Adaptive Cleaning Path Planning System and Method for Sanitation Equipment

Abstract

The present invention relates to an adaptive cleaning path planning system and method for environmental sanitation equipment, and relates to the technical field of path control. The system includes: an environmental calculation module for dynamically modeling the environment of the area to be cleaned and generating an environmental state probability; a cleaning priority module for determining the cleaning priority of each area according to the environmental state probability; a first planning module for determining a planned path for the environmental sanitation equipment according to the cleaning priority of each area; an adjustment amount module for determining the path adjustment amount of the new planned path relative to the original planned path in response to predicting a new planned path; a path adjustment module for adjusting the path of the environmental sanitation equipment according to the path adjustment amount so that the environmental sanitation equipment moves along the new planned path; and a second planning module for calculating the cleaning effect score of the environmental sanitation equipment and adjusting the new planned path according to the cleaning effect score to obtain a target path. This system can improve the accuracy of path planning for environmental sanitation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of path control, and particularly to an adaptive cleaning path planning system, method, electronic device, and non-transitory computer-readable storage medium for sanitation equipment. Background Art

[0002] In the current cleaning path planning methods for sanitation equipment, the methods based on preset rules or traditional path optimization algorithms are usually adopted. Common methods include coverage path planning (CPP) based on grid maps, A* algorithm, Dijkstra algorithm, and ant colony algorithm, etc. These methods can plan relatively optimized cleaning routes in a known environment.

[0003] However, the method based on preset rules is difficult to adapt to the dynamically changing environment, resulting in limited cleaning effects; traditional path optimization algorithms often rely on static maps and respond slowly to the randomness of sudden obstacles or garbage distribution. Summary of the Invention

[0004] The present invention aims at the technical problems existing in the prior art, and provides an adaptive cleaning path planning system, method, electronic device, and non-transitory computer-readable storage medium for sanitation equipment, which can improve the accuracy and efficiency of cleaning path planning for sanitation equipment.

[0005] The technical solution for the present invention to solve the above technical problems is as follows:

[0006] The present invention provides an adaptive cleaning path planning system for sanitation equipment, and the system includes:

[0007] An environment calculation module, configured to perform environmental dynamic modeling on the area to be cleaned and generate an environmental state probability.

[0008] A cleaning priority module, configured to determine the cleaning priority of each area according to the environmental state probability.

[0009] A first planning module, configured to determine a planned path for the sanitation equipment according to the cleaning priority of each area.

[0010] An adjustment amount module, configured to determine a path adjustment amount of the new planned path relative to the original planned path in response to predicting a new planned path.

[0011] A path adjustment module, configured to perform path adjustment on the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves along the new planned path.

[0012] A second planning module, configured to calculate a cleaning effect score of the sanitation equipment and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0013] Optionally, the environmental computing module is specifically configured to:

[0014] Construct a garbage distribution density function for representing the amount of garbage at a preset position at a preset time;

[0015] Construct an obstacle appearance probability function for representing the probability of an obstacle appearing at the preset position at the preset time.

[0016] Construct a congestion degree function for representing the degree of traffic congestion at the preset position at the preset time.

[0017] Generate an environmental state probability according to the garbage distribution density function, the obstacle appearance probability function, and the congestion degree function.

[0018] Optionally, the garbage distribution density function is constructed in the following manner:

[0019] Obtain a garbage distribution image through a vision sensor deployed on the sanitation equipment;

[0020] Use a convolutional neural network to process the garbage distribution image for garbage density grading to obtain a grading result.

[0021] Combine the grading result and historical cleaning data to establish a spatio-temporal distribution prediction model, and construct the garbage distribution density function based on the spatio-temporal distribution prediction model.

[0022] Optionally, the environmental state probability is expressed as:

[0023]

[0024] where is the position The environmental state probability at time t, is the garbage distribution density function, is the obstacle appearance probability function, is the congestion degree function, are the first weight, the second weight, and the third weight, respectively.

[0025] Optionally, the cleaning priority module is specifically configured to:

[0026] Determine the average value of the environmental state probabilities according to the environmental state probabilities of each region.

[0027] Obtain a time decay factor for representing the influence of the uncleaned duration of each region on the cleaning priority;

[0028] Obtain a pedestrian flow rating for representing the size of the pedestrian flow in each region.

[0029] Determine the cleaning priority of each area according to the average value of the environmental state probability, as well as the time decay factor and the pedestrian flow score of each area.

[0030] Optionally, the cleaning priority is expressed as:

[0031]

[0032] Wherein, is the cleaning priority of area i at time t, is the area of area i, is the time elapsed since the last cleaning of area i, is the pedestrian flow score of area i, are the fourth weight, the fifth weight and the sixth weight respectively, is the time decay factor.

