An aviation cabin simulation training system

Abstract

The invention relates to the field of aviation technology, and in particular to an aviation cabin simulation training system, comprising: a control test module, used for generating a simulation model according to currently input test data; a model partitioning module, used for partitioning the simulation model into a plurality of grid areas, and calculating a grid sensitivity coefficient of each grid area; a simulation adjustment module, used for adjusting the grid areas, and determining the test effects of users in different grid areas under different training tasks; a plan setting module, used for selecting a plan scheme corresponding to a current test according to the test effects and grid sensitivity coefficients of different grid areas, and verifying a comprehensive evaluation result of the current test according to the obtained plan scheme; and the technical effects of improving the training realism, comprehensively simulating a complex environment, accurately evaluating the training effect, and improving the training efficiency and safety are achieved.

Description

Technical Field

[0001] The invention relates to the field of aviation technology, in particular to an aviation cabin simulation training system. Background Art

[0002] At present, for simulated aviation simulation training, the main technical architecture used is distributed simulation, which has a complex vision generation and display system. It usually uses real or virtual image ball screens, column screens and other projection display devices or LED ball screens with a large field of view, and uses multi-channel vision images to generate computers to synchronously render and output the extravehicular vision images.

[0003] For example, Chinese patent publication number CN115019597A discloses a method, device and system for aviation simulation training based on cloud computing and cloud rendering, which relates to the field of aviation technology. The main technical solutions include: the flight simulator collects the head posture information and control information of the trainees and transmits them to the server, and the server performs simulated cloud computing and cloud rendering according to the head posture information, control information and control management information sent by the simulation training control terminal, and transmits the results of simulated cloud computing and cloud rendering back to the cockpit and the video transmission head display device respectively. The cockpit drives the cockpit to execute the control corresponding to the control information according to the results of simulated cloud computing, and displays the extra-cabin view obtained by cloud rendering to the user through the head display device.

[0004] The existing technology determines the data that needs to be collected and paid attention to during aviation simulation training through simulation testing. However, when the corresponding data is collected, if a more urgent fire or other emergency occurs in the cabin, it is necessary to combine the currently collected and set simulation data to train for the emergency situation, so as to improve the pertinence and training efficiency of aviation simulation. Summary of the invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: an aviation cabin simulation training system, including: a control test module, which is used to generate a simulation model according to currently input test data.

[0006] The model partitioning module is used to partition the simulation model into multiple grid areas and calculate the grid sensitivity coefficient of each grid area.

[0007] The simulation adjustment module is used to adjust the grid area and determine the test effects of users in different grid areas under different training tasks.

[0008] The plan setting module is used to select a plan corresponding to the current test according to the test results and grid sensitivity coefficients of different grid areas, and verify the comprehensive evaluation results of the current test according to the obtained plan.

[0009] The beneficial effects of the present invention are as follows: 1. The present invention generates a simulation model by controlling the test module, and the simulation adjustment module adjusts the grid area. The system can simulate a realistic aviation cabin environment, including emergency situations such as fire, making the training more realistic and targeted.

[0010] Second, the present invention divides the simulation model into multiple grid areas and calculates the sensitivity coefficient of each area. This enables the system to fully consider the characteristics and influencing factors of different areas in the cabin, such as the location and intensity of the fire source, so as to more accurately simulate emergency situations in complex environments.

[0011] 3. Through the simulation adjustment module and the plan setting module, the system can select the plan corresponding to the current test according to the test results and sensitivity coefficients of different grid areas, and verify the comprehensive evaluation results of the current test. This provides a scientific and objective evaluation basis, helping trainers to understand their training results in a timely manner, find deficiencies and make improvements.

[0012] Fourth, by introducing parameters such as grid sensitivity coefficient and fire impact index, the system can intelligently adjust the training difficulty and route plan, making the training more efficient and safe. At the same time, through simulation training, it can reduce the dependence and loss of actual aircraft and reduce the training cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0014] Figure 1 It is a system framework diagram of an aviation cabin simulation training system.

[0015] Figure 2 The present invention is a system flow chart of an aviation cabin simulation training system.

