Automatic modeling method for simulation material
By identifying the proficiency of editors and recommending high-quality parameter combinations, combining conflict detection and logical Sty growth model optimization instructions, the consistency problem of novice repeated debugging and multi-user collaborative editing in automated modeling of simulation materials is solved, improving efficiency and standardization.
Patent Information
- Application Number
- CN202510577908.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-07
- Publication Date
- 2025-08-15
AI Technical Summary
The existing automated modeling of simulation materials lacks conflict detection mechanism and parameter recommendation functions during collaborative editing of multiple users, resulting in novice users having to try and error repeatedly, which is inefficient.
Through the editor identification unit, the high and low proficiency personnel are identified, the loading parameter recommendation engine recommends high-quality parameter combinations for low-proficiency personnel, and monitors parameter modifications in real time through the conflict detection process, uses the logical Stie growth model to analyze conflict risks, generate differentiated optimization instructions, and ensure the consistency of material assets.
It improves the modeling efficiency of novice users, avoids repeated debugging, and ensures the consistency and standardization of material assets in multi-person collaboration.
Smart Images
Figure CN120494736A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of digital content generation, and in particular to an automatic modeling method for simulated materials. Background Art
[0002] Simulated materials refer to the simulation of the optical and physical properties of real materials in a digital environment, so that they present visual effects consistent with real materials when rendered. Game development, virtual production, VR / AR and other fields require the rapid generation of massive material variants, all of which require corresponding simulated materials. Traditional manual processes are difficult to meet efficiency requirements. Therefore, automated modeling techniques that automatically complete model creation, parameter configuration, data integration and other processes through computer programs and reduce manual intervention can reduce work pressure. The core of this method is to standardize and streamline repetitive operations through scripts or tool chains. Existing automated modeling of simulated materials lacks a conflict detection mechanism and parameter recommendation function during the early editing of multiple users collaborating on different instances of the same template. Novice users often need to repeat trial and error to achieve the desired effect. A more efficient and intelligent modeling solution is urgently needed. Summary of the Invention
[0003] The present invention provides an automated modeling method for simulated materials, which is used to solve the above technical problems.
[0004] A first aspect of the present invention provides an automated modeling method for simulated materials, comprising the following steps:
[0005] S1. The automatic modeling system creates a material template in the target engine, parses its original data to generate a parameter list file, bakes the material to collect data, and integrates it to generate a pak file; specifically: the automatic modeling system mobilizes the target engine to create a corresponding material instance as a material template; mobilizes the program to parse and collect the original data corresponding to the material instance, integrates the original data to obtain an adjustable parameter list file; and bakes the material, collects data from the baking process to obtain baking data, and combines the baking data with the material to obtain a pak file.
[0006] S2. The automatic modeling system mounts the pak file output by S1 through the material parameter editing module. The material parameter editing module parses the pak file to obtain an adjustable parameter list file, and converts the adjustable parameter list file into a visual interface; based on each visual interface, the editor information of the editor is obtained, and the editor identification unit of the automatic modeling system identifies the editor information to obtain the file editing status and the personnel editing status; the personnel editing status is identified, and when the personnel editing status corresponds to the low-skilled personnel status, S3 is executed; the editing status is identified, and when the editing status corresponds to the multi-person editing status, S4 is executed.
[0007] As a further improvement of the present invention, the editor identification unit is specifically:
[0008] According to the editor information, the number of editors of the visual interface corresponding to each pak file and the editing history of each editor are obtained;
[0009] Get the pre-designed minimum number of editors. When the number of editors in the visual interface exceeds the minimum number of editors, the generated file editing state is a multi-person editing state; otherwise, the generated file editing state is a regular editing state.
