Data processing method and device for simulation evaluation

Abstract

The invention discloses a data processing method and device for simulation evaluation. The method comprises the following steps: acquiring simulation result information and evaluation weight information; analyzing and processing the evaluation weight information to obtain target weight information; the target weight information comprises M target weight values; and analyzing and processing the target weight information and the simulation result information to obtain evaluation result information.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular, to a data processing method and device for simulation evaluation. Background Art

[0002] With the rapid development of technology, simulation technology plays a crucial role in many fields such as aerospace, automotive manufacturing, electronic engineering, biomedicine, etc. By constructing virtual models and environments to simulate various systems, devices or processes, it is possible to evaluate and optimize key indicators such as performance, reliability, and safety without investing a large amount of actual resources, thereby reducing R & D costs, shortening the development cycle, and improving product quality. In the field of military applications, simulation technology is becoming an important support for national defense modernization. In military training, multi-service coordinated drills can be realized through virtual battlefield environments to enhance the actual combat capabilities of troops; in the scenario of system confrontation, multi-dimensional confrontation models of sea, land, air, space, electricity, and network can be constructed to verify the effectiveness of new combat concepts and equipment; in combat simulations, strategic and tactical decisions can be assisted through red-blue confrontation deductions, war game deductions, etc. These applications all put forward higher requirements for the real-time performance, dynamic response accuracy, and complex scene restoration degree of simulation evaluation. Especially in the context of information-based warfare, the battlefield situation changes rapidly, and simulation systems need to have the capabilities of multi-source heterogeneous data fusion, large-scale parallel computing, and intelligent decision-making evaluation. In the current simulation evaluation process, how to ensure the objectivity, accuracy, and timeliness of simulation evaluation is one of the research hotspots. Therefore, a data processing method and device for simulation evaluation are provided to improve the performance and efficiency of simulation evaluation. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data processing method and device for simulation evaluation, which are beneficial to improving the performance and efficiency of simulation evaluation.

[0004] To solve the above technical problem, in the first aspect of the embodiments of the present invention, a data processing method for simulation evaluation is disclosed, and the method includes:

[0005] Obtain simulation result information and evaluation weight information;

[0006] Analyze and process the evaluation weight information to obtain target weight information; the target weight information includes M target weight values;

[0007] Analyze and process the target weight information and the simulation result information to obtain evaluation result information.

[0008] In the second aspect of the embodiments of the present invention, a data processing device for simulation evaluation is disclosed, and the device includes:

[0009] An acquisition module, configured to obtain simulation result information and evaluation weight information;

[0010] The first processing module is configured to analyze and process the evaluation weight information to obtain target weight information; the target weight information includes M target weight values.

[0011] The second processing module is configured to analyze and process the target weight information and the simulation result information to obtain evaluation result information.

[0012] A third aspect of the present invention discloses another data processing device for simulation evaluation, the device includes:

[0013] A memory storing executable program code;

[0014] A processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory and executes some or all of the steps in the data processing method for simulation evaluation disclosed in the first aspect of the embodiments of the present invention.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute some or all of the steps in the data processing method for simulation evaluation disclosed in the first aspect of the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 is a schematic diagram of the scenario of the data processing system for simulation evaluation provided by the embodiments of the present invention;

[0019] Figure 2 is a flowchart of a data processing method for simulation evaluation disclosed by the embodiments of the present invention;

[0020] Figure 3 is a schematic structural diagram of a data processing device for simulation evaluation disclosed by the embodiments of the present invention;

[0021] Figure 4 is a schematic structural diagram of another data processing device for simulation evaluation disclosed by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0023] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0024] Referring to "embodiments" herein means that a specific feature, structure or characteristic described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0025] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or description". Any embodiment described as "exemplary" in this application is not necessarily to be construed as more preferred or more advantageous than other embodiments. In order for any person skilled in the art to implement and use this application, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that this application can be implemented without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessary details from obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in this application.

[0026] It should be noted that since the method of the embodiments of this application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time, which is actually time information. It can be understood that in subsequent embodiments, if dimensions, quantities, positions, etc. are mentioned, they are all corresponding data existences for the computer device to process, and specific details will not be elaborated here.

[0027] It should be noted that a brief introduction to the artificial intelligence-related technologies that may be involved in this application is provided. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines, enabling the machines to have the functions of perception, reasoning, and decision-making.

