Vehicle monitoring method and device, electronic equipment, storage medium and vehicle

Abstract

The embodiment of the invention provides a vehicle monitoring method and device, electronic equipment, a storage medium and a vehicle, and belongs to the technical field of vehicles. The method comprises the following steps: acquiring a standard multi-dimensional mapping table; collecting current vehicle smoothness data; if the current vehicle ride comfort data does not meet the requirements of the standard multi-dimensional mapping table, judging whether the current vehicle ride comfort data is abnormal or not; and if the current vehicle ride comfort data is abnormal, judging whether the current vehicle ride comfort data is distorted or not, and obtaining a target judgment result. According to the embodiment of the invention, the ride comfort problem of the vehicle can be found in time, the ride comfort of the vehicle and the riding comfort of the vehicle are improved, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle monitoring method and device, electronic equipment, storage medium and vehicle. Background Art

[0002] Vehicle smoothness, as an important performance indicator that characterizes passenger riding comfort, directly affects the driving experience. Vehicle smoothness directly affects the reputation of riding comfort. The passenger experience will label the performance of the vehicle.

[0003] However, there are many factors that affect ride comfort, making it difficult to monitor the ride comfort of a vehicle and to detect ride comfort problems in a timely manner.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a vehicle monitoring method and device, electronic equipment, storage medium and vehicle, which can promptly detect vehicle ride comfort problems, improve vehicle ride comfort and enhance user experience.

[0006] To achieve the above object, an embodiment of the present application provides a vehicle monitoring method, which includes the following steps:

[0007] Obtain a standard multidimensional mapping table;

[0008] Collect current vehicle ride comfort data;

[0009] If the current vehicle ride comfort data does not meet the requirements of the standard multi-dimensional mapping table, determining whether the current vehicle ride comfort data is abnormal;

[0010] If the current vehicle ride comfort data is abnormal, it is determined whether the current vehicle ride comfort data is distorted to obtain a target determination result.

[0011] In some embodiments, the method further comprises:

[0012] Create a standard multidimensional mapping table.

[0013] In some embodiments, the method further comprises:

[0014] Determine whether the current vehicle's driving surface is a smooth random surface or a discrete impact surface.

[0015] In some embodiments, the standard multi-dimensional mapping table includes a stationary random road surface multi-dimensional mapping table and a discrete impact road surface multi-dimensional mapping table.

[0016] In some embodiments, the establishing of a standard multi-dimensional mapping table includes:

[0017] Collecting first test data of the test vehicle on a steady random road surface, the first test data including the load, tire pressure, driving mode, vehicle speed, road friction coefficient, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value, and pitch angle root mean square value of the test vehicle;

[0018] The first test data is used as a standard reference value to form a multi-dimensional mapping table of a smooth random road surface.

[0019] In some embodiments, the establishing of a standard multi-dimensional mapping table includes:

[0020] Collecting second test data of the test vehicle on a discrete impact road surface, the second test data including a load, tire pressure, driving mode, vehicle speed, size of an obstacle, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value, and pitch angle root mean square value of the test vehicle;

[0021] The second test data is used as a standard reference value to form a discrete impact road surface multi-dimensional mapping table.

[0022] In some embodiments, the collecting of current vehicle ride comfort data includes:

[0023] Collecting road surface characteristic data, the road surface characteristic data including at least one of a road surface friction coefficient of a steady random road surface or a size of a discrete impact road surface obstacle;

[0024] Collect the pressure value of the current vehicle's empty spring and calculate the current load of the current vehicle;

[0025] Collect the current vehicle speed, tire pressure, air spring mode, shock absorption mode and the current vehicle position;

[0026] Record the current vehicle's horizontal acceleration peak-to-peak value, horizontal acceleration RMS value, vertical acceleration peak-to-peak value, vertical acceleration RMS value, pitch angle peak-to-peak value, and pitch angle RMS value.

[0027] In some embodiments, the collecting of road surface characteristic data includes:

[0028] If the current vehicle is traveling on a smooth random road, the road friction coefficient is collected;

[0029] If the current vehicle is traveling on a discrete impact road, the size of the obstacle is collected.

[0030] In some embodiments, the method further comprises:

[0031] Determine whether the current vehicle ride comfort data conforms to the standard multi-dimensional mapping table.

[0032] In some embodiments, the method further comprises:

[0033] If the current vehicle driving road surface is a smooth random road surface, determining whether the current vehicle ride comfort data conforms to the smooth random road surface multi-dimensional mapping table;

[0034] If the current driving road surface of the vehicle is a discrete impact road surface, it is determined whether the current vehicle ride comfort data meets the requirements of the discrete impact road surface multi-dimensional mapping table.

[0035] In some embodiments, the determining whether the current vehicle ride comfort data meets the requirements of the standard multi-dimensional mapping table includes:

[0036] Plotting the current vehicle ride comfort data onto the standard multi-dimensional mapping table;

[0037] If the current vehicle ride comfort data is within the standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data complies with the standard multi-dimensional mapping table.

