A detection method and related device

Abstract

The embodiment of the application discloses a detection method and related equipment, wherein the detection method can be applied to a server, and the detection method can include: obtaining a first data set; the first data set includes M data related to an air suspension system of the first vehicle; obtaining N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system; and determining a first detection result of the air suspension system according to the N second data sets and weights corresponding to the N types of characteristics. By using the embodiment of the application, the air suspension system in the vehicle can be more comprehensively and accurately detected in real time, and driving safety is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air suspension, in particular to a detection method and related equipment. BACKGROUND

[0002] With the increasing improvement of people's living standards, people's requirements for driving a car are getting higher and higher. A high-quality suburban utility vehicle (SUV) not only has the comfort of a car, but also takes into account the performance of an off-road vehicle. The air suspension system on the market is the best choice to achieve this goal. Among them, the air suspension system can determine the change of the vehicle body height through the driving computer according to the different road conditions and the signal of the distance sensor, and then control the air compressor and the exhaust valve to make the spring automatically compress or elongate, thereby reducing or raising the ground clearance of the automobile chassis, increasing the stability of the vehicle body at high speed or the passing ability of complex road conditions, and thus improving the comfort and control feeling of the ride.

[0003] However, the gas in the air spring of the air compressor usually contains a certain amount of water, and the water contains various impurities, not pure water. During the use of the air suspension system, the impurities in the air spring will accumulate, and the rubber of the air spring will gradually age, eventually leading to rupture. In order to solve the above problems, many automobile manufacturers often choose to use a desiccant to dry the gas in the air spring, but since the service life of the desiccant itself is relatively short, if not replenished in time, the air spring will still be seriously aged and damaged in the later period. And due to the working principle of the air suspension system, the gas needs to be compressed and released frequently, thereby further shortening the service life of the rubber material.

[0004] Generally, the probability of air suspension system failure will increase exponentially with its use time, thereby bringing great driving risks to the vehicle owner. However, due to the complex structure of the air suspension system, the vehicle owner often ignores the detection and maintenance of the air suspension system when maintaining and repairing the vehicle. Therefore, most vehicle owners are unaware of the specific state of the air suspension system in their vehicles, and even if the air suspension system is close to its service life, they are unaware of this hidden danger, which will endanger the driving safety of the vehicle owner and even cause serious traffic accidents, endangering the safety of life and property of the public.

[0005] Therefore, how to realize more comprehensive and accurate detection of the air suspension system in the vehicle to ensure the driving safety of the vehicle owner is a problem to be solved. SUMMARY

[0006] The embodiments of the present application provide a detection method and related equipment, which can more comprehensively and accurately detect the air suspension system in the vehicle in real time to ensure driving safety.

[0007] In a first aspect, an embodiment of the present application provides a detection method applied to a server, which can include: obtaining a first data set; the first data set including M data related to an air suspension system of a first vehicle; M being an integer greater than or equal to 1; obtaining N second data sets; each of the N second data sets including one or more of the M data; the N second data sets corresponding to N types of features, the N types of features including one or more of an adjustment feature, a life feature and a material feature of the air suspension system; N being an integer greater than or equal to 1; determining a first detection result of the air suspension system according to the N second data sets and weights corresponding to the N types of features.

[0008] In a possible implementation, the M data in the first data set are data collected by the first vehicle during driving and / or in a parked state.

[0009] Through the method provided by the first aspect, during driving or parking of a vehicle (such as the first vehicle), the server can receive a large amount of data uploaded by the vehicle (for example, can include the data of the compressed gas volume, the released gas volume and the rising temperature of the air suspension system of the vehicle at each adjustment of the air suspension system in real time during driving of the vehicle). Then, the server can classify the received large amount of data based on different features of different data to obtain data sets corresponding to each type of feature. Finally, the server can comprehensively consider the data sets corresponding to each type of feature and the weights of each type of feature to calculate the detection result of the air suspension system (for example, calculate the current wear rate of the suspension system), thereby realizing multi-dimensional, more comprehensive and more accurate detection of the air suspension system. However, in the prior art, when detecting the air suspension system, only part of the components in the air suspension system can be detected by the corresponding detection device of the local end, thereby resulting in an incomplete and inaccurate detection result, seriously endangering the driving safety of the driver, and even causing serious traffic accidents, damaging public property and personal safety, etc. Therefore, compared with the prior art, the embodiment of the present application can upload a large amount of data related to the air suspension system collected by the vehicle in real time during driving to the server, and then based on the large amount of data, the different features of the data (such as the adjustment feature, the life feature and the material feature, etc.) and the weights of each type of feature (such as considering the influence degree of the data of different features on the use condition of the air suspension system), a more accurate and effective multi-dimensional detection system is established, thereby realizing more comprehensive and accurate real-time detection of the air suspension system, effectively avoiding traffic accidents caused by sudden failure of the air suspension system, and ensuring driving safety.

[0010] In a possible implementation, the method further includes: determining a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result includes a wear rate of the air suspension system; and the second detection result includes a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0011] In the embodiments of the present application, the server can further evaluate the failure-prone rate and the usable duration of the air suspension system based on the calculated first detection result (for example, the wear rate of the air suspension system), so as to achieve more comprehensive and multi-level detection of the air suspension system, and further enable the user to more comprehensively and intuitively master the use condition (or the health status) of the air suspension system in the vehicle, and effectively ensure driving safety.

[0012] In a possible implementation, the obtaining the first data set includes: receiving a data stream from the first vehicle; the data stream includes K data related to the air suspension system; sampling the K data included in the data stream based on an importance sampling method to obtain the first data set; the K data include the M data; and K is an integer greater than or equal to M.

[0013] In the embodiments of the present application, the vehicle can upload a large amount of collected data to the server in the form of a data stream in real time. The server can sample a large amount of data in the data stream based on an importance sampling method to obtain part of the data. It should be noted that although a large amount of data in the data stream is sampled, the data ultimately obtained by the server is still a large amount, so that the running cost and the calculation amount can be further reduced on the premise of ensuring the accuracy of the detection result, and the detection efficiency is ensured, and the like.

[0014] In a possible implementation, the method further includes: receiving a query request sent by the first vehicle; and sending the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.

[0015] In the embodiments of the present application, when a user wants to know the health status of the air suspension system in his vehicle, the vehicle can send a corresponding query request to the server, and the server receives the query request. Then, the server can send the corresponding detection result (such as the first detection result and the second detection result, that is, the wear rate, the fault-prone rate and the usable time length of the air suspension system) to the vehicle based on the query request. Thus, the user can timely master the health status of the air suspension system in his vehicle, so as to timely repair when the wear is serious or the service life is approaching, thereby avoiding sudden failure of the air suspension system during driving, effectively reducing the driving risk and ensuring driving safety.

[0016] In a possible implementation, the method can further include: determining a corresponding target terrain of the first vehicle during driving, and sending the target terrain to the first vehicle; the target terrain is used by the first vehicle to issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is one of sand, snow, rock and ice; and the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter of the air suspension system.

[0017] In the embodiments of the present application, the server can also determine the current terrain (such as sand, snow, rock or ice) of the vehicle based on the data (such as the power signal of the air suspension system) collected during driving. Then, the server can send the terrain (for example, the terrain model pre-constructed for the terrain) to the vehicle. Finally, the vehicle can issue a corresponding control strategy for the height parameter, the vibration parameter and the damping parameter of the air suspension system according to the terrain, thereby effectively improving the driving comfort and reducing the wear of the air suspension system in extreme terrain, ensuring driving safety.

[0018] In a possible implementation, the method can further include: if the first detection result and / or the second detection result meets a preset condition, sending the first detection result, the second detection result and corresponding warning information to the first vehicle; the warning information is used to warn the user to repair the air suspension system; and the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value and / or the fault-prone rate of the air suspension system is greater than a second threshold value and / or the usable time length of the air suspension system is less than a third threshold value.

[0019] In the embodiments of the present application, if any one or more of the wear rate, the failure-prone rate and the usable time length of the air suspension system calculated by the server end has endangered the driving safety (for example, the wear rate is greater than a first threshold (such as 50%), the failure-prone rate is greater than a second threshold (such as 40%), and the usable time length is less than a third threshold (such as 30 hours)), that is, the air suspension system is seriously damaged and is likely to endanger the driving safety, and needs to be repaired, the server end can directly send the detection result and corresponding warning information to the corresponding vehicle. The warning information can be used for the owner of the manhole cover vehicle to maintain the air suspension system, thereby avoiding traffic accidents caused by sudden failure of the air suspension system during driving, and effectively ensuring driving safety.

[0020] In a possible implementation, the method can further include: if the first detection result and / or the second detection result meets the preset condition, obtaining information of at least one automobile repair shop within a preset range of the first vehicle, and sending the information of the at least one automobile repair shop to the first vehicle; the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path planning.

[0021] In the embodiments of the present application, as described above, if the air suspension system is seriously damaged and is likely to endanger the driving safety, and needs to be repaired, the server end can further push the information of the automobile repair shop (or 4S shop, etc.) near the vehicle to the vehicle, such as the address of the automobile repair shop, the distance from the current vehicle, the charging price, the user evaluation and the driving path planning, etc. Thus, the owner is provided with maintenance convenience, so that the owner can timely maintain the air suspension system in the vehicle, and ensure driving safety.

[0022] In a possible implementation, the determining the first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features includes: respectively calculating a score value corresponding to each of the N types of features based on the N second data sets and a preset scoring standard; and calculating the first detection result of the air suspension system based on the score value corresponding to each of the N types of features and the weight of each of the N types of features.

[0023] In the embodiment of the present application, the server can first calculate the score values corresponding to various features based on the obtained data sets corresponding to various features and the preset scoring standard. For example, the higher the score value, the more serious the damage. Then, the server can calculate the first detection result of the air suspension system based on the score values corresponding to various features and the weights of various features. In this way, the embodiment of the present application can comprehensively consider the influence of various data in the air suspension system on the wear rate, so that the wear rate of the air suspension system calculated is more comprehensive, accurate and effective, thereby achieving more comprehensive and accurate detection of the air suspension system, effectively avoiding traffic accidents caused by sudden failure of the air suspension system, and ensuring driving safety.

[0024] In a possible implementation, the method can further include: obtaining a third data set, the third data set including P data related to the air suspension system of each of the plurality of second vehicles; P is an integer greater than 1; determining the first detection result of each of the plurality of second vehicles based on the third data set; and correcting the scoring standard and / or the weight of each of the N types of features based on the first detection result of each of the plurality of second vehicles and the first detection result of the first vehicle.

[0025] In the embodiment of the present application, the server can also receive a large amount of data collected by the air suspension system in the vehicle uploaded by a plurality of vehicles when driving or parking, and detect the air suspension system of each of the plurality of vehicles based on the above method to calculate the detection result of the air suspension system of each of the plurality of vehicles. Then, the server can correct the original scoring standard and / or the weight of each type of feature used in the calculation process based on a large amount of detection results (for example, a large amount of wear rates of air suspension systems in the vehicle calculated). Thus, the accuracy of the detection result is further improved, and traffic accidents caused by failure to timely repair the air suspension system due to inaccurate detection results are avoided, effectively ensuring driving safety.

[0026] In a possible implementation, the M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of adjustment frequency, use time, product model and product specification of the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the service life feature includes the use time; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0027] In the embodiments of the present application, the vehicle can collect data in all directions for the air suspension system during driving, for example, the data can include the compressed gas volume, the released gas volume, the rising temperature, the air compression density, and the corresponding adjustment frequency, the use time, the product model and the product specification, and the like when the air suspension system is adjusted during driving of the first vehicle. The data for detecting the air suspension system is comprehensive and rich, so that the detection result obtained by the embodiments of the present application is more comprehensive and accurate under the support of a large amount of data in all directions, and the driving safety is effectively ensured.

[0028] In a second aspect, the embodiments of the present application provide a detection method, which can include: acquiring a data stream and sending the data stream to a server; the data stream includes K data related to an air suspension system of a first vehicle; the data stream is used for the server to sample the K data included in the data stream based on an importance sampling method to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the M data are included in the K data; the M data are used for the server to obtain N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system; the N second data sets are used for the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of characteristics; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M.

[0029] In the method provided by the second aspect, the vehicle (e.g., the first vehicle) can collect data related to the air suspension system in real time during driving or parking of the vehicle (e.g., the volume of compressed gas, the volume of released gas, and the rising temperature can be collected each time the vehicle is adjusted during driving), and upload the collected large amount of data to the server in the form of a data stream in real time. Optionally, the server can sample the large amount of data in the data stream by using the importance sampling method to obtain part of the data, so as to reduce the running cost. Then, the server can classify the large amount of data obtained based on different features of different data to obtain a data set corresponding to each feature. Finally, the server can comprehensively consider the data set corresponding to each feature and the weight of each feature to calculate the detection result of the air suspension system (e.g., the current wear rate of the air suspension system), so as to realize multi-dimensional, more comprehensive and accurate detection of the air suspension system. However, in the prior art, when detecting the air suspension system, only part of the components in the air suspension system can be detected by the corresponding detection device on the local end, so that the detection result is not comprehensive and accurate, which seriously endangers the driving safety of the driver, and even causes serious traffic accidents, damages public property and personal safety, etc. Therefore, compared with the prior art, the embodiments of the present application can upload the large amount of data related to the air suspension system collected by the vehicle in real time during driving to the server, and then based on the large amount of data, establish a more accurate and effective multi-dimensional detection system based on different features of the data and the weight of each feature (e.g., considering the influence of data with different features on the use of the air suspension system), so as to realize more comprehensive and accurate real-time detection of the air suspension system, effectively avoid traffic accidents caused by sudden failure of the air suspension system, and ensure driving safety.

