Intelligent sprinkler control method, system, storage medium and program product

Abstract

An intelligent sprinkler control method, system, storage medium and program product relate to the field of sprinkler control. The method comprises: obtaining real-time dust concentration data and weather forecast data, wherein the weather forecast data includes current dust concentration, expected humidity change and rainfall information; calculating future dust concentration according to the real-time dust concentration data and the predicted dust concentration data; calculating a preset sprinkler volume parameter according to the future dust concentration, and calculating a preset sprinkler frequency parameter according to the current environmental data; controlling the current sprinkler truck according to the preset sprinkler volume parameter and the preset sprinkler frequency parameter. Implementation of the method can improve the efficiency of sprinkler dust suppression.

Description

Technical Field

[0001] The present application relates to the field of sprinkler control, and in particular to an intelligent sprinkler control method, system, storage medium and program product. Background Art

[0002] With the acceleration of urbanization and the continuous advancement of industrial development, air pollution is becoming increasingly serious, among which dust pollution is one of the main sources of pollution.

[0003] At present, the common method of sprinkling water to suppress dust is to set a fixed watering schedule, such as sprinkling water once at 6 am and 4 pm every day. During the implementation process, the staff will roughly estimate the required amount of watering based on experience and factors such as the temperature and humidity of the day, and control the actual amount of watering by adjusting the driving speed and water output of the sprinkler truck.

[0004] However, this estimation method may lead to inappropriate timing of watering or the amount of watering not matching the actual demand. For example, unnecessary watering may occur before and after rainy days, which not only fails to achieve the expected dust suppression effect, but also causes a waste of water resources. Summary of the invention

[0005] The present application provides an intelligent sprinkler control method, system, storage medium and program product for improving the efficiency of sprinkler dust suppression.

[0006] In the first aspect, the present application provides an intelligent sprinkler control method, which is applied to an intelligent sprinkler control system, the method comprising: obtaining real-time dust concentration data and obtaining weather forecast data, the weather forecast data including current dust concentration, expected humidity changes and rainfall information; calculating future dust concentration based on the real-time dust concentration data and the predicted dust concentration data; calculating a preset watering volume parameter based on the future dust concentration, and calculating a preset watering frequency parameter based on the current environmental data; and controlling the current sprinkler truck based on the preset watering volume parameter and the preset watering frequency parameter.

[0007] By adopting the above technical solution, real-time dust concentration data is obtained, and the future dust concentration is calculated by combining the current dust concentration, expected humidity changes and rainfall information in the weather forecast data. The preset watering volume parameters are calculated according to the future dust concentration, and the preset watering frequency parameters are calculated based on the current environmental data. Intelligent regulation of watering volume and frequency is realized, and the current sprinkler truck is controlled according to these accurately calculated parameters, making the watering operation more accurate and efficient. The entire process from data collection, analysis and prediction to intelligent control is automated, which improves the efficiency and effect of watering for dust suppression and reduces the waste of water resources.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the future dust concentration based on the current dust concentration, the expected humidity change and the rainfall information specifically includes: calculating the humidity attenuation coefficient of the preset future moment based on the expected humidity change; calculating the rainfall attenuation coefficient of the preset future moment based on the rainfall information; multiplying the current dust concentration with the humidity attenuation coefficient and the rainfall attenuation coefficient to obtain the future dust concentration at the preset future moment.

[0009] By adopting the above technical solution, the humidity attenuation coefficient and rainfall attenuation coefficient are calculated, the impact of humidity changes and rainfall on dust concentration is systematically considered, and these factors are combined with the current dust concentration to obtain a more accurate prediction of future dust concentration.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the preset watering amount parameter according to the future dust concentration, and calculating the preset watering frequency parameter according to the current environmental data, specifically includes: determining the target dust suppression efficiency according to the future dust concentration; calculating the preset watering amount parameter based on the target dust suppression efficiency and a preset dust suppression effect function; extracting temperature parameters, humidity parameters and wind speed parameters from the current environmental data, and calculating a preset water evaporation rate according to the temperature parameters, the humidity parameters and the wind speed parameters; and calculating a preset watering frequency parameter based on the preset water evaporation amount and a preset ground humidity threshold.

[0011] By adopting the above technical scheme, the target dust suppression efficiency is determined according to the future dust concentration, and the preset watering amount parameters are calculated in combination with the preset dust suppression effect function, so as to achieve precise control of the watering amount. The temperature, humidity and wind speed parameters are extracted from the current environmental data, and the preset water evaporation rate is calculated. The influence of environmental factors on the watering effect is considered, and the preset watering frequency parameters are calculated based on the preset water evaporation amount and the preset ground humidity threshold, thereby ensuring the continuity of ground humidity.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of controlling the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter, the method also includes: obtaining the remaining water volume of the current sprinkler truck, and calculating the sustainable operation time of the current sprinkler truck according to the remaining water volume, the preset watering volume parameter and the preset watering frequency parameter; obtaining the geographical location information and the water replenishment station location information of the current sprinkler truck, and generating an optimal water replenishment route according to the geographical location information and the water replenishment station location information; and sending the optimal water replenishment route to the client.

[0013] By adopting the above technical solution, the remaining water volume of the current sprinkler truck is obtained, and the sustainable operation time is calculated by combining the preset sprinkler volume parameters and the preset sprinkler frequency parameters, and the geographical location information of the sprinkler truck and the location information of the water replenishment station are obtained to generate the optimal water replenishment route and optimize the water replenishment process. The optimal water replenishment route is sent to the client, and the sustainable operation time is accurately calculated to avoid the situation where the sprinkler truck is interrupted due to insufficient water, thereby ensuring the continuity of the sprinkler dust suppression work.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the step of generating the optimal water replenishment route based on the geographic location information and the water replenishment station location information specifically includes: obtaining traffic condition information of the current sprinkler truck in the current area; dynamically weighting the road network in the current area based on the traffic condition information to obtain a weighted road network, and the weight of the congested section in the current area is greater than the weight of the unobstructed section; performing path planning on the weighted road network to generate multiple alternate water replenishment routes; scoring all the alternate water replenishment routes based on their total route length, estimated driving time and road complexity; and selecting the preset water replenishment route with the highest score as the optimal water replenishment route.

