A data processing method and device for simulation evaluation

Abstract

The present invention discloses a data processing method and device for simulation evaluation, which includes: obtaining 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; analyzing and processing the target weight information and 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 advancement of science and technology, simulation technology plays a vital role in numerous fields, such as aerospace, automotive manufacturing, electronic engineering, and biomedicine. By constructing virtual models and environments to simulate various systems, equipment, or processes, key indicators such as performance, reliability, and safety can be evaluated and optimized without investing significant real-world resources. This reduces R&D costs, shortens development cycles, and improves product quality. In military applications, simulation technology is becoming a crucial support for national defense modernization. In military training, virtual battlefield environments enable multi-service coordinated exercises, enhancing troops' combat capabilities. In system-of-systems confrontation scenarios, multi-dimensional confrontation models across land, sea, air, space, and power grids can be constructed to validate the effectiveness of new combat concepts and equipment. In combat simulations, red-blue confrontation simulations and wargames can be used to assist in strategic and tactical decision-making. These applications place higher demands on the real-time performance, dynamic response accuracy, and fidelity of complex scenarios in simulation evaluation. Especially in the context of information warfare, where battlefield dynamics are ever-changing, simulation systems must be capable of multi-source heterogeneous data fusion, large-scale parallel computing, and intelligent decision-making and evaluation. Ensuring objectivity, accuracy, and timeliness in simulation evaluation is a hot topic. Therefore, a data processing method and apparatus 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 conducive to improving the performance and efficiency of simulation evaluation.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a data processing method for simulation evaluation, the method comprising:

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

[0006] Analyzing and processing the evaluation weight information to obtain target weight information; the target weight information includes M target weight values;

[0007] The target weight information and the simulation result information are analyzed and processed to obtain evaluation result information.

[0008] A second aspect of an embodiment of the present invention discloses a data processing device for simulation evaluation, the device comprising:

[0009] Acquisition module, used to obtain simulation result information and evaluation weight information;

[0010] A 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 used 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 comprising:

[0013] a memory storing executable program code;

[0014] a processor coupled to a memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the data processing method for simulation evaluation disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. 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 an embodiment 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 briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0019] Figure 2 This is a flow chart of a data processing method for simulation evaluation disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural diagram of a data processing device for simulation evaluation disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a structural diagram of another data processing device for simulation evaluation disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions 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 of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0024] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0026] It should be noted that since the method of the embodiment of the present 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 is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, the corresponding data exist for the computer device to process. The details will not be repeated here.

[0027] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. 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 type of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0028] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0029] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (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 common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0030] Unimodal information is data consisting of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can often be achieved on the task.

[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 generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are generally used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiment of the present application, the large model can be a large-scale pre-trained model such as the 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, Skylark model, vivoLM model, deepseek, Tencent Yuanbao and Wenxin Yiyan, etc., which is not limited in the embodiment of the present application.

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

[0033] See also Figure 1 , Figure 1 This is a schematic diagram of a data processing system for simulation evaluation provided in an embodiment of the present application. The data processing system for simulation evaluation may include a computer device 100, in which a data processing device for simulation evaluation is integrated, such as Figure 1 Computer equipment in.

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

[0035] Analyzing and processing the evaluation weight information to obtain target weight information; the target weight information includes M target weight values;

[0036] The target weight information and the simulation result information are analyzed and processed to obtain evaluation result information.

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

[0038] In the embodiments of the present application, the computer device 100 may 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. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0039] It is 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 that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0040] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown. 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.

[0041] In addition, if Figure 1 As shown, the data processing system for simulation evaluation may further include a memory 200 for storing data, such as image data, position information, and the like.

[0042] It should be noted that Figure 1 The scenario diagram of the data processing system for simulation evaluation shown is merely an example. The data processing system and scenario for simulation evaluation described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person skilled in the art will appreciate that, with the evolution of the data processing system for simulation evaluation and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0043] The present invention discloses a data processing method and device for simulation evaluation, which are beneficial for improving the performance and efficiency of simulation evaluation. Detailed descriptions are given below.

[0044] Example 1

[0045] See also Figure 2 , Figure 2 This is a flow chart of a data processing method for simulation evaluation disclosed in an embodiment of the present invention. Figure 2 The data processing method for simulation evaluation described above is applied to a management system, such as a local server or cloud server for management, and is not limited in the embodiment of the present invention. Figure 2 As 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 the embodiment of the present invention, the target weight information includes M target weight values.