[0028] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0029] Computer Vision Technology (CV): Computer vision is a science that studies how to enable machines to "see". More specifically, it refers to using cameras and computers to replace human eyes for tasks such as object recognition and measurement in machine vision, and further performing graphic processing to make the images processed by the computer more suitable for human eyes to observe or be transmitted to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies and attempts to build artificial intelligence systems that can obtain information from images or multi-dimensional data. Computer vision technology usually includes technologies such as image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and also includes common biometric recognition technologies such as face recognition and fingerprint recognition.

[0030] Single-modal information is data of only one type, such as one of the data information types like text, image, audio, video, electromagnetic signals, etc. Multi-modal information is data information that includes at least two types of single-modal information. Further, multi-modal information is applicable to complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information of multiple modalities, higher performance and accuracy can usually be achieved in tasks.

[0031] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model usually refers to a model with hundreds of millions to trillions of parameters. The model usually needs to be trained on a large-scale dataset and requires a large amount of computing resources for optimization and adjustment. Large models are usually used to solve complex natural language processing, computer vision, speech recognition and other tasks. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos and music. In the embodiments of the present application, the large model can be large-scale pre-trained models such as ChatGPT series, BERT, XLNet, Zhipu Model, Claude, Moonshot AI Model, ChatGLM Model, Tongwen Qianyi Model, MiniMax Model, Spark Model, Llama Model, 360GPT Model, Qwen Model, Baichuan Model, Lark Model, vivoLM Model, deepseek, Tencent Yuanbao and Wenxin Yiyan, and the embodiments of the present application do not make any limitations.

[0032] The embodiments of the present application provide a data processing method, device, computer device and computer-readable storage medium for simulation evaluation, which will be described in detail below.

[0033] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the scenario of the data processing system for simulation evaluation provided by the embodiments of the present application. The data processing system for simulation evaluation may include a computer device 100, and a data processing device for simulation evaluation is integrated in the computer device 100, such as Figure 1 the computer device in

[0034] In the embodiments of the present application, the computer device 100 is mainly used to obtain simulation result information and evaluation weight information;

[0035] analyze and process the evaluation weight information to obtain target weight information; the target weight information includes M target weight values;

[0036] analyze and process the target weight information and the simulation result information to obtain evaluation result information.

[0037] It can improve the performance and efficiency of simulation evaluation.

[0038] In the embodiments of the present application, the computer device 100 can be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing.

[0039] It can be understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such devices can include: cellular or other communication devices, which have a single-line display or a multi-line display, or cellular or other communication devices without a multi-line display. Specifically, the computer device 100 can be a desktop terminal or a mobile terminal. Specifically, the computer device 100 can also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0040] Those skilled in the art can understand that Figure 1 the application environment shown in Figure 1 is only one application scenario of the solution of the present application, and does not constitute a limitation on the application scenario of the solution of the present application. Other application environments can also include more or fewer computer devices than those shown in Figure 1 For example, only 1 computer device is shown in

[0041] It can be understood that the data processing system for simulation evaluation can also include one or more other services, which are not specifically limited here. Figure 1 In addition, as shown in

[0042] the data processing system for simulation evaluation can also include a memory 200 for storing data, such as image data, location information, etc. Figure 1 It should be noted that the schematic diagram of the scenario of the data processing system for simulation evaluation shown in

[0043] is only an example. The data processing system and scenario for simulation evaluation described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the data processing system for simulation evaluation and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0044] Embodiment 1

[0045] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a data processing method for simulation evaluation disclosed in an embodiment of the present invention. Among them, Figure 2 the described data processing method for simulation evaluation is applied to a management system, such as a local server or a cloud server for management, etc., and the embodiments of the present invention do not make limitations. As Figure 2 shown, the data processing method for simulation evaluation may include the following operations:

[0046] 101. Obtain simulation result information and evaluation weight information.

[0047] 102. Analyze and process the evaluation weight information to obtain target weight information.

[0048] In an embodiment of the present invention, the target weight information includes M target weight values.

[0049] 103. Analyze and process the target weight information and the simulation result information to obtain evaluation result information.

[0050] It should be noted that the above simulation result information represents the result obtained after calculating the simulation scenario input by the user, and it includes at least four dimensions of simulation result data, such as simulation efficiency data, simulation fidelity data, simulation confrontation richness, and target detection accuracy, and the embodiments of the present invention do not make limitations. The above simulation result information is directly given by the simulation system at the end of the simulation calculation and can be directly called through a network interface. Its calculation process is prior art relative to this application, and the embodiments of the present invention do not make limitations.

