Vehicle equipment use plan simulation evaluation method

Abstract

The invention discloses a vehicle equipment use plan simulation evaluation method, and relates to the technical field of analog simulation, and the evaluation steps are as follows: S1, collecting parameter information of vehicle equipment, and establishing a vehicle equipment model; s2, performing analysis according to geographic features and environmental features of an actual field environment, the geographic features being a plurality of different landform features, and the environmental features being a meteorological environment, and constructing a specific task scene; s3, various vehicle equipment starting plan schemes are formulated, wherein the vehicle equipment starting plan schemes comprise vehicle equipment selection and deployment routes; and S4, inputting the vehicle equipment model, the established task scene and the plan scheme into the simulation model according to the formulated plan scheme. According to the method, the initial plan scheme is optimized by applying the genetic algorithm according to the obtained factor characteristics, and the optimized plan scheme is simulated and evaluated again, so that the feasibility and the effectiveness of the scheme can be checked again.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a vehicle equipment mobilization plan simulation evaluation method. Background Art

[0002] Vehicle equipment refers to the general term for vehicles and their related ancillary equipment and devices used for various tasks. In the fields of military, logistics and transportation, and emergency rescue, the reasonable mobilization plan of vehicle equipment is crucial to the efficient execution of tasks. However, the traditional vehicle equipment mobilization plan formulation often relies on experience and simple calculations, and lacks accurate simulation and evaluation methods, which may lead to resource waste, mission delays, and excessive equipment loss in the actual implementation of the plan. For example, in military operations, unreasonable vehicle scheduling may affect the timing of combat; in logistics and transportation, improper vehicle arrangement will increase transportation costs and time. There are differences between statistical data and analysis results, and there is no corresponding system prototype design and algorithm and model integration basis. In this regard, we propose a vehicle equipment mobilization plan simulation evaluation method. Summary of the invention

[0003] In order to solve the above technical problems, a vehicle equipment mobilization plan simulation evaluation method is provided. This technical solution solves the above problems of resource waste and mission risk.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: a vehicle equipment mobilization plan simulation evaluation method, the evaluation steps are:

[0005] S1. Collect parameter information of vehicle equipment and establish a vehicle equipment model;

[0006] S2. Analyze the geographical characteristics and environmental characteristics of the actual on-site environment. The geographical characteristics are various topographic features, and the environmental characteristics are meteorological environment, to build a specific task scenario;

[0007] S3. Develop various vehicle equipment activation plans, including vehicle equipment selection and deployment routes;

[0008] S4. Input the vehicle equipment model, the mission scenario and the plan into the simulation model for simulation operation. The operation status data of the vehicle equipment during the simulation is recorded in real time. The data includes the time difference and status difference of the vehicle equipment under different terrain and meteorological environment.

[0009] S5. Evaluate the obtained simulation operation status data based on statistical analysis method to obtain the characteristics of factors affecting the mobilization effect of vehicle equipment;

[0010] S6. Optimize the initial plan according to the acquired factor characteristics, optimize the plan based on genetic algorithm, including re-planning of routes and re-screening of vehicle equipment, re-simulate and evaluate the optimized plan, obtain the optimized evaluation results, and implement it based on the optimized plan.

[0011] Preferably, in step S1, parameter information of the vehicle equipment is collected in advance, including basic information, power information, usage time information and safety information of the vehicle equipment; based on the collected parameter information, a power output model of the vehicle equipment is established, and the weight, air resistance and rolling resistance factors of the current vehicle equipment are considered to establish a vehicle driving resistance model, truly simulate the actual driving conditions of the vehicle equipment, predict the fuel consumption and power requirements of the vehicle equipment under different road conditions and speeds, combine the power output model with the driving resistance model, construct a vehicle equipment power performance simulation model, and simulate the driving process of the vehicle equipment in a virtual environment.

[0012] Preferably, in step S2, the requirements of the mission scenario are determined in advance, and the mission type, including transportation, rescue and combat, is determined. The mission scenario model is constructed based on the geographic information of the mission location and the characteristics of the mission environment. The geographic information of the mission location is based on basic landform information data obtained from satellite images, and the environmental characteristic information of the mission location is based on local meteorological data obtained by the Meteorological Bureau. Based on the collected data, different mission scenarios are constructed.

