Vehicle cloud cooperative vehicle regulation and control method and storage medium

Abstract

The invention provides a vehicle cloud cooperative vehicle regulation and control method and a storage medium, and relates to the technical field of vehicle control, and the method comprises the steps: obtaining historical operation data of a target vehicle in a preset unit historical time period; working condition feature recognition is carried out on the historical operation data, and historical working condition features of the target vehicle in a preset unit historical time period are determined; according to the historical working condition characteristics of the target vehicle, a preset working condition characteristic threshold value is adopted, and historical operation scene working conditions of the target vehicle in a preset unit historical time period are determined; determining a target control strategy according to the historical operation scene working condition; and the target control strategy is issued to a vehicle controller of the target vehicle, so that the vehicle controller controls energy distribution of a power battery and a fuel battery on the target vehicle according to the target control strategy. The method is suitable for changes of vehicle working conditions in different scenes, and vehicle performance and energy management are optimized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle control, and more particularly, to a vehicle control method and a storage medium for vehicle-cloud collaboration. Background Art

[0002] With the continuous development of new energy vehicle technology, hybrid vehicles have received extensive attention. Such vehicles are usually equipped with two power sources, a power battery and a fuel cell, which can effectively improve energy utilization efficiency, reduce dependence on traditional fuel, and reduce exhaust emissions. How to reasonably allocate the energy between the power battery and the fuel cell to adapt to different vehicle operating conditions and optimize vehicle performance and energy management is an important technical challenge currently faced.

[0003] Currently, hybrid vehicles adopt relatively simple energy management strategies, such as allocating the energy of the power battery and the fuel cell based on a fixed ratio or preset rules.

[0004] However, fixed allocation ratios often cannot fully adapt to the actual operating conditions of vehicles under complex conditions, and it is difficult to adapt to the dynamic changes of vehicle conditions in different scenarios, making it impossible to accurately adjust the vehicle's operating strategy to optimize performance and energy management. Summary of the Invention

[0005] The purpose of the present application is to provide a vehicle control method and a storage medium for vehicle-cloud collaboration to address the deficiencies in the above-mentioned existing technologies, so as to adapt to the changes in vehicle conditions in different scenarios and optimize vehicle performance and energy management.

[0006] To achieve the above objective, the technical solutions adopted in the embodiments of the present application are as follows: In a first aspect, an embodiment of the present application provides a vehicle control method for vehicle-cloud collaboration, which is applied to a cloud device. The method includes: Obtain the historical operation data of the target vehicle within a preset unit historical time period; Perform working condition feature recognition on the historical operation data to determine the historical working condition features of the target vehicle within the preset unit historical time period; According to the historical working condition features of the target vehicle, use a preset working condition feature threshold to determine the historical operation scenario working condition of the target vehicle within the preset unit historical time period; Determine a target control strategy according to the historical operation scenario working condition, where the target operation scenario working condition is a control strategy for a long-distance working condition or a control strategy for a short-distance working condition; Send the target control strategy to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

[0007] Optionally, the historical operation data includes: positioning data of multiple historical trajectory points, enabling time of the power take-off, parking time, and enabling time of the air pump; the identifying of the working condition characteristics from the historical operation data to determine the historical working condition characteristics of the target vehicle in the preset unit historical time period includes: Calculating the farthest running distance of the target vehicle within the preset unit historical time period according to the positioning data of the multiple historical trajectory points; Calculating the enabling interval distance of the power take-off between two adjacent times within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the enabling time of the power take-off; Calculating the number of parking times per unit distance within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the parking time; Calculating the number of enabling times of the air pump per unit distance within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the enabling time of the air pump; Wherein, the historical working condition characteristics include: the farthest running distance, the enabling interval distance of the power take-off, the number of parking times per unit distance, and the number of enabling times of the air pump per unit distance.

[0008] Optionally, the preset working condition characteristic thresholds include: a preset running distance threshold, a preset enabling interval distance threshold of the power take-off, a preset number of parking times threshold, and a preset number of enabling times threshold of the air pump; The determining of the historical operation scenario working condition of the target vehicle in the preset unit historical time period by using the preset working condition characteristic thresholds according to the historical working condition characteristics of the target vehicle includes: Performing normalization processing on the farthest running distance according to the preset running distance threshold to obtain a first evaluation parameter; Performing normalization processing on the enabling interval distance of the power take-off according to the preset enabling interval distance threshold of the power take-off to obtain a second evaluation parameter; Performing normalization processing on the number of parking times per unit distance according to the preset number of parking times threshold to obtain a third evaluation parameter; Performing normalization processing on the number of enabling times of the air pump per unit distance according to the preset number of enabling times threshold of the air pump to determine a fourth evaluation parameter; Determining the historical operation scenario working condition according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter.

[0009] Optionally, the determining of the historical operation scenario working condition according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter includes: Perform a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter; If the target evaluation parameter is greater than or equal to a preset threshold, determine that the historical operation scenario condition is a long-distance condition; If the target evaluation parameter is less than the preset threshold, determine that the historical operation scenario condition is a short-distance condition.

[0010] Optionally, the performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter includes: Perform a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter according to a first preset weight, a second preset weight, a third preset weight, and a fourth preset weight to obtain the target evaluation parameter; Wherein, the sum of the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight is 1, and both the first preset weight and the second preset weight are greater than the third preset weight and the fourth preset weight.

[0011] Optionally, the determining a target control strategy according to the historical operation scenario condition includes: Determine whether there is a change in the condition in the preset unit historical time period according to the historical operation scenario condition; If there is no change in the condition, determine the preset control strategy corresponding to the historical operation scenario condition as the target control strategy; If there is a change in the condition, and the latest operation scenario condition is maintained within a preset number of unit historical time periods, determine the preset control strategy corresponding to the latest operation scenario condition as the target control strategy.

[0012] In a second aspect, another embodiment of the present application further provides another vehicle-cloud collaborative vehicle control method, which is applied to a vehicle controller of a target vehicle. The method includes: Upload the historical operation data of the target vehicle collected by the in-vehicle device of the target vehicle within a preset unit historical time period to a cloud device; Receive the target control strategy sent by the cloud device, where the target control strategy is the control strategy determined by the cloud device according to any one of the vehicle-cloud collaborative vehicle control methods in the first aspect above; Control the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

[0013] Optionally, controlling the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy includes: If the target control strategy is a control strategy for a long-distance working condition, obtain the current driving state of the target vehicle; If the current driving state is a constant-speed driving state, control the fuel cell to output energy based on a preset rated power; If the current driving state is an accelerating driving state or a climbing driving state, control the fuel cell to output energy based on the preset rated power, and control the power battery to supplement power; If the current driving state is a braking state, control the power battery to recover energy so that the state of charge of the power battery is within a preset range; if the state of charge recovered is still not within the preset range, control the fuel cell to charge the power battery so that the state of charge of the power battery is within the preset range.

