Analysis method for large-scale actual measurement configuration

Abstract

The invention provides an analysis method for large-scale actual measurement configuration. The analysis method comprises the following steps: step 1, acquiring all configuration information of all rectification vehicle types for rectification items; 2, grouping all the configuration information according to the declaration values of the grouping parameters; step 3, determining the visual coverage condition of each actually measured configuration by taking the configuration group as a unit; 4, starting to divergently create a branch chain by taking a single configuration as a starting point until a configuration set which can be covered by all nodes on the current branch chain is a whole group of configuration; step 5, performing de-duplication processing on the feasible solution to obtain a final rectification scheme feasible solution / set, and screening an optimal solution / set of the rectification scheme; and 6, in the feasible solution set of the rectification scheme, in combination with the actual sample vehicle production condition, selecting a scheme capable of implementing detection. According to the method, the optimal actual measurement scheme can be quickly found out, and an enterprise is helped to complete corresponding project rectification with the minimum number of sample cars, the minimum configuration of the sample cars and the minimum cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle detection and certification, and particularly relates to an analysis method for a large-scale measured configuration. Background Art

[0002] In the invention patent with the publication number CN119250846A, "Method for Generating Optimal Solutions and Feasible Solutions for Measured Schemes for Multiple Recorded Declaration Values", a method for obtaining optimal solutions and feasible solutions for measured schemes is introduced. The general principle is as follows: Suppose there are 3 configurations for a certain measured item of a certain vehicle model, namely Configuration A, B, and C. There is a certain equivalent relationship among these three configurations. As Figure 1 shown, when measuring Configuration A, Configuration A and B can be regarded as Configuration A. This relationship is called that when measuring Configuration A, it can cover Configuration A and Configuration B; similarly, when measuring Configuration B, it can cover Configuration B and C; when measuring Configuration C, it can only cover Configuration C.

[0003] The current requirement is that for a detection item, it is desired to use the fewest configurations for actual measurement to ensure that each configuration can be covered. The original patent provides a method. First, randomly select 1 configuration from these 3 configurations, and judge whether the coverage result of the selected configuration is the complete set A, B, C. If so, it means that measuring this one configuration can meet the requirements. Here, there are kinds of results when randomly selecting 1 from 3 configurations, that is, separately selecting A, B, or C. Obviously, randomly selecting any one configuration does not meet the requirements. At this time, start selecting combinations of 2 configurations, that is . At this time, there are also 3 results, which are selecting A and B, A and C, B and C. The union of the coverage results of the combination of A and B is the complete set, which is obviously okay. The coverage result of the combination of A and C is also the complete set, and the coverage result of B and C is not the complete set. Therefore, when selecting 2 configurations for actual measurement, it is the optimal solution of the detection scheme. At this time, there are 2 optimal solutions.

[0004] If you want to obtain other feasible solutions, you only need to select the results.

[0005] It can be seen from the above principle that this method fully considers all combination situations, but there are still certain problems. Suppose there are n configurations to be analyzed now. According to the above principle, if the best measured scheme requires measuring k configurations, then the calculation steps to obtain the optimal solution should be: (1) The calculation steps to obtain the feasible solution should be: (2) Previous patents addressed the problem of how to obtain the best measured solution for a single vehicle model in a certain project. In such problems, the magnitude of n is very small, and the original method was sufficient to support it. However, with the update of the detection scheme standards and requirements, the limitations of the original method will become more and more obvious.