[0013] Preferably, the plan formulation step in step S3 preliminarily screens out the types of vehicle equipment that meet the basic task requirements based on the task type and environment, adapts to various task requirements by modifying and equipping different equipment, conducts a comprehensive performance evaluation on the preliminarily screened vehicle equipment, and selects the vehicle equipment combination with a higher score and the most suitable for the task requirements as the formulated plan based on the comprehensive performance evaluation results; collects traffic map information of the task area and surrounding areas, sets corresponding attribute parameters for different types of roads, determines the task execution location, obtains coordinate information, and formulates a plan.

[0014] Preferably, the comprehensive performance evaluation calculation formula is:

[0015]

[0016] Where S is the score of comprehensive performance evaluation, w i is the weight of the i-th evaluation indicator, x iis the score of the ith evaluation index, and the evaluation index is scored based on historical data statistics; the weight is determined based on the hierarchical analysis method, by constructing a hierarchical model of the evaluation index, and having experts compare the relative importance of each index in pairs, constructing a judgment matrix, and calculating the maximum eigenvalue and eigenvector of the judgment matrix to obtain the weight of each index, and the sum of the weights is 1; the initially screened vehicle equipment is sorted according to the comprehensive performance evaluation score, and the vehicle equipment with a high score is selected. In the selection process, not only the performance score of a single vehicle should be considered, but also the synergy and complementarity between vehicles, and the combination is used as a plan.

[0017] Preferably, the simulation model in step S4 is constructed by selecting a hybrid modeling method, established through actual data analysis and learning, and RecurDyn software is selected for simulation. The constructed vehicle equipment model, mission scenario and formulated plan are input into the simulation model for simulation training, and the vehicle equipment data under simulation training is recorded in real time.

[0018] Preferably, in step S5, the statistical analysis method assumes that the effect of vehicle equipment mobilization is Y, and assumes that there are a factors affecting the effect of vehicle equipment mobilization, recorded as B 1 , B 2 , ... B a , by preprocessing the collected data, constructing a data set after processing, and performing analysis and calculation, it is found that there is a linear relationship between the factors and the effects. The calculation formula is:

[0019] Y=β 0 +β 1 B 1 +β 2 B 2 +……+β a B a +c

[0020] Among them, β0 is the intercept term, β 1 , β 2 With β a It is expressed as weight, c is the random error term, and the weight calculation formula is based on the least squares method.

[0021] Preferably, the regression coefficient β is calculated based on the least squares method to minimize the residual sum of squares. The size, positive and negative, and significance of the regression coefficient obtained through regression analysis are used to determine the impact characteristics of each factor on the vehicle equipment mobilization effect. From different angles, the characteristics of the factors affecting the vehicle equipment mobilization effect are explored to make decisions and improvement measures.

[0022] Preferably, in step S6, the initially formulated plan will be optimized based on a genetic algorithm. The genetic algorithm will make a comprehensive consideration based on the characteristics of the route-related factors that have been mastered, and regard the existing routes as individuals with different genes. These genes are reflected in the length of the route, traffic congestion in the passing area, road construction conditions, and various road properties. By simulating the biological evolution process in nature, the excellent parts of different route plans are recombined to generate new routes; for the re-screening of vehicle equipment, each available vehicle equipment is regarded as an independent individual, and it also undergoes multiple rounds of selection, crossover and mutation operations to select a set of equipment that meets the task requirements and optimize it. The entire optimized plan is then re-put into the simulation evaluation environment.

[0023] Preferably, based on the optimized plan, it is put into actual tasks. During the implementation process, the staff strictly dispatches and drives the vehicles and equipment according to the optimized route planning, pays close attention to the matching of various actual execution indicators with the simulation evaluation results, and promptly discovers problems and makes adjustments.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention optimizes the initial plan scheme by using a genetic algorithm according to the acquired factor characteristics, and re-simulates and evaluates the optimized plan scheme, so as to re-examine the feasibility and effectiveness of the plan, obtain the optimized evaluation results, and intuitively see the changes brought about by the optimization. The implementation based on such a verified optimized plan can greatly improve the success rate of actual task execution, ensure the smooth completion of the task, ensure that the developed simulation system can meet the equipment mobilization quality management and evaluation requirements, and the statistical data and analysis results are true and effective, so as to provide a support platform for the preparation and adjustment optimization of equipment mobilization plan. The developed equipment mobilization quality simulation and evaluation system is used to carry out equipment mobilization quality simulation and deduction research under the rules of random assignment, mobilization priority, and proportional extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The figure is a flowchart of the evaluation steps of the present invention. DETAILED DESCRIPTION

