Water drainage control method and system of sanitation vehicle

Abstract

The invention relates to the technical field of intelligent control, and discloses a drainage control method of a sanitation vehicle. The method comprises the following steps: acquiring environment data of a working area through a sensor, and inputting the data into a trained LSTM model; dynamically calculating water discharge amount and water discharge pressure according to the pollution predicted value, and adjusting a water discharge angle by using vehicle speed and road gradient information; and a Dijkstra algorithm is used to plan a cleaning path. Compared with the technical problems that in the prior art, drainage control parameters are fixed, path planning is lack of flexibility, and particularly under the conditions of complex road conditions and different pollution degrees, accurate drainage and efficient cleaning cannot be achieved, the method adapts to different operation conditions by introducing a prediction model, dynamically adjusting drainage parameters and intelligently planning the path, and the operation efficiency is improved. Intelligent cleaning control is realized, so that the problem of low cleaning efficiency is avoided, and the working efficiency of the sanitation vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control technology, and in particular relates to a water discharge control method and system for a sanitation vehicle. Background Art

[0002] At present, there are deficiencies in the water discharge control technology of sanitation vehicles in urban cleaning operations. For example, traditional sanitation vehicle water discharge control systems usually use fixed water discharge volume and pressure parameters, and lack the ability to adaptively adjust real-time factors such as environmental pollution level, road conditions, and traffic flow. Under dynamic conditions such as complex road conditions, highly polluted areas, and peak traffic, the existing technology cannot fully meet the needs of accurate control of water discharge volume, water resource conservation, and efficient cleaning. Therefore, there is an urgent need for a method that can achieve accurate water discharge control under different road conditions and pollution conditions based on dynamic information such as environmental pollution prediction, real-time vehicle speed, road slope, and path optimization, so as to improve the cleaning effect, resource utilization efficiency, and the intelligent level of sanitation vehicle operations. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a water release control method for a sanitation vehicle, aiming to solve the technical problems in the prior art that water release control parameters are fixed and path planning lacks flexibility, especially under complex road conditions and different pollution levels, and accurate water release and efficient cleaning cannot be achieved.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a water discharge control method for a sanitation vehicle.

[0005] The water discharge control method of the sanitation vehicle comprises:

[0006] Step S10: using air quality sensors, imaging cameras and infrared detection equipment to collect environmental data of the work area, including particulate matter concentration, garbage density and temperature information;

[0007] The environmental data of the operating area is standardized and used as the input of the trained LSTM model, and the output is the pollution prediction value P predicted ;

[0008] Step S20: According to the pollution prediction value P predicted Calculate the dynamic water discharge volume Q and water discharge pressure P;

[0009] The calculation formula of water release Q is Q = 0.5 × P predicted ;

[0010] The water discharge pressure P is divided into three levels according to the degree of pollution, specifically:

[0011] When the predicted value P predicted <50 μg / m3 When the water discharge pressure is set to 0.2MPa;

[0012] When the predicted value is 50 μg / m 3 ≤P predicted <100 μg / m 3 When the water discharge pressure is 0.3MPa;

[0013] When the predicted value P predicted >100 μg / m 3 When the water discharge pressure is 0.4MPa;

[0014] Step S30: using the vehicle-mounted GPS module to obtain the vehicle speed v in real time, and at the same time detecting the road slope α through the inclination sensor installed at the bottom of the vehicle;

[0015] According to the vehicle speed v and the road slope α, adjust the water release angle θ, and the adjustment formula is:

[0016]

[0017] Among them, sin is the sine function;

[0018] Step S40: According to the pollution prediction value P in step S20 predicted , Dijkstra algorithm is used to plan the cleaning path, and the path prioritizes the pollution value P predicted In high-speed areas, the maximum length of each planned path shall not exceed 5 kilometers, and the vehicle speed shall be controlled at 10-20 km / h;

[0019] Step S50: During the execution of the cleaning path, the laser radar sensor detects obstacles ahead. When an obstacle is detected, the system marks the area within 2 meters around the obstacle as a "no-entry area" and replans the cleaning path;

[0020] After the path is adjusted, the dynamic water release volume Q, water release pressure P, and water release angle θ are recalculated;

[0021] Step S60: After the cleaning operation is completed, the water resource consumption data and the feedback data of the cleaning area are recorded, and the adjustment coefficient β in the LSTM model is optimized and adjusted according to the feedback data.