[0006] For example, in 2024, when the Ministry of Transport's Automobile Transport Center solicited opinions on amending the JT / T 1178.1-2018 standard, hereinafter referred to as Amendment No. 1, it was proposed to cancel the speed and tonnage restrictions for active safety devices such as ESC and AEBS and turn them into standard equipment or functions. When relevant staff in the testing agency evaluated the impact of the amendment on actual work, it was found that if an enterprise had 500 vehicle models that originally did not need to install active safety devices, but with the update of the standard, they now needed to be installed. Taking the installation of ESC as an example, all 500 vehicle models would need to provide reports on the operation 02 test item. These reports could be either measured reports or deemed reports. For the 02 item, in addition to being related to the ESC model and manufacturer installed, it was also related to tire specifications, curb weight, braking components, etc. If there were multiple specifications for these parameters and components, different specifications would form different 02 item configurations according to the permutation and combination relationship. If an average of 4 02 item configurations were available for 1 vehicle model, then there would be 500 * 4 = 2000 configurations to be analyzed. If only 50 configurations needed to be measured in the actual situation to cover all configuration cases, that is, 50 configurations were measured, and any 1 of the remaining 1950 configurations could find 1 or more configurations in the 50 measured configurations for deemed equivalence. Substituting n = 2000 and k = 50 into Formula 1, the calculated result would be approximately: , according to the method of the previous patent, at least this level of operation times would be required to obtain the optimal solution result. Obviously, the problem could not be satisfactorily solved. Summary of the Invention

[0007] The present invention discloses an analysis method for a large number of measured configurations. The analysis method for a large number of measured configurations includes the following steps: Step 1: Obtain all the configuration information of all rectified vehicle models for the rectification item and integrate this information uniformly; Step 2: Group the configuration information obtained in Step 1 according to the declared values of the grouping parameters, and require that the declared values of the grouping parameters be exactly the same to be divided into the same configuration group; Step 3: Taking the configuration group as a unit, determine the deemed coverage of each configuration during measurement according to the deemed determination conditions for all the configurations in the configuration group; Step 4: In the configuration group, starting from a single configuration respectively, start to diverge and create branch chains until the set of configurations that can be covered by all the nodes on the current branch chain is the entire group of configurations; Step 5: Remove duplicates from the feasible solutions to obtain the final feasible solutions / sets of the rectification plan, and screen out the combination with the smallest number of configurations as the optimal solution / set of the rectification plan. Step 6: In the set of feasible solutions of the rectification plan, in combination with the actual production situation of the sample vehicle, select the plan that can be implemented according to the principle of the smallest number of configurations.

[0008] Furthermore, in Step 1, uniformly write the declared values of the parameters that are not applicable to the rectification model as "N / A".

[0009] Furthermore, in Step 3, sequentially traverse and analyze each configuration in this group of configurations. When this configuration is actually measured, determine whether the remaining other configurations can be regarded as the situation of this configuration until all the configurations in this group are traversed and analyzed to obtain the result after the mutual equivalence determination of this group of configurations.

[0010] Furthermore, in Step 3, the result is stored in the dictionary data format. The key of the dictionary is each configuration, and the corresponding value is the equivalent configuration situation that can be covered when this configuration is actually measured. In each value of the dictionary, the key corresponding to this value must also be included.

[0011] Furthermore, in Step 4, it includes the following steps: Step 4.1: Start creating a branch chain from each single configuration respectively, and obtain the set of configurations that can be covered by this node according to the result of Step 3. Step 4.2: Judge whether this coverage set already contains all the configurations of the entire group. If the coverage set already contains all the configurations of the group, then the current node configuration is the optimal solution of the rectification plan, and stop the analysis of the current branch. Step 4.3: If the coverage set does not contain all the configurations of the group, then a new configuration needs to be added as the next node, and update the corresponding coverage set in combination with the result of Step 3, and then repeat Step 4.2 until the set of configurations that can be covered by all the nodes on the current branch chain is the set of all configurations of the group, and the combination of the node configurations on this chain is the feasible solution of the rectification plan.

[0012] Furthermore, in Step 5, each branch chain will obtain a feasible solution of the rectification plan. These feasible solutions may be repeated and need to be de-duplicated. The result after de-duplication is the final set of feasible solutions. In the final set of feasible solutions, screen out the combinations with the smallest number of configurations, and these combinations are the optimal solutions of the rectification plan.

[0013] Further, in step 6, start judging from the solution with the least measured configurations whether implementation detection can be carried out; for each configuration, a corresponding prototype vehicle needs to be produced. If the prototype vehicle for the corresponding configuration cannot be produced, then judge in the order of increasing the number of measured configurations until a measured configuration that can implement the detection is selected.

