Self-adaptive washing and sweeping vehicle with pneumatic conveying system

Abstract

The invention relates to a self-adaptive cleaning and sweeping vehicle with a pneumatic conveying system, which comprises a vehicle body provided with a camera group and a smell sensor group; the cleaning mechanism is arranged at the bottom of the vehicle body, and the cleaning mechanism is driven by a transverse driving part to advance or retreat in the running direction of the vehicle body; the control system acquires road surface pollutant image data at different angles through the camera group, identifies a first fused image to obtain a first pollutant type and a pollution degree thereof, fuses the first fused image and the road surface pollutant smell data according to the first pollution type to obtain a fused data feature, and sends the fused data feature to the camera group; the second pollutant type and the pollution degree thereof are obtained through fusion data feature judgment, and if the first pollutant type is different from the second pollutant type and any pollution degree is larger than a preset pollution threshold value, the contact time of the cleaning mechanism and the road surface pollutants is prolonged; and a pneumatic conveying system.

Description

Technical Field

[0001] The invention relates to the field of adaptive washing and sweeping vehicles, and in particular to an adaptive washing and sweeping vehicle with a pneumatic conveying system. Background Art

[0002] The road sweeper is a road cleaning vehicle in the road sweeper series. It has multiple functions such as road cleaning, road sweeping curb and curbstone vertical brushing, low-pressure flushing, spray dust removal, etc. It generally includes a sweeping brush, a high-pressure nozzle, and a suction mechanism. The road surface can be cleaned by a rotary sweeping brush, and stubborn stains on the road surface can be washed by a high-pressure nozzle. Then, the garbage on the road surface and the waste liquid after flushing can be sucked into the trash can by the suction mechanism.

[0003] Generally, the washing and sweeping vehicle is driven by the operator to wash the road surface. Most traditional washing and sweeping vehicles use fixed sweeping brushes and high-pressure nozzles, that is, the sweeping brushes and high-pressure nozzles are set on both sides of the washing and sweeping vehicle, and the sweeping brushes and high-pressure nozzles are lowered or recovered by driving the sweeping brushes and high-pressure nozzles longitudinally. Due to the wide variety of garbage and stains on the road surface, small and light garbage such as fallen leaves and cigarette butts are easy to be swept and sucked by the washing and sweeping vehicle, while grease, candy, stickers, etc., and mixtures with other garbage, etc., are difficult to be cleaned by the washing and sweeping vehicle due to their certain viscosity. When the operator drives the washing and sweeping vehicle through the road surface, it is generally at a constant driving speed. The cleaning time between the sweeping brush and the high-pressure nozzle and the road surface is fixed. Since stains that are difficult to clean, such as grease, candy, stickers, etc., are generally tightly bonded to other garbage, road surfaces, etc., or mixed in other garbage, and garbage such as grease, candy, stickers, etc. are generally small in structure, they are difficult to be detected by the operator or the visual recognition system of the washing and sweeping vehicle. In the prior art, for example, the existing application with publication number CN115542735A, entitled Adaptive Control Method for Operating Speed ​​of a Washing and Sweeping Vehicle Based on Machine Vision, controls the cleaning speed of the cleaning components through vision, but does not adaptively adjust the cleaning time between the sweeping brush and the high-pressure nozzle and the road surface according to the type of garbage, degree of pollution, etc., resulting in problems such as incomplete cleaning.

[0004] The purpose of the present invention is to design an adaptive sweeping vehicle with a pneumatic conveying system in view of the above-mentioned problems in the prior art. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides an adaptive sweeping and cleaning vehicle with a pneumatic conveying system, which can effectively solve at least one problem existing in the above-mentioned prior art.

