Vehicle soot hidden danger prediction method and device based on big data, equipment and medium

Abstract

The present application relates to the technical field of Internet of Vehicles data application, and discloses a vehicle dust removal hidden danger prediction method and device based on big data, equipment and medium, the method comprises the following steps: obtaining historical dust removal data of different vehicles and performing data analysis, determining the dust removal hidden danger threshold corresponding to different vehicle types and the influence weight of different vehicle parameters on particulate matter accumulation under the vehicle type; obtaining the target vehicle type corresponding to the target vehicle and the target data relative to the last dust removal, and determining the corresponding dust removal hidden danger threshold and influence weight, calculating the dust removal hidden danger score of the target vehicle according to the corresponding target data and influence weight; according to the size relationship between the score and the dust removal hidden danger threshold corresponding to the target vehicle, the next dust removal time of the target vehicle is determined, the influence weight of different parameters on particulate matter accumulation is determined by introducing big data analysis, and the actual data of the target vehicle is combined to realize the prediction of the dust removal time, and the use experience of the user is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle network data application technology, specifically to a method, device, equipment, and medium for predicting potential vehicle dust removal hazards based on big data. Background Technology

[0002] With the implementation of the China VI emission standard for commercial vehicles, the biggest difference between China VI and China V vehicles lies in the engine's after-treatment system. The China VI after-treatment system adds a diesel particulate filter (DPF), also known as a particulate trap. If it is not cleaned in time, its service life will be shortened, affecting the compliance of exhaust emissions.

[0003] Incomplete combustion in the engine, and substandard fuel and lubricating oil quality, can easily produce a large amount of polymers. Salts containing elements from lubricating oil or fuel additives will adhere to the DPF (Diesel Particulate Filter). These substances accumulate over time, leading to DPF blockage and affecting the normal operation of the vehicle. Generally speaking, low-speed driving is the main cause of excessive particulate matter in engine exhaust, resulting in blockage. Compared to transport vehicles that frequently travel on highways, vehicles transporting goods in urban areas are more prone to DPF blockage. When the DPF is blocked, a corresponding warning light will usually appear on the vehicle's dashboard to indicate this.

[0004] Generally, when the DPF regeneration indicator light illuminates or flashes, it means that the vehicle needs to regenerate. You need to stop nearby, engage the handbrake, keep the engine idling, press the regeneration switch, and the engine will regenerate in place. At this time, the engine speed will rise slightly. After the engine automatically returns to idle speed and the regeneration indicator light goes out, it means that the DPF regeneration is complete and you can drive normally.

[0005] However, if the DPF captures too many particles and cannot be processed by regeneration, manual cleaning is required. The filter and its accessories need to be placed in a heating chamber to heat and decompose the urea crystals into ash, while other particles are loosened by the heat. Then, a high-speed airflow is passed through the small holes of the ceramic filter, and the pores are cleaned by multiple pressurized blows to remove the degraded carbon ash from the carrier, thus achieving the effect of clearing blockages and regeneration.

[0006] In existing technologies, manual cleaning is usually only performed when the dust removal indicator light corresponding to the vehicle's particulate filter illuminates and the particulate matter cannot be processed through regeneration. This makes it impossible to predict in advance when manual cleaning is needed, resulting in vehicle users being unable to schedule cleaning times appropriately. Summary of the Invention

[0007] In view of this, the present invention provides a method, device, equipment and medium for predicting potential vehicle dust removal hazards based on big data, in order to solve the problem that the inability to predict in advance when manual dust removal is required, which makes it impossible for vehicle users to reasonably arrange dust removal time.

[0008] In a first aspect, the present invention provides a method for predicting potential hazards during vehicle dust removal based on big data, the method comprising:

[0009] Historical dust removal data of different vehicles is obtained, and the historical dust removal data is analyzed to determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types.

[0010] Obtain target data corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning operation and the target vehicle type of the target vehicle;

[0011] Determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and calculate the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weight of the different vehicle parameters;

[0012] The next cleaning time for the target vehicle is determined based on the relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type.

[0013] By analyzing historical dust removal data of different vehicles, the influence weights of different vehicle parameters corresponding to various vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold are determined. Target data of different vehicle parameters during the operation of the target vehicle relative to the previous dust removal are obtained. Then, the dust removal hazard score of the target vehicle is determined by combining the influence weights of the vehicle parameters and comparing it with the corresponding dust removal hazard threshold to determine the time of the next dust removal. This allows for advance prediction of when the vehicle needs manual dust removal, facilitating timely dust removal by the vehicle driver.

[0014] In one optional implementation, the vehicle parameters include: running time, running mileage, and fuel quality;

[0015] The determination of the influence weights of different vehicle parameters corresponding to different vehicle types on the particulate matter accumulation state in the particulate filter includes:

[0016] Determine the weights of the effects of running time, running mileage and fuel quality on the accumulation state of particulate matter in the particulate filter for different vehicle types during operation.

[0017] The acquisition of target data corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning operation includes:

[0018] Obtain target data for the target vehicle's running time, mileage, and fuel quality relative to the previous dust cleaning operation.