[0014] Optionally, controlling the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy includes: If the target control strategy is a control strategy for a short-distance working condition, obtain the current state of charge of the power battery; If the current state of charge is within the first state-of-charge interval, control the power battery to output energy with the full required power of the target vehicle, and control the fuel cell to enter the standby state; If the current state of charge is not within the first state-of-charge interval but within the second state-of-charge interval, control the power battery to output energy with a first proportion of the required power, control the fuel cell to start and maintain a second proportion of the rated power for energy output, and use the redundant power of the fuel cell to charge the power battery; If the current state of charge is not within the second state-of-charge interval but within the third state-of-charge interval, control the fuel cell to output energy with a third proportion of the rated power, control the power battery to output energy with a fourth proportion of the required power, and use the redundant power of the fuel cell to charge the power battery; wherein, the third proportion is greater than the second proportion, and the fourth proportion is less than the first proportion.

[0015] In a third aspect, another embodiment of the present application provides a vehicle cloud collaborative vehicle control device, which is applied to a cloud device, and the device includes: An acquisition module, configured to acquire historical operation data of a target vehicle within a preset unit historical time period; An identification module, configured to identify the operating condition characteristics of the historical operation data, and determine the historical operating condition characteristics of the target vehicle in the preset unit historical time period; A determination module, configured to determine the historical operation scenario condition of the target vehicle in the preset unit historical time period by using a preset operating condition characteristic threshold according to the historical operating condition characteristics of the target vehicle; A determination module, configured to determine a target control strategy according to the historical operation scenario condition, where the target operation scenario condition is a control strategy for a long-distance condition or a control strategy for a short-distance condition; A sending module, configured to send the target control strategy to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

[0016] Fourthly, another embodiment of the present application provides another vehicle-cloud collaborative vehicle regulation device, which is applied to the vehicle controller of a target vehicle. The device includes: An acquisition module, configured to upload the historical operation data of the target vehicle collected by the in-vehicle device on the target vehicle to a cloud device within a preset unit historical time period; A receiving module, configured to receive the target control strategy sent by the cloud device, where the target control strategy is a control strategy determined by the cloud device according to any one of the vehicle-cloud collaborative vehicle regulation methods in the first aspect above; A control module, configured to control the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

[0017] Fifthly, another embodiment of the present application provides a cloud device, including: a first processor, a first memory, and a first bus. The first memory stores machine-readable instructions executable by the first processor. When the cloud device runs, the first processor communicates with the first memory through the first bus. The first processor executes the machine-readable instructions to perform the steps of any one of the vehicle-cloud collaborative vehicle regulation methods in the first aspect above.

[0018] Sixthly, another embodiment of the present application provides a vehicle controller, including: a second processor, a second memory, and a bus. The second memory stores machine-readable instructions executable by the second processor. When the vehicle controller runs, the second processor communicates with the second memory through the second bus. The second processor executes the machine-readable instructions to perform the steps of any one of the vehicle-cloud collaborative vehicle regulation methods in the second aspect above.

[0019] In a seventh aspect, another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the vehicle control method for vehicle-cloud collaboration as described in any one of the first aspect and the second aspect above.

[0020] The beneficial effects of the present application are as follows: The present application provides a vehicle control method for vehicle-cloud collaboration and a storage medium. By obtaining the historical operation data of a target vehicle within a preset unit historical time period, performing working condition feature recognition on the historical operation data, and determining the historical working condition features of the target vehicle within the preset unit historical time period, the running state of the vehicle can be accurately understood, thereby optimizing the vehicle control strategy. According to the historical working condition features of the target vehicle, using a preset working condition feature threshold, the historical operation scenario working condition of the target vehicle within the preset unit historical time period is determined; according to the historical operation scenario working condition, a target control strategy is determined. Thus, the energy of the target vehicle can be accurately managed, thereby optimizing the vehicle energy management mode and improving the performance and lifespan of the vehicle. The target control strategy is sent to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy. According to the historical operation data of the target vehicle within the preset unit historical time period, the present application determines the target control strategy of the target vehicle, which can adapt to the changes in vehicle working conditions in different scenarios, thereby accurately adjusting the vehicle control strategy to optimize vehicle performance and energy management. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic diagram of the scenario of a vehicle control method for vehicle-cloud collaboration provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the process applied to a cloud device in a vehicle control method for vehicle-cloud collaboration provided by an embodiment of the present application; Figure 3 It is a schematic diagram of the process for determining historical working condition features in a vehicle control method for vehicle-cloud collaboration provided by an embodiment of the present application; Figure 4 It is a schematic diagram for determining the farthest running distance provided by an embodiment of the present application; Figure 5Schematic flowchart of determining historical operation scenario conditions in a vehicle-cloud collaborative vehicle control method provided by an embodiment of the present application; Figure 6 Schematic flowchart of historical operation scenario conditions in another vehicle-cloud collaborative vehicle control method provided by an embodiment of the present application; Figure 7 Schematic flowchart of determining a target control strategy in a vehicle-cloud collaborative vehicle control method provided by the present application; Figure 8 Flowchart of a vehicle-cloud collaborative vehicle control method provided by another embodiment of the present application; Figure 9 Schematic flowchart of controlling energy distribution in a vehicle-cloud collaborative vehicle control method provided by an embodiment of the present application; Figure 10 Schematic flowchart of controlling energy distribution in another vehicle-cloud collaborative vehicle control method provided by an embodiment of the present application; Figure 11 Schematic diagram of a vehicle-cloud collaborative vehicle control device provided by an embodiment of the present application; Figure 12 Schematic diagram of a vehicle-cloud collaborative vehicle control device provided by another embodiment of the present application; Figure 13 Schematic diagram of the structure of a cloud device provided by an embodiment of the present application; Figure 14 Schematic diagram of the structure of a vehicle controller provided by another embodiment of the present application. Detailed implementation manners

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to actual scale. The flowcharts used in the present application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical context may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0024] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but merely represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0025] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0026] Currently, in order to solve the energy crisis and environmental pollution caused by vehicle emissions, fuel cell vehicles have entered the market. Among them, fuel cell vehicles can be hydrogen fuel cell vehicles. On the premise of meeting the vehicle's power performance, endurance, and driving range, hydrogen fuel cell vehicles can convert chemical energy from hydrogen and oxygen into electrical energy to provide power for the hydrogen fuel cell vehicle to travel, and its product is water. Since the driving route of hydrogen fuel cell vehicles is relatively fixed, during the vehicle's driving process, the vehicle usually communicates with cloud devices through a vehicle controller. The cloud device determines the vehicle's control strategy based on the vehicle status parameters sent by the vehicle controller and sends the control strategy to the vehicle, enabling the vehicle to determine the output power of the power battery and fuel cell based on the control strategy. However, in the prior art, only a single parameter or a simple threshold can be used to judge the corresponding control strategy of the vehicle. However, a single parameter often cannot comprehensively reflect the actual operating conditions of the vehicle under complex working conditions, easily leading to misjudgment, making it difficult for traditional methods to adapt to the dynamic changes of vehicle working conditions in different scenarios and unable to adjust the vehicle's operating strategy in a timely manner to optimize performance and energy management.

