Vehicle health degree determination method and electronic equipment

Abstract

The invention provides a vehicle health degree determination method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining each to-be-evaluated vehicle and an evaluation index set corresponding to each to-be-evaluated vehicle; selecting a target index matched with a preset applicable scene from the evaluation index set, and determining an index value set based on the target index; and generating the vehicle health degree of the to-be-evaluated vehicle based on the index value set. By implementing the method, the vehicle health degree can be automatically and individually determined, specifically, the index value set of the to-be-evaluated vehicle matched with the applicable scene can be obtained based on the evaluation index set, the vehicle health degree of the to-be-evaluated vehicle can be automatically generated based on the index value set, manual intervention is not needed, the efficiency of determining the vehicle health degree can be improved, and the user experience is improved. And the matching degree between the vehicle health degree and the applicable scene is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a vehicle health degree determination method and an electronic device. BACKGROUND

[0002] Generally speaking, as the service life of a vehicle increases, the health degree of the vehicle decreases, and the health degree is a state index that comprehensively reflects software and hardware faults, performance degradation and aging of electronic components and mechanical components. Since the lower the health degree of a vehicle, the higher the probability of accidents, periodically determining the health degree of a vehicle can avoid accidents in advance in the process of fleet management. In related technologies, manual health degree evaluation of vehicles is usually required for different vehicle use approaches, but this way of determining the health degree of a vehicle has the problem of low efficiency.

[0003] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0004] The purpose of the present application is to provide a vehicle health degree determination method, a vehicle health degree determination device and an electronic device, which can automatically and individually determine the health degree of a vehicle. Specifically, the index value set of the vehicle to be evaluated that matches the applicable scenario can be obtained based on the evaluation index set, and the vehicle health degree of the vehicle to be evaluated can be automatically generated based on the index value set, without human intervention, which can improve the efficiency of determining the health degree of the vehicle and improve the matching degree between the vehicle health degree and the applicable scenario.

[0005] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0006] According to an aspect of the present application, a vehicle health degree determination method is provided, which comprises:

[0007] determining a vehicle to be evaluated and an evaluation index set of the vehicle to be evaluated;

[0008] selecting a target index that matches a preset applicable scenario from the evaluation index set, and determining an index value set based on the target index;

[0009] generating a vehicle health degree of the vehicle to be evaluated based on the index value set.

[0010] According to an aspect of the present application, a vehicle health degree determination device is provided, which comprises:

[0011] an information determination unit configured to determine a vehicle to be evaluated and an evaluation index set of the vehicle to be evaluated;

[0012] The index value determination unit is configured to select a target index matching a preset applicable scenario from the evaluation index set, and determine the index value set based on the target index.

[0013] The sorting unit is configured to generate the vehicle health degree of the vehicle to be evaluated based on the index value set.

[0014] According to an aspect of the present application, a computer program product is provided, comprising a computer program configured to implement the above method.

[0015] According to an aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is configured to implement the above method when executed by a processor.

[0016] According to an aspect of the present application, an electronic device is provided, comprising a processor and a memory configured to store executable instructions of the processor, wherein the processor is configured to execute the above method by executing the executable instructions.

[0017] The exemplary embodiments of the present application can have the following partial or all beneficial effects:

[0018] In the vehicle health degree determination method provided in the example embodiment of the present application, the vehicle health degree can be determined automatically and individually, specifically, the index value set of the vehicle to be evaluated matching the applicable scenario can be obtained based on the evaluation index set, and the vehicle health degree of the vehicle to be evaluated can be automatically generated based on the index value set, without human intervention, which can improve the efficiency of determining the vehicle health degree and improve the matching degree between the vehicle health degree and the applicable scenario.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0021] Figure 1 A flowchart of a vehicle health degree determination method according to an embodiment of the present application is schematically shown;

[0022] Figure 2 A flowchart of a vehicle health degree determination method according to another embodiment of the present application is schematically shown;

[0023] Figure 3A structural block diagram of a vehicle health degree determination apparatus according to an embodiment of the present application is schematically shown;

[0024] Figure 4 A structural schematic diagram of a computer system of an electronic device suitable for implementing embodiments of the present application is schematically shown. DETAILED DESCRIPTION

[0025] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations. In the following description, numerous specific details are provided to give a thorough understanding of implementations of the application. One skilled in the relevant art will recognize, however, that the

[0026] In addition, the accompanying drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this specification. The drawings illustrate examples of the present application and, as such, a change in the drawings can be made without departing from the scope of the present application. Like reference numerals can be used to denote like elements throughout the accompanying drawings. In addition, the same reference numerals or signs are used to denote the same elements throughout the specification.

