Control method and system of unmanned sweeper capable of recycling water resources

Abstract

The invention relates to a control method and system for an unmanned sweeper capable of recycling water resources, and the method comprises the steps: W1, enabling the unmanned sweeper to run on a road, obtaining the image data information of the road surface in real time based on a vehicle-mounted camera, and obtaining the speed data information of the vehicle in real time based on a vehicle-mounted speed sensor; and W2, based on the image data information of the road surface, constructing a road surface water stain identification model based on a convolutional neural network, identifying the water stain of the road surface to obtain data information of a pixel matrix of the water stain of the road surface, and predicting the water stain recovery amount of the road surface by adopting a regression prediction algorithm for optimizing a BP neural network by adopting a sparrow algorithm to obtain the water stain recovery amount of the road surface. And the predicted data information of the water stain recovery amount of the road surface is obtained. Water stains on the road surface and excessive water resources sprayed out of a spraying opening of the sweeper are recycled, the water stains on the ground are swept, the swept water stains are filtered, recycled and reused, and the utilization rate of the water resources is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned sweepers, and in particular to a control method and system for an unmanned sweeper for recycling water resources. Background Art

[0002] With the continuous development of driverless vehicles, driverless road sweepers are also being updated and developed. These vehicles process the water stains swept from the road and then transport them to a collection tank. When the tank is full, it is dumped for disposal. During this treatment process, the wastewater is not recycled, resulting in a waste of water resources. Therefore, how to reuse the recycled water stains has become a pressing issue. Summary of the Invention

[0003] In view of the above problems, the present invention provides a control method and system for an unmanned road sweeper that recycles and utilizes water resources. It not only recycles water stains on the road surface and excess water resources sprayed from the spray nozzle of the road sweeper, but also cleans the water stains on the ground, filters, recycles and reuses the cleaned water stains, and improves the utilization rate of water resources.

[0004] In order to achieve the above-mentioned and other related objectives, the present invention provides the following technical solution: a control method for an unmanned sweeper for recycling water resources, the method comprising:

[0005] W1. The unmanned road sweeper drives on the road, acquiring real-time road image data using its onboard camera and vehicle speed data using its onboard speed sensor.

[0006] W2. Based on the image data information of the road surface, a road water stain recognition model based on a convolutional neural network is constructed to identify water stains on the road surface and to construct a pixel matrix of water stains on the road surface to obtain data information of the pixel matrix of water stains on the road surface;

[0007] W3. Based on the pixel matrix data information of the water stain on the road surface and the speed data information of the vehicle, the sparrow algorithm is used to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface, and the predicted amount of water stain recovery on the road surface is obtained;

[0008] W4. Based on the data information of the predicted amount of water stains recovered on the road surface, a water stain treatment control model for the unmanned road sweeper is constructed to control and adjust the water stain treatment of the unmanned road sweeper to obtain control data information of the unmanned road sweeper.

[0009] Furthermore, in step W2, the construction of a road surface water stain recognition model based on a convolutional neural network to recognize road surface water stains and construct a pixel matrix of road surface water stains includes:

[0010] W21. Based on the image data information of the road surface, a feature point extraction function Q of the road surface image is established.

[0011]

[0012] Wherein, x is the image data information of the road surface, α1, α2 and α3 are the feature point extraction factors of the road surface image, and the feature points of the road surface image are extracted to obtain the data information of the feature points of the road surface image;

[0013] W22. Based on the data information of the characteristic points of the road surface image, establish a road water stain recognition function R,

[0014]

[0015] Among them, y is the data information of the feature points of the road image, β1, β2 and β3 are the recognition factors of the convolutional neural network;

[0016] W23. Based on the road surface water stain recognition function R, the road surface water stains are recognized, and a pixel matrix of the road surface water stains is constructed to obtain data information of the pixel matrix of the road surface water stains.

[0017] Furthermore, the recognition factors β1, β2 and β3 of the convolutional neural network are,

[0018]

[0019] Wherein, y is the data information of the feature points of the road surface image.

[0020] Furthermore, the constraints of the feature point extraction factors α1, α2 and α3 of the road surface image are:

[0021]

[0022] Furthermore, in step W3, the use of the sparrow algorithm to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface includes:

[0023] W31. The pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input into the BP neural network for training and learning, and the weights and biases of the network are initialized to obtain the weights and bias data information of the initialized network;

[0024] W32. Based on the weights and bias data information of the initialized network, the weights and biases of the network are optimized using the sparrow optimization algorithm to obtain the optimized weights and bias data information of the network;

[0025] W33. Based on the optimized network weights and bias data information, determine the regression prediction function P of the BP neural network,

[0026]

[0027] Among them, z1 is the pixel matrix data information of the water stains on the road surface, z2 is the speed data information of the vehicle, δ1, δ2 and δ3 are the data information of the optimized network weights, and γ is the data information of the optimized bias, and the trained BP neural network is obtained;

[0028] W34. Based on the trained BP neural network, the pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input to predict the amount of water stains recovered on the road surface, and obtain the data information of the predicted amount of water stains recovered on the road surface.