[0015] By adopting the above technical solution, firstly, the traffic condition information of the current sprinkler truck in the current area is obtained to provide a real-time basis for route planning. Based on the traffic condition information, the road network in the current area is dynamically weighted to obtain a weighted road network, in which the weight of the congested section is greater than the weight of the unobstructed section, thereby ensuring the real-time and rationality of route planning. The weighted road network is path planned to generate multiple alternate water replenishment routes. The alternate water replenishment routes are scored according to their total route length, estimated driving time and road complexity, and the route with the highest score is selected as the optimal water replenishment route, thereby realizing the intelligent selection of water replenishment routes.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of controlling the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter, the method further includes: performing real-time scanning of the current road based on the lidar sensor, and dividing the current road into multiple areas to be watered according to the real-time dust concentration data; generating an optimal watering path for the current sprinkler truck according to the position data of each area to be watered, the preset watering volume parameter and the preset watering frequency parameter, the optimal watering path having the shortest total driving distance, a total watering volume corresponding to the preset watering volume parameter and the preset watering frequency parameter not greater than the remaining water volume of the current sprinkler truck and covering all the areas to be watered; and sending the optimal watering path to the client.

[0017] By adopting the above technical solution, the current road is scanned in real time using a lidar sensor, and the current road is divided into multiple areas to be watered according to real-time dust concentration data. The optimal watering path of the current sprinkler truck is generated according to the location data of each area to be watered, the preset watering volume parameters and the preset watering frequency parameters. The generated optimal watering path not only has the shortest total driving distance, but also ensures that the watering volume does not exceed the remaining water volume of the sprinkler truck, while covering all areas to be watered, thereby achieving the optimal allocation of resources. The optimal watering path is sent to the client, so that the sprinkler truck can operate according to the optimal path.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of transmitting the optimal watering path to the client, the method further includes: monitoring the current road to obtain a pavement dryness index of the current road; if the pavement dryness index is less than a preset threshold, increasing the watering amount parameter to a preset watering amount parameter adjustment value.

[0019] By adopting the above technical solution, the current road is continuously monitored to obtain the road surface dryness index, realize real-time tracking of the road surface status, and compare the road surface dryness index with the preset threshold value. When the index is less than the threshold value, the system automatically increases the watering volume parameter to the preset watering volume parameter adjustment value, thereby realizing dynamic adjustment of the watering volume and ensuring that the road surface is always kept in the optimal wet state, effectively preventing dust problems caused by excessive road drying, and avoiding water resource waste and road surface waterlogging caused by excessive watering.

[0020] In a second aspect, an embodiment of the present application provides an intelligent sprinkler control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the intelligent sprinkler control system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on an intelligent sprinkler control system, the above-mentioned intelligent sprinkler control system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on an intelligent sprinkler control system, the intelligent sprinkler control system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the intelligent sprinkler control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application obtains real-time dust concentration data, combines the current dust concentration, expected humidity changes and rainfall information in the weather forecast data, calculates the future dust concentration, calculates the preset watering volume parameters according to the future dust concentration, and calculates the preset watering frequency parameters based on the current environmental data, thereby realizing intelligent regulation of watering volume and frequency. The current sprinkler truck is controlled according to these precisely calculated parameters, making the watering operation more accurate and efficient, realizing full process automation from data collection, analysis and prediction to intelligent control, improving the efficiency and effect of watering to suppress dust, and reducing the waste of water resources.

[0025] 2. This application systematically considers the impact of humidity changes and rainfall on dust concentration by calculating the humidity attenuation coefficient and rainfall attenuation coefficient, and combines these factors with the current dust concentration to obtain a more accurate prediction of future dust concentration.

[0026] 3. This application obtains the remaining water volume of the current sprinkler truck, calculates the sustainable operation time in combination with the preset sprinkler volume parameters and the preset sprinkler frequency parameters, obtains the geographical location information of the sprinkler truck and the location information of the water replenishment station, generates the optimal water replenishment route, and optimizes the water replenishment process. The optimal water replenishment route is sent to the client, and the sustainable operation time is accurately calculated to avoid the situation where the sprinkler truck is interrupted due to insufficient water, thereby ensuring the continuity of the sprinkler dust suppression work. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the intelligent sprinkler control method in the embodiment of the present application; Figure 2 is another flow chart of the intelligent sprinkler control method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the intelligent sprinkler control system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the intelligent sprinkler control method in an embodiment of the present application.

[0031] S101. Acquire real-time dust concentration data and obtain weather forecast data, where the weather forecast data includes current dust concentration, expected humidity change, and rainfall information.

[0032] Among them, real-time dust concentration data refers to the content of suspended particulate matter in the air at the current moment, weather forecast data is used to indicate the weather forecast for a period of time in the future, predicted dust concentration data refers to the predicted value of the content of suspended particulate matter in the air for a period of time in the future, and current environmental data is used to indicate the current temperature, humidity, wind speed and other meteorological parameters.

[0033] This step is first executed after the intelligent sprinkler control system is started, and is used to obtain the basic data required to control the sprinkler. Specifically, the system first obtains the dust concentration data of the current location in real time through the dust concentration sensor (such as PM2.5, PM10 sensor) installed on the sprinkler truck. At the same time, the system will connect to the data interface of the meteorological department to obtain weather forecast data for a period of time in the future (such as 24 hours), including the predicted value of future dust concentration and current environmental parameters such as temperature, humidity, wind speed, etc.