[0027] Reference will now be made to Figure 1 , Figure 1 A flowchart of a vehicle health degree determination method according to an embodiment of the present application is schematically shown. As shown in Figure 1 , the vehicle health degree determination method can include steps S110-S130.

[0028] Step S110: determining a vehicle to be evaluated and a set of evaluation indexes of the vehicle to be evaluated.

[0029] Step S120: selecting a target index matching a preset applicable scenario from the set of evaluation indexes, and determining a set of index values based on the target index.

[0030] Step S130: generating a vehicle health degree of the vehicle to be evaluated based on the set of index values.

[0031] Implementation Figure 1The method shown can automatically and personalized determine vehicle health. Specifically, it can obtain a set of indicator values ​​for the vehicle to be evaluated that matches the applicable scenario based on a set of evaluation indicators, and automatically generate the vehicle health of the vehicle to be evaluated based on the set of indicator values ​​without human intervention. This can improve the efficiency of determining vehicle health and improve the matching degree between vehicle health and applicable scenario.

[0032] The steps described above in this example implementation will now be explained in more detail.

[0033] In step S110, the vehicle to be evaluated and the set of evaluation indicators for the vehicle to be evaluated are determined.

[0034] Specifically, the vehicles to be evaluated refer to a batch of vehicles whose health needs to be evaluated. Different vehicles to be evaluated may correspond to the same vehicle type or different vehicle types. This application does not limit the number of vehicles to be evaluated.

[0035] Furthermore, vehicle types may include, but are not limited to: intelligent driving vehicles and non-intelligent driving vehicles; or, vehicle types may include, but are not limited to: fuel-powered vehicles, electric vehicles, and hybrid vehicles; or, vehicle types may include, but are not limited to: buses, sedans, and sports cars. The classification of vehicle types depends on the classification criteria; therefore, the vehicle types obtained based on different classification criteria may be different, and this application embodiment does not limit this.

[0036] In the health evaluation process for each batch of vehicles to be evaluated, the set of evaluation indicators used can be different. The specific indicators included in the evaluation indicator set depend on the vehicle type, purpose or other conditions of each vehicle in this batch of vehicles to be evaluated. Different evaluation indicator sets may have some indicators that are the same or completely different.

[0037] Specifically, the evaluation index set includes various indicators that need to be considered when evaluating the health of vehicles of a corresponding vehicle type. Optionally, the evaluation index set can be further subdivided into multiple dimensions. For example, the evaluation index set includes the indicators A, B, C, D, E, F, and G. The evaluation index set can be divided into four dimensions: the first dimension includes A, B, and C; the second dimension includes D; the third dimension includes E and F; and the fourth dimension includes G.

[0038] When facing different industry needs, one can use either a set of evaluation indicators that are not subdivided into multiple dimensions, or a set of evaluation indicators that are subdivided into multiple dimensions. For example, when facing the needs of the display float industry, the vehicles required by this industry are only used for decoration and display, so the vehicles do not need to be drivable. Therefore, a set of evaluation indicators that are subdivided into multiple dimensions can be used. Assuming that the multiple dimensions include an appearance dimension, the indicators under the appearance dimension can be used to evaluate the vehicle's health.

[0039] The table below provides an example of a set of evaluation indicators. For each indicator, the set includes information such as symbol, statistical item, unit, and dimension. This application does not limit the scope of the examples.

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] Among these, the evaluation indicators are positively or negatively correlated with health status, depending on the specific evaluation direction of the indicator. For example, cumulative mileage is positively correlated with health status; cumulative maintenance costs are negatively correlated with health status; cumulative power consumption is negatively correlated with health status; and mechanical component failures per unit mileage are negatively correlated with health status.

[0046] In step S120, target indicators that match the preset applicable scenarios are selected from the set of evaluation indicators, and a set of indicator values ​​is determined based on the target indicators.

[0047] Specifically, the number of indicator value sets is consistent with the number of vehicles to be evaluated, and there is a one-to-one correspondence between the indicator value sets and the vehicles to be evaluated. Among them, the indicator value set includes the value of each evaluation indicator (e.g., cumulative mileage) in the evaluation indicator set (e.g., 10,000 km).

