Cleaning and sweeping vehicle operation control method, system and device and medium

Abstract

The invention discloses a cleaning and sweeping vehicle operation control method which is characterized by comprising the following steps: acquiring environmental data which comprises at least one of a pavement image, a humidity value and PM concentration; determining a pollution condition based on the environmental data; determining an operation mode based on the pollution condition; and adjusting operation parameters based on the operation mode and a parameter adjustment algorithm. The method can achieve the dynamic intelligent control of the operation behavior of the cleaning and sweeping vehicle, saves the operation resources, improves the operation quality, and is higher in practicality and industrial popularization value. The method is especially suitable for assessment scenes of both operation quality and energy conservation in cities.

Description

Technical Field

[0001] This specification relates to the technical field of washing and sweeping vehicle control, and in particular to a washing and sweeping vehicle operation control method, system, device and medium. Background Art

[0002] Most current road sweeping and cleaning vehicles have fixed operating parameters and lack the ability to intelligently switch operating intensity according to changes in road cleaning needs. This leads to problems of resource waste and inconsistent operating quality, especially in areas with different pollution levels.

[0003] Therefore, it is necessary to provide an improved washing and sweeping vehicle operation control method, system, device and medium that can realize automatic adjustment capability based on real-time road condition recognition to improve operation effectiveness. Summary of the Invention

[0004] One or more embodiments of the present specification provide a method for controlling the operation of a washing and sweeping vehicle, including: acquiring environmental data, the environmental data including at least one of a road surface image, a humidity value, and a PM concentration; determining a pollution situation based on the environmental data; determining an operation mode based on the pollution situation; and adjusting operation parameters based on the operation mode and a parameter adjustment algorithm.

[0005] In some embodiments, the method further includes: acquiring scenario data; and determining, based on the scenario data, operation parameters corresponding to various scenarios through a strategy model, where the strategy model is a machine learning model.

[0006] In some embodiments, the method further includes: obtaining the water, electricity consumption and time parameters of the washing and sweeping vehicle under various operating modes; determining the energy consumption evaluation of the washing and sweeping vehicle under different operating modes based on the water, electricity consumption and time parameters; and optimizing the operating parameters based on the energy consumption evaluation.

[0007] In some embodiments, the method further includes: acquiring historical operation data; determining a high-pollution area based on the historical operation data; and adopting a preset operation intensity in response to the current area being the high-pollution area.

[0008] At the same time, one or more embodiments of the specification provide a washing and sweeping vehicle operation control system, including a first acquisition module, configured to: acquire environmental data, the environmental data including at least one of road surface images, humidity values, and PM concentrations; a pollution assessment module, configured to: determine the pollution situation based on the environmental data; a mode determination module, configured to determine the operation mode based on the pollution situation; a parameter adjustment module, configured to adjust the operation parameters based on the operation mode and the parameter adjustment algorithm.

[0009] One or more embodiments of the present specification provide a washing and sweeping vehicle operation control device, comprising a processor, wherein the processor is configured to execute the washing and sweeping vehicle operation control method.

[0010] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for controlling a washing and sweeping vehicle operation.

[0011] Beneficial Effects: This invention enables dynamic intelligent control of sweeping vehicle operations, saving resources and improving work quality. It possesses strong practicality and industry-wide promotional value. It is particularly suitable for urban assessments that prioritize both work quality and energy conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0013] Figure 1 is a schematic diagram of a sweeper vehicle operation control system according to some embodiments of this specification;

[0014] Figure 2 is an exemplary flow chart of a method for controlling a washing and sweeping vehicle operation according to some embodiments of this specification;

[0015] Figure 3 This is an exemplary schematic diagram of the linkage control of pollution scores and parameters according to some embodiments of this specification. DETAILED DESCRIPTION

[0016] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0017] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0018] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0019] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0020] Existing technologies cannot accurately identify road pollution conditions, and operating parameters are fixed, making it impossible to achieve energy savings while ensuring cleaning results. To address this issue, this paper proposes a multi-sensor fusion recognition system combined with operational control strategy modeling and platform verification and optimization to achieve dynamic regulation and energy-saving control throughout the entire process.