[0034] In some embodiments, the acquisition of real-time dust concentration data and the acquisition of weather forecast data can be achieved in a variety of ways. Optionally, for the acquisition of real-time dust concentration data, multiple fixed dust concentration sensors can be set in the area to be sprinkled to form a sensor network. All sensors are activated at preset time intervals (such as 1 minute) to perform a scanning measurement. The measurement results are then transmitted to the central control system via a wireless network, and data fusion is performed to obtain a dust concentration distribution map of the entire area. Optionally, for the acquisition of weather forecast data, a data sharing interface can be established with the local meteorological department. Send data requests to the meteorological department at regular intervals (such as every hour). Receive and parse the returned XML format weather forecast data to extract the required predicted dust concentration and environmental parameter information. It is understandable that other methods can also be used to achieve data acquisition, such as using satellite remote sensing data, drone inspections, etc., which are not limited here.

[0035] S102: Calculate future dust concentration according to the current dust concentration, the expected humidity change and the rainfall information.

[0036] Among them, the current dust concentration refers to the content of suspended particulate matter in the air at a specific moment, usually measured in micrograms per cubic meter of air (μg / m³). The expected humidity change indicates the predicted change trend of the relative humidity of the air in the future, including the increase or decrease of humidity. Rainfall information refers to the precipitation forecast data for a certain period of time in the future, including elements such as rainfall probability, rainfall amount and duration. The future dust concentration is used to indicate the predicted value of the content of suspended particulate matter in the air within the forecast time period after considering the influence of various environmental factors. The humidity attenuation coefficient refers to the influence factor of air humidity change on dust concentration, reflecting the degree of dust concentration attenuation caused by humidity change. The rainfall attenuation coefficient indicates the degree of influence of rainfall on dust concentration, and is used to quantify the removal effect of rainfall on suspended particulate matter in the air.

[0037] This step is performed after obtaining real-time monitoring data and weather forecast data. It is mainly used to predict the dust concentration value at a certain point in the future and provide a decision-making basis for subsequent watering control. Specifically, the system first extracts the expected humidity value at a future point in time from the weather forecast data, compares it with the current humidity value, and calculates the humidity change amplitude. According to empirical data, for every 1% increase in relative humidity, the dust concentration decreases by about 0.5%, and the humidity attenuation coefficient is calculated based on this. At the same time, the rainfall forecast information is analyzed, and the rainfall attenuation coefficient is calculated based on the rainfall amount and duration, according to the empirical value that the dust concentration can be reduced by 2% per millimeter of rainfall. Finally, the current measured dust concentration value is multiplied by the two attenuation coefficients to obtain the future dust concentration forecast value after considering the influence of environmental factors.

[0038] In some embodiments, the prediction and calculation of future dust concentration can be achieved in a variety of ways: Optionally, a humidity change threshold method can be used: first set a humidity change baseline value, such as the current humidity is 60%; then calculate the difference between the expected humidity and the baseline value, such as the expected humidity is 70%, and the difference is 10%; then calculate the humidity attenuation coefficient based on the 0.5% dust concentration change corresponding to every 1% humidity change, and obtain 0.95; then analyze the rainfall in the rainfall forecast, such as the forecast rainfall is 2 mm, then the rainfall attenuation coefficient is 0.96; finally, multiply the current dust concentration of 200μg / m³ by 0.95 and 0.96 respectively, to obtain a future dust concentration of 182.4μg / m³. Optionally, a time-weighted calculation method can be used: first, the prediction time period is divided into several time units, such as one unit per hour; then the humidity change value and rainfall of each time unit are calculated; then a time weight is assigned to each time unit, and the unit weight closer to the target prediction time is larger; then the weighted attenuation coefficient of each unit is calculated; finally, all weighted attenuation coefficients are multiplied to obtain the final attenuation coefficient, and multiplied by the current dust concentration to obtain the predicted value. It is understandable that other methods can also be used to achieve the prediction of future dust concentration, such as piecewise linear calculation method, comprehensive scoring method of environmental factors, etc., which are not limited here.

[0039] S103, calculating a preset watering amount parameter according to the future dust concentration, and calculating a preset watering frequency parameter according to the current environmental data.

[0040] Among them, the future dust concentration indicates the change in the content of suspended particulate matter in the air expected in the future. The preset watering volume parameter refers to the amount of water to be sprayed determined according to the dust concentration. The current environmental data is used to indicate the current meteorological conditions such as temperature, humidity, wind speed, etc. The preset watering frequency parameter refers to the number of watering times and intervals determined according to environmental conditions.

[0041] This step is performed after obtaining the future dust concentration forecast and current environmental data, with the purpose of determining the optimal watering strategy. Specifically, the system analyzes the changing trend of future dust concentration and combines it with the predetermined air quality standard to calculate the amount of water needed to effectively suppress dust. At the same time, the system will consider environmental factors such as current temperature, humidity and wind speed to determine the optimal watering frequency.

[0042] In some embodiments, the calculation of watering volume and frequency parameters can be achieved in a variety of ways: Optionally, for the calculation of watering amount parameters, a regression model method based on machine learning can be used: collect historical data, including the optimal watering amount under different dust concentrations, watering effect scores, environmental factors, etc. These data can come from past watering records, data from air quality monitoring stations, and extract useful features from the original data. For example, the dust concentration can be discretized into multiple intervals, the moving average concentration can be calculated, and the concentration change rate can be extracted. At the same time, considering additional features such as season and time that may affect the watering effect, a gradient boosting tree model (such as XGBoost or LightGBM) is trained using the processed data. This type of model can effectively capture the nonlinear relationship between features and has strong robustness to outliers. During the training process, cross-validation is used to adjust the hyperparameters of the model to improve the generalization ability, and the predicted future dust concentration is input into the trained model to obtain a preliminary watering amount prediction value. Then, a rule-based post-processing system is designed to fine-tune the prediction results according to the actual situation. For example, if the predicted value exceeds the maximum capacity of the sprinkler truck, it is limited to a feasible range.

