All-region coverage path optimization method and system applied to water surface decontamination operation

Abstract

The invention discloses a full-area coverage path optimization method and system applied to water surface sewage disposal operation, and relates to the technical field of water surface sewage disposal path optimizing.The method comprises the steps of sewage disposal data acquisition, water surface sewage disposal path analysis, water surface sewage disposal path optimization and early warning prompt.By constructing a problem library, corresponding to the water surface sewage disposal operation, of a sewage disposal robot, the problem library of the water surface sewage disposal operation of the sewage disposal robot is established; the method comprises the following steps: analyzing a sewage disposal problem set stored in a to-be-cleaned water area corresponding to a sewage disposal robot, further forming an efficient associated optimal sewage disposal combination corresponding to sewage disposal of the sewage disposal robot, obtaining a sewage disposal operation path, analyzing a sewage disposal result of the to-be-cleaned water area corresponding to the sewage disposal robot, and when sewage disposal problems exist, knowing problem influence factors and executing path optimization. And finally, the decontamination coverage of the to-be-cleaned water area corresponding to the decontamination robot is evaluated, so that comprehensive analysis of the decontamination robot on the water surface decontamination condition is implemented, effective decontamination under corresponding full-area coverage of the water surface is realized, and the water surface decontamination efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of water surface cleaning path optimization technology, specifically to a method and system for full-area coverage path optimization applied to water surface cleaning operations. Background Technology

[0002] With the continuous development of society, various industrial constructions and the scope of human activities are constantly expanding. As a result, the phenomenon of uncivilized littering is becoming increasingly serious, with water bodies being particularly affected. Therefore, a full-area coverage path optimization method and system for water surface cleaning operations is proposed. This method utilizes cleaning robots to achieve full-area coverage cleaning of the water surface, reducing the destructive impact of pollutants on the water surface, intelligently and efficiently protecting the ecological health of the water area, and ensuring full coverage of water body cleaning.

[0003] Existing technologies, such as the invention patent application CN114721400A, which discloses a path optimization method based on ant colony algorithm and a path planning method for collecting garbage on the water surface, are as follows: S1: Model the floating garbage on the water surface and obtain the node coordinates of the floating garbage at each location; S2: Initialize parameters; S3: m ants start from the starting point; S4: Ants select the next node based on the pheromone and heuristic information of each node, and calculate the transition probability of ant k moving from node i to node j; S5: Perform global pheromone update according to the update rule; S6: Determine whether the ant has traversed all nodes or found the termination point. If so, proceed to step S7; otherwise, jump to steps S4-S5 to continue the path finding; S7: Save the search route and length of each ant; determine whether the maximum number of iterations N has been reached. If so, select the search route with the shortest length as the optimal route; otherwise, jump to step S3.

[0004] Current technology has the following problems: The aforementioned invention mainly optimizes the collection path for suspended debris on the water surface, but does not clean up various types of debris on the water surface. Therefore, the two differ in the composition and consideration of factors when considering path optimization, lacking an understanding of achieving full-area coverage cleaning of water surface debris. Consequently, it cannot construct a comprehensive cleaning solution for the current pollution status of the water surface. At the same time, it lacks effective acquisition of problems encountered by cleaning robots when performing water surface cleaning, making it difficult to avoid the recurrence of problems when cleaning robots perform water surface cleaning, reducing the effectiveness and full coverage of water surface cleaning, and also damaging the robot's function to some extent, reducing the workability of the cleaning robot. Furthermore, water surface debris exists in various types, and the factors affecting them are also different. Therefore, when the factors affecting water surface debris change, it is not detected in time, and it is impossible to ensure that the water surface cleaning path is full-coverage and efficient. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to provide a method and system for optimizing the full-area coverage path for water surface cleaning operations.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a full-area coverage path optimization method for water surface cleaning operations, including: Step 1, cleaning data acquisition: acquiring cleaning images of the cleaning robot in each historical water surface cleaning operation, constructing a problem library for the cleaning robot corresponding to the water surface cleaning operation, acquiring pollutant data corresponding to the current water area to be cleaned, and analyzing the cleaning problem set existing in the water area to be cleaned corresponding to the cleaning robot.