[0027] For this reason, the present application provides a vehicle control method for vehicle-cloud collaboration. Obtain the historical operation data of the target vehicle within a preset unit historical time period, perform working condition feature recognition on the historical operation data, determine the historical working condition features of the target vehicle within the preset unit historical time period, use a preset working condition feature threshold according to the historical working condition features of the target vehicle to determine the historical operation scenario working condition of the target vehicle within the preset unit historical time period, determine the target control strategy according to the historical operation scenario working condition, and send the target control strategy to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and fuel cell on the target vehicle according to the target control strategy. The present application determines the target control strategy of the target vehicle based on the historical operation data within the preset unit historical time period, can consider various types of data, thus more comprehensively understanding the performance and requirements of the vehicle, ensuring the accuracy of the control strategy, and thereby optimizing the performance and energy management of the vehicle.

[0028] To clearly describe the method provided in the embodiments of the present application, the vehicle control method for vehicle-cloud collaboration provided in the embodiments of the present application will be described below in conjunction with multiple drawings. The vehicle control method for vehicle-cloud collaboration provided by the present application is applied to a cloud device and a vehicle controller respectively. Figure 1 As shown in the scenario schematic diagram of a vehicle control method for vehicle-cloud collaboration provided in the embodiments of the present application, Figure 1 As shown, the vehicle controller 100 uploads the historical operation data of the target vehicle collected by the in-vehicle device on the target vehicle within a preset unit historical time period to the cloud device 200. The cloud device 200 performs working condition feature recognition on the historical operation data to determine the historical working condition features of the target vehicle within the preset unit historical time period. According to the historical working condition features of the target vehicle, using a preset working condition feature threshold, the historical operation scenario working condition of the target vehicle within the preset unit historical time period is determined. According to the historical operation scenario working condition, a target control strategy is determined, and the target control strategy is sent to the vehicle controller of the target vehicle. The vehicle controller 100 receives the target control strategy sent by the cloud device 200, and according to the target control strategy, controls the energy distribution of the power battery and the fuel cell on the target vehicle.

[0029] Figure 2 As shown in the flow schematic diagram of a vehicle control method for vehicle-cloud collaboration provided in the embodiments of the present application, Figure 2 As shown, the method includes: Step 201, obtain the historical operation data of the target vehicle within a preset unit historical time period.

[0030] Among them, the preset unit historical time period can be a driving cycle, that is, the target vehicle travels from the starting point to the ending point. It can also be one day, that is, from 0:00 to 24:00 is a unit historical time period. The historical operation data can be the daily running mileage of the target vehicle, the positioning of the target vehicle, the driving speed of the target vehicle, the number of times the power take-off of the target vehicle is turned on, and the number of times the electric power assist system of the target vehicle is started.

[0031] Optionally, the vehicle controller of the target vehicle periodically sends historical operation data to the cloud device within a preset unit historical time period. Specifically, it can send historical operation data to the cloud device every second, and the cloud device periodically receives the historical operation data according to the period sent by the vehicle controller.

[0032] Step 202, perform working condition feature recognition on the historical operation data to determine the historical working condition features of the target vehicle within a preset unit historical time period.

[0033] Among them, the historical working condition features are used to determine the historical operation scenario working condition of the target vehicle within a preset historical unit time period.

[0034] Optionally, analyze the historical operation data to determine the operating condition characteristics of each piece of historical operation data, so as to determine the historical operating condition characteristics of the target vehicle in a preset unit historical time period.

[0035] Step 203: According to the historical operating condition characteristics of the target vehicle, use a preset operating condition characteristic threshold to determine the historical operation scenario condition of the target vehicle in a preset unit historical time period.

[0036] Among them, the preset operating condition characteristic threshold is used to judge the scenario corresponding to the historical operating condition characteristics of the target vehicle. Different historical operating condition characteristics correspond to different preset operating condition thresholds, specifically determined according to the type of the target vehicle and the actual operation scenario. The embodiments of the present application do not limit this.

[0037] Optionally, compare the historical operating condition characteristics of the target vehicle with the preset operating condition characteristic thresholds respectively, so as to determine the historical operation scenario condition of the target vehicle in a preset unit historical time period.

[0038] Step 204: Determine the target control strategy according to the historical operation scenario condition.

[0039] Among them, the historical operation scenario condition can be a long-distance condition or a short-distance condition. Correspondingly, the target control strategy can be a control strategy for the long-distance condition or a control strategy for the short-distance condition.

[0040] Optionally, when all the historical operating condition characteristics of the target vehicle conform to the historical operation scenario condition corresponding to the control strategy for the long-distance condition, determine that the historical operation scenario condition of the target vehicle in the preset unit historical time period is the long-distance condition; or when some of the historical operating condition characteristics of the target vehicle conform to the historical operation scenario condition corresponding to the control strategy for the long-distance condition, determine that the historical operation scenario condition of the target vehicle in the preset unit historical time period is the long-distance condition; or compare the historical operating condition characteristics of the target vehicle with the preset operating condition characteristic thresholds through preset weight parameters to obtain corresponding parameters, and determine that the historical operation scenario condition of the target vehicle in the preset unit historical time period is the long-distance condition according to the parameters. The method for determining the control strategy for the short-distance condition is the same as that for the long-distance condition, and will not be elaborated here.

[0041] Step 205: Send the target control strategy to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

[0042] Among them, the target control strategy is used to control the energy distribution of the power battery and the fuel cell on the target vehicle.

[0043] In the embodiment of the present application, by obtaining the historical operation data of the target vehicle within a preset unit historical time period, performing working condition feature recognition on the historical operation data, and determining the historical working condition features of the target vehicle within the preset unit historical time period, the running state of the vehicle can be accurately understood, thereby optimizing the control strategy of the vehicle. According to the historical working condition features of the target vehicle, using a preset working condition feature threshold, determining the historical operation scenario working condition of the target vehicle within the preset unit historical time period; determining the target control strategy according to the historical operation scenario working condition. Accurately manage the energy of the target vehicle, thereby optimizing the energy management mode of the vehicle and improving the performance and lifespan of the vehicle. Sending the target control strategy to the vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy. The present application determines the target control strategy of the target vehicle according to the historical operation data of the target vehicle within a preset unit historical time period, adapts to the changes in the vehicle working conditions in different scenarios, and thus accurately adjusts the control strategy of the vehicle to optimize the vehicle performance and energy management.

[0044] Based on the above embodiment, the historical operation data includes: the positioning data of multiple historical trajectory points, the enabling time of the power take-off, the parking time, and the enabling time of the air pump. Therefore, the present application also provides a process for determining historical working condition features in a vehicle control method with vehicle-cloud collaboration. Figure 3 The flowchart for determining historical working condition features in a vehicle control method with vehicle-cloud collaboration provided by an embodiment of the present application is as follows. Figure 3 As shown, in step 202 above, performing working condition feature recognition on the historical operation data to determine the historical working condition features of the target vehicle within a preset unit historical time period includes: Step 301: Calculate the farthest running distance of the target vehicle within a preset unit historical time period according to the positioning data of multiple historical trajectory points.

[0045] Among them, the positioning data of the historical trajectory points includes the latitude data and longitude data of the trajectory points.

