Vehicle running control method and device, mine car, storage medium and product

Abstract

The invention discloses a vehicle driving control method and device, a mine car, a storage medium and a product, and the method comprises the steps: obtaining a dust recognition result and a haze recognition result of an environment in a vehicle driving process through image collection equipment and an Internet of Things platform; through a rainfall sensor and an Internet of Things platform, obtaining a rainfall identification result of an environment in which the vehicle is located in a driving process; according to the sand and dust recognition result, the haze recognition result and the rainfall recognition result, weather information of the environment where the vehicle is located in the running process is determined, and a control strategy matched with the weather information is adopted to control the speed limiting value and the braking force of the vehicle. According to the technical scheme, the safety coefficient of the mine car under different weather conditions can be improved, and the operation efficiency of the mine car is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a vehicle driving control method, device, mining car, storage medium, and product. Background Technology

[0002] Currently, heavy-duty trucks are one of the main forms of transportation for large open-pit mines. To improve transportation efficiency, it is particularly important to deploy automatic control systems on the mining trucks.

[0003] Existing mining trucks typically use a standard algorithm to adjust the speed limit and braking force of the vehicle based on its load. The entire control process does not take into account weather factors in the vehicle's environment. In severe weather conditions, the limited environmental awareness can easily lead to safety accidents. Summary of the Invention

[0004] This invention provides a vehicle driving control method, device, mining car, storage medium, and product, which can improve the safety factor of mining cars under different weather conditions and ensure the operating efficiency of mining cars.

[0005] According to one aspect of the present invention, a vehicle driving control method is provided, wherein an image acquisition device, a rain sensor, and an on-board terminal are deployed on the vehicle, the on-board terminal being connected to an Internet of Things (IoT) platform, the method comprising:

[0006] By using image acquisition equipment and an Internet of Things platform, the results of sand and haze identification in the environment in which the vehicle is located during its driving process are obtained;

[0007] Rainfall data is obtained from the environment in which the vehicle is located during its journey, using rain sensors and an IoT platform.

[0008] Based on the dust, haze, and rainfall identification results, the weather information of the environment in which the vehicle is located during its driving is determined, and a control strategy matching the weather information is adopted to control the vehicle's speed limit and braking force.

[0009] Optionally, the method further includes:

[0010] The image acquisition device acquires environmental images during the vehicle's movement, and a pre-trained image recognition model identifies the dust data included in the environmental images.

[0011] The real-time dust data of the environment in which the vehicle is located during its operation is transmitted to the vehicle terminal through the Internet of Things platform.

[0012] The vehicle-mounted terminal determines whether the dust data identified by the model is consistent with the real-time dust data sent by the IoT platform, and performs incremental training on the image recognition model based on the determination result.

[0013] Optionally, the rainfall identification results of the environment in which the vehicle is located during driving can be obtained through the rain sensor and the Internet of Things platform, including:

[0014] The rain sensor acquires rainfall data of the environment in which the vehicle is located during driving, and transmits the real-time rainfall data of the environment in which the vehicle is located during driving to the vehicle terminal through the Internet of Things platform.

[0015] If the rain sensor is working properly, the rain sensing data will be used as the final rain recognition result.

[0016] If the rain sensor malfunctions, the real-time rainfall data will be used as the final rainfall identification result.

[0017] Optionally, the method further includes:

[0018] The IoT platform is used to detect the corresponding working conditions and locations of the vehicle in real time during its operation.

[0019] Based on the working location and weather information of the environment in which the vehicle is located during its operation, a matching obstacle recognition device is used to identify obstacles within a preset range around the vehicle.

[0020] The vehicle's driving status is controlled based on the obstacle recognition results.

[0021] Optionally, based on the working location and weather information of the environment in which the vehicle is located during operation, a matching obstacle recognition device is used to identify obstacles within a preset range around the vehicle, including:

[0022] If the weather is clear during the vehicle's operation, the image acquisition device will identify obstacles within a preset range around the vehicle.

[0023] If the weather is not sunny during the vehicle's operation, and the vehicle is in a loading or unloading position, then the ultrasonic radar will be used to identify obstacles within a preset range around the vehicle.

[0024] If the weather is not clear during the vehicle's operation, and the vehicle is either unloaded or heavily loaded, then millimeter-wave radar will be used to identify obstacles within a preset range around the vehicle.