[0007] Step 2: Analysis of the surface cleaning path: Based on the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot, analyze the optimal cleaning combinations that the cleaning robot can perform cleaning operations on the water area to be cleaned, and then analyze the cleaning operation path of the cleaning robot on the water area to be cleaned.

[0008] Step 3: Optimization of Water Surface Cleaning Path: Monitor the cleaning robots performing cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the water area to be cleaned, analyze the factors affecting the coverage path of the cleaning robots on the water area to be cleaned when there are problems with the cleaning of the water area to be cleaned, and perform path optimization of the cleaning robots on the water area to be cleaned, and evaluate the cleaning coverage of the water area to be cleaned by the cleaning robots.

[0009] Step 4: Early Warning: An early warning will be issued when there are problems with the cleaning robot's cleaning of the water area to be cleaned, or when the cleaning robot's cleaning coverage of the water area to be cleaned is not up to standard.

[0010] In a second aspect, the present invention provides a full-area coverage path optimization system for water surface cleaning operations, comprising: a cleaning data acquisition module, used to acquire cleaning images of the cleaning robot in each historical water surface cleaning operation, construct a problem database for the cleaning robot corresponding to the water surface cleaning operation, acquire pollutant data corresponding to the current water area to be cleaned, and analyze the cleaning problem set existing in the water area to be cleaned corresponding to the cleaning robot.

[0011] The water surface cleaning path analysis module is used to analyze the optimal cleaning combinations that the cleaning robot can perform cleaning operations on the water area to be cleaned, based on the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot, and then analyze the cleaning operation path of the cleaning robot on the water area to be cleaned.

[0012] The water surface cleaning path optimization module is used to monitor the cleaning robots performing cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the corresponding water area to be cleaned, analyze the influencing factors of the cleaning path of the cleaning robots on the corresponding water area to be cleaned when there are problems in the cleaning of the corresponding water area to be cleaned, and perform path optimization of the cleaning robots on the corresponding water area to be cleaned, and evaluate the cleaning coverage of the cleaning robots on the corresponding water area to be cleaned.

[0013] The early warning terminal is used to issue early warnings when there are problems with the cleaning of the water area to be cleaned by the cleaning robot or when the cleaning coverage of the water area to be cleaned by the cleaning robot is not up to standard.

[0014] The beneficial effects of this invention are as follows: 1. This invention provides a method and system for optimizing the full-area coverage path for water surface cleaning operations. By constructing a problem database for water surface cleaning operations corresponding to the cleaning robot, and analyzing the set of cleaning problems existing in the water area to be cleaned corresponding to the cleaning robot, a highly efficient associated optimal cleaning combination for the cleaning robot is formed. The cleaning operation path is obtained by analysis, and the cleaning results of the water area to be cleaned corresponding to the cleaning robot are analyzed. When there are problems in the cleaning, the influencing factors of the problems are understood, and path optimization is performed. Finally, the cleaning coverage of the water area to be cleaned corresponding to the cleaning robot is evaluated. This enables a comprehensive analysis of the water surface cleaning situation by the cleaning robot, achieving effective cleaning under the full-area coverage of the water surface, improving the efficiency of water surface cleaning, reducing serious ecological damage caused by the failure to clean up the water surface in a timely manner, and further protecting the ecological health of the water area. At the same time, based on the monitoring and analysis of the cleaning situation under the cleaning path corresponding to the cleaning robot, the system can intelligently avoid incomplete or missed water surface cleaning, achieving intelligent and efficient water surface cleaning.

[0015] 2. Based on the construction of a problem database for water surface cleaning operations corresponding to the cleaning robot, and by analyzing the set of cleaning problems existing in the water area to be cleaned by the cleaning robot, the problem phenomena of the cleaning robot can be understood in a timely manner, and the problems can be avoided in a timely manner to prevent the recurrence of the problem.