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

[0050] It should be noted that the above-mentioned simulation result information represents the results obtained after calculating the simulation scenario input by the user, and includes simulation result data in at least four dimensions, such as simulation efficiency data, simulation realism data, simulation adversarial richness, and target detection accuracy. This is not limited in the present embodiment. The above-mentioned simulation result information is directly provided by the simulation system at the end of the simulation calculation and can be directly called through the network interface. The calculation process is prior art relative to this application and is not limited in the present embodiment.

[0051] It should be noted that the above-mentioned evaluation weight information represents the professional recognition scores of multiple experts among each other, which is not limited in the embodiment of the present invention.

[0052] It should be noted that the above-mentioned evaluation result information represents the evaluation result of the availability of the simulation calculation. When the simulation is qualified, it indicates that the reliability of the current simulation result is high. When the simulation is unqualified, the reliability of the current simulation result is poor. The embodiment of the present invention does not limit this.

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

[0054] In an optional embodiment, the evaluation weight information is analyzed and processed to obtain target weight information, including:

[0055] Verify and analyze the evaluation weight information to obtain verification result information;

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

[0057] When the verification result information is negative, the execution is triggered to obtain the evaluation weight information.

[0058] It should be noted that after the trigger execution obtains the evaluation weight information, the process of analyzing and processing the evaluation weight information to obtain the target weight information will be re-entered, and the embodiment of the present invention does not limit this.

[0059] It should be noted that the triggering of the execution to obtain the evaluation weight information can be done by uploading the previous expert group's re-scoring to the system to obtain new evaluation weight information, or by uploading the new evaluation weight information to the system after the expert group is re-formed to re-score. This is not limited in the embodiments of the present invention. Furthermore, the above-mentioned process for determining the evaluation weight information is prior art. The acquisition process of this patent is the matrix information directly generated by the user's score input into the system. This 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 embodiment of the present invention is beneficial to improving the performance and efficiency of simulation evaluation.

[0061] In another optional embodiment, target weight information is determined based on the evaluation weight information, including:

[0062] Calculating and processing the evaluation weight information using the first weight calculation model to obtain target weight information;

[0063] Among them, 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 ​​of the coordinates (i, j) and (h, j) in the evaluation weight information.

[0066] It should be noted that the evaluation weight values ​​in the above evaluation weight information are presented in matrix form, which is not limited in the present embodiment. Furthermore, the above (i, j) and (h, j) represent coordinates in the matrix, which is not limited in the present embodiment.

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

[0068] It should be noted that the above-mentioned calculation and processing of the evaluation weight information using the first weight calculation model through multiplication and exponential operations can effectively eliminate the impact of differences in subjective scores of different experts. The target weight values ​​determined by different experts will not excessively magnify 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 be evaluated and analyzed more objectively, accurately and efficiently. The embodiments of the present invention do not limit this.

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

[0070] In yet another optional embodiment, performing a verification analysis on the , and obtaining verification result information, includes:

[0071] Based on the evaluation weight information, the target weight characteristic value is determined;

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

[0073] It should be noted that the above-mentioned determination of the target weight characteristic value based on the evaluation weight information, and determination of the verification result information based on the target weight characteristic value is to first determine the core feature of the matrix constructed by the experts' scoring each other, and then evaluate whether the logic of the matrix corresponding to the constructed evaluation weight information is reasonable based on this feature, thereby providing 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 based on the target weight characteristic value, so as to improve the reliability and accuracy of the evaluation and analysis of the simulation results, which is not limited in the embodiments of the present invention.

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

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

[0076] The evaluation weight information is calculated and processed using the second weight calculation model 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] Where 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 ​​of the coordinates (a, b) and (c, b) in the evaluation weight information;

[0080] 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;

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

[0082]

[0083] Where DEQZ e Represents 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;

[0084] Utilizing a fourth weight calculation model to calculate and process the second weight value information and the evaluation weight information to obtain a target weight characteristic value;

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

[0086]

[0087] In the formula, 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 the order of numbers; pj f Represents the row vector constructed by the evaluation weight value of the f-th row in the evaluation weight information.

[0088] It should be noted that the above-mentioned 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 dominating the calculation result due to excessively large values. The embodiments of the present invention do not limit this.

[0089] It should be noted that the above-mentioned calculation and processing of the first weight value information using the third weight calculation model is to sum and average the scoring elements after normalization in the same row of the matrix, so as to obtain the scoring of different experts on the same expert, so that the total scoring result of each individual expert is quantified, which is more conducive to the analysis of the overall score. The embodiment of the present invention does not limit this.