[0043] Optionally, for the calculation of watering frequency parameters, a multi-factor weighted scoring method can be used: determine the key environmental factors that affect the watering frequency, mainly including temperature, humidity, wind speed, precipitation probability, etc. For each factor, define its direction of influence on the watering frequency (positive correlation or negative correlation), divide the possible value range of each environmental factor into multiple levels (such as 5 levels), and assign a quantitative score to each level. For example, temperature can be divided into: very low (1 point), low (2 points), moderate (3 points), high (4 points), very high (5 points). Through expert evaluation or historical data analysis, assign a weight to each environmental factor to reflect its relative importance to the watering frequency. Make sure that the sum of all weights is 1. For the given current environmental data, first determine the level and corresponding score of each factor, then multiply it by its weight, and finally add up the weighted scores of all factors to obtain a comprehensive environmental score, and establish a mapping table from score to watering frequency. For example, a comprehensive score between 0-2 corresponds to a low frequency (once every 4 hours), between 2-3 corresponds to a medium frequency (once every 2 hours), and between 3-5 corresponds to a high frequency (once every hour). Changes in environmental data are monitored in real time. When any key factor changes significantly (such as exceeding a predetermined threshold), the comprehensive score is recalculated and the watering frequency is updated.

[0044] It is understandable that other methods may be used to implement parameter calculation, which is not limited here.

[0045] S104: Control the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter.

[0046] The preset watering volume parameter indicates the amount of water to be sprayed calculated based on the dust concentration. The preset watering frequency parameter refers to the number of watering times and intervals determined based on environmental conditions. The current watering truck refers to a vehicle device that performs a watering task.

[0047] This step is executed immediately after the watering volume and frequency parameters are determined. It is the last step of the entire intelligent watering control process and directly affects the actual operation of the sprinkler truck. Specifically, the system first transmits the calculated preset watering volume parameters to the control unit of the sprinkler truck, adjusts the output pressure of the water pump and the opening of the nozzle to ensure that the amount of water sprayed each time meets the preset value. At the same time, the system formulates the driving route and stay time of the sprinkler truck according to the preset watering frequency parameters to ensure that water is sprayed at the correct location and at the correct interval. In addition, the system will monitor the watering process in real time and make fine adjustments according to actual conditions. For example, if there is a sudden increase in dust, the watering volume and frequency of the local area can be appropriately increased.

[0048] In some embodiments, the control of the sprinkler truck can be achieved in a variety of ways: Optionally, a central control system can be used: First, a GPS positioning system and a wireless communication module are installed on the sprinkler truck; then, the central control system generates a detailed sprinkler task plan based on preset parameters, including the driving route, stop points, and the amount of water to be sprinkled at each point; finally, the task plan is transmitted to the sprinkler truck in real time through a wireless network, and the on-board control unit performs the sprinkler operation according to the received instructions. This method can achieve coordinated control of multiple sprinkler trucks and improve overall operation efficiency. Optionally, an on-board intelligent control system can also be used: First, an intelligent system integrating sensors, controllers, and actuators is installed on the sprinkler truck; then, the preset watering amount and frequency parameters are input into the on-board system; finally, the on-board system autonomously plans the route and controls the sprinkler operation based on these parameters, and can dynamically adjust according to the real-time situation detected by the on-board sensors. This method enhances the autonomy of the sprinkler truck and is suitable for use in environments with poor communication conditions. It is understandable that other methods can also be used to achieve sprinkler truck control, such as distributed control systems based on edge computing, adaptive control systems combined with artificial intelligence, etc., to meet the refined control needs in different scenarios, which are not limited here.

[0049] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the intelligent sprinkler control method in the embodiment of the present application.

[0050] S201. Acquire real-time dust concentration data and obtain weather forecast data, where the weather forecast data includes current dust concentration, expected humidity change, and rainfall information.

[0051] It can be understood that this step is similar to step S101 and will not be described in detail here.

[0052] S202: Calculate future dust concentration according to the current dust concentration, the expected humidity change and the rainfall information.

[0053] This step specifically includes the following steps: Calculate the humidity attenuation coefficient at a preset future time according to the expected humidity change; Specifically, the expected humidity change indicates the trend of relative humidity change from the current moment to the preset future moment, expressed in percentage, such as from 60% to 70%. The preset future moment refers to a specific time point in the selected future, usually 1-24 hours from the current time. The humidity attenuation coefficient refers to the factor affecting the dust concentration due to humidity change, and the value range is a decimal between 0 and 1. The smaller the value, the stronger the attenuation effect of humidity on the dust concentration. For example, when the relative humidity increases from 60% to 70%, the humidity attenuation coefficient may be 0.95, indicating that the dust concentration will decay by 5%.

[0054] When calculating the humidity attenuation coefficient, first obtain the relative humidity value at the current moment and the expected relative humidity value at the preset future moment, and calculate the difference between the two. According to the relationship that every 1% increase in relative humidity leads to a 0.5% decrease in dust concentration, calculate the dust concentration attenuation ratio caused by humidity change. The specific calculation method is: assuming that the current relative humidity is 60%, the expected relative humidity is 70%, and the humidity increases by 10%, the dust concentration will decrease by 10×0.5%=5%, so the humidity attenuation coefficient is 0.95. When the expected humidity is lower than the current humidity, a similar method is used for calculation, but the attenuation coefficient will be greater than 1, indicating that the dust concentration has an upward trend.

[0055] Calculate the rainfall attenuation coefficient at the preset future time according to the rainfall information; Specifically, rainfall information includes data such as rainfall amount, rainfall duration, and rainfall intensity. The rainfall amount is measured in millimeters, indicating the depth of precipitation per unit area. The rainfall duration is measured in hours, indicating the duration of rainfall. Rainfall intensity refers to the amount of rainfall per unit time, expressed in millimeters per hour. The rainfall attenuation coefficient refers to the clearing effect of rainfall on dust concentration, and the value range is a decimal between 0 and 1. The smaller the value, the stronger the clearing effect of rainfall. For example, a rainfall attenuation coefficient of 0.8 means that rainfall reduces dust concentration by 20%.