[0010] The editor's editing history corresponds to years of work and historical abnormality rate. The years of work are divided into multiple years intervals, and a years score is set for each age interval. The corresponding years of work of each editor are matched with each age interval to obtain the corresponding years score;
[0011] Get the pre-designed benchmark anomaly rate and the full score of the historical anomaly score, and use the formula Calculate to obtain the score conversion coefficient; when the historical anomaly rate corresponding to each editor is less than the benchmark anomaly rate, the historical anomaly score corresponding to the editor is the full score for anomaly; when the historical anomaly rate is greater than the benchmark anomaly rate, calculate the difference between the historical anomaly rate of each editor and the benchmark anomaly rate to obtain the overflow anomaly rate, and substitute the overflow anomaly rate into the formula: historical anomaly score = full score for anomaly - (overflow anomaly rate × 100 × score conversion coefficient) to obtain the corresponding historical anomaly score;
[0012] The proficiency score of each editor is obtained by calculating and summing the years of experience score and historical anomaly score of each editor. When the proficiency score is greater than the pre-set proficiency threshold, the corresponding personnel editing status is generated as a high-proficiency personnel status; conversely, when the proficiency score is less than the proficiency threshold, it is generated as a low-proficiency personnel status.
[0013] S3. When the automatic modeling system identifies that the editor corresponds to a low-skilled person status, a parameter recommendation engine is loaded into the operation interface of the low-skilled person, and real-time recommendation instructions are output through the parameter recommendation engine.
[0014] As a further improvement of the present invention, the parameter recommendation engine is specifically implemented as follows:
[0015] Based on the data repository, historical high-quality material data corresponding to historical high-quality cases is obtained, high-quality material parameters are obtained based on the historical high-quality material data, a pre-set parameter matching range is obtained, the set material parameters corresponding to the current proficiency personnel are obtained, the set material parameters are matched with the parameter matching range of the high-quality material parameters to obtain the corresponding historical high-quality cases, and the high-quality material parameters corresponding to each historical high-quality case are recommended.
[0016] S4. The automatic modeling system obtains the editing operation data of each editor corresponding to the multi-person editing state through the set collaborative editing unit, and inputs the editing operation data into the conflict detection process set in the system to perform conflict detection to obtain conflict optimization instructions.
[0017] As a further improvement of the present invention, the conflict detection process is specifically implemented as follows:
[0018] Based on the editing operation data, the modification data of each configuration parameter is obtained, and the pre-set detection warning time and the upper limit of the number of people who can modify the configuration parameter are obtained. When the same configuration parameter exceeds the upper limit of the number of people who can modify the configuration parameter within the detection warning time, a conflict detection signal is output; the associated parameter group corresponding to the configuration parameter is obtained, and the configuration parameters adjusted by each user are judged through logical constraint rules. When a logical conflict occurs, a conflict detection signal is output.
[0019] When a conflict detection signal is identified, the data modification value, modified user data and constraint violation history corresponding to the modified data are obtained; the data modification value corresponding to each editor is matched with the pre-set data modification range. When the data modification value exceeds the data modification range, the median of the data modification range is marked as the benchmark reference value, and the difference between the data modification value and the benchmark reference value is calculated to obtain the conflict risk value.
[0020] Identify the modified user data of each editor to obtain the personnel editing status, and obtain the corresponding role influence value according to the personnel editing status; when the personnel editing status corresponds to the low-skilled personnel status, the corresponding role influence value is generated as the maximum influence value Ys-max;
[0021] When the personnel editing status corresponds to the highly skilled personnel status, the proficiency score S corresponding to the highly skilled personnel status is obtained, and the proficiency score is divided into multiple proficiency score intervals [S 1-min , S 1-max ]、[S 2-min , S 2-max ]、...、[S n-min , S n-max ], each proficiency score interval is given a role influence value, namely Ys1, Ys2, ..., Ysn; at the same time, the relationship between the high influence values is Ys-max>Ys1>Ys2>…>Ysn; the proficiency scores corresponding to the current highly skilled personnel are matched with each proficiency score interval to obtain the corresponding role influence value.