[0014] The beneficial effects achieved by the present invention are as follows: The present invention innovatively uses grouped parameters to achieve two key breakthroughs. On the one hand, it can reasonably divide a large number of complex configurations into multiple configuration groups, greatly optimizing the structure and efficiency of configuration management; on the other hand, since only non-grouped parameters need to be compared, this solution significantly reduces the number of parameters that need to be determined when analyzing configuration simultaneity for each configuration group. Moreover, each configuration group can carry out analysis work synchronously, and this parallel processing mode greatly shortens the overall analysis time and improves work efficiency.

[0015] In the process of exploring feasible configuration combinations, the present invention adopts recursive analysis to systematically attempt all possible configuration combinations. Each step deeply explores recursively based on the current selection. Once it is found that the current selection cannot lead to an effective solution, then by adding node configurations, attempts are made for higher-dimensional combinations until all possible solutions are found.

[0016] Taking a specific example, this solution can accurately find all measured configuration combinations that can cover all rectification configurations, and screen out the combination with the least number of configurations from them, and then determine the corresponding optimal solution and feasible solution. Compared with the original solution, the new solution can effectively control the amount of calculation and avoid unnecessary resource consumption by carefully setting the termination condition. At the same time, with the help of the backtracking search mechanism, the iterative process is accelerated, helping the enterprise quickly lock in the best measured solution. It not only significantly reduces the labor cost of enterprise certification personnel, but also helps the enterprise complete the corresponding project rectification work efficiently with the least number of prototype vehicles, the most streamlined prototype vehicle configurations and the lowest detection cost under the premise of ensuring the calculation accuracy rate, bringing significant economic benefits and time cost advantages to the enterprise. Description of the Drawings

[0017] Figure 1 It is a diagram of the simultaneity relationship between three configurations; Figure 2 It is a flowchart of an analysis method for a large number of measured configurations; Figure 3 It is a schematic diagram of the simultaneity conditions for project 02; Figure 4 It is a schematic diagram of the grouped parameters and grouped declared values for project 54 in the present invention; Figure 5 It is a diagram of the simultaneity relationship between three configurations in the second embodiment of the present invention; Figure 6 This is the flowchart for analyzing the coverage relationship with Configuration 1 as the initial node in the second embodiment of the present invention; Figure 7 This is the flowchart for analyzing the coverage relationship with Configuration 2 as the initial node in the second embodiment of the present invention; Figure 8 This is the flowchart for analyzing the coverage relationship with Configuration 3 as the initial node in the second embodiment of the present invention. Detailed implementation manners

[0018] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description progresses. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. Those skilled in the art should understand that the details and forms of the technical solutions of the present invention can be modified or replaced without departing from the spirit and scope of the present invention, but such modifications and replacements all fall within the protection scope of the present invention.

[0019] Embodiment 1 As Figure 2 shown, the Automobile Transportation Center of the Ministry of Transport intends to put forward modification opinions on the operation declaration content, and each automobile enterprise needs to prepare the rectification of the vehicle type declaration according to the content of the modification form. The corresponding rectification work can adopt an analysis method for a large number of measured configurations proposed in this application. The analysis method for a large number of measured configurations includes the following steps: Step 1: Obtain all the configuration information and corresponding configurations of all rectified vehicle types in a certain rectification project, and integrate all the vehicle type configuration results together; To obtain all the configuration information of all rectified vehicle types in a certain rectification project, it can be provided by enterprise certification personnel after manual collation, or each vehicle type can obtain the corresponding configuration according to the method provided in the patent "A Method for Generating Detection Project Configurations for Multiple Recorded Declaration Values", and then integrate all the vehicle type configuration results together to obtain a total of N configurations.

[0020] In addition, there are some special parameters here that need to be data-cleaned. Some parameters are not applicable to the corresponding rectified vehicle types. Here, it is stipulated that for non-applicable vehicle types, the declared values of these parameters are uniformly "N / A".

[0021] For example, for the parameter of the chassis production enterprise, when the rectified vehicle is a trailer, since the trailer does not have the corresponding chassis components, the declared value of the corresponding chassis production enterprise needs to be corrected to "N / A"; For another example, the wheelbase has three parameters, namely QA1231, the distance from axle I to axle II (mm), QA1232, the distance from axle II to axle III (mm), and QA1233, the distance from axle III to axle IV (mm). If the vehicle model to be rectified is a two-axle vehicle with only axles I and II, then only the declared value of QA1231 needs to be filed for this vehicle model, and QA1232 and QA1233 are not applicable to this two-axle vehicle. Therefore, the corresponding declared values also need to be corrected to N / A.