[0016] 3. Based on the analysis of the cleaning problems of the cleaning robots in the water area to be cleaned, the optimal cleaning combinations that can be associated with the cleaning robots to perform cleaning operations in the water area to be cleaned are analyzed. Then, the cleaning operation path of the cleaning robots in the water area to be cleaned is analyzed, thereby realizing the effective combination of cleaning under the cleaning operation of the cleaning robots, reducing cleaning losses and improving cleaning efficiency, and realizing intelligent and efficient water surface cleaning.

[0017] 4. Based on the analysis of the cleaning results of the cleaning robot in the water area to be cleaned, when there are problems with the cleaning, analyze the factors affecting the coverage path of the cleaning robot in the water area to be cleaned, and perform path optimization of the cleaning robot in the water area to be cleaned. Evaluate the cleaning coverage of the cleaning robot in the water area to be cleaned, so as to realize timely resolution of problems with the cleaning, avoid more serious pollution, and effectively ensure the efficiency and ecology of water area cleaning. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention.

[0020] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the full-area coverage path optimization method applied to water surface cleaning operations includes: Step 1, cleaning data acquisition: acquiring cleaning images of the cleaning robot in each historical water surface cleaning operation, constructing a problem database for the cleaning robot corresponding to the water surface cleaning operation, acquiring pollutant data corresponding to the current water area to be cleaned, and analyzing the cleaning problem set existing in the water area to be cleaned corresponding to the cleaning robot.

[0023] By constructing a problem database for water surface cleaning operations corresponding to cleaning robots, and analyzing the set of cleaning problems existing in the water areas to be cleaned by the cleaning robots, it is possible to promptly understand the problems existing in the cleaning operations of the cleaning robots, take timely measures to avoid problems, and prevent the recurrence of problems.

[0024] In a specific embodiment, the construction process of the problem library for the water surface cleaning operation of the cleaning robot is as follows: Based on the problem images of the cleaning robot exhibiting abnormal cleaning status extracted from each cleaning image, and the abnormal cleaning feature data extracted from each problem image, a cleaning abnormal feature map is constructed. At the same time, the specific abnormal manifestations of each type of dirt corresponding to the cleaning abnormal feature map are identified, and the cleaning problems and abnormal feature data of the problems when the cleaning robot cleans each type of dirt are obtained, thereby forming the problem library for the water surface cleaning operation of the cleaning robot.

[0025] It should be noted that the various types of pollutants include plants, mud, garbage, oil stains, and household waste. When cleaning, the cleaning robot may encounter various problems depending on the type of pollutant. For example, when cleaning oil stains, excessive oil content can cause suction blockage or propeller slippage. Similarly, when cleaning household waste, especially plastic or foam items, the robot may experience propeller / brush entanglement and jamming. Therefore, a problem database is constructed to address the various issues encountered by the cleaning robot when cleaning different types of waste. Specific abnormal characteristic data of the current cleaning robot when problems occur are obtained, such as trajectory deviation and motor current. This data reflects the problems that have arisen during the cleaning process.

[0026] In a specific embodiment, the analysis of the set of cleaning problems corresponding to the water area to be cleaned by the cleaning robot is carried out as follows: Dual monitoring is performed on the current water area to be cleaned using both in-water and ground-based equipment to obtain surface and bottom images of the water area. Pollutant identification is performed from both images to obtain the pollutant types corresponding to the current water area to be cleaned. These pollutant types are then compared with the pollutant types corresponding to each cleaning problem in the problem database. If the pollutant type corresponding to the current water area to be cleaned is included in the pollutant type set corresponding to a certain cleaning problem in the problem database, then the cleaning problem corresponding to that pollutant type is considered as the cleaning problem corresponding to the pollutant type of the current water area to be cleaned. This process yields the set of cleaning problems corresponding to the water area to be cleaned by the cleaning robot.

[0027] It should be noted that, based on understanding the types of pollutants present in the current water area, the types of pollutants present in the current water area are matched with the types of pollutants corresponding to each pollution control problem in the problem database. This allows us to identify the problems that may arise when using a pollution control robot to clean up the current water area, and to effectively avoid these problems when the pollution control robot cleans up these types of pollutants.

