Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a vehicle collaborative diagnosis system provided by the embodiment of the application, and as shown in fig. 1, the vehicle collaborative diagnosis system comprises a vehicle diagnosis service platform and diagnosis equipment, wherein the diagnosis equipment is a terminal tool for directly interacting with a vehicle to acquire data, the diagnosis equipment can be a vehicle-mounted diagnosis instrument, can read fault codes of the vehicle and real-time operation data such as engine rotation speed, water temperature and the like, can detect the working states of a sensor and an actuator, and provides data for vehicle diagnosis, and the vehicle diagnosis service platform is a comprehensive platform integrating data acquisition, analysis, diagnosis and maintenance guidance and can carry out advanced treatment, analysis and integration on the data. After the diagnostic equipment collects data from the vehicle, the data are transmitted to a vehicle diagnostic service platform in a wired or wireless mode, the platform receives the data and then performs analysis processing, and then the analysis result and a diagnostic instruction are fed back to the diagnostic equipment to guide the subsequent detection operation, such as prompting the diagnostic equipment to further detect specific components. In the working flow of the platform, a first vehicle acquires vehicle data of the first vehicle in real time through devices such as a vehicle-mounted sensor and a diagnosis system, the vehicle data comprise fault parameters, data processing capacity parameters, position information, vehicle characteristics, operation parameters and the like, the data are uploaded to a vehicle diagnosis service platform, and the platform determines a first fault type and a first fault level according to the fault parameters after receiving the data of the first vehicle. The platform then screens out m second vehicles associated with the first vehicle from among the plurality of vehicles based on the first fault type, the first fault level, the data processing capability parameter, and the first location information, the second vehicles also transmitting information including vehicle diagnostic data, vehicle characteristics, and operational parameters to the platform via their own data collection devices. After the platform obtains relevant data of m second vehicles, multidimensional analysis and comparison are carried out on the data of the first vehicles and the m second vehicles, for example, the first vehicle characteristics are compared with the m second vehicle characteristics to obtain vehicle characteristic similarity, the first vehicle operation parameters and the m second vehicle operation parameters are compared to obtain vehicle operation parameter similarity, the second fault type and the second fault grade of the first vehicles are determined according to the m second vehicle diagnosis data, and the first vehicle operation parameters and the m second vehicle operation parameters are compared with the fault type and the grade of the first vehicles. Through the analysis, the target fault type and the target fault grade of the first vehicle are finally determined through the platform, a diagnosis result is formed, the vehicle maintenance timeliness index is determined according to the diagnosis result, and a maintenance scheme is provided for a user by combining the energy consumption information of the first vehicle, wherein the maintenance scheme comprises the steps of determining a plurality of maintenance points, selecting the target maintenance points, planning a maintenance path and the like.
Referring to fig. 2, fig. 2 is a flowchart of a vehicle collaborative diagnosis method provided by an embodiment of the present application, and as shown in fig. 2, the vehicle collaborative diagnosis method provided by the embodiment of the present application includes, but is not limited to, the following steps:
Step S101, acquiring first vehicle diagnosis data of a first vehicle;
wherein the first vehicle diagnostic data includes a fault parameter, a data processing capability parameter, and first location information;
step S102, determining a first fault type and a first fault level of a first vehicle according to the fault parameters;
The first fault level is used for reflecting the complexity degree of the first fault type;
step S103, determining m second vehicles associated with the first vehicle according to the first fault type, the first fault level, the data processing capacity parameter and the first position information;
The distance between m second vehicles and the first vehicles is smaller than a target threshold value, wherein m is a natural number;
Step S104, second vehicle diagnosis data of each second vehicle in m second vehicles are obtained, and m second vehicle diagnosis data are obtained;
Wherein the m second vehicle diagnostic data are used for reflecting running state information of the m second vehicles;
Step 105, diagnosing the first vehicle according to m pieces of second vehicle diagnosis data to obtain diagnosis results;
and step S106, returning the diagnosis result to the first vehicle.
In one possible embodiment, first vehicle diagnostic data of a first vehicle is acquired, and first, a vehicle diagnostic service platform is connected to a diagnostic device of the first vehicle, communicates with an electronic control unit (Electronic Control Unit, ECU) of the vehicle through a standard communication protocol, and reads various fault parameters from the ECU, where the fault parameters include information of a fault code, a time when a fault occurs, a frequency of occurrence of the fault, and the like. Then, data processing capability parameters of the vehicle, such as indexes of operation speed, memory capacity, data transmission rate and the like of a central processing unit (Central Processing Unit, CPU) of the vehicle-mounted computer, are collected, and the parameters can be obtained through system information inquiry of the vehicle or special testing tools. And acquiring accurate position information of the first vehicle, including longitude and latitude, altitude and other data, through a vehicle positioning system, and updating the position information in real time.
In one possible embodiment, according to the fault parameters, the first fault type and the first fault level of the first vehicle are determined, the read fault code is compared with a preset fault code manual, the fault type of the first vehicle is identified, for example, the fault code P0101 indicates that the air flow sensor is faulty, the first fault type is the related fault of the air flow sensor, the fault level is classified according to factors such as severity of the fault, influence on the performance of the vehicle, and complexity of maintenance, for example, if the fault causes the vehicle to run abnormally, such as the engine cannot be started, the fault is determined to be a high-level fault, and if only the comfort of the vehicle is affected, such as the air conditioning refrigeration effect is not good, the fault is determined to be a low-level fault.
In one possible embodiment, m second vehicles associated with the first vehicle are determined according to the first fault type, the first fault level, the data processing capability parameter and the first position information, a preset vehicle database is obtained, the vehicle database contains a large amount of relevant information of the vehicles, such as vehicle types, fault histories, position information and the like, vehicles, the distance between the vehicles and the first vehicle is smaller than a target threshold value, which can be set according to actual conditions, such as 10 km, in the database, the candidate second vehicles are searched according to the history fault records of the candidate second vehicles, the vehicles which are the same as or similar to the first fault type are judged, and comprehensive analysis can be performed according to the occurrence position of the fault, the fault phenomenon and the like for the judgment of the similar fault types. According to the first fault level and the data processing capability parameter of the first vehicle, if the first fault level is higher, the current fault condition of the first vehicle is complex, more second vehicles are required for collaborative diagnosis, and if the data processing capability of the first vehicle is limited, fewer second vehicles are required for collaborative diagnosis, and the step aims at balancing various judging indexes so as to determine m second vehicles, so that effective collaborative diagnosis can be ensured. The priority rule is determined according to the first fault type and the first fault level, the priority rule can be distance priority or fault similarity priority, and m second vehicles are further determined from the screened candidate second vehicles according to the priority rule.
