under the current parent node (evaluation index) may be displayed through a visual graph (such as a column, a broken line, a pie chart, etc.). Therefore, the corresponding index value and the weight value are displayed in a graphical mode, and the user can understand the index value and the weight value conveniently.
In some embodiments of the application, the target assessment interface further comprises at least one first assessment instance identifier, each first assessment instance identifier being for indicating an assessment instance created for the target assessment project based on the target assessment template and a first assessment data; the above step 103 may be specifically implemented by the following step 103 a.
103a, responding to a second operation of the user on the target evaluation instance identifier and the at least one first evaluation instance identifier, and respectively completing calculation of each evaluation index of each evaluation instance indicated by the target evaluation instance identifier and each evaluation instance indicated by the at least one first evaluation instance identifier based on information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize efficiency evaluation of the target evaluation project and obtain a target efficiency evaluation result.
Wherein, the target performance evaluation result includes: the target evaluation example identifier indicates an evaluation example and each layer of evaluation indexes in the multi-layer evaluation indexes corresponding to each evaluation example in each evaluation example indicated by the at least one first evaluation example identifier, each evaluation index corresponds to an evaluation result, and weight information corresponding to each layer of evaluation indexes.
In the embodiment of the application, a plurality of evaluation examples are established for different evaluation data under the support of the same index system tree, the same weight scheme, the same evaluation scheme and the same evaluation template, and meanwhile, the evaluation calculation supporting the plurality of evaluation examples is realized in a multi-thread mode, so that the calculation efficiency is fully improved, and a progress bar prompt is given in the calculation process. Therefore, the method can meet the requirement of simultaneously evaluating equipment of various types, simultaneously evaluate test data for multiple times, realize parallel calculation and improve the processing efficiency.
In some embodiments of the present application, after the step 104, the performance evaluation method provided in the embodiment of the present application may further include the following steps 105 and 106.
105. An evaluation report is generated in response to a third operation on the target performance evaluation result.
Wherein the third operation includes setting the evaluation instance indicated by the target evaluation instance identification as a reference evaluation instance.
Wherein the evaluation report includes the target performance evaluation result and an evaluation result increment of the evaluation instance indicated by each of the first evaluation instance identifications relative to the reference evaluation instance.
In the embodiment of the application, the selection of the comparison example is supported aiming at the efficiency evaluation results of a plurality of evaluation examples; for the selected comparison example, the user can set or select the efficacy evaluation result of a certain example as a reference value; and supporting the display of the evaluation results of different examples.
Illustratively, the evaluation index parameter, index evaluation result, index weight information (the top layer and the second layer are displayed by default, and the rest of the layers are hidden in a tree form) and the evaluation structure increment of the rest of the examples relative to the reference examples of each evaluation example are output and displayed; the evaluation result increment of example a= (evaluation result of example a-reference value)/reference value x 100%.
The system automatically generates an evaluation report, which comprises an evaluation index system tree, index calculation results, weight information and the like, and the evaluation result is compared and analyzed, so that a word report can be derived.
In the embodiment of the application, the evaluation reports of the performance evaluation of a plurality of evaluation embodiments can be generated, the target performance evaluation result in the evaluation report, and the evaluation result increment of the evaluation example indicated by each first evaluation example identifier relative to the reference evaluation example. In this way, a user is facilitated to conduct a comparative analysis of performance evaluations of the multiple evaluation embodiments.