[0027] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0028] Reference Figure 1 As shown, a vehicle equipment mobilization plan simulation evaluation method, the evaluation steps are:

[0029] S1. Collect parameter information of vehicle equipment and establish a vehicle equipment model;

[0030] S2. Analyze the geographical characteristics and environmental characteristics of the actual on-site environment. The geographical characteristics are various topographic features, and the environmental characteristics are meteorological environment, to build a specific task scenario;

[0031] S3. Develop various vehicle equipment activation plans, including vehicle equipment selection and deployment routes;

[0032] S4. Input the vehicle equipment model, the mission scenario and the plan into the simulation model for simulation operation. The operation status data of the vehicle equipment during the simulation is recorded in real time. The data includes the time difference and status difference of the vehicle equipment under different terrain and meteorological environment.

[0033] S5. Evaluate the obtained simulation operation status data based on statistical analysis method to obtain the characteristics of factors affecting the mobilization effect of vehicle equipment;

[0034] S6. Optimize the initial plan according to the acquired factor characteristics, optimize the plan based on genetic algorithm, including re-planning of routes and re-screening of vehicle equipment, re-simulate and evaluate the optimized plan, obtain the optimized evaluation results, and implement it based on the optimized plan.

[0035] This application integrates various types of vehicle equipment in the form of a model, which facilitates unified management and horizontal comparative analysis among many vehicle equipment. When facing different mission scenarios and needing to select appropriate equipment, it can quickly screen out vehicles that meet the conditions, improve the efficiency of resource allocation, and also help to discover the advantages and disadvantages of different vehicle equipment, providing a basis for subsequent optimization configuration. It takes the two key dimensions of geography and environment into consideration, avoiding the problem of focusing on a single factor and ignoring other potential impacts;

[0036] Formulate a variety of vehicle equipment launch plans, covering the selection and deployment routes of vehicle equipment, providing more possibilities for responding to complex and changing mission requirements and environmental conditions. Different vehicles and equipment have their own advantages and disadvantages in different terrain and meteorological environments. With a variety of options to choose from, you can flexibly deploy according to actual conditions to find the most suitable combination for the current mission and increase the probability of successful mission execution;

[0037] By evaluating the simulation operation status data based on statistical analysis, we can dig out the characteristics of factors that really affect the mobilization effect of vehicle equipment from a large amount of seemingly complex data, find the relationship between vehicle engine power and driving speed on plateau terrain, and the key factors affecting the driving stability of muddy roads by tire pattern type. Clarifying these factors will help to accurately improve and optimize the problems.

[0038] Based on the characteristics of the factors obtained, the genetic algorithm is used to optimize the initial plan, including the re-planning of the route and the re-screening of vehicles and equipment. This process can make full use of the advantages of the genetic algorithm and continuously screen out better routes and vehicle equipment combinations like simulating biological evolution, so that the plan can be continuously improved on the original basis and gradually approach the optimal configuration, thereby improving the efficiency of vehicle equipment mobilization, reducing costs, and enhancing the reliability of task execution.

[0039] In step S1, parameter information of vehicle equipment is collected in advance, including basic information, power information, usage time information and safety information of vehicle equipment; based on the collected parameter information, a power output model of vehicle equipment is established, and the weight, air resistance and rolling resistance factors of the current vehicle equipment are considered to establish a vehicle driving resistance model, truly simulate the actual driving conditions of the vehicle equipment, predict the fuel consumption and power requirements of the vehicle equipment under different road conditions and speeds, combine the power output model with the driving resistance model, construct a vehicle equipment power performance simulation model, and simulate the driving process of the vehicle equipment in a virtual environment.

[0040] This application can form a comprehensive understanding of vehicle equipment by collecting multi-dimensional parameters such as basic information, power information, usage time information and safety information of vehicle equipment, and establish a vehicle equipment power output model and a driving resistance model that considers weight, air resistance and rolling resistance factors. The two are combined to construct a power performance simulation model, which helps to deeply analyze the interaction between power and resistance of the vehicle during driving.