[0022] Step 2: Group all the configurations in the rectification items according to the grouping parameters. According to the same type determination conditions issued by the Automobile Transportation Center of the Ministry of Transport, extract the parameters for which the declared values of the specified declared values must be the same to determine that the configurations are regarded as the same as the grouping parameters; group the N configurations provided in Step 1 according to the declared values of the grouping parameters, and the configurations with the declared values of all the grouping parameters being the same are divided into one group.

[0023] In a specific embodiment, such as Figure 3 As shown, according to the determination conditions of the same type provided by the Automobile Transportation Center of the Ministry of Transport, in the test item numbered 54, only when the declared values of the parameters such as the chassis manufacturer, the category of the automatic emergency braking system (AEBS), the model of the automatic emergency braking system (AEBS), the manufacturer of the automatic emergency braking system (AEBS), the model of the anti-lock braking system (ABS) controller, and the manufacturer of the anti-lock braking system (ABS) controller are exactly the same, can they be used as the necessary conditions for determining whether the relevant vehicle configurations can be regarded as the same. Based on this, the above-mentioned parameters can be defined as grouping parameters, which play a key role in classification and definition during the grouping process.

[0024] Divide these N configurations into n configuration groups according to the declared values of the grouping parameters. When the declared values of the grouping parameters are exactly the same, the corresponding configurations are divided into the same configuration group.

[0025] In this embodiment, for the rectification of 54 items of operation, as shown in Table 1, which is the content sorted according to the same type determination conditions issued by the Ministry of Transport, it is known that the grouping parameters for 54 items should be: QB002, QJ001, QJ015, QJ017, QJ019, QJ021, QJ022, QB0815, QB0816, QB0817, QB0818, QB003, QA067, QB0741, QB0171, QB0751, QB0161, QA025, QA024, QA026, QJ029, QJ030, QJ071, QJ072, QJ031, QJ032, QA0481, QA029, QA027, QA028, QA064.

[0026] In addition, if the declared values of several parameters in the grouping parameters cannot be determined. For example, for 54 items related to rectification, it may be necessary to replace ABES components. However, if the enterprise has not determined which AEBS supplier to use, then the corresponding AEBS category, model, and manufacturer are all unknown. In this case, the corresponding parameters need to be ignored. Based on this, the original steps are continued. In the final result, multiplying by the number of declared value groups of the ignored parameters can also obtain the final result. For example, if the AEBS category, model, and manufacturer are ignored and the original steps are continued for analysis, it is found that at least 5 configurations need to be actually measured to complete the rectification. However, if the enterprise determines that it will use 3 sets of AEBS parts later, then configurations need to be prepared for actual measurement.

[0027] Table 1 Operating 54 equivalent conditions

[0028] Parameter code Parameter Equivalent requirement QB002 Chassis manufacturer Same QA025 Automatic Emergency Braking System (AEBS) category Same QA024 Automatic Emergency Braking System (AEBS) model Same QA026 Automatic Emergency Braking System (AEBS) manufacturer Same QJ029 Antilock Braking System (ABS) controller model Same QJ030 Antilock Braking System (ABS) controller manufacturer Same QJ071 Electronic Braking System (EBS) controller model Same Q072 Electronic Braking System (EBS) control manufacturer Same QJ031 Electronic Stability Control System (ESC) model Same QJ032 Electronic Stability Control System (ESC) manufacturer Same QA0481 Automatic Emergency Braking System (AEBS) front obstacle detection sensor category Same QA029 Automatic Emergency Braking System (AEBS) front obstacle detection sensor quantity Same QA027 Automatic Emergency Braking System (AEBS) front obstacle detection sensor model Same QA028 Automatic Emergency Braking System (AEBS) front obstacle detection sensor manufacturer Same QA064 Automatic Emergency Braking System (AEBS) front obstacle detection sensor installation position Same QA0701 Gross vehicle weight Same or decreased QA094 Total mass of articulated vehicle (kg) Same or decreased QJ001 Type of service braking system Same QB009 Tire size Tire section width and static load radius change by the same or decreased by 5% QJ015 Brake caliper model Same QJ017 Brake disc model Same QJ019 Brake drum model Same QJ021 Brake shoe model Same QJ022 Brake lining specification model Same QB0815 Number of tires (first axle) Same QB0816 Number of tires (second axle) Same QB0817 Number of tires (third axle) Same QB0818 Number of tires (fourth axle) Same QB003 Number of axles Same QB0461 Axle arrangement Same QA067 Vehicle classification Same In addition, when processing 54 items and other items, since the declared value of the parameter QB0461 is non-standard language, for different declared values, although they are different text descriptions, their Chinese meanings are the same. Logically, they should also be regarded as the same. However, in the actual batch processing process, it is difficult to determine whether different Chinese descriptions have the same Chinese meaning. Therefore, "determining whether the declared values of QB0461 are the same" needs to be equivalently transformed into "determining whether the declared values of QB0741, QB0171, QB0751, and QB0161 are the same".