In one possible embodiment, the second vehicle diagnostic data of each of the m second vehicles is obtained, and the m second vehicle diagnostic data is respectively connected with the m second vehicles in a communication way, and the diagnostic data of each second vehicle is obtained through diagnostic equipment or other data acquisition modes, wherein the second vehicle diagnostic data comprises, but not limited to, real-time operation parameters of the vehicles, such as engine speed, vehicle speed, fuel consumption and the like, and fault codes, sensor data, such as temperature sensor, pressure sensor data and the like, and historical maintenance records of the vehicles and the like.
In one possible embodiment, the first vehicle is diagnosed according to m pieces of second vehicle diagnosis data to obtain a diagnosis result, and the m pieces of second vehicle diagnosis data and the first vehicle diagnosis data are comprehensively analyzed, for example, fault codes of the first vehicle and the second vehicle are compared, the occurrence frequency and the combination condition of the fault codes are analyzed to judge whether a common problem exists, and meanwhile, the operation parameters of the vehicles are combined to analyze the influence of the fault on the performance of the vehicles.
In one possible embodiment, the diagnosis result is returned to the first vehicle, and the diagnosis result is sent back to the display screen or the vehicle-mounted information system of the first vehicle through a wireless communication technology, wherein the diagnosis result comprises the forms of text description, diagrams and the like, so that a driver or a maintenance person can conveniently know the fault condition and the treatment advice.
In the embodiment of the application, the fault parameters, the data processing capacity parameters and the position information of the vehicle are comprehensively acquired, the fault parameters are favorable for rapidly positioning the fault types, the data processing capacity parameters provide basis for determining a proper collaborative diagnosis scheme, and the position information is convenient for screening nearby related vehicles, so that the diagnosis efficiency is improved. The related vehicles are screened by comprehensively considering various factors, so that the selected second vehicles and the first vehicles are ensured to have similarity in fault types, detailed diagnosis data of a plurality of second vehicles are acquired, diagnosis information sources are enriched, the data of different vehicles are compared and analyzed, the commonality and the individuality of the faults can be found, and more reference bases are provided for more accurately diagnosing the faults of the first vehicles. The data of multiple vehicles are comprehensively analyzed, fault reasons can be deeply excavated, the accuracy and the reliability of diagnosis are improved, and compared with the diagnosis of a single vehicle, the collaborative diagnosis of the multiple vehicles can more comprehensively consider various factors, and misdiagnosis and missed diagnosis are avoided.
Optionally, step S103, determining m second vehicles associated with the first vehicle according to the first fault type, the first fault level, the data processing capability parameter, and the first location information may include the steps of:
step S201, acquiring vehicle road parameters of a first vehicle;
Step S202, determining a collaborative diagnosis accuracy according to vehicle road parameters, a first fault type and a first fault level, wherein the collaborative diagnosis accuracy is used for reflecting the accuracy when diagnosing a first vehicle through vehicle diagnosis data of a plurality of vehicles;
step S203, determining a vehicle diagnosis timeliness index of the first vehicle according to the first fault type and the first fault level, wherein the vehicle diagnosis timeliness index is used for reflecting the diagnosis efficiency required by the first vehicle;
step S204, determining the target quantity according to the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capacity parameter;
Step S205, determining n associated vehicles of the first vehicle according to the first position information, wherein n is a natural number;
step S206, determining the association priority of each association vehicle in the n association vehicles to obtain n association priorities;
and S207, screening the n associated vehicles according to the target number and the n associated priorities to obtain m second vehicles.
In one possible embodiment, a vehicle road parameter of the first vehicle is obtained, which may be a road type, a road surface condition, a gradient, a curve radius, a vehicle flow, a traffic density, a pedestrian density, a traffic signal, and so on. The vehicle road parameters also include weather parameters, which may be temperature, humidity, wind speed and direction, and precipitation conditions, and surrounding object parameters, which may be obstacle distance, surrounding vehicle speed and direction of travel, and roadside facility location.
In one possible embodiment, the collaborative diagnosis accuracy is determined according to the vehicle road parameter, the first fault type and the first fault level, historical vehicle diagnosis data is obtained, including collaborative diagnosis results under different road parameters such as road gradient, congestion degree, road surface condition and the like, fault types and fault levels, the historical data are trained through a machine learning algorithm, a correlation model between the road parameter, the fault type, the fault level and the collaborative diagnosis accuracy is established, the vehicle road parameter, the first fault type and the first fault level of the current first vehicle are input into the trained correlation model, and the corresponding collaborative diagnosis accuracy is output through the model according to input information.
In one possible embodiment, the vehicle diagnostic age indicator of the first vehicle is determined based on the first fault type and the first fault level, the corresponding diagnostic age indicator is determined based on different fault types and fault levels, for example, the diagnosis is determined to be completed within 10 minutes for a low level simple fault, such as a light fault, the diagnosis time is determined to be 30 minutes for a high level complex fault, such as an engine severe fault, or the diagnostic age indicator of different fault types is determined in combination with the driving state of the first vehicle, for example, the diagnosis time may be 30 minutes if the first vehicle is in an idle state, and the required diagnosis efficiency of the first vehicle is higher if the first vehicle is in a driving state and the first fault type has a higher correlation with the current driving state. And according to the first fault type and the first fault grade of the first vehicle, searching a corresponding diagnosis aging requirement from the aging standard, and taking the diagnosis aging requirement as a vehicle diagnosis aging index of the first vehicle.
In one possible embodiment, the target number is determined according to the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capability parameter, a comprehensive evaluation function is established, the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capability parameter are taken as input variables, wherein the evaluation function can be expressed as f=a×collaborative diagnosis accuracy+b×vehicle diagnosis aging index+c×data processing capability parameter, F is the evaluation function, and a, b and c are weights of the parameters respectively. And performing simulation calculation on different associated vehicle numbers, substituting the corresponding collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capacity parameter under each number into an evaluation function to obtain corresponding evaluation values, and selecting the associated vehicle number corresponding to the maximum evaluation value as the target number.
In one possible embodiment, n associated vehicles of the first vehicle are determined according to the first position information, and all vehicles within a target range from the first vehicle are screened out in a vehicle database to serve as candidate associated vehicles. And further screening the candidate associated vehicles, and judging whether the vehicle has the diagnostic data sharing capability or not according to the type of the vehicle. For example, only vehicles which are the same type as the first vehicle and have a data communication function are selected as the associated vehicles, and n associated vehicles are finally determined.
In one possible embodiment, the associated priority of each of the n associated vehicles is determined, and the n associated priorities are obtained, and the fault similarity between the associated vehicle and the first vehicle is analyzed, that is, whether the associated vehicle experiences the same or similar fault as the first vehicle, the higher the similarity, the higher the priority, and the closer the associated vehicle is to the first vehicle for a specific vehicle fault type, such as abnormal engine noise, the higher the referenceable priority.
In one possible embodiment, the n associated vehicles are screened according to the target number and the n associated priorities to obtain m second vehicles, the n associated vehicles are ranked according to the associated priorities from high to low, and the ranked front target number of associated vehicles is selected as the m second vehicles.