[0016] Figure 3 The present invention is a simulation training flow chart of an aviation cabin simulation training system. DETAILED DESCRIPTION

[0017] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0018] See also Figure 1 , Figure 2 , Figure 3, an aviation cabin simulation training system, comprising: a control test module, a model partition module, a simulation adjustment module and a plan setting module; the control test module, as the starting point of the system, is responsible for receiving the test data input by the user; according to the test data, the control test module generates a simulation model, and the control test module passes it to the model partition module for further processing; the model partition module receives the simulation model from the control test module; the model partition module passes the partition information and sensitivity coefficient data to the simulation adjustment module; the simulation adjustment module receives the partition information and grid sensitivity coefficient from the model partition module; after the simulation adjustment is completed, the module passes the test effect data of each grid area to the plan setting module; the plan setting module receives the test effect data from the simulation adjustment module and the grid sensitivity coefficient of the model partition module; based on this information, the plan setting module selects a plan corresponding to the current test; finally, the plan setting module may feed back the evaluation results to the control test module for iterative optimization or as part of the system output.

[0019] The control test module is used to generate a simulation model based on the currently input test data.

[0020] The model partitioning module is used to partition the simulation model into multiple grid areas and calculate the grid sensitivity coefficient of each grid area.

[0021] The simulation adjustment module is used to adjust the grid area and determine the test effects of users in different grid areas under different training tasks.

[0022] The plan setting module is used to select a plan corresponding to the current test according to the test results and grid sensitivity coefficients of different grid areas, and verify the comprehensive evaluation results of the current test according to the obtained plan.

[0023] When generating a simulation model in the control test module, a simulation model related to the current test is generated according to the currently input test data. The input test data includes the location, intensity, fuel characteristics, and cabin layout of the fire source to obtain the scene and location that currently needs to be tested, and set the initial conditions and boundary conditions for the current scene.

[0024] The initial condition means setting the values ​​of the currently input test data according to the initial situation. The set values ​​include the positions of users and crew members in the cabin and the corresponding flight mission and flight time of the aircraft. The boundary condition means the temperature, pressure and ventilation flow rate under normal operation of the cabin.

[0025] The set grid sensitivity coefficient is expressed as the ratio between the change in simulation results and the change in grid size. In order to verify that the currently divided grid area can obviously reflect the detailed changes in the cabin, the evacuation time corresponding to the emergency event in the cabin is used as the basis for setting the grid sensitivity coefficient at this time.

[0026] The model partitioning module partitions the simulation model into multiple grid areas. The implementation method requires calculating the diameter of each grid area. The simulation model is divided into multiple grid areas according to the calculated diameter of the grid area. The grid area that needs to be set currently can be divided according to the calculated diameter. The heat release amount, air density, specific heat capacity of air, and ambient temperature of the currently set fire source are obtained to obtain the diameter of the grid area. ;in, represents the diameter of the mesh area, Indicates the heat release of the fire source, represents the air density, is the specific heat capacity of air, Indicates the ambient temperature, represents the acceleration due to gravity, represents pi, represents the exponential constant, Represents the initial diameter ratio coefficient. The range of the diameter ratio coefficient setting is The diameter ratio coefficient is used to adjust the size of the currently set grid area to prevent the currently set grid area from being too small. The set grid area is not only used to detect existing critical events, but also needs to assist in identifying general behavior events to improve the training effect. At this time, the first half of the calculation represents the diameter of the fire source, and the second half is a way to adjust the currently set grid area according to the diameter of the generated fire source, so that the grid area that needs to be verified can be accurately identified in the corresponding situation.

[0027] The method for obtaining the grid sensitivity coefficient at this time includes determining the temperature change curve of the adjacent grid area when a fire occurs in the currently set grid area, simulating the spread path of the fire; according to the spread path of the fire, determining the evacuation time of each grid area, and outputting the change ratio value of the evacuation time as the grid sensitivity coefficient.