[0030] It should be understood that the execution subject of the second aspect is the first vehicle, the specific content of the second aspect corresponds to the content of the first aspect, and the corresponding features and beneficial effects of the second aspect can be referred to the description of the first aspect. To avoid repetition, the detailed description is appropriately omitted here.

[0031] In a possible implementation, the first detection result is used by the server to determine a second detection result of the air suspension system based on the first detection result; the first detection result includes a wear rate of the air suspension system; and the second detection result includes a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0032] In a possible implementation, the method further includes: sending a query request to the server; and receiving the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

[0033] In a possible implementation, the method further includes: receiving a target terrain sent by the server, and issuing a corresponding regulation strategy for the air suspension system according to the target terrain; the target terrain is a terrain corresponding to the first vehicle in a driving process determined by the server; the target terrain is one of sand, snow, rock, and ice; and the regulation strategy includes a regulation strategy for at least one of a height parameter, a vibration parameter, and a damping parameter corresponding to the air suspension system.

[0034] In a possible implementation, the method further includes: if the first detection result and / or the second detection result meets a preset condition, receiving the first detection result, the second detection result, and corresponding warning information sent by the server; the warning information is used to warn a user to maintain the air suspension system; and the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

[0035] In a possible implementation, the method further includes: if the first detection result and / or the second detection result meets a preset condition, receiving information of at least one automobile repair shop within a preset range of the first vehicle sent by the server; the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations, and driving path planning.

[0036] In a possible implementation, the M pieces of data include at least one of a compressed gas volume, a released gas volume, an ascending temperature, an air compression density, and a regulation frequency, a use duration, a product model, and a product specification of the air suspension system; the second data set corresponding to the regulation feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one ascending temperature, the at least one air compression density, and the regulation frequency; the second data set corresponding to the life feature includes the use duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0037] In a third aspect, an embodiment of the present application provides a detection device, applied to a server, and the device includes:

[0038] The first obtaining unit is configured to obtain a first data set, wherein the first data set comprises M data related to an air suspension system of a first vehicle, and M is an integer greater than or equal to 1.

[0039] The second obtaining unit is configured to obtain N second data sets, wherein each of the N second data sets comprises one or more data in the M data, and the N second data sets correspond to N types of features, the N types of features comprising one or more of an adjustment feature, a service life feature and a material feature of the air suspension system, and N is an integer greater than or equal to 1.

[0040] The first determining unit is configured to determine a first detection result of the air suspension system according to the N second data sets and weights corresponding to the N types of features.

[0041] In a possible implementation, the apparatus further comprises:

[0042] The second determining unit is configured to determine a second detection result of the air suspension system based on the first detection result of the air suspension system, wherein the first detection result comprises a wear rate of the air suspension system, and the second detection result comprises a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0043] In a possible implementation, the first obtaining unit is specifically configured to:

[0044] receive a data stream from the first vehicle, wherein the data stream comprises K data related to the air suspension system;

[0045] sample the K data included in the data stream based on an importance sampling apparatus to obtain the first data set, wherein the K data comprises the M data, and K is an integer greater than or equal to M.

[0046] In a possible implementation, the apparatus further comprises:

[0047] The receiving unit is configured to receive a query request sent by the first vehicle.

[0048] The first sending unit is configured to send the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.

[0049] In a possible implementation, the apparatus further comprises:

[0050] The second sending unit is configured to determine a corresponding target terrain of the first vehicle in a driving process, and send the target terrain to the first vehicle; the target terrain is used for the first vehicle to issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is one of sand, snow, rock and ice; and the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

[0051] In a possible implementation, the apparatus further includes:

[0052] The third sending unit is configured to send the first detection result, the second detection result and corresponding warning information to the first vehicle if the first detection result and / or the second detection result meets a preset condition; the warning information is used for warning a user to maintain the air suspension system; and the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

[0053] In a possible implementation, the apparatus further includes:

[0054] The fourth sending unit is configured to acquire information of at least one automobile repair shop within a preset range of the first vehicle and send the information of the at least one automobile repair shop to the first vehicle if the first detection result and / or the second detection result meets the preset condition; and the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path plans.

[0055] In a possible implementation, the first determining unit is specifically configured to:

[0056] Based on the N second data sets and a preset scoring standard, a score value corresponding to each of the N types of features is respectively calculated;

[0057] Based on the score value corresponding to each of the N types of features and a weight of each of the N types of features, the first detection result of the air suspension system is calculated.

[0058] In a possible implementation, the apparatus further includes:

[0059] The third acquiring unit is configured to acquire a third data set, the third data set including P data related to air suspension systems of a plurality of second vehicles; P is an integer greater than 1;

[0060] The third determining unit is configured to determine, based on the third data set, a first detection result of each of the plurality of second vehicles.

[0061] The correcting unit is configured to correct a weight of each of the scoring standard and / or the N types of features based on the first detection result of each of the plurality of second vehicles and the first detection result of the first vehicle.

[0062] In a possible implementation, the M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of the adjustment frequency, the use duration, the product model and the product specification of the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the life feature includes one or more of the use duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0063] In a possible implementation, the second obtaining unit is specifically configured to:

[0064] The M data are classified based on the N types of features to obtain N second data sets corresponding to the N types of features.

[0065] In a fourth aspect, an embodiment of the present application provides a detection device, which can include:

[0066] An obtaining unit is configured to obtain a data stream and send the data stream to a server; the data stream includes K data related to an air suspension system of a first vehicle; the data stream is used by the server to sample the K data included in the data stream based on an importance sampling method to obtain a first data set corresponding thereto; the first data set includes M data related to the air suspension system of the first vehicle; the M data are included in the K data; the M data are used by the server to obtain N second data sets; each of the N second data sets includes one or more of the M data; the N second data sets correspond to N types of features, and the N types of features include one or more of an adjustment feature, a life feature and a material feature of the air suspension system; the N second data sets are used by the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of features; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M.

[0067] In a possible implementation, the first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection result comprises a wear rate of the air suspension system; and the second detection result comprises a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0068] In a possible implementation, the apparatus further includes:

[0069] a sending unit, configured to send a query request to the server;

[0070] a first receiving unit, configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

[0071] In a possible implementation, the apparatus further includes:

[0072] a second receiving unit, configured to receive a target terrain sent by the server, and issue a corresponding regulation strategy to the air suspension system according to the target terrain; the target terrain is a terrain corresponding to the first vehicle in a driving process and determined by the server; the target terrain is one of sand, snow, rock and ice; and the regulation strategy comprises a regulation strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

[0073] In a possible implementation, the apparatus further includes:

[0074] a third receiving unit, configured to receive the first detection result, the second detection result and corresponding warning information sent by the server if the first detection result and / or the second detection result meets a preset condition; the warning information is used to warn a user to maintain the air suspension system; and the preset condition comprises that the wear rate of the air suspension system is greater than a first threshold, and / or the failure-prone rate of the air suspension system is greater than a second threshold, and / or the usable duration of the air suspension system is less than a third threshold.

[0075] In a possible implementation, the apparatus further includes:

[0076] a fourth receiving unit, configured to receive information of at least one automobile repair shop within a preset range of the first vehicle sent by the server if the first detection result and / or the second detection result meets a preset condition; the information comprises at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path planning.

[0077] In a possible implementation, the M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of the adjustment frequency, the use duration, the product model and the product specification of the air suspension system related to the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the service life feature includes the use duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0078] In a fifth aspect, an embodiment of the present application provides a server, which includes a processor configured to support the server to implement corresponding functions in the detection method provided in the first aspect. The server can further include a memory coupled with the processor, which stores necessary program instructions and data of the server. The server can further include a communication interface for communication between the server and other devices or communication networks.

[0079] In a sixth aspect, an embodiment of the present application provides an intelligent vehicle, which is a first vehicle, and the intelligent vehicle includes a processor configured to support the intelligent vehicle to implement corresponding functions in the detection method provided in the second aspect. The intelligent vehicle can further include a memory coupled with the processor, which stores necessary program instructions and data of the intelligent vehicle. The intelligent vehicle can further include a communication interface for communication between the intelligent vehicle and other devices or communication networks.

[0080] In a seventh aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the detection method process of any one of the first aspect or the detection method process of any one of the second aspect is implemented.

[0081] In an eighth aspect, an embodiment of the present application provides a computer program, which includes instructions. When the computer program is executed by a computer, the computer can execute the detection method process of any one of the first aspect or the detection method process of any one of the second aspect.

[0082] In a ninth aspect, an embodiment of the present application provides a chip system, which can include the detection device of any one of the third aspect, to implement the functions involved in the detection method process of any one of the first aspect. Alternatively, the chip system can include the detection device of any one of the fourth aspect, to implement the functions involved in the detection method process of any one of the second aspect. In a possible design, the chip system further includes a memory, and the memory is configured to store program instructions and data necessary for the detection method. The chip system can be composed of a chip, or can include a chip and other discrete devices. BRIEF DESCRIPTION OF DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments of the present application or the background art will be described below.

[0084] Figure 1 is a structural schematic diagram of an air suspension system.

[0085] Figure 2a is a fault rate analysis schematic diagram of an air suspension system provided by an embodiment of the present application.

[0086] Figure 2b is a fault cause analysis schematic diagram of an air suspension system provided by an embodiment of the present application.

[0087] Figure 3 is a schematic diagram of an automatic detection system for an air pump of an automobile air suspension system.

[0088] Figure 4a is a functional block diagram of an intelligent vehicle provided by an embodiment of the present application.

[0089] Figure 4b is a structural schematic diagram of an air suspension system provided by an embodiment of the present application.

[0090] Figure 5 is a system architecture schematic diagram of a detection method provided by an embodiment of the present application.

[0091] Figure 6a is an application scenario schematic diagram provided by an embodiment of the present application.

[0092] Figure 6b is another application scenario schematic diagram provided by an embodiment of the present application.

[0093] Figure 7 is a flowchart of a detection method provided by an embodiment of the present application.

[0094] Figure 8 is a flowchart of another detection method provided by an embodiment of the present application.

[0095] Figure 9 This is an overall flowchart of a detection method provided in an embodiment of this application.

[0096] Figure 10 This is a schematic diagram of data sampling provided in an embodiment of this application.

[0097] Figure 11 This is a schematic diagram of a terrain recognition process provided in an embodiment of this application.

[0098] Figure 12a This is a schematic diagram of a damping adjustment provided in an embodiment of this application.

[0099] Figure 12b This is a schematic diagram of another damping adjustment provided in an embodiment of this application.

[0100] Figure 13 This is an overall flowchart of another detection method provided in the embodiments of this application.

[0101] Figure 14 This is a schematic diagram of the structure of a detection device provided in an embodiment of this application.

[0102] Figure 15 This is a schematic diagram of another detection device provided in an embodiment of this application.

[0103] Figure 16 This is a schematic diagram of the structure of a server provided in an embodiment of this application.

[0104] Figure 17 This is a schematic diagram of the structure of an intelligent vehicle provided in an embodiment of this application. Detailed Implementation

[0105] The embodiments of this application will now be described with reference to the accompanying drawings.

[0106] First, some of the technical terms used in this application will be explained to facilitate understanding by those skilled in the art.

[0107] (1) Air suspension. Please refer to [link / reference]. Figure 1 , Figure 1 This is a structural diagram of an air suspension system. (Example) Figure 1 As shown, the air suspension system in the vehicle includes an air pump (or air compressor), air springs, shock absorbers, a control unit, and control wiring, etc. Each air pump can be independent, and the contraction and release of the air pumps can be controlled by electrical signals. Optionally, the air suspension system may also include exhaust valves, a dynamic chassis control unit, and multiple sensors. Figure 1The plurality of sensors can include, for example, front and rear axle body height sensors, a plurality of body acceleration sensors in different directions, a plurality of air spring extension acceleration sensors, and the like, and will not be described here.

[0108] The basic technical solution of the air suspension mainly includes an air spring containing compressed air and a shock absorber with variable damping. Compared with the traditional steel automobile suspension system, the air suspension has many advantages, the most important of which is that the spring constant, i.e., the softness or hardness of the spring, can be automatically adjusted as needed. For example, when driving at high speed, the suspension can be hardened to improve the stability of the vehicle body, and when driving at low speed for a long time, the control unit considers that the vehicle is passing through a bumpy road, and the suspension is softened to improve the comfort of shock absorption.

[0109] Further, the acceleration of the wheels caused by the impact of the ground can also be one of the parameters considered by the air spring when automatically adjusting. For example, when driving at high speed through a curve, the air springs and shock absorbers of the outer wheels will automatically harden to reduce the roll of the vehicle body, and when emergency braking, the electronic module will also strengthen the hardness of the springs and shock absorbers of the front wheels to reduce the inertial inclination of the vehicle body. Therefore, the vehicle equipped with air springs has higher handling limits and comfort than other vehicles.