[0021] Figure 1 It is an exemplary module diagram of a washing and sweeping vehicle operation control system according to some embodiments of this specification.

[0022] In some embodiments, as Figure 1 As shown, the washing and sweeping vehicle operation control system 100 may include a first acquisition module 110 , a pollution assessment module 120 , a mode determination module 130 , and a parameter adjustment module 140 .

[0023] In some embodiments, the first acquisition module is configured to acquire environmental data, where the environmental data includes at least one of a road surface image, a humidity value, and a PM concentration.

[0024] In some embodiments, the pollution assessment module is configured to determine pollution conditions based on the environmental data.

[0025] In some embodiments, the mode determination module is configured to determine an operation mode based on the pollution situation.

[0026] In some embodiments, the parameter adjustment module is configured to adjust the operation parameters based on the operation mode and the parameter adjustment algorithm.

[0027] In some embodiments, the washing and sweeping vehicle operation control system also includes a second acquisition module, which is configured to: acquire scene data; a parameter determination module, which is configured to determine the operation parameters corresponding to various scenes based on the scene data through a strategy model, and the strategy model is a machine learning model.

[0028] In some embodiments, the washing and sweeping vehicle operation control system also includes a third acquisition module, which is configured to: obtain the water, electricity consumption and time parameters of the washing and sweeping vehicle under various operation modes; an energy consumption evaluation module, which is configured to determine the energy consumption evaluation of the washing and sweeping vehicle under different operation modes based on the water, electricity consumption and time parameters; and a parameter optimization module, which is configured to optimize the operation parameters based on the energy consumption evaluation.

[0029] In some embodiments, the washing and sweeping vehicle operation control system also includes a fourth acquisition module, which is configured to: acquire historical operation data; an area determination module, which is configured to: determine a high-pollution area based on the historical operation data; and adopt a preset operation intensity in response to the current area being the high-pollution area.

[0030] It should be noted that the above description of the control system and its modules for the washing and sweeping vehicle operation is for convenience only and does not limit this specification to the scope of the embodiments. It is understandable that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form a subsystem connected with other modules without deviating from the principles. In some embodiments, Figure 1 The first acquisition module 110, pollution assessment module 120, pattern determination module 130, and parameter adjustment module 140 can be separate modules within a system, or a single module can implement the functions of two or more of the aforementioned modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are within the scope of protection of this specification.

[0031] Figure 2 This is an exemplary flow chart of a method for controlling a washing and sweeping vehicle operation according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a washing and sweeping vehicle operation control system.

[0032] Step 210: Acquire environmental data, where the environmental data includes at least one of a road surface image, a humidity value, and a PM concentration.

[0033] In some embodiments, the washing and sweeping vehicle operation control system can collect road surface images, humidity values ​​(%), PM concentration and other information based on the AI ​​vision system and humidity and particulate matter sensors.

[0034] Step 220: Determine the pollution situation based on the environmental data.

[0035] In some embodiments, as Figure 3As shown, the control system of the washing and sweeping vehicle operation can determine the pollution score S corresponding to the pollution situation based on the collected road image, humidity value (%), PM concentration and other information by looking up a table or other methods. dirt .

[0036] Step 230: Determine an operation mode based on the pollution situation.

[0037] In some embodiments, the washing and sweeping vehicle operation control system may query a database based on historical experience statistics according to the pollution situation to determine the operation mode.

[0038] Step 240: Adjust the operating parameters based on the operating mode and the parameter adjustment algorithm.

[0039] In some embodiments, as Figure 3 As shown, the washing and sweeping vehicle operation control system can be combined with the adjustment function to adjust the operation parameters based on the operation mode and parameter adjustment algorithm.