[0022] Preferably, in step S20, in step S10, the model of the air quality sensor is PMS5003, the resolution of the imaging camera is 1280x720, and the model of the infrared detection device is MLX90640.

[0023] Preferably, in step S10, the LSTM model is trained based on historical environmental data, the input is standardized historical environmental data, and the output is the actual pollution prediction value.

[0024] Preferably, in step S10, an improved loss function is used in the LSTM model training process, and the formula is:

[0025]

[0026] Among them, S is the improved loss function, N is the number of historical environment data samples, i is the i-th historical environment data sample, y i is the actual predicted value, is the pollution value predicted by the model, w i is the weighting coefficient;

[0027] The loss function S is used to adjust the weight parameters of each layer of the model.

[0028] Preferably, the weighting coefficient w i The calculation method is:

[0029]

[0030] Among them, β is the adjustment coefficient, which is used to control the weight amplification ratio of high-pollution areas; max(y) is the maximum pollution value in all historical data.

[0031] Preferably, in step S20, the water discharge pressure is adjusted by controlling the opening of a valve, and the control valve model is DF300.

[0032] Preferably, in step S30, the vehicle-mounted GPS module model is Ublox NEO-M8N, and the tilt sensor model is SCA103T-D05.

[0033] The present invention also provides a water discharge control system for a sanitation vehicle, comprising:

[0034] A data acquisition module, which uses air quality sensors, imaging cameras and infrared detection equipment to collect environmental data of the work area, including particulate matter concentration, garbage density and temperature information;

[0035] The environmental data of the operating area is standardized and used as the input of the trained LSTM model, and the output is the pollution prediction value P predicted ;

[0036] The water release control module is used to predicted Calculate the dynamic water discharge volume Q and water discharge pressure P;

[0037] The calculation formula of water release Q is Q = 0.5 × P predicted ;

[0038] The water discharge pressure P is divided into three levels according to the degree of pollution, specifically:

[0039] When the predicted value P predicted <50 μg / m 3 When the water discharge pressure is set to 0.2MPa;

[0040] When the predicted value is 50 μg / m 3 ≤P predicted <100 μg / m 3 When the water discharge pressure is 0.3MPa;

[0041] When the predicted value P predicted >100 μg / m 3 When the water discharge pressure is 0.4MPa;

[0042] An angle adjustment module is used to obtain the vehicle speed v in real time using the vehicle-mounted GPS module, and detect the road slope α through the inclination sensor installed at the bottom of the vehicle;

[0043] According to the vehicle speed v and the road slope α, adjust the water release angle θ, and the adjustment formula is:

[0044]

[0045] Among them, sin is the sine function;

[0046] The path planning module is used to calculate the pollution prediction value P in step S20. predicted , Dijkstra algorithm is used to plan the cleaning path, and the path prioritizes the pollution value P predicted In high-speed areas, the maximum length of each planned path shall not exceed 5 kilometers, and the vehicle speed shall be controlled at 10-20 km / h;

[0047] The obstacle detection and path adjustment module is used to detect obstacles in front through the lidar sensor during the execution of the cleaning path. When an obstacle is detected, the system marks the area within 2 meters around the obstacle as a "no-entry area" and replans the cleaning path;

[0048] After the path is adjusted, the dynamic water release volume Q, water release pressure P, and water release angle θ are recalculated;

[0049] The feedback optimization module records the feedback data including water resource consumption data and cleaning area after the cleaning operation is completed, and optimizes and adjusts the adjustment coefficient β in the LSTM model according to the feedback data.

[0050] The present invention also provides a water discharge control device for a sanitation vehicle, comprising a memory, a processor, and a water discharge control program for the sanitation vehicle stored in the memory and executable on the processor. When the water discharge control program for the sanitation vehicle is executed by the processor, the water discharge control method for the sanitation vehicle is implemented.

[0051] The present invention also provides a computer program product, including a water discharge control program for a sanitation vehicle, and when the water discharge control program for the sanitation vehicle is executed by a processor, the water discharge control method for the sanitation vehicle is implemented.

[0052] The beneficial effect of the present invention is that compared with the prior art in which water discharge control parameters are fixed and path planning lacks flexibility, especially under complex road conditions and conditions of different pollution levels, the technical problem of being unable to achieve accurate water discharge and efficient cleaning, the present invention introduces a prediction model, dynamically adjusts water discharge parameters and intelligent path planning, adapts to different operating conditions, and realizes intelligent cleaning control, thereby avoiding the problem of low cleaning efficiency and improving the operating efficiency of sanitation vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 The present invention is a flowchart of a first embodiment of a method for controlling water discharge of a sanitation vehicle.