[0019] By determining the respective influence weights of vehicle runtime, mileage, and fuel quality on the particulate matter accumulation status in the particulate filter, and combining these vehicle parameters relative to the previous cleaning, the current particulate matter accumulation status of the target vehicle can be determined more accurately.

[0020] In one optional implementation, the target data corresponding to the oil quality is obtained as follows:

[0021] The oil quality is determined based on the proportion of the duration of oil quality abnormality during the previous cleaning operation of the target vehicle relative to the total operating time.

[0022] By statistically analyzing the percentage of time the target vehicle experienced abnormal oil quality during the previous cleaning operation, we can more accurately reflect the overall oil quality of the target vehicle during that operation, thus enabling us to more accurately calculate the cleaning hazard score of the target vehicle in the future.

[0023] In one optional implementation, the step of calculating the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weights of the different vehicle parameters includes:

[0024] Determine the preset scoring ranges corresponding to running time, running mileage, and fuel quality;

[0025] Based on the relationship between the target data corresponding to the running time, running mileage, and fuel quality and their respective preset scoring intervals, the scores corresponding to the running time, running mileage, and fuel quality of the target vehicle are determined.

[0026] The dust removal hazard score of the target vehicle is calculated based on the corresponding scores and influence weights of the target vehicle's running time, running mileage, and oil quality.

[0027] By determining the relationship between the target data corresponding to different vehicle parameters and the preset scoring range, the corresponding score can be determined. This allows for the unification of the target data corresponding to different parameters, thereby more accurately determining the target vehicle's hazard score.

[0028] In one optional implementation, determining the next cleaning time for the target vehicle based on the relationship between the target vehicle's dust removal hazard score and the dust removal hazard threshold corresponding to the target vehicle type includes:

[0029] Determine the proportional relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type, and determine the running time of the target vehicle relative to the last dust removal;

[0030] Based on the aforementioned proportional relationship and the target vehicle's running time relative to the previous dust cleaning, the next dust cleaning time for the target vehicle is determined.

[0031] By determining the ratio between the target vehicle's dust removal hazard score and the dust removal hazard threshold, as well as the target vehicle's running time relative to the last dust removal, the next dust removal time for the target vehicle can be determined more accurately, thus improving the accuracy of the next dust removal time prediction.

[0032] In an optional implementation, the method further includes:

[0033] A dust removal reminder will be sent based on the next dust removal time of the target vehicle.

[0034] By providing timely reminders based on the next dust removal time, drivers can plan their dust removal schedule in advance, thus improving the user experience.

[0035] In an optional implementation, the method further includes:

[0036] Based on the locations where cleaning prompts disappeared in the historical cleaning data, the location of the cleaning service station was determined;

[0037] After issuing a dust removal reminder based on the next dust removal time of the target vehicle, the method further includes:

[0038] The location of cleaning service stations around the target vehicle is indicated.

[0039] By analyzing the locations where cleaning reminders disappeared from historical cleaning data, the location of cleaning service stations can be determined. When reminding a target vehicle to clean, the location of nearby cleaning service stations can be further provided, making it easier for users to find the nearest cleaning service station.

[0040] Secondly, the present invention provides a vehicle dust removal hazard prediction device based on big data, the device comprising:

[0041] The historical data analysis module is used to acquire historical dust removal data of different vehicles, perform data analysis on the historical dust removal data, and determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types.

[0042] The target data determination module is used to obtain target data corresponding to different vehicle parameters of the target vehicle during the previous dust removal operation and the target vehicle type of the target vehicle;

[0043] The hazard scoring calculation module is used to determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and to calculate the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weight of the different vehicle parameters.

[0044] The dust removal time determination module is used to determine the next dust removal time for the target vehicle based on the relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type.

[0045] Thirdly, the present invention provides a computer device, comprising: 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 computer instructions to perform the vehicle dust removal hazard prediction method based on big data as described in the first aspect or any corresponding embodiment.

[0046] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the big data-based vehicle dust removal hazard prediction method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for predicting potential vehicle dust removal hazards based on big data, according to an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating another method for predicting potential vehicle dust removal hazards based on big data, according to an embodiment of the present invention.

[0050] Figure 3 This is a flowchart illustrating a vehicle dust removal time prediction method according to an embodiment of the present invention;

[0051] Figure 4 This is a structural block diagram of a vehicle dust removal hazard prediction device based on big data according to an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] With the implementation of the China VI emission standard for commercial vehicles, the biggest difference between China VI and China V vehicles lies in the engine's after-treatment system. The China VI after-treatment system adds a diesel particulate filter (DPF), also known as a particulate trap. If it is not cleaned in time, its service life will be shortened, affecting the compliance of exhaust emissions.

[0055] Incomplete combustion in the engine, and substandard fuel and lubricating oil quality, can easily produce a large amount of polymers. Salts containing elements from lubricating oil or fuel additives will adhere to the DPF (Diesel Particulate Filter). These substances accumulate over time, leading to DPF blockage and affecting the normal operation of the vehicle. Generally speaking, low-speed driving is the main cause of excessive particulate matter in engine exhaust, resulting in blockage. Compared to transport vehicles that frequently travel on highways, vehicles transporting goods in urban areas are more prone to DPF blockage. When the DPF is blocked, a corresponding warning light will usually appear on the vehicle's dashboard to indicate this.