[0048] Specifically, as vehicle production increases, the number of vehicles entering the market grows, and in some cases, these vehicles need to be processed in batches. For example, autonomous vehicle management companies manage a large number of autonomous vehicles with varying degrees of wear and tear, requiring them to calculate the health status of each vehicle individually and, based on this health status, promptly scrap, resell, or dismantle vehicles that no longer meet usage requirements. Similarly, used car recycling companies also manage a large number of autonomous vehicles and, likewise, need to periodically calculate the health status of each vehicle.

[0049] The technical solution provided in this application can be adapted to various vehicle screening needs. Whether it is an autonomous vehicle management company, a used car recycling company, or other companies, they can determine the corresponding applicable scenarios based on their personalized vehicle screening needs (e.g., "I want to screen out aesthetically pleasing vehicles for use as display floats"). These applicable scenarios may include, but are not limited to, driving scenarios, decoration scenarios, and sales scenarios. Furthermore, target indicators matching the applicable scenarios can be selected from the set of evaluation indicators. For each vehicle identifier, a set of indicator values ​​used to generate vehicle health is obtained based on the target indicators. This set of indicator values ​​includes the indicator values ​​under each target indicator.

[0050] In step S130, the vehicle health score of the vehicle to be evaluated is generated based on the set of indicator values.

[0051] As an optional embodiment of step S130, the vehicle health score of the vehicle to be evaluated is generated based on the set of indicator values, including:

[0052] Step S1301: Convert multiple indicator values ​​in the indicator value set into standardized indicators to obtain multiple standardized indicator sets;

[0053] Step S1302: Determine the standardized sample covariance matrix based on multiple standardized index sets;

[0054] Step S1303: Calculate the eigenvectors based on the standardized sample covariance matrix;

[0055] Step S1304: Generate vehicle health based on entropy method, superior and inferior solution distance method, and the feature vector.

[0056] As can be seen, by implementing this optional embodiment, a feature vector for evaluating health can be determined by standardizing the index values, and the vehicle health determined based on this feature vector has higher accuracy.

[0057] Specifically, assuming the number of vehicles to be evaluated is n, and the set of evaluation indicators includes m evaluation indicators, then the set of indicator values ​​includes values ​​corresponding to each of the m evaluation indicators; where n > m. Based on each set of indicator values, (X) can be determined. ij ) n×m matrix, Among them, X ij This refers to the j-th index value of the i-th vehicle.

[0058] This involves transforming multiple indicator values ​​in the set of indicator values ​​into standardized indicators, resulting in multiple sets of standardized indicators. Specifically, this can be achieved by transforming the indicator values ​​in each set of indicator values ​​into unidirectional indicator values ​​x. ij , For each metric j, Xmin =min(X) ij |1≤i≤n)andX max =max(X ij |1≤i≤nW, the transformed index x ij (1≤i≤n; 1≤j≤m) are positively correlated with health. Therefore, the vehicle sample mean μ can be calculated based on this. j and standard deviation And based on the vehicle sample mean μ j and standard deviation Generate standardized index y ij Thus, we can obtain Y = (y ij ) n×m That is, a set of multiple standardized indicators; among which, i represents the vehicle serial number, j represents the index variable serial number, and x represents the vehicle serial number. ij Let y be the value of the j-th indicator variable for the i-th vehicle. ij For x ij The standardized value.

[0059] Specifically, the standardized sample covariance matrix is ​​determined based on multiple standardized indicator sets, which can be implemented as follows: based on Y = (y ij ) n×m The standardized sample covariance matrix R can be determined.

[0060]

[0061] Specifically, the eigenvectors are calculated based on the standardized sample covariance matrix. This can be achieved as follows: Since the relationship between eigenvalues ​​and eigenvectors is constrained to Rα = λα and (R - λE)α = 0, therefore... Here, E is the identity matrix, and the determinant |D| can be solved using the method of algebraic cofactors. m |=|R-λE|=0; specifically, the determinant |D m | Expand by the first line to get Among them, a 1j It is the determinant of the first row |D m | The element in the first row and j-th column (e.g., a) 11 =r 11 -λ), A 1j Yes |D m The m-1 order determinant remaining after discarding the elements in the first row and the j-th column Based on the method of expanding the first row, the determinant |D m Continue expanding to an m-2 order determinant to obtain This continues until the first-order determinant contains only one element. Furthermore, we can set |D m|=0 This yields an m-order univariate equation in λ, and solving this equation yields m eigenvalues ​​λ. k (k = 1, 2, ..., m); where each eigenvalue λ k Corresponding to an m-dimensional eigenvector α k Based on this, (R-λ) can be solved. k E)α k =0 to determine the vector α k, α k The corresponding eigenvalues ​​represent the contribution of the eigenvector to the health score calculation process. Wherein, α... k =(α 1k ,α 2k ,…α mk ).