[0051] It should be noted that the above evaluation weight information represents the professional recognition scores among multiple experts, and the embodiments of the present invention do not make limitations.

[0052] It should be noted that the above evaluation result information represents the evaluation result of the usability of the simulation calculation. When it is a qualified simulation, it indicates that the current simulation result has high reliability. When it is an unqualified simulation, the current simulation result has poor reliability, and the embodiments of the present invention do not make limitations.

[0053] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0054] In an optional embodiment, analyzing and processing the evaluation weight information to obtain target weight information includes:

[0055] Performing verification analysis on the evaluation weight information to obtain verification result information;

[0056] When the verification result information is "yes", based on the evaluation weight information, determine the target weight information;

[0057] When the verification result information is "no", trigger the execution of obtaining the evaluation weight information.

[0058] It should be noted that after the above-mentioned triggering of the execution of obtaining the evaluation weight information, the process of re-analyzing and processing the evaluation weight information to obtain the target weight information will be re-entered, which is not limited in the embodiments of the present invention.

[0059] It should be noted that the above-mentioned triggering of the execution of obtaining the evaluation weight information can be that the previous expert group re-scores and uploads it to the system to obtain new evaluation weight information, or a new expert group is re-formed to re-score and upload it to the system to obtain new evaluation weight information, which is not limited in the embodiments of the present invention. Further, the determination process of the above-mentioned evaluation weight information is prior art, and the acquisition process of this patent is the matrix information directly generated from the scores input by the user into the system, which is not limited in the embodiments of the present invention.

[0060] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0061] In another optional embodiment, determining the target weight information based on the evaluation weight information includes:

[0062] Use the first weight calculation model to calculate and process the evaluation weight information to obtain the target weight information;

[0063] Wherein, the first weight calculation model is:

[0064]

[0065] In the formula, MBQZ i represents the i-th target weight value in the target weight information; PJ ij and PJ hj respectively represent the evaluation weight values with coordinates (i, j) and (h, j) in the evaluation weight information.

[0066] It should be noted that the evaluation weight values in the above-mentioned evaluation weight information are presented in matrix form, which is not limited in the embodiments of the present invention. Further, the (i, j) and (h, j) above represent the coordinates in the matrix, which is not limited in the embodiments of the present invention.

[0067] It should be noted that the above-mentioned M represents the order of the matrix, which is not limited in the embodiments of the present invention.

[0068] It should be noted that the above calculation and processing of the evaluation weight information using the first weight calculation model through product and exponential operations can effectively eliminate the influence brought by the subjective scoring differences of different experts on each other. The determined target weight values of different experts will not overly amplify the contribution of high-scoring indicators, nor will they ignore the shortcomings of low-scoring indicators. The results are more interpretable and reasonable, and can evaluate and analyze more objectively, accurately and efficiently. The embodiments of the present invention do not make limitations in this regard.

[0069] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0070] In another optional embodiment, a verification analysis is performed to obtain verification result information, including:

[0071] Based on the evaluation weight information, a target weight eigenvalue is determined;

[0072] Based on the target weight eigenvalue, the verification result information is determined.

[0073] It should be noted that the above determination of the target weight eigenvalue based on the evaluation weight information and the determination of the verification result information based on the target weight eigenvalue are to first determine the core characteristics of the matrix constructed by the scores of experts for each other, and then evaluate whether the logic of the matrix corresponding to the constructed evaluation weight information is reasonable according to this characteristic, so as to provide a reliable basis for the subsequent evaluation and analysis of the simulation results based on the target weight value determined by the verification result information determined based on the target weight eigenvalue, so as to improve the reliability and accuracy of the simulation result evaluation and analysis. The embodiments of the present invention do not make limitations in this regard.