As shown in fig. 8, the embodiment of the present application provides a visual performance evaluation system, which mainly includes several key steps of an evaluation engineering management module (corresponding to an evaluation engineering management interface), an index system management module (corresponding to an index system interface), a weight and evaluation scheme setting module (corresponding to a weight scheme interface and an evaluation scheme interface), an evaluation template management module (corresponding to an evaluation template interface), an evaluation instance management module (corresponding to an evaluation instance interface), an evaluation calculation module, an evaluation result visualization module, a report generation module, and an algorithm library tool module,
The evaluation engineering management module supports that the current evaluation engineering can be created, edited and stored aiming at equipment in different fields, the stored historical evaluation engineering is inquired, and the equipment can be opened again. The index system management module can provide a visual user interface, is convenient to quickly create an index system tree, and has the functions of creating a new index system tree, deleting an index system tree and editing an index system tree (the functions of renaming an index system tree, modifying an evaluation index node, deleting an evaluation index node, adding an evaluation index node and the like). The weight and evaluation scheme setting module can set a weight algorithm and an evaluation algorithm required by each index evaluation in the index system tree independently or uniformly; the method comprises a weight scheme module and an evaluation scheme module, wherein the weight scheme module has a weight scheme adding function, a weight scheme deleting function and a weight scheme editing function (weight scheme renaming, weight algorithm modifying evaluation index nodes and the like), the evaluation scheme module has an evaluation scheme adding function, an evaluation scheme deleting function and an evaluation scheme editing function (evaluation scheme renaming, evaluation algorithm modifying evaluation index nodes and the like), and the setting of the evaluation algorithm comprises the selection of an aggregation model algorithm and an evaluation model algorithm. The evaluation template management module comprises a type setting module for evaluating data, a data source and bottom evaluation index mapping module and a data preprocessing module. The evaluation template management module can select evaluation data from a file or a database, set the mapping relation between a data source and a bottom index, and support the setting of various data preprocessing algorithms such as normalization, variance solving and the like for the data source. The evaluation template management module also has the functions of creating an evaluation template, deleting the evaluation template and editing the evaluation template. The evaluation example management module comprises functions of creating an evaluation example, editing the evaluation example and deleting the evaluation example, and can be used for expanding a support interface mode to access simulation data. The evaluation calculation module firstly checks an evaluation index system, a weight algorithm, an evaluation algorithm, a data source and a data preprocessing flow, and after the checking is completed, efficiency evaluation analysis is realized through parallel calculation; the evaluation result visualization module presents the performance evaluation calculation result in forms, graphs and the like; the report generation module may automatically generate a performance assessment analysis report for the equipment.
In some embodiments of the present application, the performance evaluation system may further include an evaluation task management module, where the evaluation task management module mainly functions as a task for associating the index system tree, the weight scheme, and the evaluation scheme, and creates a corresponding evaluation template and an evaluation instance based on the evaluation task. The assessment task management module has a delete assessment task function, edit assessment task function (rename, modify at least one of an associated index system tree, weight scheme, and assessment scheme function) and add assessment template function.
Illustratively, in connection with fig. 8, as shown in fig. 9, the overall flow of creating an evaluation project, performing evaluation calculation based on the created evaluation project, and exhibiting the evaluation result includes: creating an evaluation project, constructing an index system tree, setting a weight algorithm and an evaluation algorithm for the index system tree based on an algorithm library, creating an evaluation task based on the index system tree, an evaluation scheme and the weight scheme, adding an evaluation template and an evaluation example for the evaluation task, performing evaluation operation based on the evaluation example, and displaying an evaluation result (data visualization) and outputting an evaluation report (data output). The evaluation tasks are tasks of the associated index system tree, the weight scheme and the evaluation scheme, and an evaluation template and an evaluation instance corresponding to the evaluation tasks.
In the embodiment of the application, an algorithm library is preset in the efficiency evaluation system, and comprises an evaluation algorithm library, a weight algorithm library, a data preprocessing algorithm library and the like. The method can realize independent use of the evaluation algorithms, each evaluation algorithm has a detailed use guide, is internally provided with the evaluation algorithm commonly used for evaluation, and supports expansion of the user-defined algorithm. The evaluation algorithm library comprises 70 evaluation algorithms such as TOPSIS, ADC, principal component analysis, factor analysis, gray whitening weight function clustering, fuzzy synthesis, SEA, support vector machine, neural network, delphi and PAU. The evaluation algorithm can be used in an index calculation flow, and a specific index analysis evaluation method is supported to be defined in a flow modeling mode. The weight algorithm library comprises an AHP, a fuzzy analytic hierarchy process, a cyclic ratio coefficient process, an expert scoring process, an entropy weight process, a maximum dispersion process and the like. Meanwhile, the system provides an expansion mechanism of a custom analysis and evaluation algorithm, supports expansion modes such as formulas, scripts, DLLs and the like, and the supported script languages comprise JavaScript, R, mathScript, python and the like; the main evaluation analysis method provides an algorithm guide function, assists personnel to familiarize with the method, provides convenience for various methods, and can be independently used from an index calculation flow.