[0025] Optionally, the method further includes:

[0026] Obtain information on the uphill and downhill driving of the vehicle and the vehicle in front;

[0027] The braking distance of the vehicle is controlled based on the weather information of the environment in which the vehicle is located during its driving, as well as the uphill and downhill driving information of the vehicle and the vehicle in front.

[0028] According to another aspect of the present invention, a vehicle driving control device is provided, wherein an image acquisition device, a rain sensor, and an on-board terminal are deployed on the vehicle, the on-board terminal being connected to an Internet of Things (IoT) platform, and the device includes:

[0029] The sand and fog recognition module is used to obtain sand and fog recognition results of the environment in which the vehicle is located during driving through the image acquisition device and the Internet of Things platform;

[0030] The rainfall recognition module is used to obtain the rainfall recognition results of the environment in which the vehicle is located during driving through the rainfall sensor and the Internet of Things platform;

[0031] The weather analysis module is used to determine the weather information of the environment in which the vehicle is located during driving based on the dust recognition results, haze recognition results, and rainfall recognition results, and to control the speed limit and braking force of the vehicle by adopting a control strategy that matches the weather information.

[0032] According to another aspect of the present invention, a mining car is provided, the mining car comprising:

[0033] At least one processor; and a memory communicatively connected to said at least one processor;

[0034] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle driving control method according to any embodiment of the present invention.

[0035] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle driving control method according to any embodiment of the present invention.

[0036] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the vehicle driving control method according to any embodiment of the present invention.

[0037] The technical solution provided by this invention acquires dust and haze recognition results of the environment in which the vehicle is located during its operation through image acquisition equipment and an Internet of Things (IoT) platform. It also acquires rainfall recognition results of the environment in which the vehicle is located during its operation through a rain sensor and the IoT platform. Based on the dust, haze, and rainfall recognition results, the weather information of the environment in which the vehicle is located during its operation is determined. A control strategy matching the weather information is then adopted to control the speed limit and braking force of the vehicle. This provides a method for controlling the speed limit and braking force of the mining truck by combining weather factors of the environment in which the truck is located. This can improve the safety factor of the mining truck under different weather conditions and ensure the operational efficiency of the mining truck.

[0038] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a vehicle driving control method provided according to an embodiment of the present invention;

[0041] Figure 2 This is a flowchart of another vehicle driving control method provided according to an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of a vehicle driving control system according to an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of a vehicle driving control device according to an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram of the structure of a mining car that implements the vehicle driving control method of this invention. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0047] Figure 1 This is a flowchart illustrating a vehicle driving control method provided in an embodiment of the present invention. This embodiment is applicable to controlling the speed limit and braking force of a mining truck during its operation, based on weather information about the environment in which the truck is located. This method can be executed by a vehicle driving control device, which can be implemented in hardware and / or software. The vehicle is equipped with an image acquisition device, a rain sensor, and an on-board terminal (Terminal Box, TBox), which is connected to an Internet of Things (IoT) platform. Figure 1 As shown, the method includes:

[0048] Step 110: Obtain the dust and haze identification results of the environment in which the vehicle is located during its driving process through image acquisition equipment and IoT platform.

[0049] In this embodiment, the image acquisition device can be a camera or other electronic device with image acquisition capabilities, such as a scanner, and this embodiment is not limited to this. The Internet of Things platform refers to a service platform for remote connection of the vehicle terminal.

[0050] During the vehicle's operation, the image acquisition device can acquire images of the vehicle's surroundings and identify dust data included in the images to obtain a first dust identification result. Simultaneously, the IoT platform can also query dust data of the vehicle's environment based on its real-time driving location via a pre-integrated third-party weather service application programming interface (API) to obtain a second dust identification result.

[0051] In this step, optionally, after obtaining the first dust identification result and the second dust identification result through the above method, the severity of dust in the two can be compared, and the dust identification result with the higher severity can be used as the final dust identification result of the environment in which the vehicle is located during driving.

[0052] In this embodiment, specifically, the image acquisition device can also identify the haze data included in the environmental image using a similar method described above to obtain a first haze identification result. Simultaneously, the IoT platform can also query the haze data of the vehicle's environment based on the vehicle's real-time driving location using a pre-integrated third-party weather service API, thereby obtaining a second haze identification result. Finally, the haze identification result with the higher severity is taken as the final haze identification result for the environment in which the vehicle is located during its driving process.

[0053] Step 120: Obtain the rainfall identification results of the environment in which the vehicle is located during driving through the rain sensor and the Internet of Things platform.