[0046] Optionally, calculate the latitude mean and latitude standard deviation of multiple historical trajectory points according to the latitude data of the historical trajectory points, and calculate the longitude mean and longitude standard deviation of multiple historical trajectory points according to the longitude data of the historical trajectory points. Calculate the bounding box of multiple historical trajectory points according to the latitude mean, latitude standard deviation, longitude mean, and longitude standard deviation of multiple historical trajectory points, and determine the farthest running distance of the target vehicle within a preset unit historical time period according to the diagonal of the bounding box. For example, the diagonal of the bounding box can be used as the farthest running distance of the target vehicle within a preset unit historical time period.

[0047] Optionally, subtract twice the standard deviation of the latitudes of multiple historical trajectory points from the mean latitude to obtain the lower latitude limit of the bounding box, and add twice the standard deviation of the latitudes of multiple historical trajectory points to the mean latitude to obtain the upper latitude limit of the bounding box. Subtract twice the standard deviation of the longitudes of multiple historical trajectory points from the mean longitude to obtain the lower longitude limit of the bounding box, and add twice the standard deviation of the longitudes of multiple historical trajectory points to the mean longitude to obtain the upper longitude limit of the bounding box.

[0048] Exemplarily, Figure 4 FIG. is a schematic diagram for determining the farthest running distance provided by an embodiment of the present application. As Figure 4 shown, the dashed double arrow is the farthest distance between the starting point and the ending point, the dashed box is the bounding box constructed based on multiple historical trajectory points, the solid double arrow is the diagonal of the bounding box, the actual farthest running distance of the target vehicle is 4 kilometers, and the diagonal distance of the bounding box is 3.5 kilometers. Therefore, the farthest running distance of the target vehicle can be determined according to the diagonal of the bounding box.

[0049] Step 302: Calculate the enabling interval distance between two adjacent times within a preset unit historical time period according to the positioning data of multiple historical trajectory points and the enabling time of the power take-off.

[0050] The enabling of the power take-off is used to indicate that the target vehicle is loading or unloading goods. The enabling interval distance between two adjacent times within a preset unit historical time period can be the interval distance between the loading and unloading of the target vehicle.

[0051] Optionally, according to the positioning data of multiple historical trajectory points and the enabling time of the power take-off, determine the historical trajectory points of the target vehicle when the power take-off is enabled, and determine the enabling interval distance between two adjacent times within a preset unit historical time period according to the vehicle instrument mileage values corresponding to the historical trajectory points.

[0052] Step 303: Calculate the number of stops per unit distance within a preset unit historical time period according to the positioning data of multiple historical trajectory points and the parking time.

[0053] The unit distance can be one kilometer, which is specifically determined according to the actual situation, and the embodiments of the present application do not limit this.

[0054] Optionally, according to the positioning data of multiple historical trajectory points and the parking time, determine the positioning data of the target vehicle when the target vehicle stops, and calculate the number of stops per unit distance within a preset unit historical time period according to the positioning data of the target vehicle when the target vehicle stops.

[0055] Exemplarily, if the vehicle travels 10 kilometers in 1 hour and stops 5 times, then the number of stops per unit distance is 0.5 times per kilometer.

[0056] Step 304: Calculate the number of times the air pump is enabled per unit distance within a preset unit historical time period based on the positioning data of multiple historical trajectory points and the air pump enabling time.

[0057] Among them, the historical operating condition characteristics include: the farthest operating distance, the enabling interval distance of the power take-off, the number of stops per unit distance, and the number of times the air pump is enabled per unit distance.

[0058] Among them, the unit distance can be one kilometer, which is specifically determined according to the actual situation, and the embodiments of the present application do not limit this. The air pump enabling is used to indicate whether the air pump of the target vehicle is in a working state.

[0059] Optionally, based on the positioning data of multiple historical trajectory points and the air pump enabling time, determine the positioning data of the target vehicle at the air pump enabling time of the target vehicle, and calculate the number of times the air pump is enabled per unit distance within a preset unit historical time period according to the positioning data of the target vehicle at the air pump enabling time of the target vehicle.

[0060] For example, if the vehicle travels 10 kilometers within 1 hour and the air pump is enabled 5 times, then the number of times the air pump is enabled per unit distance is 0.5 times / kilometer.

[0061] In the embodiments of the present application, by calculating the farthest operating distance, the enabling interval distance of the power take-off between two adjacent times per unit distance, the number of stops per unit distance, and the number of times the air pump is enabled per unit distance within a preset unit historical time period to determine the historical operating condition characteristics, the historical operating condition characteristics of the target vehicle can be accurately determined, thereby optimizing the control strategy of the target vehicle and reducing unnecessary energy consumption and losses.

[0062] On the basis of the above embodiments, the preset operating condition characteristic thresholds include: a preset operating distance threshold, a preset power take-off enabling interval distance threshold, a preset number of stops threshold, and a preset air pump enabling number threshold. Therefore, the present application also provides a process for determining the historical operation scenario conditions in a vehicle-cloud collaborative vehicle regulation method. Figure 5 For the process schematic diagram of determining the historical operation scenario conditions in a vehicle-cloud collaborative vehicle regulation method provided by the embodiments of the present application, as Figure 5 shown, in step 203 above, according to the historical operating condition characteristics of the target vehicle and using the preset operating condition characteristic thresholds, determine the historical operation scenario conditions of the target vehicle within a preset unit historical time period, including: Step 501: Standardize the farthest operating distance according to the preset operating distance threshold to obtain a first evaluation parameter.

[0063] Among them, the preset operating distance threshold can be 20 kilometers, and the standardization process is to determine that the first evaluation parameter is 1 or 0.

[0064] Optionally, compare the preset operating distance threshold with the farthest operating distance to standardize the farthest operating distance and obtain a first evaluation parameter. If the farthest operating distance is greater than the preset operating distance threshold, determine that the first evaluation parameter is 1; otherwise, the first evaluation parameter is 0.

[0065] Step 502: Standardize the power take-off enabling interval distance according to the preset power take-off enabling interval distance threshold to obtain a second evaluation parameter.

[0066] Among them, the preset power take-off enabling interval distance threshold can be 20 kilometers, and the standardization process is to determine that the second evaluation parameter is 1 or 0.

[0067] Optionally, compare the preset power take-off enabling interval distance threshold with the power take-off enabling interval distance to standardize the power take-off enabling interval distance and obtain a second evaluation parameter. If the power take-off enabling interval distance is greater than the preset power take-off enabling interval distance threshold, determine that the second evaluation parameter is 1; otherwise, the second evaluation parameter is 0.

[0068] Step 503: Standardize the number of stops per unit distance according to the preset number of stop thresholds to obtain a third evaluation parameter.

[0069] Among them, the preset number of stop thresholds can be two stops per kilometer, and the standardization process is to determine that the third evaluation parameter is 1 or 0.

[0070] Optionally, compare the preset number of stop thresholds with the number of stops per unit distance to standardize the number of stops per unit distance and obtain a third evaluation parameter. If the number of stops per unit distance is greater than the preset number of stop thresholds, determine that the third evaluation parameter is 0; otherwise, the third evaluation parameter is 1.