[0028] After the size of the grid area is determined, the temperature change curve can be verified based on the obtained grid area size, and the grid sensitivity coefficient corresponding to each set grid area can be obtained in turn; the change ratio value of the evacuable time is output as the grid sensitivity coefficient, which realizes the quantitative evaluation of the grid sensitivity; this quantitative evaluation not only helps to intuitively understand the sensitivity of each grid area to fire, but also provides a strong basis for further decision-making analysis, so as to quickly understand which areas are more sensitive. At the same time, the change rate ratio of the evacuable time is selected from two time points after the fire occurs, one is the initial time point of the fire, and the other is the time point after a fixed time interval after the fire occurs. The fixed time interval passed at this time can be any one of 10 seconds, 30 seconds and 40 seconds; the evacuable time calculated at the two time points is compared with the standard evacuation time, and the compared values ​​are subtracted, and the difference between the compared values ​​is used as the output change rate ratio of the evacuable time.

[0029] The processing method for the temperature change curve of adjacent grid areas is to solve the continuity equation, momentum equation, turbulence equation and energy equation in sequence to obtain the temperature change curve of adjacent grid areas; solve the continuity equation to ensure mass conservation; solve the momentum equation to obtain the velocity field; solve the turbulence equation to describe the turbulence intensity and structure around the flame; solve the energy equation to obtain the temperature field, and identify the temperature change curve of the grid area according to the obtained temperature field.

[0030] The continuity equation for the setup is expressed as: ;in, represents the air density, Indicates time, represents the air velocity vector, represents the gradient operator, where the continuity equation is used to verify the value of the air velocity vector.

[0031] The momentum equation is expressed as, ;in, Indicates air pressure, represents the stress tensor, Represents external force, which is gravity in this case; what is verified at this time is whether momentum is conserved.

[0032] The turbulence equation that needs to be verified is expressed as follows: First, determine the turbulent kinetic energy k, and its equation is expressed as shown below.

[0033] ;in, represents the dynamic viscosity, represents the turbulent viscosity, is the turbulent Planck constant, ,at this time represents the transposed matrix, represents the kinetic energy generated by turbulence; Represents the source term, which is used to describe the impact of other logistics processes; Represents the dissipation rate.

[0034] After determining the turbulent kinetic energy k, the corresponding dissipation rate is determined The evolution of is given by the following equation.

[0035] ;in, is the turbulent Planck constant, Represents the source term, which is used to describe the impact of other logistics processes; , represents an empirical constant.

[0036] The energy equation is expressed as, ;in, represents the temperature of the corresponding grid area, is the specific heat capacity of air, represents thermal conductivity, Indicates the heat release of the fire source, Indicates the heat generated by chemical reactions, such as the heat generated by the burning of other items near the fire source in the cabin; at this time, the energy conservation of the heat generated by the air flow when the fire source burns is verified. Expressed as and The sum of the correlation values ​​of .

[0037] According to the obtained energy equation, the corresponding temperature in different grid areas can be calculated. At this time, the corresponding temperature change curve can be identified to obtain the overall temperature change.

[0038] The direction with the largest temperature change in adjacent grid areas is identified as the diffusion route of the fire; the time when the cabin door reaches the target temperature under the corresponding fire diffusion route is determined, and the time when the cabin door reaches the target temperature is used as the set evacuation time.

[0039] The target temperature set for the cabin door is 65℃. The time when the cabin door reaches the target temperature is the instant when the temperature of the cabin door reaches 65 degrees. At this time, the evacuation time available under the corresponding circumstances can be known.

[0040] The mesh sensitivity coefficient reflects the ratio between the change in simulation results and the change in mesh size. In cabin simulation training, an appropriate mesh sensitivity coefficient can ensure the accuracy of simulation results, thereby providing flight attendants with a realistic training environment. When the mesh sensitivity coefficient is set appropriately, the simulation system can more accurately capture the changes in details in the cabin.

[0041] The setting of the grid sensitivity coefficient also directly affects the efficiency and effect of training. If the grid sensitivity coefficient is too high, that is, the change in grid size has too great an impact on the simulation results, then a finer grid division may be required to ensure the accuracy of the simulation. However, this will increase the amount of calculation and slow down the simulation speed, which will in turn affect the training efficiency. Conversely, if the grid sensitivity coefficient is too low, the simulation results may be too rough and cannot truly reflect the actual situation in the cabin, thereby reducing the training effect. Therefore, a reasonable grid sensitivity coefficient can improve the training efficiency while ensuring the accuracy of the simulation.