[0054] In this embodiment, optionally, the rain sensor can be a piezoelectric sensor or a capacitive sensor, etc., and this embodiment does not limit it. The rain sensor can determine the first rainfall identification result of the environment in which the vehicle is located during driving based on the deformation result of the piezoelectric material or the change in capacitance value after the raindrop impact.

[0055] Meanwhile, the IoT platform can also query the rainfall data of the vehicle's environment based on the vehicle's real-time driving location through a pre-integrated third-party weather service API, thereby obtaining a second rainfall recognition result. Finally, by combining the first and second rainfall recognition results, the final rainfall recognition result of the environment in which the vehicle is located during its driving process is determined.

[0056] Step 130: Based on the dust recognition results, haze recognition results, and rainfall recognition results, determine the weather information of the environment in which the vehicle is located during its driving process, and adopt a control strategy that matches the weather information to control the speed limit and braking force of the vehicle.

[0057] In this embodiment, optionally, before the vehicle travels, eight different weather levels can be pre-defined based on the severity of sandstorms, haze, and rainfall. These levels include: normal clear weather, light sandstorm / haze + light rainfall, light sandstorm / haze + moderate rainfall, light sandstorm / haze + heavy rainfall, moderate sandstorm / haze + light to moderate rainfall, moderate sandstorm / haze + heavy rainfall, heavy sandstorm / haze + light to moderate rainfall, and heavy sandstorm / haze + heavy rainfall. Then, corresponding speed limits and braking force control strategies are set for each weather level, as shown in Table 1.

[0058] Table 1

[0059]

[0060] After determining the dust, haze, and rainfall identification results of the environment in which the vehicle is located during its driving process through the above steps, the current weather level of the environment in which the vehicle is located can be determined based on the dust, haze, and rainfall identification results. Then, a matching control strategy can be obtained by querying Table 1 based on the current weather level, and the speed limit and braking force of the vehicle can be controlled according to the control strategy.

[0061] The technical solution provided by this invention acquires dust and haze recognition results of the environment in which the vehicle is located during its operation through image acquisition equipment and an Internet of Things (IoT) platform. It also acquires rainfall recognition results of the environment in which the vehicle is located during its operation through a rain sensor and the IoT platform. Based on the dust, haze, and rainfall recognition results, the weather information of the environment in which the vehicle is located during its operation is determined. A control strategy matching the weather information is then adopted to control the speed limit and braking force of the vehicle. This provides a method for controlling the speed limit and braking force of the mining truck by combining weather factors of the environment in which the truck is located. This can improve the safety factor of the mining truck under different weather conditions and ensure the operational efficiency of the mining truck.

[0062] Figure 2 A flowchart of another vehicle driving control method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the method includes:

[0063] Step 210: Obtain the dust and haze identification results of the environment in which the vehicle is located during its driving process through image acquisition equipment and IoT platform.

[0064] In one embodiment of this example, the method further includes: acquiring environmental images of the vehicle during its driving process using an image acquisition device, and identifying dust data included in the environmental images using a pre-trained image recognition model; transmitting real-time dust data of the environment in which the vehicle is located during its driving process to an in-vehicle terminal via an Internet of Things (IoT) platform; determining, via the in-vehicle terminal, whether the dust data identified by the model is consistent with the real-time dust data transmitted by the IoT platform, and incrementally training the image recognition model based on the determination result.

[0065] In one specific embodiment, the image recognition model can be an artificial intelligence model obtained by pre-training a neural network model using a large number of environmental dust and haze images. After the image acquisition device acquires environmental images during vehicle movement, it can input the environmental images into the image recognition model built into the device. The model can extract features from the environmental images based on pre-trained model parameters and identify dust data in the environmental images based on the feature extraction results. Optionally, the dust data may include dust images and dust density data, etc.

[0066] In this embodiment, the IoT platform can also obtain real-time dust data of the vehicle's environment based on the vehicle's real-time driving location through a pre-integrated third-party weather service API, and send the real-time dust data to the vehicle terminal.

[0067] The vehicle-mounted terminal can determine whether the dust data identified by the image recognition model is consistent with the real-time dust data sent by the IoT platform. If the severity of the real-time dust data sent by the IoT platform is higher than that identified by the image recognition model, the IoT platform can push the environmental images during vehicle operation, the dust data identified by the image recognition model, and the real-time dust data to the algorithm optimization personnel. The algorithm optimization personnel will then determine the standard dust data corresponding to the environmental images. The IoT platform will then send the environmental images and standard dust data to the image acquisition device, which will use them as new training samples to incrementally train the image recognition model.