[0090] It should be noted that the column vector constructed by the above-mentioned second weight values ​​in numerical order represents that the second weight values ​​calculated by the third weight calculation model are arranged in the order of calculation to construct a column vector, which is not limited in the embodiment of the present invention.

[0091] It should be noted that the above-mentioned calculation and processing of the second weight value information and the evaluation weight information using the fourth weight calculation model is a quadratic nonlinear fusion calculation of the vector of the comprehensive score obtained by normalization and summing and averaging with each element in the initial matrix, thereby realizing the re-fusion of the original features in the score 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 score weight information, so as to evaluate whether the logic of the matrix corresponding to the constructed evaluation weight information is reasonable based on this feature. The embodiment of the present invention does not limit this.

[0092] It can be seen that implementing the data processing method for simulation evaluation described in the embodiment 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 characteristic value, the verification result information is determined, including:

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

[0095] Among them, the fifth weight calculation model is:

[0096] DJYZ=(MBQZ-M) / ((M-1)·YZ);

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

[0098] Determine whether the value to be verified is less than the verification threshold and obtain the verification result;

[0099] When the verification judgment result is yes, determining the verification result information is yes;

[0100] When the verification judgment result is no, the verification result information is determined to be no.

[0101] It should be noted that the above calculation coefficient is obtained by checking the check value table. Furthermore, the above check value table can be set by the user or obtained by analyzing the large model based on the historical check value table. The embodiment of the present invention does not limit this. Further, the above check value table can be:

[0102] M value Checksum 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-mentioned calculation and processing of the target weight characteristic value by using the fifth weight calculation model is to further analyze whether the subjective evaluation of each other by experts is 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 characteristic parameters that deeply reflect the scoring relationship between each other, that is, the value to be verified, which is more conducive to the fair and objective evaluation analysis of the evaluation weight, and the embodiments of the present invention do not limit this.

[0104] It should be noted that the above verification threshold is a value between 0.01-0.02, such as 0.01 or 0.015, which is not limited in the embodiment of the present invention.

[0105] It can be seen that implementing the data processing method for simulation evaluation described in the embodiment 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] Interactively analyze and process the target weight information and simulation result information to obtain the simulation evaluation value;

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

[0109] When the evaluation result is yes, the evaluation result information is determined to be simulation qualified;

[0110] When the evaluation judgment result is negative, the evaluation result information is determined to be simulation failure.

[0111] It should be noted that the above-mentioned evaluation threshold can be set by the user or obtained based on analysis of historical evaluation thresholds. Furthermore, the value of the above-mentioned evaluation threshold is between 6 and 9, such as 7 or 8, which is not limited in the embodiment of the present invention.

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

[0113] Display the simulation result information on the display screen;

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

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

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

[0117]

[0118] Where PGZ represents the simulation evaluation value; MBQZ p Represents the pth 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 qth pending score value in the score of the simulation result data of the pth dimension.

[0119] It should be noted that the scoring of the simulation result data of the dimension in the above user input operation has several pending scoring values ​​to reflect the evaluation results of the expert when viewing the simulation result information displayed on the display screen, which is not limited in the embodiment of the present invention.

[0120] It should be noted that the above-mentioned 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 results on the display screen during the evaluation so that each expert can view the data in real time, and then input the data into the system based on each person's evaluation situation, thereby obtaining an objective score that is not interfered with by each other (the scores of other people cannot be seen in the scoring system), and use the scoring analysis model to perform a comprehensive evaluation, so as to first perform an equal weight evaluation of the simulation result data of a certain dimension by summing and averaging, and then use the weights to perform a comprehensive evaluation of each dimension separately, thereby realizing the simulation result evaluation of equal weight first 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 the experts, and comprehensively reflecting the accurate and objective influence of the evaluation of each dimension on the overall evaluation result, thereby improving the accuracy and reliability of the simulation result evaluation, which is not limited in the embodiments of the present invention.

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

[0122] Example 2

[0123] See also Figure 3 , Figure 3 Schematic diagram of a data processing device for simulation evaluation disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 3 As shown, the device may include:

[0124] Acquisition module 201, used to obtain simulation result information and evaluation weight information;

[0125] The first processing module 202 is used 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 used to analyze and process the target weight information and the simulation result information to obtain evaluation result information.