[0055] Figure 2 The present invention is a schematic diagram of equipment for a water release control method for a sanitation vehicle. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1: Figure 1 , which is a flow chart of a first embodiment of a water discharge control method for a sanitation vehicle of the present invention, and provides a first embodiment of a water discharge control method for a sanitation vehicle of the present invention.

[0058] In the first embodiment, the water discharge control method of the sanitation vehicle includes:

[0059] Step S10: using air quality sensors, imaging cameras and infrared detection equipment to collect environmental data of the work area, including particulate matter concentration, garbage density and temperature information;

[0060] The environmental data of the operating area is standardized and used as the input of the trained LSTM model, and the output is the pollution prediction value P predicted ;

[0061] It should be noted that air quality sensors are used to detect particle concentrations. For example, the PM2.5 sensor model is PMS5003, which can accurately collect particle concentration data. Imaging cameras are used to capture garbage distribution images and extract garbage density information from them. The camera resolution is usually set to 1280x720 to ensure image clarity. Infrared detection devices (such as MLX90640) are used to collect temperature information, which helps to improve the accuracy of pollution prediction, especially when temperature has a greater impact.

[0062] It is understandable that in order to improve the prediction accuracy of the model, different types of sensor data need to be standardized. The standardized data can eliminate the differences in dimensions and ranges between different data sources, making the LSTM model more stable for training and prediction. Specific standardization methods include minimum-maximum standardization or Z-score standardization.

[0063] It should be understood that in order to adapt to different operating areas and pollution conditions, the input data of the LSTM model can be expanded according to actual conditions, such as adding data dimensions such as humidity and wind speed, to further improve the applicability and accuracy of the prediction.

[0064] For example, during the peak operation period of the day, when the particle concentration reaches 80 micrograms per cubic meter, the garbage density is 15 pieces per square meter, and the temperature is 25°C, the pollution value predicted by the LSTM model is 100. This predicted value can be used to dynamically adjust the water discharge volume and pressure in subsequent steps to meet the needs of cleaning operations.

[0065] Step S20: According to the pollution prediction value P predicted Calculate the dynamic water discharge volume Q and water discharge pressure P;

[0066] The calculation formula of water release Q is Q = 0.5 × P predicted ;

[0067] The water discharge pressure P is divided into three levels according to the degree of pollution, specifically:

[0068] When the predicted value P predicted <50 μg / m 3 When the water discharge pressure is set to 0.2MPa;

[0069] When the predicted value is 50 μg / m 3 ≤P predicted <100 μg / m 3 When the water discharge pressure is 0.3MPa;

[0070] When the predicted value P predicted >100 μg / m 3 When the water discharge pressure is 0.4MPa;

[0071] It should be noted that the adjustment coefficient (0.5) in the dynamic water discharge calculation formula is obtained through multiple experimental optimizations, so that the system can maintain effective cleaning capabilities under low pollution value conditions, and appropriately increase the water discharge under high pollution value conditions to achieve efficient cleaning effects.

[0072] It is understandable that the three levels of water discharge pressure are set according to the degree of pollution in order to achieve accurate water resource utilization in different pollution environments. For example, in a low-pollution environment, reducing the water discharge pressure can effectively save water resources, while appropriately increasing the pressure in a high-pollution area can enhance the cleaning effect. The three levels of water discharge pressure (0.2MPa, 0.3MPa, 0.4MPa) can be appropriately adjusted according to the environmental needs of specific cities to meet the cleaning standards and water resource use restrictions in different regions.

[0073] For example, when the predicted pollution value is 80, the calculated water discharge volume Q = 0.5 × 80 = 40L / min, and the water discharge pressure is set to 0.3MPa. At this time, the system operates in a moderately polluted area, maintaining a moderate cleaning effect and effectively controlling water resource consumption.

[0074] Step S30: using the vehicle-mounted GPS module to obtain the vehicle speed v in real time, and at the same time detecting the road slope α through the inclination sensor installed at the bottom of the vehicle;

[0075] According to the vehicle speed v and the road slope α, adjust the water release angle θ, and the adjustment formula is:

[0076]

[0077] Among them, sin is the sine function;

[0078] It should be noted that the initial water release angle of 45° in the formula is the optimal water release angle under flat roads and low vehicle speeds, which can ensure water flow coverage and cleaning effect. The adjustment coefficients of 0.5 and 1.2 are determined through experiments and are used to adjust the sensitivity to the influence of vehicle speed and slope, respectively.