[0068] In this embodiment, specifically, the image recognition model can also identify haze data included in the environmental image in a similar manner as described above; the real-time haze data of the environment in which the vehicle is located during driving is sent to the vehicle terminal through the Internet of Things platform; the vehicle terminal determines whether the haze data identified by the model is consistent with the real-time haze data sent by the Internet of Things platform, and performs incremental training on the image recognition model based on the determination result.

[0069] The advantage of this setup is that the recognition function of the image acquisition device can be continuously optimized through real-time weather data sent by the IoT platform, thereby improving the accuracy of the recognition results of the image acquisition device and ensuring its adaptability to complex road conditions.

[0070] Step 220: Obtain the rainfall identification results of the environment in which the vehicle is located during driving through the rain sensor and the Internet of Things platform.

[0071] In one embodiment of this invention, obtaining rainfall identification results of the environment in which the vehicle is located during driving through the rain sensor and the Internet of Things (IoT) platform includes: acquiring rainfall sensing data of the environment in which the vehicle is located during driving through the rain sensor, and transmitting the real-time rainfall data of the environment in which the vehicle is located during driving to the vehicle terminal through the IoT platform; if the rain sensor is working normally, the rainfall sensing data is used as the final rainfall identification result; if the rain sensor is malfunctioning, the real-time rainfall data is used as the final rainfall identification result.

[0072] The advantage of this setup is that, compared to the existing technology where rainfall is determined solely by a rain sensor, it avoids inaccurate rainfall readings affecting vehicle safety in the event of a rain sensor malfunction. This improves vehicle safety and ensures operational efficiency.

[0073] Step 230: Based on the dust recognition results, haze recognition results, and rainfall recognition results, determine the weather information of the environment in which the vehicle is located during its driving process.

[0074] Step 240: Detect the corresponding working condition position of the vehicle in real time through the IoT platform.

[0075] In this embodiment, the IoT platform can pre-set the loading point, unloading point, and electronic fence of the driving path corresponding to the vehicle. During the vehicle's journey, it can determine whether the vehicle is in a loading position, an empty driving state, a heavy driving state, or an unloading position based on the pre-set location markers and the weighing results of the vehicle's load sensor.

[0076] Step 250: Based on the working location and the weather information of the environment in which the vehicle is located during driving, a matching obstacle recognition device is used to identify obstacles within a preset range around the vehicle, and the driving status of the vehicle is controlled based on the obstacle recognition results.

[0077] In one embodiment of this example, based on the working location and weather information of the environment in which the vehicle is located during its operation, a matching obstacle recognition device is used to identify obstacles within a preset range around the vehicle, including:

[0078] Step 251: If the weather is clear during the vehicle's operation, the image acquisition device will identify obstacles within a preset range around the vehicle.

[0079] Step 252: If the weather is not sunny during the vehicle's operation and the vehicle is in a loading or unloading position, then the ultrasonic radar is used to identify obstacles within a preset range around the vehicle.

[0080] Step 253: If the weather is not sunny during the vehicle's operation, and the vehicle is either unloaded or heavily loaded, then the obstacles within a preset range around the vehicle are identified using millimeter-wave radar.

[0081] For example, if the weather is clear during vehicle operation, obstacles around the vehicle can be directly identified using the onboard camera, including loading positions, empty driving status, heavy driving status, and unloading positions. If the weather is not clear, ultrasonic radar can be used to identify obstacles around the vehicle at loading and unloading positions, specifically for short-range (e.g., less than 5 meters) obstacle identification scenarios such as when approaching an excavator or reversing to unload. If the weather is not clear, millimeter-wave radar can be used to identify obstacles around the vehicle at both empty and heavy driving statuses, specifically for long-range (e.g., greater than 10 meters) obstacle identification scenarios.

[0082] In addition, obstacles within 5 to 10 meters can be identified by fusing algorithms from ultrasonic radar and millimeter-wave radar.

[0083] The advantage of this setup is that it uses matching obstacle recognition devices to process obstacles at different distances, which can fully utilize the algorithmic advantages of millimeter-wave radar and ultrasonic radar, thus improving the accuracy of obstacle recognition results.