In the embodiment of the application, the collaborative diagnosis accuracy is determined through the vehicle road parameters, the first fault type and the first fault level, the effect of collaborative diagnosis of multiple vehicles can be estimated more accurately, different road conditions can influence the vehicle faults, and the diagnosis can be more in accordance with the actual conditions by combining the factors, so that the diagnosis accuracy is improved. According to the first fault type and the first fault level, the vehicle diagnosis timeliness index is determined, so that the diagnosis time can be reasonably arranged under different fault conditions, the diagnosis efficiency is improved, and the problems of excessive time-consuming diagnosis of simple faults or insufficient time of complex fault diagnosis are avoided. The target quantity is determined by integrating the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capacity parameter, and the quantity of the related vehicles participating in collaborative diagnosis can be reasonably selected on the premise of ensuring the diagnosis quality and the diagnosis efficiency, so that the waste or the deficiency of resources is avoided. And determining and screening the association priority of the associated vehicles, so that the m finally selected second vehicles can most effectively assist the first vehicle in diagnosis, and vehicles with high fault similarity and close distance are preferentially selected, thereby improving the collaborative diagnosis effect.
Optionally, step S204, determining the target number according to the co-diagnosis accuracy, the vehicle diagnosis aging index and the data processing capability parameter, may include the following steps:
Step 301, determining a reference number according to the collaborative diagnosis accuracy and a preset first mapping relation, wherein the first mapping relation is used for reflecting the mapping relation between the collaborative diagnosis accuracy and the reference number;
step S302, determining reference data processing quantity corresponding to the reference quantity;
Step S303, acquiring diagnosis duration of the first vehicle;
step S304, determining the data processing time length according to the data processing capacity parameter, the reference data processing amount and the diagnosis time length;
and step S305, determining the target number according to the reference number, the vehicle diagnosis timeliness index and the data processing time length.
In one possible embodiment, the reference number is determined according to the collaborative diagnosis accuracy and a preset first mapping relation, first, a first mapping relation table or a function model is established through historical collaborative diagnosis case data, and the mapping relation table comprises the optimal reference number corresponding to different collaborative diagnosis accuracy. After the collaborative diagnosis accuracy of the current first vehicle is obtained, searching and matching are carried out in the established first mapping relation, if a mapping relation table is adopted, the reference number closest to the current accuracy can be found through a linear interpolation method and the like, and if the current first vehicle is a function model, the collaborative diagnosis accuracy is directly substituted into a function to calculate the reference number.
In one possible embodiment, the reference data processing amount corresponding to the reference number is determined, the reference data processing amount corresponding to the reference number is counted or calculated in advance for each reference number, then the diagnosis time length of the first vehicle is obtained, the diagnosis time length information is extracted from the diagnosis record or the related log of the first vehicle, the data processing time length is determined according to the data processing capacity parameter, the reference data processing amount and the diagnosis time length, the data processing capacity parameter of the first vehicle can be the data processing speed, namely the data amount which can be processed in unit time, then the target number is determined according to the reference number, the vehicle diagnosis time efficiency index and the data processing time length, and the diagnosis of the first vehicle is determined according to the vehicle diagnosis time efficiency index. And establishing an evaluation function, integrating the reference quantity, the vehicle diagnosis aging index and the data processing time length, for example, when the data processing time length exceeds the vehicle diagnosis aging index, properly reducing the reference quantity, and recalculating the data processing time length until the aging requirement is met, otherwise, properly increasing the reference quantity on the premise of meeting the aging requirement to improve the accuracy of collaborative diagnosis, and finally determining the target quantity which can meet the diagnosis aging requirement and ensure a certain collaborative diagnosis effect through continuous adjustment and optimization.
In the embodiment of the application, the reference quantity is determined through the preset first mapping relation, the proper cooperative diagnosis vehicle quantity range can be quickly found by means of historical experience, the diagnosis efficiency is improved, the reference data processing quantity corresponding to the reference quantity is determined, the data processing capacity parameter and the diagnosis duration are combined to determine the data processing duration, the data processing requirement and the data processing capacity under the current condition can be determined, and the diagnosis delay or failure caused by overlarge data quantity or insufficient processing capacity is avoided. The target quantity is determined according to the vehicle diagnosis timeliness index, so that the whole diagnosis process is always completed within a specified time, the time requirement of an actual application scene is met, the timeliness and the practicability of diagnosis are improved, a plurality of factors are comprehensively considered to determine the target quantity, the blind increase or decrease of the quantity of vehicles participating in collaborative diagnosis is avoided, the optimal configuration of resources is realized, and the cost and the resource consumption are reduced on the premise of guaranteeing the diagnosis quality.
Optionally, step S205, determining n associated vehicles of the first vehicle according to the first location information may include the following steps:
Step S401, determining a target threshold according to the first fault type and the first fault level;
step S402, determining a target association range according to the first position information and a target threshold value;
step S403, obtaining k associated vehicles in a target associated range according to the first position information, wherein k is a natural number greater than or equal to n;
Step S404, acquiring a first vehicle characteristic and a first vehicle operation parameter of a first vehicle;
Step S405, acquiring vehicle characteristics and vehicle operation parameters of each associated vehicle in k associated vehicles to obtain k associated vehicle characteristics and k associated vehicle operation parameters;
Step S406, comparing the first vehicle characteristic with k associated vehicle characteristics to obtain k vehicle characteristic similarities;
Step S407, comparing the first vehicle operation parameters with k related vehicle operation parameters to obtain k vehicle operation parameter similarity;
And step S408, determining n associated vehicles from the k associated vehicles according to the k vehicle characteristic similarities and the k vehicle operation parameter similarities.
In one possible embodiment, a target threshold is determined according to a first fault type and a first fault level, a comprehensive evaluation system of the fault type and the fault level is established, and the distance range rules of the associated vehicle required to be referenced under different fault levels of different fault types are analyzed in combination with historical vehicle maintenance case data. For example, for severe engine faults, it may be necessary to find the associated vehicle in a larger area, as such faults are more complex, requiring more references to different vehicle conditions, while for some simple component faults, the associated vehicle of reference value may be found in a smaller area.
In one possible embodiment, according to the analysis result, target thresholds corresponding to different fault types and fault level combinations are set, and after the first fault type and the first fault level of the first vehicle are determined, the set corresponding relationship is searched for, so that the target threshold required by the diagnosis is obtained. Then, a target association range is determined according to the first position information and the target threshold value, and a circular or polygonal target association range is determined according to the target threshold value by taking the first position as the center. If the target threshold is in units of distance, such as 5 km, a circle is drawn by taking the first vehicle position as the center of a circle and 5 km as the radius, the region in the circle is the target association range, if the target threshold is defined by other modes, such as covering a certain number of administrative areas, the target association range is defined according to the corresponding rule.