[0062] As an optional embodiment of step S1304, generating vehicle health based on the entropy method, the superior-inferiority solution distance method, and the feature vector includes:

[0063] Step S13041: Determine the contribution ranking of the feature vectors by sorting the feature values ​​in descending order, and determine the target feature vector based on the contribution ranking;

[0064] Step S13042: Normalize the scores of the target feature vectors to obtain a normalized decision matrix, and calculate the weight of the normalized target feature vectors of the vehicle to be evaluated.

[0065] Step S13043: Determine the information entropy of the target feature vector based on the entropy method, and determine the weighted decision matrix based on the information entropy and weights;

[0066] Step S13044: Generate the ideal solution and negative ideal solution corresponding to each vehicle to be evaluated based on the superior-inferior solution distance method and the weighted decision matrix;

[0067] Step S13045: Calculate vehicle health based on ideal solution and negative ideal solution.

[0068] As can be seen, implementing this optional embodiment can automatically generate high-precision vehicle health information for user reference based on entropy method, superior-inferior solution distance method, and feature vector.

[0069] Specifically, based on the entropy method, the distance between superior and inferior solutions method, and the generation of ideal and negative ideal solutions corresponding to each vehicle to be evaluated using eigenvectors, this can be implemented as follows: determining the eigenvector α by sorting the eigenvalues ​​in descending order. k The contribution ranking can be used to clarify the contribution ranking of each feature vector α. k The contribution rate is calculated, and the top Q vectors are selected and retained as target feature vectors (also known as principal components) for calculating health score based on their cumulative contribution rate. Here, the contribution rate of the target feature vectors to the total variance should be... The cumulative contribution rate of the target feature vector should be Therefore, according to C Q-1 ≤0.9≤C Q We can clearly define the first Q vectors as target feature vectors. The score of the k-th target feature vector of the i-th vehicle is... Where, α jQ This represents the j-th element of the k-th target feature vector, which can also be understood as feature vector α. k The j-th component.

[0070] Furthermore, the scores of the target feature vector can be normalized to obtain a normalized decision matrix: Then, the weight of the j-th normalized target feature vector of the i-th vehicle to be evaluated can be calculated: And based on the entropy method, the information entropy of the j-th target feature vector is determined as follows: The weights are: Furthermore, the weighted decision matrix can be determined based on information entropy and weights: V = [v ij ] = [w j n ij ].

[0071] Based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), an ideal solution corresponding to each vehicle to be evaluated can be generated using a weighted decision matrix. and negative ideal solution in, In addition, the TOPISI method is a multi-criteria decision analysis method that can measure the degree of closeness between the evaluated object and the ideal goal.

[0072] Specifically, calculating vehicle health based on the ideal solution and the negative ideal solution can be implemented as follows: Based on TOPSIS, according to the ideal solution A... i And negative ideal solution A - Calculate the vehicle health score c for each vehicle to be evaluated. i , Among them, vehicle health refers to the degree to which the vehicle being evaluated closely approximates the ideal solution. and Let represent the distances between the i-th vehicle and the ideal solution and the negative ideal solution, respectively.

[0073] As an optional embodiment, the number of vehicles to be evaluated is multiple, and it also includes:

[0074] Step S150: Sort the vehicle health of multiple vehicles to be evaluated to obtain the vehicle health ranking results of multiple vehicles to be evaluated.

[0075] As can be seen, implementing this optional embodiment makes it easier for users to understand the distribution of vehicle health based on the vehicle health ranking results.

[0076] Specifically, the vehicle health ranking results include each vehicle to be evaluated arranged in descending or ascending order of health.

[0077] As an optional embodiment, it also includes:

[0078] Step S160: Based on the vehicle health of the multiple vehicles to be evaluated, select target vehicles that meet the health conditions from the multiple vehicles to be evaluated.