[0084] Step 260: Obtain the uphill and downhill driving information of the vehicle and the vehicle in front, and control the braking distance of the vehicle based on the weather information of the environment in which the vehicle is located during its driving process, as well as the uphill and downhill driving information of the vehicle and the vehicle in front.

[0085] In this embodiment, specifically, based on the uphill and downhill driving information of the vehicle and the vehicle in front, four driving scenarios can be identified: two vehicles uphill, two vehicles downhill, the vehicle in front driving on a flat road while the vehicle in front is downhill, and the vehicle in front driving uphill while the vehicle in front is on a flat road. Then, a corresponding braking distance control strategy is set for each weather level and driving scenario. The braking distance control strategy includes the target braking distance of the vehicle under specific weather levels and driving scenarios.

[0086] In one specific embodiment, after controlling the braking distance of the vehicle, the braking distance, the vehicle's state of charge (SOC) and the brake pedal opening can be combined to determine whether the vehicle needs to actively brake. If so, the braking force of the vehicle is controlled according to step 270.

[0087] Step 270: Adopt a control strategy that matches the weather information to control the speed limit and braking force of the vehicle.

[0088] The technical solution provided by this invention acquires dust and haze recognition results of the environment in which the vehicle is located during its operation through image acquisition equipment and an Internet of Things (IoT) platform, and acquires rainfall recognition results of the environment in which the vehicle is located during its operation through a rain sensor and an IoT platform. Based on the dust, haze, and rainfall recognition results, the weather information of the environment in which the vehicle is located during its operation is determined. The IoT platform detects the corresponding working position of the vehicle in real time during its operation. Based on the working position and the weather information of the environment in which the vehicle is located during its operation, a matching obstacle recognition device is used to identify obstacles within a preset range around the vehicle. The driving state of the vehicle is controlled based on the obstacle recognition results. The uphill and downhill driving information of the vehicle and the vehicle in front is acquired. Based on the weather information and the uphill and downhill driving information of the vehicle and the vehicle in front, the braking distance of the vehicle is controlled. By adopting a control strategy that matches the weather information to control the speed limit and braking force of the vehicle, the safety factor of the mining truck under different weather conditions can be improved, and the operating efficiency of the mining truck can be guaranteed.

[0089] Based on the above embodiments, this embodiment also provides a vehicle driving control system, such as... Figure 3 As shown. The vehicle driving control system includes an Internet of Things platform, an in-vehicle terminal TBox, a vehicle control unit (VCU), a rain sensor, a load sensor, an ultrasonic radar, a millimeter-wave radar, a camera, and a controller.

[0090] The vehicle controller is connected to a rain sensor, a load sensor, an ultrasonic radar, a millimeter-wave radar, a camera and controller, and a TBox. The TBox is also connected to an IoT platform, which in turn is connected to the camera and controller.

[0091] The IoT platform is used to send real-time sandstorm data, real-time haze data, and real-time rainfall data to the TBox; the vehicle controller is used to combine the real-time weather data sent by the IoT platform with the weather data sensed by the camera and rain sensor to determine the weather information of the environment in which the vehicle is located during driving, and to use a control strategy that matches the weather information to control the speed limit and braking force of the vehicle.

[0092] The camera and controller are used to identify sand and haze data in the environment where the vehicle is located. When the real-time weather data sent by the IoT platform is inconsistent with the weather data identified by the camera, the identified weather data is uploaded to the IoT platform so that the IoT platform can optimize and train the identification model.

[0093] The vehicle controller is also used to identify obstacles within a preset range around the vehicle based on the vehicle's corresponding working position and the weather information of the surrounding environment, using matching obstacle recognition devices (such as cameras, ultrasonic radar, and millimeter-wave radar) for different obstacle distances, and to control the vehicle's driving status based on the obstacle recognition results.

[0094] pass Figure 3 The vehicle driving control system shown can improve the safety factor of mining trucks under different weather conditions and ensure the operating efficiency of mining trucks.

[0095] Figure 4 This is a schematic diagram of a vehicle driving control device provided in an embodiment of the present invention. The vehicle is equipped with an image acquisition device, a rain sensor, and an on-board terminal. The on-board terminal is connected to an Internet of Things (IoT) platform, such as... Figure 4 As shown, the device includes: a sand and fog recognition module 310, a rainfall recognition module 320, and a weather analysis module 330.