[0110] Further, the air suspension can also integrate the traditional chassis lifting technology. For example, when the vehicle is driving at high speed, the height of the vehicle body is automatically lowered to improve the ground adhesion performance and ensure good high-speed driving stability while reducing wind resistance and fuel consumption. When the vehicle is driving slowly through a bumpy road, the chassis is automatically raised to improve the passing performance. In addition, the air suspension system can also automatically maintain the horizontal height of the vehicle body, regardless of whether it is empty or full, the height of the vehicle body can be constant, so that the suspension travel of the suspension system remains unchanged under any load, so that the shock absorption characteristics are basically not affected. Therefore, even if the vehicle is fully loaded, the vehicle body is easy to control.

[0111] However, compared with the traditional suspension (such as the coil spring suspension system), due to the complexity of the air adjustable suspension structure, the probability and frequency of failure are generally higher. Please refer to Figure 2a , Figure 2a is a schematic diagram of the failure rate analysis of the air suspension system provided by the embodiment of the present application. As Figure 2a indicated, the failure rate of the air suspension system tends to increase exponentially with the use time. Further, please refer to Figure 2b , Figure 2b is a schematic diagram of the failure cause analysis of the air suspension system provided by the embodiment of the present application. As Figure 2bAs shown in the table, the air leakage of the distribution valve (i.e., the exhaust valve) and the aging of the rubber each account for 20% of the causes of air suspension system failure, the air pipe leakage and the air spring leakage each account for 13% of the causes of air suspension system failure, and so on. The failure of the air suspension system greatly endangers the driving safety and thus causes serious traffic accidents. Therefore, how to more comprehensively and accurately monitor the air suspension system in the vehicle in real time and timely warn the user is particularly important to ensure the driving safety of the user.

[0112] (2) Importance sampling is one of the variance reduction techniques. Importance sampling is a variance reduction algorithm for rare events. It introduces bias in a controlled way, increases rare events, and reduces running time. In system design, the mathematical expectation of a target distribution function is approximated by a random weighted average of a relatively simple distribution function, and a bias function is added to make the system produce more decision errors and thus more important events. The relatively simple distribution function is called importance density function or bias function, and the weight value is approximately proportional to the likelihood ratio of the two distributions. By modifying the importance density function and introducing the importance weight, the number of simulation samples can be greatly reduced, so that the simulation result with given accuracy can be obtained in a shorter running time. In short, the importance sampling algorithm is to cover the points that contribute greatly to the integral as much as possible within a limited number of sampling times.

[0113] First, in order to facilitate the understanding of the embodiments of the present application, further analyze and propose the technical problems to be solved by the present application. In the prior art, the detection technology of the air suspension system includes various technical solutions, the following exemplary enumeration is as follows.

[0114] Please refer to Figure 3 , Figure 3 is a schematic diagram of an automatic detection system of an air pump of an automobile air suspension system. As Figure 3 shown, the automatic detection system of the air pump can include a DC power supply module, a programmable logic controller, an analog quantity acquisition module, and an air path leakage detection module, etc. The DC power supply module is electrically connected with the programmable logic controller, the programmable logic controller is electrically connected with the DC motor of the air pump through a DC controller, and the analog quantity acquisition module is electrically connected with the programmable logic controller. As Figure 3As shown, the air path leakage detection module includes a balance comparison cavity, a pressure stabilizing cavity and a flow tester, the pressure stabilizing cavity is respectively provided with an air path switching valve between the balance comparison cavity and the flow tester, and the pressure stabilizing cavity is provided with an air path switching valve between the exhaust port of the air pump. The balance comparison cavity, the pressure stabilizing cavity and the flow tester are respectively electrically connected with the analog quantity acquisition module, and the pressure sensor is respectively arranged between the analog quantity acquisition module and the pressure stabilizing cavity and the balance comparison cavity. Among them, the analog quantity acquisition module is connected with a current sensor for measuring the current value and / or a voltmeter for measuring the voltage value. The detection efficiency of the air pump automatic detection system is high, the accuracy is good, the human error and missed detection can be avoided, and the quality of the air suspension system air pump can be effectively improved.

[0115] Further, the air pump automatic detection system of the automobile air suspension system can further include a two-dimensional code generator and a printing device, the two-dimensional code generator is electrically connected with the programmable logic controller and the printing device, by directly generating a two-dimensional code pattern, product data can be permanently saved with the product, etc., which will not be described here.

[0116] Optionally, the user can interact with the programmable logic controller through the man-machine interaction interface. Among them, the user can set the detection parameters through the programmable logic controller, can also select a manual detection mode to detect individual items of the air pump, or select an automatic detection mode to sequentially detect all detection items provided by the air pump automatic detection system of the automobile air suspension system, etc., which will not be described here.

[0117] The disadvantages of the scheme are: as described above, the air pump automatic detection system of the automobile air suspension system provided by the scheme can accurately and efficiently detect the existing state of the air pump in the air suspension system by setting corresponding modules and controllers. However, for the air suspension system with complex structure and many components, the above scheme only involves the detection of the air pump, the detection range is narrow, the detection result is one-sided, and it is not referenceable. In short, the above scheme cannot comprehensively and accurately detect and evaluate the overall state of the air suspension system.

[0118] In summary, the above-mentioned solutions cannot realize efficient, accurate and comprehensive detection of the air suspension system by using the existing general vehicle hardware architecture and air suspension system, etc., so as to guarantee the driving safety when the user drives the vehicle with the air suspension system. Therefore, in order to solve the problem that the current air suspension system detection technology does not meet the actual business requirements, the technical problems actually solved by the embodiments of the present application include the following aspects: (1) Based on a large amount of data collected by the air suspension system during the driving of the vehicle, the air suspension system in the vehicle is comprehensively and accurately detected in real time, so as to avoid traffic accidents caused by air suspension system failure, thereby guaranteeing the driving safety of the user, etc. (2) Based on the detection result, the use of the air suspension system is further estimated, and in an emergency (for example, in the case that the air suspension system is severely worn and is estimated to have little safe service life and is prone to failure), the vehicle owner is warned to remind the vehicle owner to timely maintain or replace the air suspension system, so as to avoid traffic accidents caused by air suspension system failure during driving, thereby effectively guaranteeing the driving safety.

[0119] Please refer to Figure 4a , Figure 4a is a functional block diagram of an intelligent vehicle provided by an embodiment of the present application. The detection method provided by an embodiment of the present application can be applied to an intelligent vehicle 200 as shown in Figure 4a In an embodiment, the intelligent vehicle 200 can be configured in a fully or partially autonomous driving mode. When the intelligent vehicle 200 is in the autonomous driving mode, the intelligent vehicle 200 can be operated without human interaction.

[0120] The intelligent vehicle 200 can include various subsystems, such as an air suspension system 201, a travel system 202, a sensing system 204, a control system 206, one or more peripheral devices 208, a power supply 210, a computer system 212 and a user interface 216. Optionally, the intelligent vehicle 200 can include more or fewer subsystems, and each subsystem can include multiple elements. In addition, each subsystem and element of the intelligent vehicle 200 can be interconnected by wire or wirelessly.

[0121] The air suspension system 201 can include various components for air suspension during travel of the intelligent vehicle 200. In one embodiment, the air suspension system 201 can include air springs, air compressors, shock absorbers, and the like. Optionally, in one embodiment, the air suspension system 201 can further include a corresponding data acquisition module, which can acquire data of the air suspension system 201 during travel of the intelligent vehicle 200 or when the intelligent vehicle 200 is parked, such as air compression density, compressed gas volume, released gas volume, and usage time of the air suspension system, etc. when the air compressor is adjusted each time. Optionally, in one embodiment, the air suspension system 201 can further include a corresponding communication module, which can establish a communication connection with a remote server through a wireless network, and then upload the acquired data to the server, so that the server can comprehensively and accurately detect the air suspension system 201 in the intelligent vehicle 200 based on the data through a detection method provided by the present application. Further, the detection result and the like sent by the server can also be received through the corresponding communication module in the air suspension system 201. Thus, the user can timely master the health status of the air suspension system 201 and maintain or replace the air suspension system 201 when necessary (for example, when the wear rate of the air suspension system 201 is detected to have exceeded 60%), to ensure driving safety. Optionally, in some possible embodiments, the air suspension system 201 can also be arranged in the traveling system 202, etc., which is not limited in the embodiments of the present application.

[0122] The traveling system 202 can include components that provide power motion for the intelligent vehicle 200. In one embodiment, the traveling system 202 can include an engine 218, an energy source 219, a transmission 220, and wheels 221. The engine 218 can be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine composed of a gasoline engine and an electric motor, or a hybrid engine composed of an internal combustion engine and an air compression engine. The engine 218 can convert the energy source 219 into mechanical energy.

[0123] Examples of the energy source 219 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 219 can also provide energy for other systems of the intelligent vehicle 200.

[0124] The transmission 220 can transmit mechanical power from the engine 218 to the wheels 221. The transmission 220 can include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 220 can further include other devices, such as a clutch. The drive shaft can include one or more shafts that can be coupled to one or more wheels 221.

[0125] The sensing system 204 may include a plurality of sensors that can be used to sense information about the environment surrounding the intelligent vehicle 200 (e.g., the terrain, motor vehicles, non-motor vehicles, pedestrians, roadblocks, traffic signs, traffic lights, animals, buildings, and vegetation around the intelligent vehicle 200, etc.). Figure 4a As shown, the sensing system 204 may include a positioning system 222 (which may be a Global Positioning System (GPS), BeiDou Navigation Satellite System, or other positioning systems), an inertial measurement unit (IMU) 224, a radar 226, a laser rangefinder 228, a camera 230, and a computer vision system 232, etc. The sensing system 204 may also include one or more sensors from the internal systems of the intelligent vehicle 200, such as an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc. In one embodiment, the sensor system 204 may further include one or more sensors for collecting data from the air suspension system 201, such as sensors for collecting air pressure or temperature rise within the air springs, etc. The collected data can be uploaded to a server for monitoring the air suspension system and ensuring driving safety.

[0126] The positioning system 222 can be used to estimate the geographic location of the intelligent vehicle 200. The IMU 224 is used to sense changes in the position and orientation of the intelligent vehicle 200 based on inertial acceleration. In one embodiment, the IMU 224 can be a combination of an accelerometer and a gyroscope.

[0127] Radar 226 can use radio signals to sense objects in the surrounding environment of the intelligent vehicle 200. In some embodiments, radar 226 can also be used to sense the speed and / or direction of travel of vehicles around the intelligent vehicle 200, etc.

[0128] The laser rangefinder 228 can use lasers to sense objects in the environment in which the intelligent vehicle 200 is located. In some embodiments, the laser rangefinder 228 may include one or more laser sources, one or more laser scanners, one or more detectors, and other system components.

[0129] Camera 230 can be used to capture multiple images of the surrounding environment of the intelligent vehicle 200. Camera 230 can be a still camera or a video camera.

[0130] The computer vision system 232 can operate to process and analyze images captured by the camera 230 in order to identify objects and / or features in the environment surrounding the intelligent vehicle 200. The objects and / or features can include terrain, motor vehicles, non-motor vehicles, pedestrians, buildings, traffic signals, road boundaries, and obstacles, among others. The computer vision system 232 can use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 232 can send the identified terrain to the air suspension system 201, which can issue corresponding control strategies to its internal components based on the terrain. For example, if the intelligent vehicle 200 is currently driving on a rocky terrain, the air suspension system 201 can adjust the vehicle chassis of the intelligent vehicle 200 accordingly, and increase the damping to improve the driving comfort, among others.

[0131] The control system 206 controls the operation of the intelligent vehicle 200 and its components. The control system 206 can include various elements, including the throttle 234, the braking unit 236, and the steering system 240.

[0132] The throttle 234 is used to control the speed of the engine 218 and, in turn, the speed of the intelligent vehicle 200.

[0133] The braking unit 236 is used to control the deceleration of the intelligent vehicle 200. The braking unit 236 can use friction to slow down the wheels 221. In other embodiments, the braking unit 236 can convert the kinetic energy of the wheels 221 into electrical current. The braking unit 236 can also take other forms to slow down the speed of the wheels 221 and, in turn, control the speed of the intelligent vehicle 200.

[0134] The steering system 240 can operate to adjust the direction of travel of the intelligent vehicle 200.

[0135] Of course, in one instance, the control system 206 can include additional components in addition to those shown and described, either in addition to or in place of those shown. Or some of the components shown above can be reduced.

[0136] The intelligent vehicle 200 interacts with external sensors, other vehicles, other computer systems, or users through the peripherals 208. The peripherals 208 can include a wireless communication system 246, an on-board computer 248, a microphone 250, and / or a speaker 252. In some embodiments, the collected data of the air suspension system 201 can be uploaded to a server through the wireless communication system 246, and the detection results of the air suspension system 201 can be requested from the server and received from the server through the wireless communication system 246, among others, which are not limited in the embodiments of the present application.

[0137] In some embodiments, the peripheral devices 208 provide a means for a user of the intelligent vehicle 200 to interact with the user interface 216. For example, the on-board computer 248 can provide information to a user of the intelligent vehicle 200. The user interface 216 can also operate the on-board computer 248 to receive input from the user. The on-board computer 248 can be operated through a touch screen. In other cases, the peripheral devices 208 can provide a means for the intelligent vehicle 200 to communicate with other devices located within the vehicle. For example, the microphone 250 can receive audio (e.g., voice commands or other audio input) from a user of the intelligent vehicle 200. Similarly, the speaker 252 can output audio to a user of the intelligent vehicle 200.