[0022] Based on the constraint violation history, the violation time of the historical violation records corresponding to each editor is obtained, the number of historical violation records in the pre-set analysis time period is obtained to obtain the interval violation amount, and the interval violation frequency is calculated by ratioing the interval violation amount with the length of the analysis time period. When the interval violation frequency is greater than the pre-set violation frequency threshold, the interval violation frequency and the violation frequency threshold are calculated as the difference to obtain the interval violation impact value; the conflict risk value, role impact value and interval violation impact value are comprehensively analyzed through the logistic growth model to obtain the comprehensive growth amount.
[0023] As a further improvement of the present invention, a comprehensive analysis of the conflict risk value, role impact value and interval violation impact value is performed using a logistic growth model. The specific analysis process is as follows:
[0024] The logistic growth model is expressed as Where η represents the conflict risk value, role impact value and interval violation impact value; the conflict risk value, role impact value and interval violation impact value are normalized and taken as their numerical values, and are recorded as R, C, and V respectively; the conflict risk value R is substituted into the expression formula of the logistic growth model Calculate the corresponding conflict logistic growth β R ; Where K represents the preset upper limit of the conflict risk; a and b are both constants, and a>0; with the increase of R, β R Will continue to grow and approach K R Similarly, we get the role logistic growth β corresponding to the role impact value and the interval violation impact value C and role logistic growth β V ; The conflict logistic growth, role logistic growth and role logistic growth are calculated using the weighted formula The comprehensive growth amount P is calculated, where are all preset weight factors; when the comprehensive growth amount is greater than the preset growth upper limit, the corresponding conflict optimization instruction is generated as the key optimization target; conversely, when the comprehensive growth amount is less than the growth upper limit, the corresponding conflict optimization instruction is generated as the general optimization target; the execution of the conflict optimization instruction includes but is not limited to the use of the CRDT algorithm to automatically merge numerical parameters, a pop-up interface to display the modified values of each user, and the current user can choose to keep the version and provide the function of undoing to the previous version.
[0025] S5. When the user completes editing, the material serialization module serializes the asset information edited by the user into a specific file for persistent storage; specifically: after the user completes editing, the template traceability data and differentiated parameter set are automatically integrated through the pre-set material serialization storage module; the template traceability data includes the unique identifier, version number and engine environment configuration of the basic material template; the differentiated parameter set is a key-value pair that records the parameter modifications made by the user, and the default parameters are referenced through the template to avoid redundancy.
[0026] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:
[0027] 1. The present invention integrates the editor's years of work and historical abnormality rate through the editor identification unit to accurately distinguish between high and low skilled personnel status; when identified as low skilled personnel, the parameter recommendation engine matches and recommends verified high-quality parameter combinations based on historical high-quality material data, avoiding repeated debugging caused by novices' lack of experience.
[0028] 2. The present invention monitors parameter modification data in real time through a conflict detection process in scenarios where multiple people are editing the same material template at the same time; based on the logistic growth model, it comprehensively analyzes the conflict risk value, role impact value, and interval violation impact value to generate differentiated conflict optimization instructions and ensure the consistency and standardization of material assets in team collaboration. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.
[0030] Figure 1 is a flow chart of the method of the present invention;
[0031] Figure 2 This is a principle block diagram of the present invention. DETAILED DESCRIPTION
[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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.
[0033] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1-2 In an embodiment of the present invention, an embodiment of an automatic modeling method for simulated materials includes:
[0034] S1. Preprocessing stage: The automatic modeling system mobilizes the target engine to create the corresponding material instance as a material template; mobilizes the program to parse and collect the original data corresponding to the material instance, and outputs the original data to obtain an adjustable parameter list file; and bakes and packages the material, collects data from the baking process to obtain baking data, and combines the baking data with the material to obtain a material pak file.
[0035] Target engines include but are not limited to Unreal Engine, Unity, and Cry Engine. These target engines are professional digital content creation platforms with integrated development environments. They provide a rich tool set that supports the creation, editing, rendering, and integration of materials with other scene elements. They can present virtual scenes in a visual form and achieve a certain degree of automated operations through programming or scripting.