[0074] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0075] In another optional embodiment, based on the evaluation weight information, determining a target weight eigenvalue includes:

[0076] Using a second weight calculation model to calculate and process the evaluation weight information to obtain first weight value information; the first weight value information includes a plurality of first weight values;

[0077] Among them, the second weight calculation model is:

[0078]

[0079] In the formula, DYQZ ab represents the first weight value numbered ab in the first weight value information; PJ ab and PJ cbrespectively represent the evaluation weight values with coordinates (a, b) and (c, b) in the evaluation weight information;

[0080] Use the third weight calculation model to calculate and process the first weight value information to obtain the second weight value information; the second weight value information includes a number of second weight values;

[0081] Among them, the third weight calculation model is:

[0082]

[0083] In the formula, DEQZ e represents the e-th second weight value in the second weight value information; DYQZ db represents the first weight value with the number db in the first weight value information;

[0084] Use the fourth weight calculation model to calculate and process the second weight value information and the evaluation weight information to obtain the target weight eigenvalue;

[0085] Among them, the fourth weight calculation model is:

[0086]

[0087] In the formula, MBQZ represents the target weight eigenvalue; DEQZ f represents the f-th second weight value in the second weight value information; deqz represents the column vector constructed by the second weight values in the second weight value information in the order of numbers; pj f represents the row vector constructed by the evaluation weight values in the f-th row of the evaluation weight information.

[0088] It should be noted that the above calculation and processing of the evaluation weight information using the second weight calculation model performs row normalization on the matrix so that the sum of the elements in the same row is 1, thereby eliminating the subjective scoring differences of different scoring elements in the same row, ensuring the fairness of weight distribution, and avoiding a certain element from dominating the calculation result due to a large value. The embodiments of the present invention do not make limitations.

[0089] It should be noted that the above calculation and processing of the first weight value information using the third weight calculation model is to sum and average the scoring elements after row normalization of the same row of the matrix, so as to obtain the scoring situation of different experts on the same expert, quantify the total scoring results of each expert individual, and thus be more conducive to the analysis of the overall scoring. The embodiments of the present invention do not make limitations.

[0090] It should be noted that the above column vector constructed by the second weight values in the order of numbers represents that the second weight values calculated by the third weight calculation model are arranged according to the calculated order, so as to construct a column vector. The embodiments of the present invention do not make limitations.

[0091] It should be noted that the above calculation and processing of the second weight value information and the evaluation weight information by using the fourth weight calculation model are based on the quadratic non-linear fusion calculation of the comprehensive scoring vector obtained by normalization, summation and averaging with each element in the initial matrix, so as to realize the re-fusion of the original features in the scoring matrix and the deep features extracted by the second weight calculation model and the third weight calculation model, so as to obtain deep features that can deeply reflect the scoring weight information, so as to evaluate whether the logic of the matrix corresponding to the constructed evaluation weight information is reasonable according to this feature. The embodiments of the present invention do not make any limitations.

[0092] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0093] In an optional embodiment, based on the target weight eigenvalue, the verification result information is determined, including:

[0094] The target weight eigenvalue is calculated and processed by using the fifth weight calculation model to obtain the value to be verified;

[0095] Wherein, the fifth weight calculation model is:

[0096] DJYZ = (MBQZ - M) / ((M - 1)·YZ);

[0097] In the formula, DJYZ represents the value to be verified; YZ represents the calculation coefficient;

[0098] It is judged whether the value to be verified is less than the verification threshold to obtain the verification judgment result;

[0099] When the verification judgment result is yes, it is determined that the verification result information is yes;

[0100] When the verification judgment result is no, it is determined that the verification result information is no.

[0101] It should be noted that the above calculation coefficient is obtained by looking up the verification value table according to M. Further, the above verification value table can be set by the user or obtained by the large model analysis according to the historical verification value table. The embodiments of the present invention do not make any limitations. Further, the above verification value table can be:

[0102] M value Check value 1 0 2 0 3 0.51 4 0.88 5 1.11 6 1.23 7 1.3 8 1.39 9 1.42 10 1.46

[0103] It should be noted that the above calculation and processing of the target weight eigenvalue by using the fifth weight calculation model is to further analyze whether the subjective evaluations among experts are objective by analyzing the relationship between the characteristics of the evaluation weight matrix and the order of the evaluation weight matrix itself, so as to further form a characteristic parameter reflecting the scoring relationship among each other, that is, the value to be verified, which is more conducive to the fair and objective evaluation and analysis of the evaluation weight. The embodiments of the present invention do not make any limitations in this regard.

[0104] It should be noted that the above verification threshold is a value between 0.01 and 0.02, such as 0.01 or 0.015. The embodiments of the present invention do not make any limitations in this regard.