In one possible embodiment, k associated vehicles in the target associated range are obtained according to the first location information, and screening is performed in the database according to the first location information and the target associated range to find out all vehicles in the target associated range, wherein the vehicles are candidate associated vehicles. And further screening and confirming candidate associated vehicles, excluding some obviously irrelevant vehicles, such as different types of vehicles, vehicles incapable of providing data, and the like, and finally determining k associated vehicles.
In one possible embodiment, a first vehicle characteristic of a first vehicle is obtained, the first vehicle characteristic including static information of a make, a model, a year of production, a configuration, etc. of the vehicle, and a first vehicle operating parameter including dynamic information of an engine speed, a vehicle speed, fuel consumption, various sensor data, etc. And acquiring the vehicle characteristics and the vehicle operation parameters of each associated vehicle in the k associated vehicles to acquire k associated vehicle characteristics and k associated vehicle operation parameters, establishing communication connection with each vehicle for the k associated vehicles respectively, remotely acquiring the vehicle characteristics and the vehicle operation parameters through a network if the vehicle has a vehicle networking function, and acquiring related data through a portable data acquisition device deployed on the vehicle if the vehicle does not have the vehicle networking function under the condition of consent of a vehicle owner.
In one possible embodiment, the first vehicle feature is compared with k associated vehicle features to obtain k vehicle feature similarities, a rule-based matching method may be used for discrete features such as a vehicle brand, a model, etc., and a corresponding similarity calculation algorithm may be used for numerical or quantifiable features such as a production year, a configuration, etc. And sequentially calculating each feature of the first vehicle and the corresponding features of k associated vehicles to obtain feature similarity of each associated vehicle and the first vehicle, and finally forming k vehicle feature similarity data.
In a possible embodiment, the first vehicle operation parameter and the k associated vehicle operation parameters are compared to obtain k vehicle operation parameter similarities, and for the dynamic operation parameters, as the data are changed along with time, a proper time window is selected for data sampling and comparison, and a proper similarity calculation method is also adopted for carrying out one-by-one comparison on the operation parameters of the first vehicle and each associated vehicle, so that the operation parameter similarities of each associated vehicle and the first vehicle are calculated, and k vehicle operation parameter similarity data are obtained.
In one possible embodiment, according to the k vehicle feature similarities and the k vehicle operation parameter similarities, n associated vehicles are determined from the k associated vehicles, and the associated vehicles with the vehicle feature similarities and the vehicle operation parameter similarities higher than the preset similarity threshold value in the k associated vehicles are used as n associated vehicles.
According to the method and the device for determining the target threshold according to the fault type and the grade, the target association range is determined, the vehicle range possibly related to the first vehicle fault can be accurately screened out, blind searching is avoided, and the screening efficiency and pertinence of the associated vehicles are improved. The characteristics and the operation parameters of the first vehicle and the related vehicles are acquired, and the similarity of the first vehicle and the related vehicles is compared in a multi-dimension manner, so that the similarity of the related vehicles and the first vehicle can be estimated from multiple angles. The final n associated vehicles are determined by comprehensively considering the similarity of the vehicle characteristics and the operation parameters, and have higher similarity with the first vehicle in multiple aspects, so that information with more reference value can be provided when the collaborative diagnosis is carried out, and the accuracy of the fault diagnosis of the first vehicle is improved. In the process of determining the associated vehicles, the uncorrelated vehicles are gradually screened and eliminated, so that data acquisition and analysis of a large number of uncorrelated vehicles are avoided, the utilization of resources is effectively optimized, and unnecessary calculation and communication costs are reduced.
Optionally, step S206, determining the association priority of each of the n associated vehicles, to obtain n association priorities, may include the following steps:
step S501, obtaining second position information of each associated vehicle in n associated vehicles to obtain n second position information;
Step S502, determining n associated distances according to the first position information and n pieces of second position information;
step S503, determining n vehicle feature similarities corresponding to n associated vehicles in the k vehicle feature similarities;
Step S504, determining n vehicle operation parameter similarities corresponding to n related vehicles in the k vehicle operation parameter similarities;
Step S505, determining a target weight group according to the first fault type and the first fault level, wherein the target weight group comprises a first weight, a second weight and a third weight;
And S506, determining n associated priorities according to the n associated distances, the n vehicle feature similarities, the n vehicle operation parameter similarities and the target weight group.
In one possible embodiment, second position information of each associated vehicle in the n associated vehicles is obtained, n second position information is obtained, n associated distances are determined according to the first position information and the n second position information, n vehicle feature similarities corresponding to the n associated vehicles in the k vehicle feature similarities are determined, and n vehicle operation parameter similarities corresponding to the n associated vehicles in the k vehicle operation parameter similarities are determined. Determining a target weight group according to the first fault type and the first fault level, wherein the target weight group comprises a first weight, a second weight and a third weight, a mapping relation table of the fault type, the fault level and the weight group is established, different fault types and fault level combinations correspond to different weight groups in the mapping relation table, after the first fault type and the first fault level of the first vehicle are determined, the first weight group corresponding to the first fault type and the first fault level of the first vehicle are searched and matched in the mapping relation table, the weight of the first weight corresponding to the association distance in the target weight group, the weight of the second weight corresponding to the similarity of the vehicle characteristics and the weight of the similarity of the third weight corresponding to the vehicle operation parameters are found. According to the n associated distances, the n vehicle feature similarities, the n vehicle operation parameter similarities and the target weight groups, n associated priorities are determined, after the associated priorities of the n associated vehicles are calculated, the calculation results are ordered, the associated priority sequence of each associated vehicle is determined from high to low, and the n associated priorities are obtained.
According to the embodiment of the application, the association degree of the associated vehicle and the first vehicle can be comprehensively estimated from a plurality of dimensions such as the space position, the hardware characteristics of the vehicle, the real-time running state and the like by acquiring the position information of the associated vehicle and calculating the association distance and combining the similarity of the characteristics of the vehicle and the similarity of the running parameters. The weight group is determined according to the first fault type and the fault level, so that key factors can be highlighted when the association priority is calculated, the association vehicle which is most helpful to the first vehicle fault diagnosis is screened out more pertinently, time and calculation resources are prevented from being wasted on a large amount of irrelevant or low-association vehicle data, and therefore diagnosis efficiency is improved. The method has the advantages that the correlation priority is determined by comprehensively considering multiple factors, so that in the collaborative diagnosis process, the vehicles which are closely correlated with the first vehicle in multiple key aspects can be preferentially selected, the vehicles can provide information with more reference value, the fault cause of the first vehicle can be analyzed more accurately, the diagnosis result is optimized, and the accuracy and reliability of diagnosis are improved. By establishing the mapping relation between the fault type, the grade and the weight group, the emphasis point of the associated vehicle can be flexibly adjusted and evaluated according to different fault conditions, the adaptability of the system to various complex fault scenes is enhanced, and the diversified vehicle fault diagnosis requirements can be better met.