[0056] In the process of calculating the rainfall attenuation coefficient, the accumulated rainfall before the preset future time is first extracted from the rainfall information. According to the relationship that each millimeter of rainfall causes a 2% decrease in dust concentration, the dust concentration attenuation ratio caused by rainfall is calculated. The specific calculation method is: assuming that the accumulated rainfall before the preset time is 5 mm, the dust concentration will decrease by 5×2%=10%, so the rainfall attenuation coefficient is 0.9. If the forecast shows no rainfall, the rainfall attenuation coefficient is 1, indicating that rainfall has no effect on dust concentration.

[0057] The current dust concentration is multiplied by the humidity attenuation coefficient and the rainfall attenuation coefficient to obtain the future dust concentration at the preset future time.

[0058] Specifically, the current dust concentration refers to the content of suspended particulate matter in the air measured at the current moment, in micrograms per cubic meter. The humidity attenuation coefficient and the rainfall attenuation coefficient respectively indicate the degree of influence of humidity change and rainfall on dust concentration. The future dust concentration refers to the dust concentration value predicted at a preset future moment under the dual effects of humidity and rainfall.

[0059] The calculation process uses the method of multiplying the three, multiplying the current measured dust concentration value with the humidity attenuation coefficient and the rainfall attenuation coefficient respectively. This calculation method reflects the cumulative impact of environmental factors on dust concentration. The specific calculation method is: assuming that the current dust concentration is 200 micrograms / cubic meter, the humidity attenuation coefficient is 0.95, and the rainfall attenuation coefficient is 0.9, then the dust concentration at the preset future time is 200×0.95×0.9=171 micrograms / cubic meter. This result shows that under the combined effect of increased humidity and rainfall, the dust concentration at the preset future time will be reduced to 171 micrograms / cubic meter.

[0060] S203, calculating a preset watering amount parameter according to the future dust concentration, and calculating a preset watering frequency parameter according to the current environmental data.

[0061] This step specifically includes the following steps: determining a target dust suppression efficiency according to the future dust concentration.

[0062] The preset watering amount parameter is calculated based on the target dust suppression efficiency and the preset dust suppression effect function.

[0063] The target dust suppression efficiency refers to the dust concentration reduction ratio to be achieved according to the expected air quality standard. The preset dust suppression effect function is an empirical equation that reflects the amount of dust concentration that can be reduced by a unit of watering under given environmental conditions. The preset watering amount parameter is the amount of water that needs to be sprinkled to achieve the target dust suppression efficiency.

[0064] In specific implementation, a lookup table that gives the target dust suppression efficiency can be pre-established. For example, when the target efficiency is 50%, the lookup table returns 0.5. At the same time, a dust suppression effect function is fitted based on past data, such as each ton of water can reduce the dust concentration by 50μg / m3. Enter the target dust suppression efficiency of 0.5 and the current concentration value of 300μg / m3, and the function calculates the required watering amount parameter as 3 tons.

[0065] A temperature parameter, a humidity parameter and a wind speed parameter are extracted from the current environmental data, and a preset water evaporation rate is calculated based on the temperature parameter, the humidity parameter and the wind speed parameter.

[0066] The current environmental data includes parameters such as temperature, humidity and wind speed. Extraction refers to selecting three parameters for calculating the evaporation rate. The water evaporation rate reflects the evaporation consumption rate of the water sprayed per unit time.

[0067] In specific implementation, the real-time monitoring values ​​of temperature, humidity and wind speed can be obtained by querying the API interface of the current environmental data. Then input them into the empirical evaporation rate calculation formula: evaporation rate EVP=aTEMP+bHUMI+c*WIND, where a / b / c are the coefficients of different parameters, temperature is positively correlated, humidity is negatively correlated, and wind speed is positively correlated. The calculated evaporation rate is the preset water evaporation rate parameter.

[0068] The preset watering frequency parameter is calculated based on the preset water evaporation amount and the preset ground humidity threshold.

[0069] Water evaporation refers to the evaporation consumption of watering per unit time. The ground humidity threshold is a humidity reference value to determine whether additional watering is needed. The preset watering frequency parameter is the number of watering times required to keep the ground humidity greater than the threshold.

[0070] In specific implementation, a ground humidity threshold can be preset, for example, 70%. The evaporation in 1 hour is calculated to be 0.5 tons. The current ground humidity is 80%. If no water is added after 2 hours, the ground humidity will drop to 75%, which is lower than the threshold. Therefore, the preset watering frequency parameter is once every 2 hours to maintain the ground humidity above the threshold. Real-time detection of ground humidity, when it is lower than the threshold, watering is carried out in advance, otherwise, watering is delayed, so as to realize dynamic adjustment of the watering frequency.

[0071] For example, first, the preset water evaporation (EVP) needs to be determined, which can be determined by empirical formula or experimental data. Assume that the empirical formula is: evaporation rate EVP = aTEMP+bHUMI+c*WIND, where TEMP is temperature, HUMI ​​is humidity, WIND is wind speed, and a, b, and c are empirical coefficients obtained by fitting historical data. Assuming the following parameters: temperature 30°C, humidity 60%, wind speed 3 m / s, a=0.1, b=-0.05, c=0.2, the preset water evaporation is 0.6 tons / hour. Next, the effect of watering on ground humidity needs to be calculated. Assuming that the amount of watering each time is 1 ton, each ton of water can increase the ground humidity by 10%. The ground humidity needs to be kept greater than the preset ground humidity threshold. Assuming that the preset ground humidity threshold is 70%, the current ground humidity is 80%, and the evaporation will cause the ground humidity to decrease. The evaporation per hour is 0.6 tons, which is equivalent to a 6% decrease in humidity. Therefore, the time required for the humidity to drop from 80% to 70% is (80%-70%) / 6%=1.67 hours. Therefore, in order to keep the ground humidity above 70%, watering is required every 1.67 hours. In addition, the ground humidity can be detected in real time, and the frequency of watering can be adjusted dynamically according to the actual situation. For example, when the humidity drops to 75%, watering can be carried out in advance, or watering can be delayed to save water resources. The specific implementation method includes obtaining environmental parameters such as temperature, humidity and wind speed in real time through sensors, calculating the amount of water evaporation per hour using empirical formulas, monitoring the ground humidity in real time, judging whether watering is needed through a preset threshold, and controlling the sprinkler equipment to perform the sprinkler operation according to the calculated sprinkler frequency parameters.