[0056] Generally, when the DPF regeneration indicator light illuminates or flashes, it means that the vehicle needs to regenerate. You need to stop nearby, engage the handbrake, keep the engine idling, press the regeneration switch, and the engine will regenerate in place. At this time, the engine speed will rise slightly. After the engine automatically returns to idle speed and the regeneration indicator light goes out, it means that the DPF regeneration is complete and you can drive normally.

[0057] However, if the DPF captures too many particles and cannot be processed by regeneration, manual cleaning is required. The filter and its accessories need to be placed in a heating chamber to heat and decompose the urea crystals into ash, while other particles are loosened by the heat. Then, a high-speed airflow is passed through the small holes of the ceramic filter, and the pores are cleaned by multiple pressurized blows to remove the degraded carbon ash from the carrier, thus achieving the effect of clearing blockages and regeneration.

[0058] In existing technologies, manual cleaning is usually only performed when the dust removal indicator light corresponding to the vehicle's particulate filter illuminates and the particulate matter cannot be processed through regeneration. This makes it impossible to predict in advance when manual cleaning is needed, resulting in vehicle users being unable to schedule cleaning times appropriately.

[0059] To address this, this invention provides a big data-based method for predicting potential vehicle cleaning hazards. By analyzing historical cleaning data of different vehicles, the method determines the influence weights of different vehicle parameters corresponding to various vehicle types on the accumulation state of particulate matter in the particulate trap and the cleaning hazard threshold. It obtains target data of different vehicle parameters of the target vehicle during the previous cleaning process, and then combines the influence weights corresponding to the vehicle parameters to determine the cleaning hazard score of the target vehicle. The score is then compared with the corresponding cleaning hazard threshold to determine the time of the next cleaning. This method can predict in advance when the vehicle needs manual cleaning, making it easier for the vehicle driver to clean the vehicle in a timely manner.

[0060] According to an embodiment of the present invention, a method for predicting potential vehicle dust removal hazards based on big data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment provides a method for predicting potential vehicle dust removal hazards based on big data, which can be used for the aforementioned dust removal time prediction. Figure 1 This is a flowchart of a method for predicting potential vehicle dust removal hazards based on big data, according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0062] Step S101: Obtain historical dust removal data for different vehicles, perform data analysis on the historical dust removal data, and determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types.

[0063] During vehicle operation, relevant operational data is uploaded to the vehicle network data platform. The vehicle network data platform contains a large amount of historical operational data for different vehicles. This historical data includes data related to vehicle dust removal, such as vehicle oil quality, running time, mileage, and the generation and disappearance time of DPF fault codes, which are statistically recorded during vehicle operation.

[0064] For example, the time when the vehicle dust removal reminder disappears and the time when the vehicle issues a dust removal reminder again. The time when the vehicle dust removal reminder disappears can be understood as the vehicle completing manual dust removal, and therefore the dust removal reminder disappears. The time when the vehicle issues a dust removal reminder can be understood as the particulate matter in the vehicle's DPF device accumulating again to a state that requires cleaning, and manual dust removal is required.

[0065] By acquiring vehicle parameter data during the time periods corresponding to these two time intervals—that is, vehicle parameter data during the process from when the vehicle is cleaned of particulate matter to when it is fully refilled—big data analysis is performed on this vehicle parameter data to determine the influence weight of different vehicle parameters on particulate matter accumulation. This includes the influence weight of vehicle parameters such as vehicle runtime and fuel quality during vehicle operation, as well as the corresponding vehicle cleaning hazard thresholds for various vehicles.

[0066] Specifically, big data analysis can be used to determine the scoring range corresponding to different vehicle parameters, and to uniformly score and quantify different parameters during vehicle operation. Then, combined with the corresponding weights, the threshold for vehicle dust removal hazards can be determined. The vehicle dust removal hazard threshold means that if the scores of various different parameters combined with the corresponding weights reach the threshold, it means that the vehicle particulate matter has filled the DPF device and dust removal is required. The specific implementation method is not limited; this is just an example implementation method.

[0067] Step S102: Obtain target data and target vehicle type corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning process.

[0068] This can be understood as follows: before predicting the dust removal time of a target vehicle, it is necessary to first obtain the target data corresponding to different vehicle parameters during the target vehicle's operation relative to the previous dust removal process. That is, the target data corresponding to different vehicle parameters during the target vehicle's operation at the current moment relative to the previous dust removal moment. Simultaneously, the vehicle type of the target vehicle needs to be determined. Typically, vehicles equipped with DPF devices and requiring manual dust removal are trucks. Among trucks, different types correspond to different truck models, i.e., vehicle types.

[0069] Step S103: Determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and calculate the dust removal hazard score of the target vehicle based on the target data corresponding to different vehicle parameters and the influence weight of different vehicle parameters.

[0070] After determining the vehicle type corresponding to the target vehicle, the influence weights of different vehicle parameters corresponding to different vehicle types determined in step S101 on the particulate matter accumulation state in the DPF device are used to determine the influence weights of different vehicle parameters corresponding to the target vehicle type and the corresponding dust removal hazard thresholds.