[0033] Optionally, the first planning module is specifically configured to:

[0034] Calculate the weighted sum of the cleaning priority of each area and the coverage of the path to obtain the cleaning benefit;

[0035] Construct an energy consumption function representing the energy consumed during the movement of the sanitation equipment from one area to another area.

[0036] Construct a time consumption function representing the time consumed during the movement of the sanitation equipment from one area to another area.

[0037] Determine a planned path for the sanitation equipment according to the cleaning benefit, the energy consumption function and the time consumption function.

[0038] Optionally, the adjustment amount module is specifically configured to:

[0039] Obtain an adjustment coefficient for representing the adjustment amplitude of the control path.

[0040] Obtain a second time decay factor for controlling the influence degree of time on the path adjustment.

[0041] Obtain the time interval since the last path adjustment.

[0042] Process the difference between the new planned path and the original planned path according to the adjustment coefficient, the second time decay factor and the time interval to determine the path adjustment amount.

[0043] Optionally, the second planning module is specifically configured to:

[0044] Obtain the resource consumption of the sanitation equipment at the preset moment.

[0045] Construct a resource consumption function based on the resource consumption of the sanitation equipment at the preset moment.

[0046] Calculate the cleaning effect score of the sanitation equipment according to the resource consumption function, the environmental state probability of each area, and the cleaning priority.

[0047] The present invention also provides an adaptive cleaning path planning method for sanitation equipment, and the method includes:

[0048] Perform environmental dynamic modeling on the area to be cleaned to generate environmental state probabilities.

[0049] Determine the cleaning priority of each area according to the environmental state probability.

[0050] Determine a planned path for the sanitation equipment according to the cleaning priority of each area.

[0051] In response to predicting a new planned path, determine the path adjustment amount of the new planned path relative to the original planned path.

[0052] Adjust the path of the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves along the new planned path.

[0053] Calculate the cleaning effect score of the sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0054] In addition, to achieve the above object, the present invention also proposes an electronic device, including: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the adaptive cleaning path planning method for sanitation equipment as described above.

[0055] In addition, to achieve the above object, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, the adaptive cleaning path planning method for sanitation equipment as described above is implemented.

[0056] The beneficial effects of the present invention are:

[0057] (1) Through environmental dynamic modeling, the present invention can sense environmental characteristics such as garbage distribution density, obstacle appearance probability, and congestion degree in real time, enabling the sanitation equipment to respond to environmental changes in a timely manner, avoiding wasting time in high-congestion areas or obstacle-dense areas, thereby improving cleaning efficiency.

[0058] (2) The present invention constructs a multi-dimensional task priority evaluation function, comprehensively considering factors such as garbage distribution in the area, the time interval since the last cleaning, and the pedestrian flow, and preferentially cleans high-priority areas to ensure the most effective utilization of limited cleaning resources.

[0059] (3) The present invention comprehensively evaluates factors such as the degree of cleanliness, cleaning requirements, and resource consumption, monitors the completion of cleaning tasks in real time, promptly discovers problems such as insufficient cleaning or resource waste, and adjusts the cleaning strategy according to the evaluation results to ensure that the cleaning quality meets the expected standards.

[0060] In summary, through a series of innovative methods and mechanisms, the present invention realizes the adaptive planning and optimization of the cleaning path of sanitation equipment, and has significant beneficial effects in improving cleaning efficiency, optimizing resource utilization, enhancing cleaning quality, strengthening system flexibility and adaptability, reducing the cost of manual intervention, and improving the level of urban environmental management. It has important practical significance for promoting the intelligent development of the sanitation industry and improving the urban environmental quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a scenario diagram of the adaptive cleaning path planning method for the sanitation equipment provided by the present invention;

[0062] Figure 2 It is a schematic structural diagram of the adaptive cleaning path planning system for the sanitation equipment provided by the present invention;

[0063] Figure 3 It is a flow chart of the adaptive cleaning path planning method for the sanitation equipment provided by the present invention;

[0064] Figure 4 It is a schematic hardware structure diagram of an electronic device provided by the present invention;

[0065] Figure 5 It is a schematic hardware structure diagram of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0067] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0068] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0069] Please refer to Figure 1 , Figure 1 which is a scenario diagram of the adaptive cleaning path planning method for the sanitation equipment provided by the present invention. As Figure 1 shown, the terminal and the server are connected through a network, for example, through a wired or wireless network connection, etc. Among them, the terminal may include, but is not limited to, portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, inquiry machines, and advertising machines. Among them, the server provides various business services for users, including service push servers, user recommendation servers, etc.