[0038] In some embodiments, determining whether the current vehicle ride comfort data is abnormal includes:

[0039] If the current vehicle driving road surface is a smooth random road surface, obtaining a driving-in threshold, obtaining the current vehicle driving-in times according to the current vehicle smoothness data, and judging whether the current vehicle driving-in times is greater than the driving-in threshold, if the current vehicle driving-in times is greater than the driving-in threshold, judging that the current vehicle smoothness data is abnormal;

[0040] If the current vehicle is traveling on a discrete impact road surface, obtain the size of the historical obstacle, and determine whether the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle. If the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle, determine that the current vehicle smoothness data is abnormal.

[0041] In some embodiments, the determining whether the current vehicle ride comfort data is distorted to obtain a target determination result includes:

[0042] Obtain historical ride quality data;

[0043] Establishing a historical multi-dimensional mapping table according to the historical ride quality data;

[0044] Plotting the current vehicle ride comfort data onto the historical multi-dimensional mapping table;

[0045] If the current vehicle ride comfort data is not within the reference value range of the historical multi-dimensional mapping table, the target judgment result is determined as a problem with the ride comfort of the current vehicle.

[0046] In some embodiments, the method further comprises:

[0047] If the target judgment result is that the current vehicle ride comfort data is distorted, the target judgment result is transmitted to the human-computer interaction system through the Internet of Vehicles device;

[0048] In response to the first instruction, the target judgment result is displayed on a notification page of the human-computer interaction system.

[0049] To achieve the above object, another aspect of the embodiment of the present application provides a vehicle monitoring device, the device comprising:

[0050] A standard acquisition module, used to acquire a standard multi-dimensional mapping table;

[0051] Data acquisition module, used to collect current vehicle ride comfort data;

[0052] an abnormality judgment module, configured to judge whether the current vehicle ride comfort data has an abnormality if the current vehicle ride comfort data does not meet the requirements of the standard multi-dimensional mapping table;

[0053] The distortion judgment module is used to judge whether the current vehicle ride comfort data is distorted if there is an abnormality in the current vehicle ride comfort data, and obtain a target judgment result.

[0054] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0055] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0056] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a vehicle, which is used to implement the above-mentioned method.

[0057] The embodiments of the present application include at least the following beneficial effects: the present application provides a vehicle monitoring method and device, an electronic device, a storage medium and a vehicle. The scheme obtains a standard multi-dimensional mapping table; collects the current vehicle smoothness data; if the current vehicle smoothness data does not meet the requirements of the standard multi-dimensional mapping table, determines whether the current vehicle smoothness data has an abnormality; if the current vehicle smoothness data has an abnormality, determines whether the current vehicle smoothness data has distortion, and obtains a target judgment result. Obtaining the target judgment result by monitoring the vehicle is conducive to timely discovering the vehicle's smoothness problems, improving the vehicle's smoothness and vehicle ride comfort, and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of a vehicle monitoring method provided by an embodiment of the present application;

[0059] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0060] Figure 3 yes Figure 1 A flow chart of step S103 in FIG.

[0061] Figure 4 yes Figure 1 Another flow chart of step S103 in ;

[0062] Figure 5 yes Figure 1 Flow chart of step S104 in FIG.

[0063] Figure 6 It is a flow chart of the steps of establishing a standard multi-dimensional mapping table of the vehicle monitoring method provided in the embodiment of the present application;

[0064] Figure 7 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0065] Figure 8 is a flow chart of a vehicle monitoring method provided in an embodiment of the present application applied to a vehicle ride comfort monitoring strategy;

[0066] Fig. 9 is a schematic diagram of the structure of a vehicle monitoring device provided in an embodiment of the present application;

[0067] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0069] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0070] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0072] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0073] 1) Pitch: It is used to describe the inclination angle of the vehicle relative to the horizontal plane, that is, the angle between the longitudinal axis of the vehicle and the horizontal plane.

[0074] Vehicle smoothness, as an important performance indicator that characterizes passenger riding comfort, directly affects the driving experience. Vehicle smoothness directly affects the reputation of riding comfort. The passenger experience will label the performance of the vehicle.

[0075] However, there are many factors that affect ride comfort, making it difficult to monitor the ride comfort of a vehicle and to detect ride comfort problems in a timely manner.

[0076] In summary, the technical problems existing in the relevant technologies need to be improved.

[0077] In view of this, a vehicle monitoring method, device, equipment and medium are provided in an embodiment of the present application. The scheme obtains a standard multi-dimensional mapping table; collects the current vehicle smoothness data; if the current vehicle smoothness data does not meet the requirements of the standard multi-dimensional mapping table, determines whether the current vehicle smoothness data has an abnormality; if the current vehicle smoothness data has an abnormality, determines whether the current vehicle smoothness data has distortion, and obtains a target judgment result, which is conducive to timely discovering the vehicle's smoothness problems, improving the vehicle's smoothness and vehicle ride comfort, and enhancing the user experience.

[0078] The vehicle monitoring method provided in the embodiment of the present application relates to the field of vehicle technology. The vehicle monitoring method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the vehicle monitoring method, etc., but is not limited to the above forms.

[0079] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0080] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0081] Figure 1 is an optional flow chart of the vehicle monitoring method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0082] Step S101, obtaining a standard multi-dimensional mapping table.