[0071] This can be understood through an example. For instance, in step S101, it is determined that for vehicle type A, the weight corresponding to vehicle parameter 1 is 0.3, the weight corresponding to vehicle parameter 2 is 0.3, and the weight corresponding to vehicle parameter 3 is 0.4. The dust removal hazard threshold for vehicle type A is 30 points. Using this example, different vehicle types correspond to different weight relationships and dust removal hazard thresholds. If the target vehicle type is A, then the weights of its corresponding vehicle parameters are determined as shown in the example above: the weight corresponding to vehicle parameter 1 is 0.3, the weight corresponding to vehicle parameter 2 is 0.3, and the weight corresponding to vehicle parameter 3 is 0.4.

[0072] After determining the weights of different vehicle parameters for the target vehicle type, different types of data corresponding to different vehicle parameters can be uniformly quantified using preset scoring rules. These scoring rules can be determined through data analysis in step S101, or they can be pre-set scoring standards before data analysis; there are no specific restrictions. Then, by multiplying the scores corresponding to different types of data for different vehicle parameters by their respective weights, the dust removal hazard score for the target vehicle can be obtained.

[0073] Step S104: Determine the next cleaning time for the target vehicle based on the relationship between the target vehicle's cleaning hazard score and the cleaning hazard threshold corresponding to the target vehicle type.

[0074] After determining the dust removal hazard score of the target vehicle, the dust removal hazard score can be used to reflect the accumulation of particulate matter in the particle collector of the target vehicle, and the dust removal hazard threshold corresponding to the target vehicle can be used to represent the threshold score corresponding to the particle collector of the target vehicle being in a state of full accumulation of particulate matter.

[0075] By comparing the current dust removal hazard score of the target vehicle with the corresponding dust removal hazard threshold, the next dust removal call time for the target vehicle can be determined. For example, if the target vehicle has been dusted for two months since its last dust removal, has a current dust removal hazard score of 20, and a dust removal hazard threshold of 30, then the next dust removal call time for the target vehicle can be considered to be one month from now.

[0076] The vehicle dust removal hazard prediction method based on big data provided in this embodiment analyzes historical dust removal data of different vehicles to determine the influence weight of different vehicle parameters corresponding to various vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold. It obtains target data of different vehicle parameters of the target vehicle during the operation of the previous dust removal, and then combines the influence weight of the vehicle parameters to determine the dust removal hazard score of the target vehicle. It compares the score with the corresponding dust removal hazard threshold to determine the time of the next dust removal. It can predict in advance when the vehicle needs to be manually cleaned, so that the vehicle driver can clean the vehicle in a timely manner.

[0077] According to an embodiment of the present invention, another embodiment of a vehicle dust removal hazard prediction method based on big data is provided, which can be used for the aforementioned dust removal time prediction. Figure 2 This is a flowchart of another vehicle dust removal hazard prediction method based on big data according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0078] Step S201: Obtain historical dust removal data for different vehicles, perform data analysis on the historical dust removal data, and determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types.

[0079] Specifically, in step S201 above, the vehicle parameters include: running time, running mileage, and fuel quality.

[0080] Determine the influence weights of different vehicle parameters corresponding to different vehicle types on the particulate matter accumulation state in the particulate filter, including:

[0081] Determine the weights of the effects of running time, mileage, and fuel quality on the accumulation state of particulate matter in the particulate filter for different vehicle types during operation.

[0082] It can be understood that during vehicle operation, the main factors affecting particulate matter accumulation within the vehicle's DPF (Device Filter) are three vehicle parameters: vehicle operating time, vehicle mileage, and fuel quality during operation. These three factors are the primary causes of particulate matter accumulation. Therefore, when conducting big data analysis on historical data, it is necessary to analyze these three parameters and determine their respective weights in influencing particulate matter accumulation within the particulate filter.

[0083] Determining the influence weights of the three vehicle parameters on particulate matter accumulation through data analysis can more accurately identify the influence weights of the vehicle parameters that have the main impact on particulate matter accumulation, thereby further improving the accuracy of subsequent dust removal time prediction.

[0084] Step S202: Obtain target data and target vehicle type corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning process.

[0085] Specifically, in step S202, target data corresponding to different vehicle parameters of the target vehicle during the previous dust removal operation are obtained, including:

[0086] Obtain target data for the target vehicle's running time, mileage, and fuel quality relative to the previous dust cleaning operation.

[0087] This can be understood as follows: in the above steps, specific vehicle parameters were determined when analyzing historical data, namely, the weights corresponding to running time, running mileage, and fuel quality. Therefore, when predicting the next cleaning time for actual vehicles, target data for the corresponding vehicle parameters should also be obtained, namely, the running time, running mileage, and fuel quality of the target vehicle relative to the previous cleaning operation.

[0088] By obtaining the running time, mileage, and oil quality of the target vehicle relative to the last cleaning operation, the current particulate matter accumulation of the target vehicle can be determined more accurately, thereby predicting the next cleaning time more accurately.

[0089] Specifically, the method for obtaining the target data corresponding to oil quality is as follows:

[0090] The oil quality is determined by the proportion of the duration of oil quality abnormality during the previous cleaning operation of the target vehicle relative to the total operating time.