[0029] Furthermore, in step W32, the optimization of the network weights and biases using the sparrow optimization algorithm includes:

[0030] W321. Based on the weights and bias data of the initialized network, the sparrow population is initialized, the parameters of the sparrow population are determined, and the data information of the initialized sparrow population is obtained;

[0031] W322. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individual,

[0032]

[0033] Among them, r is the data information of the initialized sparrow population, η1, η2 and η3 are the fitness determining factors of the individual sparrow population, and the fitness values of the individual sparrow population are calculated to obtain the data information of the fitness values of the individual sparrow population;

[0034] W323. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function F,

[0035]

[0036] Among them, h is the data information of the fitness value of the sparrow population individuals, λ1, λ2 and λ3 are the target optimization factors, and the weights and biases of the network are optimized to obtain the data information of the weights and biases of the optimized network.

[0037] Furthermore, in step W4, the water stain treatment control model of the unmanned sweeper is constructed to control and adjust the water stain treatment of the unmanned sweeper, including:

[0038] W41. Based on the data information of the predicted road surface water stain recovery amount, construct a data set of the predicted road surface water stain recovery amount;

[0039] W42. Input the predicted road surface water stain recovery amount data set into the unmanned road sweeper water stain treatment control model for training and learning, and determine the unmanned road sweeper water stain treatment control function G,

[0040]

[0041] Among them, g is the dataset of the predicted road surface water stain recovery amount, μ1, μ2, and μ3 are the water stain treatment control factors of the unmanned sweeper, and the trained unmanned sweeper water stain treatment control model is obtained;

[0042] W43. Based on the trained control model for water stain treatment of the unmanned road sweeper, the data information of the predicted water stain recovery amount of the road surface is input, the water stain treatment of the unmanned road sweeper is controlled and adjusted, and the control data information of the unmanned road sweeper is obtained.

[0043] Furthermore, the control data information of the unmanned sweeper includes speed data information of the unmanned sweeper, data information of the water stain purification efficiency of the unmanned sweeper, and data information of the water stain recovery speed of the unmanned sweeper.

[0044] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a control system for an unmanned sweeper for recycling and utilizing water resources, including a computer device that is programmed or configured to execute any one of the steps of the control method for an unmanned sweeper for recycling and utilizing water resources.

[0045] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the control methods for an unmanned sweeper for recycling water resources.

[0046] The present invention has the following positive effects:

[0047] 1. The present invention identifies road water stains by constructing a convolutional neural network-based road water stain recognition model, and predicts the amount of road water stain recovery by combining the sparrow algorithm to optimize the BP neural network regression prediction algorithm. This method not only recycles water stains on the road surface and excess water resources sprayed from the spray nozzles of road sweepers, but also cleans the water stains on the ground, filters, recovers, and reuses the cleaned water stains, thereby improving the utilization rate of water resources.

[0048] 2. The present invention controls and adjusts the water stain treatment of the unmanned sweeper by constructing a water stain treatment control model for the unmanned sweeper. It can not only treat and purify the cleaned water stains during the operation of the unmanned sweeper, thereby improving the operating efficiency of the unmanned sweeper, but also has strong adaptability and can perform corresponding control and adjustment according to different road conditions, thereby improving the cleaning efficiency of the unmanned sweeper. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the method flow of the present invention;

[0050] Figure 2 Schematic diagram of the process of the road surface water stain recognition model based on convolutional neural network of the present invention;

[0051] Figure 3 Schematic diagram of the process of optimizing the BP neural network regression prediction algorithm using the sparrow algorithm of the present invention;

[0052] Figure 4 This is a flow chart of the construction of a water stain treatment control model for an unmanned road sweeper according to the present invention. DETAILED DESCRIPTION

[0053] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0054] Example 1: Figure 1 As shown, a control method for an unmanned sweeper for recycling water resources comprises:

[0055] W1. The unmanned road sweeper drives on the road, acquiring real-time road image data using its onboard camera and vehicle speed data using its onboard speed sensor.