Optionally, for step S207, screening n associated vehicles according to the target number and n associated priorities to obtain m second vehicles, in a possible embodiment, referring to fig. 3, fig. 3 is an application scenario diagram of a vehicle collaborative diagnosis method based on a common vehicle provided by the embodiment of the present application, when a first vehicle is a common vehicle, there is no fleet binding relationship with surrounding vehicles, and due to diversity of driving roads, the surrounding vehicles of the first vehicle may have a low distribution density degree, and then the number of associated vehicles in the target associated range is less, and at this time, vehicles with low similarity of vehicle characteristics and low similarity of vehicle operation parameters may also be used as second vehicles, and vehicles outside the target associated range are used as other vehicles.
Optionally, for step S207, the n associated vehicles are screened according to the target number and the n associated priorities to obtain m second vehicles, in a possible embodiment, referring to fig. 4, fig. 4 is an application scenario diagram of a vehicle collaborative diagnosis method based on a large-scale fleet according to an embodiment of the present application, as shown in fig. 4, when a first vehicle belongs to a vehicle in the fleet, the associated vehicles in the target association range may be more, and the similarity between the first vehicle and the vehicle types of the associated vehicles is higher, and when the target number is smaller, and when the associated priorities are determined according to the association distance, the associated vehicle with a relatively close association distance with the first vehicle may be used as the second vehicle.
Optionally, step S105 is performed to diagnose the first vehicle according to m pieces of second vehicle diagnosis data, so as to obtain a diagnosis result, including:
step S601, determining a second fault type and a second fault level of each second vehicle in m second vehicles according to m second vehicle diagnosis data to obtain m second fault types and m second fault levels;
step S602, comparing the first fault type with m second fault types to determine m fault type similarity;
step S603, comparing the first fault level with m second fault levels to determine m fault level similarity;
step S604, determining m associated priorities corresponding to m second vehicles in the n associated priorities;
step S605, determining a target fault type and a target fault level of the first vehicle according to m associated priorities, m fault type similarities and m fault level similarities;
And step S606, taking the target fault type and the target fault level as diagnosis results.
In one possible embodiment, the second fault type and the second fault level of each of the m second vehicles are determined according to the m second vehicle diagnosis data, so as to obtain m second fault types and m second fault levels, the fault code data are compared with a standard fault code library, the corresponding fault types are identified, for example, the fault code P0171 indicates that the fuel system is too thin, and then the fault type of the second vehicle may be a fuel supply related problem. According to factors such as the influence degree of the fault on the vehicle performance, the maintenance difficulty, the possible consequences and the like, the fault grade is determined, for example, the fault that the engine cannot be started seriously affects the normal use of the vehicle, the maintenance is complex, the fault can be judged to be a high-grade fault, the fault with small influence on the main functions of the vehicle, such as the fact that the lamp in the vehicle is not on, can be judged to be a low-grade fault, and m second fault types and m second fault grades are finally obtained.
In one possible embodiment, the first fault type is compared with m second fault types, m fault type similarity is determined, a feature vector library described by the fault types is established, the first fault type and the m second fault types are respectively converted into feature vectors, for example, for the engine fault types, the related components such as an oil nozzle, a spark plug and the like, fault phenomena such as shaking, flameout and the like can be used as features, and the existence or severity of each feature is represented by numerical values to form the feature vectors. And sequentially calculating the feature vector of the first fault type and the feature vectors of the m second fault types through a similarity calculation method to obtain m numerical values, wherein the numerical values represent the similarity between the first fault type and each second fault type, and the numerical values are closer to 1, the higher the similarity is, the closer to 0, and the lower the similarity is.
In one possible embodiment, the first fault level and the m second fault levels are compared, m fault level similarities are determined, a quantization standard of the fault levels is defined, for example, the fault level is divided into 1-5 levels, 1 level is the lowest level, 5 level is the highest level, for the first fault level and each second fault level, a numerical processing is performed according to the quantization standard, then the difference between the first fault level and each second fault level is obtained through a difference calculation, the difference degree is converted into the similarity, m fault level similarities are obtained, and similarly, the closer the value is to 1, the higher the similarity is.
In one possible embodiment, m associated priorities corresponding to m second vehicles in the n associated priorities are determined, and the target fault type and the target fault level of the first vehicle are determined according to the m associated priorities, the m fault type similarities and the m fault level similarities, if the fault type similarities of the plurality of second vehicles with higher associated priorities in the m second vehicles are higher, for example, when the first fault type of the first vehicle is engine abnormal sound and the second fault type of the second vehicle with higher associated priorities in the m second vehicles is also engine abnormal sound, the fault type of the first vehicle may not be engine abnormal sound, and the fault may be misdiagnosis caused by environmental noise outside the vehicle.
In the embodiment of the application, the reference which is most in line with the fault condition of the first vehicle can be found from a plurality of similar cases by comparing the fault types and the fault grades of m second vehicles and utilizing similarity calculation and comprehensive evaluation, so that the target fault type and the target fault grade of the first vehicle can be more accurately determined, and the possibility of misdiagnosis is reduced. Considering the associated priorities of m second vehicles, the vehicle fault information closely associated with the first vehicle is preferentially referred, so that the diagnosis process can more effectively utilize the data of other vehicles, and the diagnosis efficiency and quality are improved. The fault type similarity, the fault level similarity and the associated priority are comprehensively considered, the faults of the first vehicle are analyzed from multiple dimensions, the limitation of single-dimension judgment is avoided, and the diagnosis result is more comprehensive and reliable.
Optionally, the vehicle collaborative diagnosis method provided by the embodiment of the application further includes the following steps:
step 701, determining a vehicle maintenance aging index according to a diagnosis result, wherein the vehicle maintenance aging index is used for reflecting the maintenance efficiency required by the first vehicle;
Step S702, energy consumption information of a first vehicle is obtained;
Step 703, determining a plurality of maintenance points according to the vehicle maintenance aging index and the energy consumption information;
Step S704, acquiring maintenance point information of each maintenance point in a plurality of maintenance points to obtain a plurality of maintenance point information;
step S705, determining the maintenance time and the target maintenance point of the first vehicle according to the plurality of maintenance point information and the vehicle maintenance aging index;
step S706, determining a maintenance path according to the first position information and the third position information of the target maintenance point;
step S707, a maintenance scheme of the first vehicle is generated according to the maintenance time and the maintenance path.