[0036] S2. Material parameter editing module: The automatic modeling system mounts the pak file output by S1 through the material parameter editing module. The material parameter editing module parses the pak file to obtain an adjustable parameter list file, and converts the adjustable parameter list file into a visual interface; the editor information of the editor is obtained based on each visual interface, and the editor information is identified through the editor identification unit of the automatic modeling system to obtain the file editing status and the personnel editing status; the personnel editing status is identified, and when the personnel editing status corresponds to the low-skilled personnel status, S3 is executed; the editing status is identified, and when the editing status corresponds to the multi-person editing status, S4 is executed.
[0037] Editor identification unit, which is specifically:
[0038] According to the editor information, the number of editors of the visual interface corresponding to each pak file and the editing history of each editor are obtained;
[0039] Get the pre-designed minimum number of editors. When the number of editors in the visual interface exceeds the minimum number of editors, the generated file editing state is a multi-person editing state; otherwise, the generated file editing state is a regular editing state.
[0040] The editor's editing history corresponds to years of work and historical abnormality rate. The years of work are divided into multiple years intervals, and a years score is set for each age interval. The corresponding years of work of each editor are matched with each age interval to obtain the corresponding years score;
[0041] Get the pre-designed benchmark anomaly rate and the full score of the historical anomaly score, and use the formula Calculate to obtain the score conversion coefficient; when the historical anomaly rate corresponding to each editor is less than the benchmark anomaly rate, the historical anomaly score corresponding to the editor is the full score for anomaly; when the historical anomaly rate is greater than the benchmark anomaly rate, calculate the difference between the historical anomaly rate of each editor and the benchmark anomaly rate to obtain the overflow anomaly rate, and substitute the overflow anomaly rate into the formula: historical anomaly score = full score for anomaly - (overflow anomaly rate × 100 × score conversion coefficient) to obtain the corresponding historical anomaly score;
[0042] The proficiency score of each editor is obtained by calculating and summing the years of experience score and historical anomaly score of each editor. When the proficiency score is greater than the pre-set proficiency threshold, the corresponding personnel editing status is generated as a high-proficiency personnel status; conversely, when the proficiency score is less than the proficiency threshold, it is generated as a low-proficiency personnel status.
[0043] S3. Low-skill recommendation engine: When the automatic modeling system identifies that the editor is in the low-skill status, the parameter recommendation engine is loaded into the operation interface of the low-skilled person, and real-time recommendation instructions are output through the parameter recommendation engine.
[0044] The parameter recommendation engine is implemented as follows:
[0045] Based on the data repository, historical high-quality material data corresponding to historical high-quality cases are obtained, high-quality material parameters are obtained based on the historical high-quality material data, a pre-set parameter matching range is obtained, the set material parameters corresponding to the current proficiency personnel are obtained, the set material parameters are matched with the parameter matching range of the high-quality material parameters to obtain the corresponding historical high-quality cases, and the high-quality material parameters corresponding to each historical high-quality case are recommended; the material parameters include but are not limited to color and lighting parameters, surface property parameters and reflection and refraction parameters.
[0046] S4, collaborative editing stage: The automatic modeling system obtains the editing operation data of each editor corresponding to the multi-person editing state through the set collaborative editing unit, and inputs the editing operation data into the conflict detection process set in the system for conflict detection to obtain conflict optimization instructions.
[0047] The conflict detection process is specifically implemented as follows:
[0048] According to the editing operation data, the modification data of each configuration parameter is obtained, and the pre-set detection warning time and the upper limit of the number of modifications are obtained. When the same configuration parameter exceeds the upper limit of the number of modifications within the detection warning time, a conflict detection signal is output; the associated parameter group corresponding to the configuration parameter is obtained, and the configuration parameters adjusted by each user are judged through the logical constraint rules, and a conflict detection signal is output when a logical conflict occurs; the logical constraint rules are violated when multiple interrelated configuration parameters in the same parameter group are modified separately. For example, "metallicity" and "roughness" are adjusted by users A and B respectively, and are modified to metallicity = 0.8 and roughness = 0.6 respectively. At this time, it is judged by the logical constraint rules that it violates the physical constraints and constitutes a logical conflict.