[0079] As can be seen, implementing this optional embodiment can select a target vehicle that meets the user's needs in response to health conditions.

[0080] Specifically, health conditions can be implemented in any form, such as instructions or statements. Health conditions are used to limit the conditions that target vehicles need to meet, and the number of target vehicles can be one or more.

[0081] As an optional embodiment, it also includes:

[0082] Step S170: If the vehicle health of the target vehicle meets the recommended criteria, determine the set of indicators used to generate the vehicle health; determine the industry systems to which the indicator set is applicable; and push the recommended information corresponding to the target vehicle to each industry system.

[0083] It is evident that implementing this optional embodiment can improve the utilization rate of vehicle resources.

[0084] Specifically, when the vehicle health of a target vehicle meets one or more recommended criteria (e.g., a perfect appearance score), a set of indicators for generating the vehicle health score can be proactively determined, and the corresponding industry systems (e.g., taxi industry system, parade float industry system, etc.) can be identified. Pushing these target vehicles to the industry systems can help reuse the target vehicles. When a user needs to dispose of their fleet or when their fleet is not suitable for the company's development plan, the implementation of this application can be triggered to determine target vehicles that can be recommended to the corresponding industry. Since the target vehicles have a high degree of matching with their industry, the target vehicles are more likely to be reused, which can help improve resource utilization.

[0085] As an optional embodiment, it also includes:

[0086] Step S180: For the remaining vehicles other than the target vehicle among the multiple vehicles to be evaluated, generate and output scrapping prompt information.

[0087] It is evident that implementing this optional embodiment can mitigate risks to some extent by scrapping the remaining vehicles.

[0088] Specifically, for remaining vehicles, users can be advised to scrap them to prevent harmful vehicles from entering the market and causing damage.

[0089] As an optional embodiment, it also includes:

[0090] Step S190: Identify the remaining vehicles other than the target vehicle among the multiple vehicles to be evaluated; determine the first indicator from the set of indicator values, and use the first indicator as the common indicator of the remaining vehicles, wherein the indicator value of the first indicator of each remaining vehicle is higher than a preset threshold.

[0091] As can be seen, by implementing this optional embodiment, the remaining vehicles can be used in the corresponding scenarios based on their commonalities, thereby improving the utilization rate of the remaining vehicles.

[0092] For example, assuming the remaining vehicles are unsuitable for the autonomous driving industry due to low health, common indicators can be extracted from the set of indicator values ​​corresponding to each remaining vehicle. If the indicator values ​​of the same common indicator (e.g., appearance) for all remaining vehicles are higher than a preset threshold, and there exists an applicable scenario corresponding to the common indicator (e.g., a float display scenario), specific vehicles suitable for the applicable scenario can be selected from the remaining vehicles, and recommendation information containing the specific vehicles can be pushed to the industry system corresponding to the applicable scenario. Furthermore, if there is no applicable scenario corresponding to the common indicator, a scrapping warning message can be generated and output.

[0093] Furthermore, as an optional embodiment, the common indicators include several, including:

[0094] Based on multiple common indicators, determine the set of common indicator values;

[0095] Based on the set of common indicator values, the reuse evaluation value of the remaining vehicles is generated;

[0096] Vehicles that meet the reuse criteria are selected from the remaining vehicles based on their reuse evaluation values.

[0097] As can be seen, by implementing this optional embodiment, the remaining vehicles can be re-evaluated based on the set of common indicator values ​​to select specific vehicles that meet the conditions for reuse, thereby improving the utilization rate of vehicle resources.

[0098] Specifically, the method for generating reuse evaluation values ​​for remaining vehicles based on a set of common indicator values ​​can be as follows: convert the common indicator values ​​in each set of common indicator values ​​into standardized common indicators to obtain multiple standardized common indicator sets; determine the standardized common sample covariance matrix based on multiple standardized common indicator sets; calculate the eigenvectors used for reuse evaluation values ​​based on the standardized common sample covariance matrix; and generate reuse evaluation values ​​corresponding to each vehicle to be evaluated based on the entropy method, the superior-inferior solution distance method, and the eigenvectors.

[0099] Please see Figure 2 , Figure 2 A flowchart illustrating a vehicle health determination method according to another embodiment of this application is shown. Figure 2 As shown, the method for determining vehicle health includes steps S210 to S270.

[0100] Step S210: Determine each vehicle to be evaluated and the set of evaluation indicators corresponding to each vehicle to be evaluated.

