Park dynamic information management method

Abstract

The invention discloses a park dynamic information management method. The method comprises the following steps: acquiring detection items and detection sites of a park; acquiring the detection items which cannot be performed at the same time as a mutual exclusion combination; obtaining a day detection task of the park, constructing a plurality of time sequence paths for each detection site, and randomly filling the time sequence paths with corresponding detection items in the day detection task; each detection site randomly outputs a time sequence path for superposition, total time sequence paths are obtained, and the total time sequence paths with conflicts are removed according to mutual exclusion combination; and optimizing the overall time sequence path based on the shortest time, obtaining an optimal overall time sequence path, forming the detection items in the optimal overall time sequence path into a scheduling table according to a time sequence, and outputting the scheduling table, so as to avoid inaccurate detection results caused by influence when the detection items are implemented through evaluation of mutual exclusion combination. And when optimization is carried out based on the shortest time, the detection efficiency can be improved, and the detection time can be shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of smart park management, and in particular to a park dynamic information management method. Background Art

[0002] With the rapid development of the automotive industry, automobile testing parks, as core locations for testing vehicle safety performance, environmental indicators, and comprehensive performance, have a direct impact on vehicle quality control and industry compliance through their management efficiency and testing accuracy. Currently, testing projects within testing parks are becoming increasingly diverse and complex, covering dozens of types including safety technology testing, environmental emission testing, and special testing for new energy vehicles. Each testing project has significantly different requirements for equipment, personnel, and site environment.

[0003] The park's dynamic information management mainly includes the management of equipment, personnel, and testing projects. The management of testing tasks still relies mainly on work experience or simple rules. There is no planning of testing projects to shorten the working hours of staff. At the same time, there is no systematic consideration of resource conflicts between testing projects (such as the exclusive demand for shared equipment and the skill matching of technical personnel) and environmental interference (such as the impact of vibration equipment on noise detection accuracy and the pollution of exhaust emissions on adjacent testing projects). This leads to low testing efficiency, serious waste of resources, and even deviations in test results and compliance risks. Summary of the Invention

[0004] In view of this, the present invention proposes a park dynamic information management method, which can reasonably sort the scheduling of inspection items, avoid conflicting inspection items at the same time, and ensure the accuracy of inspection results.

[0005] The technical solution of the present invention is achieved as follows:

[0006] A park dynamic information management method comprises the following steps:

[0007] Step S1: Obtain the testing items available in the park and the park plan, and determine the testing site according to the park plan;

[0008] Step S2: Evaluate the impact of the testing items performed at the corresponding testing site on the testing items performed at other testing sites, and obtain the testing items that cannot be performed simultaneously as mutually exclusive combinations;

[0009] Step S3: Obtain the inspection tasks for the day in the park, construct several time series paths for each inspection site, and randomly fill the corresponding inspection items in the inspection tasks for the day into the time series paths;

[0010] Step S4: Each detection site randomly outputs a time series path for superposition to obtain an overall time series path, and the conflicting overall time series paths are eliminated based on mutually exclusive combinations;

[0011] Step S5: Optimize the overall time series path based on the shortest time, and obtain the optimal overall time series path. Output the inspection items in the optimal overall time series path in chronological order as a schedule.

[0012] Preferably, the specific steps of step S1 include:

[0013] Step S11: Obtain the service list displayed by the park, and extract the testing items that the park can provide from the service list;

[0014] Step S12: Obtain planning information of the park, identify several testing sites from the planning information, and obtain the distances between the testing sites.

[0015] Preferably, the specific steps of step S2 include:

[0016] Step S21: Obtain the correlation between the test items and the test sites, and perform one-to-one matching between the test items and the test sites based on the correlation;

[0017] Step S22: Randomly select two testing sites and input matching testing items for simulation. Evaluate the impact of the two testing items based on shared equipment, shared technicians, and distance between the testing sites. Based on the impact, output the testing items that cannot be performed simultaneously as mutually exclusive combinations.

[0018] Step S23: Repeat step S22 to obtain multiple mutually exclusive combinations.

[0019] Preferably, the specific steps of step S21 are: obtaining descriptions of the test items and test sites, extracting keywords from the descriptions, calculating the relevance of the keywords using the Pearson correlation coefficient method, and matching the test items and test sites one by one according to the relevance.