[0041] In step S2, the requirements of the mission scenario are determined in advance, and the mission type is determined, including transportation, rescue and combat. The mission scenario model is constructed based on the geographic information of the mission location and the characteristics of the mission environment. The geographic information of the mission location is based on basic landform information data obtained from satellite images, and the environmental characteristic information of the mission location is based on local meteorological data obtained by the Meteorological Bureau. Based on the collected data, different mission scenarios are constructed.

[0042] The geographical information and environmental characteristics of different mission locations in this application vary greatly. By collecting multi-faceted data to build mission scenarios, the formulated plans can adapt to various complex and changing environmental conditions.

[0043] The plan formulation step in step S3 preliminarily screens out the types of vehicle equipment that meet the basic mission requirements based on the mission type and environment, and adapts to various mission requirements by modifying and equipping different equipment. A comprehensive performance evaluation is conducted on the preliminarily screened vehicle equipment. Based on the comprehensive performance evaluation results, the vehicle equipment combination with a higher score and the most suitable for the mission requirements is selected as the formulated plan; traffic map information of the mission area and surrounding areas is collected, corresponding attribute parameters are set for different types of roads, the mission execution location is determined, coordinate information is obtained, and a plan is formulated.

[0044] This application preliminarily screens out vehicle equipment types that meet basic mission requirements based on mission types and environments, and then adapts to a variety of mission requirements by modifying and equipping them with different equipment. This approach ensures that the selected vehicle equipment is highly consistent with specific tasks, conducts a comprehensive performance evaluation on the preliminarily screened vehicle equipment, and selects the vehicle equipment combination with a higher score and the most suitable for mission requirements based on the evaluation results, avoiding problems that may arise from selecting vehicle equipment based on a single indicator.

[0045] The comprehensive performance evaluation calculation formula is:

[0046]

[0047] Where S is the score of comprehensive performance evaluation, w i is the weight of the i-th evaluation indicator, x i is the score of the ith evaluation index, and the evaluation index is scored based on historical data statistics; the weight is determined based on the hierarchical analysis method, by constructing a hierarchical model of the evaluation index, and having experts compare the relative importance of each index in pairs, constructing a judgment matrix, and calculating the maximum eigenvalue and eigenvector of the judgment matrix to obtain the weight of each index, and the sum of the weights is 1; the initially screened vehicle equipment is sorted according to the comprehensive performance evaluation score, and the vehicle equipment with a high score is selected. In the selection process, not only the performance score of a single vehicle should be considered, but also the synergy and complementarity between vehicles, and the combination is used as a plan.

[0048] The evaluation indicators of this application are scored based on historical data statistics, which ensures that the scoring of each indicator is rooted in past actual conditions, reflects the actual performance of vehicle equipment in different scenarios, and is objective and reliable.

[0049] In step S4, the simulation model is constructed by selecting a hybrid modeling method, established through actual data analysis and learning, and RecurDyn software is selected for simulation. The constructed vehicle equipment model, mission scenario, and formulated plan are input into the simulation model for simulation training, and the vehicle equipment data under simulation training is recorded in real time.

[0050] This application has obvious advantages in choosing RecurDyn software for simulation. The software has powerful functions in multi-body dynamics and is particularly suitable for simulating systems with complex mechanical structures and motion relationships such as vehicle equipment. It can accurately handle the interactions, motion transmission and force conditions between the various components of the vehicle.

[0051] In step S5, the statistical analysis method assumes that the effect of vehicle equipment mobilization is Y, and there are a factors that affect the effect of vehicle equipment mobilization, recorded as B. 1 , B 2 , ... B a , by preprocessing the collected data, constructing a data set after processing, and performing analysis and calculation, it is found that there is a linear relationship between the factors and the effects. The calculation formula is:

[0052] Y=β 0 +β 1 B 1 +β 2 B 2 +……+β a B a +c

[0053] Among them, β0 is the intercept term, β 1 , β 2 With β a It is expressed as weight, c is the random error term, and the weight calculation formula is based on the least squares method.

[0054] The regression coefficient β is calculated based on the least squares method to minimize the residual sum of squares. The size, positive and negative, and significance of the regression coefficient obtained through regression analysis are used to determine the impact characteristics of each factor on the vehicle equipment mobilization effect. From different angles, the characteristics of the factors that affect the vehicle equipment mobilization effect are explored to make decisions and improvement measures.