[0083] Specifically, the standard multidimensional mapping table includes a steady random road surface multidimensional mapping table and a discrete impact road surface multidimensional mapping table.

[0084] In some embodiments, a standard multi-dimensional mapping table is established.

[0085] Optionally, first test data of the test vehicle on a stable random road surface is collected, and the first test data is used as a standard reference value to form a stable random road surface multidimensional mapping table. The first test data includes the load, tire pressure, driving mode, vehicle speed, road friction coefficient of the stable random road surface, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value and pitch angle root mean square value of the test vehicle, and the driving mode is used to determine the air spring mode and the shock absorption mode.

[0086] For example, if the driving mode is comfort mode, the air spring mode adopts a lower stiffness to provide a softer suspension effect, reduce the impact of the road on the vehicle body, and improve ride comfort. At the same time, the shock absorption mode adopts a lower damping coefficient to absorb more vibrations, making the vehicle more stable during driving.

[0087] Optionally, collect second test data of the test vehicle on the discrete impact road surface, and use the second test data as a standard reference value to form a discrete impact road surface multi-dimensional mapping table. The second test data includes the test vehicle's load, tire pressure, driving mode, vehicle speed, size of obstacles on the discrete impact road surface, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value, and pitch angle root mean square value;

[0088] The present application does not specifically limit the data collection method, and it can be flexibly selected in combination with actual monitoring needs. For example, data collection can be performed using vehicle-mounted sensors, or by image recognition and other methods.

[0089] Furthermore, a pre-established standard multi-dimensional mapping table is obtained.

[0090] In this embodiment, a standard multi-dimensional mapping table is obtained to prepare for the subsequent determination of whether the current vehicle ride comfort data conforms to the standard multi-dimensional mapping table.

[0091] Step S102, collecting current vehicle ride comfort data;

[0092] Specifically, the current vehicle ride comfort data includes road surface characteristic data, pressure value of an empty spring, vehicle speed, tire pressure, an empty spring mode, a shock absorption mode, GPS position, horizontal acceleration, vertical acceleration and pitch angle, and the road surface characteristic data includes at least one of a road surface friction coefficient of a steady random road surface or a size of a discrete impact road obstacle;

[0093] Optionally, it is determined whether the current driving road surface of the vehicle is a smooth random road surface or a discrete impact road surface.

[0094] In some embodiments, road characteristic data is collected, the pressure value of the air spring of the current vehicle is collected and the current load of the current vehicle is calculated; the vehicle speed, tire pressure, air spring mode, shock absorption mode and the current position of the current vehicle are collected; the horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value and pitch angle root mean square value of the current vehicle are recorded.

[0095] In this embodiment, collecting the current vehicle ride comfort data helps to monitor the ride comfort performance of the vehicle in a timely manner, thereby performing problem handling or vehicle maintenance.

[0096] Step S103: if the current vehicle ride comfort data does not conform to the standard multi-dimensional mapping table, it is determined whether the current vehicle ride comfort data is abnormal.

[0097] In some embodiments, it is determined whether the current vehicle ride comfort data conforms to a standard multi-dimensional mapping table.

[0098] Optionally, if the current driving road surface of the vehicle is a smooth random road surface, it is determined whether the current vehicle smoothness data conforms to the smooth random road surface multi-dimensional mapping table.

[0099] Optionally, if the current driving road surface of the vehicle is a discrete impact road surface, it is determined whether the current vehicle ride comfort data meets the requirements of the discrete impact road surface multi-dimensional mapping table.

[0100] In some embodiments, the current vehicle ride comfort data is mapped onto a standard multi-dimensional mapping table; if the current vehicle ride comfort data is within a standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data complies with the standard multi-dimensional mapping table, wherein the standard multi-dimensional mapping table includes a standard reference value range.

[0101] Furthermore, it is determined whether there is any abnormality in the current vehicle ride comfort data.

[0102] In some embodiments, if the current vehicle is traveling on a smooth random road, the entry threshold is obtained, the number of times the current vehicle enters is obtained according to the current vehicle ride comfort data, and it is determined whether the number of times the current vehicle enters is greater than the entry threshold. If the number of times the current vehicle enters is greater than the entry threshold, it is determined that the current vehicle ride comfort data is abnormal. Among them, GPS positioning information is obtained according to the current vehicle ride comfort data to obtain the number of times the current vehicle enters.

[0103] In some embodiments, if the current vehicle is traveling on a discrete impact road surface, the size of the historical obstacle is obtained, and it is determined whether the size of the obstacle in the current vehicle ride comfort data is the same as the size of the historical obstacle. If the size of the obstacle in the current vehicle ride comfort data is the same as the size of the historical obstacle, it is determined that the current vehicle ride comfort data is abnormal. The size of the historical obstacle is the size of the recorded obstacle.

[0104] Furthermore, the size of the obstacle in the current vehicle ride comfort data is saved as the size of the historical obstacle.