[0070] It should be noted that Figure 1 the scenario diagram of the adaptive cleaning path planning method for the sanitation equipment shown is only an example. The terminal, server, and application scenarios described in the embodiments of the present invention are for more clearly explaining the technical solutions of the embodiments of the present invention, and do not impose limitations on the technical solutions provided by the embodiments of the present invention. Those of ordinary skill in the art can know that with the evolution of the system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0071] Among them, the terminal can be used for:

[0072] Performing environmental dynamic modeling on the area to be cleaned to generate an environmental state probability;

[0073] Determining the cleaning priority of each area according to the environmental state probability;

[0074] Determine a planned path for the sanitation equipment according to the cleaning priority of each said area;

[0075] In response to predicting a new planned path, determine the path adjustment amount of the new planned path relative to the original planned path;

[0076] Adjust the path of the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves according to the new planned path;

[0077] Calculate the cleaning effect score of the sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0078] Please refer to Figure 2 , Figure 2 , which is a schematic structural diagram of the adaptive cleaning path planning system for the sanitation equipment provided by the present invention.

[0079] As Figure 2 shown, the adaptive cleaning path planning system for the sanitation equipment proposed in the embodiment of the present invention includes:

[0080] An environment calculation module 201, configured to perform dynamic modeling on the area to be cleaned to generate an environmental state probability.

[0081] A cleaning priority module 202, configured to determine the cleaning priority of each said area according to the environmental state probability.

[0082] A first planning module 203, configured to determine a planned path for the sanitation equipment according to the cleaning priority of each said area.

[0083] An adjustment amount module 204, configured to determine the path adjustment amount of the new planned path relative to the original planned path in response to predicting a new planned path;

[0084] A path adjustment module 205, configured to adjust the path of the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves according to the new planned path.

[0085] A second planning module 206, configured to calculate the cleaning effect score of the sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0086] In some embodiments, the environment calculation module 201 is specifically configured to:

[0087] Construct a garbage distribution density function for representing the amount of garbage at a preset position at a preset moment.

[0088] Construct an obstacle appearance probability function for representing the probability of an obstacle appearing at the preset position at the preset moment.

[0089] Construct a congestion degree function for representing the traffic congestion degree at the preset position at the preset moment.

[0090] Generate an environmental state probability according to the garbage distribution density function, the obstacle appearance probability function, and the congestion degree function.

[0091] In some embodiments, the garbage distribution density function is constructed in the following manner:

[0092] Obtain a garbage distribution image through a vision sensor deployed on the sanitation equipment;

[0093] Use a convolutional neural network to process the garbage distribution image for garbage density grading to obtain a grading result.

[0094] Establish a spatio-temporal distribution prediction model by combining the grading result and historical cleaning data, and construct the garbage distribution density function based on the spatio-temporal distribution prediction model.

[0095] In some embodiments, the environmental state probability is expressed as:

[0096]

[0097] Wherein, is the position The environmental state probability at time t, is the garbage distribution density function, is the obstacle appearance probability function, is the congestion degree function, are the first weight, the second weight, and the third weight respectively.

[0098] In specific implementation, the garbage distribution density function D(x, y, t) represents the garbage distribution density at the preset position (x, y) at the preset moment t. This function reflects the amount of garbage at the preset spatial position at a specific time. The higher the garbage density, the higher the urgency of cleaning in this area.

[0099] Specifically, the distribution of garbage can be detected in real time through sensors (such as cameras, lidar, etc.) installed on the sanitation equipment. Combining historical cleaning records, predict the current garbage distribution. Consider the influence of factors such as weather and human flow on garbage distribution. For example, strong wind weather may make the garbage distribution more dispersed.

[0100] The obstacle occurrence probability function O(x, y, t) represents the probability of an obstacle appearing at the preset position (x, y) at the preset time t. It is used to evaluate the obstacles that sanitation equipment may encounter during the cleaning process (such as pedestrians, vehicles, temporary obstacles, etc.). The higher the obstacle occurrence probability, the more cautious the cleaning path planning needs to be to avoid collisions.

[0101] Specifically, the presence of obstacles can be detected in real time through sensors such as lidar and cameras. Combining with a high-precision map, areas where possible obstacles are located (such as construction areas, fixed obstacles, etc.) are pre-marked. Real-time traffic information is obtained through the traffic monitoring system to predict the occurrence probability of dynamic obstacles (such as vehicles and pedestrians).

[0102] The congestion degree function C(x, y, t) represents the congestion degree at the preset position (x, y) at the preset time t. It reflects the traffic congestion situation that sanitation equipment may encounter during the cleaning process. The higher the congestion degree, the more detour plans need to be considered in the cleaning path planning to reduce the cleaning time.