In one possible embodiment, according to the diagnosis result, determining a vehicle maintenance aging index, and establishing a corresponding relation database of fault types, fault grades and maintenance aging indexes, wherein the database is used for determining suggested maintenance duration ranges corresponding to different fault types and grades. And inquiring corresponding maintenance timeliness indexes in the database according to the determined target fault type and target fault grade of the first vehicle. Energy consumption information of the first vehicle is then obtained, including energy consumption data of the vehicle over a period of time, such as fuel consumption or electric energy consumption. And determining a plurality of maintenance points according to the vehicle maintenance aging index and the energy consumption information, establishing a database containing a plurality of maintenance point information, wherein the database comprises positions of the maintenance points, maintenance capability such as repairable fault types and grades, service quality evaluation and the like, screening out maintenance points capable of completing maintenance within a specified time according to the vehicle maintenance aging index, combining the energy consumption information of a first vehicle and distance factors of the maintenance points, preferentially selecting the maintenance points with a relatively short distance, reducing the energy consumption of the vehicle in the repair process, and determining a plurality of maintenance points meeting the conditions through the screening. The method comprises the steps of obtaining maintenance point information of each maintenance point in a plurality of maintenance points to obtain the plurality of maintenance point information, extracting detailed information from a maintenance point database for each determined maintenance point, wherein the detailed information comprises specific addresses of the maintenance points, business hours, technical qualification of maintenance teams, equipment conditions of maintenance equipment and the like, determining maintenance time and target maintenance points of a first vehicle according to the plurality of maintenance point information and vehicle maintenance aging indexes, analyzing maintenance capacity and service quality information of each maintenance point, and predicting actual maintenance time of each maintenance point by combining the vehicle maintenance aging indexes. And comprehensively considering factors such as maintenance time, maintenance price, service quality and the like, calculating comprehensive scores of all maintenance points, selecting the maintenance point with the highest comprehensive score as a target maintenance point, and determining the corresponding maintenance time. Then, a maintenance path is determined according to the first position information and the third position information of the target maintenance point, the first position information of the first vehicle and the third position information of the target maintenance point are input by using map navigation software or an online map service, an optimal maintenance path from the current position of the first vehicle to the target maintenance point is calculated according to traffic conditions, road types and traffic rules, a path planning result can comprise detailed path description such as a driving direction, intersection turning information and predicted driving time, and finally a maintenance scheme of the first vehicle is generated according to the maintenance time and the maintenance path.
In a possible embodiment, referring to fig. 5, fig. 5 is an application scenario diagram of a vehicle maintenance method provided by the embodiment of the present application, as shown in fig. 5, five maintenance points in the diagram are determined according to a vehicle maintenance aging index and energy consumption information, and each maintenance point conforms to the vehicle maintenance aging index and energy consumption information of a first vehicle, where the maintenance point 1 is in a moving direction of the first vehicle, and then the maintenance point 1 may be regarded as a target maintenance point.
According to the embodiment of the application, the maintenance aging index is determined according to the diagnosis result, so that maintenance work can be completed within reasonable time, inconvenience to a vehicle owner caused by overlong maintenance time is avoided, meanwhile, the maintenance points with strong maintenance capability and high efficiency are selected by screening and determining the target maintenance points, the maintenance efficiency is further improved, the maintenance points with relatively close distance are selected by combining energy consumption information, the energy consumption of the vehicle in the repair process is reduced, the energy cost is reduced, and meanwhile, the target maintenance points are determined by comprehensively considering factors such as maintenance price, and the like, so that the control of the maintenance cost is facilitated. The maintenance points are screened and evaluated, so that the vehicle can obtain the most suitable maintenance service, the configuration of maintenance resources is optimized, and the utilization efficiency of the maintenance resources is improved.
In a possible embodiment, referring to fig. 6, fig. 6 is a flowchart of a method for determining a vehicle collaborative diagnosis according to an embodiment of the present application, as shown in fig. 6, a single diagnosis accuracy is determined first, where the single diagnosis accuracy is used to reflect an accuracy when a first vehicle is diagnosed by self data of the first vehicle, then the single diagnosis accuracy is compared with the collaborative accuracy to obtain a comparison result, whether to perform collaborative diagnosis is determined according to the comparison result, and when the single diagnosis accuracy is higher than the collaborative diagnosis accuracy or higher than a preset accuracy threshold, it is determined that the first vehicle does not need to perform collaborative diagnosis, and the first fault type and the first fault level are used as a diagnosis result of the first vehicle. For example, when the failure type of the first vehicle is that the vehicle lamp is not on, the single diagnosis accuracy of the failure type is higher, so that collaborative diagnosis is not needed, when the failure type of the first vehicle is that abnormal sound of the engine and the like may be misjudged due to environmental factors, the single diagnosis accuracy is lower, and at the moment, collaborative diagnosis is carried out on the first vehicle.
In one possible embodiment, vehicle diagnosis data of the target vehicle is obtained, when the vehicle diagnosis data shows that the target function of the target vehicle may be abnormal, relevant diagnosis data corresponding to the target function of the surrounding same type of vehicle is obtained, collaborative diagnosis is performed on the target vehicle based on the relevant diagnosis data and the vehicle diagnosis data, and further, corresponding measures are taken based on collaborative diagnosis results. For example, if an engine of one truck is abnormally noisy, the system may obtain engine operating data and environmental information from other trucks on the same route, and determine whether the fault is prevalent or related to factors such as particular road segments, weather conditions, etc., by comparing and analyzing the relevant diagnostic data to the vehicle diagnostic data. When a slight abnormality occurs in a brake system of one bus, the brake data of surrounding buses under the same road condition are combined for analysis, so that whether the bus needs to be maintained immediately or can be continuously operated to the next maintenance point for processing is judged.
In the shared automobile platform, a plurality of automobiles are distributed at different places, when the automobile faults are identified, cooperative diagnosis is carried out by means of peripheral shared automobile data, for example, when the battery capacity of one shared automobile is abnormally reduced, the battery performance of the nearby automobile under the same service time and environment is analyzed, and whether the battery of the automobile has problems or is influenced by external factors, such as the faults of a charging facility or the high-power consumption driving behavior of a specific area is judged.
Further, countermeasures can be taken based on the collaborative diagnosis result, for example, for sharing a bicycle, mechanical components and an electronic lock system of the bicycle are monitored, when the brake of one bicycle fails, the brake service condition and maintenance record of the nearby bicycle are acquired, whether the problem is batch is determined, so that the same batch of vehicles can be checked and maintained in time, and riding safety of users is guaranteed.
In an urban intelligent traffic network or on a highway, remote diagnosis of multi-vehicle cooperation can be combined with data of a traffic management center to provide support for traffic flow optimization and road safety, for example, when a plurality of vehicles frequently fail in an electronic system on a certain road section, the traffic management center can adjust traffic signals in time according to diagnosis information according to the running state and road conditions of the vehicles to guide other vehicles to avoid the road section.