[0105] In some embodiments, if the current vehicle ride comfort data conforms to the standard multi-dimensional mapping table or the current vehicle ride comfort data does not have an abnormality, the current vehicle ride comfort data is saved as historical ride comfort data, wherein the historical ride comfort data is used to determine whether the current vehicle ride comfort data is distorted.

[0106] In this embodiment, if the current vehicle ride comfort data does not conform to the standard multi-dimensional mapping table, it is determined whether the current vehicle ride comfort data has an abnormality. Timely identification of the abnormality helps to discover vehicle ride comfort problems and improve vehicle safety and comfort.

[0107] Step S104: if the current vehicle ride comfort data is abnormal, determine whether the current vehicle ride comfort data is distorted to obtain a target determination result.

[0108] In some embodiments, historical ride comfort data is obtained; a historical multi-dimensional mapping table is established based on the historical ride comfort data; current vehicle ride comfort data is plotted on the historical multi-dimensional mapping table; if the current vehicle ride comfort data is not within a reference value range of the historical multi-dimensional mapping table, a target judgment result is determined as distortion of the current vehicle ride comfort data and a problem with the ride comfort of the current vehicle. The historical multi-dimensional mapping table includes a reference value range.

[0109] It is understandable that if the current vehicle ride comfort data is not completely within the reference value range of the historical multi-dimensional mapping table, the target judgment result is determined as the current vehicle ride comfort data is distorted and there is a problem with the ride comfort of the current vehicle.

[0110] Furthermore, if the current vehicle ride comfort data is completely within the reference value range of the historical multi-dimensional mapping table, the target judgment result is determined as the current vehicle ride comfort data is distorted and there is a problem with the ride comfort of the current vehicle.

[0111] In some embodiments, if the target judgment result is that the current vehicle smoothness data is distorted, the target judgment result is transmitted to the human-computer interaction system through the Internet of Vehicles device; in response to the first instruction, the target judgment result is displayed on the notification page of the human-computer interaction system, which is conducive to relevant personnel to promptly discover the vehicle's smoothness problems, and deal with the vehicle's smoothness problems, thereby improving the user experience.

[0112] In this embodiment, if there is an abnormality in the current vehicle smoothness data, it is determined whether the current vehicle smoothness data is distorted, and a target judgment result is obtained, which is conducive to timely discovering the vehicle's smoothness problems, improving the vehicle's smoothness and vehicle ride comfort, and enhancing the user experience.

[0113] Steps S101 to S104 shown in the embodiment of the present application are performed by obtaining a standard multi-dimensional mapping table; collecting the current vehicle smoothness data; if the current vehicle smoothness data does not meet the requirements of the standard multi-dimensional mapping table, determining whether the current vehicle smoothness data has an abnormality; if the current vehicle smoothness data has an abnormality, determining whether the current vehicle smoothness data has distortion, and obtaining a target judgment result, which is conducive to timely discovering vehicle smoothness problems, improving the vehicle smoothness and vehicle ride comfort, and enhancing user experience.

[0114] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S204:

[0115] Step S201, collecting road surface characteristic data.

[0116] In step S201 of some embodiments, if the current vehicle driving on a road surface is a smooth random road surface, the road surface friction coefficient is collected; if the current vehicle driving on a road surface is a discrete impact road surface, the size of the obstacle is collected.

[0117] Step S202, collecting the pressure value of the current vehicle's empty spring and calculating the current load of the current vehicle.

[0118] In step S202 of some embodiments, the pressure value of the empty spring of the current vehicle is collected and the current load of the current vehicle is calculated according to the pressure value of the empty spring of the current vehicle.

[0119] Step S203, collecting the current vehicle speed, tire pressure, air spring mode, shock absorption mode and current vehicle position.

[0120] Step S204, recording the peak-to-peak value of horizontal acceleration, the root mean square value of horizontal acceleration, the peak-to-peak value of vertical acceleration, the root mean square value of vertical acceleration, the peak-to-peak value of pitch angle, and the root mean square value of pitch angle of the current vehicle.

[0121] In step S204 of some embodiments, the horizontal acceleration, vertical acceleration and pitch angle of the current vehicle are collected, and the peak-to-peak value of the horizontal acceleration, the root mean square value of the horizontal acceleration, the peak-to-peak value of the vertical acceleration, the root mean square value of the vertical acceleration, the peak-to-peak value of the pitch angle and the root mean square value of the pitch angle are determined based on the collected information, and then the peak-to-peak value of the horizontal acceleration, the root mean square value of the horizontal acceleration, the peak-to-peak value of the vertical acceleration, the root mean square value of the vertical acceleration, the peak-to-peak value of the pitch angle and the root mean square value of the pitch angle of the current vehicle are recorded.

[0122] In this embodiment, the current vehicle ride comfort data is collected to provide real-time data support for vehicle monitoring, which helps to monitor the vehicle ride comfort performance in a timely manner, thereby performing problem handling or vehicle maintenance.

[0123] See also Figure 3 In some embodiments, step S103 may include but is not limited to steps S301 to S302:

[0124] Step S301, plotting the current vehicle ride comfort data onto a standard multi-dimensional mapping table.