[0029] The principle of grouping is that only when the declared values of all grouping parameters are exactly the same can they be grouped into one group. As Figure 4 shown, for example, the declared values of the parameters in the first group are: 'N / A', 'dual-circuit air brake, front and rear disc brakes', '22.5', 'φ430X45', 'N / A', 'N / A', '22.5', '2', '4', '4', 'N / A', '3', 'tractor vehicle', '1', '2', 'the first axle', 'the second axle, the third axle'. Only when the declared values corresponding to the grouping parameters are these values respectively can they be assigned to the first group.

[0030] Step 3: Taking the configuration group as a unit, determine the deemed coverage of each configuration during actual measurement based on the deemed judgment conditions for all the configurations in the configuration group; and obtain the deemed coverage of the configurations in other configuration groups using the same scheme. Analyze each configuration in all configuration groups in turn. According to the judgment logic of the deemed requirements, analyze: when any configuration is actually measured, whether the remaining other configurations in this configuration group can be deemed as this configuration, until all the configurations in this configuration group are analyzed. Obtain the result after mutual deemed judgment of the configurations in this group and store the result in a special dictionary data format. The key of the dictionary is a single configuration, and the corresponding value is the deemed configuration situation that can be covered when this configuration is actually measured. In each value of the dictionary, the key corresponding to this value must also be included.

[0031] Step 4: Starting from a single configuration, diverge to create a branch chain. Obtain the set of configurations that can be covered by this node according to the result of Step 3. Determine whether this coverage set is the entire set of configurations. If so, take the configuration of this node as the optimal solution of the rectification plan and stop the analysis of the current branch chain. If it is not the entire set of configurations, new configurations need to be added as the next node, and at the same time, update the corresponding coverage set until the set of configurations that can be covered by all the nodes on the current branch chain is the entire set of configurations. At this time, the combination of the node configurations on this chain is the feasible solution of the rectification plan; Step 4.1: Starting from a single configuration respectively, diverge to create a branch chain. According to the result of Step 3, obtain the set of configurations that this node can cover; Step 4.2: Judge whether this coverage set already contains all the configurations of the entire group. If the coverage set already contains the entire set of configurations, then the current node configuration is the optimal solution of the rectification plan and stop the analysis of the current branch; Step 4.3: If the coverage set does not contain the entire set of configurations, new configurations need to be added as the next node, and update the corresponding coverage set in combination with the result of Step 3, and then repeat Step 4.2 until the set of configurations that can be covered by all the nodes on the current branch chain is the entire set of configurations. At this time, the combination of the node configurations on this chain is the feasible solution of the rectification plan; Step 5: De-duplicate the feasible solutions to obtain the final set of feasible solutions for the rectification plan, and select the combination with the least number of configurations as the optimal solution of the rectification plan; Each branch chain will obtain a feasible solution for the rectification plan. These feasible solutions may be repeated and need to be de-duplicated. The result after de-duplication is the final set of feasible solutions. In the final set of feasible solutions, select the combination with the least number of configurations. These combinations are the optimal solutions of the rectification plan.