In the embodiment of the application, extensible efficacy evaluation of weaponry is supported, and multi-level index system tree creation is provided; providing a performance evaluation implementation method and tool with complete functions; the mainstream weight scheme setting algorithm and the evaluation algorithm are supported, and the evaluation algorithm can be customized. According to the technical scheme, an evaluation index system is dynamically constructed through a visual evaluation interface, a weight and an evaluation scheme are set, real-time evaluation data or offline evaluation data are accessed, the efficiency of the weapon equipment is evaluated, and finally an evaluation result is displayed in a data table and graph mode to generate an evaluation report.
The present application also provides a performance evaluation device, fig. 10 is a schematic structural diagram of the performance evaluation device provided by the present application, as shown in fig. 10, the performance evaluation device includes:
a receiving module 601, configured to receive a first operation of a user, where the first operation is used to create a target evaluation project; the display module 602 is configured to respond to the first operation, and display a target evaluation interface corresponding to the target evaluation project, where the target evaluation interface includes a target index system identifier, a target weight scheme identifier, a target evaluation template identifier, and a target evaluation instance identifier; the target index system identifier is used for indicating a target index system tree corresponding to the target evaluation project, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation project, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identification is used for indicating an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data; the evaluation module 603 is configured to respond to a second operation of the user on the target evaluation instance identifier, and complete calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier, and the target evaluation template identifier, so as to implement performance evaluation of the target evaluation project, and obtain a target performance evaluation result.
In some embodiments of the present application, the evaluation project management interface provides a new evaluation project function for a user, where the first operation includes an operation of the user on the evaluation project management interface for identifying a new target evaluation project, an operation of editing a target index system tree indicated by the target index system identification, an operation of editing each weight algorithm indicated by the target weight scheme identification, an operation of editing each evaluation algorithm indicated by the target evaluation scheme identification, an operation of editing a target evaluation template indicated by the target evaluation template identification, and an operation of editing target evaluation data indicated by the target evaluation instance identification; the assessment engineering management interface also provides at least one of the following functions for the user: querying the evaluation engineering function, loading the evaluation engineering function, modifying the evaluation engineering function, deleting the evaluation engineering function, importing the evaluation engineering function, and exporting the evaluation engineering function.
In some embodiments of the present application, the display module 602 is further configured to display, before receiving the first operation of the user, a first evaluation interface corresponding to a first evaluation project, where the first evaluation interface includes the target index system identifier, the target weight scheme identifier, the target evaluation template identifier, and the target evaluation instance identifier; the first operation includes an operation for copying the first evaluation project to generate the target evaluation project.
In some embodiments of the present application, the target index system tree is a preset index system tree, or the target index system tree is a user-defined index system tree, or the target index system tree is generated based on at least one of the following user operations on the basis of the preset index system tree: adding index nodes, editing index nodes and deleting index nodes; the tree structure of the target index system tree is constructed in a graphical mode.
In some embodiments of the present application, the display module 602 is further configured to, in response to a second operation of the target evaluation instance identifier by the user, complete calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier, and the target evaluation template identifier, so as to implement performance evaluation of the target evaluation project, and display a target performance evaluation result after obtaining the target performance evaluation result, where the target performance evaluation result includes: each layer of evaluation indexes in the multi-layer evaluation indexes corresponds to an evaluation result of each evaluation index, and weight information corresponding to each layer of evaluation indexes.
In some embodiments of the application, the target assessment interface further comprises at least one first assessment instance identifier, each first assessment instance identifier being for indicating an assessment instance created for the target assessment project based on the target assessment template and a first assessment data; the evaluation module 603 is specifically configured to, in response to a second operation of the user on the target evaluation instance identifier and the at least one first evaluation instance identifier, respectively complete calculation of each evaluation index of each evaluation instance indicated by the target evaluation instance identifier and each evaluation instance indicated by the at least one first evaluation instance identifier based on information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier, and the target evaluation template identifier, so as to implement performance evaluation of the target evaluation project, and obtain a target performance evaluation result; the target performance evaluation result includes: the target evaluation example identifier indicates an evaluation example and each layer of evaluation indexes in the multi-layer evaluation indexes corresponding to each evaluation example in each evaluation example indicated by the at least one first evaluation example identifier, each evaluation index corresponds to an evaluation result, and weight information corresponding to each layer of evaluation indexes.