[0125] In step S301 of some embodiments, the current vehicle ride comfort data is converted into a format and then plotted on a standard multi-dimensional mapping table, wherein the current vehicle ride comfort data and the standard multi-dimensional mapping table need to be axis aligned, data interpolated, and normalized.

[0126] Optionally, a data visualization tool is used to generate a visualization chart based on the current vehicle ride comfort data and the standard multi-dimensional mapping table, which is helpful for clearly and quickly judging whether the current vehicle ride comfort data conforms to the standard multi-dimensional mapping table.

[0127] In this embodiment, the current vehicle ride comfort data is plotted on the standard multi-dimensional mapping table, which is helpful to intuitively see the degree of compliance of the vehicle ride comfort data with the standard multi-dimensional mapping table, and facilitates subsequent judgment.

[0128] Step S302: If the current vehicle ride comfort data is within the standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data conforms to the standard multi-dimensional mapping table.

[0129] In step S302 of some embodiments, if the current vehicle ride comfort data is completely within the standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data complies with the standard multi-dimensional mapping table.

[0130] It is understandable that if the current vehicle ride comfort data is not completely within the standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data does not conform to the standard multi-dimensional mapping table.

[0131] Furthermore, a compliance threshold is set, and if the degree of matching between the current vehicle ride comfort data and the standard reference value range of the standard multi-dimensional mapping table is higher than the compliance threshold, it is determined that the current vehicle ride comfort data complies with the standard multi-dimensional mapping table.

[0132] In this embodiment, if the current vehicle smoothness data is within the standard reference value range of the standard multidimensional mapping table, it is determined that the current vehicle smoothness data conforms to the standard multidimensional mapping table, so that the vehicle smoothness conforms to the design standards, which helps to improve the comfort and safety of the vehicle.

[0133] See also Figure 4 In some embodiments, step S103 may include but is not limited to steps S401 to S402:

[0134] Step S401, if the current vehicle is traveling on a smooth random road surface, obtain the entry threshold, obtain the number of times the current vehicle enters according to the current vehicle smoothness data, and determine whether the number of times the current vehicle enters is greater than the entry threshold. If the number of times the current vehicle enters is greater than the entry threshold, it is determined that the current vehicle smoothness data is abnormal.

[0135] In step S401 of some embodiments, if the current driving road of the vehicle is a smooth random road, a preset driving-in threshold is obtained, wherein the driving-in threshold may be defined by a developer or may be customized by a user.

[0136] Optionally, GPS positioning information is obtained according to the GPS position of the current vehicle ride comfort data, thereby obtaining the current vehicle entry times. The GPS positioning information is the historical GPS information of the current vehicle, and is used to determine the current vehicle entry times.

[0137] Further, it is determined whether the current vehicle entry times are greater than the entry threshold. If the current vehicle entry times are greater than the entry threshold, it is determined that the current vehicle ride comfort data is abnormal.

[0138] It can be understood that the GPS position of the current vehicle ride comfort data is saved as the historical GPS information of the current vehicle.

[0139] Exemplarily, the entry threshold is 4 times, and the GPS positioning information is obtained according to the GPS position of the current vehicle ride comfort data. The historical GPS information indicates that the vehicle has entered 4 times in history. It can be understood that the current number of vehicle entries is 5 times, and it is considered that the current vehicle ride comfort data is abnormal.

[0140] Step S402, if the current vehicle is traveling on a discrete impact road surface, obtain the size of the historical obstacle, and determine whether the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle. If the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle, it is determined that the current vehicle smoothness data is abnormal.

[0141] In step S402 of some embodiments, if the size of the obstacle in the current vehicle ride comfort data is different from the size of the historical obstacle, it is determined that there is no abnormality in the current vehicle ride comfort data.

[0142] Further, if the size of the obstacle in the current vehicle ride comfort data is different from the size of the historical obstacle, the size of the obstacle in the current vehicle ride comfort data is saved as the size of the historical obstacle.

[0143] See also Figure 5 In some embodiments, step S304 may also include but is not limited to steps S501 to S504:

[0144] Step S501, obtaining historical ride comfort data.

[0145] In step S501 of some embodiments, the current road section is determined according to the current vehicle smoothness data, the historical road section is determined according to the historical smoothness data, and the historical smoothness data of the historical road section that is the same as the current road section is obtained according to the entry threshold.

[0146] For example, if the entry threshold is 4 times, the historical smoothness data of the previous 4 historical sections that are the same as the current section are obtained.

[0147] Step S502: establishing a historical multi-dimensional mapping table according to historical ride comfort data.

[0148] In step S502 of some embodiments, historical ride comfort data is used as a reference value to form a historical multi-dimensional mapping table.

[0149] Step S503: plotting the current vehicle ride comfort data onto a historical multi-dimensional mapping table.

[0150] In step S503 of some embodiments, the current vehicle ride comfort data is converted into a format and then plotted on a historical multi-dimensional mapping table, wherein the current vehicle ride comfort data and the historical multi-dimensional mapping table need to be axis-aligned, data interpolated, and normalized.

[0151] Step S504: if the current vehicle ride comfort data is not within the reference value range of the historical multi-dimensional mapping table, the target judgment result is determined as a problem with the ride comfort of the current vehicle.