[0028] Step 2: Analysis of the surface cleaning path: Based on the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot, analyze the optimal cleaning combinations that the cleaning robot can perform cleaning operations on the water area to be cleaned, and then analyze the cleaning operation path of the cleaning robot on the water area to be cleaned.

[0029] Based on the analysis of the cleaning problems of the cleaning robots in the water area to be cleaned, the optimal cleaning combinations that can be associated with the cleaning robots to perform cleaning operations in the water area to be cleaned are obtained. Then, the cleaning operation path of the cleaning robots in the water area to be cleaned is obtained, thereby realizing the effective combination of cleaning under the cleaning operation of the cleaning robots, reducing cleaning losses and improving cleaning efficiency, and realizing intelligent and efficient water surface cleaning.

[0030] In a specific embodiment, the analysis yields optimal cleaning combinations for the cleaning robot to perform cleaning operations on the water area to be cleaned. The specific analysis process is as follows: Based on the set of cleaning problems existing in the water area to be cleaned by the cleaning robot, the abnormal performance characteristics of the cleaning robot when cleaning each type of pollutant have problems are extracted, and the hierarchical association between pollution type, typical anomalies, and feature data is obtained. Based on the hierarchical association, a problem association library for each type of pollutant by the cleaning robot is constructed. The hierarchical association of pollutant problems for each type of pollutant in the water area to be cleaned by the current cleaning robot is extracted. At the same time, the similar feature data between the hierarchical associations of pollutant problems for each type of pollutant in the water area to be cleaned by the current cleaning robot is extracted. A problem cancellation feature library between specific anomalies of each type of pollutant is established. When the cleaning problems of a certain type of pollutant and another type of pollutant constitute anomaly problem parameter cancellation, the cleaning association combination of the two types of pollutants is performed. In this way, the optimal cleaning combinations for the cleaning robot to perform cleaning operations on the water area to be cleaned are obtained.

[0031] It should be noted that, based on the analysis of the current water area's existing problems and abnormal characteristics, data associations are formed under pollution types, abnormal characteristics, and abnormal characteristic data. This yields specific abnormal manifestations and their presentation characteristics for each type of pollutant, establishing a hierarchical relationship among these three elements. Based on this hierarchical relationship, a database of hierarchical relationships for each type of pollutant is created. Furthermore, based on the types of pollutants present in the water area to be cleaned by the current cleaning robot, the database extracts the specific pollution problems and their hierarchical formation relationships for each pollutant type. Finally, based on the similarity features between the specific formation hierarchical relationships of each pollutant type in the water area to be cleaned by the cleaning robot, a problem offset feature database is established between specific pollution problems corresponding to each pollutant type. When similar data exist in the formation hierarchy of specific pollution problems, the cleaning associations of the two types of pollutants are analyzed to obtain the optimal cleaning combinations that the cleaning robot can perform for the water area to be cleaned. For example, if the pollutant type is oil, the abnormal characteristic is suction port blockage, and the abnormal characteristic data is low suction flow, then when the robot performs oil cleaning, the suction port will be blocked due to the heavy oil, resulting in a reduced suction volume. If the pollutant type is plastic from household waste, the abnormal characteristic is suction port blockage, and the abnormal characteristic data is interrupted suction flow, then when the robot performs plastic cleaning, the suction port will be blocked, resulting in interrupted suction. This establishes a hierarchical relationship. When the robot performs oil or plastic cleaning, the similar data is low suction flow and interrupted suction flow. Therefore, the two are combined for cleaning, and the solutions to these problems are used as common execution measures for both situations.