[0138] The wireless communication system 246 can wirelessly communicate with one or more devices directly or via a communication network. For example, the wireless communication system 246 can use third generation mobile networks (3G) cellular communication, such as code division multiple access (CDMA), global system for mobile communications (GSM) / general packet radio service (GPRS), or fourth generation mobile networks (4G) cellular communication, such as long term evolution (LTE). Or fifth generation mobile networks (5G) cellular communication. The wireless communication system 246 can also utilize wireless-fidelity (WIFI) and wireless local area network (WLAN) communication. In some embodiments, the wireless communication system 246 can communicate directly with devices using infrared links, Bluetooth, etc. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 246 can include one or more dedicated short range communications (DSRC) devices, which can include public and / or private data communication between vehicles and / or roadside stations.

[0139] Power source 210 can provide power to the various components of intelligent vehicle 200. In one embodiment, power source 210 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries can be configured as the power source to provide power to the various components of intelligent vehicle 200. In some embodiments, power source 210 and energy source 219 can be implemented together, as in some all-electric vehicles.

[0140] Some or all of the functionality of intelligent vehicle 200 is controlled by computer system 212. Computer system 212 can include at least one processor 213 that executes instructions 215 stored in a non-transitory computer readable medium, such as memory 214. Computer system 212 can also be a plurality of computing devices that control individual components or subsystems of intelligent vehicle 200 in a distributed manner.

[0141] Processor 213 can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a dedicated device such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Although Figure 4a Although the processor and memory are functionally illustrated as being within the same block in computer system 212, it should be understood that the processor or memory can actually comprise multiple processors or memories that are not stored within the same physical housing. For example, the memory can be a hard drive or other storage medium located in a housing different from that of computer system 212. Accordingly, reference to a processor or memory will be understood to encompass reference to a collection of processors or memories that can or can not operate in parallel. Rather than using a single processor to perform the steps described herein, for example, some components of sensing system 204 can each have their own processor that only performs calculations related to the functionality specific to that component.

[0142] In various aspects described herein, processor 213 can be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor.

[0143] In some embodiments, the memory 214 can include instructions 215 (e.g., program logic) that can be executed by the processor 213 to perform various functions of the intelligent vehicle 200, including those described above. The memory 214 can also include additional instructions, including instructions to send data to, receive data from, interact with, and / or control one or more of the air suspension system 201, the propulsion system 202, the sensing system 204, the control system 206, and the peripherals 208.

[0144] In addition to the instructions 215, the memory 214 can also store data, such as product specifications of various components within the air suspension system 201, product models (e.g., product model of air springs is rubber A-001, etc.), usage time of the air suspension system 201, terrain models (such as ice, snow, sand, and rock, etc.), and air suspension control strategies corresponding to each of the terrain models, etc. In some embodiments, the memory 214 can also store, for example, road maps, route information, location, direction, speed, and other such vehicle data of the vehicle, and other information, etc. Such information can be used by the air suspension system 201 or the computer system 212 in the intelligent vehicle 200 during travel of the intelligent vehicle 200. For example, a corresponding terrain model can be determined according to the current road conditions, etc., and then a control strategy of the air suspension system 201 can be further determined to obtain a better driving experience.

[0145] The user interface 216 is used to provide information to or receive information from a user of the intelligent vehicle 200. Optionally, the user interface 216 can include one or more input / output devices within the set of peripherals 208, such as the wireless communication system 246, the vehicle-to-computer 248, the microphone 250, and the speaker 252.

[0146] Optionally, one or more of the above components can be installed separately from or associated with the intelligent vehicle 200. For example, the memory 214 can exist partially or entirely separately from the intelligent vehicle 200. The above components can be communicatively coupled together in a wired and / or wireless manner.

[0147] In summary, the intelligent vehicle 200 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawnmower, an amusement park vehicle, a construction device, a trolley, a golf cart, a train, and a cart, etc., and embodiments of the present application are not limited in this regard.

[0148] It can be understood that, Figure 4a The functional block diagram of the intelligent vehicle in FIG. 1 is only an exemplary embodiment in embodiments of the present application, and the intelligent vehicle in embodiments of the present application includes but is not limited to the above structure.

[0149] Please refer to Figure 4a , Figure 4b is a structural diagram of an air suspension system provided by an embodiment of the present application. The air suspension system 10 can be the air suspension system 201 in the intelligent vehicle 200 shown in the above Figure 4a . As shown in the above Figure 4b , the air suspension system 10 can include an air spring 101, an air compressor 102, a shock absorber 103, a data acquisition module 104, a communication module 105, and a control module 106, etc. Among them, the specific functions of the air spring 101, the air compressor 102, and the shock absorber 103 can refer to the description in the above professional language explanation, which will not be repeated here. It can be understood that part or all of the data acquisition unit 104, the communication unit 105, and the control unit 106 can also be integrated together, and the embodiments of the present application do not make specific limitations thereto.

[0150] Among them, the control module 106 can control each component in the air suspension system 10 to adjust (for example, control the air spring 101, the air compressor 102, and the shock absorber 103 to adjust).

[0151] Among them, the data acquisition module 104 can periodically and real-time collect the corresponding data of the air suspension system 10 during the driving process of the intelligent vehicle, for example, collect the release gas volume, the compressed gas volume, the air compression density, and the rising temperature of the air compressor 102 each time it is adjusted. The usage time and the adjustment frequency of the air suspension system 10 can also be collected, wherein the adjustment frequency can be the adjustment frequency of the air compressor, and specifically can be the damping adjustment frequency, etc., and the embodiments of the present application do not make specific limitations thereto.

[0152] The communication module 105 can communicate through various wireless communication modes such as but not limited to 2G, 3G, 4G, 5G, etc., can also be WIFI, dedicated short range communication technology (DSRC), or long term evolution-vehicle technology (LTE-V), etc., or can also be a wired communication mode connected through a data line, etc. The communication module 105 can establish a communication connection with a remote server, and the communication module 105 can receive the original data collected by the above data acquisition module 104, or the data obtained after the original sensor data is preprocessed by the data acquisition module 104, and upload the data to the server. The server can comprehensively and accurately detect the air suspension system 10 in the intelligent vehicle 200 based on a large amount of collected data.

[0153] Optionally, the data acquisition module 104 can also periodically acquire a power signal of the air suspension system 10, and send the acquired power signal to the control module 106. Correspondingly, the control module 106 can receive the power signal, and calculate a corresponding power spectrum, power spectral density, spectral density, and Gaussian pulse value statistics per unit time, etc. based on the power signal. Then, the control module 106 can determine the corresponding terrain during the driving of the intelligent vehicle 200 based on the power spectrum, power spectral density, spectral density, and Gaussian pulse value statistics per unit time calculated above, and the model parameters of the plurality of terrain models respectively preset. Finally, the control module 106 can issue a corresponding regulation and control strategy based on the current terrain, to ensure the comfort of driving in any terrain. Optionally, the data acquisition module 104 can also send the acquired power signal to the communication module 105, and the communication module 105 can send the received power signal to the server, so that the server determines the current terrain based on the power signal, and feeds back the terrain to the intelligent vehicle 200 (for example, the server can send the determined terrain to the communication module 105, and the communication module 105 sends the terrain to the control module 106), and finally realizes different regulation and control strategies of the air suspension system in different terrains, to ensure the comfort of driving in any terrain.

[0154] Optionally, in some possible embodiments, the interiors of the air spring 101, the air compressor 102, and the shock absorber 103 can also be separately provided with respective data acquisition modules, communication modules, control modules, etc. to realize corresponding functions, which are not limited in the embodiments of the present application.

[0155] It can be understood that, Figure 5 The structure of the air suspension system in the above embodiment is only an exemplary implementation in the embodiments of the present application, and the structure of the air suspension system in the embodiments of the present application includes but is not limited to the above structure.

[0156] In order to facilitate the understanding of the embodiments of the present application, the system architecture of one of the air suspension system detection methods based on the embodiments of the present application will be described first. Please refer to Figure 5 , Figure 5 is a system architecture diagram of a detection method provided by the embodiments of the present application. The detection method provided by the embodiments of the present application can be applied to the system architecture as shown in Figure 5 or similar system architecture. As shown in Figure 4a , the system architecture can include a server 100 and a plurality of intelligent vehicles, specifically including intelligent vehicles 200a, 200b, and 200c, etc. Among them, the intelligent vehicles 200a, 200b, and 200c can be the intelligent vehicle 200 described in the above Figure 5 corresponding embodiments, and optionally, as shown in Figure 4bAs shown, the intelligent vehicles 200a, 200b and 200c can be internally provided with corresponding air suspension systems (for example, the air suspension system 10 shown in the corresponding embodiment). Figure 5 As shown, the intelligent vehicles 200a, 200b and 200c can be internally provided with corresponding air suspension systems (for example, the air suspension system 10 shown in the corresponding embodiment). Figure 5 As shown, the intelligent vehicles 200a, 200b and 200c can establish a communication connection with the server through a wireless network (such as WIFI, Bluetooth, mobile network, etc.) or the like. Alternatively, the intelligent vehicles 200a, 200b and 200c can also establish a communication connection through the network, which is not limited in the embodiment of the present application.

[0157] Next, taking the server 100 and the intelligent vehicle 200a as an example, a detection method provided by the embodiment of the present application is described in detail. As shown in Figure 5 As shown, during the driving of the intelligent vehicle 200a by the user, the air suspension system in the vehicle can be in an activated state. When encountering uneven road surfaces, the air suspension system will automatically adjust accordingly to dampen the vehicle body and ensure the driving comfort of the user. At each adjustment of the air suspension system, the intelligent vehicle 200a can collect data for each aspect of the air suspension system and upload the collected large amount of data to the server 100 in real time through the network. After receiving the large amount of data uploaded by the intelligent vehicle 200a, the server 100 can input the received large amount of data into a pre-constructed detection model to obtain the detection result of the air suspension system in the intelligent vehicle 200a. Alternatively, through the detection model, the received large amount of data can be first classified based on pre-set multi-class data features (such as adjustment features, material features and service life features, etc.) to obtain a data set corresponding to each class of features. Then, the score value corresponding to each data set can be calculated based on a pre-set scoring standard, and finally, the detection result of the air suspension system in the intelligent vehicle 200a (such as the wear rate of the air suspension system) can be calculated based on the score value corresponding to each data set and the weight corresponding to each class of features. Further, the server 100 can also formulate a corresponding maintenance suggestion based on the detection result and push the maintenance suggestion and the corresponding detection result to the intelligent vehicle 200a through the network as shown in Figure 5 As shown, the user can timely grasp the health status of the air suspension system in the vehicle and timely perform maintenance. At this point, the server 100 has completed the detection of the air suspension system based on the large amount of data collected and uploaded by the vehicle in real time, and comprehensively considered the influence of different categories of data on the health status of the air suspension system, thereby comprehensively and accurately detecting the air suspension system.

[0158] Alternatively, as shown in Figure 5As shown, the server 100 can also receive data uploaded by other vehicles such as the intelligent vehicles 200b and 200c, and obtain detection results of the air suspension systems in the intelligent vehicles 200b and 200c and other vehicles based on the detection model. Then, the server can optimize the detection model based on the obtained large amount of detection results. For example, if the calculated wear rates of the vehicles are almost equal, such as in the interval of 10%-12%, one or more parameters in the detection model can be corrected, such as the correction of the above-mentioned scoring standard and / or the weight of each feature, so as to make the detection results more accurate.

[0159] It can be understood that, with the development of automobile electrification and intelligence, more and more vehicle data begin to be uploaded to the cloud, and the cloud (i.e., the server as shown) can evaluate the state of the vehicle based on big data analysis. Figure 5 The embodiments of the present application can use the data uploaded by the vehicle to the cloud to comprehensively and accurately detect the air suspension system in the vehicle, and further provide accurate maintenance and repair suggestions, which can greatly reduce traffic accidents caused by air suspension system failure and ensure driving safety.

[0160] In summary, the intelligent vehicles 200a, 200b and 200c in the embodiments of the present application can be cars, trucks, motorcycles, buses, ships, airplanes, helicopters, lawn mowers, recreational vehicles, amusement park vehicles, construction equipment, trolleys, golf carts, trains and trolleys, etc. with the above-mentioned functions. Alternatively, the intelligent vehicles 200a, 200b and 200c can also be intelligent cars with an assisted driving system or a full-automatic driving system (an intelligent car which integrates computer, modern sensing, information fusion, communication, artificial intelligence and automatic control technologies, and is a high-tech comprehensive system with functions of environment perception, planning and decision making, and multi-level assisted driving), and can also be wheeled mobile robots or other machine devices, etc. The embodiments of the present application do not make specific limitations on this. The server 100 in the embodiments of the present application can be a server or a chip in the server with the above-mentioned functions, and can be a server, a server cluster composed of multiple servers, or a cloud computing service center, etc. Alternatively, the server 100 can also be an application related to the detection of the air suspension system of the intelligent vehicles 200a, 200b and 200c, etc. The embodiments of the present application do not make specific limitations on this. Alternatively, the server 100 can also be a terminal device such as a smart phone, a tablet computer, a notebook computer and a desktop computer, etc.

[0161] It can be understood that, the above-mentioned Figure 5The system architecture of the detection method shown is only an exemplary embodiment in the embodiments of the present application, and the system architecture of the detection method in the embodiments of the present application includes but is not limited to the above Figure 6a The system architecture shown.