[0152] In step S504 of some embodiments, if the current vehicle ride comfort data is not completely within the reference value range of the historical multi-dimensional mapping table, it is determined that the current vehicle ride comfort data does not conform to the historical multi-dimensional mapping table.

[0153] Furthermore, a compliance threshold is set, and if the degree of matching between the current vehicle ride comfort data and the reference value range of the historical multi-dimensional mapping table is higher than the compliance threshold, it is determined that the current vehicle ride comfort data complies with the historical multi-dimensional mapping table.

[0154] See also Figure 6 In some embodiments, the vehicle monitoring method provided in the embodiments of the present application further includes the step of establishing a standard multi-dimensional mapping table, and the step of establishing a standard multi-dimensional mapping table includes but is not limited to the following steps:

[0155] Step 1: Test the peak-to-peak value and RMS of ax, az and pitch for typical stable random road surfaces with different road roughness.

[0156] Specifically, the road roughness is the road friction coefficient, ax is the horizontal acceleration, az is the vertical acceleration, pitch is the pitch angle, and RMS is the root mean square value.

[0157] In some embodiments, first test data of a test vehicle on a typical smooth random road surface is collected.

[0158] Step 2: Test the peak-to-peak and RMS values ​​of ax, az and pitch for typical discrete impact road surfaces with different obstacle sizes.

[0159] In some embodiments, second test data of the test vehicle on a discrete impact road surface is collected.

[0160] Step 3, forming reasonable peak-to-peak and RMS values ​​of ax and az corresponding to load, tire pressure, air spring mode, CDC damping mode, vehicle speed, road roughness / discrete obstacle size as standard reference values.

[0161] Specifically, the air spring mode and the CDC damping mode are determined according to the driving mode.

[0162] In some embodiments, the first test data and the second test data are used as standard reference values ​​to form reasonable peak-to-peak values ​​and RMS values ​​of ax and az corresponding to load, tire pressure, air spring mode, CDC shock absorption mode, vehicle speed, road roughness / discrete obstacle size, thereby forming a standard multi-dimensional MAP.

[0163] In some embodiments, the vehicle monitoring method provided in the embodiments of the present application is applied to a vehicle ride comfort monitoring strategy. The application scenario diagram is shown in FIG. Figure 7 As shown, the vehicle monitoring method provided in the embodiment of the present application is applied to the flow chart of the vehicle ride comfort monitoring strategy as shown in Figure 8 As shown, Figure 8 The method may include, but is not limited to, the following steps:

[0164] Step 1: Profiling the vehicle's smoothness characteristics in the user's environment.

[0165] In some embodiments, based on the user's driving conditions, a portrait of the vehicle's ride comfort in the user's usage environment is created.

[0166] Step 2: Identify the roughness / discrete obstacles on the road section through the camera.

[0167] Specifically, roughness is the friction coefficient of the road surface.

[0168] In some embodiments, the roughness of the corresponding smooth random road surface or the size of discrete impact road obstacles on the road section that the vehicle is traveling through is received by the camera.

[0169] Step 3, calculate the corresponding vehicle load through the air spring pressure.

[0170] In some embodiments, the current load of the vehicle is calculated by the pressure value of the empty spring.

[0171] Step 4, label GPS location / vehicle speed / load / tire pressure / air spring mode / CDC shock absorption mode.

[0172] Specifically, tagging is used to identify and track vehicle status.

[0173] In some embodiments, the smoothness data of the current vehicle is recorded through GPS position / vehicle speed / load / tire pressure / air spring mode / CDC shock absorption mode.

[0174] Step 5, record the peak-to-peak value and RMS corresponding to ax and az.

[0175] Specifically, ax is the horizontal acceleration, az is the vertical acceleration, pitch is the pitch angle, and RMS is the root mean square value.

[0176] In some embodiments, the peak-to-peak and RMS values ​​of ax, az and pitch of the front and rear axles of the vehicle are recorded.

[0177] Step 6, plot the data to a multidimensional MAP.

[0178] Specifically, the multi-dimensional MAP is a standard multi-dimensional mapping table.

[0179] In some embodiments, the vehicle smoothness data is plotted onto a multi-dimensional MAP of the vehicle's smoothness in the user's environment corresponding to the current vehicle speed, tire pressure, air spring mode, CDC shock absorption mode, and the vehicle's GPS corresponding to the vehicle's position.

[0180] Step 7, compare whether the current data is within the standard reference value setting range, if so, it is considered that there is no problem.

[0181] In some embodiments, after collecting the above smoothness-related data in the current scenario, the current data is first compared to see if it is within the standard reference value setting range and the standard smoothness MAP drawn before leaving the factory. If so, it is considered that there is no problem with the smoothness of the current vehicle.

[0182] Step 8, judging whether the data corresponds to the vehicle position that has been driven in within A times, and if it is within A times, it is considered that there is no problem.

[0183] Specifically, A times is the driving-in threshold.