[0103] Specifically, real-time traffic flow data can be obtained through the traffic monitoring system to evaluate the congestion degree. Combining with historical traffic data, the current congestion situation is predicted. The surrounding traffic conditions can be sensed in real time through the vehicle's own sensors (such as radar and cameras).

[0104] Weight coefficients They are the weight coefficients of the garbage distribution density, obstacle occurrence probability, and congestion degree respectively, and the sum of the three is equal to 1. It is used to adjust the contribution of the three factors to the environmental state probability P(x, y, t). Different application scenarios and requirements can optimize the path planning by adjusting the weights.

[0105] If the primary goal of the cleaning task is to clean up the garbage as soon as possible, then the weight of can be increased. If there are many pedestrians and vehicles in the cleaning area and special attention needs to be paid to safety, then the weight of can be increased. If the cleaning task needs to be completed within a limited time and traffic congestion has a greater impact on the cleaning efficiency, then the weight of can be increased.

[0106] Through this formula, the environmental state probability P(x, y, t) of each position at each moment can be calculated dynamically. This probability value can be used for subsequent cleaning task priority evaluation and path planning. Combining P(x, y, t) with other factors (such as area of the region, last cleaning time, etc.), the cleaning priority of each region is evaluated. According to the distribution of P(x, y, t), the optimal cleaning path is planned to avoid high-congestion areas and areas with high obstacle probability, and give priority to cleaning areas with high garbage distribution density.

[0107] By updating D(x, y, t), O(x, y, t), and C(x, y, t) in real time, the present invention can adapt to the dynamic changes of the environment. By adjusting the weight coefficients, the path planning can be optimized according to different application scenarios and requirements. Considering three important factors of garbage distribution, obstacles, and congestion, the path planning becomes more comprehensive and reasonable.

[0108] In some embodiments, the cleaning priority module is specifically configured to:

[0109] Determine the average environmental state probability according to the environmental state probabilities of the respective regions;

[0110] Obtain a time decay factor for representing the influence of the duration since the last cleaning of each region on the cleaning priority;

[0111] Obtain a pedestrian flow rating for representing the magnitude of the pedestrian flow in each region;

[0112] Determine the cleaning priority of each region according to the average environmental state probability, and the time decay factor and pedestrian flow rating of each region.

[0113] In some embodiments, the cleaning priority is expressed as:

[0114]

[0115] Wherein, is the cleaning priority of region i at time t, is the area of region i, is the time since the last cleaning of region i, is the pedestrian flow rating of region i, are the fourth weight, the fifth weight, and the sixth weight respectively, is the time decay factor.

[0116] In specific implementation, this formula is used to evaluate the cleaning priority of region i at time t , considering multiple factors to determine which regions need to be cleaned preferentially.

[0117] Average environmental state probability , this part calculates the average value of the environmental state probabilities P(x, y, t) at all positions within region i, where is the area of region i. By calculating the average environmental state probability within the region, the overall cleaning demand of the region can be quantified. If the average environmental state probability of a region is high, it indicates that the garbage distribution density, the probability of obstacles appearing, or the degree of congestion in the region is high, and it needs to be cleaned preferentially. First, traverse all positions (x, y) within region i and calculate the environmental state probability P(x, y, t) at each position. Then sum up the environmental state probabilities of all positions and divide by the area of the region .

[0118] Time decay factor , which represents the time since the last cleaning of region i The impact on the cleaning priority, that is, the impact of the duration of non-cleaning in region i on the cleaning priority, where is the first time decay factor. As time goes by, the cleaning demand of the region will gradually increase. The first time decay factor is used to control the degree of influence of time on the cleaning priority. If is small, the influence of time on the cleaning priority is large; if is large, the influence of time on the cleaning priority is small. is the time since the last cleaning of region i (the unit can be hours, days, etc.). is an exponential decay function, and as time increases, its value gradually decreases.

[0119] Pedestrian flow score represents the pedestrian flow score of region i, reflecting the size of the pedestrian flow in the region. Regions with a high pedestrian flow usually generate more garbage, so they require a higher cleaning priority. The pedestrian flow score can be determined based on real-time data (such as camera monitoring) or historical data (such as statistical pedestrian flow). The pedestrian flow can be monitored in real time through sensors installed in the region (such as cameras, infrared sensors, etc.). It is also possible to count the pedestrian flow in the region based on historical data and give a score value.

[0120] are the weight coefficients of the average environmental state probability, the first time decay factor, and the pedestrian flow score respectively, and the sum of the three is equal to 1. They are used to adjust the contribution sizes of the three factors to the cleaning priority R(i, t). Different application scenarios and requirements can optimize the evaluation of the cleaning priority by adjusting the weights.