[0105] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0106] In another optional embodiment, the target weight information and the simulation result information are analyzed and processed to obtain evaluation result information, including:

[0107] The target weight information and the simulation result information are interactively analyzed and processed to obtain a simulation evaluation value;

[0108] It is judged whether the simulation evaluation value is greater than or equal to the evaluation threshold to obtain an evaluation judgment result;

[0109] When the evaluation judgment result is yes, it is determined that the evaluation result information is that the simulation is qualified;

[0110] When the evaluation judgment result is no, it is determined that the evaluation result information is that the simulation is unqualified.

[0111] It should be noted that the above evaluation threshold can be set by the user or obtained by analyzing the historical evaluation threshold. Further, the value of the above evaluation threshold is between 6 and 9, such as 7 or 8. The embodiments of the present invention do not make any limitations in this regard.

[0112] In this optional embodiment, as an optional implementation manner, the above interactive analysis and processing of the target weight information and the simulation result information to obtain a simulation evaluation value includes:

[0113] The simulation result information is displayed on the display screen;

[0114] In response to the user's input operation, simulation scoring information is obtained; the simulation scoring information includes several pieces of scoring information to be processed; each piece of scoring information to be processed includes several scoring values to be processed;

[0115] The scoring analysis model is used to calculate and process the simulation scoring information and the target weight information to obtain a simulation evaluation value;

[0116] Among them, the scoring analysis model is:

[0117]

[0118] In the formula, PGZ represents the simulation evaluation value; MBQZ p represents the p-th target weight value in the target weight information; N represents the dimension of the simulation result data in the simulation result information; PFZ qp represents the q-th to-be-processed score value in the score of the simulation result data for the p-th dimension.

[0119] It should be noted that in the above input operation of the user, there are several to-be-processed score values for the score of the simulation result data of the dimension, so as to reflect the evaluation result of the expert when viewing the simulation result information displayed on the display screen. The embodiments of the present invention do not make any limitations.

[0120] It should be noted that the above interactive analysis and processing of the target weight information and the simulation result information to obtain the simulation evaluation value is to first display the simulation result on the display screen during the evaluation, so that each expert can view the data in real time, and then input it into the system according to the evaluation situation of each person, so as to obtain an objective score that is not interfered with by each other (others' scores cannot be seen in the scoring system), and use the scoring analysis model for comprehensive evaluation, so as to first perform the same-weight evaluation of summing and averaging the simulation result data of a certain dimension, and then use the weight to perform separate comprehensive evaluations on each dimension, thereby realizing the simulation result evaluation of first same weight and then difference, that is, objectively reflecting the objective evaluation of each dimension of the simulation result, eliminating the influence of the subjective scoring differences of experts, and also being able to comprehensively reflect the accurate and objective influence of the evaluation situation of each dimension on the overall evaluation result, improving the accuracy and reliability of the simulation result evaluation. The embodiments of the present invention do not make any limitations.

[0121] It can be seen that implementing the data processing method for simulation evaluation described in the embodiments of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0122] Embodiment 2

[0123] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a data processing device for simulation evaluation disclosed in the embodiments of the present invention. Among them, Figure 3 the described device can be applied to a management system, such as a local server or a cloud server for management, etc. The embodiments of the present invention do not make any limitations. As Figure 3 shown, the device may include:

[0124] An acquisition module 201, configured to acquire simulation result information and evaluation weight information;

[0125] The first processing module 202 is configured to analyze and process the evaluation weight information to obtain target weight information; the target weight information includes M target weight values.

[0126] The second processing module 203 is configured to analyze and process the target weight information and the simulation result information to obtain evaluation result information.

[0127] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is beneficial to improving the performance and efficiency of simulation evaluation.

[0128] In another alternative embodiment, as Figure 3 shown, analyzing and processing the evaluation weight information to obtain target weight information includes:

[0129] Performing verification analysis on the evaluation weight information to obtain verification result information;

[0130] When the verification result information is yes, determining the target weight information based on the evaluation weight information;

[0131] When the verification result information is no, triggering the execution of obtaining the evaluation weight information.

[0132] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is beneficial to improving the performance and efficiency of simulation evaluation.

[0133] In yet another alternative embodiment, as Figure 3 shown, determining the target weight information based on the evaluation weight information includes:

[0134] Using the first weight calculation model to calculate and process the evaluation weight information to obtain target weight information;

[0135] Among them, the first weight calculation model is:

[0136]

[0137] In the formula, MBQZ i represents the i-th target weight value in the target weight information; PJ ij and PJ hj respectively represent the evaluation weight values with coordinates (i, j) and (h, j) in the evaluation weight information.