[0042] At the same time, when setting the grid area, the user who is currently undergoing simulation training will be regarded as a point. At this time, the grid area corresponding to this point is also the user's corresponding grid area. Therefore, the adjustment settings of different grid areas at this time are to determine how the user is tested if he is in the corresponding grid area, thereby improving the accuracy and effectiveness of the training.

[0043] The test results for each grid area in the simulation adjustment module are expressed as verifying the event type that occurs to the user during training, and determining the user's corresponding reaction time and average processing time when each event occurs based on the acquired event type.

[0044] According to the temperature change curve of the identified grid area, the number of grid areas currently affected is determined, and the current fire impact index is judged. According to the fire impact index, the route plan set in the current cabin is determined; the tendency index corresponding to the temperature change curve is obtained, and the user's action set is determined according to the obtained tendency index; the test score is set according to the current user's action set and route plan.

[0045] The test results include test score, reaction time and average processing time as output.

[0046] The fire impact index is obtained by obtaining the change ratio of the fire source diameter. , the distance ratio between the current fire source and the hatch , the ratio of the number of grid areas directly affected by the fire source in front of the escape route to the number of all grid areas on the entire escape route , and the fire influence coefficient is obtained .

[0047] ;in, represents the fire source growth coefficient, represents the position weight value, Indicates the route weight value.

[0048] For the change ratio of the fire source diameter set at this time, the proportional value of the change of each fire source before and after the collection interval is obtained according to the collection interval of every ten seconds. The fire source growth coefficient is a coefficient set according to the current degree of fire growth. For example, it can be set to a weight value less than 1 such as 0.09, 0.2, etc. according to the fire intensity.

[0049] For the set distance ratio between the fire source and the cabin door, by verifying the ratio of the current distance between the fire source and the cabin door to the preset safety distance, it is verified whether there is a greater risk at the current fire time. The set location weight value is used to evaluate whether there is a higher risk at the location where the fire source occurs.

[0050] The ratio of the number of grid areas directly affected by the fire source ahead to the number of all grid areas on the entire escape route is used to determine whether the current fire source will affect escape and whether other problems are likely to occur in this case. The set route weight value is used to evaluate whether the escape route affected by the fire source is available, so as to adjust the current escape route.

[0051] According to the fire impact index, the implementation method of determining the route plan currently set in the cabin is to compare the fire impact coefficients of the grid areas close to the cabin door, select the grid area corresponding to the minimum value of the fire impact coefficient of the grid area close to the cabin door as the retrieval area, and select the route with the highest correlation with the retrieval area from the preset route plan library as the currently implemented route plan.

[0052] The calculation method for selecting the route with the highest relevance to the search area from the preset route plan library is to determine whether the starting point and the end point of the route plan in the search route plan library are closest to the location of the search area, and whether the coverage of the route plan with the search area is greater than a coverage threshold. The coverage is to determine whether each point in the route plan library overlaps with the search area. At this time, the number of points that need to overlap is greater than a set coverage threshold. When the searched path meets the above conditions, the plan that meets the conditions is set as the route plan; that is, the route with the highest relevance to the search area from the preset route plan library is represented by the route whose starting point and the end point are closest to the location of the search area, and whose coverage of the route plan with the search area is greater than the coverage threshold.

[0053] The tendency index represents the change value of temperature in different grid areas, that is, the tendency index is the cumulative change value of temperature in each grid area. At this time, the period for calculating the cumulative change value is 60 seconds, and then the temperature change value is calculated every 10 seconds. The temperature change values ​​are added up to get the cumulative change value. The cumulative change value of temperature within 60 seconds is used as the tendency index at this time to understand the current temperature change.

[0054] The implementation method of determining the user's action set includes: determining the user's action behavior under the corresponding event when an event occurs, and determining the probability of occurrence of the corresponding action behavior under different events; according to the probability of occurrence of the corresponding action behavior, identifying the current target population, and determining the action set corresponding to the action behavior.

[0055] The implementation method for determining the probability of occurrence of corresponding action behaviors under different events is to obtain the first action behavior information of the user under the corresponding event. The first action behavior information simulates the action data of the user under different fire events to adjust the guidance and guidance that should be adopted in the corresponding situation to improve the training of relevant emergency situations. At the same time, the simulated emergency situation at this time is based on the current tendency index, and the content that matches the current situation is screened out from the corresponding emergency situation to simulate the user's action data, and the action probability that may occur in the currently selected situation is verified in turn.