[0049] When a conflict detection signal is identified, the data modification value, modified user data and constraint violation history corresponding to the modified data are obtained; the data modification value corresponding to each editor is matched with the pre-set data modification range. When the data modification value exceeds the data modification range, the median of the data modification range is marked as the benchmark reference value, and the difference between the data modification value and the benchmark reference value is calculated to obtain the conflict risk value.
[0050] Identify the modified user data of each editor to obtain the personnel editing status, and obtain the corresponding role influence value according to the personnel editing status; when the personnel editing status corresponds to the low-skilled personnel status, the corresponding role influence value is generated as the maximum influence value Ys-max;
[0051] When the personnel editing status corresponds to the highly skilled personnel status, the proficiency score S corresponding to the highly skilled personnel status is obtained, and the proficiency score is divided into multiple proficiency score intervals [S 1-min , S 1-max ]、[S 2-min , S 2-max ]、...、[S n-min , S n-max ], each proficiency score interval is given a role influence value, namely Ys1, Ys2, ..., Ysn; at the same time, the relationship between the high influence values is Ys-max>Ys1>Ys2>…>Ysn; the proficiency scores corresponding to the current highly skilled personnel are matched with each proficiency score interval to obtain the corresponding role influence value.
[0052] Based on the constraint violation history, the violation time of the historical violation records corresponding to each editor is obtained, the number of historical violation records within the pre-set analysis time period is obtained to obtain the interval violation amount, and the interval violation amount is calculated by ratioing the length of the analysis time period to obtain the interval violation frequency. When the interval violation frequency is greater than the pre-set violation frequency threshold, the interval violation frequency and the violation frequency threshold are subtracted to obtain the interval violation impact value.
[0053] Logistic growth model A comprehensive analysis of the conflict risk value, role impact value, and interval violation impact value is performed, where η represents the conflict risk value, role impact value, and interval violation impact value; the conflict risk value, role impact value, and interval violation impact value are normalized and taken as their numerical values, and are recorded as R, C, and V respectively; the conflict risk value R is substituted into the expression formula of the logistic growth model Calculate the corresponding conflict logistic growth β R ; Where K represents the preset upper limit of the conflict risk; a and b are both constants, and a>0; with the increase of R, β R Will continue to grow and approach K R Similarly, we get the role logistic growth β corresponding to the role impact value and the interval violation impact value C and role logistic growth β V ; The conflict logistic growth, role logistic growth and role logistic growth are calculated using the weighted formula The comprehensive growth amount P is calculated, where They are all preset weight factors, and the sum is 1. The specific values can be 0.23, 0.42 and 0.25 respectively; when the comprehensive growth is greater than the preset growth upper limit, the corresponding conflict optimization instruction is generated as the key optimization target; conversely, when the comprehensive growth is less than the growth upper limit, the corresponding conflict optimization instruction is generated as the general optimization target.
[0054] The execution of conflict optimization instructions includes but is not limited to using the CRDT algorithm to automatically merge numerical parameters, a pop-up interface to display the modified values of each user, and the current user can choose to retain the version and provide the function of undoing to the previous version.
[0055] S5, post-processing stage: After the user completes editing, the template traceability data and differentiated parameter sets are automatically integrated through the pre-set material serialization storage module; the template traceability data includes the unique identifier, version number and engine environment configuration of the basic material template; the differentiated parameter set is a key-value pair that records the parameter modifications made by the user, and the default parameters are referenced through the template to avoid redundancy.
[0056] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof 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 embodiments of the present invention.