[0020] Preferably, the specific steps of step S22 are:

[0021] Step S221: randomly select two testing sites and input matching testing items into the testing sites for simulation;

[0022] Step S222: Extract the equipment list required for the two inspection items and obtain the shared equipment demand. If the shared equipment supply in the park is less than the shared equipment demand, output the two inspection items as a mutually exclusive combination.

[0023] Step S223: extract the types of technicians required for the two inspection projects and obtain the demand for shared technicians. If the supply of shared technicians is less than the demand for shared technicians, output the two inspection projects as a mutually exclusive combination.

[0024] Step S224: When the impact of the detection items on the environment cannot be reduced by the distance between the detection sites, the two detection items are output as a mutually exclusive combination.

[0025] Preferably, the specific steps of step S3 are:

[0026] Step S31: Obtain the inspection tasks of the park on the same day, and obtain the inspection items from the inspection tasks on the same day;

[0027] Step S32: construct a time series path for the test site, randomly select test items corresponding to the test site and fill them into the time series path in chronological order until all test items corresponding to the test site are selected;

[0028] Step S33: Repeat step S32 to obtain multiple time series paths for each detection site.

[0029] Preferably, when filling the test items in step S32, the start time of the next test item is made later than the end time of the previous test item.

[0030] Preferably, the specific steps of step S4 are:

[0031] Step S41: Each detection site randomly outputs a time series path, which are then time-aligned and superimposed to form an overall time series path;

[0032] Step S42: repeat step S41 to obtain several overall time series paths;

[0033] Step S43: Evaluate any moment in the overall time series path to determine whether there is a mutually exclusive combination, and eliminate the overall time series path with a mutually exclusive combination.

[0034] Preferably, the specific steps of step S5 are:

[0035] Step S51, initialize the gray wolf population and set the number of iterations;

[0036] Step S52: randomly select an overall time series path and calculate the fitness value of the gray wolf based on the shortest time;

[0037] Step S53: Select another overall time series path, calculate the fitness value, and retain the overall time series path with a larger fitness value compared with the previous calculation;

[0038] Step S54: perform iterative calculations and output the overall time series path with the largest fitness value as the optimal overall time series path;

[0039] Step S55: Output the inspection items in the optimal overall time series path in chronological order as a schedule.

[0040] Preferably, the number of iterations is the number of overall time series paths.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] A park dynamic information management method of the present invention evaluates the impact of detection items that the park can provide, determines whether two detection items can be carried out at the same time, and treats detection items that cannot be carried out at the same time as a mutually exclusive combination, and then fills each detection site with detection items based on time sorting, obtains a time series path, and thus obtains the scheduling of detection items on the detection site. The time series paths of different detection sites are combined to obtain the overall time series path of a single day. The overall time series path reflects the overall scheduling of a single day, and then the overall time series paths with conflicts can be eliminated according to the mutually exclusive combination to avoid mutual conflicts when the detection items are implemented, thereby ensuring the accuracy of the detection results. Finally, the optimal overall time series path is obtained from the overall time series path, so that the detection items can be implemented in sequence according to the sorting in the optimal overall time series path, and the time required for the implementation of all detection items is guaranteed to be the shortest, thereby improving the detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flow chart of a park dynamic information management method of the present invention;

[0045] Figure 2 Flowchart of step S1 of a park dynamic information management method of the present invention;

[0046] Figure 3 This is a flow chart of step S2 of a park dynamic information management method of the present invention;

[0047] Figure 4 This is a flow chart of step S22 of a park dynamic information management method of the present invention;

[0048] Figure 5 Flowchart of step S3 of a park dynamic information management method of the present invention;

[0049] Figure 6 Flowchart of step S4 of a park dynamic information management method of the present invention;

[0050] Figure 7 Flowchart of step S5 of a park dynamic information management method of the present invention; DETAILED DESCRIPTION

[0051] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0052] See also Figures 1 to 7 The present invention provides a method for managing park dynamic information, comprising the following steps:

[0053] Step S1: Obtain the testing items available in the park and the park plan, and determine the testing site according to the park plan;

[0054] Step S2: Evaluate the impact of the testing items performed at the corresponding testing site on the testing items performed at other testing sites, and obtain the testing items that cannot be performed simultaneously as mutually exclusive combinations;

[0055] Step S3: Obtain the inspection tasks for the day in the park, construct several time series paths for each inspection site, and randomly fill the corresponding inspection items in the inspection tasks for the day into the time series paths;

[0056] Step S4: Each detection site randomly outputs a time series path for superposition to obtain an overall time series path, and the conflicting overall time series paths are eliminated based on mutually exclusive combinations;

[0057] Step S5: Optimize the overall time series path based on the shortest time, and obtain the optimal overall time series path. Output the inspection items in the optimal overall time series path in chronological order as a schedule.