[0091] This can be understood as follows: during vehicle operation, if the vehicle's fuel quality is poor, the vehicle will issue a warning to remind the driver that the current fuel quality is poor. Therefore, when determining the fuel quality of a target vehicle relative to the last cleaning operation, we can determine the total operating time of the target vehicle between the current moment and the last cleaning moment, as well as the duration of the vehicle's fuel quality warning. The fuel quality during that operation can be determined by the proportion of time the vehicle spends warning about the fuel quality abnormality.

[0092] By statistically analyzing the percentage of time the target vehicle experienced abnormal oil quality during the previous cleaning operation, we can more accurately reflect the overall oil quality of the target vehicle during that operation, thus enabling us to more accurately calculate the cleaning hazard score of the target vehicle in the future.

[0093] Step S203: Determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and calculate the dust removal hazard score of the target vehicle based on the target data corresponding to different vehicle parameters and the influence weight of different vehicle parameters.

[0094] Specifically, in step S203, the dust removal hazard score of the target vehicle is calculated based on the target data corresponding to different vehicle parameters and the influence weights of different vehicle parameters, including:

[0095] Determine the preset scoring ranges corresponding to running time, running mileage, and fuel quality;

[0096] Based on the relationship between the target data and the preset scoring ranges corresponding to running time, running mileage, and fuel quality, the scores corresponding to the running time, running mileage, and fuel quality of the target vehicle are determined.

[0097] The dust removal hazard score of the target vehicle is calculated based on the corresponding scores and influence weights of the target vehicle's running time, running mileage, and oil quality.

[0098] It can be understood that the vehicle's running time, running mileage, and oil quality are three different types of vehicle parameters. Therefore, if we want to determine the current dust removal hazard score of the vehicle based on these three different vehicle parameters and their corresponding weights, we need to use a scoring mechanism to uniformly quantify these three different types of vehicle parameters.

[0099] Therefore, the preset scoring ranges corresponding to running time, running mileage and fuel quality can be determined in advance, and then the scores corresponding to the different parameter ranges can be determined according to the target data corresponding to the different vehicle parameters of the target vehicle.

[0100] For example, for vehicle running time, the scores for ≤10000, 10000 < ≤11000, 10000 < ≤11000, and 10000 < ≤11000 are 7, 8, 9, and 10 respectively; for vehicle mileage, the scores for ≤8000, 8000 < ≤9000, 9000 < ≤10000, and 10000 < mileage are 7, 8, 9, and 10 respectively; for fuel quality during vehicle operation, the percentage of time the fuel quality is abnormal relative to the previous cleaning operation is determined as the failure rate, and the percentage of time the fuel quality is not abnormal relative to the previous cleaning operation is the pass rate. The corresponding fuel quality score is determined based on the pass rate range. The specific fuel quality scores are shown in Table 1.

[0101] Table 1

[0102]

[0103] After determining the preset scoring ranges for running time, running mileage, and fuel quality, and combining the target data for running time, running mileage, and fuel quality, the scores for the current target vehicle's running time, running mileage, and fuel quality are determined. Then, the scores for each vehicle parameter are multiplied by their corresponding weights and summed to obtain the target vehicle's dust removal hazard score.

[0104] By determining the relationship between the target data corresponding to different vehicle parameters and the preset scoring range, the corresponding score can be determined. This allows for the unification of the target data corresponding to different parameters, thereby more accurately determining the target vehicle's hazard score.

[0105] Step S204: Determine the next cleaning time for the target vehicle based on the relationship between the target vehicle's cleaning hazard score and the cleaning hazard threshold corresponding to the target vehicle type.

[0106] Specifically, in step S204, based on the relationship between the target vehicle's dust removal hazard score and the dust removal hazard threshold corresponding to the target vehicle type, the next dust removal time for the target vehicle is determined, including:

[0107] Determine the proportional relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type, and determine the running time of the target vehicle relative to the last dust removal.

[0108] Based on the proportional relationship and the running time of the target vehicle relative to the previous dust cleaning, the next dust cleaning time of the target vehicle is determined.

[0109] This can be understood as follows: the dust removal hazard threshold corresponding to the target vehicle type is used to indicate that the particulate matter inside the DPF device of the target vehicle has accumulated to the point where it needs to be cleaned. The dust removal hazard score of the target vehicle is used to indicate the current particulate matter accumulation status inside the DPF device of the target vehicle. Combined with the time taken for the particulate matter in the target vehicle to accumulate to the current state, that is, the running time of the target vehicle relative to the last dust removal, the time it takes for the DPF device in the target vehicle to be fully accumulated can be calculated.

[0110] For example, if the target vehicle was cleaned two months ago, and today, two months later, the calculated dust removal hazard score for the target vehicle is 20 points, while the corresponding dust removal hazard threshold is 30 points, it means that the target vehicle's current DPF device has accumulated two-thirds of its load over the two months of operation. Therefore, the predicted next dust removal time is one month from now.

[0111] By determining the ratio between the target vehicle's dust removal hazard score and the dust removal hazard threshold, as well as the target vehicle's running time relative to the last dust removal, the next dust removal time for the target vehicle can be determined more accurately, thus improving the accuracy of the next dust removal time prediction.

[0112] Step S205: Send a dust cleaning reminder based on the next dust cleaning time of the target vehicle.