[0056] W2. Based on the image data information of the road surface, a road water stain recognition model based on a convolutional neural network is constructed to identify water stains on the road surface and to construct a pixel matrix of water stains on the road surface to obtain data information of the pixel matrix of water stains on the road surface;

[0057] W3. Based on the pixel matrix data information of the water stain on the road surface and the speed data information of the vehicle, the sparrow algorithm is used to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface, and the predicted amount of water stain recovery on the road surface is obtained;

[0058] W4. Based on the data information of the predicted amount of water stains recovered on the road surface, a water stain treatment control model for the unmanned road sweeper is constructed to control and adjust the water stain treatment of the unmanned road sweeper to obtain control data information of the unmanned road sweeper.

[0059] In this embodiment, if Figure 2 As shown, in step W2, the construction of a road surface water stain recognition model based on a convolutional neural network, identifying road surface water stains, and constructing a pixel matrix of road surface water stains includes:

[0060] W21. Based on the image data information of the road surface, a feature point extraction function Q of the road surface image is established.

[0061]

[0062] Wherein, x is the image data information of the road surface, α1, α2 and α3 are the feature point extraction factors of the road surface image, and the feature points of the road surface image are extracted to obtain the data information of the feature points of the road surface image;

[0063] W22. Based on the data information of the characteristic points of the road surface image, establish a road water stain recognition function R,

[0064]

[0065] Among them, y is the data information of the feature points of the road image, β1, β2 and β3 are the recognition factors of the convolutional neural network;

[0066] W23. Based on the road surface water stain recognition function R, the road surface water stains are recognized, and a pixel matrix of the road surface water stains is constructed to obtain data information of the pixel matrix of the road surface water stains.

[0067] In this embodiment, the recognition factors β1, β2 and β3 of the convolutional neural network are,

[0068] Wherein, y is the data information of the feature points of the road surface image.

[0069] In this embodiment, the constraints of the feature point extraction factors α1, α2, and α3 of the road surface image are:

[0070]

[0071] In this embodiment, if Figure 3 As shown, in step W3, the method of using the sparrow algorithm to optimize the BP neural network regression prediction algorithm to predict the water stain recovery amount of the road surface includes:

[0072] W31. The pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input into the BP neural network for training and learning, and the weights and biases of the network are initialized to obtain the weights and bias data information of the initialized network;

[0073] W32. Based on the weights and bias data information of the initialized network, the weights and biases of the network are optimized using the sparrow optimization algorithm to obtain the optimized weights and bias data information of the network;

[0074] W33. Based on the optimized network weights and bias data information, determine the regression prediction function P of the BP neural network,

[0075]

[0076] Among them, z1 is the pixel matrix data information of the water stains on the road surface, z2 is the speed data information of the vehicle, δ1, δ2 and δ3 are the data information of the optimized network weights, and γ is the data information of the optimized bias, and the trained BP neural network is obtained;

[0077] W34. Based on the trained BP neural network, the pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input to predict the amount of water stains recovered on the road surface, and obtain the data information of the predicted amount of water stains recovered on the road surface.

[0078] In this embodiment, in step W32, the optimization of the network weights and biases using the sparrow optimization algorithm includes:

[0079] W321. Based on the weights and bias data of the initialized network, the sparrow population is initialized, the parameters of the sparrow population are determined, and the data information of the initialized sparrow population is obtained;

[0080] W322. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individual,

[0081]

[0082] Among them, r is the data information of the initialized sparrow population, η1, η2 and η3 are the fitness determining factors of the individual sparrow population, and the fitness values of the individual sparrow population are calculated to obtain the data information of the fitness values of the individual sparrow population;

[0083] W323. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function F,

[0084]

[0085] Among them, h is the data information of the fitness value of the sparrow population individuals, λ1, λ2 and λ3 are the target optimization factors, and the weights and biases of the network are optimized to obtain the data information of the weights and biases of the optimized network.

[0086] Example 2: Based on the control method of an unmanned sweeper for recycling water resources in Example 1, the present invention is further illustrated and described below.

[0087] like Figure 1 As shown, a control method for an unmanned sweeper for recycling water resources comprises:

[0088] W1. The unmanned road sweeper drives on the road, acquiring real-time road image data using its onboard camera and vehicle speed data using its onboard speed sensor.

[0089] W2. Based on the image data information of the road surface, a road water stain recognition model based on a convolutional neural network is constructed to identify water stains on the road surface and to construct a pixel matrix of water stains on the road surface to obtain data information of the pixel matrix of water stains on the road surface;

[0090] W3. Based on the pixel matrix data information of the water stain on the road surface and the speed data information of the vehicle, the sparrow algorithm is used to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface, and the predicted amount of water stain recovery on the road surface is obtained;

[0091] W4. Based on the data information of the predicted amount of water stains recovered on the road surface, a water stain treatment control model for the unmanned road sweeper is constructed to control and adjust the water stain treatment of the unmanned road sweeper to obtain control data information of the unmanned road sweeper.