[0121] It can be understood that if the primary goal of the cleaning task is to clean up the garbage as soon as possible, then weight can be increased. If the cleaning task needs to consider the time interval since the last cleaning, then weight can be increased. If the cleaning task needs to prioritize regions with a high pedestrian flow, then Weight.

[0122] Through this formula, the cleaning priority R(i, t) of each area at each moment can be dynamically calculated. According to the cleaning priority R(i, t), areas with high cleaning priority are planned first to ensure the most effective utilization of limited cleaning resources. More cleaning equipment or manpower can be allocated to areas with high cleaning priority to improve cleaning efficiency.

[0123] The present invention comprehensively considers three important factors: environmental status, time interval, and pedestrian flow, making the evaluation of cleaning priority more comprehensive and reasonable. By updating the environmental status probability P(x, y, t) and pedestrian flow score in real time , it can adapt to the dynamic changes of the environment. By adjusting the weight coefficient and the first time decay factor , the evaluation of cleaning priority can be optimized according to different application scenarios and requirements.

[0124] In some embodiments, the first planning module is specifically configured to:

[0125] Calculate the weighted sum of the cleaning priority of each area and the coverage degree of the path to obtain the cleaning benefit.

[0126] Construct an energy consumption function representing the energy consumed during the movement of the sanitation equipment from one area to another area.

[0127] Construct a time consumption function representing the time consumed during the movement of the sanitation equipment from one area to another area.

[0128] Determine a planned path for the sanitation equipment according to the cleaning benefit, the energy consumption function, and the time consumption function.

[0129] In some embodiments, the planned path can be expressed as:

[0130]

[0131] Among them, is the planned path, is the coverage length of the path in area i, is the energy consumption function from area i to j, is the time consumption function from area i to j, are the seventh weight, the eighth weight, and the ninth weight respectively.

[0132] In specific implementation, this formula is used to evaluate and optimize the cleaning path of the sanitation equipment , and the optimal cleaning path is designed by comprehensively considering factors such as cleaning priority, energy consumption, and time consumption.

[0133] The product of the cleaning priority and the coverage length , which calculates the weighted sum of the cleaning priority R(i,t) and the coverage length L(i) of the path in each region i. The cleaning priority R(i,t) reflects the cleaning demand of region i, while the coverage length L(i) represents the actual cleaning scope of the path in that region. By multiplying the two, the cleaning benefit of the path in each region can be quantified. It is used to adjust the importance of the cleaning benefit in the overall path optimization. When calculating, first traverse all regions i passed by the path. For each region i, calculate the product of its cleaning priority R(i,t) and the coverage length L(i). Add up the products of all regions to obtain the total cleaning benefit.

[0134] Energy consumption function , which calculates the weighted sum of the energy consumption function E(i,j) of the path from region i to region j. The energy consumption function E(i,j) reflects the energy consumed by the sanitation equipment during the movement from region i to region j. By minimizing the energy consumption, the energy efficiency of the cleaning task can be improved and the operating cost can be reduced. It is used to adjust the importance of the energy consumption in the overall path optimization. When calculating, first traverse all adjacent region pairs (i,j) in the path. For each pair of regions (i,j), calculate its energy consumption function E(i,j). Add up the energy consumption of all region pairs to obtain the total energy consumption.

[0135] Time consumption function , which calculates the weighted sum of the time consumption function T(i,j) of the path from region i to region j. The time consumption function T(i,j) reflects the time consumed by the sanitation equipment during the movement from region i to region j. By minimizing the time consumption, the efficiency of the cleaning task can be improved and more cleaning work can be ensured to be completed within a limited time. It is used to adjust the importance of the time consumption in the overall path optimization. When calculating, first traverse all adjacent region pairs (i,j) in the path. For each pair of regions (i,j), calculate its time consumption function T(i,j). Finally, add up the time consumption of all region pairs to obtain the total time consumption.

[0136] They are the weight coefficients of the cleaning benefit, energy consumption, and time consumption respectively, and the sum of the three is 1. It is used to adjust the contribution of the three factors to the path optimization objective function . Different application scenarios and requirements can optimize the path planning by adjusting the weights.

[0137] If the primary goal of the cleaning task is to maximize the cleaning benefit, then The weight. If the cleaning task needs to consider energy efficiency, then the weight of can be increased. If the cleaning task needs to be completed within a limited time, then the weight of can be increased.

[0138] Through this formula, the cleaning path of the sanitation equipment can be dynamically evaluated and optimized. The goal of path optimization is to maximize , that is, while meeting the cleaning efficiency, minimizing energy consumption and time consumption. The specific steps are as follows: Generate multiple possible cleaning paths. Use the formula to evaluate the advantages and disadvantages of each path. Select the path with the largest

[0139] value as the optimal path.

