Internet of vehicles big data monitoring method, device and equipment

Abstract

The invention provides an Internet of Vehicles big data monitoring method, device and equipment, relates to the technical field of big data processing, and aims to solve the problems that in the prior art, monitoring information of vehicle power is not comprehensive, and early warning is not timely. The method comprises the following steps: acquiring motor parameters of a vehicle through a vehicle networking system; determining a load value of the motor based on the motor parameter, and determining a duration that the load value is continuously higher than a preset motor load threshold value as a continuous duration; and according to the duration, sending early warning information to the vehicle.

Description

Technical Field

[0001] The present application relates to the field of big data processing technology, and in particular to a method, device and equipment for monitoring big data of an Internet of Vehicles. Background Art

[0002] Since the advent of computers, the Internet era has gradually been integrated into all aspects of human life. Among them, the application of Internet of Vehicles is strongly related to travel. At present, Internet of Vehicles is mainly used in the service industry, including in-vehicle navigation, remote one-button start, remote car locking, etc. In recent years, major car companies have developed driving behavior recognition systems to judge fatigue driving, dangerous driving and other behaviors. However, most of the above systems use fixed fault codes for identification, and do not perform dynamic monitoring from the perspective of optimal driving or energy consumption analysis, resulting in incomplete monitoring information and untimely warnings.

[0003] Therefore, it is necessary to provide a vehicle networking big data monitoring method to solve the above problems. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method, device and equipment for monitoring big data of Internet of Vehicles to solve the problems in the prior art of incomplete monitoring information of vehicle power and untimely early warning.

[0005] To achieve the above technical objectives, this application provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present specification provides a method for monitoring big data of an Internet of Vehicles, including:

[0007] Obtain the vehicle's motor parameters through the Internet of Vehicles system;

[0008] Determine the load value of the motor based on the motor parameters, and determine the duration for which the load value is continuously higher than a preset motor load threshold as the duration;

[0009] According to the duration, a warning message is sent to the vehicle.

[0010] In one implementation, obtaining motor parameters of a vehicle through a vehicle networking system includes:

[0011] Determine the time to obtain the motor parameters based on a preset time interval through the Internet of Vehicles system;

[0012] Obtain the target motor parameters of the vehicle at each moment;

[0013] The target motor parameters are determined as the motor parameters at a time corresponding to the target motor parameters.

[0014] In one implementation, determining the load value of the motor based on the motor parameters, and determining the duration for which the load value is continuously higher than a preset motor load threshold as the duration, includes:

[0015] Determining a load value of the motor based on motor parameters, the motor parameters including at least one of the following: motor transient temperature, motor running time, motor peak torque, and motor power;

[0016] When the load value is not higher than the motor load threshold, the duration is reset to zero;

[0017] When the load value is higher than the motor load threshold, the duration is accumulated starting from the time corresponding to the load value.

[0018] In one implementation, when the load value is higher than the motor load threshold, starting from the time corresponding to the load value, the duration is accumulated, including:

[0019] When the load value at the current moment is higher than the motor load threshold, a preset time interval is added to the duration.

[0020] In one implementation, issuing a warning message to the vehicle according to the duration includes:

[0021] Determine the ratio of the duration to the preset unit duration;

[0022] When the ratio is greater than a preset threshold, a warning message is sent to the vehicle.

[0023] In one embodiment, the preset threshold includes a first threshold, a second threshold and a third threshold that increase in sequence. When the ratio is greater than the preset threshold, a warning message is sent to the vehicle, including:

[0024] When the ratio is greater than the first threshold and less than the second threshold, a third-level warning message is issued to the vehicle;

[0025] When the ratio is greater than the second threshold and less than the third threshold, a second-level warning message is issued to the vehicle;

[0026] When the ratio is greater than the third threshold, a first-level warning message is issued to the vehicle.

[0027] In one embodiment, the method further comprises:

[0028] Prompt the driver of the vehicle with driving operation suggestions corresponding to the warning information.

[0029] In a second aspect, the embodiments of this specification provide a vehicle networking big data monitoring device, including:

[0030] An acquisition unit, used for acquiring motor parameters of the vehicle through the Internet of Vehicles system;

[0031] A processing unit, configured to determine a load value of the motor based on the motor parameters, and determine a duration for which the load value is continuously higher than a preset motor load threshold as a duration;

[0032] The early warning unit is used to send early warning information to the vehicle according to the duration.