[0113] After calculating the next dust cleaning time for the target vehicle, the driver can be directly notified of this time via voice announcement, display screen, or indicator light reminder. Specific conditions can also be set, such as triggering a reminder only when the next dust cleaning time falls within a certain range. The specific settings can be tailored to the specific circumstances.

[0114] Specifically, in the above steps regarding big data analysis of historical data, the location of the cleaning service station can be determined based on the location where the cleaning prompts disappeared in the historical cleaning data.

[0115] This can be understood as follows: when the vehicle dust cleaning reminder disappears, it means that the vehicle has been dusted. Therefore, the location of the hour when the vehicle dust cleaning reminder appears in the historical data can be used to determine the location of the dust cleaning service station.

[0116] Specifically, after reminding the target vehicle of its next cleaning time, the method also includes:

[0117] The location of cleaning service stations near the target vehicle is indicated.

[0118] This can be understood as follows: after notifying the vehicle that it needs to be cleaned and the next cleaning time, the system can indicate the location of a nearby cleaning service station based on the location of the cleaning service station determined from historical data.

[0119] By analyzing the locations where cleaning reminders disappeared from historical cleaning data, the location of cleaning service stations can be determined. When reminding a target vehicle to clean, the location of nearby cleaning service stations can be further provided, making it easier for users to find the nearest cleaning service station.

[0120] The vehicle dust removal hazard prediction method based on big data provided in this invention analyzes historical data of different vehicles to determine the influence weights of running time, mileage, and fuel quality on particulate matter accumulation in the DPF device of different vehicle types during operation, as well as the corresponding dust removal hazard thresholds. Then, target data for the corresponding vehicle parameters of the target vehicle are acquired, and different types of parameter data are uniformly quantified according to a preset scoring range. The influence weights of each parameter are then combined to calculate the current dust removal hazard score of the target vehicle. By comparing the dust removal hazard score with the corresponding dust removal hazard threshold of the target vehicle, the next dust removal time of the target vehicle is determined and a corresponding reminder is given. This method can effectively predict the next dust removal time of the target vehicle, reminding users to bring the vehicle in for maintenance before congestion times. Furthermore, it only requires data analysis of historical data to determine the influence weights and corresponding thresholds, without requiring vehicle modifications, resulting in low prediction costs.

[0121] To facilitate understanding of the application background of the above method embodiments, the DPF device for vehicles will be explained here. DPF, scientifically known as a particulate filter, is made of porous wall-flow ceramic material and coated with a precious metal coating. It consists of four parts: encapsulation, clamp, carrier, and gasket. Its main function is to capture carbon soot particles and other particulate matter in diesel vehicle exhaust. It's similar to putting a mask on the exhaust pipe, filtering impurities in the exhaust to ensure that the PM content in the exhaust meets emission standards.

[0122] As a particulate filter, the DPF will collect a large amount of particulate matter during long-term operation. If it is not cleaned in time, it will cause the DPF to become clogged.

[0123] When the particulate matter captured by the DPF reaches the treatment limit, truck drivers need to process it. High temperatures and other reaction conditions are used to burn off the captured particulate matter, turning it into ash that is emitted into the atmosphere with the exhaust gas. Currently, DPFs are mainly divided into active regeneration and passive regeneration. If the blockage is severe, manual cleaning may also be necessary.

[0124] Passive regeneration refers to the process where, when a vehicle is traveling at high speed, the system detects that the particulate matter in the DPF device has reached a certain level. The exhaust gas will automatically rise to a higher temperature to convert nitrogen monoxide into nitrogen dioxide, which will then oxidize carbon particles and automatically remove the deposited particles from the filter.

[0125] Active regeneration refers to the process where, when the particulate matter captured by the filter reaches a preset limit, additional fuel is injected to increase exhaust temperature and burn off the collected carbon particles. A clogged DPF (Diesel Particulate Filter) can affect a vehicle's power and fuel consumption. Imagine a mask covered in dust; to breathe easily, we need to increase our breathing intensity. The same principle applies to the DPF. As impurities accumulate in the DPF, it increases exhaust back pressure, affecting the smoothness of engine exhaust and consequently impacting engine power and fuel consumption.

[0126] To aid in understanding the above embodiments of the invention, an exemplary flowchart of a vehicle dust removal time prediction method is provided according to an embodiment of the present invention, such as... Figure 3 As shown, the vehicle's dust removal cycle is first monitored to obtain relevant historical data on the dust removal process of various vehicles. Specifically, this data can be monitored through a vehicle network data platform. In this historical data, the appearance of a DPF (Device Filter) fault code indicates abnormal particulate matter accumulation inside the DPF, requiring cleaning; the disappearance of the fault code indicates that the DPF has completed particulate matter cleaning. Therefore, data such as fuel consumption, vehicle runtime, and mileage can be statistically analyzed for different vehicles from the completion of particulate matter cleaning to the point of particulate matter abnormality. Data analysis is then performed to determine the weights of different vehicle parameters and the threshold for identifying potential hazards for different vehicle types.