[0006] The technical solution of the present invention is: An adaptive sweeping and cleaning vehicle with a pneumatic conveying system, comprising: A vehicle body, wherein the vehicle body is provided with a camera group and an odor sensor group; A cleaning mechanism, arranged at the bottom of the vehicle body, the cleaning mechanism being driven by a transverse driving member so as to move forward or backward along the travel direction of the vehicle body; A control system connected to the camera group, the odor sensor group, and the transverse driving member, wherein the control system obtains road pollutant image data at different angles through the camera group, fuses the road pollutant image data at different angles to obtain a first fused image, and obtains a first pollutant type and its pollution degree through the first fused image identification, and the control system obtains road pollutant odor data, fuses the first fused image with the road pollutant odor data according to the first pollution type to obtain a fused data feature, and obtains a second pollutant type and its pollution degree through the fused data feature judgment, and if the first pollutant type and the second pollutant type are different and any of the pollution degrees is greater than a preset pollution threshold, the transverse driving member is controlled to drive the cleaning mechanism to retreat along the driving direction of the vehicle body, thereby increasing the contact time between the cleaning mechanism and the road pollutants; The pneumatic conveying system is arranged at the bottom of the rear part of the vehicle body and is used to suck up the pollutants on the road surface after being swept by the cleaning mechanism.

[0007] Furthermore, the camera group includes at least a front camera, a left camera, and a right camera; and the odor sensor group is arranged at the bottom of the vehicle body.

[0008] Further, the road surface pollutant image data at different angles are obtained, and the road surface pollutant image data at different angles are fused to obtain a first fused image. The first pollutant type and its pollution degree are obtained by identifying the first fused image, including: Acquire a front image, a left image, and a right image at the same time point, and fuse the front image, the left image, and the right image at the same time point to obtain a first fused image; The first fused image is input into a pre-trained first pollutant detection model to obtain a first pollutant type and a pollution degree thereof, wherein the pollutant types include solid waste, particulate dust, and liquid pollutants, and the pollution degrees include high pollution, medium pollution, and low pollution.

[0009] Further, fusing the front image, the left image, and the right image at the same time point to obtain a first fused image includes: The deep features of the front image, left image, and right image are extracted through the pre-trained convolutional neural network. The deep features of the front image, the left image, and the right image are weightedly fused using an attention mechanism, and the weighted fused features are reconstructed into the first fused image using a decoder.

[0010] Further, the road pollutant odor data is obtained, and the first fused image and the road pollutant odor data are fused according to the first pollution type to obtain the fused data features including: Extract visual features of the first fused image through a pre-trained convolutional neural network ; The road pollution odor data is input into the long short-term memory network model to extract the odor characteristics. ; Use the embedded features of the first pollutant type as the query vector , respectively calculate the visual features through the attention mechanism and odor characteristics The corresponding weight is calculated as follows: , , in, For visual features The corresponding weights, Odor characteristics The corresponding weights, is the attention mechanism function; Based on visual features and odor characteristics The corresponding weights will be visual features and odor characteristics Fusion is performed to obtain fusion data features , the calculation formula is as follows: .

[0011] Furthermore, obtaining the second pollutant type and its pollution degree by fusing the data features includes: inputting the fused data features into a pre-trained second pollutant detection model to obtain the second pollutant type and its pollution degree.

[0012] Furthermore, controlling the transverse driving member to drive the cleaning mechanism to move backward along the driving direction of the vehicle body to increase the contact time between the cleaning mechanism and the road surface pollutants includes: The pollutant type, pollution degree, and speed adjustment of the cleaning mechanism are used as state variables, and the time required for the cleaning mechanism to clean the road pollutants is used as the action variable to build a cleaning time reinforcement learning model. The reward function is constructed by cleaning the effect value. When the reward value of the reward function is higher than the reward threshold, it is marked as a positive reward signal, otherwise it is marked as a negative reward signal. The reinforcement learning model is optimized according to the reward signal. Inputting the current first pollutant type and the second pollutant type and the corresponding pollution degree into the cleaning time reinforcement learning model to obtain the cleaning time required; The driving speed of the lateral driving member to drive the cleaning mechanism to move backward along the driving direction of the vehicle body is controlled, so that the contact time between the cleaning mechanism and the road surface pollutants meets the required cleaning time when the vehicle body maintains the driving speed.

[0013] Furthermore, when the vehicle body maintains the driving speed, if the maximum contact time between the cleaning mechanism and the road pollutants is less than the time required for cleaning, an alarm signal is issued.

[0014] Furthermore, the cleaning mechanism includes a sweeping brush and a high-pressure nozzle.