In some embodiments of the present application, the performance evaluation apparatus further comprises: the generating module is used for responding to a third operation on the target efficiency evaluation result after the target efficiency evaluation result is displayed, and the third operation comprises setting an evaluation example indicated by the target evaluation example identifier as a reference evaluation example; wherein the evaluation report includes the target performance evaluation result and an evaluation result increment of the evaluation instance indicated by each of the first evaluation instance identifications relative to the reference evaluation instance.
It should be noted that the performance evaluation device may be an electronic device in the foregoing method embodiment of the present application, or may be a functional module and/or a functional entity in the electronic device that can implement a function of the device embodiment, which is not limited by the embodiment of the present application.
In the embodiment of the present application, each module may implement the performance evaluation method provided by the above method embodiment, and may achieve the same technical effect, so that repetition is avoided and redundant description is omitted.
As shown in fig. 11, the embodiment of the present application further provides an electronic device 700, where the electronic device 700 may be the electronic device or the server. The electronic device 700 includes: the processor 701, the memory 702, and the computer program stored in the memory 702 and capable of running on the processor 701, when executed by the processor 701, implement the respective processes executed by the performance evaluation method described above, and achieve the same technical effects, and are not repeated here.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements each process executed by the performance evaluation method, and can achieve the same technical effect, so that repetition is avoided, and no further description is provided herein.
The computer readable storage medium may be a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or the like.
The present application provides a computer program product comprising: the computer program product, when run on a computer, causes the computer to implement the performance assessment method described above.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
The foregoing description, for purposes of explanation, has been presented in conjunction with specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the embodiments to the precise forms disclosed above. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles and the practical application, to thereby enable others skilled in the art to best utilize the embodiments and various embodiments with various modifications as are suited to the particular use contemplated.
Claims (10)
1. A method of efficacy assessment, the method comprising:
receiving a first operation of a user, wherein the first operation is used for creating a target evaluation project;
responding to the first operation, displaying a target evaluation interface corresponding to a target evaluation project, wherein the target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identification is used for indicating a target index system tree corresponding to the target evaluation engineering, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation engineering, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identifier is used for indicating an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data;
And responding to a second operation of the user on the target evaluation instance identifier, and completing calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize performance evaluation of the target evaluation engineering and obtain a target performance evaluation result.
2. The method of claim 1, wherein the assessment engineering management interface provides a user with new assessment engineering functions, and wherein the first operation includes an operation of a user on the assessment engineering management interface for new objective assessment engineering identifications, an operation of editing an objective index system tree indicated by the objective index system identifications, an operation of editing each weighting algorithm indicated by the objective weighting scheme identifications, an operation of editing each assessment algorithm indicated by the objective assessment scheme identifications, an operation of editing an objective assessment template indicated by the objective assessment template identifications, and an operation of editing objective assessment data indicated by the objective assessment instance identifications;
the assessment engineering management interface also provides the user with at least one of the following functions: querying the evaluation engineering function, loading the evaluation engineering function, modifying the evaluation engineering function, deleting the evaluation engineering function, importing the evaluation engineering function, and exporting the evaluation engineering function.
3. The method of claim 1, wherein prior to the receiving the first operation by the user, the method further comprises:
displaying a first evaluation interface corresponding to a first evaluation project, wherein the first evaluation interface comprises the target index system identifier, the target weight scheme identifier, the target evaluation template identifier and the target evaluation instance identifier;
the first operation includes an operation for copying the first evaluation project to generate the target evaluation project.
4. The method of claim 1, wherein the target metric system tree is a preset metric system tree, or the target metric system tree is a user-defined metric system tree, or the target metric system tree is generated based on at least one of the following user operations on the basis of the preset metric system tree: adding index nodes, editing index nodes and deleting index nodes;
the tree structure of the target index system tree is constructed in a graphical mode.