[0140] In some embodiments, the adjustment amount module is specifically configured to:

[0141] Obtain an adjustment coefficient for representing the adjustment amplitude of the control path;

[0142] Obtain a second time decay factor for controlling the influence degree of time on the path adjustment;

[0143] Obtain the time interval from the current time to the last path adjustment;

[0144] According to the adjustment coefficient, the second time decay factor and the time interval, process the difference between the newly planned path and the original planned path, and determine the path adjustment amount.

[0145] In some embodiments, the path adjustment amount can be expressed as:

[0146]

[0147] Wherein, is the path adjustment amount at time t, is the newly planned path,

[0148] is the original planned path, is the adjustment coefficient, is the second time decay factor, is the time interval from the current time to the last path adjustment.

[0149] In specific implementation, this formula is used to describe the path adjustment mechanism of sanitation equipment in a dynamic environment, and determines the amount of path adjustment based on the difference in the optimal objective function values of the new and old paths and the time interval.

[0150] Amount of path adjustment represents the amount of adjustment to the cleaning path at time t. This adjustment amount determines the degree of change of the path in the dynamic environment. If the adjustment amount is large, it means that the path needs to be changed significantly; if the adjustment amount is small, it means that the path is relatively stable.

[0151] Difference in the optimal objective function values between the newly planned path and the original planned path , this part calculates the newly planned path and the original planned path of the optimal objective function value difference.

[0152] Optimal objective function is used to evaluate the quality of the path. If the optimal objective function value of the newly planned path is greater than the optimal objective function value of the original planned path , it means that the new path is better and needs to be adjusted.

[0153] When calculating, the path optimization objective function mentioned above can be used to calculate the values of the new path and the original path respectively. Then calculate the difference between the two .

[0154] Adjustment coefficient is used to control the amplitude of path adjustment and determines the sensitivity of path adjustment. If is large, the path adjustment will be more sensitive, that is, it responds more strongly to the difference between the new and old paths; if is small, the path adjustment will be more gentle, that is, it responds weakly to the difference between the new and old paths. Usually is a positive number, and its value range can be adjusted according to actual needs.

[0155] This part is an exponential decay function, indicating the influence of the time interval on path adjustment. It is used to control the timeliness of path adjustment. If the time interval is large, it means that a long time has passed since the last adjustment, and the amplitude of path adjustment will decrease; if the time interval is small, it means that the time since the last adjustment is short, and the amplitude of path adjustment will be large. is the time interval between the current time t and the last adjustment time. is the second time decay factor, which is used to control the degree of influence of time on path adjustment.

[0156] Through this formula, the cleaning path of the sanitation equipment can be dynamically adjusted to adapt to environmental changes. The specific steps are as follows: Use the optimization objective function to evaluate the newly planned path and the original planned path . Calculate the path adjustment amount according to the formula . Finally, update the cleaning path according to the adjustment amount .

[0157] By evaluating the difference in the optimization objective function values of the new and old paths in real time, the present invention can dynamically adjust the cleaning path to adapt to environmental changes. Through the time influence factor , the timeliness of path adjustment can be controlled to avoid instability caused by frequent adjustments. By adjusting the adjustment coefficient and the second time decay factor , the path adjustment mechanism can be optimized according to different application scenarios and requirements.

[0158] In some embodiments, the second planning module is specifically configured to:[[]]

[0159] Obtain the resource consumption of the sanitation equipment at the preset moment;

[0160] Construct a resource consumption function according to the resource consumption of the sanitation equipment at the preset moment;

[0161] Calculate the cleaning effect score of the sanitation equipment according to the resource consumption function, the environmental state probability and the cleaning priority of each region.

[0162] In some embodiments, the cleaning effect score can be expressed as:

[0163]

[0164] wherein, is the cleaning effect score, is the resource consumption function, are the tenth weight, the eleventh weight and the twelfth weight respectively.

[0165] In specific implementation, this formula is used to evaluate the cleaning effect of the sanitation equipment at time t. It comprehensively considers factors such as environmental state probability, cleaning priority and resource consumption to quantify the completion of the cleaning task.

[0166] The complement of the environmental state probability , this part calculates the sum of the complements of the environmental state probabilities of all positions (i.e., ). The environmental state probability P(x, y, t) reflects the cleaning demand at the position (x, y) at time t. Its complement It reflects the cleanliness of the position (x, y). By adding up the cleanliness of all positions, the overall cleaning effect can be obtained.