[0015] Therefore, the present invention provides the following effects and / or advantages: This application fuses road pollutant images from different perspectives (such as the front side, left side, and right side) to form a complete description of scene characteristics. Image fusion retains the key information of each perspective, more comprehensively reflects the shape, color, and distribution characteristics of pollutants, and reduces the risk of occlusion or information loss. Inputting the fused first fused image into a pre-trained pollutant detection model can effectively extract the deep characteristics of the pollutants. Accurately identify the type of the first pollutant (such as solid waste, liquid pollutants, particulate dust) and its degree of pollution. Therefore, by controlling the cleaning mechanism that can move forward or backward in the direction of travel of the vehicle body according to the corresponding type and degree of pollution, it is possible to keep or slowly pass through the road pollutants, thereby increasing the cleaning time to improve the cleaning effect.

[0016] This application eliminates the limitations of single-modal data by fusing image and odor data. Images provide information such as the spatial distribution, appearance shape, and occlusion effect of pollutants; odors supplement the characteristics of chemical properties (such as toxicity, harmfulness, or corrosiveness). After multimodal fusion, pollutant characteristics can be more comprehensively expressed, providing a more accurate reference for pollution identification and cleaning strategy generation. According to the characteristics of the first pollutant type, the fusion weight of image data and odor data is dynamically adjusted to ensure importance matching.

[0017] This application can provide richer feature judgments for second pollutant detection by fusing data features, helping the model to further explore hidden information from a single type of pollutant.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0019] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic structural diagram of an embodiment of the present invention.

[0021] Description of reference numerals: Vehicle body 1, cleaning mechanism 2, lateral driving member 3, pneumatic conveying system 4. DETAILED DESCRIPTION

[0022] In order to facilitate understanding by those skilled in the art, the structure of the present invention is further described in detail with reference to the embodiments in conjunction with the accompanying drawings: refer to Figure 1 , an adaptive sweeper with a pneumatic conveying system, comprising: A vehicle body 1, wherein the vehicle body 1 is provided with a camera group and an odor sensor group; A cleaning mechanism 2 is disposed at the bottom of the vehicle body 1, and the cleaning mechanism 2 is driven by a transverse driving member 3 so as to move forward or backward along the travel direction of the vehicle body 1; In this embodiment, the vehicle body 1 can be directly adopted from the prior art. The vehicle body 1 serves as a driving force for the cleaning mechanism 2 to travel on the road surface, and the vehicle body 1 is also a power source for the cleaning mechanism 2 and the transverse driving member 3. One of the core improvements of this embodiment is that the cleaning mechanism 2 is driven by the transverse driving member 3, so that it can move forward or backward. During the forward movement of the vehicle, if the transverse driving member 3 drives the cleaning mechanism 2 to move backward, the moving speed of the cleaning mechanism 2 relative to the road surface is less than the moving speed of the vehicle relative to the road surface, thereby increasing the contact time between the cleaning mechanism 2 and the road surface, and thus improving the cleaning effect of the cleaning mechanism 2.

[0023] A control system connected to the camera group, the odor sensor group, and the transverse driving member 3, wherein the control system obtains road pollutant image data at different angles through the camera group, fuses the road pollutant image data at different angles to obtain a first fused image, and obtains a first pollutant type and its pollution degree through the first fused image recognition, and the control system obtains road pollutant odor data, fuses the first fused image with the road pollutant odor data according to the first pollution type to obtain a fused data feature, and obtains a second pollutant type and its pollution degree through the fused data feature judgment, and if the first pollutant type and the second pollutant type are different and any of the pollution degrees is greater than a preset pollution threshold, the transverse driving member 3 is controlled to drive the cleaning mechanism 2 to retreat along the driving direction of the vehicle body 1, thereby increasing the contact time between the cleaning mechanism 2 and the road pollutants; As introduced in the background technology, grease, candy, stickers, and mixtures thereof with other garbage are difficult to be cleaned by a cleaning vehicle due to their certain stickiness. Moreover, these grease, candy, stickers, and the like are small in size and easily stick to the road surface. It is difficult for the driver of the vehicle 1 or the control system of the vehicle 1 to recognize that the road pollutants contain these stubborn stains, and it is also impossible to adaptively adjust the contact time between the cleaning mechanism 2 and the road pollutants. This embodiment fuses the road pollutant images of the camera group to form a complete scene characteristic description, and then inputs the fused first fused image into a pre-trained pollutant detection model, which can effectively extract the deep characteristics of the pollutants and accurately identify the type of the first pollutant and its degree of pollution. Then, by integrating the odor data of road pollutants, such as grease, candy, stickers, etc. with special odors, the type of pollutants and their degree of pollution in the road pollutants after combining the image and odor can be identified, so that it can be known whether the influence of stubborn stains on road pollutants is large. If it is large, it will affect the second pollutant type and its degree of pollution obtained by judging the fusion data characteristics, thereby obtaining a second pollutant type different from the first pollutant type. For example, if a pile of leaves contains a large amount of grease, the pollutant type calculated by the first fused image is leaves, and the pollutant type calculated by the second pollutant type may be grease; or if a pile of leaves contains a small amount of grease, the pollutant type calculated by the first fused image is leaves, and the pollutant type calculated by the second pollutant type may be leaves; therefore, by judging the pollutant types of the two, the influence of the proportion of stubborn stains can be identified.