In summary, in the embodiment of the application, first vehicle diagnosis data of a first vehicle is acquired, wherein the first vehicle diagnosis data includes a fault parameter, a data processing capability parameter and first position information, then a first fault type and a first fault level of the first vehicle are determined according to the fault parameter, the first fault level is used for reflecting the complexity degree of the first fault type, m second vehicles associated with the first vehicle are determined according to the first fault type, the first fault level, the data processing capability parameter and the first position information, wherein the distance between the m second vehicles and the first vehicle is smaller than a target threshold value, m is a natural number, then second vehicle diagnosis data of each second vehicle in the m second vehicles are acquired, m first vehicle diagnosis data are obtained, the m second vehicle diagnosis data are used for reflecting the running state information of the m second vehicles, diagnosis is performed on the first vehicle according to the m second vehicle diagnosis data, and a diagnosis result is obtained, and the diagnosis result is returned to the first vehicle. Therefore, the first fault type and the first fault level of the first vehicle are determined, m second vehicles associated with the first vehicle are determined according to the first fault type, the first fault level, the data processing capacity parameter and the first position information, the first vehicle is diagnosed according to m second vehicle diagnosis data, a diagnosis result is obtained, and when the vehicles are in fault, diagnosis is performed through vehicle cooperation, so that the diagnosis accuracy can be improved.
The foregoing details of the method according to the embodiments of the present invention and the apparatus according to the embodiments of the present invention are provided below.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a vehicle collaborative diagnosis apparatus according to an embodiment of the present application. As shown in fig. 7, the vehicle cooperative diagnosis device 800 includes an acquisition unit 801 and a processing unit 802;
an acquisition unit 801 for acquiring first vehicle diagnostic data of a first vehicle, the first vehicle diagnostic data including a failure parameter, a data processing capability parameter, and first position information;
A processing unit 802, configured to determine a first fault type and a first fault level of the first vehicle according to the fault parameter, where the first fault level is used to reflect a complexity level of the first fault type;
determining m second vehicles associated with the first vehicle according to the first fault type, the first fault level, the data processing capacity parameter and the first position information, wherein the distance between the m second vehicles and the first vehicle is smaller than a target threshold value;
Obtaining second vehicle diagnosis data of each second vehicle in m second vehicles to obtain m second vehicle diagnosis data, wherein the m second vehicle diagnosis data are used for reflecting running state information of the m second vehicles;
Diagnosing the first vehicle according to the m second vehicle diagnosis data to obtain a diagnosis result;
and returning the diagnosis result to the first vehicle.
In one possible embodiment, the processing unit 802 is specifically configured to, in determining m second vehicles associated with the first vehicle according to the first fault type, the first fault level, the data processing capability parameter, and the first location information:
Acquiring vehicle road parameters of a first vehicle;
Determining a collaborative diagnosis accuracy according to the vehicle road parameter, the first fault type and the first fault level, wherein the collaborative diagnosis accuracy is used for reflecting the accuracy when diagnosing the first vehicle through the vehicle diagnosis data of a plurality of vehicles;
determining a vehicle diagnosis timeliness index of the first vehicle according to the first fault type and the first fault level, wherein the vehicle diagnosis timeliness index is used for reflecting the diagnosis efficiency required by the first vehicle;
Determining target quantity according to the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capacity parameter;
Determining n associated vehicles of the first vehicle according to the first position information, wherein n is a natural number;
determining the association priority of each association vehicle in the n association vehicles to obtain n association priorities;
And screening the n associated vehicles according to the target number and the n associated priorities to obtain m second vehicles.
In one possible embodiment, the processing unit 802 is specifically configured to determine the target number according to the collaborative diagnostic accuracy, the vehicle diagnostic timeliness indicator, and the data processing capability parameter:
determining a reference number according to the collaborative diagnosis accuracy and a preset first mapping relation, wherein the first mapping relation is used for reflecting the mapping relation between the collaborative diagnosis accuracy and the reference number;
determining reference data processing quantity corresponding to the reference quantity;
Acquiring a diagnosis duration of a first vehicle;
determining a data processing time length according to the data processing capacity parameter, the reference data processing amount and the diagnosis time length;
And determining the target number according to the reference number, the vehicle diagnosis timeliness index and the data processing time length.
In one possible embodiment, the processing unit 802 is specifically configured to, in determining n associated vehicles of the first vehicle according to the first location information:
determining a target threshold according to the first fault type and the first fault level;
Determining a target association range according to the first position information and a target threshold value;
k associated vehicles in the target association range are acquired according to the first position information, wherein k is a natural number greater than or equal to n;
acquiring a first vehicle characteristic and a first vehicle operating parameter of a first vehicle;
Acquiring vehicle characteristics and vehicle operation parameters of each associated vehicle in k associated vehicles to obtain k associated vehicle characteristics and k associated vehicle operation parameters;
Comparing the first vehicle characteristic with k associated vehicle characteristics to obtain k vehicle characteristic similarities;
comparing the first vehicle operation parameters with k related vehicle operation parameters to obtain k vehicle operation parameter similarity;
And determining n associated vehicles from the k associated vehicles according to the k vehicle characteristic similarities and the k vehicle operation parameter similarities.
In one possible embodiment, in determining the associated priority of each of the n associated vehicles, the processing unit 802 is specifically configured to:
Acquiring second position information of each associated vehicle in the n associated vehicles to obtain n second position information;
Determining n associated distances according to the first position information and the n second position information;
Determining n vehicle feature similarities corresponding to n associated vehicles in the k vehicle feature similarities;
Determining n vehicle operation parameter similarities corresponding to n associated vehicles in the k vehicle operation parameter similarities;
Determining a target weight group according to the first fault type and the first fault level, wherein the target weight group comprises a first weight, a second weight and a third weight;
And determining n associated priorities according to the n associated distances, the n vehicle characteristic similarities, the n vehicle operation parameter similarities and the target weight group.
In one possible embodiment, the processing unit 802 is specifically configured to, in diagnosing the first vehicle according to the m second vehicle diagnostic data, obtain a diagnosis result:
Determining a second fault type and a second fault level of each second vehicle in the m second vehicles according to the m second vehicle diagnosis data to obtain m second fault types and m second fault levels;
Comparing the first fault type with m second fault types to determine m fault type similarity;
Comparing the first fault level with m second fault levels to determine m fault level similarity;
Determining m associated priorities corresponding to m second vehicles in the n associated priorities;
Determining a target fault type and a target fault level of the first vehicle according to the m associated priorities, the m fault type similarities and the m fault level similarities;
and taking the target fault type and the target fault level as diagnosis results.
In a possible embodiment, the processing unit 802 is further configured to:
determining a vehicle maintenance aging index according to the diagnosis result, wherein the vehicle maintenance aging index is used for reflecting the maintenance efficiency required by the first vehicle;
Acquiring energy consumption information of a first vehicle;
determining a plurality of maintenance points according to the vehicle maintenance aging index and the energy consumption information;
acquiring maintenance point information of each maintenance point in a plurality of maintenance points to obtain a plurality of maintenance point information;
Determining maintenance time and a target maintenance point of the first vehicle according to the plurality of maintenance point information and the vehicle maintenance aging index;
Determining a maintenance path according to the first position information and the third position information of the target maintenance point;
And generating a maintenance scheme of the first vehicle according to the maintenance time and the maintenance path.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a diagnostic device according to an embodiment of the present application. As shown in fig. 8, the diagnostic device 900 includes a transceiver 901, a processor 902, and a memory 903, which are connected by a bus 904. The memory 903 is used to store computer programs and data, and the data stored in the memory 903 may be transferred to the processor 902. The diagnostic device 900 may be the vehicle co-diagnostic apparatus 800, and the processor 902 may be the acquisition unit 801 and the processing unit 802.