[0071] Step 504: Standardize the number of air pump enabling times per unit distance according to the preset number of air pump enabling times threshold to determine a fourth evaluation parameter.

[0072] Among them, the preset number of air pump enabling times threshold can be two times per kilometer, and the standardization process is to determine that the fourth evaluation parameter is 1 or 0.

[0073] Optionally, compare the preset number of air pump enabling times threshold with the number of air pump enabling times per unit distance to standardize the number of air pump enabling times per unit distance and obtain a fourth evaluation parameter. If the number of air pump enabling times per unit distance is greater than the preset number of air pump enabling times threshold, determine that the third evaluation parameter is 0; otherwise, the third evaluation parameter is 1.

[0074] Step 505: Determine the historical operation scenario condition according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter.

[0075] Optionally, the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter can be averaged to determine the historical operation scenario condition according to the average value; or the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter can be weighted and calculated to determine the historical operation scenario condition; or other calculation methods, which are not limited in the embodiments of the present application.

[0076] In the embodiments of the present application, by determining the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter, the historical operation scenario condition is determined, and the historical operation scenario condition is determined according to the weighted calculation of the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter. Through the evaluation of multiple dimensions, the present application can quickly identify the historical operation scenario condition.

[0077] On the basis of the above embodiments, the present application also provides a process of the historical operation scenario condition in another vehicle-cloud collaborative vehicle control method. Figure 6 It is a schematic flow chart of the historical operation scenario condition in another vehicle-cloud collaborative vehicle control method provided by the embodiments of the present application. As Figure 6 shown, in step 505 above, according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter, determining the historical operation scenario condition includes: Step 601: Perform a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter.

[0078] Optionally, according to the historical data corresponding to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter, weights corresponding to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter are respectively assigned, and a weighted sum calculation is performed on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter. Specifically, the weights corresponding to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter are determined according to the own parameters and operating conditions of the target vehicle, which are not limited in the embodiments of the present application.

[0079] Step 602: If the target evaluation parameter is greater than or equal to the preset threshold, determine that the historical operation scenario condition is a long-distance condition.

[0080] Among them, the preset threshold can be 0.5. Since the total score is usually higher for the long-distance condition after the feature combination, by setting the threshold, the operation scenario condition can be effectively distinguished.

[0081] Step 603: If the target evaluation parameter is less than the preset threshold, determine that the historical operation scenario condition is a short-distance condition.

[0082] In the embodiments of the present application, a weighted sum operation is performed on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter, and the historical operation scenario condition is determined according to the target evaluation parameter. The target evaluation parameter can be dynamically adjusted to optimize the control strategy.

[0083] Based on the above embodiments, the present application further provides a process for determining a target evaluation parameter in a vehicle-cloud collaborative vehicle regulation method. In step 601 above, a weighted sum operation is performed on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter, including: Performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter according to the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight to obtain a target evaluation parameter; Among them, the sum of the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight is 1, and both the first preset weight and the second preset weight are greater than the third preset weight and the fourth preset weight. The first preset weight and the second preset weight may be the same or different, and the third preset weight and the fourth preset weight may be the same or different.

[0084] Exemplarily, the first preset weight and the second preset weight are 0.4 respectively, the third preset weight and the fourth preset weight are 0.1 respectively, the preset threshold may be 0.5. Performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter according to the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight to obtain a target evaluation parameter. If the target evaluation parameter is greater than 0.5, it is a long-distance condition, otherwise it is a short-distance condition.

[0085] In the embodiments of the present application, performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter according to the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight to obtain a target evaluation parameter can dynamically adapt to business requirements, enhance the accuracy of determining the operation scenario, and avoid a certain parameter dominating the result.

[0086] Based on the above embodiments, the present application further provides a process for determining a target control strategy in a vehicle-cloud collaborative vehicle control method. Figure 7 It is a schematic flowchart of a process for determining a target control strategy in a vehicle-cloud collaborative vehicle control method provided by the present application, as Figure 7 shown. In step 204 above, determining a target control strategy according to the historical operation scenario condition includes: Step 701: Determine whether there is a change in the operating condition in a preset unit historical time period according to the historical operating scenario condition.

[0087] Optionally, if the preset unit historical time period is one day, then compare the historical operating scenario condition with the historical operating scenario condition of the previous preset unit historical time period to determine whether there is a change in the operating condition in the preset unit historical time period.

[0088] Step 702: If there is no change in the operating condition, determine the preset control strategy corresponding to the historical operating scenario condition as the target control strategy.

[0089] Optionally, if the historical operating scenario condition is the same as the historical operating scenario condition of the previous preset unit historical time period, then there is no change in the operating condition, and determine the preset control strategy corresponding to the historical operating scenario condition as the target control strategy.

[0090] Step 703: If there is a change in the operating condition and the latest operating scenario condition is maintained in a preset number of unit historical time periods, determine the preset control strategy corresponding to the latest operating scenario condition as the target control strategy.

[0091] Among them, the preset number of unit historical time periods can be two unit historical time periods.

[0092] Optionally, if the historical operating scenario condition is different from the historical operating scenario condition of the previous preset unit historical time period, then there is a change in the operating condition. If the latest operating scenario condition is maintained in two unit historical time periods, determine the preset control strategy corresponding to the latest operating scenario condition as the target control strategy.

[0093] For example, if the historical operating scenario condition is a long-distance condition and the historical operating scenario condition of the previous preset unit historical time period is a short-distance condition, then there is a change in the operating condition. If the long-distance condition is maintained in two unit historical time periods, determine the preset control strategy corresponding to the long-distance condition as the target control strategy.

[0094] In the embodiment of the present application, according to the historical operating scenario condition, determine whether there is a change in the operating condition in the preset unit historical time period, so as to determine the target control strategy. The present application can quickly identify the change of the vehicle operating scenario within the preset time period, dynamically switch the target control strategy, and optimize the resource utilization rate.

[0095] On the basis of the above embodiment, another embodiment of the present application further provides a vehicle regulation method for vehicle-cloud collaboration, which is applied to the vehicle controller of the target vehicle. Figure 8 For the flow of a vehicle regulation method for vehicle-cloud collaboration provided by another embodiment of the present application, as Figure 8 shown, this method includes: Step 801: Upload the historical operation data of the target vehicle collected by the in-vehicle device of the target vehicle to the cloud device within a preset unit historical time period.

[0096] Among them, the in-vehicle device includes devices such as a positioning device, a power take-off controller, and a pressure sensor, and the embodiments of the present application do not limit this. The historical operation data includes: positioning data of historical trajectory points, enabling time of the power take-off, parking time, and enabling time of the air pump.

[0097] Optionally, within a preset unit historical time period, the in-vehicle device of the target vehicle uploads the positioning data of the trajectory points on the target vehicle, the enabling time of the power take-off, the parking time, and the enabling time of the air pump to the cloud device. The preset unit historical time period can be 1 second.

[0098] Step 802: Receive the target control strategy sent by the cloud device, where the target control strategy is a control strategy determined by the cloud device according to the vehicle regulation method of vehicle-cloud collaboration.