[0024] Some pollutants, such as chemical liquids, oil stains, or decomposed garbage residues, may be more easily detected by smell alone. Odor sensors can provide important supplementary perception information by sensing the chemical gases emitted by pollutants. Combining images and odors can more comprehensively identify the type and degree of pollution of pollutants. The odor sensor group is mainly used to detect volatile chemical gases or characteristic odors of road pollutants, so the odor sensor can use a variety of odor sensor combinations, such as electrochemical gas sensors (which can detect ammonia and hydrogen sulfide gases produced by food corruption in the market, etc.), metal oxide semiconductor gas sensors (which can detect volatile gases of chemical liquids and grease pollutants on the road), PID photoionization gas sensors (which can detect volatile gases of paint, chemical cleaning agents, oil stains, etc.).

[0025] In the above, it is mentioned that the first pollutant type and the second pollutant type can be calculated, and at this time, the cleaning time can be dynamically adjusted according to whether these types are the same and the degree of pollution. Specifically, the cleaning mechanism 2 is driven by the transverse driving member 3 to move forward or backward along the driving direction of the vehicle body 1, so that the transverse driving member 3 can extend backward when the vehicle moves forward, so that the cleaning mechanism 2 extends backward to extend the contact time between the cleaning mechanism 2 and the pollutants on the road surface, so that the pollutants are cleaned off the road surface and can be sucked up by the pneumatic conveying system 4 of the vehicle.

[0026] The pneumatic conveying system 4 is arranged at the bottom of the rear of the vehicle body 1 and is used to suck up the road pollutants after being cleaned by the cleaning mechanism 2 .

[0027] Furthermore, the camera group includes at least a front camera, a left camera, and a right camera; and the odor sensor group is arranged at the bottom of the vehicle body 1.

[0028] Further, the road surface pollutant image data at different angles are obtained, and the road surface pollutant image data at different angles are fused to obtain a first fused image. The first pollutant type and its pollution degree are obtained by identifying the first fused image, including: Acquire a front image, a left image, and a right image at the same time point, and fuse the front image, the left image, and the right image at the same time point to obtain a first fused image; The first fused image is input into a pre-trained first pollutant detection model to obtain a first pollutant type and a pollution degree thereof, wherein the pollutant types include solid waste, particulate dust, and liquid pollutants, and the pollution degrees include high pollution, medium pollution, and low pollution.

[0029] In this step, image data from a single angle may have blind spots or incomplete information (such as occlusion or light influence), resulting in inaccurate pollutant detection. By acquiring road image data from multiple angles, including the front, left, and right sides, and using deep feature extraction and attention mechanism for weighted fusion, a first fused image with a global view is generated. Image data from different angles may have differences in perspective, resolution, or features, and direct fusion may result in information loss or redundancy. Deep features are extracted through pre-trained convolutional neural networks, and weighted fusion of features is performed in combination with attention mechanism to highlight key features and suppress irrelevant information.

[0030] Traditional methods have difficulty in simultaneously achieving accurate identification of multiple pollutant types (solid waste, particulate dust, liquid pollutants) and their pollution levels (high, medium, low), especially in complex scenarios. This solution uses a pre-trained YOLO model as the first pollutant detection model. Through training and learning, the YOLO model can identify three types of solid waste, particulate dust, and liquid pollutants, and return the results as specific pollutant types through category confidence scores.