5. The method according to any one of claims 1 to 4, wherein in response to the second operation of the target evaluation instance identifier by the user, based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier, and the target evaluation template identifier, the calculation of each evaluation index of the target evaluation instance is completed, so as to implement the performance evaluation of the target evaluation project, and after obtaining a target performance evaluation result, the method further includes:
Displaying the target performance evaluation result, wherein the target performance evaluation result comprises: each layer of evaluation indexes in the multi-layer evaluation indexes is provided with an evaluation result corresponding to each evaluation index, and weight information corresponding to each layer of evaluation indexes is provided.
6. The method of claim 5, wherein the target assessment interface further comprises at least one first assessment instance identifier, each first assessment instance identifier being for indicating an assessment instance created for the target assessment project based on the target assessment template and one first assessment data;
the responding to the second operation of the user on the target evaluation instance identifier, completing the calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier, so as to realize the performance evaluation of the target evaluation engineering and obtain a target performance evaluation result, including:
responding to the second operation of the user on the target evaluation instance identifier and the at least one first evaluation instance identifier, and respectively completing calculation of each evaluation index of each evaluation instance indicated by the target evaluation instance identifier and each evaluation instance indicated by the at least one first evaluation instance identifier based on information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize efficiency evaluation of the target evaluation project and obtain a target efficiency evaluation result;
The target efficacy evaluation result includes: the target evaluation example identifier indicates an evaluation example and the at least one first evaluation example identifier indicates each layer of evaluation indexes in the multi-layer evaluation indexes corresponding to each evaluation example, each evaluation index corresponds to an evaluation result, and the weight information corresponding to each layer of evaluation indexes.
7. The method of claim 6, wherein after displaying the target performance assessment result, the method further comprises:
generating an evaluation report in response to a third operation on the target performance evaluation result, wherein the third operation comprises setting an evaluation instance indicated by the target evaluation instance identification as a reference evaluation instance;
wherein the assessment report includes the target efficacy assessment results, and the first assessment instance identifies an indicated assessment instance as an assessment result increment relative to the baseline assessment instance.
8. An efficacy assessment device, comprising:
the receiving module is used for receiving a first operation of a user, wherein the first operation is used for creating a target evaluation project;
the display module is used for responding to the first operation and displaying a target evaluation interface corresponding to the target evaluation project, wherein the target evaluation interface comprises a target index system identifier, a target weight scheme identifier, a target evaluation template identifier and a target evaluation instance identifier; the target index system identification is used for indicating a target index system tree corresponding to the target evaluation engineering, the target index system tree is used for indicating multiple layers of evaluation indexes corresponding to the target evaluation engineering, and each layer of evaluation indexes comprises multiple evaluation indexes; the target weight scheme identifies a weight algorithm for indicating a layer-by-layer aggregate calculation process of the multi-layer assessment index from a bottom-layer assessment index to a top-layer assessment index; the target evaluation scheme identifies an evaluation algorithm for indicating a layer-by-layer aggregate calculation process from the bottom-layer evaluation index to the top-layer evaluation index; the target evaluation template identifier is used for indicating a target evaluation template, and the target evaluation template comprises the type of evaluation data corresponding to the target evaluation project, the mapping relation between each data source in the evaluation data corresponding to the evaluation project and one evaluation index in the bottom evaluation index, and a data preprocessing algorithm corresponding to each data source; the target evaluation instance identifier is used for indicating an evaluation instance created for the target evaluation project based on the target evaluation template and target evaluation data;
The evaluation module is used for responding to the second operation of the user on the target evaluation instance identifier, completing the calculation of each evaluation index of the target evaluation instance based on the information corresponding to the target index system identifier, the target weight scheme identifier, the target evaluation scheme identifier and the target evaluation template identifier so as to realize the efficiency evaluation of the target evaluation engineering and obtain a target efficiency evaluation result.
9. An electronic device, comprising: a processor for executing a computer program stored in a memory, which when executed by the processor implements the steps of the performance assessment method of any one of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the performance evaluation method according to any one of claims 1-7.
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