[0127] For example, in data analysis, Naive Bayes can be used for statistical analysis of events such as fuel quality, duration, and mileage. For instance, let H represent vehicle speed, and the speed variation be divided into three levels: h1, h2, and h3. The geographical range is represented by G, also divided into three levels: g1, g2, and g3. Additionally, time is represented by W, divided into two levels: w1 and w2. The most recent week and one week ago are represented as w1 and w2, respectively. Through past and current data, we can statistically determine the probability of occurrence of h and g when the conditions for events such as fuel quality, duration, and mileage (i.e., P(H|W)) and P(G|W). H, G, and W take values ​​within the previously mentioned grading range. Assuming the speed change H and the geographical range G are relatively independent, the event is transformed into calculating: P = P(W|G,H) = P(W|G) * P(W|H), which is transformed into: P = [P(G|W)P(W) / P(G)] * [P(H|W)P(W) / P(H)]. In the above formula, P(G|W) and P(H|W) are the probabilities calculated previously, while P(W) is the probability of events such as fuel quality, duration, and mileage occurring within a week and a week ago. It is possible to calculate the probabilities P(W1) of fuel quality, duration, and mileage within a week and the probability P(W2) of events occurring a week ago, while P(G) and P(H) are constants for each category.

[0128] Probability of factors such as fuel type, duration, and mileage within a week:

[0129] P1=P(w1|G,H)=[P(G|W1)P(W1) / P(G)]*[P(H|W1)P(W1) / P(H)].

[0130] Probability of factors such as fuel type, duration, and mileage a week ago:

[0131] P2=P(w2|G,H)=[P(G|W2)P(W2) / P(G)]*[P(H|W2)P(W2) / P(H)].

[0132] When all terms on the right side of an equation are known, for example, the change in vehicle speed on a certain day is h1, the range is g1, and then the magnitudes of P1 and P2 are compared.

[0133] The calculated information is mapped onto a data table to compare changes in fuel quality, duration, mileage, etc.

[0134] A general Naive Bayes classification approach: Let 'a' be a category to be classified, and each 'a' be a feature attribute of 'x' (vehicle speed, time, region); there is a set of categories; calculations are made for factors such as fuel type, duration, mileage, and region (range, time, vehicle density). The above data analysis method is merely an exemplary approach; specific analysis methods are not limited, as long as the influence weights of different vehicle parameters on particulate matter accumulation and the corresponding dust removal hazard thresholds for different vehicle types can be determined.

[0135] Next, the last DPF cleaning time of the target vehicle is determined, along with the vehicle's runtime and mileage compared to that time. In some cases, to ensure the accuracy of the prediction results, the vehicle's fuel quality during that runtime can be further obtained. By combining the influence weights of each vehicle parameter, the current dust removal hazard score of the target vehicle is calculated. This score is then compared with the dust removal hazard threshold corresponding to the vehicle type to determine whether the DPF of the target vehicle needs cleaning. If cleaning is required, a DPF congestion alarm is issued to remind the driver to clean the DPF of the vehicle.

[0136] The vehicle dust removal prediction method based on big data provided in this invention has high real-time performance when predicting dust removal, can communicate with torque in 10ms, and has low cost. It only requires big data analysis of historical data and acquisition of relevant data of the target vehicle, without any other costs. The specific functions can be integrated on the existing vehicle CAN bus module without hardware modification costs and are easy to use.

[0137] This embodiment also provides a vehicle dust removal hazard prediction device based on big data. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0138] This embodiment provides a vehicle dust removal hazard prediction device based on big data, such as... Figure 4 As shown, it includes:

[0139] The historical data analysis module 401 is used to acquire historical dust removal data of different vehicles, perform data analysis on the historical dust removal data, determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap, and the dust removal hazard threshold corresponding to different vehicle types.

[0140] The target data determination module 402 is used to obtain the target data and the target vehicle type of the target vehicle corresponding to different vehicle parameters during the previous dust removal process.

[0141] The hazard scoring calculation module 403 is used to determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and to calculate the dust removal hazard score of the target vehicle based on the target data corresponding to different vehicle parameters and the influence weight of different vehicle parameters.

[0142] The dust removal time determination module 404 is used to determine the next dust removal time for the target vehicle based on the relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type.

[0143] In some alternative implementations, vehicle parameters include: running time, mileage, and fuel quality;

[0144] Determine the influence weights of different vehicle parameters corresponding to different vehicle types on the particulate matter accumulation state in the particulate filter, including:

[0145] Determine the weights of the effects of running time, running mileage and fuel quality on the accumulation state of particulate matter in the particulate filter for different vehicle types during operation.

[0146] Obtain target data corresponding to different vehicle parameters during the operation of the target vehicle relative to the previous dust cleaning process, including:

[0147] Obtain target data for the target vehicle's running time, mileage, and fuel quality relative to the previous dust cleaning operation.

[0148] In some optional implementations, the target data corresponding to oil quality is obtained in the following ways:

[0149] The oil quality is determined by the proportion of the duration of oil quality abnormality during the previous cleaning operation of the target vehicle relative to the total operating time.

[0150] In some optional implementations, the dust removal hazard score of the target vehicle is calculated based on the target data corresponding to different vehicle parameters and the influence weights of different vehicle parameters, including:

[0151] Determine the preset scoring ranges corresponding to running time, running mileage, and fuel quality;

[0152] Based on the relationship between the target data and the preset scoring ranges corresponding to running time, running mileage, and fuel quality, the scores corresponding to the running time, running mileage, and fuel quality of the target vehicle are determined.