[0184] In some embodiments, if the smoothness data collected in the current scenario is not on the standard smoothness MAP, then based on GPS positioning, it is confirmed whether the vehicle position corresponding to the current data is within A times. If it is within A times, it is considered that there is no problem, that is, there is no problem with the smoothness of the current vehicle.

[0185] It should be noted that smooth random roads and typical discrete impact roads need to be judged separately. For a typical discrete impact road, if the obstacle corresponding to the current entry is different from the previously recorded obstacle, it is considered that there is no problem with the smoothness of the current vehicle.

[0186] Step 9: Determine whether the currently generated data is distorted. If not, it is considered to be problematic.

[0187] Specifically, the currently generated data is the current vehicle smoothness data.

[0188] In some embodiments, if the GPS positioning confirms that the current data corresponds to a vehicle position that has entered the vehicle more than A times, then further determine whether the data is distorted by comparing it with the vehicle ride characteristics portrait MAP (i.e., historical MAP or historical multi-dimensional mapping table) under the user usage environment corresponding to the previously generated data; if there is distortion, then it is determined that there is a problem with the vehicle ride. In this case, the historical data of the previous A times is obtained, and the historical MAP is formed based on the historical data.

[0189] Step 10, notify the 4S store or the driver that there may be abnormalities in the vehicle comfort and it is recommended to check the vehicle status.

[0190] In some embodiments, the judgment result (ie, the target judgment result) is notified to the 4S store or the driver, indicating that there is an abnormality in the ride comfort of the current vehicle, and it is recommended to perform a status check on the vehicle.

[0191] The embodiment of the present application identifies the roughness of the road surface identified by the vehicle camera, the size of the obstacle corresponding to the discrete impact (such as the width and height of the deceleration zone, the size difference of the manhole cover, the depth and size of the pothole, etc.), the ax perceived by the vehicle-mounted inertial navigation, the ay perceived by the vehicle-mounted inertial navigation, the pitch perceived by the vehicle-mounted inertial navigation, the vehicle speed, the tire pressure of the four tires, the pressure corresponding to the four-corner air springs, and the vehicle position corresponding to the GPS; the perception signal on the vehicle side is transmitted to the vehicle smoothness monitoring strategy corresponding to the cloud big data processing unit through the Internet of Vehicles such as TBOX. The vehicle smoothness monitoring strategy will process and analyze the data, and after analyzing whether there is a problem with the smoothness of the current vehicle, the result will be sent to the 4S store, or transmitted to the vehicle's human-computer interaction system through the Internet of Vehicles equipment to inform the driver, which is conducive to timely discovering the vehicle's smoothness problems, improving the vehicle's smoothness and vehicle ride comfort, and enhancing the user experience.

[0192] See also Fig. 9 The embodiment of the present application also provides a vehicle monitoring device, which can implement the above-mentioned vehicle monitoring method, and the device includes:

[0193] A standard acquisition module 901 is used to acquire a standard multi-dimensional mapping table;

[0194] The data collection module 902 is used to collect the current vehicle ride comfort data;

[0195] The abnormality judgment module 903 is used to judge whether the current vehicle ride comfort data is abnormal if the current vehicle ride comfort data does not conform to the standard multi-dimensional mapping table;

[0196] The distortion judgment module 904 is used to judge whether the current vehicle ride comfort data is distorted if there is an abnormality in the current vehicle ride comfort data, and obtain a target judgment result.

[0197] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0198] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above vehicle monitoring method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0199] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0200] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0201] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0202] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the vehicle monitoring method of the embodiment of this application;

[0203] Input / output interface 1003, used to implement information input and output;

[0204] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0205] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0206] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0207] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned vehicle monitoring method is implemented.

[0208] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0209] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0210] The vehicle monitoring method, vehicle monitoring device, electronic device, storage medium and vehicle provided in the embodiments of the present application obtain a standard multi-dimensional mapping table; collect current vehicle smoothness data; if the current vehicle smoothness data does not meet the requirements of the standard multi-dimensional mapping table, determine whether the current vehicle smoothness data has an abnormality; if the current vehicle smoothness data has an abnormality, determine whether the current vehicle smoothness data has distortion, and obtain a target judgment result, which is conducive to timely discovering vehicle smoothness problems, improving vehicle smoothness and vehicle ride comfort, and enhancing user experience.

[0211] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0212] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0213] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0214] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0215] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0216] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0217] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0218] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0219] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0220] If the integrated unit is implemented 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 this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0221] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A vehicle monitoring method, characterized in that: The method comprises the following steps: Obtain a standard multidimensional mapping table; Collect current vehicle ride comfort data; If the current vehicle ride comfort data does not meet the requirements of the standard multi-dimensional mapping table, determining whether the current vehicle ride comfort data is abnormal; If the current vehicle ride comfort data is abnormal, it is determined whether the current vehicle ride comfort data is distorted to obtain a target determination result.

2. The method according to claim 1, characterized in that The method further comprises: Create a standard multidimensional mapping table.

3. The method according to claim 1, characterized in that The method further comprises: Determine whether the current vehicle's driving surface is a smooth random surface or a discrete impact surface.

4. The method according to claim 1, characterized in that: The standard multi-dimensional mapping table includes a steady random road surface multi-dimensional mapping table and a discrete impact road surface multi-dimensional mapping table.