[0032] Step 6: Select the actually measured configurations that can be implemented from all possible combinations of actually measured configurations.

[0033] Starting from the solution with the least measured configuration, check one by one whether implementation detection can be carried out; if the existing sample vehicles are not sufficient to meet the detection, judge in the way of increasing the measured configuration quantity until a measured configuration that can implement the detection is selected.

[0034] It stands to reason that obtaining the optimal solution is an ideal solution to solve the operation rectification task, which means that the enterprise can complete the rectification task with the least number of sample vehicles, the least detection configuration, the least test cost, and the least enterprise cost. However, in the actual application process, the actual situation often needs to be considered. For example, a certain configuration in the optimal solution or the optimal solution set cannot be produced due to the lack of some parts, but due to the timeliness of the rectification, the rectification task must be ensured to be completed. At this time, it is necessary to find a configuration that can be produced in the feasible solution or the feasible solution set with the configuration number +1. If the feasible solution or the feasible solution set corresponding to the current configuration number cannot guarantee actual production, continue to increase the configuration number by 1 until a feasible solution that meets the requirements can be found.

[0035] Embodiment 2 As Figure 5 shown, under three specific configuration combinations, the analysis method provided by the present invention for a large-scale measured configuration is as follows: Starting from Configuration 1: The current combination is [Configuration 1], and the covered set is {Configuration 1, Configuration 2} First, try to add Configuration 2. The new combination is [Configuration 1, Configuration 2], and the new covered set is {Configuration 1, Configuration 2, Configuration 3}, which meets the termination condition. Add [Configuration 1, Configuration 2] to the result.

[0036] Secondly, try to add Configuration 3. The new combination is [Configuration 1, Configuration 3], and the new covered set is {Configuration 1, Configuration 2, Configuration 3}, which meets the termination condition. Add [Configuration 1, Configuration 3] to the result.

[0037] As Figure 6 shown, all the chain branches with Configuration 1 as the initial node have been analyzed.

[0038] Starting from Configuration 2: The current combination is [Configuration 2], and the covered set is {Configuration 2, Configuration 3}.

[0039] First, try to add Configuration 1. The new combination is [Configuration 2, Configuration 1], and the new covered set is {Configuration 1, Configuration 2, Configuration 3}, which meets the termination condition. Add [Configuration 1, Configuration 2] to the result (it will be deduplicated later because it is repeated with the previous one after sorting).

[0040] Secondly, try to add Configuration 3. The new combination is [Configuration 2, Configuration 3], and the new coverage set is {Configuration 2, Configuration 3}. Since the termination condition is not met, continue to add nodes.

[0041] At this time, only Configuration 1 can be added. The new combination is [Configuration 2, Configuration 3, Configuration 1], and the new coverage set is {Configuration 1, Configuration 2, Configuration 3}. Since the termination condition is met, add [Configuration 2, Configuration 3, Configuration 1] to the result.

[0042] As Figure 7 shown, all the chain branches starting from Configuration 2 have been analyzed.

[0043] Starting from Configuration 3: The current combination is [Configuration 3], and the covered set is {Configuration 3}.

[0044] First, try to add Configuration 1. The new combination is [Configuration 3, Configuration 1], and the new coverage set is {Configuration 1, Configuration 2, Configuration 3}. Since the termination condition is met, add [Configuration 1, Configuration 3] to the result (it will be deduplicated later because it is a duplicate after sorting).

[0045] Secondly, try to add Configuration 2. The new combination is [Configuration 3, Configuration 2], and the new coverage set is {Configuration 2, Configuration 3}. Since the termination condition is not met, a new node needs to be added.

[0046] At this time, only Configuration 1 can be added. The new combination is [Configuration 3, Configuration 2, Configuration 1], and the new coverage set is {Configuration 1, Configuration 2, Configuration 3}. Since the termination condition is met, add [Configuration 3, Configuration 2, Configuration 1] to the result (it will be deduplicated later because it is a duplicate after sorting).

[0047] As Figure 8 shown, all the chain branches starting from Configuration 3 have been analyzed.