[0058] A park dynamic information management method of the present invention is applied in an automobile inspection park to optimize the inspection project scheduling of the park, wherein the inspection projects that the park can provide services and the park planning are first obtained, and the inspection site can be determined through the park planning. Different inspection sites correspond to different inspection projects, and the same inspection site may be suitable for multiple inspection projects. The distances between different inspection sites are also different. If two inspection sites are close, the inspection projects may affect the environment when they are carried out, such as generating noise, which in turn affects the inspection projects on nearby inspection sites. At the same time, the resources required for different inspection projects may overlap, resulting in the inability to carry out two inspection projects at the same time. Therefore, when scheduling inspection projects, it is necessary to first evaluate the degree of impact between the inspection projects, simulate the implementation of the inspection projects on the corresponding inspection sites, and judge whether they will affect the inspection projects on other inspection sites. The judgment result can obtain the inspection items that cannot be carried out at the same time, and output them as mutually exclusive combinations. Then, the inspection tasks of the park on that day can be obtained. From the inspection tasks of the day, the inspection items that need to be implemented in the park on that day can be obtained. For each inspection site in the park, several time series paths are constructed respectively, and each time series path is filled with inspection items of different arrangements and combinations. Different inspection sites can obtain several time series paths. At this time, each inspection site will correspond to multiple time series paths, and each inspection site can output a time series path. The time series paths output by each inspection site are superimposed to obtain the overall time series path. The overall time series path represents the inspection project schedule of different inspection sites in the park on that day. Then, according to the mutually exclusive combination, the conflicting parts in the overall time series path can be eliminated to avoid the inspection project affecting other inspection projects during implementation, thereby ensuring the accuracy of the inspection results.

[0059] The overall time series path after elimination is the specific schedule of the inspection items that the park can choose. The number of overall time series paths will be relatively large, and it is necessary to select an optimal overall time series path. Therefore, the present invention optimizes the overall time series path based on the shortest time. The park can implement the inspection items according to the schedule of the inspection items in the obtained optimal overall time series path, and the implementation time is shortest, which can improve the efficiency of automobile inspection in the park.

[0060] Preferably, the specific steps of step S1 include:

[0061] Step S11: Obtain the service list displayed by the park, and extract the testing items that the park can provide from the service list;

[0062] Step S12: Obtain planning information of the park, identify several testing sites from the planning information, and obtain the distances between the testing sites.

[0063] The service list displayed by the park can be used to extract the inspection items that the park can provide services. The confirmation of the inspection site requires obtaining the planning information of the park. According to the planning information, different areas of the park can be determined. The equipment and structures in different areas will be suitable for different inspection items. Therefore, the inspection site can be confirmed, and the distance between the inspection sites can be calculated for backup.

[0064] Preferably, the specific steps of step S2 include:

[0065] Step S21: Obtain the correlation between the test items and the test sites, and perform one-to-one matching between the test items and the test sites based on the correlation;

[0066] Step S22: Randomly select two testing sites and input matching testing items for simulation. Evaluate the impact of the two testing items based on shared equipment, shared technicians, and distance between the testing sites. Based on the impact, output the testing items that cannot be performed simultaneously as mutually exclusive combinations.

[0067] Step S23: Repeat step S22 to obtain multiple mutually exclusive combinations.

[0068] After obtaining the test items and test sites, it is necessary to determine at which test site the test items can be implemented. You can first obtain the descriptions of the test items and test sites, extract keywords from the descriptions, and use the Pearson correlation coefficient method to calculate the relevance of the keywords. According to the relevance, the test items and test sites are matched one by one. A single test site can be used for multiple test items. Then, the degree of influence between the test items is determined through simulated testing. When evaluating the degree of influence, the three perspectives of equipment, technicians, and distance are considered, and mutually exclusive combinations are obtained. Mutually exclusive combinations include two test items that cannot be performed at the same time. After repeated simulated testing, several groups of mutually exclusive combinations can be obtained.