[0092] In this embodiment, if Figure 4 As shown, in step W4, the construction of the water stain treatment control model of the unmanned sweeper and the control and adjustment of the water stain treatment of the unmanned sweeper include:

[0093] W41. Based on the data information of the predicted road surface water stain recovery amount, construct a data set of the predicted road surface water stain recovery amount;

[0094] W42. Input the predicted road surface water stain recovery amount data set into the unmanned road sweeper water stain treatment control model for training and learning, and determine the unmanned road sweeper water stain treatment control function G,

[0095]

[0096] Among them, g is the dataset of the predicted road surface water stain recovery amount, μ1, μ2, and μ3 are the water stain treatment control factors of the unmanned sweeper, and the trained unmanned sweeper water stain treatment control model is obtained;

[0097] W43. Based on the trained control model for water stain treatment of the unmanned road sweeper, the data information of the predicted water stain recovery amount of the road surface is input, the water stain treatment of the unmanned road sweeper is controlled and adjusted, and the control data information of the unmanned road sweeper is obtained.

[0098] In this embodiment, the control data information of the unmanned sweeper includes speed data information of the unmanned sweeper, water stain purification efficiency data information of the unmanned sweeper, and water stain recovery speed data information of the unmanned sweeper.

[0099] In this embodiment, the present invention provides a control system for an unmanned sweeper for recycling water resources, including a computer device that is programmed or configured to execute any one of the steps of the control method for an unmanned sweeper for recycling water resources.

[0100] In this embodiment, an unmanned sweeper that can recycle water resources includes a cleaning mechanism, a water resource purification and recovery mechanism, a sewage tank, a spraying system water tank, an unmanned driving perception system, an unmanned driving planning and control system, and an unmanned driving positioning system.

[0101] The cleaning mechanism includes a variety of side brush heads, side brush motors, roller brush motors, and roller brushes that are suitable for different road surfaces. The water resource purification and recovery mechanism includes a sewage coarse filtration mechanism, a sewage treatment mechanism, a sewage fine filtration mechanism, etc.

[0102] When the unmanned sweeper is in automatic driving mode, it uses the perception system to identify the condition of the road surface. If there is water on the identified road surface, this information is fed back to the planning and control system. The control system makes a decision and sends an instruction to the VCU. After receiving the instruction, the VCU sends it to the side brush motor.

[0103] After the side brush motor receives the command from the VCU, the vehicle stops to switch the brush head. After switching, the vehicle continues to clean the water stains and garbage on the road.

[0104] The unmanned road sweeper transports the water stains and garbage cleaned on the road to the sewage coarse filtration mechanism, sends the filtered sewage to the sewage treatment mechanism, and transports the filtered garbage to the garbage bin. The sewage treatment mechanism purifies and precipitates the sewage, and then transports it to the sewage fine filtration mechanism. The filtered sewage is then transported back to the spraying system water tank.

[0105] The spraying system uses recycled water to spray during cleaning.

[0106] In this embodiment, the present invention further provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the control methods for an unmanned sweeper for recycling water resources.

[0107] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0108] In summary, the present invention not only recycles water stains on the road surface and excess water resources sprayed from the spray nozzles of the road sweeper, but also cleans the water stains on the ground, filters, recycles and reuses the cleaned water stains, and improves the utilization rate of water resources.

[0109] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A control method for an unmanned sweeper for recycling water resources, characterized in that: The method comprises: W1. The unmanned road sweeper drives on the road, acquiring real-time road image data using its onboard camera and vehicle speed data using its onboard speed sensor. W2. Based on the image data information of the road surface, a road water stain recognition model based on a convolutional neural network is constructed to identify water stains on the road surface and to construct a pixel matrix of water stains on the road surface to obtain data information of the pixel matrix of water stains on the road surface; W3. Based on the pixel matrix data information of the water stain on the road surface and the speed data information of the vehicle, the sparrow algorithm is used to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface, and the predicted amount of water stain recovery on the road surface is obtained; W4. Based on the data information of the predicted amount of water stains recovered on the road surface, a water stain treatment control model for the unmanned road sweeper is constructed to control and adjust the water stain treatment of the unmanned road sweeper to obtain control data information of the unmanned road sweeper.