[0040] As an example only, the control system for a washing and sweeping vehicle can automatically select the operating mode gear according to the pollution score and adjust the sweeping brush speed ω, the water spray pressure P, and the suction force F. The adjustment function is as follows:

[0041] ω=ω base +k1·S dirt ;

[0042] P=P base +k2·S dirt ;

[0043] F=F base +k3·S dirt ; Among them, k1, k2, and k3 are environmental adaptability coefficients, which can be determined based on regional and weather data to ensure optimal operation results in different regions and weather conditions. base 、P base 、F base They are basic sweeping speed, basic water spray pressure, and basic suction, which can be obtained based on presets.

[0044] In some embodiments, the method further includes: acquiring scenario data; and determining, based on the scenario data, operation parameters corresponding to various scenarios through a strategy model, where the strategy model is a machine learning model.

[0045] Further explanation of the strategy model is provided below.

[0046] In some embodiments, the vehicle's control system can also pre-configure strategies for different scenarios. For example, for fallen leaves, high speed, medium water volume, and medium suction are recommended; for mud, slow speed, high water volume, and strong suction are recommended; and for dust, high suction and controllable water pressure are recommended. The vehicle's control system also employs fuzzy control or neural networks to implement multivariable nonlinear control, and strategies can be dynamically configured on the platform.

[0047] In some embodiments, the method further includes: obtaining the water, electricity consumption and time parameters of the washing and sweeping vehicle under various operating modes; determining the energy consumption evaluation of the washing and sweeping vehicle under different operating modes based on the water, electricity consumption and time parameters; and optimizing the operating parameters based on the energy consumption evaluation.

[0048] In some embodiments, the washing and sweeping vehicle operation control system can record the water and electricity consumption and time parameters in each mode to build an energy efficiency model: E total =a1·T+a2·∫P(t)dt+a3·∫ω(t)dt evaluates the energy consumption performance in different modes and provides a basis for strategy optimization. total For energy consumption evaluation, T is the time consumption, ∫P(t)dt is the energy consumption caused by water spraying, ∫ω(t)dt is the energy consumption caused by vacuuming, and a1, a2, and a3 are weight coefficients, which can be preset or adjusted based on the mode.

[0049] In some embodiments, the vehicle's control system can determine the operating parameters with the lowest energy consumption estimate that meet cleaning requirements in each mode, thereby optimizing the operating parameters. Actual measurements show that energy savings can reach 20% in lightly polluted areas.

[0050] In some embodiments, the method further includes: acquiring historical operation data; determining a high-pollution area based on the historical operation data; and adopting a preset operation intensity in response to the current area being the high-pollution area.

[0051] In some embodiments, the control system for the washing and sweeping vehicle operation can be modeled based on historical data to identify high-pollution areas and set the operation intensity of the high-pollution areas in advance, such as: Rpollute = Nhigh dirt / Ntotal and adjust the preset parameters before entering the area next time to achieve strategy memory and prediction. Among them, Rpollute(x,y) is the intensity adjustment coefficient, Nhigh dirt is the number of high-pollution areas, Ntotal is the number of all cleaning areas, and the preset operation intensity is as follows:

[0052] ω=ω base *(1+Rpollute);

[0053] P=Pbase *(1+Rpollute);

[0054] F=F base *(1+Rpollute).

[0055] In some embodiments, the policy model may be a machine learning model, such as a neural network model (NN).

[0056] In some embodiments, the input of the strategy model may include scenario data, and the output may include job parameters corresponding to the scenario.

[0057] In some embodiments, the washing and sweeping vehicle operation control system may train the strategy model based on the first training sample set.

[0058] The first sample set includes a first training sample and a corresponding first label.

[0059] In some embodiments, the first training sample includes sample scene data. The washing and sweeping vehicle operation control system may obtain the first training sample from historical data of the sample washing and sweeping vehicle.

[0060] In some embodiments, the washing and sweeping vehicle operation control system can determine multiple historical operation data from the historical data, use the corresponding qualified cleaning data as the first training sample, and use the actual operation parameters corresponding to the scene data as the first label.