Claims
1. An automated modeling method for simulated materials, characterized in that: The steps include: S1. The automatic modeling system creates a material template in the target engine, parses its raw data to generate a parameter list file, bakes the material collection data, and integrates it to generate a pak file; S2. The automatic modeling system mounts the pak file through the material parameter editing module, and the material parameter editing module parses the pak file to obtain an adjustable parameter list file, and converts the adjustable parameter list file into a visual interface; based on each of the visual interfaces, the editor information of the editor is obtained, and the editor identification unit of the automatic modeling system identifies the editor information to obtain a file editing status and a person editing status; the person editing status is identified, and when the person editing status corresponds to a low-skilled person status, step S3 is executed; Identify the editing state, and execute step S4 when the editing state corresponds to a multi-person editing state; S3. When the automatic modeling system identifies that the editor is in a low-skilled personnel state, a parameter recommendation engine is loaded into the operation interface of the low-skilled personnel, and real-time recommended instructions are output through the parameter recommendation engine; S4. The automatic modeling system obtains the editing operation data of each editor in the multi-person editing state through the collaborative editing unit, and inputs the editing operation data into the conflict detection process set in the system to perform conflict detection and obtain conflict optimization instructions; S5, post-processing stage: When the user completes editing, the material serialization module serializes the asset information edited by the user into a specific file for storage.
2. The method for automated modeling of simulated materials according to claim 1, wherein: The specific execution of S1 is as follows: the automatic modeling system mobilizes the target engine to create a corresponding material instance as a material template; mobilizes the program to parse and collect the original data corresponding to the material instance, and outputs the original data to obtain an adjustable parameter list file; and bakes the material, and collects data of the baking process to obtain baking data, and combines the baking data with the material to obtain a pak file.
3. The automated modeling method for simulated materials according to claim 1, characterized in that: The editor identification unit obtains the number of editors of the visual interface corresponding to each pak file and the editing history of each editor according to the editor information; obtains the pre-designed minimum number of editors, and when the number of editors of the visual interface exceeds the minimum number of editors, generates the file editing state as a multi-person editing state; otherwise, generates the file editing state as a normal editing state; The editor's editing history corresponds to years of work and historical abnormality rate. The years of work are divided into multiple years intervals, and a years score is set for each age interval. The corresponding years of work of each editor are matched with each age interval to obtain the corresponding years score; Get the pre-designed benchmark anomaly rate and the full score of the historical anomaly score, and use Calculate to obtain the score conversion coefficient; when the historical anomaly rate corresponding to each editor is less than the benchmark anomaly rate, the historical anomaly score corresponding to the editor is the full score for anomaly; when the historical anomaly rate is greater than the benchmark anomaly rate, calculate the difference between the historical anomaly rate of each editor and the benchmark anomaly rate to obtain the overflow anomaly rate, and substitute the overflow anomaly rate into the formula: historical anomaly score = full score for anomaly - (overflow anomaly rate × 100 × score conversion coefficient) to obtain the corresponding historical anomaly score; The proficiency score of each editor is obtained by calculating and summing the years of experience score and historical anomaly score of each editor. When the proficiency score is greater than the pre-set proficiency threshold, the corresponding personnel editing status is generated as a high-proficiency personnel status; conversely, when the proficiency score is less than the proficiency threshold, it is generated as a low-proficiency personnel status.
4. The automated modeling method for simulated materials according to claim 1, wherein: The parameter recommendation engine is specifically implemented as follows: based on the data repository, historical high-quality material data corresponding to historical high-quality cases is obtained; based on the historical high-quality material data, high-quality material parameters are obtained; a pre-set parameter matching range is obtained; the set material parameters corresponding to the current skilled personnel are obtained; the set material parameters are matched with the parameter matching range of the high-quality material parameters to obtain the corresponding historical high-quality cases; and the high-quality material parameters corresponding to each historical high-quality case are recommended.