[0069] Preferably, the specific steps of step S22 are:

[0070] Step S221: randomly select two testing sites and input matching testing items into the testing sites for simulation;

[0071] Step S222: Extract the equipment list required for the two inspection items and obtain the shared equipment demand. If the shared equipment supply in the park is less than the shared equipment demand, output the two inspection items as a mutually exclusive combination.

[0072] Step S223: extract the types of technicians required for the two inspection projects and obtain the demand for shared technicians. If the supply of shared technicians is less than the demand for shared technicians, output the two inspection projects as a mutually exclusive combination.

[0073] Step S224: When the impact of the detection items on the environment cannot be reduced by the distance between the detection sites, the two detection items are output as a mutually exclusive combination.

[0074] When evaluating the impact of a testing project, we will consider the equipment, technicians, and distance. If two testing projects share common equipment or common types of technicians, we need to determine whether the park can provide a sufficient number of equipment and technicians. If not, it means that the equipment or technicians can only serve one testing project at the same time. Therefore, the corresponding two testing projects will be output as mutually exclusive combinations. In addition, the testing projects will have an impact on the environment during implementation. For example, noise will be generated during a collision test. If the two testing sites are too close, the noise will be less reduced during the transmission process, which will affect the project for noise detection. Therefore, the corresponding two testing projects can be output as mutually exclusive projects. At the same time, based on whether the different impacts of the testing projects on the environment will be reduced by distance, it is evaluated whether other testing projects are mutually exclusive projects.

[0075] Preferably, the specific steps of step S3 are:

[0076] Step S31: Obtain the inspection tasks of the park on the same day, and obtain the inspection items from the inspection tasks on the same day;

[0077] Step S32: Construct a time series path for the test site, randomly select test items corresponding to the test site, and fill them into the time series path in chronological order until all test items corresponding to the test site are selected. When filling in the test items, the start time of the next test item is later than the end time of the previous test item.

[0078] Step S33: Repeat step S32 to obtain multiple time series paths for each detection site.

[0079] After determining the ownership relationship between all inspection items and inspection sites in the park and whether they are mutually exclusive combinations, it is necessary to plan the inspection tasks for the day in the park. The corresponding inspection items can be extracted from the inspection tasks for the day in the park, and then a time series path is constructed based on the inspection sites used by the inspection items. Several time series paths will be constructed for each inspection site, and each time path will be filled with corresponding inspection items, and the order of filling is different. Therefore, each inspection site can obtain multiple time series paths with different arrangement orders. In order to ensure the normal progress of the inspection items on the same time series path, it is necessary to ensure that the end time and start time of adjacent inspection items do not overlap.

[0080] Preferably, the specific steps of step S4 are:

[0081] Step S41: Each detection site randomly outputs a time series path, which are then time-aligned and superimposed to form an overall time series path;

[0082] Step S42: repeat step S41 to obtain several overall time series paths;

[0083] Step S43: Evaluate any moment in the overall time series path to determine whether there is a mutually exclusive combination, and eliminate the overall time series path with a mutually exclusive combination.

[0084] Each time series path ranks the inspection tasks at each inspection site. After each inspection site outputs a time series path and superimposes them, the overall time series path can be obtained. The overall time series path is the specific ranking of the inspection projects in the park on that day, and different time series path combinations can obtain different overall time series paths. After obtaining the overall time series path, any moment of the overall time series path is extracted to determine whether there is a mutually exclusive combination at any moment. If so, it is proposed. If not, it indicates that there is no conflict between the inspection tasks in the overall time series path.

[0085] Preferably, the specific steps of step S5 are:

[0086] Step S51, initialize the gray wolf population, set the number of iterations, and the number of iterations is the number of overall time series paths;

[0087] Step S52: randomly select an overall time series path and calculate the fitness value of the gray wolf based on the shortest time;

[0088] Step S53: Select another overall time series path, calculate the fitness value, and retain the overall time series path with a larger fitness value compared with the previous calculation;

[0089] Step S54: perform iterative calculations and output the overall time series path with the largest fitness value as the optimal overall time series path;

[0090] Step S55: Output the inspection items in the optimal overall time series path in chronological order as a schedule.