[0056] Based on the obtained first action behavior information, an action classification set corresponding to the first action behavior information is generated, and the action classification set is used to classify the currently identified first action behavior information, for example, into escape behavior, alarm behavior, and rescue behavior, to ensure that each action has a clear classification; for each event, the occurrence frequency of the corresponding first action behavior information under each action classification set is counted; the occurrence frequency of the first action behavior information is output as the occurrence probability of the corresponding action behavior.

[0057] The current target population is identified and the action set corresponding to the action behavior is determined by: obtaining the characteristics of the target population, which indicates the probability of the target population selecting the action behavior in an emergency; matching the characteristics of the target person with the occurrence probability of the action behavior, and taking the action behavior with a similarity greater than a similarity threshold as the user's action set.

[0058] At this time, the method of matching the characteristics of the target person with the probability of occurrence of the action behavior is to calculate according to the probability of the target person selecting the action behavior and the probability of occurrence of the action behavior calculated at this time. The calculation method is to obtain the corresponding similarity between the two probability values ​​according to the calculation method of the Pearson correlation coefficient, and calculate the corresponding actions at this time in turn to select the actions whose similarity is greater than the similarity threshold and form the user's action set.

[0059] In order to verify the probability of the current target population choosing an action behavior in an emergency situation, it is necessary to select an action behavior corresponding to the current tendency index from the preset action behavior library based on the obtained tendency index, and calculate the probability of the action behavior to assist in determining the user's action set.

[0060] When setting the test score based on the current user's action set and route plan, it is also necessary to verify the rationality of the action set and set the test score for the current training based on the rationality of the action set and the route plan.

[0061] Verify whether each action in the current action set is continuous, and get the score corresponding to the action set based on the continuity score and rationality score of the action. The score of the route plan is to verify the time and number of people passing through the current route plan in the simulation test, and the sum of the score of the action set and the score of the route plan is used as the set test score.

[0062] The continuity score verified at this time is to determine whether the previous action in the currently selected action set can naturally lead to the next action. If so, the score is set to 1, otherwise it is set to 0; at the same time, the rationality score verifies whether the current action and the corresponding emergency situation can correspond. If so, it is set to 1, otherwise it is 0; the route plan score compares the passing time and the number of passengers with the preset standard values, and the sum of the ratios is used as the route plan score set at this time, so that the test score in the test score can be obtained.

[0063] In the plan setting module, in order to determine the results that can be obtained from the corresponding test, the implementation method of the corresponding comprehensive evaluation results is shown as follows.

[0064] When selecting a plan, at least one target area and a plan corresponding to the target area are obtained based on the test results and grid sensitivity coefficients of different grid areas; the target area represents an area set by the current user under the standard moving distance, which is used to judge how far the user has moved and can handle the situation according to the current moving distance. The plan set at this time is selected based on the weighted average of the test results and grid sensitivity coefficient of the selected target area in the previous test, and the plan corresponding to the weighted average is selected for setting.

[0065] The standard moving distance is 5 meters. At this time, the maximum distance between the selected target area and the grid area where the current user is located is 5 meters. At this time, it is necessary to select the corresponding plan for the user within these 5 meters to verify the corresponding processing method.

[0066] Determine the execution time and execution score of each target area in the execution plan, and take the average of the execution time and execution scores of all target areas as the current comprehensive evaluation result.

[0067] For example, the user is currently in grid A, and the standard moving distance is 5 meters; the system analyzes the test results and sensitivity coefficients of adjacent grids; such as grids B, C, D, etc., the interval with grid A is no more than 5 meters.

[0068] Assuming that the test effect of grid B is the best and the sensitivity coefficient is moderate, grid B is selected as the target area; the system selects a corresponding plan based on the weighted average of grid B, such as route optimization plan B.

[0069] The system predicts that the execution time of route optimization plan B in grid B is 30 seconds; at the same time, based on historical data and simulation tests, the execution score of this plan in grid B is 85 points, with a full score of 100 points.