[0031] When detecting pollutants, the YOLO model will output a bounding box of the target, which describes the rectangular range of the area where the pollutants are located. The total number of pixels in the bounding box The number of pixels covered with actual contamination It can be used as an indicator to determine the degree of pollution. The calculation formula is: Specific: Highly polluted: Polluted pixel ratio ≥80%, moderate pollution: 60%≤proportion of polluted pixels <80%, low pollution: percentage of polluted pixels <60%.

[0032] The ratio of pollutant pixels to bounding box pixels is used as the basis to avoid relying directly on the absolute value of the pollutant area, because the scope of road pollution may be affected by viewing angle and distance, while the ratio is relatively stable. Whether it is solid waste (evenly distributed lumps) or liquid pollutants (which may be irregularly shaped or splashed), the ratio as a standard is universal.

[0033] Further, fusing the front image, the left image, and the right image at the same time point to obtain a first fused image includes: The deep features of the front image, left image, and right image are extracted through the pre-trained convolutional neural network. The deep features of the front image, the left image, and the right image are weightedly fused using an attention mechanism, and the weighted fused features are reconstructed into the first fused image using a decoder.

[0034] Further, the road pollutant odor data is obtained, and the first fused image and the road pollutant odor data are fused according to the first pollution type to obtain the fused data features including: Extract visual features of the first fused image through a pre-trained convolutional neural network ; The road pollution odor data is input into the long short-term memory network model to extract the odor characteristics. ; Use the embedded features of the first pollutant type as the query vector , respectively calculate the visual features through the attention mechanism and odor characteristics The corresponding weight is calculated as follows: , , in, For visual features The corresponding weight, Odor characteristics The corresponding weight, is the attention mechanism function; Based on visual features and odor characteristics The corresponding weights will be visual features and odor characteristics Fusion is performed to obtain fusion data features , the calculation formula is as follows: .

[0035] In this step, the data type collected by the odor sensor is usually an analog or digital quantity reflecting the characteristics of a specific gas. These odor data are usually time series data formed by sampling from multiple sensors at different time points. LSTM (Long Short-Term Memory Network Model) is good at processing this time series and can capture the dynamic characteristics of pollutant odors.

[0036] The importance of fusing visual features and odor features varies depending on the type of pollutant. In pollutant detection tasks involving multimodal features (visual and odor), different types of pollutants rely on visual and odor features to different degrees. For example, for solid waste (such as paper scraps and plastic bottles), the visual features are very obvious (shape, outline, color), and the visual features contribute more to recognition, while the odor features may be irrelevant and have a lower weight. For liquid pollutants (such as oil stains and sewage), vision may only be able to capture the area of ​​the pollutant, while the odor features supplement its composition information (such as whether there is a foul smell). The odor features contribute more when judging the type of pollutant and the degree of pollution. In the attention mechanism, the query vector It is possible to dynamically focus on which parts of the visual and odor features are more important for this type of pollutant, improving the accuracy of the fused data.

[0037] Based on visual features and odor characteristics The corresponding weights will be visual features and odor characteristics Fusion is performed to obtain fusion data features In the process, the corresponding weights are increased or decreased according to the detected images and odors. For example, for garbage mixed with leaves and grease, when a small amount of grease odor is detected in the leaves, the weight of the odor can be slightly increased or kept unchanged, so that the final fused data features are mainly leaves; or when a large amount of grease odor is detected in the leaves, the weight of the odor can be greatly increased, so that the final fused data features are mainly grease.

[0038] Furthermore, obtaining the second pollutant type and its pollution degree by fusing the data features includes: inputting the fused data features into a pre-trained second pollutant detection model to obtain the second pollutant type and its pollution degree.