[0079] It is understandable that the conversion of the vehicle speed v from km / h to m / s (by dividing by 3.6) is to unify the units and thus ensure the accuracy of the formula calculation. Through this conversion, the change in vehicle speed can be directly reflected in the adjustment of the water release angle, ensuring that the water flow angle is adaptively adjusted when the vehicle speed is high, avoiding excessive water loss.

[0080] It should be understood that the water release angle will automatically adjust as the road slope changes to ensure that the water flow can cover more area in front when going downhill and reduce the forward deviation of the water flow when going uphill, thereby improving the efficiency and effectiveness of cleaning.

[0081] For example, when the vehicle speed v = 36 km / h (converted to 10 m / s) and the slope α = 15, the calculated water release angle is:

[0082]

[0083] At this angle, the water flow rate can adapt to changes in slope and vehicle speed to achieve a higher cleaning effect.

[0084] Step S40: According to the pollution prediction value P in step S20 predicted , Dijkstra algorithm is used to plan the cleaning path, and the path prioritizes the pollution value P predicted In high-speed areas, the maximum length of each planned path shall not exceed 5 kilometers, and the vehicle speed shall be controlled at 10-20 km / h;

[0085] It should be noted that the Dijkstra algorithm is an algorithm commonly used to find the shortest path. Using the Dijkstra algorithm here can ensure that the sanitation vehicle passes through areas with higher pollution prediction values ​​first to achieve higher cleaning efficiency, while avoiding unnecessary detours, thereby saving water resources and fuel consumption.

[0086] It is understandable that giving priority to areas with high pollution values ​​during route planning can enable sanitation vehicles to focus on cleaning areas with more serious pollution and improve cleaning efficiency. This can not only ensure the cleaning effect, but also reduce resource consumption in low-pollution areas.

[0087] It should be understood that the maximum length of route planning (5 kilometers) is set based on the urban road environment and the time limit of the cleaning task. This ensures that the sanitation vehicle can complete the cleaning task within a reasonable time and avoids exceeding the set range and causing uneven cleaning effects.

[0088] For example, when there are multiple polluted areas in the operation area and the pollution values ​​are 50, 80 and 120 respectively, the system will give priority to the area with a pollution value of 120 to ensure that the highly polluted areas are cleaned first. If the total path length is found to exceed 5 kilometers during the planning process, the area with the highest pollution value will be cleaned first, and the low-pollution area will be abandoned if the distance is insufficient to ensure the timeliness of the cleaning task.

[0089] Step S50: During the execution of the cleaning path, the laser radar sensor detects obstacles ahead. When an obstacle is detected, the system marks the area within 2 meters around the obstacle as a "no-entry area" and replans the cleaning path;

[0090] After the path is adjusted, the dynamic water release volume Q, water release pressure P, and water release angle θ are recalculated;

[0091] It should be noted that lidar sensors (such as model Hokuyo UST-10LX) can accurately detect obstacles in front and have a detection distance of 10 meters and an accuracy of 40 mm, ensuring that obstacles are discovered in time during cleaning tasks, ensuring the safety of sanitation vehicles and the continuity of cleaning operations.

[0092] It is understandable that marking the 2-meter range around the obstacle as a "no-entry zone" is to ensure that the sanitation vehicle does not approach the obstacle when replanning the route, thereby avoiding the risk of collision with the cleaning vehicle. This distance was obtained through multiple tests and can provide sufficient safety buffer at different operating speeds.

[0093] It should be understood that the dynamic water discharge volume, water discharge pressure and water discharge angle are recalculated because the new cleaning path may cover different contaminated areas and have different vehicle speed and slope requirements. By recalculating the water discharge parameters, the system can adapt to the new cleaning path to ensure the optimization of cleaning effect and resource utilization.

[0094] Step S60: After the cleaning operation is completed, the water resource consumption data and the feedback data of the cleaning area are recorded, and the adjustment coefficient β in the LSTM model is optimized and adjusted according to the feedback data.

[0095] It should be noted that water consumption data and feedback data from clean areas are important indicators for evaluating cleaning effects and resource utilization efficiency. Water consumption data can help determine whether water resources are used reasonably under different pollution levels, while feedback data from clean areas can reflect the matching degree between the pollution value predicted by the LSTM model and the actual cleaning needs.