5. The method according to claim 2, characterized in that: The establishing of the standard multi-dimensional mapping table comprises: Collecting first test data of the test vehicle on a steady random road surface, the first test data including the load, tire pressure, driving mode, vehicle speed, road friction coefficient, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value, and pitch angle root mean square value of the test vehicle; The first test data is used as a standard reference value to form a multi-dimensional mapping table of a smooth random road surface.

6. The method according to claim 2, characterized in that The establishing of the standard multi-dimensional mapping table comprises: Collecting second test data of the test vehicle on a discrete impact road surface, the second test data including a load, tire pressure, driving mode, vehicle speed, size of an obstacle, horizontal acceleration peak-to-peak value, horizontal acceleration root mean square value, vertical acceleration peak-to-peak value, vertical acceleration root mean square value, pitch angle peak-to-peak value, and pitch angle root mean square value of the test vehicle; The second test data is used as a standard reference value to form a discrete impact road surface multi-dimensional mapping table.

7. The method according to claim 3, characterized in that The collecting of current vehicle ride comfort data includes: Collecting road surface characteristic data, the road surface characteristic data including at least one of a road surface friction coefficient of a steady random road surface or a size of a discrete impact road surface obstacle; Collect the pressure value of the current vehicle's empty spring and calculate the current load of the current vehicle; Collect the current vehicle speed, tire pressure, air spring mode, shock absorption mode and the current vehicle position; Record the current vehicle's horizontal acceleration peak-to-peak value, horizontal acceleration RMS value, vertical acceleration peak-to-peak value, vertical acceleration RMS value, pitch angle peak-to-peak value, and pitch angle RMS value.

8. The method according to claim 7, characterized in that The collecting of road surface characteristic data includes: If the current vehicle is traveling on a smooth random road, the road friction coefficient is collected; If the current vehicle is traveling on a discrete impact road, the size of the obstacle is collected.

9. The method according to claim 1, characterized in that: The method further comprises: Determine whether the current vehicle ride comfort data meets the requirements of the standard multi-dimensional mapping table.

10. The method according to claim 4, characterized in that The method further comprises: If the current driving road surface of the vehicle is a smooth random road surface, determining whether the current vehicle ride comfort data meets the requirements of the smooth random road surface multidimensional mapping table; If the current driving road surface of the vehicle is a discrete impact road surface, it is determined whether the current vehicle ride comfort data meets the requirements of the discrete impact road surface multi-dimensional mapping table.

11. The method according to claim 9, characterized in that The determining whether the current vehicle ride comfort data meets the requirements of the standard multi-dimensional mapping table includes: Plotting the current vehicle ride comfort data onto the standard multi-dimensional mapping table; If the current vehicle ride comfort data is within the standard reference value range of the standard multi-dimensional mapping table, it is determined that the current vehicle ride comfort data meets the requirements of the standard multi-dimensional mapping table.

12. The method according to claim 3, characterized in that The determining whether the current vehicle ride comfort data is abnormal includes: If the current vehicle driving road surface is a smooth random road surface, obtaining a driving-in threshold, obtaining the current vehicle driving-in times according to the current vehicle smoothness data, and judging whether the current vehicle driving-in times is greater than the driving-in threshold, if the current vehicle driving-in times is greater than the driving-in threshold, judging that the current vehicle smoothness data is abnormal; If the current vehicle is traveling on a discrete impact road surface, obtain the size of the historical obstacle, and determine whether the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle. If the size of the obstacle in the current vehicle smoothness data is the same as the size of the historical obstacle, determine that the current vehicle smoothness data is abnormal.

13. The method according to claim 3, characterized in that The determining whether the current vehicle ride comfort data is distorted to obtain a target determination result includes: Obtain historical ride quality data; Establishing a historical multi-dimensional mapping table according to the historical ride quality data; Plotting the current vehicle ride comfort data onto the historical multi-dimensional mapping table; If the current vehicle ride comfort data is not within the reference value range of the historical multi-dimensional mapping table, the target judgment result is determined as a problem with the ride comfort of the current vehicle.

14. The method according to claim 1, characterized in that The method further comprises: If the target judgment result is that the current vehicle ride comfort data is distorted, the target judgment result is transmitted to the human-computer interaction system through the Internet of Vehicles device; In response to the first instruction, the target judgment result is displayed on a notification page of the human-computer interaction system.

15. A vehicle monitoring device, characterized in that: The device comprises: A standard acquisition module, used to acquire a standard multi-dimensional mapping table; Data acquisition module, used to collect current vehicle ride comfort data; an abnormality judgment module, configured to judge whether the current vehicle ride comfort data has an abnormality if the current vehicle ride comfort data does not meet the requirements of the standard multi-dimensional mapping table; The distortion judgment module is used to judge whether the current vehicle ride comfort data is distorted if there is an abnormality in the current vehicle ride comfort data, and obtain a target judgment result.

16. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 14 when executing the computer program.

17. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 14 is implemented.

18. A vehicle, characterized in that: The vehicle is used to implement the method according to any one of claims 1 to 14.

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