[0162] In order to facilitate understanding of the embodiments of the present application, the following exemplary application scenarios to which the detection method in the present application is applicable are listed.

[0163] Please refer to Figure 6a , Figure 6a is a schematic diagram of an application scenario provided by the embodiments of the present application. As Figure 4b shown, the application scenario can be a sandy land (or desert), including an intelligent vehicle 200 and a server 100. Among them, the intelligent vehicle 200 can be built-in with an air suspension system, including a plurality of devices (such as air springs, air compressors, shock absorbers, etc.) for air suspension. Optionally, the air suspension system can be the air suspension system 10 shown in Figure 6a . As Figure 6a shown, the intelligent vehicle 200 and the server 100 can establish a communication connection through a network. During the driving process of the intelligent vehicle 200 or when the intelligent vehicle 200 is parked, the intelligent vehicle 200 can collect data of the air suspension system in the vehicle and upload the collected data to the server 100 through the network. Then, the server 100 can detect the air suspension system in the intelligent vehicle 200 based on the uploaded data by using a detection method provided by the embodiments of the present application, and obtain a corresponding detection result. Optionally, if a user wants to know the health status of the current air suspension system, the user can send a query request to the server 100 through the intelligent vehicle 200 (for example, through a related application program running in the intelligent vehicle 200, or a related button provided in the intelligent vehicle 200, etc.). Then, the server 100 can send the corresponding detection result to the intelligent vehicle 200 based on the query request. Optionally, the user can also send a query request to the server 100 through a related application program running on a smart phone, and correspondingly, the server 100 can also push the detection result to the smart phone. Optionally, the server 100 can also actively send the detection result to the intelligent vehicle 200, for example, in the case that the air suspension system is severely worn and has reached the end of its service life, the server 100 can immediately send the detection result to the intelligent vehicle 200, and send corresponding maintenance suggestions and safety warnings, etc., to remind the user that the current air suspension system is in a high degree of danger, and if it is continued to be used, it is easy to fail, and the air suspension system needs to be repaired in time, so as to ensure driving safety.

[0164] Optionally, in order to meet the driving comfort requirement under different terrains, an interpretable modeling can be performed on each terrain in advance to obtain a plurality of terrain models. Optionally, the server 100 and the intelligent vehicle 200 can both maintain the plurality of terrain models, that is, can both store the plurality of terrain models. During the driving of the intelligent vehicle 200, the intelligent vehicle 200 can periodically collect the power signal of the air suspension system thereof and upload the power signal to the server 100. The server 100 can determine the corresponding terrain model (that is, identify the terrain currently driven by the intelligent vehicle 200) based on the power signal through the pre-constructed algorithm model (for example, the algorithm model can include power spectrum calculation, power spectral density calculation, Gaussian pulse value statistics in unit time, and frequency spectrum density calculation based on the power signal, etc.). Then, the server 100 can send the terrain model to the intelligent vehicle 200. After receiving the terrain model, the intelligent vehicle 200 can issue corresponding control strategies to each device in the air suspension system based on the terrain model, so as to ensure the driving comfort and safety under different terrains. For example, as shown in Figure 6b , the current terrain is a sand terrain, the intelligent vehicle 200 can issue corresponding control strategies to each device in the air suspension system according to the sand terrain, for example, trigger the air suspension to actively vibrate at a high frequency, so as to prevent the intelligent vehicle 200 from sinking into a sand pit, etc.

[0165] Please refer to Figure 6b , Figure 6b is another application scenario provided by the embodiment of the present application. As shown in Figure 6a , the application scenario can be a snow-covered road, including the intelligent vehicle 200 and the server 100, wherein the introduction of each part can refer to the related description in the above Figure 6b corresponding embodiment, which will not be described here. As shown in Figure 7 , the current terrain is a snow terrain, the intelligent vehicle can issue corresponding control strategies to each device in the air suspension system according to the snow terrain, for example, trigger the air suspension to lower the height of the vehicle base, so as to improve the driving stability and ensure the driving safety on the slippery road surface such as snow-covered road. For another example, if the current road surface is covered with thick snow, the air suspension can also be triggered to raise the height of the vehicle base, so as to prevent the intelligent vehicle from sinking into a snow pit, etc.

[0166] Optionally, the server 100 can also iteratively update each terrain model and the algorithm model for identifying the terrain, and constantly optimize, so as to better ensure the driving comfort and safety under different terrains and meet the user demand.

[0167] It should be noted that the above scenarios are only exemplary, and the detection method provided by the embodiments of the present application can also be applied to other scenarios in addition to the two application scenarios exemplified above, etc., which are not limited by the embodiments of the present application.

[0168] Please refer to Figure 7 , Figure 5 is a flowchart of a detection method provided by the embodiments of the present application, which can be applied to the system architecture of the detection method described above Figure 5 , wherein the first vehicle can be any one of the intelligent vehicles 200a, 200b and 200c in the system architecture described above Figure 4b , wherein the air suspension system can be the air suspension system 10 described above Figure 5 , wherein the server can be the server 100 in the system architecture described above Figure 7 , and can be used to support and execute the method flow shown in Figure 7 . The following will be described from the server side Figure 8 , which can include the following steps S701-S703:

[0169] Step S701: obtaining a first data set; the first data set includes M data related to the air suspension system of the first vehicle.

[0170] Specifically, the server obtains a first data set, which can include M data related to the air suspension system of the first vehicle. The M data can be data related to the air suspension system collected by the first vehicle during driving or in a parked state, and M is an integer greater than or equal to 1.

[0171] Optionally, the M data can include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, etc. collected when the air suspension system is adjusted, and can also include multiple ones of the adjustment frequency, usage time, product model and product specification of the air suspension system, etc., which are not limited by the embodiments of the present application. In this way, a large number of different types of data can provide effective support for the subsequent detection process, greatly improving the comprehensiveness and accuracy of the detection results.

[0172] Step S702: obtaining N second data sets; each of the N second data sets includes one or more of the M data, and the N second data sets correspond to N types of features.

[0173] Specifically, after obtaining the first data set, the server can classify the M data in the first data set based on the preset N types of characteristics to obtain N second data sets corresponding to the N types of characteristics. Obviously, each of the N second data sets includes one or more of the M data. Optionally, the N types of characteristics can include one or more of an adjustment characteristic, a service life characteristic, and a material characteristic of the air suspension system, and N is an integer greater than or equal to 1.

[0174] Table 1

[0175]

[0176] For example, the second data set corresponding to the adjustment characteristic can include one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the service life characteristic can include the service time of the air suspension system (for example, 128 hours, 58 days, or 1 year, etc.); and the second data set corresponding to the material characteristic can include the product model and the product specification (for example, the material used for the air spring is rubber with a product model of A-001 and a product specification of B-001, etc.).

[0177] Step S703: determining a first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of characteristics.

[0178] Specifically, after classifying the N second data sets, the server can calculate the first detection result of the air suspension system based on the N second data sets and the respective weights of the N types of characteristics. Optionally, the first detection result can be a wear rate of the air suspension system. For example, the weight of the adjustment characteristic can be 40%, the weight of the material characteristic can be 30%, and the weight of the service life characteristic can be 30%, etc. That is, it can be considered that the adjustment characteristic (such as the adjustment frequency and the rising temperature, etc.) has a greater impact on the quality or health status of the air suspension system. For another example, the weight of the adjustment characteristic can be 20%, the weight of the material characteristic can be 50%, and the weight of the service life characteristic can be 30%, etc. That is, it can be considered that the material characteristic (such as the product model and the product specification, etc.) has a greater impact on the quality or health status of the air suspension system. For example, if the quality is poor or the product model is too old, the wear or failure rate of the air suspension system can be higher, etc. Details are not described herein.

[0179] By using the embodiments of the present application, a large amount of data collected in real time by the vehicle during driving for the air suspension system can be uploaded to the server, and then a more accurate and effective multi-dimensional detection system can be established based on different characteristics of the data and respective weights of various characteristics (for example, considering the influence degree of different characteristic data on the use condition of the air suspension system), under the support of the large amount of data, so as to realize more comprehensive and accurate real-time detection of the air suspension system, effectively avoid traffic accidents caused by sudden failure of the air suspension system, and ensure driving safety.

[0180] Please refer to Figure 8 , Figure 5 is a flowchart of another detection method provided by the embodiments of the present application. The method can be applied to the system architecture of the detection method described in the above Figure 5 , wherein the first vehicle can be any one of the intelligent vehicles 200a, 200b and 200c in the system architecture described in the above Figure 4b , wherein the air suspension system can be the air suspension system 10 described in the above Figure 5 , wherein the server can be the server 100 in the system architecture described in the above Figure 8 , and can be used to support and execute the method flow shown in the above Figure 8 . The following will be described from the interaction side of the server and the first vehicle. The method can include the following steps S801-S809: Figure 7

[0181] Step S801: Obtain a data stream.

[0182] Specifically, in order to ensure the real-time of the detection result, the embodiments of the present application can use the method of stream computing to process the data stream. The first vehicle can collect data related to the air suspension system when driving or parking, so as to obtain the corresponding data stream. The data stream can include K data. Optionally, step S801 can refer to step S701 in the above corresponding embodiments, which will not be described here. Figure 9

[0183] Step S802: The first vehicle sends the data stream to the server.

[0184] Specifically, the first vehicle can upload the data stream obtained by continuously collecting data for the air suspension system during driving to the server in real time. Optionally, please refer to Figure 9 , Figure 9 is a whole flowchart of a detection method provided by the embodiments of the present application. Step S802 can refer to step S11 in the above Figure 9 , as Figure 10 ​​​In step S11, the intelligent vehicle (i.e., the first vehicle) reports data to the server.

[0185] In step S803, the server samples K data included in the data stream based on the importance sampling method to obtain a first data set; the first data set includes M data.

[0186] Specifically, the server can sample K data included in the data stream based on the importance sampling method to obtain a first data set, and the first data set includes M data. It can be understood that the K data includes the M data, and K is an integer greater than or equal to M. Optionally, as described above, in order to reduce the calculation amount and operation cost of the server and improve the detection efficiency, the server can perform detection on the air suspension system based on a part of the data collected and uploaded by the first vehicle.

[0187] Referring to Figure 10 , Figure 10 is a schematic diagram of data sampling provided by an embodiment of the present application. It can be understood that the time point of air suspension adjustment often has strong randomness, and the adjustment frequency is high during busy hours (i.e., the first vehicle collects and uploads data extremely frequently during busy hours), and often reaches a peak. As Figure 10 indicates, a big data analysis structure can be constructed, assuming that the actual adjustment distribution probability function is p(z), and the peak point of the function is the busy hour of the vehicle dynamic adjustment of the air suspension. In this way, the server can use the naive Bayes model to classify the weight during the sampling process of the uploaded data stream, for example Figure 10 indicates kq(z) at the peak, so that the server can increase data during the busy hour of air suspension adjustment (i.e., when the air suspension adjustment frequency is high, and then the first vehicle collects and uploads data more frequently), and reduce data sampling during the idle time of air suspension adjustment, thereby obtaining the first data set. The data included in the first data set obtained by sampling can be as shown in the table in Figure 7 , which will not be described here. For example, if the air suspension system has a high adjustment frequency from the 5th minute to the 20th minute, and the first vehicle collects and uploads 40 data, the server can sample 30 data; if the air suspension system has a very low adjustment frequency from the 40th minute to the 55th minute, and the first vehicle collects and uploads only 5 data, the server can sample 3 data. In this way, the importance sampling method can make the distribution of the sampling points more consistent with the actual situation within a limited sampling time or sampling number, and the sampling efficiency is higher, which provides a large amount of effective data support for the subsequent detection process.

[0188] Step S804: The service end classifies the M data based on the preset N types of features to obtain N second data sets corresponding to the N types of features.

[0189] Specifically, step S804 can refer to the above Figure 7 Corresponding to step S702 in the embodiment, details are not repeated here. Optionally, the developer can construct an interpretable classification model on the service end based on algorithms such as support vector machine (SVM) and neural network (NN) in advance. As described above, by inputting the M data into the classification model, the features of each of the M data can be analyzed and extracted, and then classified based on different features, and finally the N second data sets are obtained, and the like. In this way, by analyzing the features through big data, deep analysis and integration are performed on the corresponding features, and a large amount of data supports behind the features, which can improve the interpretability of the features.

[0190] Step S805: The service end determines the first detection result of the air suspension system based on the N second data sets and the respective weights of the N types of features.

[0191] Specifically, step S805 can refer to the above Figure 9 Corresponding to step S703 in the embodiment, details are not repeated here.

[0192] Optionally, step S805 can also refer to step S12 in Figure 9 As shown in Figure 9 , the developer can construct a calculation model on the service end in advance, through which the score value corresponding to the adjustment feature a1 (for example, if the full score is 10 points, then the a1 can be 5 points, and generally, the higher the score value, the more serious the damage of the air suspension system), the score value corresponding to the material feature a2, and the score value corresponding to the life feature a3 can be calculated based on the preset scoring standard and the second data set corresponding to each feature. Further, as shown in Figure 9 , the weight of the adjustment feature is p1, the weight of the material feature is p2, and the weight of the life feature is p3, then the wear rate (i.e., the first detection result) of the air suspension system a1*p1+a2*p2+a3*p3 can be calculated.