[0101] Step S220: For each vehicle to be evaluated, obtain the set of indicator values ​​used to generate the vehicle health score based on the set of evaluation indicators.

[0102] Step S230: Convert the index values ​​in each set of index values ​​into standardized indexes to obtain multiple sets of standardized indexes. Determine the standardized sample covariance matrix based on the multiple sets of standardized indexes. Calculate the feature vector for evaluating health based on the standardized sample covariance matrix. Generate the ideal solution and negative ideal solution corresponding to each vehicle to be evaluated based on the entropy method, the distance between good and bad solutions, and the feature vector.

[0103] Step S240: For each vehicle to be evaluated, calculate the vehicle health based on the ideal solution and the negative ideal solution to obtain the vehicle health corresponding to each vehicle to be evaluated.

[0104] Step S250: Select target vehicles that meet the health criteria from the vehicles to be evaluated based on the health status of each vehicle.

[0105] Step S260: If the vehicle health of the target vehicle meets the recommended criteria, determine the set of indicators used to generate the vehicle health, determine the industry systems to which the indicator set is applicable, and push the recommended information corresponding to the target vehicle to the industry systems.

[0106] Step S270: For the remaining vehicles in each vehicle to be evaluated, excluding the target vehicle, generate and output scrapping prompt information; or, based on the set of indicator values ​​corresponding to each remaining vehicle, extract the common indicators corresponding to each remaining vehicle, wherein the indicator values ​​of the same common indicator corresponding to each remaining vehicle are all higher than a preset threshold. If there is an applicable scenario corresponding to the common indicator, then select specific vehicles applicable to the applicable scenario from each remaining vehicle.

[0107] It should be noted that steps S210 to S270 are related to... Figure 1 The steps and their embodiments shown correspond to each other. Figure 2 The diagram shows the combination. Figure 1 This is one optional implementation of some embodiments, but it does not mean that... Figure 1 The embodiments must be in accordance with Figure 2 The steps are executed in a specific order. For detailed implementation methods of steps S210 to S270, please refer to [reference needed]. Figure 1 The steps and their embodiments shown are not described in detail here.

[0108] It is evident that implementation Figure 2 The method shown can automatically and personalized determine vehicle health. Specifically, it can obtain a set of indicator values ​​for the vehicle to be evaluated that matches the applicable scenario based on a set of evaluation indicators, and automatically generate the vehicle health of the vehicle to be evaluated based on the set of indicator values ​​without human intervention. This can improve the efficiency of determining vehicle health and improve the matching degree between vehicle health and applicable scenario.

[0109] Please see Figure 3 , Figure 3 The diagram schematically illustrates a structural block diagram of a vehicle health determination device according to one embodiment of this application. The vehicle health determination device 300 and... Figure 1 The methods shown correspond to those described, such as Figure 3 As shown, the vehicle health determination device 300 includes:

[0110] Information determination unit 301 is used to determine the vehicle to be evaluated and the set of evaluation indicators for the vehicle to be evaluated;

[0111] The indicator value determination unit 302 is used to select target indicators that match the preset applicable scenarios from the evaluation indicator set, and determine the indicator value set based on the target indicators.

[0112] The sorting unit 303 is used to generate the vehicle health score of the vehicle to be evaluated based on the set of indicator values.

[0113] It is evident that implementation Figure 3The device shown can automatically and individually determine vehicle health. Specifically, it can obtain a set of indicator values ​​for the vehicle to be evaluated that matches the applicable scenario based on a set of evaluation indicators, and automatically generate the vehicle health of the vehicle to be evaluated based on the set of indicator values ​​without human intervention. This can improve the efficiency of determining vehicle health and improve the matching degree between vehicle health and applicable scenario.

[0114] As an optional embodiment, the sorting unit 303 generates the vehicle health score of the vehicle to be evaluated based on the set of indicator values, including:

[0115] Multiple indicator values ​​in the indicator value set are transformed into standardized indicators to obtain multiple standardized indicator sets;

[0116] Determine the standardized sample covariance matrix based on multiple standardized indicator sets;

[0117] eigenvectors are calculated based on the standardized sample covariance matrix.

[0118] Vehicle health is generated based on the entropy method, the superior-inferior solution distance method, and the feature vector.