[0055] By calculating the regression coefficient β through the least squares method, the impact of each factor on the vehicle equipment mobilization effect can be quantified. The size of the regression coefficient intuitively reflects the relative importance of each factor in affecting the mobilization effect. If the absolute value of the regression coefficient corresponding to a certain factor is large, it means that the change of the factor has a more significant impact on the vehicle equipment mobilization effect; conversely, if the absolute value of the coefficient is small, its impact is relatively weak. This precise quantitative method allows us to clearly distinguish the role of different factors in the entire influencing system and avoid the deviation caused by judging the importance of factors based solely on subjective feelings or experience.

[0056] In step S6, the initial plan will be optimized based on the genetic algorithm. The genetic algorithm will make a comprehensive consideration based on the characteristics of the route-related factors that have been mastered, and regard the existing routes as individuals with different genes. These genes are reflected in the length of the route, traffic congestion in the passing area, road construction conditions, and various road properties. By simulating the process of biological evolution in nature, the excellent parts of different route plans are recombined to generate new routes; for the re-screening of vehicle equipment, each available vehicle equipment is regarded as an independent individual, and it also undergoes multiple rounds of selection, crossover and mutation operations to select a set of equipment that meets the task requirements and optimize it. The entire optimized plan is then re-put into the simulation evaluation environment.

[0057] This application will put the entire optimized plan back into the simulation evaluation environment, which can fully verify the optimization effect before actual implementation. By simulating the operation of vehicle equipment according to the optimization plan in real scenarios, it can intuitively see the changes in key indicators such as whether the transportation time is shortened, whether the mission success rate is improved, and whether the cost is reduced. Problems or deficiencies that may still exist in the optimization plan can be discovered in advance, so as to further adjust and improve it, and avoid potential risks and losses caused by untested implementation.

[0058] Based on the optimized plan, we put it into actual tasks. During the implementation process, the staff strictly dispatched and drove vehicles and equipment according to the optimized route plan, paid close attention to the matching between various actual execution indicators and simulation evaluation results, discovered problems in time and made adjustments.

[0059] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. A vehicle equipment mobilization plan simulation evaluation method, characterized in that: The evaluation steps are: S1. Collect parameter information of vehicle equipment and establish a vehicle equipment model; S2. Analyze the geographical characteristics and environmental characteristics of the actual on-site environment. The geographical characteristics are various topographic features, and the environmental characteristics are meteorological environment, to build a specific task scenario; S3. Develop various vehicle equipment activation plans, including vehicle equipment selection and deployment routes; S4. Input the vehicle equipment model, the mission scenario and the plan into the simulation model for simulation operation. The operation status data of the vehicle equipment during the simulation is recorded in real time. The data includes the time difference and status difference of the vehicle equipment under different terrain and meteorological environment. S5. Evaluate the obtained simulation operation status data based on statistical analysis method to obtain the characteristics of factors affecting the mobilization effect of vehicle equipment; S6. Optimize the initial plan according to the acquired factor characteristics, optimize the plan based on genetic algorithm, including re-planning of routes and re-screening of vehicle equipment, re-simulate and evaluate the optimized plan, obtain the optimized evaluation results, and implement it based on the optimized plan.

2. A vehicle equipment mobilization plan simulation evaluation method according to claim 1, characterized in that: In step S1, parameter information of vehicle equipment is collected in advance, including basic information, power information, usage time information and safety information of vehicle equipment; based on the collected parameter information, a power output model of vehicle equipment is established, and the weight, air resistance and rolling resistance factors of the current vehicle equipment are considered to establish a vehicle driving resistance model, truly simulate the actual driving conditions of the vehicle equipment, predict the fuel consumption and power requirements of the vehicle equipment under different road conditions and speeds, combine the power output model with the driving resistance model, construct a vehicle equipment power performance simulation model, and simulate the driving process of the vehicle equipment in a virtual environment.

3. A vehicle equipment mobilization plan simulation evaluation method according to claim 1, characterized in that: In step S2, the requirements of the mission scenario are determined in advance, and the mission type is determined, including transportation, rescue and combat. The mission scenario model is constructed based on the geographic information of the mission location and the characteristics of the mission environment. The geographic information of the mission location is based on basic landform information data obtained from satellite images, and the environmental characteristic information of the mission location is based on local meteorological data obtained by the Meteorological Bureau. Based on the collected data, different mission scenarios are constructed.