[0039] Furthermore, controlling the transverse driving member 3 to drive the cleaning mechanism 2 to move backward along the driving direction of the vehicle body 1 to increase the contact time between the cleaning mechanism 2 and the road surface pollutants includes: The pollutant type, pollution degree, and speed adjustment of the cleaning mechanism 2 are taken as state variables, and the time required for the cleaning mechanism 2 to clean the road pollutants is taken as the action variable to construct a cleaning time reinforcement learning model. The reward function is constructed by cleaning the effect value. When the reward value of the reward function is higher than the reward threshold, it is marked as a positive reward signal, otherwise it is marked as a negative reward signal. The reinforcement learning model is optimized according to the reward signal. Inputting the current first pollutant type and the second pollutant type and the corresponding pollution degree into the cleaning time reinforcement learning model to obtain the cleaning time required; The driving speed of the lateral driving member 3 to drive the cleaning mechanism 2 to move backward along the driving direction of the vehicle body 1 is controlled so that the contact time between the cleaning mechanism 2 and the road surface pollutants meets the required cleaning time when the vehicle body 1 maintains the driving speed.

[0040] In this step, different pollutant types, pollution degrees, etc. can be input as state variables, and the time required for the cleaning mechanism 2 to clean road pollutants can be used as action variables. For example, the pollutant type is leaves mixed with grease, the pollution degree is light, and the rotation speed of the sweeping brush during cleaning is 30r / min. At this time, the cleaning mechanism 2 needs to clean for 10s, etc. A large amount of existing data can be used as training data for the reinforcement learning model, so as to obtain the ability to automatically output the required cleaning time according to the pollutant type, pollution degree, and the operating speed adjustment amount of the cleaning mechanism 2.

[0041] Then, simply relying on the cleaning effect (such as the reduction of pollution level) may ignore the efficiency problem, while simply relying on efficiency (such as cleaning time) may sacrifice the cleaning effect. We construct a reward function R based on the cleaning effect and cleaning time, and use the weight and Balance the priority of cleaning effect and time. The evaluation of cleaning effect needs to rely on the comparison of the degree of contamination before and after cleaning, and the degree of contamination needs to be extracted from multi-view (back, left, right) images. Through image fusion technology, the back image, left image and right image are fused into the second fused image, which is input into the pre-trained first pollutant detection model (YOLO model) to obtain the degree of contamination after cleaning. This multi-view fusion can reduce the occlusion or omission problems that may exist in a single view and improve the accuracy of contamination assessment.

[0042] Then, the required cleaning time can be automatically outputted based on the identified first pollutant type and second pollutant type and the corresponding pollution degree.

[0043] Finally, the output speed of the transverse drive member 3 is adjusted according to the time required for cleaning. For example, if the vehicle body 1 has a forward speed of 0.05 m / s and the cleaning time is 3 seconds, the transverse drive member 3 will continue to output backward at 0.05 m / s for 3 seconds, so that the cleaning mechanism 2 remains above the road surface pollutants for 3 seconds, and then the transverse drive member 3 is retracted.

[0044] Furthermore, when the vehicle body 1 maintains the driving speed, if the maximum contact time between the cleaning mechanism 2 and the road pollutants is less than the time required for cleaning, an alarm signal is issued.

[0045] Since the output length of the transverse drive member 3 is limited, if the time required for cleaning cannot be met after the transverse drive member 3 is fully extended, the vehicle body 1 needs to stop driving, and an alarm signal can be issued to remind the driver to stop for a certain time.

[0046] Furthermore, the cleaning mechanism 2 includes a sweeping brush and a high-pressure nozzle.

[0047] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily being directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. An adaptive sweeper with a pneumatic conveying system, characterized in that: include: A vehicle body (1), wherein the vehicle body (1) is provided with a camera group and an odor sensor group; A cleaning mechanism (2) is arranged at the bottom of the vehicle body (1), and the cleaning mechanism (2) is driven by a transverse driving member (3) so as to move forward or backward along the travel direction of the vehicle body (1); A control system connected to the camera group, the odor sensor group, and the transverse driving member (3), wherein the control system acquires road pollutant image data at different angles through the camera group, fuses the road pollutant image data at different angles to obtain a first fused image, identifies a first pollutant type and its pollution degree through the first fused image, acquires road pollutant odor data, fuses the first fused image with the road pollutant odor data according to the first pollution type, obtains fused data features, determines a second pollutant type and its pollution degree through the fused data features, and if the first pollutant type and the second pollutant type are different and any of the pollution degrees is greater than a preset pollution threshold, controls the transverse driving member (3) to drive the cleaning mechanism (2) to retreat along the driving direction of the vehicle body (1), thereby increasing the contact time between the cleaning mechanism (2) and the road pollutant; The pneumatic conveying system (4) is arranged at the bottom of the rear of the vehicle body (1) and is used to suck up pollutants on the road surface after being cleaned by the cleaning mechanism (2).