[0096] The sand and fog recognition module 310 is used to obtain the sand and fog recognition results of the environment in which the vehicle is located during driving through the image acquisition device and the Internet of Things platform;

[0097] The rainfall recognition module 320 is used to obtain the rainfall recognition results of the environment in which the vehicle is located during driving through the rainfall sensor and the Internet of Things platform;

[0098] The weather analysis module 330 is used to determine the weather information of the environment in which the vehicle is located during driving based on the dust recognition results, haze recognition results and rainfall recognition results, and to control the speed limit and braking force of the vehicle by adopting a control strategy that matches the weather information.

[0099] The technical solution provided by this invention acquires dust and haze recognition results of the environment in which the vehicle is located during its operation through image acquisition equipment and an Internet of Things (IoT) platform. It also acquires rainfall recognition results of the environment in which the vehicle is located during its operation through a rain sensor and the IoT platform. Based on the dust, haze, and rainfall recognition results, the weather information of the environment in which the vehicle is located during its operation is determined. A control strategy matching the weather information is then adopted to control the speed limit and braking force of the vehicle. This provides a method for controlling the speed limit and braking force of the mining truck by combining weather factors of the environment in which the truck is located. This can improve the safety factor of the mining truck under different weather conditions and ensure the operational efficiency of the mining truck.

[0100] Based on the above embodiments, the sand and fog recognition module 310 includes:

[0101] The model recognition unit is used to acquire environmental images during the vehicle's driving process through the image acquisition device, and to identify the sand and dust data included in the environmental images through a pre-trained image recognition model.

[0102] The dust data transmission unit is used to transmit real-time dust data of the environment in which the vehicle is located during driving to the vehicle terminal through the Internet of Things platform;

[0103] The dust data judgment unit is used to judge whether the dust data identified by the model is consistent with the real-time dust data sent by the Internet of Things platform through the vehicle terminal, and to perform incremental training on the image recognition model based on the judgment result.

[0104] Rainfall recognition module 320 includes:

[0105] The rainfall data acquisition unit is used to acquire rainfall sensing data of the environment in which the vehicle is located during driving through the rainfall sensor, and to send the real-time rainfall data of the environment in which the vehicle is located during driving to the vehicle terminal through the Internet of Things platform.

[0106] The rainfall data filtering unit is used to take the rainfall sensing data as the final rainfall identification result if the rainfall sensor is working properly, and to take the real-time rainfall data as the final rainfall identification result if the rainfall sensor is malfunctioning.

[0107] The device further includes:

[0108] The obstacle recognition module is used to detect the corresponding working position of the vehicle in real time through the Internet of Things platform; based on the working position and the weather information of the environment in which the vehicle is located during driving, it uses a matching obstacle recognition device to identify obstacles within a preset range around the vehicle; and controls the driving state of the vehicle based on the obstacle recognition results.

[0109] The braking distance control module is used to acquire the uphill and downhill driving information of the vehicle and the vehicle in front; and to control the braking distance of the vehicle based on the weather information of the environment in which the vehicle is located during its driving process, as well as the uphill and downhill driving information of the vehicle and the vehicle in front.

[0110] The obstacle recognition module includes:

[0111] The first obstacle recognition unit is used to identify obstacles within a preset range around the vehicle through the image acquisition device if the weather is sunny during the vehicle's operation.

[0112] The second obstacle recognition unit is used to identify obstacles within a preset range around the vehicle using ultrasonic radar if the weather is not sunny during the vehicle's operation and the vehicle is in a loading or unloading position.

[0113] The third obstacle recognition unit is used to identify obstacles within a preset range around the vehicle using millimeter-wave radar if the weather is not sunny during the vehicle's operation and the vehicle is either unloaded or heavily loaded.

[0114] The above-described apparatus can execute the methods provided in all the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the above methods. Technical details not described in detail in the embodiments of the present invention can be found in the methods provided in all the foregoing embodiments of the present invention.

[0115] Figure 5 A schematic diagram of the structure of a mining car 10 that can be used to implement an embodiment of the present invention is shown.

[0116] like Figure 5 As shown, the mining vehicle 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the processor 11. The memory stores computer programs executable by the processor 11. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 can also store various programs and data required for the operation of the mining vehicle 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0117] Multiple components in the mining vehicle 10 are connected to the input / output interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the mining vehicle 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as vehicle driving control methods.

[0119] In some embodiments, the vehicle driving control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the mining car 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into random access memory 13 and executed by processor 11, one or more steps of the vehicle driving control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle driving control method by any other suitable means (e.g., by means of firmware).