4. A vehicle equipment mobilization plan simulation evaluation method according to claim 1, characterized in that: In the plan formulation step of step S3, the vehicle equipment types that meet the basic mission requirements are preliminarily screened out based on the mission type and environment, and the vehicle equipment preliminarily screened out is adapted to various mission requirements by modification and equipping with different equipment. The comprehensive performance evaluation is conducted on the preliminarily screened vehicle equipment. Based on the comprehensive performance evaluation results, the vehicle equipment combination with a higher score and the most suitable for the mission requirements is selected as the formulated plan; Collect traffic map information of the mission area and surrounding areas, set corresponding attribute parameters for different types of roads, determine the mission execution location, obtain coordinate information, and formulate a plan.

5. A vehicle equipment mobilization plan simulation evaluation method according to claim 4, characterized in that: The comprehensive performance evaluation calculation formula is: Where S is the score of comprehensive performance evaluation, w i is the weight of the i-th evaluation indicator, x i is the score of the i-th evaluation indicator, and the evaluation indicator is scored based on historical data statistics; The weights are determined based on the analytic hierarchy process. A hierarchical model of evaluation indicators is constructed. Experts compare the relative importance of each indicator in pairs, build a judgment matrix, calculate the maximum eigenvalue and eigenvector of the judgment matrix, and obtain the weight of each indicator. The sum of the weights is 1. The preliminarily screened vehicles and equipment are ranked according to the comprehensive performance evaluation scores, and the vehicles and equipment with high scores are selected. In the selection process, not only the performance scores of individual vehicles should be considered, but also the synergy and complementarity between vehicles, and the combination is used as a plan.

6. A vehicle equipment mobilization plan simulation evaluation method according to claim 1, characterized in that: In step S4, the simulation model is constructed by selecting a hybrid modeling method, established through actual data analysis and learning, and RecurDyn software is selected for simulation. The constructed vehicle equipment model, mission scenario, and formulated plan are input into the simulation model for simulation training, and the vehicle equipment data under simulation training is recorded in real time.

7. The vehicle equipment mobilization plan simulation evaluation method according to claim 1 is characterized in that: In step S5, the statistical analysis method assumes that the effect of vehicle equipment mobilization is Y, and there are a factors that affect the effect of vehicle equipment mobilization, which are recorded as B1, B2, ... B a , by preprocessing the collected data, constructing a data set after processing, and performing analysis and calculation, it is found that there is a linear relationship between the factors and the effects. The calculation formula is: Y=β0+β1B1+β2B2+……+β a B a +c Among them, β0 is the intercept term, β1, β2 and β a It is expressed as weight, c is the random error term, and the weight calculation formula is based on the least squares method.

8. A vehicle equipment mobilization plan simulation evaluation method according to claim 7, characterized in that: The regression coefficient β is calculated based on the least squares method to minimize the residual sum of squares. The size, positive and negative, and significance of the regression coefficient obtained through regression analysis are used to determine the impact characteristics of each factor on the vehicle equipment mobilization effect. From different angles, the characteristics of the factors that affect the vehicle equipment mobilization effect are explored to make decisions and improvement measures.

9. A vehicle equipment mobilization plan simulation evaluation method according to claim 1, characterized in that: In step S6, the genetic algorithm will optimize the initially formulated plan. The genetic algorithm will comprehensively consider the characteristics of the route-related factors that have been mastered, and regard the existing routes as individuals with different genes. These genes are reflected in the length of the route, traffic congestion in the passing area, road construction conditions, and various road properties. By simulating the biological evolution process in nature, the excellent parts of different route plans are recombined to generate new routes. For the re-screening of vehicle equipment, each available vehicle equipment is regarded as an independent individual, and it also undergoes multiple rounds of selection, crossover and mutation operations to select the equipment set that meets the mission requirements and optimize it. The entire optimized plan is then re-put into the simulation evaluation environment.

10. A vehicle equipment mobilization plan simulation evaluation method according to claim 9, characterized in that: Based on the optimized plan, we put it into actual tasks. During the implementation process, the staff strictly dispatched and drove vehicles and equipment according to the optimized route plan, paid close attention to the matching between various actual execution indicators and simulation evaluation results, discovered problems in time and made adjustments.

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