[0138] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is beneficial to improving the performance and efficiency of simulation evaluation.

[0139] In yet another alternative embodiment, as Figure 3As shown, the evaluation weight information is verified and analyzed to obtain verification result information, including:

[0140] Based on the evaluation weight information, a target weight eigenvalue is determined;

[0141] Based on the target weight eigenvalue, the verification result information is determined.

[0142] It can be seen that implementing Figure 3 the data processing device for simulation evaluation described is conducive to improving the performance and efficiency of simulation evaluation.

[0143] In another alternative embodiment, as Figure 3 shown, based on the evaluation weight information, a target weight eigenvalue is determined, including:

[0144] The evaluation weight information is calculated and processed using a second weight calculation model to obtain first weight value information; the first weight value information includes a number of first weight values;

[0145] Among them, the second weight calculation model is:

[0146]

[0147] In the formula, DYQZ ab represents the first weight value numbered ab in the first weight value information; PJ ab and PJ cb respectively represent the evaluation weight values with coordinates (a, b) and (c, b) in the evaluation weight information;

[0148] The first weight value information is calculated and processed using a third weight calculation model to obtain second weight value information; the second weight value information includes a number of second weight values;

[0149] Among them, the third weight calculation model is:

[0150]

[0151] In the formula, DEQZ e represents the e-th second weight value in the second weight value information; DYQZ db represents the first weight value numbered db in the first weight value information;

[0152] The second weight value information and the evaluation weight information are calculated and processed using a fourth weight calculation model to obtain the target weight eigenvalue;

[0153] Among them, the fourth weight calculation model is:

[0154]

[0155] Wherein, MBQZ represents the target weight eigenvalue; DEQZ f represents the f-th second weight value in the second weight value information; deqz represents the column vector constructed by the second weight values in the second weight value information in the order of numbers; pj f represents the row vector constructed by the evaluation weight values in the f-th row of the evaluation weight information.

[0156] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is beneficial to improving the performance and efficiency of simulation evaluation.

[0157] In another alternative embodiment, as Figure 3 shown, based on the target weight eigenvalue, the verification result information is determined, including:

[0158] Using the fifth weight calculation model to perform calculation processing on the target weight eigenvalue to obtain the value to be verified;

[0159] Among them, the fifth weight calculation model is:

[0160] DJYZ = (MBQZ - M) / ((M - 1)·YZ);

[0161] In the formula, DJYZ represents the value to be verified; YZ represents the calculation coefficient;

[0162] Judge whether the value to be verified is less than the verification threshold to obtain the verification judgment result;

[0163] When the verification judgment result is yes, determine that the verification result information is yes;

[0164] When the verification judgment result is no, determine that the verification result information is no.

[0165] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is beneficial to improving the performance and efficiency of simulation evaluation.

[0166] In another alternative embodiment, as Figure 3 shown, analyzing and processing the target weight information and the simulation result information to obtain the evaluation result information, including:

[0167] Performing interactive analysis and processing on the target weight information and the simulation result information to obtain the simulation evaluation value;

[0168] Judge whether the simulation evaluation value is greater than or equal to the evaluation threshold to obtain the evaluation judgment result;

[0169] When the evaluation judgment result is yes, determine that the evaluation result information is that the simulation is qualified;

[0170] When the evaluation judgment result is no, determine that the evaluation result information is that the simulation is unqualified.

[0171] It can be seen that implementing Figure 3 the described data processing device for simulation evaluation is conducive to improving the performance and efficiency of simulation evaluation.

[0172] Embodiment III

[0173] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of another data processing device for simulation evaluation disclosed in the embodiments of the present invention. Among them, Figure 4 the described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. As Figure 4 shown, the device may include:

[0174] A memory 301 storing executable program codes;

[0175] A processor 302 coupled to the memory 301;

[0176] The processor 302 calls the executable program codes stored in the memory 301 to execute the steps in the data processing method for simulation evaluation described in Embodiment I.

[0177] Embodiment IV

[0178] The embodiments of the present invention disclose a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the data processing method for simulation evaluation described in Embodiment I.

[0179] Embodiment V

[0180] The embodiments of the present invention disclose a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the data processing method for simulation evaluation described in Embodiment I.