[0120] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0123] To provide interaction with the user, the systems and technologies described herein can be implemented on a mining truck, which includes: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the mining truck. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0125] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0126] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0127] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle travel control method characterized by comprising: An image acquisition device, a rain sensor and a vehicle terminal are deployed on a vehicle, the vehicle terminal is connected with an Internet of Things platform, and the method comprises the following steps: Through the image acquisition device and the Internet of Things platform, a sand and dust identification result and a haze identification result of an environment in which the vehicle is located during driving are obtained; Through the rain sensor and the Internet of Things platform, a rain identification result of the environment in which the vehicle is located during driving is obtained; According to the sand and dust identification result, the haze identification result and the rain identification result, weather information of the environment in which the vehicle is located during driving is determined, and a control strategy matched with the weather information is adopted to control a speed limit value and a braking force of the vehicle.

2. The method of claim 1, wherein, The method further comprises the following steps: Through the image acquisition device, an environmental image during driving of the vehicle is obtained, and sand and dust data included in the environmental image is identified through a pre-trained image recognition model; Through the Internet of Things platform, real-time sand and dust data of the environment in which the vehicle is located during driving is downloaded to the vehicle terminal; Through the vehicle terminal, it is judged whether the sand and dust data identified by the model is consistent with the real-time sand and dust data downloaded by the Internet of Things platform, and the image recognition model is incrementally trained according to the judgment result.

3. The method of claim 1, wherein, Through the rain sensor and the Internet of Things platform, a rain identification result of an environment in which the vehicle is located during driving is obtained, comprising the following steps: Through the rain sensor, rain perception data of the environment in which the vehicle is located during driving is obtained, and through the Internet of Things platform, real-time rain data of the environment in which the vehicle is located during driving is downloaded to the vehicle terminal; If the rain sensor is working normally, the rain perception data is taken as the final rain identification result; If the rain sensor is working abnormally, the real-time rain data is taken as the final rain identification result.

4. The method of claim 1, wherein, The method further comprises the following steps: Through the Internet of Things platform, a corresponding working condition position during driving of the vehicle is detected in real time; According to the working condition position and weather information of the environment in which the vehicle is located during driving, a matched obstacle identification device is adopted to identify obstacles within a preset range around the vehicle; According to the obstacle identification result, the driving state of the vehicle is controlled.

5. The method of claim 4, wherein, According to the working condition position and weather information of the environment in which the vehicle is located during driving, a matched obstacle identification device is adopted to identify obstacles within a preset range around the vehicle, comprising the following steps: If the weather of the environment in which the vehicle is located during driving is fine, the image acquisition device is used to identify obstacles within a preset range around the vehicle; If the weather of the environment in which the vehicle is located during driving is not fine, and the vehicle is in a loading position or an unloading position, an ultrasonic radar is used to identify obstacles within a preset range around the vehicle; If the weather of the environment in which the vehicle is located during driving is not fine, and the vehicle is in an empty driving state or a heavy load driving state, a millimeter wave radar is used to identify obstacles within a preset range around the vehicle.

6. The method of claim 1, wherein, The method further comprises the following steps: Slope driving information of the vehicle and a preceding vehicle is obtained; According to the weather information of the environment where the vehicle is located during driving and the uphill and downhill driving information of the vehicle and the front vehicle, the brake distance of the vehicle is controlled.

7. A vehicle travel control device characterized by comprising: An image acquisition device, a rain sensor and a vehicle terminal are deployed on a vehicle, the vehicle terminal is connected with an Internet of Things platform, and the device comprises: A sand and dust identification module is configured to acquire sand and dust identification results and haze identification results of an environment where the vehicle is located during driving through the image acquisition device and the Internet of Things platform; A rain identification module is configured to acquire rain identification results of the environment where the vehicle is located during driving through the rain sensor and the Internet of Things platform; A weather analysis module is configured to determine weather information of the environment where the vehicle is located during driving according to the sand and dust identification results, the haze identification results and the rain identification results, and to control the speed limit value and the braking force of the vehicle by using a control strategy matched with the weather information.

8. A mine car characterized by, The mine car comprises: at least one processor; and a memory connected with the at least one processor in communication; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle driving control method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to execute the vehicle driving control method of any one of claims 1-6 when executed.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program implements the vehicle driving control method according to any one of claims 1-6 when executed by the processor.

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