[0033] In one implementation, the acquisition unit is specifically configured to:

[0034] Determine the time to obtain the motor parameters based on a preset time interval through the Internet of Vehicles system;

[0035] Obtain the target motor parameters of the vehicle at each moment;

[0036] The target motor parameters are determined as the motor parameters at a time corresponding to the target motor parameters.

[0037] In one embodiment, the processing unit is specifically configured to:

[0038] Determining a load value of the motor based on motor parameters, the motor parameters including at least one of the following: motor transient temperature, motor running time, motor peak torque, and motor power;

[0039] When the load value is not higher than the motor load threshold, the duration is reset to zero;

[0040] When the load value is higher than the motor load threshold, the duration is accumulated starting from the time corresponding to the load value.

[0041] In one embodiment, the processing unit is specifically configured to:

[0042] When the load value at the current moment is higher than the motor load threshold, a preset time interval is added to the duration.

[0043] In one implementation, the early warning unit is specifically used to:

[0044] Determine the ratio of the duration to the preset unit duration;

[0045] When the ratio is greater than a preset threshold, a warning message is sent to the vehicle.

[0046] In one embodiment, the preset threshold includes a first threshold, a second threshold, and a third threshold that increase in sequence, and the early warning unit is specifically used to:

[0047] When the ratio is greater than the first threshold and less than the second threshold, a third-level warning message is issued to the vehicle;

[0048] When the ratio is greater than the second threshold and less than the third threshold, a second-level warning message is issued to the vehicle;

[0049] When the ratio is greater than the third threshold, a first-level warning message is issued to the vehicle.

[0050] In one embodiment, the early warning unit is further used for:

[0051] Prompt the driver of the vehicle with driving operation suggestions corresponding to the warning information.

[0052] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for Internet of Vehicles big data monitoring of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0053] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for monitoring big data of the Internet of Vehicles as described in any one of the above items is implemented.

[0054] In a fifth aspect, an embodiment of the present specification provides a computer program product or a computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the vehicle network big data monitoring method as described in any one of the above items.

[0055] It can be seen from the above technical scheme that the present application provides a method, device and equipment for monitoring big data of the Internet of Vehicles. The method first obtains the motor parameters of the vehicle through the Internet of Vehicles system, and then determines the load value of the motor based on the motor parameters, and determines the duration for which the load value is continuously higher than the preset motor load threshold as the duration. Finally, according to the duration, a warning message is issued to the vehicle. By obtaining the motor parameters of the vehicle, the load condition of the vehicle motor is analyzed, and a warning is issued to the vehicle driver accordingly to avoid improper use of the vehicle. While improving the vehicle's use cycle, it also provides vehicle R&D personnel with motor power share data, which is conducive to optimizing the next generation of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0057] Figure 1 A flowchart of a vehicle networking big data monitoring method provided for an implementation of this specification;

[0058] Figure 2 A schematic diagram of a specific process of a vehicle networking big data monitoring method provided for the implementation of this specification;

[0059] Figure 3 A schematic diagram of the structure of a vehicle networking big data monitoring device provided for the implementation of this specification;

[0060] Figure 4 A schematic diagram of the structure of an electronic device provided for an implementation of this specification. DETAILED DESCRIPTION

[0061] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons skilled in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0062] Unless the context requires otherwise, throughout the specification, "plurality" means "at least two", and "including" is interpreted as an open, inclusive meaning, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0063] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0064] Overview

[0065] As mentioned in the background technology, in a vehicle, continuous high-power output can cause serious heating inside the motor. If the cooling system of the motor cannot dissipate heat in time, it may cause aging of the insulating material, causing the insulating material of the motor's internal windings to age faster due to high temperature, resulting in a shortened service life. Or the components may be deformed or damaged. High temperature may cause the key components of the motor (such as the rotor and stator) to deform or be damaged due to thermal expansion. At the same time, it will cause certain damage to the power devices and battery system in the motor controller, and may also cause premature aging or damage to components of the transmission system such as gears and bearings, and even cause safety hazards.

[0066] Since the advent of computers, the Internet era has gradually been integrated into all aspects of human life. Among them, the application of Internet of Vehicles is strongly related to travel. At present, Internet of Vehicles is mainly used in the service industry, including in-vehicle navigation, remote one-button start, remote car locking, etc. In recent years, major car companies have developed driving behavior recognition systems to judge fatigue driving, dangerous driving and other behaviors. However, most of the above systems use fixed fault codes for identification, and do not perform dynamic monitoring from the perspective of optimal driving or energy consumption analysis, resulting in incomplete monitoring information and untimely warnings. At the same time, for vehicle manufacturers, it is impossible to make the best design or matching application from this information, and it is impossible to provide directional guidance for the vehicle.