[0091] The present invention adopts the gray wolf optimization algorithm to optimize the overall time series path. By calculating the fitness value of each overall time series path and then comparing them, the overall time series path with the largest fitness value is obtained, and it is output as the optimal overall time series path. The time arrangement of the detection items in the optimal overall time series path is the schedule of the detection items of the park on that day. Arranging the detection items according to the schedule can achieve the shortest detection time, improve detection efficiency, and reduce detection costs.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A park dynamic information management method, characterized in that: The following steps are involved: Step S1: Obtain the testing items available in the park and the park plan, and determine the testing site according to the park plan; Step S2: Evaluate the impact of the testing items performed at the corresponding testing site on the testing items performed at other testing sites, and obtain the testing items that cannot be performed simultaneously as mutually exclusive combinations; Step S3: Obtain the inspection tasks for the day in the park, construct several time series paths for each inspection site, and randomly fill the corresponding inspection items in the inspection tasks for the day into the time series paths; Step S4: Each detection site randomly outputs a time series path for superposition to obtain an overall time series path, and the conflicting overall time series paths are eliminated based on mutually exclusive combinations; Step S5: Optimize the overall time series path based on the shortest time, and obtain the optimal overall time series path. Output the inspection items in the optimal overall time series path in chronological order as a schedule.

2. A park dynamic information management method according to claim 1, characterized in that: The specific steps of step S1 include: Step S11: Obtain the service list displayed by the park, and extract the testing items that the park can provide from the service list; Step S12: Obtain planning information of the park, identify several testing sites from the planning information, and obtain the distances between the testing sites.

3. A park dynamic information management method according to claim 2, characterized in that: The specific steps of step S2 include: Step S21: Obtain the correlation between the test items and the test sites, and perform one-to-one matching between the test items and the test sites based on the correlation; Step S22: Randomly select two testing sites and input matching testing items for simulation. Evaluate the impact of the two testing items based on shared equipment, shared technicians, and distance between the testing sites. Based on the impact, output the testing items that cannot be performed simultaneously as mutually exclusive combinations. Step S23: Repeat step S22 to obtain multiple mutually exclusive combinations.

4. A park dynamic information management method according to claim 3, characterized in that: The specific steps of step S21 are: obtaining descriptions of the test items and test sites, extracting keywords from the descriptions, calculating the relevance of the keywords using the Pearson correlation coefficient method, and matching the test items and test sites one by one based on the relevance.

5. A park dynamic information management method according to claim 3, characterized in that: The specific steps of step S22 are: Step S221: randomly select two testing sites and input matching testing items into the testing sites for simulation; Step S222: Extract the equipment list required for the two inspection items and obtain the shared equipment demand. If the shared equipment supply in the park is less than the shared equipment demand, output the two inspection items as a mutually exclusive combination. Step S223: extract the types of technicians required for the two inspection projects and obtain the demand for shared technicians. If the supply of shared technicians is less than the demand for shared technicians, output the two inspection projects as a mutually exclusive combination. Step S224: When the impact of the detection items on the environment cannot be reduced by the distance between the detection sites, the two detection items are output as a mutually exclusive combination.

6. A park dynamic information management method according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Obtain the inspection tasks of the park on the same day, and obtain the inspection items from the inspection tasks on the same day; Step S32: construct a time series path for the test site, randomly select test items corresponding to the test site and fill them into the time series path in chronological order until all test items corresponding to the test site are selected; Step S33: Repeat step S32 to obtain multiple time series paths for each detection site.

7. A park dynamic information management method according to claim 6, characterized in that: When filling the detection items, the step S32 makes the start time of the next detection item later than the end time of the previous detection item.

8. A park dynamic information management method according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Each detection site randomly outputs a time series path, which are then time-aligned and superimposed to form an overall time series path; Step S42: repeat step S41 to obtain several overall time series paths; Step S43: Evaluate any moment in the overall time series path to determine whether there is a mutually exclusive combination, and eliminate the overall time series path with a mutually exclusive combination.

9. A park dynamic information management method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51, initialize the gray wolf population and set the number of iterations; Step S52: randomly select an overall time series path and calculate the fitness value of the gray wolf based on the shortest time; Step S53: Select another overall time series path, calculate the fitness value, and retain the overall time series path with a larger fitness value compared with the previous calculation; Step S54: perform iterative calculations and output the overall time series path with the largest fitness value as the optimal overall time series path; Step S55: Output the inspection items in the optimal overall time series path in chronological order as a schedule.

10. A park dynamic information management method according to claim 9, characterized in that: The number of iterations is the number of overall time series paths.

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