[0099] Step 803: According to the target control strategy, control the energy distribution of the power battery and the fuel cell on the target vehicle.

[0100] In the embodiments of the present application, the historical operation data of the target vehicle is uploaded to the cloud device, the target control strategy sent by the cloud is received, and the energy distribution of the power battery and the fuel cell on the target vehicle is controlled according to the target control strategy. The present application can optimize the energy distribution of the target vehicle, reduce the energy consumption of the target vehicle, make more efficient use of limited energy, and thus extend the driving range of the vehicle.

[0101] On the basis of the above embodiments, the present application also provides a process for controlling energy distribution in a vehicle regulation method of vehicle-cloud collaboration. Figure 9 It is a schematic diagram of the process for controlling energy distribution in a vehicle regulation method of vehicle-cloud collaboration provided by the embodiments of the present application. As Figure 9 shown, in the above Step 803, according to the target control strategy, controlling the energy distribution of the power battery and the fuel cell on the target vehicle includes: Step 901: If the target control strategy is a control strategy for long-distance working conditions, obtain the current driving state of the target vehicle.

[0102] Among them, the driving state of the target vehicle may include: uniform driving, accelerating driving, climbing driving, and braking state.

[0103] Optionally, if the target control strategy is a control strategy for long-distance working conditions, obtain the vehicle speed sensor, acceleration sensor, and positioning device on the target vehicle to determine the current driving state of the target vehicle.

[0104] Step 902: If the current driving state is a constant speed driving state, then control the fuel cell to output energy based on a preset rated power.

[0105] Among them, the preset rated power can be 80%. When the total power of the fuel cell is 120 kWh, then the preset rated power can be 96 kW. The specific preset rated power is determined according to the fuel cell of the target vehicle and the parameters of the target vehicle, and the embodiments of the present application do not limit this. The constant speed driving state can include: low-speed constant speed driving and high-speed constant speed driving.

[0106] Optionally, if the current driving state is a constant speed driving state, it indicates that the target vehicle has no need for acceleration or deceleration, then control the fuel cell to output energy based on a preset rated power.

[0107] Optionally, if the current driving state is a constant speed driving state, it indicates that the target vehicle has no need for acceleration or deceleration, then determine the power demand of the target vehicle at this speed according to the opening of the accelerator pedal depressed by the driver at this speed, use the power demand of the target vehicle at this speed as the preset rated power, and control the fuel cell to output energy based on the preset rated power.

[0108] Step 903: If the current driving state is an accelerating driving state or a climbing driving state, then control the fuel cell to output energy based on a preset rated power, and control the power battery to supplement power.

[0109] Optionally, if the current driving state is an accelerating driving state or a climbing driving state, it indicates that only using the fuel cell to supply energy to the target vehicle may not be able to ensure the power demand of the target vehicle. Then control the fuel cell to output energy based on a preset rated power, and control the power battery to supplement power, so that the target vehicle can still drive stably when the current driving state is an accelerating driving state or a climbing driving state.

[0110] Step 904: If the current driving state is a braking state, then control the power battery to recover energy, so that the state of charge of the power battery is within a preset range; if the state of charge recovered is still not within the preset range, then control the fuel cell to charge the power battery, so that the state of charge of the power battery is within the preset range.

[0111] Among them, the preset range can be 30% - 70%.

[0112] Optionally, if the current driving state is a braking state, then control the power battery to recover energy, so that the state of charge of the power battery is within a preset range. If the state of charge recovered is still not within the preset range, then control the fuel cell to supply energy to the target vehicle and charge the power battery at the same time, so that the state of charge of the power battery is within the preset range.

[0113] In the embodiments of the present application, according to the vehicle control strategy and the vehicle driving state of the target vehicle, the control strategies for the fuel cell and the power battery are determined, which can improve the energy utilization rate of the fuel cell and the power battery, extend the vehicle cruising range and the service life of the fuel cell and the power battery, and improve the vehicle performance.

[0114] Based on the above embodiments, the present application also provides a process for controlling energy distribution in another vehicle-cloud collaborative vehicle regulation method. Figure 10 It is a schematic flow chart for controlling energy distribution in another vehicle-cloud collaborative vehicle regulation method provided by the embodiments of the present application. As Figure 10 shown, in step 803 above, according to the target control strategy, controlling the energy distribution of the power battery and the fuel cell on the target vehicle includes: Step 1001, if the target control strategy is the control strategy for short-distance working conditions, obtain the current state of charge of the power battery.

[0115] Among them, the current state of charge parameter of the power battery is determined by the battery management system in the power battery.

[0116] Optionally, if the target control strategy is the control strategy for short-distance working conditions, obtain the current state of charge of the power battery through the battery management system of the power battery.

[0117] Step 1002, if the current state of charge is within the first state-of-charge interval, control the power battery to output energy at the full demand power of the target vehicle, and control the fuel cell to enter the standby state.

[0118] Among them, the first state-of-charge interval is the interval where the state-of-charge parameter is greater than 90%.

[0119] Optionally, if the current state of charge is within the first state-of-charge interval, it means that the power battery can meet the power supply demand of the target vehicle. Then control the power battery to output energy at the full demand power of the target vehicle, and control the fuel cell to enter the standby state. At this time, the fuel cell does not work.

[0120] Step 1003, if the current state of charge is not within the first state-of-charge interval but within the second state-of-charge interval, control the power battery to output energy at the first proportional demand power, control the fuel cell to start and maintain the second proportional rated power for energy output, and use the redundant power of the fuel cell to charge the power battery.

[0121] Among them, the second state-of-charge interval is the interval where the state-of-charge parameter is greater than 75% and less than 90%. The first proportional demand power is between 70% - 90% of the target vehicle system demand, and the second proportional rated power can be between 10% - 30%. The embodiments of the present application do not limit this.

[0122] Optionally, if the current state of charge is not within the first state-of-charge interval but within the second state-of-charge interval, control the power battery to output a first proportion of the required power for energy output. If the power battery may not be able to meet the system requirements of the target vehicle at this time, control the fuel cell to start and maintain a second proportion of the rated power for energy output, and use the redundant power of the fuel cell to charge the power battery. Specifically, when the output power of the fuel cell meets the system requirements of the target vehicle, use the redundant power of the fuel cell to charge the power battery.

[0123] Step 1004: If the current state of charge is not within the second state-of-charge interval but within the third state-of-charge interval, control the fuel cell to output energy at a third proportion of the rated power, control the power battery to output energy at a fourth proportion of the required power, and use the redundant power of the fuel cell to charge the power battery.

[0124] Among them, the third proportion can be 40%, the third proportion is greater than the second proportion, the fourth proportion can be 80%, and the fourth proportion is less than the first proportion. The third state-of-charge interval is the interval where the state-of-charge parameter is less than 75%, and the embodiments of the present application do not limit this.

[0125] Optionally, if the current state of charge is not within the second state-of-charge interval but within the third state-of-charge interval, and at this time the power battery cannot meet the power supply requirements of the system, control the fuel cell to output energy at a third proportion of the rated power, control the power battery to output energy at a fourth proportion of the required power, and use the redundant power of the fuel cell to charge the power battery.