[0119] As can be seen, by implementing this optional embodiment, a feature vector for evaluating health can be determined by standardizing the index values, and the vehicle health determined based on this feature vector has higher accuracy.

[0120] As an optional embodiment, the sorting unit 303 generates vehicle health based on the entropy method, the superior-inferior solution distance method, and the feature vector, including:

[0121] The contribution ranking of the feature vectors is determined by sorting the feature values ​​in descending order, and the target feature vector is determined based on the contribution ranking.

[0122] The scores of the target feature vectors are normalized to obtain a normalized decision matrix, and the weight of the normalized target feature vector of the vehicle to be evaluated is calculated.

[0123] The information entropy of the target feature vector is determined based on the entropy method, and the weighted decision matrix is ​​determined based on the information entropy and the weights.

[0124] Based on the superior-inferiority distance method and weighted decision matrix, ideal and negative ideal solutions are generated for each vehicle to be evaluated.

[0125] Vehicle health is calculated based on the ideal solution and the negative ideal solution.

[0126] As can be seen, implementing this optional embodiment can automatically generate high-precision vehicle health information for user reference based on entropy method, superior-inferior solution distance method, and feature vector.

[0127] As an optional embodiment, it also includes:

[0128] The vehicle health status of multiple vehicles to be evaluated is sorted to obtain the vehicle health status ranking results.

[0129] As can be seen, implementing this optional embodiment makes it easier for users to understand the distribution of vehicle health based on the vehicle health ranking results.

[0130] As an optional embodiment, it also includes:

[0131] Based on the vehicle health status of the multiple vehicles to be evaluated, target vehicles that meet the health status criteria are selected from the multiple vehicles to be evaluated.

[0132] As can be seen, implementing this optional embodiment can select a target vehicle that meets the user's needs in response to health conditions.

[0133] As an optional embodiment, it also includes:

[0134] The indicator set determination unit is used to determine the indicator set used to generate the vehicle health score if the vehicle health score of the target vehicle meets the recommended standards.

[0135] Industry system determination unit, used to determine the various industry systems to which the indicator set applies;

[0136] The information push unit is used to push recommended information corresponding to the target vehicle to various industry systems.

[0137] It is evident that implementing this optional embodiment can improve the utilization rate of vehicle resources.

[0138] As an optional embodiment, it also includes:

[0139] The scrapping notification unit is used to generate and output scrapping notification information for the remaining vehicles other than the target vehicle among multiple vehicles to be evaluated.

[0140] It is evident that implementing this optional embodiment can mitigate risks to some extent by scrapping the remaining vehicles.

[0141] As an optional embodiment, it also includes:

[0142] The remaining vehicle determination unit is used to determine the remaining vehicles other than the target vehicle among multiple vehicles to be evaluated.

[0143] The common index determination unit is used to determine the first index from the set of index values ​​and use the first index as the common index of the remaining vehicles, wherein the index value of the first index of each remaining vehicle is higher than a preset threshold.

[0144] The specific vehicle determination unit is used to select specific vehicles suitable for the applicable scenario from the remaining vehicles if there is an applicable scenario corresponding to the common index.

[0145] As can be seen, by implementing this optional embodiment, the remaining vehicles can be used in the corresponding scenarios based on their commonalities, thereby improving the utilization rate of the remaining vehicles.

[0146] As an optional embodiment, the specific vehicle determination unit selects specific vehicles suitable for the applicable scenario from the remaining vehicles, including:

[0147] Based on multiple common indicators, determine the set of common indicator values;

[0148] Based on the set of common indicator values, the reuse evaluation value of the remaining vehicles is generated;

[0149] Vehicles that meet the reuse criteria are selected from the remaining vehicles based on their reuse evaluation values.

[0150] As can be seen, by implementing this optional embodiment, the remaining vehicles can be re-evaluated based on the set of common indicator values ​​to select specific vehicles that meet the conditions for reuse, thereby improving the utilization rate of vehicle resources.

[0151] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0152] Since the functional modules of the vehicle health determination device in the example embodiments of this application correspond to the steps of the vehicle health determination method in the example embodiments described above, for details not disclosed in the device embodiments of this application, please refer to the embodiments of the vehicle health determination method described above. Also, since the functional modules of the vehicle health determination device in the example embodiments of this application correspond to the steps of the vehicle health determination method in the example embodiments described above, for details not disclosed in the device embodiments of this application, please refer to the embodiments of the vehicle health determination method described above.