[0067] Therefore, it is necessary to provide a vehicle networking big data monitoring method to avoid the above problems.

[0068] In order to solve the problems of incomplete monitoring information of vehicle power and untimely warning in existing calculations, in the technical solution of the present application, the motor parameters of the vehicle are first obtained through the Internet of Vehicles system, and then the load value of the motor is determined based on the motor parameters, and the time during which the load value continues to be higher than the preset motor load threshold is determined as the duration. Finally, according to the duration, a warning message is issued to the vehicle. By obtaining the motor parameters of the vehicle, the load condition of the vehicle motor is analyzed, and a warning is issued to the vehicle driver accordingly to avoid improper use of the vehicle. While improving the vehicle's service life, it also provides vehicle R&D personnel with motor power share data, which is conducive to optimizing the next generation of products.

[0069] Based on the above-mentioned inventive concept, the vehicle network big data monitoring method provided in the implementation mode of this specification is exemplarily described below.

[0070] Exemplary Methods

[0071] The present specification provides a method for monitoring big data of an Internet of Vehicles. Figure 1 As shown, including:

[0072] S101. Obtain motor parameters of the vehicle through the Internet of Vehicles system.

[0073] During specific implementation, the current vehicle motor parameters are read in real time according to the vehicle networking system. Specifically, the time for obtaining the motor parameters can be determined according to a preset time interval, and then the vehicle motor parameters are obtained when the time is reached. Then, the vehicle motor parameters obtained at the time are used as the motor parameters of the corresponding time period according to preset rules. For example, the preset time length is 5s. Assuming that the time for obtaining the motor parameters is 8 minutes, 8 minutes 5 seconds, and 8 minutes 10 seconds, the motor parameters at 8 minutes 5 seconds can be used as the motor signal power from 8 minutes to 8 minutes 5 seconds. Of course, it can also be set in other ways, such as taking the average of two times, etc. The embodiments of the present disclosure are not limited to this.

[0074] S102: Determine a load value of the motor based on the motor parameters, and determine a duration for which the load value is continuously higher than a preset motor load threshold as a duration.

[0075] In a specific implementation, the motor parameters may include at least one of the following: motor transient temperature, motor running time, motor peak torque, and motor power. Specifically, one or more of the following may be included for judgment.

[0076] In one example, the motor parameter is described as the motor power. Then, according to the Internet of Vehicles system, the current vehicle motor power P is read in real time. i , and divide the signal into hours. At the same time, the motor load threshold is set to the motor power threshold P e .

[0077] In another example, the motor parameters include motor power and motor peak torque, then the load value is the vector weighted value of the motor power and the motor peak torque, and the motor load threshold is a comprehensive consideration of the motor power and the motor peak torque, that is, when the motor power and the motor peak torque are both greater than the preset values, the load value is higher than the motor load threshold. Of course, the load value can also be judged and set in other mathematical forms. For example, based on normalized load judgment, the motor power and the motor peak torque can be normalized respectively so that they can be compared within the same range, and the power and torque can be normalized within the range of [0,1], and then a weighted average is performed on them as a comprehensive evaluation of the load value. The load judgment can also be based on fuzzy logic, and the load can be judged by setting fuzzy sets of motor power and peak torque. Assuming that the motor power and torque belong to the three fuzzy sets of "low", "medium" and "high" respectively, fuzzy reasoning rules (such as Mamdan-type reasoning) can be used to comprehensively evaluate the load, and finally the fuzzy output of the load is obtained through fuzzy reasoning, and then the final load value is obtained through defuzzification. Of course, other forms can also be selected to judge and set the load value, and the embodiments of the present invention are not limited to this.

[0078] In another example, the motor parameters include motor power, motor transient temperature and motor running time. The motor parameters are used to judge the load value, that is, the motor load threshold is the motor power threshold. When determining high load, the motor transient temperature and motor running time are used for auxiliary judgment.

[0079] During the initial calculation, the duration is first cleared to zero. If the intermediate motor parameter is not greater than the motor load threshold, the duration is cleared to zero. If the motor parameter is higher than the motor load threshold, the duration is recorded as the duration from this moment on until the intermediate motor parameter is no greater than the motor load threshold, and then the duration is cleared to zero.