[0072] S204: Control the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter.

[0073] It can be understood that this step is similar to step S104 and will not be described in detail here.

[0074] S205, obtaining the remaining water volume of the current sprinkler truck, and calculating the sustainable operation time of the current sprinkler truck according to the remaining water volume, the preset sprinkler volume parameter and the preset sprinkler frequency parameter.

[0075] Obtaining the current remaining water volume of the sprinkler truck refers to real-time detection of the water volume in the sprinkler truck's water tank, which can generally be achieved by setting a water level sensor. The preset watering volume parameter is the amount of water that needs to be sprayed calculated based on the dust concentration. The preset watering frequency parameter is the number of watering times and intervals determined based on environmental conditions. Based on these three parameters, the sustainable operation time of the current sprinkler truck can be calculated, that is, under the current remaining water volume, according to the preset watering volume and frequency parameters, the sprinkler truck can continue to work for a certain length of time.

[0076] In specific implementation, multiple water level sensors can be set in the water tank to detect the water volume in the water tank in real time. The sensor data is read to the on-board computing unit, and combined with the preset watering volume and frequency parameters that have been calculated, the sustainable operation time is calculated according to the formula of (remaining water volume) / (single watering volume × watering frequency). For example, if the remaining water volume is 10 tons, the single watering volume is 2 tons, and the watering frequency is once per hour, the sustainable operation time is 10 / (2×1)=5 hours. The computing unit will continuously monitor the water volume and automatically recalculate the sustainable duration to guide the vehicle's operation plan.

[0077] S206: Obtain the geographical location information and water supply station location information of the current sprinkler truck, and obtain the traffic condition information of the current sprinkler truck in the current area.

[0078] The purpose of obtaining the current geographic location information of the sprinkler truck is to determine the real-time coordinates of the sprinkler truck. This is usually obtained through the vehicle's GPS system. The purpose of obtaining the location information of the water supply station is to determine the nearby water supply point, that is, the location where the sprinkler truck needs to go to replenish water. The information includes the name of the water supply station, location coordinates, etc. The purpose of obtaining the traffic status information of the current area is to determine the dynamic traffic that needs to be considered when selecting the vehicle's driving route.

[0079] In specific implementation, the vehicle needs to be equipped with a GPS positioning module and cooperate with a digital map system to determine the real-time location coordinates of the vehicle. At the same time, a location database of water filling stations is pre-stored, containing information on all possible water filling stations. It is also necessary to connect to a real-time traffic information platform to obtain information such as traffic congestion and traffic restrictions in the area surrounding the vehicle. Combining these three aspects of information, the system can plan the optimal water filling route. For example, choose the nearest water filling station while bypassing congested sections to complete water filling in the shortest time.

[0080] S207. Dynamically weight the road network in the current area based on the traffic condition information to obtain a weighted road network, wherein the weight of the congested road section in the current area is greater than the weight of the unobstructed road section.

[0081] Dynamically weighting the road network based on traffic information means evaluating the importance of road segments in the map numerically based on the real-time traffic conditions of the roads. A high weight on a congested section means it is more difficult to pass; a low weight on an unobstructed section means it is more convenient to pass. The weighted map is more conducive to the path planning algorithm to search for the shortest water replenishment route.

[0082] In specific implementation, a regional road network map database can be established in advance. After obtaining real-time traffic information, the system will assign a dynamic weight value to each road based on the degree of congestion, traffic restrictions, etc. For example, the weight of congested sections is 1.0, slow sections is 0.8, and smooth sections is 0.2. Then, based on the weighted graph theory algorithm, the shortest path is calculated on the weighted map, that is, the optimal water replenishment route that pays attention to time factors is obtained. The route will automatically bypass congested sections and prefer smooth roads with lower weights.

[0083] S208: Perform path planning on the weighted road network to generate multiple candidate water replenishment routes.

[0084] Weighted road network refers to a map road network with dynamically weighted roads. Path planning refers to searching for one or more continuous road paths from the starting point to the end point on the road network. Alternative water replenishment routes refer to multiple optional water replenishment routes obtained through path planning.

[0085] In specific implementation, after obtaining the weighted road network data, the Dijkstra algorithm, A* algorithm and other path planning algorithms can be used to search for the shortest path on the road network map with the current location of the sprinkler truck as the starting point and the target water filling station as the end point. During the search process, the algorithm will automatically avoid congested roads with larger weights and give priority to unblocked roads with smaller weights. In the end, multiple candidate water filling routes with similar distances but different road node combinations can be generated.

[0086] S209: Score all the candidate water replenishment routes according to their total length, estimated travel time, and road complexity.

[0087] Scoring alternate water refueling routes refers to quantitatively evaluating each alternate route using indicators such as total route length, estimated driving time, and road complexity. The total route length refers to the total number of kilometers. The estimated driving time is calculated based on the road speed limit. Road complexity refers to the number of turns included in the route, which affects the difficulty of driving.

[0088] In specific implementation, the comprehensive score of each route can be calculated according to the following formula: Score = aLength + bTime + c*Complexity. Among them, a, b, and c are coefficients for adjusting the weights of each indicator. The system substitutes the indicator data of each route into the formula to calculate the comprehensive score, and the route with the highest score is the optimal route. Machine learning models can also be used to learn scoring rules from a large amount of historical route data to achieve automatic scoring.