2. The control method of the unmanned sweeper for recycling water resources according to claim 1, characterized in that: In step W2, the construction of a road surface water stain recognition model based on a convolutional neural network, identifying water stains on the road surface, and constructing a pixel matrix of the water stains on the road surface includes: W21. Based on the image data information of the road surface, a feature point extraction function Q of the road surface image is established. Wherein, x is the image data information of the road surface, α1, α2 and α3 are the feature point extraction factors of the road surface image, and the feature points of the road surface image are extracted to obtain the data information of the feature points of the road surface image; W22. Based on the data information of the characteristic points of the road surface image, establish a road water stain recognition function R, Among them, y is the data information of the feature points of the road image, β1, β2 and β3 are the recognition factors of the convolutional neural network; W23. Based on the road surface water stain recognition function R, the road surface water stains are recognized, and a pixel matrix of the road surface water stains is constructed to obtain data information of the pixel matrix of the road surface water stains.

3. The control method of the unmanned sweeper for recycling water resources according to claim 2 is characterized in that: The recognition factors β1, β2 and β3 of the convolutional neural network are, Wherein, y is the data information of the feature points of the road surface image.

4. The control method of the unmanned sweeper for recycling water resources according to claim 2 is characterized in that: The constraints of the feature point extraction factors α1, α2 and α3 of the road image are:

5. The control method of the unmanned sweeper for recycling water resources according to claim 1, characterized in that: In step W3, the method of using the sparrow algorithm to optimize the BP neural network regression prediction algorithm to predict the amount of water stain recovery on the road surface includes: W31. The pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input into the BP neural network for training and learning, and the weights and biases of the network are initialized to obtain the weights and bias data information of the initialized network; W32. Based on the weights and bias data information of the initialized network, the weights and biases of the network are optimized using the sparrow optimization algorithm to obtain the optimized weights and bias data information of the network; W33. Based on the optimized network weights and bias data information, determine the regression prediction function P of the BP neural network, Among them, z1 is the pixel matrix data information of the water stains on the road surface, z2 is the speed data information of the vehicle, δ1, δ2 and δ3 are the data information of the optimized network weights, and γ is the data information of the optimized bias, and the trained BP neural network is obtained; W34. Based on the trained BP neural network, the pixel matrix data information of the water stains on the road surface and the speed data information of the vehicle are input to predict the amount of water stains recovered on the road surface, and obtain the data information of the predicted amount of water stains recovered on the road surface.

6. The control method of the unmanned sweeper for recycling water resources according to claim 5, characterized in that: In step W32, the optimization of the network weights and biases using the sparrow optimization algorithm includes: W321. Based on the weights and bias data of the initialized network, the sparrow population is initialized, the parameters of the sparrow population are determined, and the data information of the initialized sparrow population is obtained; W322. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individual, Among them, r is the data information of the initialized sparrow population, η1, η2 and η3 are the fitness determining factors of the individual sparrow population, and the fitness values of the individual sparrow population are calculated to obtain the data information of the fitness values of the individual sparrow population; W323. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function F, Among them, h is the data information of the fitness value of the sparrow population individuals, λ1, λ2 and λ3 are the target optimization factors, and the weights and biases of the network are optimized to obtain the data information of the weights and biases of the optimized network.

7. The control method of the unmanned sweeper for recycling water resources according to claim 1, characterized in that: In step W4, the construction of the water stain treatment control model of the unmanned sweeper and the control and adjustment of the water stain treatment of the unmanned sweeper include: W41. Based on the data information of the predicted road surface water stain recovery amount, construct a data set of the predicted road surface water stain recovery amount; W42. Input the predicted road surface water stain recovery amount data set into the unmanned road sweeper water stain treatment control model for training and learning, and determine the unmanned road sweeper water stain treatment control function G, Among them, g is the dataset of the predicted road surface water stain recovery amount, μ1, μ2, and μ3 are the water stain treatment control factors of the unmanned sweeper, and the trained unmanned sweeper water stain treatment control model is obtained; W43. Based on the trained control model for water stain treatment of the unmanned road sweeper, the data information of the predicted water stain recovery amount of the road surface is input, the water stain treatment of the unmanned road sweeper is controlled and adjusted, and the control data information of the unmanned road sweeper is obtained.

8. The control method of the unmanned sweeper for recycling water resources according to claim 7 is characterized in that: The control data information of the unmanned sweeper includes speed data information of the unmanned sweeper, data information of the water stain purification efficiency of the unmanned sweeper, and data information of the water stain recovery speed of the unmanned sweeper.

9. A control system for an unmanned sweeper for recycling water resources, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the control method of the unmanned sweeper for recycling water resources as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program that is programmed or configured to execute the control method for the unmanned sweeper for recycling water resources as described in any one of claims 1 to 8.

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