[0070] If only one target area grid B is considered, the average execution time is 30 seconds and the average execution score is 85 points; if there are multiple target areas, the system will repeat the above steps for each area and calculate the overall average execution time and execution score of all areas; this overall average will be used as the current comprehensive evaluation result to evaluate the overall performance of route optimization plan B or other alternative plans.

[0071] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. An aviation cabin simulation training system, characterized in that: include: A control test module is used to generate a simulation model according to the currently input test data; A model partitioning module is used to partition the simulation model into multiple grid areas and calculate the grid sensitivity coefficient of each grid area; The simulation adjustment module is used to adjust the grid area and determine the test effect of users in different grid areas under different training tasks; The plan setting module is used to select a plan corresponding to the current test according to the test results and grid sensitivity coefficients of different grid areas, and verify the comprehensive evaluation results of the current test according to the obtained plan; The test results for each grid area in the simulation adjustment module are expressed as follows: verifying the event type that occurs during user training, and determining the user's corresponding reaction time and average processing time when each event occurs based on the acquired event type; According to the change curve of the temperature of the identified grid area, the number of grid areas currently affected is determined, and the current fire impact index is judged. According to the fire impact index, the route plan set in the current cabin is determined; the tendency index corresponding to the temperature change curve is obtained, and the user's action set is determined according to the obtained tendency index; the test score is set according to the current user's action set and route plan; Output test results include test score, response time and average processing time; The method for obtaining the fire impact index is to obtain the change ratio of the fire source diameter , the distance ratio between the current fire source and the hatch , the ratio of the number of grid areas directly affected by the fire source in front of the escape route to the number of all grid areas on the entire escape route , and the fire influence coefficient is obtained : ； in, represents the fire source growth coefficient, represents the position weight value, Indicates the route weight value; According to the fire impact index, the implementation method of determining the route plan currently set in the cabin is to compare the fire impact coefficients of the grid areas close to the cabin door, select the grid area corresponding to the minimum fire impact coefficient of the grid area close to the cabin door as the search area, and select the route with the highest correlation with the search area from the preset route plan library as the currently implemented route plan; The implementation method of determining the user's action set includes: determining the user's action behavior under the corresponding event when an event occurs, and determining the probability of occurrence of the corresponding action behavior under different events; according to the probability of occurrence of the corresponding action behavior, identifying the current target population, and determining the action set corresponding to the action behavior.

2. An aviation cabin simulation training system according to claim 1, characterized in that: The test data input into the control test module includes the location, intensity, fuel characteristics, and cabin layout of the fire source.

3. The aviation cabin simulation training system according to claim 1, characterized in that: The model partitioning module partitions the simulation model into multiple grid areas. The diameter of each grid area needs to be calculated. The simulation model is divided into multiple grid areas according to the calculated diameter of the grid area. The heat release amount, air density, specific heat capacity of air, and ambient temperature of the currently set fire source are obtained to obtain the diameter of the grid area: ； in, represents the diameter of the mesh area, Indicates the heat release of the fire source, represents the air density, is the specific heat capacity of air, Indicates the ambient temperature, represents the acceleration due to gravity, represents pi, represents the initial diameter scale factor, Represents an exponential constant.

4. The aviation cabin simulation training system according to claim 1, characterized in that: The grid sensitivity coefficient is obtained by determining the temperature change curve of the adjacent grid area when a fire occurs in the currently set grid area, simulating the fire diffusion path; according to the fire diffusion path, determining the evacuation time of each grid area, and outputting the change ratio value of the evacuation time as the grid sensitivity coefficient.

5. The aviation cabin simulation training system according to claim 4, characterized in that: The direction with the largest temperature change in adjacent grid areas is identified as the diffusion route of the fire; the time when the cabin door reaches the target temperature under the corresponding fire diffusion route is determined, and the time when the cabin door reaches the target temperature is used as the set evacuation time.

6. The aviation cabin simulation training system according to claim 1, characterized in that: In the plan setting module, the implementation of the corresponding comprehensive evaluation results is as follows: According to the test results and grid sensitivity coefficients of different grid areas, obtain at least one target area and a plan corresponding to one target area; determine the execution time and execution score of each target area in the execution plan, and take the average of the execution time and execution scores of all target areas as the current comprehensive evaluation result.

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