[0167] When calculating, first traverse all positions (x, y) to calculate the environmental state probability P(x, y, t) of each position. Then calculate the cleanliness of each position (1 - P(x, y, t)). Then add up the cleanliness of all positions.

[0168] Total cleaning priority What is calculated is the total cleaning priority of all areas The sum. The cleaning priority R(i, t) reflects the cleaning demand of area i at time t. By adding up the cleaning priorities of all areas, the overall cleaning demand can be obtained. When calculating, first traverse all areas i to calculate the cleaning priority R(i, t) of each area. Add up the cleaning priorities of all areas.

[0169] The resource consumption function C(t), this part represents the resource consumption at time t, such as energy consumption, time consumption, etc. The resource consumption function C(t) reflects the resource consumption required to complete the cleaning task. By minimizing the resource consumption, the efficiency of the cleaning task can be improved. When calculating, design the resource consumption function C(t) according to the actual resource consumption situation.

[0170] They are the weight coefficients of cleanliness, cleaning demand, and resource consumption respectively, and the sum of the three satisfies 1. It is used to adjust the contribution of the three factors to the cleaning effect score Q(t). Different application scenarios and requirements can optimize the evaluation of the cleaning effect by adjusting the weights. If the primary goal of the cleaning task is to improve cleanliness, then the weight can be increased. If the cleaning task needs to consider the cleaning demand, then the weight can be increased. If the cleaning task needs to be completed with limited resources, then the weight can be increased.

[0171] In some embodiments, the cleaning effect score can be calculated first, then the path optimization requirement can be evaluated, then the newly planned path can be adjusted, the path adjustment amount can be calculated, then the target path can be updated, and finally the target path can be executed.

[0172] In the above way, the present invention can dynamically evaluate the cleaning effect of the sanitation equipment. The cleaning effect score can be used to monitor the completion of the cleaning task and optimize the cleaning strategy. The present invention comprehensively considers three important factors of cleanliness, cleaning demand, and resource consumption, making the evaluation of the cleaning effect more comprehensive and reasonable. By updating the environmental state probability, cleaning priority, and resource consumption function in real time, it can adapt to the dynamic changes of the environment.

[0173] Please refer toFigure 3 , a flowchart of the adaptive cleaning path planning method for the environmental sanitation equipment of the present invention is provided, including the following steps:

[0174] Step 301: Perform environmental dynamic modeling on the area to be cleaned to generate environmental state probabilities;

[0175] Step 302: Determine the cleaning priorities of the respective areas according to the environmental state probabilities;

[0176] Step 303: Determine a planned path for the environmental sanitation equipment according to the cleaning priorities of the respective areas;

[0177] Step 304: In response to predicting a new planned path, determine the path adjustment amount of the new planned path relative to the original planned path;

[0178] Step 305: Adjust the path of the environmental sanitation equipment according to the path adjustment amount, so that the environmental sanitation equipment moves along the new planned path;

[0179] Step 306: Calculate the cleaning effect score of the environmental sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0180] Please refer to Figure 4 , Figure 4 , which is a schematic diagram of an embodiment of the electronic device provided by an embodiment of the present invention. As Figure 4 shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0181] Perform environmental dynamic modeling on the area to be cleaned to generate environmental state probabilities;

[0182] Determine the cleaning priorities of the respective areas according to the environmental state probabilities;

[0183] Determine a planned path for the environmental sanitation equipment according to the cleaning priorities of the respective areas;

[0184] In response to predicting a new planned path, determine the path adjustment amount of the new planned path relative to the original planned path;

[0185] Adjust the path of the environmental sanitation equipment according to the path adjustment amount, so that the environmental sanitation equipment moves along the new planned path;

[0186] Calculate the cleaning effect score of the environmental sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0187] Please refer toFigure 5 , Figure 5 is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 5 shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0188] Perform environmental dynamic modeling on the area to be cleaned to generate an environmental state probability;

[0189] Determine the cleaning priority of each area according to the environmental state probability;

[0190] Determine a planned path for the sanitation equipment according to the cleaning priority of each area;

[0191] In response to predicting a new planned path, determine the path adjustment amount of the new planned path relative to the original planned path;

[0192] Adjust the path of the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves along the new planned path;

[0193] Calculate the cleaning effect score of the sanitation equipment, and adjust the new planned path according to the cleaning effect score to obtain a target path.