[0193] To sum up, to realize the detection method provided in the embodiments of the present application, a developer can construct a detection model in advance on the server side. The detection model may, for example, include the classification model and the calculation model described above, and can realize the functions of data classification and calculation of the first detection result according to different weights described above. In this way, the server side can input the collected data uploaded by the vehicle in real time into the detection model, thereby efficiently and accurately obtaining the first detection result of the air suspension system, realizing real-time monitoring of the state of the air suspension system, and greatly reducing the accident rate caused by faults of the air suspension system.

[0194] Optionally, the server side can periodically detect the air suspension system and periodically update the detection result based on a preset period (for example, 1 hour or 30 minutes, etc.) and the data continuously collected and uploaded by the first vehicle, thereby ensuring the real-time and effectiveness of the detection result.

[0195] Optionally, the server can further obtain a third data set, which can include P data related to the air suspension systems of the second vehicles, for example, the P data can be data collected by the air suspension systems of the second vehicles when the second vehicles are driving or parking, etc., wherein P can be an integer greater than 1. Then, the server can obtain the first detection result of each of the second vehicles based on the third data set and by using the above-mentioned method for calculating the first detection result. Secondly, the server can analyze and compare the first detection result of each of the second vehicles and the first detection result of the first vehicle. For example, the server can analyze and compare the wear rates of a large number of vehicles to check whether the wear rates are consistent with the actual distribution of the wear rates. Obviously, if the wear rates of a large number of vehicles are distributed in the same interval, for example, all around 10%, it can be considered that there is a problem in the current detection process, and the classification model, the scoring standard, or the weight distribution can be not perfect. Therefore, the server can further modify the classification model, the scoring standard, and / or the weight of each of the N types of features in the detection process based on the big data (i.e., the wear rates of a large number of vehicles), so as to make the detection result more accurate and avoid the risk of traffic accidents caused by inaccurate detection results. Optionally, as mentioned above, since the detection result of the first vehicle can be updated periodically based on the continuously uploaded data, the server can also modify the classification model, the scoring standard, and / or the weight of each of the N types of features in the detection process based on the detection results of the first vehicle obtained at different times. For example, if the server obtains the wear rate of the first vehicle at 9 am as 30%, at 10 am as 50%, and at 11 am as 10%, it can be determined that there is a problem in the current detection process based on the change of the wear rate that does not conform to the real-time situation, and the developer can further optimize the detection process, etc., which will not be described here.

[0196] Step S806: The server determines the second detection result of the air suspension system based on the first detection result of the air suspension system

[0197] Specifically, the server can further calculate a second detection result of the air suspension system based on the calculated first detection result. For example, the server can further evaluate or predict the failure-prone rate and the serviceable duration (or evaluate whether the serviceable duration is within a safe duration range, etc.) of the air suspension system based on the wear rate of the air suspension system, etc. The embodiments of the present application do not make specific limitations in this regard. Alternatively, the second detection result can also include an evaluation of whether the air suspension system needs to be maintained, etc. Alternatively, the calculated first detection result and the second detection result can be stored in the server and can be carried with a corresponding unique identifier for recording that the first detection result and the second detection result correspond to the first vehicle, etc. The embodiments of the present application do not make specific limitations in this regard.

[0198] Step S807: The first vehicle sends a query request to the server.

[0199] Specifically, if the user wants to know the health status of the air suspension system in the first vehicle, the first vehicle can send a query request to the server. Alternatively, step S807 can also refer to step S13a in Figure 9 .

[0200] Step S808: The server sends the first detection result and the second detection result to the first vehicle.

[0201] Specifically, after receiving the query request sent by the first vehicle, the server can determine the first detection result and the second detection result corresponding to the first vehicle based on the query request, and send the first detection result and the second detection result to the first vehicle. Alternatively, the server can also send only the first detection result or only the second detection result based on the actual needs of the user, etc. The embodiments of the present application do not make specific limitations in this regard. Alternatively, step S808 can also refer to step S13b in Figure 9 .

[0202] Step S809: If the first detection result and / or the second detection result meet a preset condition, the server sends the first detection result and the second detection result to the first vehicle

[0203] Specifically, if the first detection result and / or the second detection result meet a preset condition, the server can also actively send the first detection result and the second detection result to the first vehicle. Alternatively, step S809 can refer to Figure 11Optionally, for example, in the case that the wear rate of the air suspension system is greater than a first threshold (for example, 40%) and / or the failure-prone rate is greater than a second threshold (for example, 50%) and / or the available use time is less than a third threshold (for example, 12 hours), that is, in the case that the air suspension system is severely worn and is prone to failure and is not suitable for continued use, in order to ensure the driving safety of the user, the server can immediately send the first vehicle the corresponding first detection result, second detection result and warning information. Optionally, the first vehicle can remind the user through a central display screen, instrument panel or voice warning after receiving the warning information, so that the user can timely maintain the air suspension system to avoid traffic accidents.

[0204] Optionally, if the first detection result and / or the second detection result meet the preset condition, the server can further formulate a corresponding maintenance plan and obtain information of at least one automobile repair shop within a preset range of the first vehicle, and push the maintenance plan and the information of the at least one automobile repair shop to the first vehicle, so that the user can timely and accurately and efficiently maintain the air suspension system to ensure driving safety. The information can include the respective names, addresses, distances from the first vehicle, charging prices, user evaluations and driving path planning of the at least one automobile repair shop, and the like, which are not limited in the embodiments of the present application.

[0205] Optionally, in order to ensure driving comfort and safety in different terrains, the server in the embodiments of the present application can also determine a target terrain corresponding to the current driving process of the first vehicle and send the target terrain to the first vehicle. The first vehicle can obtain the optimal air suspension mode under the target terrain based on the target terrain and issue a corresponding control strategy, so as to adapt to different terrain driving requirements and also reduce the wear of the air suspension system in extreme terrain and prolong the service life of the air suspension system. Optionally, the target terrain can be any one of sand, snow, rock and ice, and the control strategy can include a control strategy for at least one of the height parameter, vibration parameter and damping parameter of the air suspension system.

[0206] Optionally, referring to Figure 11 , Figure 11 is a flowchart of terrain identification provided by the embodiments of the present application. As shown in Figure 11 , the developer can use least squares method to fit the characteristics under different terrains in advance on the cloud (that is, the above-mentioned server), perform explanatory driving description, and thus perform interpretable modeling on each terrain to obtain a plurality of terrain models. Optionally, as shown in Figure 11As shown, during the operation of the first vehicle, the first vehicle can periodically collect the power signal of its air suspension system (e.g., Figure 11 The diagram shows the carrier envelope phase (CEP). This power signal is then uploaded to the cloud. The cloud can use this power signal, through a pre-built algorithm model (such as...). Figure 11 As shown, the algorithm model can be "R in =model(power spectral density (PS), power spectral density (PSD), Gaussian pulse (Gaussian pulse), frequency density (frequency density))", which can include power spectrum calculation, power spectral density calculation, Gaussian pulse value statistics per unit time, and frequency density calculation based on the power signal) to determine the terrain model corresponding to the current driving conditions. Then, the cloud can send the terrain model to the first vehicle. After receiving the terrain model, the first vehicle can maintain the terrain model locally, formulate corresponding control strategies based on the terrain model, and issue corresponding control strategies to various devices in the air suspension system, thereby ensuring real-time dynamic adjustment of various devices in the air suspension system under different terrains, and thus ensuring driving comfort and safety. Optionally, such as Terrain As shown, the cloud can also iteratively update the algorithm model to improve the accuracy and efficiency of terrain recognition. Optionally, the cloud can also optimize various terrain models, and so on.

[0207] Optionally, even when the vehicle is not connected to the cloud (i.e., not connected to the internet), it can still perform terrain recognition based on its own collected power signals, locally maintained algorithm models, and various terrain models. Based on the identified terrain, it can then issue corresponding control strategies to multiple devices within the air suspension system, and so on. Optionally, the vehicle may also include one or more sensors (e.g., radar and cameras), and the vehicle can perform terrain recognition through these sensors. For example, it can analyze the current terrain using images captured by the camera, etc. This application embodiment does not specifically limit this aspect. Optionally, please refer to Table 2 below.

[0208] Table 2

[0209] Height (mm) Shake (times / sec) Damping (Newton / (meter / sec)) Sand Snow A1 B1 C1 No adjustment A2 Rock C2 Ice A3 B3 C3 No adjustment A4 Figure 12a C4

[0210] As shown in Table 2 above, the control strategy can mainly include different strategy levels of control for the three parameters of height, vibration and damping. The corresponding devices in the air suspension system can adjust accordingly after receiving the control strategy of each parameter. Next, the control strategy for each parameter is described in detail.

[0211] Height (mm): The height is the actual distance between the vehicle (specifically the vehicle base) and the ground. Generally, different vehicle sizes have different distances from the ground, but the range is usually 430-460 mm, and the adjustable range is -25-+25 mm. The corresponding electronic components in the air suspension system can adjust according to the height parameter in different terrains. For example, in uneven terrains such as rocks, the height parameter control strategy can be +25 mm to maximize the distance between the vehicle and the ground, thereby avoiding damage or getting stuck of the vehicle base on rocks, etc. Thus, A3 in Table 2 can be greater than A1, A2 and A4.

[0212] Vibration (times / sec): Full name can be vibration frequency, i.e. the number of times the air suspension is actively triggered to vibrate per second. The triggering terrain of the vibration can be desert, rock, etc. When it is identified that the current terrain is sand or rock, the first vehicle can issue an instruction to trigger the air suspension to actively vibrate, thereby preventing the vehicle from sinking into a sand pit or muddy road surface and ensuring driving safety. Alternatively, the vibration frequency and / or vibration amplitude can be different based on different terrains, which is not limited in the embodiments of the present application. As shown in Table 2 above, in relatively smooth terrains such as snow and ice, no vibration adjustment can be made, i.e. the air suspension is not actively triggered to vibrate.

[0213] Damping (Newton / (meter / sec)): Unit speed force value. Generally, to achieve different damping, different volumes of air need to be filled into the air spring of the air suspension system. For example, when driving in uneven terrains such as rocks or mountains, a larger volume of air is often filled to increase the damping and maintain smooth driving. However, the adjustment of damping often has some differences due to different materials of the air suspension devices (i.e. the materials used in the air spring). Therefore, the embodiments of the present application can also standardize the damping after measurement based on different device materials. Alternatively, please refer to Figure 12a , Figure 12a for a schematic diagram of damping adjustment provided by the embodiments of the present application. As shown in Figure 12b , in some possible embodiments, the rebound damping and compression damping can be fitted respectively to obtain the best damping adjustment strategy to better adapt to different terrains. Alternatively, please refer to Figure 12b , Figure 12bis another schematic diagram of the damping adjustment provided by the embodiment of the present application. As shown in Figure 12b , the dashed line therein is the fitting curve of the temperature / pressure without damping adjustment (i.e. in the real driving condition), each dot therein is the measured temperature / pressure after the damping adjustment, and the solid line therein is the fitting curve of the multiple measured temperature / pressures after the damping adjustment. It can be understood that, in general, the higher the temperature / pressure, the greater the damping, as shown in Figure 13 , the straight line part above the dashed line can represent the case of increasing damping, and the straight line part below the dashed line can represent the case of decreasing damping. Alternatively, the effects of pressure, temperature and material on damping can also be comprehensively analyzed to obtain the granularity value of each pulse adjustment (i.e. each time the air spring is filled with gas for damping adjustment), such as pulse=model(temperature, pressure, material), etc., so as to obtain a better damping adjustment strategy to adapt to different terrains, etc., which is not limited in the embodiment of the present application.

[0214] Alternatively, in addition to the above-mentioned terrain identification active triggering of damping adjustment, the user can also manually adjust the damping based on his own driving needs, for example, if the user wants to obtain a more intense driving experience, he can manually operate to reduce the damping, and for example, if the user wants to obtain a smooth driving experience, he can manually operate to increase the damping. Correspondingly, in addition to the above-mentioned manual damping adjustment method, the user can also manually switch the terrain mode according to his own needs, for example, he can select the default highway terrain mode during rock terrain driving, so as to reduce the damping of the air suspension, thereby enhancing the real experience and control feeling of driving, etc.

[0215] Please refer to Figure 13 , Figure 13 is the overall flowchart of another detection method provided by the embodiment of the present application. As shown in Figure 13 , as described above, the embodiment of the present application is completed through the interaction of the vehicle end (i.e. the first vehicle described above) and the cloud end (i.e. the server end described above). Among them, the collection and reporting of the air suspension system usage data are performed at the vehicle end, and the extraction of data features and the analysis of models are performed at the cloud end. As shown in Figure 13 , the server end can use big data analysis technology to focus on analyzing the health status and terrain model of the air suspension system. As shown in Figure 14As shown, the vehicle end can mainly receive the health status information sent by the cloud end through active query and cloud end active push two ways to synchronize the health status information, and after synchronization, it can be pushed to the vehicle owner and provide corresponding query function, etc. For the terrain selection (or terrain identification) function, the vehicle end can synchronize the terrain model with the cloud end, and then issue instructions based on the current terrain model on the vehicle end to adaptively adjust the air suspension. In this way, the embodiments of the present application can effectively monitor the state of the air suspension system in real time based on big data analysis, reducing the accident rate caused by air suspension system failure. Further, the embodiments of the present application can also bring better driving experience and riding experience based on terrain identification and intelligent adjustment, and can also prolong the service life of the air suspension system, etc. It can be understood that in the process of electrification of the air suspension, data gives it intelligence, and through the embodiments of the present application, the data value can be better played.