[0061] In some embodiments, for each historical operation data, the washing and sweeping vehicle operation control system can determine whether it is qualified based on its cleaning quality and the like.

[0062] In some embodiments, the washing and sweeping vehicle operation control system can perform multiple rounds of iterations, at least one of which includes: selecting one or more first training samples from a first sample data set, inputting the one or more first training samples into an initial strategy model, and obtaining model prediction outputs corresponding to the one or more first training samples; substituting the model prediction outputs corresponding to the one or more first training samples and the first labels of the one or more first training samples into a predefined loss function formula to calculate the value of the loss function; and reversely updating the model parameters in the initial strategy model based on the value of the loss function; this step can be performed using various methods. For example, the update can be based on the gradient descent method. When the iteration end condition is met, the iteration ends and a trained strategy model is obtained.

[0063] In some embodiments, the washing and sweeping vehicle operation control system can continuously optimize the strategy model through the latest data accumulation and learning to improve accuracy and adaptability.

[0064] In summary, the present invention can achieve dynamic intelligent control of the operation behavior of washing and sweeping vehicles, saving operation resources and improving operation quality, and has strong practicality and industry promotion value. It is particularly suitable for urban assessment scenarios where both operation quality and energy conservation are equally important.

[0065] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0066] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0067] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0068] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for controlling a washing and sweeping vehicle operation, characterized in that: include: Acquiring environmental data, the environmental data including at least one of a road surface image, a humidity value, and a PM concentration; determining pollution conditions based on the environmental data; determining an operation mode based on the pollution situation; Based on the operation mode and the parameter adjustment algorithm, the operation parameters are adjusted.

2. The method according to claim 1, characterized in that The method further comprises: Get scene data; Based on the scenario data, the operation parameters corresponding to each scenario are determined through a strategy model, and the strategy model is a machine learning model.

3. The method according to claim 2, characterized in that The method further comprises: Obtain water and electricity consumption and time parameters of the washing and sweeping vehicle under various operation modes; Determine the energy consumption evaluation of the washing and sweeping vehicle in different operation modes based on the water and electricity consumption and time parameters; The operating parameters are optimized based on the energy consumption assessment.

4. The method according to claim 1, wherein The method further comprises: Get historical job data; determining high-pollution areas based on the historical operation data; In response to the current area being the high-pollution area, a preset operating intensity is adopted.

5. A washing and sweeping vehicle operation control system, characterized in that: include A first acquisition module is configured to: acquire environmental data, wherein the environmental data includes at least one of a road surface image, a humidity value, and a PM concentration; A pollution assessment module is configured to: determine pollution conditions based on the environmental data; a mode determination module, configured to determine an operation mode based on the pollution situation; The parameter adjustment module is configured to adjust the operation parameters based on the operation mode and the parameter adjustment algorithm.

6. The system according to claim 5, characterized in that The system further comprises: The second acquisition module is configured to: acquire scene data; The parameter determination module is configured to determine the operation parameters corresponding to various scenarios based on the scenario data through a strategy model, where the strategy model is a machine learning model.

7. The system according to claim 6, characterized in that The system further comprises: The third acquisition module is configured to: obtain water and electricity consumption and time parameters of the washing and sweeping vehicle in various operation modes; an energy consumption evaluation module configured to determine an energy consumption evaluation of the washing and sweeping vehicle in different operation modes based on the water and electricity consumption and time parameters; A parameter optimization module is configured to optimize the operation parameters based on the energy consumption evaluation.

8. The system according to claim 5, wherein: The system further comprises: The fourth acquisition module is configured to: acquire historical operation data; The region determination module is configured to: determining high-pollution areas based on the historical operation data; In response to the current area being the high-pollution area, a preset operating intensity is adopted.

9. A washing and sweeping vehicle operation control device, characterized in that: The method comprises a processor configured to execute the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method according to any one of claims 1 to 4.

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