5. The automated modeling method for simulated materials according to claims 1 and 3, characterized in that: The specific execution method of the conflict detection process is as follows: Obtain the modification data of each configuration parameter based on the editing operation data, obtain the pre-set detection warning time and the upper limit of the number of people who can modify the configuration parameter, and output a conflict detection signal when the same configuration parameter exceeds the upper limit of the number of people who can modify the configuration parameter within the detection warning time; Obtain the associated parameter group corresponding to the configuration parameters, judge the configuration parameters adjusted by each user through logical constraint rules, and output a conflict detection signal when a logical conflict occurs; When a conflict detection signal is identified, obtaining a data modification value corresponding to the modified data, modified user data, and constraint violation history; Match the data modification value corresponding to each editor with the pre-set data modification range. When the data modification value exceeds the data modification range, mark the median of the data modification range as the benchmark reference value, and calculate the difference between the data modification value and the benchmark reference value to obtain the conflict risk value; Identify the modified user data of each editor to obtain the personnel editing status, and obtain the corresponding role influence value based on the personnel editing status; When the personnel editing status corresponds to the low-skilled personnel status, the corresponding role influence value generated is the highest influence value Ys-max; When the personnel editing status corresponds to the highly skilled personnel status, the proficiency score S corresponding to the highly skilled personnel status is obtained, and the proficiency score is divided into multiple proficiency score intervals [S 1-min , S 1-max ]、[S 2-min , S 2-max ]、...、[S n-min , S n-max ], each proficiency score interval is given a role influence value, namely Ys1, Ys2, ..., Ysn; at the same time, the relationship between the high influence values is Ys-max>Ys1>Ys2>...>Ysn; the proficiency score corresponding to each current highly skilled personnel is matched with each proficiency score interval to obtain the corresponding role influence value; Based on the constraint violation history, the violation time of the historical violation records corresponding to each editor is obtained, the number of historical violation records in the pre-set analysis time period is obtained to obtain the interval violation amount, and the interval violation frequency is calculated by ratioing the interval violation amount with the length of the analysis time period. When the interval violation frequency is greater than the pre-set violation frequency threshold, the interval violation frequency and the violation frequency threshold are calculated as the difference to obtain the interval violation impact value; the conflict risk value, role impact value and interval violation impact value are comprehensively analyzed through the logistic growth model to obtain the comprehensive growth amount.
6. The automated modeling method for simulated materials according to claim 5, characterized in that: The logistic growth model is used to comprehensively analyze the conflict risk value, role impact value, and interval violation impact value. The specific analysis process is as follows: The logistic growth model is expressed as Where η represents the conflict risk value, role impact value and interval violation impact value; the conflict risk value, role impact value and interval violation impact value are normalized and taken as their numerical values, and are recorded as R, C, and V respectively; the conflict risk value R is substituted into the expression formula of the logistic growth model Calculate the corresponding conflict logistic growth β R ; Where K represents the preset upper limit of the conflict risk; a and b are both constants, and a>0; with the increase of R, β R Will continue to grow and approach K R Similarly, we get the role logistic growth β corresponding to the role impact value and the interval violation impact value C and role logistic growth β V ; The conflict logistic growth, role logistic growth and role logistic growth are calculated using the weighted formula The comprehensive growth amount P is calculated, where are all preset weight factors; when the comprehensive growth amount is greater than the preset growth upper limit, the corresponding conflict optimization instruction is generated as the key optimization target; conversely, when the comprehensive growth amount is less than the growth upper limit, the corresponding conflict optimization instruction is generated as the general optimization target.
7. The automated modeling method for simulated materials according to claim 1, characterized in that: The S5 is specifically as follows: after the user completes editing, the template traceability data and the differentiated parameter set are automatically integrated through the pre-set material serialization storage module; the template traceability data includes the unique identifier, version number and engine environment configuration of the basic material template; the differentiated parameter set is a key-value pair that records the parameter modifications made by the user.