[0080] Still using the above example, if P i ―P e ≥0, then start recording the duration. If P is not satisfied i ―P e ≥0, the duration will be initialized.

[0081] S103: Send a warning message to the vehicle according to the duration.

[0082] In specific implementation, whether to issue a warning message to the vehicle is determined based on the ratio of the duration to the preset unit duration. Specifically, when the ratio is greater than a preset threshold, a warning message is issued to the vehicle. Of course, there may be multiple preset thresholds, and the corresponding warning messages may be divided into levels. For example, when the ratio is greater than a preset first threshold and less than a second threshold, a third-level warning message is issued to the vehicle. When the ratio is greater than a preset second threshold and less than a third threshold, a second-level warning message is issued to the vehicle. When the ratio is greater than a preset third threshold, a first-level warning message is issued to the vehicle. It should be noted that the values ​​of the first threshold, the second threshold and the third threshold can be set according to needs, and the embodiments of the present invention do not limit this.

[0083] In this step, corresponding driving operation suggestions may also be sent based on the warning information, such as appropriate deceleration or greater deceleration.

[0084] In one example, when the first-level warning is reached, it is sent to the car company's big data center through the car networking port, which collects it and compares it with historical data to determine whether driving abnormalities occur. If the average deviation from the historical data curve is greater than a certain value, such as ≥2, an abnormal driving behavior reminder is output. This information is then sent to the back end to query whether the vehicle has changed the driver or whether the driver's status is abnormal, and after confirmation, the driver is reminded through the vehicle-side APP.

[0085] When the second-level warning is reached, based on the above-mentioned first-level preload information processing, the motor temperature and motor torque analysis are added. For example, if the motor temperature ≥140℃ or the motor torque ≥218Nm lasts for more than 30 minutes, a reminder will be directly issued through the vehicle-side APP. At the same time, the abnormal driving behavior will be recorded once, saved to the big data background, and this information will be transmitted to the insurance port of the car company for reference for other operations in the future. When the third-level warning is reached, based on the above-mentioned second-level preload information processing, the information will be transmitted to the back end through big data to make after-sales preparations. At the same time, when the driver does not operate according to the warning reminder, the big data middle platform records the intense driving once. If the intense driving continues for a certain period of time, such as more than 10 minutes, the vehicle will enter the protection mechanism, actively intervene, and reduce the speed. At the same time, to ensure safe driving, big data will further analyze whether there are abnormalities in other vehicle signals. If there is fault information, a map search will be conducted in advance to query the information of nearby service stations, and remind the driver to go for vehicle inspection as soon as possible.

[0086] In a vehicle networking big data monitoring method provided by the present embodiment, the motor parameters of the vehicle are first obtained through the vehicle networking system, and then the load value of the motor is determined based on the motor parameters, and the time during which the load value is continuously higher than the preset motor load threshold is determined as the duration, and finally, according to the duration, a warning message is issued to the vehicle. By obtaining the motor parameters of the vehicle, the load condition of the vehicle motor is analyzed, and a warning is issued to the vehicle driver accordingly to avoid improper use of the vehicle. While improving the vehicle's service life cycle, it also provides vehicle R&D personnel with motor power share data, which is conducive to optimizing the next generation of products.

[0087] In one example, if Figure 2 As shown, taking the preset time interval of 1s, the preset unit time length of 1h, and the load value as an example, the Internet of Vehicles big data monitoring method mentioned in this embodiment is described in detail.

[0088] Step 1: Read the current vehicle motor parameters P in real time according to the Internet of Vehicles system i , and divide the signal into hours, and set the motor load threshold P e , data initialization, set N 1 =N 2 =N 3 =0, i=1, where N 1 、N 2 、N 3 Correspondingly represent the first-level warning, second-level warning, and third-level warning. i is used for time counting. Here, i+1 is calculated every time 1 second passes.

[0089] Step 2, initialize the count j = 1, j is used to represent the duration. Then calculate the current motor power P i Make a judgment, if Pi ―P e ≥0, is false, executes the return operation, and initializes the count j=1, that is, the duration is cleared to zero.

[0090] Step 3: Same as step 2, calculate the current motor power P i If P i ―P e ≥0, is true, and the operation i=i+1, j=j+1 is executed, where i=i+1 represents the timestamp corresponding to the current data, in seconds, and j=j+1 represents the number of continuous powers that meet step 3. Since the time span is 1 second, it can also represent continuous time; then the operation Q=j / 3600 is executed, where 3600 is the number of seconds in each hour, that is, Q is the proportion of continuous power that meets the requirements per hour.