[0181] The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0182] Through the specific descriptions of the above embodiments, those skilled in the art can clearly understand that each implementation can be achieved by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0183] Finally, it should be noted that: the data processing method and device for simulation evaluation disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method for simulation evaluation, characterized in that: The method comprises: Obtain simulation result information and evaluation weight information; Analyzing and processing the evaluation weight information to obtain target weight information; the target weight information includes M target weight values; The target weight information and the simulation result information are analyzed and processed to obtain evaluation result information.

2. The data processing method for simulation evaluation according to claim 1, characterized in that: The analyzing and processing the evaluation weight information to obtain target weight information includes: Performing verification analysis on the evaluation weight information to obtain verification result information; When the verification result information is yes, determining target weight information based on the evaluation weight information; When the verification result information is negative, the step of obtaining the evaluation weight information is triggered.

3. The data processing method for simulation evaluation according to claim 2, characterized in that: The step of determining target weight information based on the evaluation weight information includes: Using a first weight calculation model to calculate the evaluation weight information to obtain target weight information; Wherein, the first weight calculation model is: In the formula, MBQZ i represents the i-th target weight value in the target weight information; PJ ij and PJ hj Respectively represent the evaluation weight values ​​of the coordinates (i, j) and (h, j) in the evaluation weight information.

4. The data processing method for simulation evaluation according to claim 2, characterized in that: The verification and analysis of the evaluation weight information to obtain verification result information includes: Based on the evaluation weight information, determining a target weight characteristic value; Based on the target weight characteristic value, verification result information is determined.

5. The data processing method for simulation evaluation according to claim 4, characterized in that: The step of determining a target weight characteristic value based on the evaluation weight information includes: The evaluation weight information is calculated and processed using a second weight calculation model to obtain first weight value information; the first weight value information includes a plurality of first weight values; Wherein, the second weight calculation model is: In the formula, DYQZ ab represents the first weight value numbered ab in the first weight value information; PJ ab and PJ cb Respectively represent the evaluation weight values ​​of the coordinates (a, b) and (c, b) in the evaluation weight information; Using a third weight calculation model to calculate and process the first weight value information to obtain second weight value information; the second weight value information includes a plurality of second weight values; Wherein, the third weight calculation model is: Where DEQZ e Indicates the e-th second weight value in the second weight value information; DYQZ db A first weight value numbered db in the first weight value information; Using a fourth weight calculation model to calculate and process the second weight value information and the evaluation weight information to obtain a target weight feature value; Wherein, the fourth weight calculation model is: Where, MBQZ represents the target weight eigenvalue; DEQZ f represents the fth second weight value in the second weight value information; deqz represents the column vector constructed by the second weight values ​​in the second weight value information in numerical order; pj f A row vector constructed by representing the evaluation weight values ​​of the fth row in the evaluation weight information.

6. The data processing method for simulation evaluation according to claim 4, characterized in that: The step of determining verification result information based on the target weight characteristic value includes: Using the fifth weight calculation model to calculate the target weight characteristic value to obtain a value to be verified; Wherein, the fifth weight calculation model is: DJYZ=(MBQZ-M) / ((M-1)·YZ); In the formula, DJYZ represents the value to be checked; YZ represents the calculation coefficient; Determine whether the value to be verified is less than a verification threshold, and obtain a verification result; When the verification judgment result is yes, determining the verification result information is yes; When the verification judgment result is no, it is determined that the verification result information is no.

7. The data processing method for simulation evaluation according to claim 1, characterized in that: The analyzing and processing the target weight information and the simulation result information to obtain the evaluation result information includes: Interactively analyzing and processing the target weight information and the simulation result information to obtain a simulation evaluation value; Determine whether the simulation evaluation value is greater than or equal to an evaluation threshold, and obtain an evaluation judgment result; When the evaluation result is yes, determining the evaluation result information as simulation qualified; When the evaluation judgment result is no, it is determined that the evaluation result information is simulation failure.

8. A data processing device for simulation evaluation, characterized in that: The device comprises: An acquisition module is used to obtain simulation result information and evaluation weight information; A first processing module is used to analyze and process the evaluation weight information to obtain target weight information; the target weight information includes M target weight values; The second processing module is used to analyze and process the target weight information and the simulation result information to obtain evaluation result information.

9. A data processing device for simulation evaluation, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data processing method for simulation evaluation according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the data processing method for simulation evaluation according to any one of claims 1 to 7.

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