[0126] Based on the same inventive concept, an on-vehicle and cloud collaborative vehicle control device corresponding to an on-vehicle and cloud collaborative vehicle control method is further provided in the embodiments of the present application. The device is applied to a cloud device. Since the principle of solving problems by the device in the embodiments of the present application is similar to that of the above-mentioned on-vehicle and cloud collaborative vehicle control method in the embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0127] Figure 11 It is a schematic diagram of an on-vehicle and cloud collaborative vehicle control device provided by an embodiment of the present application, as Figure 11 shown. The device includes: An acquisition module 1101, configured to acquire historical operation data of a target vehicle within a preset unit historical time period; An identification module 1102, configured to perform working condition feature identification on the historical operation data to determine the historical working condition features of the target vehicle within the preset unit historical time period; A determination module 1103, configured to determine a historical operating scenario condition of the target vehicle in a preset unit historical time period by using a preset condition feature threshold according to the historical condition features of the target vehicle; A determination module 1103, configured to determine a target control strategy according to the historical operating scenario condition, where the target operating scenario condition is a control strategy for a long-distance condition or a control strategy for a short-distance condition; A sending module 1104, configured to send the target control strategy to a vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of a power battery and a fuel cell on the target vehicle according to the target control strategy.

[0128] Optionally, the historical operation data includes: positioning data of multiple historical trajectory points, enabling time of a power take-off, parking time, and enabling time of an air pump; the recognition module 1102 is specifically configured to: calculate the farthest running distance of the target vehicle in a preset unit historical time period according to the positioning data of the multiple historical trajectory points; calculate the enabling interval distance of the power take-off between two adjacent times in a preset unit historical time period according to the positioning data of the multiple historical trajectory points and the enabling time of the power take-off; calculate the number of parking times per unit distance in a preset unit historical time period according to the positioning data of the multiple historical trajectory points and the parking time; calculate the number of air pump enabling times per unit distance in a preset unit historical time period according to the positioning data of the multiple historical trajectory points and the enabling time of the air pump; wherein, the historical condition features include: the farthest running distance, the enabling interval distance of the power take-off, the number of parking times per unit distance, and the number of air pump enabling times per unit distance.

[0129] Optionally, the preset condition feature threshold includes: a preset running distance threshold, a preset power take-off enabling interval distance threshold, a preset parking times threshold, and a preset air pump enabling times threshold; the recognition module 1102 is specifically configured to: perform normalization processing on the farthest running distance according to the preset running distance threshold to obtain a first evaluation parameter; perform normalization processing on the enabling interval distance of the power take-off according to the preset power take-off enabling interval distance threshold to obtain a second evaluation parameter; perform normalization processing on the number of parking times per unit distance according to the preset parking times threshold to obtain a third evaluation parameter; perform normalization processing on the number of air pump enabling times per unit distance according to the preset air pump enabling times threshold to determine a fourth evaluation parameter; determine the historical operating scenario condition according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter.

[0130] Optionally, the recognition module 1102 is specifically configured to: perform a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter; If the target evaluation parameter is greater than or equal to a preset threshold, determine that the historical operation scenario condition is a long-distance condition; If the target evaluation parameter is less than the preset threshold, determine that the historical operation scenario condition is a short-distance condition.

[0131] Optionally, the recognition module 1102 is specifically configured to: perform a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter according to a first preset weight, a second preset weight, a third preset weight, and a fourth preset weight to obtain a target evaluation parameter; Wherein, the sum of the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight is 1, and both the first preset weight and the second preset weight are greater than the third preset weight and the fourth preset weight.

[0132] Optionally, the determination module 1103 is specifically configured to: determine whether there is a condition change in a preset unit historical time period according to the historical operation scenario condition; If there is no condition change, determine that the preset control strategy corresponding to the historical operation scenario condition is the target control strategy; If there is a condition change and the latest operation scenario condition is maintained in a preset number of unit historical time periods, determine that the preset control strategy corresponding to the latest operation scenario condition is the target control strategy.

[0133] Descriptions of the processing procedures of the various modules in the device and the interaction procedures between the various modules may refer to the relevant descriptions in the above method embodiments and will not be elaborated here.

[0134] Based on the same inventive concept, another embodiment of the present application further provides a vehicle control device for vehicle-cloud collaboration corresponding to a vehicle control method for vehicle-cloud collaboration, which is applied to a vehicle controller. Since the principle of solving problems by the device in the embodiments of the present application is similar to that of the above vehicle control method for vehicle-cloud collaboration in the embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0135] Figure 12 For a schematic diagram of a vehicle control device for vehicle-cloud collaboration provided in another embodiment of the present application, as Figure 12 shown, the device includes: The acquisition module 1201 is configured to upload the historical operation data of the target vehicle collected by the in-vehicle device on the target vehicle in a preset unit historical time period to the cloud device; A receiving module 1202, configured to receive a target control strategy sent by a cloud device, where the target control strategy is a control strategy determined by the cloud device according to a vehicle regulation method for vehicle-cloud collaboration; A control module 1203, configured to control the energy distribution of a power battery and a fuel cell on a target vehicle according to the target control strategy.

[0136] Optionally, the control module 1203 is specifically configured to: if the target control strategy is a control strategy for a long-distance working condition, obtain the current driving state of the target vehicle; If the current driving state is a constant-speed driving state, control the fuel cell to output energy based on a preset rated power; If the current driving state is an accelerating driving state or a climbing driving state, control the fuel cell to output energy based on a preset rated power, and control the power battery to supplement power; If the current driving state is a braking state, control the power battery to perform energy recovery so that the state of charge of the power battery is within a preset range; if the state of charge after energy recovery is still not within the preset range, control the fuel cell to charge the power battery so that the state of charge of the power battery is within the preset range.

[0137] Optionally, the control module 1203 is specifically configured to: if the target control strategy is a control strategy for a short-distance working condition, obtain the current state of charge of the power battery; If the current state of charge is within a first state-of-charge interval, control the power battery to output energy at the full required power of the target vehicle, and control the fuel cell to enter a standby state; If the current state of charge is not within the first state-of-charge interval but within a second state-of-charge interval, control the power battery to output energy at a first ratio of the required power, control the fuel cell to start and maintain a second ratio of the rated power for energy output, and use the redundant power of the fuel cell to charge the power battery; If the current state of charge is not within the second state-of-charge interval but within a third state-of-charge interval, control the fuel cell to output energy at a third ratio of the rated power, and control the power battery to output energy at a fourth ratio of the required power, and use the redundant power of the fuel cell to charge the power battery; where the third ratio is greater than the second ratio, and the fourth ratio is less than the first ratio.

[0138] This application embodiment also provides a cloud device, Figure 13A schematic structural diagram of a cloud device provided by an embodiment of the present application, including: a first processor 1301, a first memory 1302, and optionally, a first bus 1303 may also be included. The first memory 1302 stores machine-readable instructions executable by the first processor 1301. When the cloud device 200 runs, the first processor 1301 communicates with the first memory 1302 through the first bus 1303, and the machine-readable instructions are executed by the first processor 1301 to perform the steps of the vehicle control method for vehicle-cloud collaboration.