[0153] Please see Figure 4 , Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0154] It should be noted that, Figure 4The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0155] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0156] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0157] Specifically, according to embodiments of this application, the processes described in the above-described flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in the methods and apparatus of this application.

[0158] An exemplary embodiment of this disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the aforementioned vehicle health determination method.

[0159] In one embodiment, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, NAND flash memory, etc.

[0160] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0161] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0162] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic fields, and infrared radiation. Electronic devices can convert signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, the processor of the electronic device to execute) the method steps of various exemplary embodiments of this disclosure. For example, the vehicle health determination method described above can be executed, which includes the following steps: determining the vehicle to be evaluated and a set of evaluation indicators for the vehicle to be evaluated; selecting target indicators from the set of evaluation indicators that match a preset applicable scenario, and determining a set of indicator values ​​based on the target indicators; and generating the vehicle health of the vehicle to be evaluated based on the set of indicator values.

[0163] By executing the above methods and steps through a computer program, vehicle health can be determined automatically and in a personalized manner. Specifically, it can obtain a set of indicator values ​​for the vehicle to be evaluated that matches the applicable scenario based on a set of evaluation indicators, and automatically generate the vehicle health of the vehicle to be evaluated based on the set of indicator values. This can improve the efficiency of determining vehicle health and enhance the matching degree between vehicle health and applicable scenarios without human intervention.

[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0165] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0166] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

Claims

1. A method for determining vehicle health, characterized in that, include: Determine the vehicle to be evaluated and the set of evaluation indicators for the vehicle to be evaluated; Select target indicators that match the preset applicable scenarios from the set of evaluation indicators, and determine the set of indicator values ​​based on the target indicators; Based on the set of indicator values, the vehicle health score of the vehicle to be evaluated is generated.

2. The method according to claim 1, characterized in that, Based on the set of indicator values, the vehicle health score of the vehicle to be evaluated is generated, including: Multiple indicator values ​​in the set of indicator values ​​are converted into standardized indicators to obtain multiple sets of standardized indicators. Based on the aforementioned set of standardized indicators, the standardized sample covariance matrix is ​​determined; Based on the standardized sample covariance matrix, the feature vector is calculated; The vehicle health score is generated based on the entropy method, the superior-inferior solution distance method, and the feature vector.

3. The method according to claim 2, characterized in that, The vehicle health score is generated based on the entropy method, the superior-inferiority distance method, and the feature vector, including: The contribution ranking of the feature vectors is determined by sorting the feature values ​​in descending order, and the target feature vector is determined based on the contribution ranking. The scores of the target feature vectors are normalized to obtain a normalized decision matrix, and the weight of the normalized target feature vector of the vehicle to be evaluated is calculated. The information entropy of the target feature vector is determined based on the entropy method, and the weighted decision matrix is ​​determined based on the information entropy and the weights. Based on the superior-inferiority distance method and weighted decision matrix, ideal and negative ideal solutions are generated for each vehicle to be evaluated. The vehicle health is calculated based on the ideal solution and the negative ideal solution.

4. The method according to claim 1, characterized in that, The number of vehicles to be evaluated is multiple, and the method further includes: The vehicle health scores of the multiple vehicles to be evaluated are sorted to obtain the vehicle health score ranking results.

5. The method according to claim 4, characterized in that, Also includes: Based on the vehicle health status of the multiple vehicles to be evaluated, target vehicles that meet the health status criteria are selected from the multiple vehicles to be evaluated.

6. The method according to claim 5, characterized in that, Also includes: For the remaining vehicles among the multiple vehicles to be evaluated, excluding the target vehicle, generate and output scrapping prompt information.

7. The method according to claim 5, characterized in that, Also includes: Identify the remaining vehicles among the plurality of vehicles to be evaluated, excluding the target vehicle; A first indicator is determined from the set of indicator values, and the first indicator is used as a common indicator for the remaining vehicles, wherein the indicator value of the first indicator for each of the remaining vehicles is higher than a preset threshold.

8. The method according to claim 7, characterized in that, The common indicators are multiple, and the method further includes: Based on the aforementioned common indicators, a set of common indicator values ​​is determined; Based on the set of common index values, the reuse evaluation value of the remaining vehicles is generated; Based on the reuse evaluation value, vehicles that meet the reuse conditions are selected from the remaining vehicles.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1-8 by executing the executable instructions.

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