[0216] Please refer to Figure 14 , Figure 14 is a structural schematic diagram of a detection device provided by the embodiments of the present application. The detection device 30 can be applied to the above-mentioned service end, as shown in Figure 7 The detection device 30 can include a first acquisition unit 301, a second acquisition unit 302, and a first determination unit 303, and the detailed description of each unit is as follows.

[0217] The first acquisition unit 301 is configured to acquire a first data set; the first data set includes M data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1;

[0218] The second acquisition unit 302 is configured to acquire N second data sets; each of the N second data sets includes one or more of the M data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of the adjustment characteristics, the life characteristics and the material characteristics of the air suspension system; N is an integer greater than or equal to 1;

[0219] The first determination unit 303 is configured to determine a first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of characteristics.

[0220] In a possible implementation, the device 30 further includes:

[0221] The second determining unit 304 is configured to determine a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result comprises a wear rate of the air suspension system; and the second detection result comprises a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0222] In a possible implementation, the first obtaining unit 301 is specifically configured to:

[0223] receive a data stream from the first vehicle; the data stream comprises K data related to the air suspension system;

[0224] sample the K data included in the data stream based on an importance sampling device to obtain the first data set; the K data include the M data; and K is an integer greater than or equal to M.

[0225] In a possible implementation, the device 30 further includes:

[0226] The receiving unit 305 is configured to receive a query request sent by the first vehicle.

[0227] The first sending unit 306 is configured to send the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.

[0228] In a possible implementation, the device 30 further includes:

[0229] The second sending unit 307 is configured to determine a corresponding target terrain of the first vehicle in a driving process, and send the target terrain to the first vehicle; the target terrain is used for the first vehicle to issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is one of sand, snow, rock and ice; and the control strategy comprises a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

[0230] In a possible implementation, the device 30 further includes:

[0231] The third sending unit 308 is configured to send the first detection result, the second detection result and corresponding warning information to the first vehicle if the first detection result and / or the second detection result meets a preset condition; the warning information is used to warn a user to maintain the air suspension system; the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

[0232] In a possible implementation, the apparatus 30 further includes:

[0233] The fourth sending unit 309 is configured to acquire information of at least one automobile repair shop within a preset range of the first vehicle and send the information of the at least one automobile repair shop to the first vehicle if the first detection result and / or the second detection result meets the preset condition; the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path planning.

[0234] In a possible implementation, the first determining unit 303 is specifically configured to:

[0235] respectively calculate score values corresponding to the N types of features based on the N second data sets and a preset scoring standard;

[0236] calculate the first detection result of the air suspension system based on the score values corresponding to the N types of features and respective weights of the N types of features.

[0237] In a possible implementation, the apparatus 30 further includes:

[0238] The third acquiring unit 310 is configured to acquire a third data set, the third data set including P data related to respective air suspension systems of a plurality of second vehicles; P is an integer greater than 1;

[0239] The third determining unit 311 is configured to determine respective first detection results of the plurality of second vehicles based on the third data set.

[0240] The correction unit 312 is configured to correct the scoring standard and / or respective weights of the N types of features based on the respective first detection results of the plurality of second vehicles and the first detection result of the first vehicle.

[0241] In a possible implementation, the M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of the adjustment frequency, the use time length, the product model and the product specification of the air suspension system related to the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the service life feature includes the use time length; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0242] In a possible implementation, the second acquisition unit 302 is specifically configured to:

[0243] Based on the N types of features, the M data are classified to obtain N second data sets corresponding to the N types of features.

[0244] It should be noted that the functions of each functional unit in the detection device described in the embodiments of the present application can refer to the related descriptions of steps S701-S703 in the method embodiments described above in the Figure 8 , and can also refer to the related descriptions of steps S801-S809 in the method embodiments described above in the Figure 14 , which will not be described here in detail.

[0245] Figure 15 Each unit in the foregoing embodiments can be implemented in software, hardware or a combination thereof. The unit implemented in hardware can include a logic circuit, an arithmetic circuit or an analog circuit, etc. The unit implemented in software can include program instructions, which are regarded as a software product and stored in a memory, and can be run by a processor to implement relevant functions, as described above.

[0246] Please refer to Figure 15 , Figure 15 is a structural schematic diagram of a detection device provided by the embodiments of the present application. The detection device 40 can be applied to the first vehicle described above, as shown in Figure 7 , the detection device 40 can include an acquisition unit 401, and the detailed description of each unit is as follows.

[0247] The acquisition unit 401 is configured to acquire a data stream and send the data stream to a server; the data stream includes K data related to an air suspension system of a first vehicle; the data stream is used for the server to sample the K data included in the data stream based on an importance sampling method to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the M data are included in the K data; the M data are used for the server to obtain N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of features, and the N types of features include one or more of an adjustment feature, a service life feature and a material feature of the air suspension system; the N second data sets are used for the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of features; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M.

[0248] In a possible implementation, the first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection result includes a wear rate of the air suspension system; and the second detection result includes a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

[0249] In a possible implementation, the apparatus 40 further includes:

[0250] The sending unit 402 is configured to send a query request to the server.

[0251] The first receiving unit 403 is configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

[0252] In a possible implementation, the apparatus 40 further includes:

[0253] The second receiving unit 406 is configured to receive a target terrain sent by the server and issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is a terrain corresponding to the first vehicle in a driving process and determined by the server; the target terrain is one of sand, snow, rock and ice; and the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

[0254] In a possible implementation, the apparatus 40 further includes:

[0255] The third receiving unit 404 is configured to receive the first detection result, the second detection result and corresponding warning information sent by the server if the first detection result and / or the second detection result meets a preset condition; the warning information is used to warn a user to maintain the air suspension system; the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

[0256] In a possible implementation, the apparatus 40 further includes:

[0257] The fourth receiving unit 405 is configured to receive information of at least one automobile repair shop within a preset range of the first vehicle sent by the server if the first detection result and / or the second detection result meets a preset condition; the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path planning.

[0258] In a possible implementation, the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of adjustment frequency, usable duration, product model and product specification of the air suspension system; the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the service life feature includes the usable duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

[0259] It should be noted that the functions of each functional unit in the detection apparatus described in the embodiments of the present application can refer to the related descriptions of steps S701-S703 in the method embodiments described above, and can also refer to the related descriptions of steps S801-S809 in the method embodiments described above, which will not be described here in detail. Figure 8 Figure 15

[0260] Figure 16 Each unit in the above method embodiments can be implemented in software, hardware or a combination thereof. The unit implemented in hardware can include a logic circuit, an algorithm circuit or an analog circuit, etc. The unit implemented in software can include program instructions, which are regarded as a kind of software product, stored in a memory and executed by a processor to implement related functions, as previously described.​​

[0261] Based on the description of the method embodiments and the device embodiments, the embodiments of the present application further provide a server. Please refer to Figure 16 , Figure 7 is a structural schematic diagram of a server provided by the embodiments of the present application. The server at least includes a processor 1001, an input device 1002, an output device 1003 and a computer readable storage medium 1004. The server can further include other general-purpose components, which are not described here in detail. Among them, the processor 1001, the input device 1002, the output device 1003 and the computer readable storage medium 1004 in the server can be connected through a bus or other means.

[0262] The processor 1001 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs of the above solutions.

[0263] The memory in the server can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disc storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but not limited to. The memory can exist independently and be connected to the processor through a bus. The memory can also be integrated with the processor.

[0264] The computer-readable storage medium 1004 can be stored in the server's memory. The computer-readable storage medium 1004 is used to store a computer program, which includes program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1004. The processor 1001 (or CPU (Central Processing Unit)) is the computing and control core of the server, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method flows or corresponding functions. In one embodiment, the processor 1001 described in this application embodiment can be used to perform a series of processes for air suspension system detection, including: acquiring a first data set; the first data set includes M data related to the air suspension system of a first vehicle; M is an integer greater than or equal to 1; acquiring N second data sets; each of the N second data sets includes one or more data from the M data sets; the N second data sets correspond to N types of features, the N types of features include one or more of the adjustment features, life features, and material features of the air suspension system; N is an integer greater than or equal to 1; determining a first detection result of the air suspension system based on the weights corresponding to the N second data sets and the N types of features, etc.

[0265] It should be noted that the functions of each functional unit in the server described in this application embodiment can be found in the above description. Figure 8 For further details regarding steps S701-S703 in the embodiments described above, please refer to the above descriptions. Figure 17 The relevant descriptions of steps S801-S809 in the method embodiment are not repeated here.

[0266] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0267] The embodiment of the present application further provides a computer readable storage medium (Memory), which is a memory device in a server, used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the server, and of course can include an extended storage medium supported by the server. The computer readable storage medium provides a storage space, which stores an operating system of the server. In addition, one or more instructions suitable for being loaded and executed by the processor 1001 are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory; and optionally can also be at least one computer readable storage medium located away from the aforementioned processor.

[0268] The embodiment of the present application further provides a computer program, which includes instructions, when the computer program is executed by a computer, so that the computer can execute part or all steps of any one of the detection methods.

[0269] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0270] Based on the description of the above method embodiments and device embodiments, the embodiment of the present application further provides an intelligent vehicle. Please refer to Figure 17 , Figure 17 is a structural schematic diagram of an intelligent vehicle provided by the embodiment of the present application, which can be the first vehicle described above, and can include an air suspension system. As Figure 7 indicated, the intelligent vehicle at least includes a processor 1101, an input device 1102, an output device 1103 and a computer readable storage medium 1104, and can further include other general-purpose components, which are not described here in detail. Among them, the processor 1101, the input device 1102, the output device 1103 and the computer readable storage medium 1104 in the intelligent vehicle can be connected through a bus or other means.

[0271] The processor 1101 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program.

[0272] The memory in the intelligent vehicle can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory can exist independently and be connected to the processor through a bus. The memory can also be integrated with the processor.

[0273] The computer readable storage medium 1104 can be stored in the memory of the intelligent vehicle, and is used for storing a computer program including program instructions. The processor 1101 is a computing core and a control core of the intelligent vehicle, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method process or a corresponding function. In one embodiment, the processor 1101 described in the embodiment of the present application can be used for a series of processes of air suspension system detection, including: acquiring a data stream and sending the data stream to a server; the data stream includes K data related to the air suspension system of the first vehicle; the data stream is used for the server to sample the K data included in the data stream based on an importance sampling method to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the K data includes the M data; the M data is used for the server to obtain N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of the adjustment characteristics, the life characteristics and the material characteristics of the air suspension system; the N second data sets are used for the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of characteristics; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M, and the like.

[0274] It should be noted that the functions of the functional units in the intelligent vehicle described in the embodiments of the present application can refer to the related descriptions of steps S701-S703 in the method embodiments described above, and can also refer to the related descriptions of steps S801-S809 in the method embodiments described above, which will not be described here. Figure 8 ​

[0275] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can refer to the related description of other embodiments.

[0276] ​​The embodiment of the present application further provides a computer readable storage medium (Memory), which is a memory device in the intelligent vehicle and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the intelligent vehicle, and of course can also include an extended storage medium supported by the intelligent vehicle. The computer readable storage medium provides a storage space, which stores an operating system of the intelligent vehicle. In addition, one or more instructions suitable for being loaded and executed by the processor 1101 are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory; and optionally can also be at least one computer readable storage medium located away from the aforementioned processor.

[0277] The embodiment of the present application further provides a computer program, which includes instructions, when the computer program is executed by a computer, so that the computer can execute part or all steps of any one of the detection methods.

[0278] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0279] The terms "first", "second", "third", and "fourth" and the like in the specification of the present application and claims and the drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to the process, method, product or device.

[0280] In this document, the term "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0281] As used in this description, the terms "component," "module," "system", and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized, partially localized, or distributed across two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal).

[0282] It should be noted that, for the method embodiments, the acts performed in the method embodiments can be performed in an order different than the order described, and that various activities described with respect to the methods can be modified or omitted, and various other activities not described can be added to the methods. Similarly, for the non-transitory computer-readable medium embodiments, the description as to what each non-transitory computer-readable medium embodiment should or should not have can be applied to each and every one of the non-transitory computer-readable medium embodiments described herein, independently of one another and independently of the description above.

[0283] In several embodiments provided by the present disclosure, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the apparatus embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be implemented by using some interfaces of the units or other means, and can be in electric or other forms.

[0284] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or can be distributed on a plurality of network units. In actual implementation, some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0285] In addition, each of the functional units in each of the embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0286] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc., and specifically can be a processor in a computer device) to perform all or part of the steps of the methods according to the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, magnetic disk, optical disk, read-only memory (ROM) or random access memory (RAM), and various other media that can store program codes.