The processor 902 is configured to read a computer program in the memory 903 to perform the following operations:
acquiring first vehicle diagnostic data of a first vehicle, wherein the first vehicle diagnostic data comprises fault parameters, data processing capacity parameters and first position information;
Determining a first fault type and a first fault grade of the first vehicle according to the fault parameters, wherein the first fault grade is used for reflecting the complexity degree of the first fault type;
determining m second vehicles associated with the first vehicle according to the first fault type, the first fault level, the data processing capacity parameter and the first position information, wherein the distance between the m second vehicles and the first vehicle is smaller than a target threshold value;
Obtaining second vehicle diagnosis data of each second vehicle in m second vehicles to obtain m second vehicle diagnosis data, wherein the m second vehicle diagnosis data are used for reflecting running state information of the m second vehicles;
Diagnosing the first vehicle according to the m second vehicle diagnosis data to obtain a diagnosis result;
and returning the diagnosis result to the first vehicle.
In one possible embodiment, in determining m second vehicles associated with a first vehicle based on a first fault type, a first fault level, a data processing capability parameter, and first location information, the processor 902 is specifically configured to:
Acquiring vehicle road parameters of a first vehicle;
Determining a collaborative diagnosis accuracy according to the vehicle road parameter, the first fault type and the first fault level, wherein the collaborative diagnosis accuracy is used for reflecting the accuracy when diagnosing the first vehicle through the vehicle diagnosis data of a plurality of vehicles;
determining a vehicle diagnosis timeliness index of the first vehicle according to the first fault type and the first fault level, wherein the vehicle diagnosis timeliness index is used for reflecting the diagnosis efficiency required by the first vehicle;
Determining target quantity according to the collaborative diagnosis accuracy, the vehicle diagnosis aging index and the data processing capacity parameter;
Determining n associated vehicles of the first vehicle according to the first position information, wherein n is a natural number;
determining the association priority of each association vehicle in the n association vehicles to obtain n association priorities;
And screening the n associated vehicles according to the target number and the n associated priorities to obtain m second vehicles.
In one possible embodiment, the processor 902 is specifically configured to, in determining the target number based on the co-diagnostic accuracy, the vehicle diagnostic age indicator, and the data processing capability parameter:
determining a reference number according to the collaborative diagnosis accuracy and a preset first mapping relation, wherein the first mapping relation is used for reflecting the mapping relation between the collaborative diagnosis accuracy and the reference number;
determining reference data processing quantity corresponding to the reference quantity;
Acquiring a diagnosis duration of a first vehicle;
determining a data processing time length according to the data processing capacity parameter, the reference data processing amount and the diagnosis time length;
And determining the target number according to the reference number, the vehicle diagnosis timeliness index and the data processing time length.
In one possible embodiment, the processor 902 is specifically configured to, in determining n associated vehicles of the first vehicle based on the first location information:
determining a target threshold according to the first fault type and the first fault level;
Determining a target association range according to the first position information and a target threshold value;
k associated vehicles in the target association range are acquired according to the first position information, wherein k is a natural number greater than or equal to n;
acquiring a first vehicle characteristic and a first vehicle operating parameter of a first vehicle;
Acquiring vehicle characteristics and vehicle operation parameters of each associated vehicle in k associated vehicles to obtain k associated vehicle characteristics and k associated vehicle operation parameters;
Comparing the first vehicle characteristic with k associated vehicle characteristics to obtain k vehicle characteristic similarities;
comparing the first vehicle operation parameters with k related vehicle operation parameters to obtain k vehicle operation parameter similarity;
And determining n associated vehicles from the k associated vehicles according to the k vehicle characteristic similarities and the k vehicle operation parameter similarities.
In one possible embodiment, in determining the associated priority of each of the n associated vehicles, resulting in n associated priorities, the processor 902 is specifically configured to:
Acquiring second position information of each associated vehicle in the n associated vehicles to obtain n second position information;
Determining n associated distances according to the first position information and the n second position information;
Determining n vehicle feature similarities corresponding to n associated vehicles in the k vehicle feature similarities;
Determining n vehicle operation parameter similarities corresponding to n associated vehicles in the k vehicle operation parameter similarities;
Determining a target weight group according to the first fault type and the first fault level, wherein the target weight group comprises a first weight, a second weight and a third weight;
And determining n associated priorities according to the n associated distances, the n vehicle characteristic similarities, the n vehicle operation parameter similarities and the target weight group.
In one possible embodiment, the processor 902 is specifically configured to perform the following operations in diagnosing the first vehicle according to m second vehicle diagnostic data to obtain a diagnosis result:
Determining a second fault type and a second fault level of each second vehicle in the m second vehicles according to the m second vehicle diagnosis data to obtain m second fault types and m second fault levels;
Comparing the first fault type with m second fault types to determine m fault type similarity;
Comparing the first fault level with m second fault levels to determine m fault level similarity;
Determining m associated priorities corresponding to m second vehicles in the n associated priorities;
Determining a target fault type and a target fault level of the first vehicle according to the m associated priorities, the m fault type similarities and the m fault level similarities;
and taking the target fault type and the target fault level as diagnosis results.
In one possible embodiment, the processor 902 is further configured to:
determining a vehicle maintenance aging index according to the diagnosis result, wherein the vehicle maintenance aging index is used for reflecting the maintenance efficiency required by the first vehicle;
Acquiring energy consumption information of a first vehicle;
determining a plurality of maintenance points according to the vehicle maintenance aging index and the energy consumption information;
acquiring maintenance point information of each maintenance point in a plurality of maintenance points to obtain a plurality of maintenance point information;
Determining maintenance time and a target maintenance point of the first vehicle according to the plurality of maintenance point information and the vehicle maintenance aging index;
Determining a maintenance path according to the first position information and the third position information of the target maintenance point;
And generating a maintenance scheme of the first vehicle according to the maintenance time and the maintenance path.
Embodiments of the present application also provide a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any one of the vehicle co-diagnosis methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the vehicle co-diagnostic methods described in the method embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the modules, merely a logical function division, and there may be additional manners of dividing actual implementations, such as multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software program modules.
The integrated modules, if implemented in the form of software program modules, may be stored in a computer readable memory for sale or use as a stand alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. The Memory includes a U disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc. which can store the program codes.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.