[0194] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0195] Those skilled in the art should understand that the embodiments of the present invention can be provided as a system, a method, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0196] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the process Figure 1one or more processes and / or blocks Figure 1 a system for the functions specified in one or more blocks

[0197] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction system that implements the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks

[0199] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention

[0200] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations

Claims

1. An adaptive cleaning path planning system for sanitation equipment, characterized in that: The system comprises: The environmental calculation module is used to perform environmental dynamic modeling on the area to be cleaned and generate environmental state probabilities; A cleaning priority module, used to determine the cleaning priority of each of the areas according to the environmental state probability; A first planning module is used to determine a planning path for the sanitation equipment according to the cleaning priority of each of the areas; An adjustment module, configured to determine, in response to predicting a new planned path, a path adjustment amount of the new planned path relative to the original planned path; A path adjustment module, used for adjusting the path of the sanitation equipment according to the path adjustment amount, so that the sanitation equipment moves along the new planned path; A second planning module is used to calculate the cleaning effect score of the sanitation equipment and adjust the new planned path according to the cleaning effect score to obtain a target path; The environment calculation module is specifically used for: Constructing a garbage distribution density function for representing the amount of garbage at a preset location at a preset time; Constructing an obstacle occurrence probability function for representing the probability of an obstacle appearing at the preset position at the preset time; Constructing a congestion degree function for representing the traffic congestion degree of the preset location at the preset time; Generate an environmental state probability according to the garbage distribution density function, the obstacle occurrence probability function and the congestion degree function; The cleaning priority module is specifically used for: Determining an average value of the environmental state probability according to the environmental state probability of each of the areas; Obtaining a time decay factor for indicating the effect of the duration of uncleanness of each area on the cleaning priority; Obtaining a human flow score for indicating the magnitude of human flow in each of the areas; Determine the cleaning priority of each area according to the average value of the environmental state probability, as well as the time decay factor and the human flow score of each area; The first planning module is specifically used for: Calculate the weighted sum of the cleaning priority of each area and the coverage degree of the path to obtain the cleaning benefit; Constructing an energy consumption function representing the energy consumed by the sanitation equipment during movement from one area to another area; Constructing a time consumption function representing the time consumed in the process of moving the sanitation equipment from one area to another area; A planned path is determined for the sanitation equipment according to the cleaning benefit, the energy consumption function and the time consumption function.

2. The adaptive cleaning path planning system for sanitation equipment according to claim 1, characterized in that: The garbage distribution density function is constructed in the following way: Acquire garbage distribution images through visual sensors deployed on the sanitation equipment; Using a convolutional neural network to process the garbage distribution image to perform garbage density classification and obtain a classification result; A spatiotemporal distribution prediction model is established in combination with the classification results and historical cleaning data, and the garbage distribution density function is constructed based on the spatiotemporal distribution prediction model.

3. The adaptive cleaning path planning system for sanitation equipment according to claim 2, characterized in that: The environmental state probability is expressed as: in, It's location The probability of the environment state at time t is: is the garbage distribution density function, is the obstacle appearance probability function, is a function of congestion level, They are the first weight, the second weight and the third weight respectively.

4. The adaptive cleaning path planning system for sanitation equipment according to claim 3, characterized in that: The cleaning priorities are expressed as: in, is the cleaning priority of area i at time t, is the area of ​​region i, is the time since area i was last cleaned, is the traffic score of area i, They are the fourth weight, the fifth weight and the sixth weight, is the time decay factor.

5. The adaptive cleaning path planning system for sanitation equipment according to claim 4, characterized in that: The adjustment module is specifically used for: Obtaining an adjustment coefficient for indicating an adjustment range of a control path; Obtaining a second time attenuation factor for controlling the degree of influence of time on path adjustment; Get the time interval between the current and last path adjustment; The difference between the new planned path and the original planned path is processed according to the adjustment coefficient, the second time attenuation factor and the time interval to determine the path adjustment amount.

6. The adaptive cleaning path planning system for sanitation equipment according to claim 5, characterized in that: The second planning module is specifically used for: Obtaining resource consumption of the sanitation equipment at the preset time; Constructing a resource consumption function according to the resource consumption of the sanitation equipment at the preset time; The cleaning effect score of the sanitation equipment is calculated based on the resource consumption function, the environmental state probability and the cleaning priority of each area.

7. A planning method for an adaptive cleaning path planning system for sanitation equipment according to claim 6, characterized in that: The method comprises: Perform environmental dynamic modeling on the area to be cleaned and generate environmental state probabilities; Determining the cleaning priority of each of the areas according to the environmental state probability; Determine the planned routes for sanitation equipment according to the cleaning priorities of each of the areas; In response to predicting a new planned path, determining a path adjustment amount of the new planned path relative to the original planned path; Adjusting the path of the sanitation equipment according to the path adjustment amount so that the sanitation equipment moves along the new planned path; The cleaning effect score of the sanitation equipment is calculated, and the new planned path is adjusted according to the cleaning effect score to obtain a target path.

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