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

[0128] In another optional embodiment, as Figure 3 As shown, the evaluation weight information is analyzed and processed to obtain the target weight information, including:

[0129] Verify and analyze the evaluation weight information to obtain verification result information;

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

[0131] When the verification result information is negative, the execution is triggered to obtain the evaluation weight information.

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

[0133] In another optional embodiment, Figure 3 As shown, based on the evaluation weight information, the target weight information is determined, including:

[0134] Calculating and processing the evaluation weight information using the first weight calculation model 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 ​​of the coordinates (i, j) and (h, j) in the evaluation weight information.

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

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

[0140] Based on the evaluation weight information, the target weight characteristic value is determined;

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

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

[0143] In another optional embodiment, Figure 3 As shown, based on the evaluation weight information, the target weight feature value is determined, including:

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

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

[0146]

[0147] Where 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;

[0148] 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;

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

[0150]

[0151] Where DEQZ e Represents 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;

[0152] Utilizing a fourth weight calculation model to calculate and process the second weight value information and the evaluation weight information to obtain a target weight characteristic value;

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

[0154]

[0155] In the formula, 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 the order of numbers; pj f Represents the row vector constructed by the evaluation weight value of the f-th row in the evaluation weight information.

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

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

[0158] The target weight characteristic value is calculated and processed using the fifth weight calculation model to obtain a 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 checked; YZ represents the calculation coefficient;

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

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

[0164] When the verification judgment result is no, the verification result information is determined to be no.

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

[0166] In another optional embodiment, Figure 3 As shown, the target weight information and simulation result information are analyzed and processed to obtain evaluation result information, including:

[0167] Interactively analyze and process the target weight information and simulation result information to obtain the simulation evaluation value;

[0168] Determine whether the simulation evaluation value is greater than or equal to the evaluation threshold, and obtain an evaluation judgment result;

[0169] When the evaluation result is yes, the evaluation result information is determined to be simulation qualified;

[0170] When the evaluation judgment result is negative, the evaluation result information is determined to be simulation failure.

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

[0172] Example 3

[0173] See also Figure 4 , Figure 4 This is a structural diagram of another data processing device for simulation evaluation disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0174] A memory 301 storing executable program code;

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

[0176] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data processing method for simulation evaluation described in the first embodiment.

[0177] Example 4

[0178] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data processing method for simulation evaluation described in the first embodiment.

[0179] Example 5

[0180] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data processing method for simulation evaluation described in the first embodiment.

[0181] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0182] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, 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 are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various 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; Analyzing and processing the target weight information and the simulation result information to obtain evaluation result information; The analyzing and processing of 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, triggering the execution of obtaining the evaluation weight information; The verification analysis of the evaluation weight information to obtain the verification result information includes: Determining a target weight characteristic value based on the evaluation weight information; Based on the target weight characteristic value, the verification result information is determined 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: Where 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 Represents the e-th second weight value in the second weight value information; DYQZ db Representing the 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 the order of numbers; pj f A row vector representing the evaluation weight values ​​of the f-th row in the evaluation weight information.

2. The data processing method for simulation evaluation according to claim 1, characterized in that: Determining target weight information based on the evaluation weight information includes: Calculating and processing the evaluation weight information using a first weight calculation model 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.

3. The data processing method for simulation evaluation according to claim 1, characterized in that: Determining verification result information based on the target weight characteristic value includes: Calculating the target weight characteristic value using a fifth weight calculation model to obtain a value to be verified; Wherein, the fifth weight calculation model is: DJYZ=(MBQZ-M) / ((M-1)·YZ); Wherein, 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 judgment result; When the verification judgment result is yes, determining the verification result information is yes; When the verification judgment result is no, the verification result information is determined to be no.

4. 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 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, the evaluation result information is determined to be simulation unqualified.

5. A data processing device for simulation evaluation, characterized in that: The device comprises: Acquisition module, used to obtain simulation result information and evaluation weight information; A 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; A second processing module is used to analyze and process the target weight information and the simulation result information to obtain evaluation result information; The analyzing and processing of 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, triggering the execution of obtaining the evaluation weight information; The verification analysis of the evaluation weight information to obtain the verification result information includes: Determining a target weight characteristic value based on the evaluation weight information; Based on the target weight characteristic value, the verification result information is determined 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: Where 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 Represents the e-th second weight value in the second weight value information; DYQZ db Representing the 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 the order of numbers; pj f A row vector representing the evaluation weight values ​​of the f-th row in the evaluation weight information.

6. 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 4.

7. 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 4.

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