[0089] S210: Using the preset water replenishment route with the highest score as the optimal water replenishment route.

[0090] The one with the highest score is selected from multiple candidate water replenishment routes as the optimal water replenishment route for final planning. The optimal route meets the conditions of short driving time, short route length and low road difficulty to the greatest extent while ensuring that the destination is reached.

[0091] In specific implementation, after scoring the candidate routes, the system will sort the scoring results. The route with the highest score and significantly better than other routes will be selected as the optimal water replenishment route. If multiple routes have similar scores, multiple options will be provided. The final optimal route will be used as the water replenishment navigation route for the sprinkler truck and transmitted to the on-board navigation system to complete the navigation for the sprinkler truck to replenish water.

[0092] S211. Send the optimal water replenishment route to the client.

[0093] In the previous steps, the optimal water replenishment route has been calculated. This is the optimal path from the current location of the sprinkler truck to the nearest water replenishment station. The node sequence, coordinate information, route length and time of the route can be sent to the client in character form in the standard JSON data format through the 4G network. The client system will parse the JSON data, convert and display it as a planned route annotation on the on-board digital map, including complete water replenishment navigation information such as each turning point and road section.

[0094] S212: Scan the current road in real time based on the laser radar sensor, and divide the current road into multiple areas to be sprinkled with water according to the real-time dust concentration data.

[0095] Real-time scanning of the current road based on lidar refers to using the lidar sensor installed on the sprinkler truck to send out laser pulse signals to scan both sides of the road and detect the return signal to obtain road condition information. Dividing the road into multiple areas to be sprinkled means dividing the road into several sections according to the dust concentration, so that concentrated watering can be carried out in the high-concentration areas later.

[0096] In specific implementation, the laser radar performs line scanning of the road at a certain angle and frequency, and fuses the scanning results of the laser radar with the real-time dust concentration data of the dust concentration sensor (such as PM2.5 and PM10 sensors) to obtain a road dust distribution map. According to the preset dust concentration level, such as 50μg / m3 as level one and 100μg / m3 as level two, the real-time scanning results divide the areas with different dust levels. The high-concentration areas will be used as priority watering areas, forming multiple road sections to be watered.

[0097] S213. Generate an optimal watering path for the current sprinkler truck according to the location data of each area to be watered, the preset watering volume parameter and the preset watering frequency parameter. The optimal watering path has the shortest total driving distance, and the total watering volume corresponding to the preset watering volume parameter and the preset watering frequency parameter is not greater than the remaining water volume of the current sprinkler truck and covers all the areas to be watered.

[0098] The location data of each area to be watered comes from the scanning results of the laser radar. The preset watering volume parameters are calculated according to the dust concentration. The preset watering frequency parameters are calculated according to environmental factors. The optimal watering path takes into account the short driving distance, the total watering volume matching the vehicle's water volume, and covering all areas to be watered.

[0099] In specific implementation, the problem can be modeled as a traveling salesman problem, with each area to be watered as a node and the path length as a weight. An approximate algorithm is used to calculate the shortest cycle path covering all areas, that is, the watering sequence. The total watering volume on the path is calculated. If it exceeds the water volume on the vehicle, the number of cycles is gradually reduced until the total watering volume constraint is met. If it cannot be met, the number of nodes is reduced to obtain the final path.

[0100] For example, first load the location data of each area to be watered, including the coordinate range, shape, length and other information of each area, load the preset watering volume parameters, calculate the required watering volume for each area based on the concentration, load the preset watering frequency parameters: the watering frequency calculated based on environmental factors, load the remaining water volume of the current sprinkler truck: the available water volume in the onboard water tank, and establish a mathematical model for the problem: the objective function - minimize the total length of the path, and the constraints are: (1) the path covers all areas to be watered, (2) the cumulative watering volume on the path <= the remaining water volume in the water tank, (3) the watering volume of each area >= the preset watering volume parameters of the area, and (4) the watering frequency meets the preset watering frequency parameters. For example, meta-heuristic algorithms such as genetic algorithms and simulated annealing algorithms are used to initialize the population, randomly generate multiple paths, each path represents a possible watering route, evaluate the fitness of the path, score it according to the objective function and constraints, retain the paths with higher scores to the next generation of the population, use crossover, mutation and other operators to generate new paths, repeat the above iterative operations until the optimal solution with the highest score is obtained, and output the optimal path to obtain the watering route that meets all constraints and has the shortest length, which is output as the optimal watering path result.

[0101] S214: Send the optimal watering path to the client.

[0102] Sending the optimal watering path to the client means transmitting the precise watering route calculated by the server to the on-board information terminal on the watering truck.

[0103] In specific implementation, the results of watering path planning can be transmitted to the vehicle information system of the sprinkler truck in real time through the 4G network. The system integrates a GPS navigation module, which can read and analyze the watering path data and display it on a digital map for the driver to follow during the watering operation. At the same time, the sprinkler truck can also be remotely controlled to automatically execute the preset optimal watering path when necessary.

[0104] S215: Monitor the current road to obtain a road surface dryness index of the current road.

[0105] First, an industrial camera is used to continuously shoot the road ahead so that the image covers the distance from the front of the vehicle to a certain distance, and a sequence of road pavement images is obtained. The road pavement images are input into a preset image processing algorithm to achieve road area segmentation, wet area detection, etc., and the number of wet pixels in each image is output. Combined with the accurate vehicle speed measured by the vehicle-mounted radar, the percentage of wet pixels in the image within a unit distance is calculated, that is, the wetness of the road surface. The continuously shot road pavement images are input into the image processing algorithm to detect the color, saturation, and brightness information of each pixel point, and compared with the pre-stored dry road surface template image to calculate the proportion of wet pixels. Combined with the vehicle speed information, the average percentage of wet pixels per meter of road, that is, the wetness of the road surface, can be obtained. If the proportion of wet pixels is too low, the watering effect of the road section is evaluated to be poor.