[0091] Step 4, execute Q=j / 3600 operation according to step 3, and further judge the continuous power time proportion Q. If Q≥50% is false, further judge whether Q≥30% is true. If it is false, further judge whether Q≥10% is true. If it is false, return to step 3 and repeat the cycle.

[0092] Step 5: further determine whether Q ≥ 10% is true according to step 4. If so, count it and execute N 3 =N 3 +1, if N 3 >1, a third-level warning will be reported. Correspondingly, during the third-level warning, the owner can be reminded that the current high-power continuous use time of the motor is 10% to 30%. If the situation permits, the vehicle speed can be appropriately reduced.

[0093] If Q ≥ 30% is true, if it is true, count it and execute N 2 =N 2 +1, if N 2 >1, a level 2 warning will be issued. Correspondingly, during the level 2 warning, the car owner can be reminded that the current high-power continuous use time of the motor is 30% to 50%, and it is recommended to reduce the speed appropriately to reduce the motor power.

[0094] If Q ≥ 50% is true, if it is true, count it and execute N 1 =N 1 +1, if N 1 >1, a first-level warning will be reported. Correspondingly, during the first-level warning, the owner can be reminded that the current high-power continuous use time of the motor is more than 50%, and it is recommended to reduce the speed in a short period of time to ensure that the motor does not overheat.

[0095] According to the algorithm logic of the above steps, false alarms of warning levels can be avoided. At the same time, by judging by decreasing the proportion step by step from high to low, the system judgment time can be saved.

[0096] Through the above-mentioned Internet of Vehicles big data monitoring method, the vehicle motor power usage can be analyzed in real time, and warnings can be issued according to different warning levels to give users buffer time to make correct and reasonable judgments. At the same time, by actively reminding users, the product life cycle can be increased and maintenance costs can be saved. For automobile company R&D personnel, the motor power share data can be used to make more accurate positioning and matching for the next generation of products.

[0097] Exemplary Devices

[0098] In an exemplary embodiment of the present specification, a vehicle networking big data monitoring device 300 is also provided, such as Figure 3 As shown, including:

[0099] The acquisition unit 301 is used to acquire motor parameters of the vehicle through the Internet of Vehicles system;

[0100] The processing unit 302 is used to determine the load value of the motor based on the motor parameters, and determine the duration for which the load value is continuously higher than a preset motor load threshold as a duration;

[0101] The warning unit 303 is used to send a warning message to the vehicle according to the duration.

[0102] In one implementation, the acquiring unit 301 is specifically configured to:

[0103] Determine the time to obtain the motor parameters based on a preset time interval through the Internet of Vehicles system;

[0104] Obtain the target motor parameters of the vehicle at each moment;

[0105] The target motor parameters are determined as the motor parameters at a time corresponding to the target motor parameters.

[0106] In one implementation, the processing unit 302 is specifically configured to:

[0107] Determining a load value of the motor based on motor parameters, the motor parameters including at least one of the following: motor transient temperature, motor running time, motor peak torque, and motor power;

[0108] When the load value is not higher than the motor load threshold, the duration is reset to zero;

[0109] When the load value is higher than the motor load threshold, the duration is accumulated starting from the time corresponding to the load value.

[0110] In one implementation, the processing unit 302 is specifically configured to:

[0111] When the load value at the current moment is higher than the motor load threshold, a preset time interval is added to the duration.

[0112] In one implementation, the early warning unit 303 is specifically configured to:

[0113] Determine the ratio of the duration to the preset unit duration;

[0114] When the ratio is greater than a preset threshold, a warning message is sent to the vehicle.

[0115] In one implementation, the preset threshold includes a first threshold, a second threshold, and a third threshold that increase in sequence, and the early warning unit 303 is specifically used to:

[0116] When the ratio is greater than the first threshold and less than the second threshold, a third-level warning message is issued to the vehicle;

[0117] When the ratio is greater than the second threshold and less than the third threshold, a second-level warning message is issued to the vehicle;

[0118] When the ratio is greater than the third threshold, a first-level warning message is issued to the vehicle.

[0119] In one implementation, the early warning unit 303 is further configured to:

[0120] Prompt the driver of the vehicle with driving operation suggestions corresponding to the warning information.