[0139] An embodiment of the present application also provides a vehicle controller. Figure 14 A schematic structural diagram of a vehicle controller provided by another embodiment of the present application, including: a second processor 1401, a second memory 1402, and optionally, a second bus 1403 may also be included. The second memory 1402 stores machine-readable instructions executable by the second processor 1401. When the vehicle controller 100 runs, the second processor 1401 communicates with the second memory 1402 through the second bus 1403, and the machine-readable instructions are executed by the second processor 1401 to perform the steps of the vehicle control method for vehicle-cloud collaboration.

[0140] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it performs the steps of the vehicle control method for vehicle-cloud collaboration.

[0141] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the method embodiments, and will not be repeated in the present application. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0142] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0143] The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A vehicle control method based on vehicle-cloud collaboration, characterized in that: Applied to a cloud device, the method includes: Obtain historical operation data of the target vehicle within a preset unit historical time period; Performing operating condition feature recognition on the historical operating data to determine the historical operating condition features of the target vehicle in the preset unit historical time period; According to the historical operating condition characteristics of the target vehicle, a preset operating condition characteristic threshold is used to determine the historical operation scenario operating condition of the target vehicle in the preset unit historical time period; Determine a target control strategy according to the historical operating scenario conditions, wherein the target operating scenario conditions are a control strategy for long-distance conditions or a control strategy for short-distance conditions; The target control strategy is issued to a vehicle controller of the target vehicle, so that the vehicle controller controls the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy.

2. The method according to claim 1, characterized in that The historical operation data includes: positioning data of multiple historical trajectory points, the enabling time of the power take-off, the parking time, and the enabling time of the air pump; the operating condition feature recognition of the historical operation data to determine the historical operating condition features of the target vehicle in the preset unit historical time period includes: Calculating the maximum running distance of the target vehicle within the preset unit historical time period according to the positioning data of the multiple historical trajectory points; Calculate the enabling interval distance of two adjacent power take-offs within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the enabling time of the power take-off; Calculate the number of unit distance parking times within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the parking time; Calculate the number of times the air pump is enabled per unit distance within the preset unit historical time period according to the positioning data of the multiple historical trajectory points and the air pump enabling time; Among them, the historical operating condition characteristics include: the longest running distance, the power take-off enabling interval distance, the number of stops per unit distance, and the number of air pump enabling times per unit distance.

3. The method according to claim 2, characterized in that The preset operating condition characteristic thresholds include: a preset running distance threshold, a preset power take-off enabling interval distance threshold, a preset parking times threshold, and a preset pumping enabling times threshold; The determining of the historical operating scenario operating condition of the target vehicle in the preset unit historical time period by using a preset operating condition characteristic threshold according to the historical operating condition characteristics of the target vehicle includes: Normalizing the farthest running distance according to the preset running distance threshold to obtain a first evaluation parameter; Standardizing the power take-off enabling interval distance according to the preset power take-off enabling interval distance threshold to obtain a second evaluation parameter; The number of stops per unit distance is normalized according to the preset number of stops threshold to obtain a third evaluation parameter; The fourth evaluation parameter is determined by normalizing the number of times the air pump is enabled per unit distance according to the preset inflation enable number threshold; The historical operating scenario conditions are determined according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter and the fourth evaluation parameter.

4. The method according to claim 3, characterized in that The determining the historical operation scenario condition according to the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter includes: Performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter; If the target evaluation parameter is greater than or equal to a preset threshold, determining that the historical operating scenario condition is a long-distance condition; If the target evaluation parameter is less than the preset threshold, it is determined that the historical operating scenario condition is a short-distance condition.

5. The method according to claim 4, characterized in that The performing a weighted sum operation on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to obtain a target evaluation parameter includes: According to the first preset weight, the second preset weight, the third preset weight and the fourth preset weight, a weighted sum operation is performed on the first evaluation parameter, the second evaluation parameter, the third evaluation parameter and the fourth evaluation parameter to obtain the target evaluation parameter; The sum of the first preset weight, the second preset weight, the third preset weight and the fourth preset weight is 1, and the first preset weight and the second preset weight are both greater than the third preset weight and the fourth preset weight.

6. The method according to claim 1, characterized in that Determining the target control strategy according to the historical operating scenario conditions includes: According to the historical operating scenario operating conditions, determine whether there is an operating condition change in the preset unit historical time period; If there is no change in the operating condition, determining the preset control strategy corresponding to the operating condition of the historical operation scenario as the target control strategy; If there is a change in operating conditions, and the latest operating scenario operating conditions are maintained within a preset number of unit historical time periods, the preset control strategy corresponding to the latest operating scenario operating conditions is determined to be the target control strategy.

7. A vehicle control method based on vehicle-cloud collaboration, characterized in that: Applied to a vehicle controller of a target vehicle, the method comprises: Uploading the historical operation data of the target vehicle collected by the on-board device of the target vehicle within a preset unit historical time period to the cloud device; Receiving a target control strategy issued by the cloud device, wherein the target control strategy is a control strategy determined by the cloud device according to the vehicle control method for vehicle-cloud collaboration as claimed in claim 1; According to the target control strategy, the energy distribution of the power battery and the fuel cell on the target vehicle is controlled.

8. The method according to claim 7, characterized in that The step of controlling the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy includes: If the target control strategy is a control strategy for long-distance operation, obtaining the current driving state of the target vehicle; If the current driving state is constant speed driving, controlling the fuel cell to output energy based on a preset rated power; If the current driving state is accelerating driving or climbing driving, controlling the fuel cell to output energy based on the preset rated power, and controlling the power battery to supplement power; If the current driving state is a braking state, the power battery is controlled to recover energy so that the state of charge of the power battery is within a preset range; if the state of charge after energy recovery is still not within the preset range, the fuel cell is controlled to charge the power battery so that the state of charge of the power battery is within the preset range.

9. The method according to claim 7, characterized in that: The step of controlling the energy distribution of the power battery and the fuel cell on the target vehicle according to the target control strategy includes: If the target control strategy is a control strategy for a short-distance operating condition, obtaining a current state of charge of the power battery; If the current state of charge is within the first charge interval, controlling the power battery to output energy at the full required power of the target vehicle, and controlling the fuel cell to enter a standby state; If the current state of charge is not within the first charge interval but within the second charge interval, the power battery is controlled to output a first proportion of the required power for energy output, the fuel cell is controlled to start and maintain a second proportion of the rated power for energy output, and the redundant power of the fuel cell is used to charge the power battery; If the current state of charge is not within the second charge interval but within the third charge interval, the fuel cell is controlled to output energy at a third ratio of the rated power, and the power battery is controlled to output energy at a fourth ratio of the required power, and the power battery is charged with the redundant power of the fuel cell; wherein the third ratio is greater than the second ratio, and the fourth ratio is less than the first ratio.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the vehicle control method for vehicle-cloud collaboration described in any one of claims 1 to 9 are executed.

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