[0048] So far, all the combinations of all branches have been completed. After deduplication, there are the following combination results in the solution of the plan at this time. These 3 measured solutions are all feasible solutions for the rectification plan: The first measured solution: [Configuration 1, Configuration 2] The second measured solution: [Configuration 1, Configuration 3] The third measured solution: [Configuration 1, Configuration 2, Configuration 3] Among them, the first 2 require the fewest number of measured configurations, which is 2. Therefore, the first 2 are the optimal solution sets of the measured solutions.

[0049] The above are only the specific steps of the present invention, which do not constitute any limitation to the protection scope of the present invention; all technical solutions formed by equivalent transformation or equivalent substitution fall within the scope of the protection of the present invention; the parts not elaborated in detail in the present invention belong to the well-known technologies in the art.

Claims

1. An analysis method for large-scale measured configurations, characterized in that The analysis method for a large number of measured configurations comprises the following steps: Step 1, obtain all configuration information of all modified models for the modification project, and integrate the information; Step 2: group the configuration information obtained in step 1 according to the declared values ​​of the grouping parameters. The declared values ​​of the grouping parameters must be completely consistent to be classified into the same configuration group. Step 3: Taking the configuration group as a unit, determine the deemed coverage of each configuration in the configuration group according to the deemed judgment condition during the actual measurement; Step 4: In the configuration group, start with a single configuration and create branch chains divergently until the configuration set that can be covered by all nodes on the current branch chain is the entire group of configurations; Step 5: perform deduplication processing on the feasible solutions to obtain the final feasible solution / set of the rectification plan, and select the combination with the least number of configurations as the optimal solution / set of the rectification plan; Step 6: From the feasible solution set of the rectification plan, based on the actual production situation of the prototype vehicle and in accordance with the principle of minimizing the number of configurations, select the plan that can be tested.

2. The analysis method for large-scale measured configurations according to claim 1, characterized in that In step 1, the declared values ​​of the parameters that are not applicable to the modified vehicle models are uniformly written as "N / A".

3. The analysis method for large-scale measured configurations according to claim 1, wherein In step 3, each configuration in the group of configurations is traversed and analyzed in turn. When the configuration is actually measured, it is determined whether the remaining configurations can be regarded as the same as the configuration, until all the configurations in the group are traversed and analyzed, and the result after the mutual determination of the configurations in the group is obtained.

4. The analysis method for large-scale measured configurations according to claim 3, characterized in that, In step 3, the result is stored in a dictionary data format, where the key of the dictionary is each configuration, and the corresponding value is the deemed configuration that can be covered when the configuration is actually measured. Each value in the dictionary must also include the key corresponding to this value.

5. The analysis method for large-scale actual measurement configuration according to claim 1, characterized in that, In step 4, the following steps are included: In step 4.1, starting from a single configuration, start diverging and creating a branch chain, and according to the result of step 3, obtain the configuration set that the node can cover; In step 4.2, determine whether the covering set already contains all the configurations of the entire group. If the covering set already contains the entire group of configurations, then the current node configuration is the optimal solution for the rectification plan, and stop the analysis of the current branch; In step 4.3, if the covering set does not contain the entire set of configurations, a new configuration needs to be added as the next node, and the corresponding covering set needs to be updated based on the result of step 3. Then, step 4.2 is repeated until the configuration set that can be covered by all nodes on the current branch chain is the entire set of configurations. The node configuration combination on the chain is the feasible solution to the rectification plan.

6. The analysis method for large-scale actual measurement configuration according to claim 1, characterized in that, In step 5, each branch chain will obtain a feasible solution for the rectification plan. These feasible solutions may be repeated and need to be deduplicated. The result obtained after deduplication is the final set of feasible solutions. In the final set of feasible solutions, the combinations with the least number of configurations are selected. These combinations are the optimal solutions for the rectification plan.

7. The analysis method for large-scale actual measurement configuration according to claim 1, characterized in that In step 6, it is determined whether the test can be carried out starting from the scheme with the least measured configuration; each configuration needs to produce a corresponding prototype vehicle. If the prototype vehicle of the corresponding configuration cannot be produced, the determination is made in an increasing manner according to the number of measured configurations until a measured configuration that can be tested is selected.

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