2. The adaptive sweeper with pneumatic conveying system according to claim 1, characterized in that: The camera group comprises at least a front camera, a left camera, and a right camera; the odor sensor group is arranged at the bottom of the vehicle body (1).

3. The adaptive sweeper with pneumatic conveying system according to claim 1, characterized in that: Acquiring road pollutant image data at different angles, fusing the road pollutant image data at different angles to obtain a first fused image, and identifying the first pollutant type and its pollution degree through the first fused image include: Acquire a front image, a left image, and a right image at the same time point, and fuse the front image, the left image, and the right image at the same time point to obtain a first fused image; The first fused image is input into a pre-trained first pollutant detection model to obtain a first pollutant type and a pollution degree thereof, wherein the pollutant types include solid waste, particulate dust, and liquid pollutants, and the pollution degrees include high pollution, medium pollution, and low pollution.

4. The adaptive sweeper with pneumatic conveying system according to claim 3, characterized in that: Fusing the front image, the left image, and the right image at the same time point to obtain a first fused image includes: The deep features of the front image, left image, and right image are extracted through the pre-trained convolutional neural network. The deep features of the front image, the left image, and the right image are weightedly fused using an attention mechanism, and the weighted fused features are reconstructed into the first fused image using a decoder.

5. The adaptive sweeper with pneumatic conveying system according to claim 3, characterized in that: Acquire road pollutant odor data, fuse the first fused image and the road pollutant odor data according to the first pollution type, and obtain fused data features including: Extract visual features of the first fused image through a pre-trained convolutional neural network ; The road pollution odor data is input into the long short-term memory network model to extract the odor characteristics. ; Use the embedded features of the first pollutant type as the query vector , respectively calculate the visual features through the attention mechanism and odor characteristics The corresponding weight is calculated as follows: ， ， in, For visual features The corresponding weights, Odor characteristics The corresponding weights, is the attention mechanism function; Based on visual features and odor characteristics The corresponding weights will be visual features and odor characteristics Fusion is performed to obtain fusion data features , the calculation formula is as follows: 。 6. The adaptive sweeper with pneumatic conveying system according to claim 1, characterized in that: Determining the second pollutant type and its pollution degree by fusing the data features includes: inputting the fused data features into a pre-trained second pollutant detection model to obtain the second pollutant type and its pollution degree.

7. The adaptive sweeper with pneumatic conveying system according to claim 1, characterized in that: Controlling the transverse driving member (3) to drive the cleaning mechanism (2) to move backward along the travel direction of the vehicle body (1) to increase the contact time between the cleaning mechanism (2) and road surface pollutants comprises: The pollutant type, the degree of pollution, and the speed adjustment of the cleaning mechanism (2) are taken as state variables, and the time required for the cleaning mechanism (2) to clean the road pollutants is taken as the action variable to construct a cleaning time reinforcement learning model; The reward function is constructed by cleaning the effect value. When the reward value of the reward function is higher than the reward threshold, it is marked as a positive reward signal, otherwise it is marked as a negative reward signal. The reinforcement learning model is optimized according to the reward signal. Inputting the current first pollutant type and the second pollutant type and the corresponding pollution degree into the cleaning time reinforcement learning model to obtain the cleaning time required; The driving speed of the lateral driving member (3) driving the cleaning mechanism (2) to move backward along the driving direction of the vehicle body (1) is controlled so that the contact time between the cleaning mechanism (2) and the road surface contaminants satisfies the time required for cleaning when the vehicle body (1) maintains the driving speed.

8. The adaptive sweeper with pneumatic conveying system according to claim 7, characterized in that: When the vehicle body (1) maintains the driving speed, if the maximum contact time between the cleaning mechanism (2) and the road surface pollutants is less than the time required for cleaning, an alarm signal is issued.

9. The adaptive sweeper with pneumatic conveying system according to claim 1, characterized in that: The cleaning mechanism (2) comprises a sweeping brush and a high-pressure nozzle.

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