[0106] S216: If the road surface dryness index is less than a preset threshold, increase the watering amount parameter to a preset watering amount parameter adjustment value.

[0107] Pavement dryness index: A quantitative indicator reflecting the wetness of the road surface. In this scheme, it is obtained by calculating the percentage of wet pixels on the road surface based on the image.

[0108] The preset threshold refers to the critical dryness index value that determines whether the watering amount needs to be adjusted, and the watering amount parameter adjustment value refers to the new watering parameter value that is increased when the actual dryness index is lower than the threshold.

[0109] Next, the specific implementation process is explained in detail: according to the road material and the watering target, the critical dryness index threshold is determined, such as 50% wet pixel percentage, and the real-time road wet pixel percentage calculated by the image processing algorithm is compared with the preset threshold. If the actual dryness index is lower than the threshold, the watering parameter is increased. For example, if the original parameter is 10 liters / square meter, the new parameter is 15 liters / square meter after increasing by 50%. The adjusted watering volume parameter is updated to the watering system control module for subsequent watering.

[0110] The following describes the intelligent sprinkler control system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device of an intelligent sprinkler control system in an embodiment of the present application.

[0111] It should be noted that Figure 3 The structure of the intelligent sprinkler control system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0112] like Figure 3 As shown, the intelligent sprinkler control system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0113] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0114] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0115] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0117] Specifically, the intelligent sprinkler control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the intelligent sprinkler control method provided in the above embodiment is implemented.

[0118] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the intelligent sprinkler control system described in the above embodiment; or may exist independently without being assembled into the intelligent sprinkler control system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the intelligent sprinkler control system, the intelligent sprinkler control system implements the intelligent sprinkler control method provided in the above embodiment.

[0119] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0120] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0121] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. An intelligent sprinkler control method, characterized in that: Applied to an intelligent sprinkler control system, the method comprises: Acquiring real-time dust concentration data and obtaining weather forecast data, wherein the weather forecast data includes current dust concentration, expected humidity change and rainfall information; calculating a future dust concentration according to the current dust concentration, the expected humidity change and the rainfall information; Calculating a preset watering amount parameter according to the future dust concentration, and calculating a preset watering frequency parameter according to the current environmental data; The current sprinkler truck is controlled according to the preset watering volume parameter and the preset watering frequency parameter.

2. The method according to claim 1, characterized in that The step of calculating the future dust concentration according to the current dust concentration, the expected humidity change and the rainfall information specifically includes: Calculating a humidity attenuation coefficient at a preset future time according to the expected humidity change; Calculating the rainfall attenuation coefficient at the preset future time according to the rainfall information; The current dust concentration is multiplied by the humidity attenuation coefficient and the rainfall attenuation coefficient to obtain the future dust concentration at the preset future time.

3. The method according to claim 1, characterized in that The step of calculating the preset watering amount parameter according to the future dust concentration and calculating the preset watering frequency parameter according to the current environmental data specifically includes: determining a target dust suppression efficiency based on the future dust concentration; Calculating a preset watering amount parameter based on the target dust suppression efficiency and a preset dust suppression effect function; Extracting temperature parameters, humidity parameters and wind speed parameters from the current environmental data, and calculating a preset water evaporation rate based on the temperature parameters, the humidity parameters and the wind speed parameters; The preset watering frequency parameter is calculated based on the preset water evaporation amount and the preset ground humidity threshold.

4. The method according to claim 1, characterized in that: After the step of controlling the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter, the method further includes: Obtaining the remaining water volume of the current sprinkler truck, and calculating the sustainable operation time of the current sprinkler truck according to the remaining water volume, the preset sprinkler volume parameter and the preset sprinkler frequency parameter; Obtaining the current geographical location information and water supply station location information of the sprinkler truck, and generating an optimal water supply route according to the geographical location information and the water supply station location information; The optimal water replenishment route is sent to the client.

5. The method according to claim 4, characterized in that The step of generating the optimal water replenishment route according to the geographical location information and the water replenishment station location information specifically includes: Obtaining traffic condition information of the current sprinkler truck in the current area; Dynamically weighting the road network in the current area based on the traffic condition information to obtain a weighted road network, wherein the weight of the congested road section in the current area is greater than the weight of the unobstructed road section; Performing path planning on the weighted road network to generate a plurality of alternate water replenishment routes; Score all the alternate water replenishment routes according to their total route length, estimated travel time and road complexity; The preset hydration route with the highest score is used as the optimal hydration route.

6. The method according to claim 1, characterized in that After the step of controlling the current sprinkler truck according to the preset watering volume parameter and the preset watering frequency parameter, the method further includes: Scanning the current road in real time, and dividing the current road into a plurality of areas to be sprinkled with water according to the real-time dust concentration data; Generate an optimal watering path for the current watering truck according to the location data of each area to be watered, the preset watering amount parameter and the preset watering frequency parameter, wherein the optimal watering path has the shortest total driving distance, the total watering amount corresponding to the preset watering amount parameter and the preset watering frequency parameter is not greater than the remaining water amount of the current watering truck, and covers all the areas to be watered; The optimal watering path is sent to the client.

7. The method according to claim 6, characterized in that After the step of sending the optimal watering path to the client, the method further includes: Monitoring the current road to obtain a road surface dryness index of the current road; If the road surface dryness index is less than a preset threshold, the watering amount parameter is increased to a preset watering amount parameter adjustment value.

8. An intelligent sprinkler control system, characterized in that: The intelligent sprinkler control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the intelligent sprinkler control system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the intelligent sprinkler control system, the intelligent sprinkler control system executes the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an intelligent sprinkler control system, the intelligent sprinkler control system is enabled to execute the method according to any one of claims 1 to 7.

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