[0096] It can be understood that the purpose of optimizing and adjusting the adjustment coefficient β in the LSTM model is to continuously improve the prediction accuracy of the model according to the actual cleaning effect, so as to more accurately control the water release volume and pressure in future cleaning tasks, and further improve the cleaning efficiency and water resource utilization.

[0097] It should be understood that the optimization adjustment of the adjustment coefficient β can be achieved through the feedback training mechanism of machine learning. For example, based on the feedback data after cleaning, the actual pollution cleaning effect is compared with the predicted value, and the value of β is updated through error back propagation to enhance the prediction accuracy of the model under similar pollution conditions.

[0098] For example, if the cleaning feedback data shows that the actual cleaning effect in a certain polluted area is lower than expected, the pollution prediction value of the LSTM model in this area can be improved by increasing the value of the adjustment coefficient β, thereby increasing the amount of water released in this area in subsequent cleaning tasks to achieve better cleaning effects.

[0099] In addition, the water discharge control system for a sanitation vehicle provided by the present invention adopts a water discharge control method for a sanitation vehicle in the above embodiment, which can solve the technical problem of water discharge control for a sanitation vehicle. Compared with the prior art, the beneficial effects of the water discharge control system for a sanitation vehicle provided by the present invention are the same as the beneficial effects of the water discharge control method for a sanitation vehicle provided by the above embodiment, and the other technical features of the water discharge control system for a sanitation vehicle are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0100] The present invention provides a water discharge control device for a sanitation vehicle, please refer to Figure 2, a water discharge control device for a sanitation vehicle comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a water discharge control method for a sanitation vehicle in the above-mentioned embodiment 1. A water discharge control device for a sanitation vehicle in an embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A water discharge control device for a sanitation vehicle is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. A water discharge control device for a sanitation vehicle may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of a water discharge control device for a sanitation vehicle are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow a water discharge control device for a sanitation vehicle to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a water discharge control device for a sanitation vehicle with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0101] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of the water discharge control method for a sanitation vehicle as described above are implemented. The computer program product provided by the present invention can solve the technical problem of water discharge control for a sanitation vehicle. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the water discharge control method for a sanitation vehicle provided by the above embodiment, and will not be described in detail here.

[0102] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0103] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0104] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A water discharge control method for a sanitation vehicle, characterized in that: Methods include: Step S10: using air quality sensors, imaging cameras and infrared detection equipment to collect environmental data of the work area, including particulate matter concentration, garbage density and temperature information; The environmental data of the operating area is standardized and used as the input of the trained LSTM model, and the output is the pollution prediction value P predicted ; Step S20: According to the pollution prediction value P predicted Calculate the dynamic water discharge volume Q and water discharge pressure P; The calculation formula of water release Q is Q = 0.5 × P predicted ; The water discharge pressure P is divided into three levels according to the degree of pollution, specifically: When the predicted value P predicted <50μg / m 3 When the water discharge pressure is set to 0.2MPa; When the predicted value is 50Ig / m 3 P predicted <100 μg / m 3 When the water discharge pressure is 0.3MPa; When the predicted value P predicted >100 μg / m 3 When the water discharge pressure is 0.4MPa; Step S30: using the vehicle-mounted GPS module to obtain the vehicle speed v in real time, and at the same time detecting the road slope α through the inclination sensor installed at the bottom of the vehicle; According to the vehicle speed v and the road slope α, adjust the water release angle θ, and the adjustment formula is: Among them, sin is the sine function; Step S40: According to the pollution prediction value P in step S20 predicted , Dijkstra algorithm is used to plan the cleaning path, and the path prioritizes the pollution value P predicted In high-speed areas, the maximum length of each planned path shall not exceed 5 kilometers, and the vehicle speed shall be controlled at 10-20 km / h; Step S50: During the execution of the cleaning path, the laser radar sensor detects obstacles ahead. When an obstacle is detected, the system marks the area within 2 meters around the obstacle as a "no-entry area" and replans the cleaning path; After the path is adjusted, the dynamic water release volume Q, water release pressure P, and water release angle θ are recalculated; Step S60: After the cleaning operation is completed, the water resource consumption data and the feedback data of the cleaning area are recorded, and the adjustment coefficient β in the LSTM model is optimized and adjusted according to the feedback data.

2. A water discharge control method for a sanitation vehicle as claimed in claim 1, characterized in that: In step S10, the model of the air quality sensor is PMS5003, the resolution of the imaging camera is 1280x720, and the model of the infrared detection device is MLX90640.