[0153] The dust removal hazard score of the target vehicle is calculated based on the corresponding scores and influence weights of the target vehicle's running time, running mileage, and oil quality.

[0154] In some optional implementations, the next cleaning time for the target vehicle is determined based on the relationship between the target vehicle's dust removal hazard score and the dust removal hazard threshold corresponding to the target vehicle type, including:

[0155] Determine the proportional relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type, and determine the running time of the target vehicle relative to the last dust removal.

[0156] Based on the proportional relationship and the running time of the target vehicle relative to the previous dust cleaning, the next dust cleaning time of the target vehicle is determined.

[0157] In some alternative embodiments, the apparatus is also used for:

[0158] Send a dust removal reminder based on the target vehicle's next dust removal time.

[0159] In some alternative embodiments, the apparatus is also used for:

[0160] Based on the locations where cleaning prompts disappeared in historical cleaning data, the location of the cleaning service station was determined.

[0161] After sending a dust removal reminder based on the target vehicle's next dust removal time, the method also includes:

[0162] The location of cleaning service stations near the target vehicle is indicated.

[0163] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0164] In this embodiment, the vehicle dust removal hazard prediction device based on big data is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0165] This invention also provides a computer device having the above-described features. Figure 4 The device shown is a vehicle dust removal hazard prediction device based on big data.

[0166] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0167] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0168] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0169] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0170] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0171] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0172] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0173] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0174] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting potential hazards during vehicle dust removal based on big data, characterized in that, The method includes: Historical dust removal data of different vehicles is obtained, and the historical dust removal data is analyzed to determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types. Obtain target data corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning operation and the target vehicle type of the target vehicle; Determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and calculate the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weight of the different vehicle parameters; The next cleaning time for the target vehicle is determined based on the relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type.

2. The method according to claim 1, characterized in that, The vehicle parameters include: running time, running mileage, and fuel quality; The determination of the influence weights of different vehicle parameters corresponding to different vehicle types on the particulate matter accumulation state in the particulate filter includes: Determine the weights of the effects of running time, running mileage and fuel quality on the accumulation state of particulate matter in the particulate filter for different vehicle types during operation. The acquisition of target data corresponding to different vehicle parameters of the target vehicle during the previous dust cleaning operation includes: Obtain target data for the target vehicle's running time, mileage, and fuel quality relative to the previous dust cleaning operation.

3. The method according to claim 2, characterized in that, The method for obtaining the target data corresponding to the oil quality is as follows: The oil quality is determined based on the proportion of the duration of oil quality abnormality during the previous cleaning operation of the target vehicle relative to the total operating time.

4. The method according to claim 2, characterized in that, The step of calculating the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weights of the different vehicle parameters includes: Determine the preset scoring ranges corresponding to running time, running mileage, and fuel quality; Based on the relationship between the target data corresponding to the running time, running mileage, and fuel quality and their respective preset scoring intervals, the scores corresponding to the running time, running mileage, and fuel quality of the target vehicle are determined. The dust removal hazard score of the target vehicle is calculated based on the corresponding scores and influence weights of the target vehicle's running time, running mileage, and oil quality.

5. The method according to claim 1, characterized in that, The step of determining the next cleaning time for the target vehicle based on the relationship between the target vehicle's dust removal hazard score and the dust removal hazard threshold corresponding to the target vehicle type includes: Determine the proportional relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type, and determine the running time of the target vehicle relative to the last dust removal; Based on the aforementioned proportional relationship and the target vehicle's running time relative to the previous dust cleaning, the next dust cleaning time for the target vehicle is determined.

6. The method according to claim 1, characterized in that, The method further includes: A dust removal reminder will be sent based on the next dust removal time of the target vehicle.

7. The method according to claim 6, characterized in that, The method further includes: Based on the locations where cleaning prompts disappeared in the historical cleaning data, the location of the cleaning service station was determined; After issuing a dust removal reminder based on the next dust removal time of the target vehicle, the method further includes: The location of cleaning service stations around the target vehicle is indicated.

8. A vehicle dust removal hazard prediction device based on big data, characterized in that, The device includes: The historical data analysis module is used to acquire historical dust removal data of different vehicles, perform data analysis on the historical dust removal data, and determine the influence weight of different vehicle parameters corresponding to different vehicle types on the accumulation state of particulate matter in the particulate trap and the dust removal hazard threshold corresponding to different vehicle types. The target data determination module is used to obtain target data corresponding to different vehicle parameters of the target vehicle during the previous dust removal operation and the target vehicle type of the target vehicle; The hazard scoring calculation module is used to determine the dust removal hazard threshold and the influence weight of different vehicle parameters corresponding to the target vehicle type, and to calculate the dust removal hazard score of the target vehicle based on the target data corresponding to the different vehicle parameters and the influence weight of the different vehicle parameters. The dust removal time determination module is used to determine the next dust removal time for the target vehicle based on the relationship between the dust removal hazard score of the target vehicle and the dust removal hazard threshold corresponding to the target vehicle type.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle dust removal hazard prediction method based on big data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the vehicle dust removal hazard prediction method based on big data as described in any one of claims 1 to 7.

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