[0287] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of detection, characterized in that, Applied to a server, the method comprises: obtaining a first data set; the first data set comprises M data related to the air suspension system of a first vehicle; M is an integer greater than or equal to 1; obtaining N second data sets; each of the N second data sets comprises one or more of the M data; the N second data sets correspond to N types of characteristics, which include one or more of the adjustment characteristics, the life characteristics and the material characteristics of the air suspension system; N is an integer greater than or equal to 1; determining a first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of characteristics; the first detection result comprises a wear rate of the air suspension system; the determination of the first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of characteristics comprises: based on the N second data sets and a preset scoring standard, the score values corresponding to the N types of characteristics are respectively calculated; based on the score values corresponding to the N types of characteristics and the weights of the N types of characteristics, the first detection result of the air suspension system is calculated; the obtaining of the N second data sets comprises: based on the N types of characteristics, the M data are classified to obtain the N second data sets corresponding to the N types of characteristics; the method further comprises: obtaining a third data set, which comprises P data related to the air suspension systems of a plurality of second vehicles respectively; P is an integer greater than 1; based on the third data set, determining the first detection results of the plurality of second vehicles respectively; based on the first detection results of the plurality of second vehicles respectively and the first detection result of the first vehicle, the scoring standard and / or the weights of the N types of characteristics are corrected; wherein, when the distribution of the wear rates in the first detection results of the first vehicle and the plurality of second vehicles does not conform to the actual distribution of the wear rates, it is determined that the scoring standard and / or the weights of the N types of characteristics are corrected.

2. The method of claim 1, wherein, the method further comprises: based on the first detection result of the air suspension system, determining a second detection result of the air suspension system; the second detection result comprises a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

3. The method of claim 2, wherein, the obtaining of the first data set comprises: receiving a data stream from the first vehicle; the data stream comprises K data related to the air suspension system; based on an importance sampling method, sampling the K data included in the data stream to obtain the first data set; the K data include the M data; K is an integer greater than or equal to M.

4. The method according to any one of claims 2-3, characterized in that, the method further comprises: receiving a query request sent by the first vehicle; based on the query request, sending the first detection result and the second detection result of the air suspension system to the first vehicle.

5. The method according to any one of claims 1 to 3, characterized in that, the method further comprises: determining a target terrain corresponding to the first vehicle during driving, and sending the target terrain to the first vehicle; the target terrain is used by the first vehicle to issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is one of sand, snow, rock and ice; the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

6. The method of any one of claims 2-3, wherein, The method further comprises: if the first detection result and / or the second detection result meets a preset condition, sending the first detection result, the second detection result and corresponding warning information to the first vehicle; the warning information is used to warn the user to maintain the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and / or the failure-prone rate of the air suspension system is greater than a second threshold and / or the usable duration of the air suspension system is less than a third threshold.

7. The method of claim 6, wherein, The method further comprises: if the first detection result and / or the second detection result meets the preset condition, obtaining information of at least one automobile repair shop within a preset range of the first vehicle, and sending the information of the at least one automobile repair shop to the first vehicle; the information includes at least one of the respective addresses of the at least one automobile repair shop, the distances between the at least one automobile repair shop and the first vehicle, the charging prices, the user evaluations and the driving path planning.

8. The method according to any one of claims 1 to 3, characterized in that, The M data includes at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of adjustment frequencies, use durations, product models and product specifications of the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density and the adjustment frequency; the second data set corresponding to the life feature includes the use duration; the second data set corresponding to the material feature includes one or more of the product model and the product specification.

9. A method of detection, characterized in that It comprises: obtaining a data stream and sending the data stream to a server; the data stream includes K data related to the air suspension system of the first vehicle; The data stream is used for the server to sample the K data included in the data stream based on an importance sampling method, to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the K data include the M data; the M data are used for the server to obtain N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of an adjustment characteristic, a life characteristic, and a material characteristic of the air suspension system; the N second data sets are used for the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of characteristics; the first detection result includes a wear rate of the air suspension system; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M; The N second data sets are specifically used for the server to respectively calculate score values corresponding to the N types of characteristics based on the N second data sets and a preset scoring standard; and the first detection result of the air suspension system is calculated based on the score values corresponding to the N types of characteristics and the weights of the N types of characteristics; The M data are specifically used for the server to classify the M data based on the N types of characteristics to obtain N second data sets corresponding to the N types of characteristics; The scoring standard and / or the weights of the N types of characteristics are results of correction based on first detection results of a plurality of second vehicles and the first detection result of the first vehicle; the first detection results of the plurality of second vehicles are determined based on third data sets, and the third data sets include P data related to air suspension systems of the plurality of second vehicles; P is an integer greater than 1; when the distribution of the wear rates in the first detection results of the first vehicle and the plurality of second vehicles does not conform to the actual distribution of the wear rates, it is determined that the scoring standard and / or the weights of the N types of characteristics are corrected.

10. The method of claim 9, wherein, The first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection result includes a wear rate of the air suspension system; and the second detection result includes a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

11. The method of claim 10, wherein, The method further includes: sending a query request to the server; receiving the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

12. The method according to any one of claims 9-11, characterized in that, The method further includes: receive the target terrain sent by the server, and issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is a terrain corresponding to the first vehicle in a driving process determined by the server; the target terrain is one of sand, snow, rock, and ice; the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter, and a damping parameter corresponding to the air suspension system.

13. The method according to any one of claims 10 and 11, characterized in that, The method further includes: if the first detection result and / or the second detection result meets a preset condition, receiving the first detection result, the second detection result, and corresponding warning information sent by the server; the warning information is used to warn a user to maintain the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value and / or the failure-prone rate of the air suspension system is greater than a second threshold value and / or the usable duration of the air suspension system is less than a third threshold value.

14. The method of claim 13, wherein, The method further includes: if the first detection result and / or the second detection result meets a preset condition, receiving information of at least one car repair shop within a preset range of the first vehicle sent by the server; the information includes at least one of respective addresses of the at least one car repair shop, distances between the at least one car repair shop and the first vehicle, charging prices, user evaluations, and driving path plans.

15. The method of any one of claims 9-11, wherein, The M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of adjustment frequencies, use durations, product models, and product specifications of the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life feature includes the use duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

16. A detection device, characterized in that The device is applied to a server and includes: a first obtaining unit configured to obtain a first data set; the first data set includes M data related to an air suspension system of a first vehicle; M is an integer greater than or equal to 1; a second obtaining unit configured to obtain N second data sets; each of the N second data sets includes one or more of the M data; the N second data sets correspond to N types of features, and the N types of features include one or more of an adjustment feature, a life feature, and a material feature of the air suspension system; N is an integer greater than or equal to 1; a first determining unit configured to determine a first detection result of the air suspension system according to the N second data sets and weights corresponding to the N types of features; the first detection result includes a wear rate of the air suspension system; and a second determining unit configured to determine a second detection result of the air suspension system according to the N second data sets and the N types of features; the second detection result includes a failure-prone rate of the air suspension system. The first determining unit is specifically configured to: Based on the N second data sets and a preset scoring standard, a score value corresponding to each of the N types of features is respectively calculated; Based on the score value corresponding to each of the N types of features and the weight of each of the N types of features, the first detection result of the air suspension system is calculated; The second obtaining unit is specifically configured to: Based on the N types of features, the M data are classified to obtain N second data sets corresponding to the N types of features; The device further comprises: A third obtaining unit is configured to obtain a third data set, the third data set comprising P data related to air suspension systems of a plurality of second vehicles; P is an integer greater than 1; A third determining unit is configured to determine, based on the third data set, a first detection result of each of the plurality of second vehicles; A correction unit is configured to correct the scoring standard and / or the weight of each of the N types of features based on the first detection result of each of the plurality of second vehicles and the first detection result of the first vehicle; when the distribution of the wear rate in the first detection result of the first vehicle and the plurality of second vehicles does not conform to the actual distribution of the wear rate, the scoring standard and / or the weight of each of the N types of features is determined to be corrected.

17. The apparatus of claim 16, wherein, The device further comprises: A second determining unit is configured to determine, based on the first detection result of the air suspension system, a second detection result of the air suspension system; the second detection result comprises a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

18. The apparatus of claim 17, wherein, The first obtaining unit is specifically configured to: Receive a data stream from the first vehicle; the data stream comprises K data related to the air suspension system; Sample, based on an importance sampling device, the K data included in the data stream to obtain the first data set; the K data include the M data; K is an integer greater than or equal to M.

19. The apparatus of any of claims 17-18, wherein, The device further comprises: A receiving unit is configured to receive a query request sent by the first vehicle; A first sending unit is configured to send, based on the query request, the first detection result and the second detection result of the air suspension system to the first vehicle.

20. The apparatus of any one of claims 16-18, wherein, The device further comprises: A second sending unit is configured to determine a target terrain corresponding to the first vehicle in the driving process and send the target terrain to the first vehicle; the target terrain is used by the first vehicle to issue a corresponding control strategy for the air suspension system according to the target terrain; the target terrain is one of sand, snow, rock and ice; the control strategy comprises a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

21. The apparatus of any of claims 17-18, wherein, The device further comprises: The third sending unit is configured to send the first detection result, the second detection result, and corresponding warning information to the first vehicle if the first detection result and / or the second detection result meets a preset condition; the warning information is used to warn a user to maintain the air suspension system; the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

22. The apparatus of claim 21, wherein, The device further includes: The fourth sending unit is configured to acquire information of at least one automobile repair shop within a preset range of the first vehicle and send the information of the at least one automobile repair shop to the first vehicle if the first detection result and / or the second detection result meets the preset condition; the information includes at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations, and driving path plans.

23. The apparatus of any one of claims 16-18, wherein, The M data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, and a plurality of adjustment frequency, usable duration, product model, and product specification of the air suspension system; the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one rising temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life feature includes the usable duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

24. A detection device, characterized by The device includes: The acquisition unit is configured to acquire a data stream and send the data stream to a server; the data stream includes K data related to an air suspension system of a first vehicle; the data stream is used for the server to sample the K data included in the data stream based on an importance sampling method, to acquire a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the K data include the M data; the M data are used for the server to acquire N second data sets; each of the N second data sets includes one or more data of the M data; the N second data sets correspond to N types of features, the N types of features including one or more of an adjustment feature, a life feature, and a material feature of the air suspension system; the N second data sets are used for the server to determine a first detection result of the air suspension system based on the N second data sets and weights corresponding to the N types of features; the first detection result includes a wear rate of the air suspension system; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M. The N second data sets are specifically used for the server to calculate a score value corresponding to each of the N types of features based on the N second data sets and a preset scoring standard; and the server calculates the first detection result of the air suspension system based on the score value corresponding to each of the N types of features and a weight of each of the N types of features. The M data are specifically used for the server to classify the M data based on the N types of features to obtain N second data sets corresponding to the N types of features. The scoring standard and / or the weight of each of the N types of features is a result of correction based on a first detection result of each of a plurality of second vehicles and the first detection result of the first vehicle; the first detection result of each of the plurality of second vehicles is determined based on a third data set including P data related to air suspension systems of the plurality of second vehicles; P is an integer greater than 1; and when a distribution of the wear rate in the first detection result of the first vehicle and the plurality of second vehicles does not conform to an actual distribution of the wear rate, it is determined that the scoring standard and / or the weight of each of the N types of features is corrected.

25. The apparatus of claim 24, wherein, The first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; and the second detection result includes a failure-prone rate of the air suspension system and a usable duration of the air suspension system.

26. The apparatus of claim 25, wherein, The device further includes: a sending unit configured to send a query request to the server; a first receiving unit configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

27. The apparatus of any one of claims 24-26, wherein, The device further includes: a second receiving unit configured to receive a target terrain sent by the server and issue a corresponding control strategy to the air suspension system according to the target terrain; the target terrain is a terrain corresponding to the first vehicle in a driving process determined by the server; the target terrain is one of sand, snow, rock and ice; and the control strategy includes a control strategy for at least one of a height parameter, a vibration parameter and a damping parameter corresponding to the air suspension system.

28. The apparatus of any one of claims 25 and 26, wherein, The device further includes: a third receiving unit configured to receive the first detection result, the second detection result and corresponding warning information sent by the server if the first detection result and / or the second detection result meets a preset condition; the warning information is used to warn a user to maintain the air suspension system; and the preset condition includes that the wear rate of the air suspension system is greater than a first threshold value, and / or the failure-prone rate of the air suspension system is greater than a second threshold value, and / or the usable duration of the air suspension system is less than a third threshold value.

29. The apparatus of claim 28, wherein, The device further includes: The fourth receiving unit is configured to receive information of at least one automobile repair shop within a preset range of the first vehicle sent by the server if the first detection result and / or the second detection result meets a preset condition, wherein the information comprises at least one of respective addresses of the at least one automobile repair shop, distances between the at least one automobile repair shop and the first vehicle, charging prices, user evaluations and driving path plans.

30. The apparatus of any one of claims 24-26, wherein, The M data include at least one of a compressed gas volume, a released gas volume, a temperature rise, an air compression density, and a plurality of adjustment frequencies, a use duration, a product model and a product specification of the air suspension system; wherein the second data set corresponding to the adjustment feature includes one or more of the at least one compressed gas volume, the at least one released gas volume, the at least one temperature rise, the at least one air compression density and the adjustment frequency; the second data set corresponding to the service life feature includes the use duration; and the second data set corresponding to the material feature includes one or more of the product model and the product specification.

31. A server, comprising: The device comprises a processor and a memory, wherein the processor and the memory are connected, the memory is configured to store program code, and the processor is configured to call the program code to execute the method in any one of claims 1 to 8.

32. An intelligent vehicle, characterized by The intelligent vehicle is a first vehicle, and comprises a processor and a memory, wherein the processor and the memory are connected, the memory is configured to store program code, and the processor is configured to call the program code to execute the method in any one of claims 9 to 15.

33. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 8 or the method in any one of claims 9 to 15.

34. A computer program product, characterised in that, The computer program product comprises instructions, and when the instructions are executed by a computer, the computer executes the method in any one of claims 1 to 8 or the method in any one of claims 9 to 15.

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