3. A water discharge control method for a sanitation vehicle as claimed in claim 1, characterized in that: In step S10, the LSTM model is trained based on historical environmental data, the input is the standardized historical environmental data, and the output is the actual pollution prediction value; The LSTM model consists of an input layer, three hidden layers, a fully connected layer, and an output layer; The input layer receives standardized historical environmental data; The hidden layers process the input data layer by layer, extracting short-term, medium-term and long-term features in turn; The fully connected layer integrates the short-term, medium-term and long-term feature information of the hidden layer to obtain a single pollution prediction value; The output layer finally outputs the predicted pollution value P predicted , used to guide the setting of water release control parameters of sanitation vehicles.

4. A water discharge control method for a sanitation vehicle as claimed in claim 1, characterized in that: In step S10, an improved loss function is used in the LSTM model training process, and the formula is: Among them, S is the improved loss function, N is the number of historical environment data samples, i is the i-th historical environment data sample, y i is the actual pollution value, is the pollution value predicted by the model, w i is the weighting coefficient; the loss function S is used to adjust the weight parameters of each layer of the model.

5. A water discharge control method for a sanitation vehicle as claimed in claim 4, characterized in that: Weighting coefficient w i The calculation method is: Among them, β is the adjustment coefficient, which is used to control the weight amplification ratio of high-pollution areas; max(y) is the maximum pollution value in all historical data.

6. A water discharge control method for a sanitation vehicle as claimed in claim 1, characterized in that: In step S20, the water discharge pressure is adjusted by controlling the opening of the valve, and the control valve model is DF300.

7. A water discharge control method for a sanitation vehicle as claimed in claim 1, characterized in that: In step S10 and step S30, the vehicle-mounted GPS module model is Ublox NEO-M8N, and the tilt sensor model is SCA103T-D05.

8. A water discharge control system for a sanitation vehicle, characterized in that: The water discharge control system of the sanitation vehicle comprises: A data acquisition module, which uses air quality sensors, imaging cameras and infrared detection equipment to collect environmental data of the work area, including particulate matter concentration, garbage density and temperature information; The environmental data of the operating area is standardized and used as the input of the trained LSTM model, and the output is the pollution prediction value P predicted ; The water release control module is used to predicted Calculate the dynamic water discharge volume Q and water discharge pressure P; The calculation formula of water release Q is Q = 0.5 × P predicted ; The water discharge pressure P is divided into three levels according to the degree of pollution, specifically: When the predicted value P predicted <50μg / m 3 When the water discharge pressure is set to 0.2MPa; When the predicted value is 50 μg / m 3 P predicted <100 μg / m 3 When the water discharge pressure is 0.3MPa; When the predicted value P predicted >100 μg / m 3 When the water discharge pressure is 0.4MPa; An angle adjustment module is used to obtain the vehicle speed v in real time using the vehicle-mounted GPS module, and detect the road slope α through the inclination sensor installed at the bottom of the vehicle; According to the vehicle speed v and the road slope α, adjust the water release angle θ, and the adjustment formula is: Among them, sin is the sine function; The path planning module is used to calculate the pollution prediction value P in step S20. predicted , Dijkstra algorithm is used to plan the cleaning path, and the path prioritizes the pollution value P predicted In high-speed areas, the maximum length of each planned path shall not exceed 5 kilometers, and the vehicle speed shall be controlled at 10-20 km / h; The obstacle detection and path adjustment module is used to detect obstacles in front through the lidar sensor during the execution of the cleaning path. When an obstacle is detected, the system marks the area within 2 meters around the obstacle as a "no-entry area" and replans the cleaning path; After the path is adjusted, the dynamic water release volume Q, water release pressure P, and water release angle θ are recalculated; The feedback optimization module records the feedback data including water resource consumption data and cleaning area after the cleaning operation is completed, and optimizes and adjusts the adjustment coefficient β in the LSTM model according to the feedback data.

9. A water discharge control device for a sanitation vehicle, characterized in that: The water discharge control device of the sanitation vehicle comprises: a memory, a processor and a water discharge control program of the sanitation vehicle stored in the memory and executable on the processor. When the water discharge control program of the sanitation vehicle is executed by the processor, the water discharge control method of the sanitation vehicle described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a water discharge control program for a sanitation vehicle, and when the water discharge control program for the sanitation vehicle is executed by a processor, the water discharge control method for the sanitation vehicle described in any one of claims 1 to 7 is implemented.

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