[0121] The Internet of Vehicles big data monitoring device provided in this embodiment belongs to the same application concept as the Internet of Vehicles big data monitoring method provided in the above embodiments of this application, and can execute the Internet of Vehicles big data monitoring method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the Internet of Vehicles big data monitoring method. For technical details not fully described in this embodiment, please refer to the specific processing content of the Internet of Vehicles big data monitoring method provided in the above embodiments of this application, and will not be repeated here.

[0122] Exemplary Devices

[0123] In an exemplary embodiment of the present specification, an electronic device is also provided, such as Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the vehicle network big data monitoring method, which includes:

[0124] Obtain the vehicle's motor parameters through the Internet of Vehicles system;

[0125] Determine the load value of the motor based on the motor parameters, and determine the duration for which the load value is continuously higher than a preset motor load threshold as the duration;

[0126] According to the duration, a warning message is sent to the vehicle.

[0127] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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 a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0128] Exemplary computer program products and storage media

[0129] In addition to the above-mentioned methods and devices, the vehicle networking big data monitoring method provided in the embodiments of this specification may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the vehicle networking big data monitoring method according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0130] The computer program product can be written in any combination of one or more programming languages ​​to write program codes for executing the operations of the embodiments of this specification, and the programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages.

[0131] In addition, an embodiment of the present specification also provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the vehicle network big data monitoring method according to various embodiments of the present specification described in the above "Exemplary Method" section of the present specification.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments only express several implementation methods of this specification, and the descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the solutions provided by the embodiments of this specification. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of this specification, which all belong to the protection scope of this specification. Therefore, the protection scope of the patent of this specification shall be based on the attached claims.

Claims

1. A vehicle networking big data monitoring method, characterized in that: include: Obtain the vehicle's motor parameters through the Internet of Vehicles system; Determine a load value of the motor based on the motor parameter, and determine a duration during which the load value is continuously higher than a preset motor load threshold as a duration; According to the duration, a warning message is sent to the vehicle.

2. The method according to claim 1, characterized in that The obtaining of the motor parameters of the vehicle through the vehicle networking system includes: Determine the time to obtain the motor parameters based on a preset time interval through the Internet of Vehicles system; Acquire target motor parameters of the vehicle at each said moment; The target motor parameter is determined as the motor parameter at a time corresponding to the target motor parameter.

3. The method according to claim 1, characterized in that The step of determining the load value of the motor based on the motor parameter, and determining the duration for which the load value is continuously higher than a preset motor load threshold as a duration, includes: Determining a load value of the motor based on the motor parameters, wherein the motor parameters include at least one of the following: motor transient temperature, motor running time, motor peak torque, and motor power; When the load value is not higher than the motor load threshold, clearing the duration; When the load value is higher than the motor load threshold, the duration is accumulated starting from the time corresponding to the load value.

4. The method according to claim 3, characterized in that When the load value is higher than the motor load threshold, starting from the time corresponding to the load value, accumulating the duration, including: When the load value at the current moment is higher than the motor load threshold, the preset time interval is added to the duration.

5. The method according to claim 1, characterized in that The sending of warning information to the vehicle according to the duration includes: Determine the ratio of the duration to a preset unit duration; When the ratio is greater than a preset threshold, a warning message is sent to the vehicle.

6. The method according to claim 5, characterized in that The preset threshold value includes a first threshold value, a second threshold value and a third threshold value which increase in sequence. When the ratio is greater than the preset threshold value, issuing a warning message to the vehicle includes: When the ratio is greater than the first threshold and less than the second threshold, issuing a third-level warning message to the vehicle; When the ratio is greater than the second threshold and less than the third threshold, issuing a second-level warning message to the vehicle; When the ratio is greater than the third threshold, a first-level warning message is issued to the vehicle.

7. The method according to claim 1, characterized in that The method further comprises: Prompt the driver of the vehicle with driving operation suggestions corresponding to the warning information.

8. A vehicle networking big data monitoring device, characterized in that: include: An acquisition unit, used for acquiring motor parameters of the vehicle through the Internet of Vehicles system; a processing unit, configured to determine a load value of the motor based on the motor parameters, and determine a duration for which the load value is continuously higher than a preset motor load threshold as a duration; The early warning unit is used to send early warning information to the vehicle according to the duration.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle network big data monitoring method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the Internet of Vehicles big data monitoring method according to any one of claims 1 to 7.

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