[0032] In a specific embodiment, the analysis obtains the cleaning operation path of the cleaning robot corresponding to the water area to be cleaned. The specific analysis process is as follows: Based on the comprehensive feature data of the cleaning robot performing cleaning operations in the water area to be cleaned, which can be associated with the optimal cleaning combination, the comprehensive feature data is imported into the waste combination quality evaluation model, and the combination quality value corresponding to each associated optimal cleaning combination is output. Then, the combination quality values ​​corresponding to each associated optimal cleaning combination are arranged in descending order. The cleaning area of ​​the water area to be cleaned is divided according to the order from the first to the last quality value, and the cleaning operation path corresponding to the cleaning operation of the water area to be cleaned is executed in ascending order of the quality values.

[0033] It should be noted that the combined quality value is the output natural number, including 1, 2, 3, etc.; the quality value is used to reflect the efficiency of the cleaning robot under each associated quality combination, so as to plan the cleaning path of the cleaning robot to be cleaned in a full coverage and efficient manner; the comprehensive feature data includes cleaning rate and cleaning coverage, which are the required data based on obtaining the combined quality value.

[0034] It should be noted that the construction of the high-quality assessment model for waste combinations involves the following steps: First, the comprehensive characteristic data of the waste cleaning robot performing waste cleaning operations on the water area to be cleaned is preprocessed. After the data preprocessing is completed, the model architecture is selected based on the amount of data, and the data is divided into three sets according to a set ratio, including a training set, a validation set, and a test set. During the training process, techniques such as early stopping are used to prevent the model from overfitting. The conclusions of the validation set are used as the conclusion indicators after the model training. After the high-quality assessment model for waste combinations completes the training and validation of the data, the model construction is completed, thus forming the high-quality assessment model for waste combinations. The construction process of the full coverage assessment model for water surface waste cleaning paths in this paper is the same as that of this model, and will not be elaborated further here.

[0035] Step 3: Optimization of Water Surface Cleaning Path: Monitor the cleaning robots performing cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the water area to be cleaned, analyze the factors affecting the coverage path of the cleaning robots on the water area to be cleaned when there are problems with the cleaning of the water area to be cleaned, and perform path optimization of the cleaning robots on the water area to be cleaned, and evaluate the cleaning coverage of the water area to be cleaned by the cleaning robots.

[0036] Based on the analysis of the cleaning results of the cleaning robot in the water area to be cleaned, when problems occur in the cleaning process, the factors affecting the coverage path of the cleaning robot in the water area to be cleaned are analyzed, and the path optimization of the cleaning robot in the water area to be cleaned is performed. The cleaning coverage of the cleaning robot in the water area to be cleaned is evaluated, so as to realize the timely resolution of problems in the cleaning process, avoid more serious pollution, and effectively ensure the efficiency and ecology of water area cleaning.

[0037] In a specific embodiment, the analysis of the cleaning results of the cleaning robot for the water area to be cleaned is carried out as follows: The cleaning feedback data of the cleaning robot for the water area to be cleaned is compared with a preset reference cleaning feedback data range. If one or more pieces of the cleaning feedback data of the cleaning robot for the water area to be cleaned are not included in the preset reference cleaning feedback data range, it is determined that there is a problem with the cleaning of the water area to be cleaned by the cleaning robot, and the cleaning path is not fully covered by the area. If all the cleaning feedback data of the cleaning robot for the water area to be cleaned are included in the preset reference cleaning feedback data range, it is determined that there is no problem with the cleaning of the water area to be cleaned by the cleaning robot, and the cleaning path is fully covered by the area. The remaining water area cleaning will continue to be performed according to the path.

[0038] It should be noted that the reference cleaning feedback range is a cleaning value preset by the cleaning management personnel for the cleaning robot. It is used to directly provide feedback on whether the cleaning operation of the cleaning robot is effectively cleaning the area it is cleaning according to the cleaning path, so as to realize timely handling of cleaning problems and ensure that the dirt in the area patrolled by the cleaning robot is effectively cleaned with full coverage. The cleaning feedback data includes cleaning time and robot wear and tear.

[0039] In a specific embodiment, the analysis of the influencing factors of the coverage path of the cleaning robot in the water area to be cleaned is carried out as follows: The pollutant content of the water area to be cleaned when there is a problem with the cleaning by the cleaning robot is obtained. The pollutant content of the water area to be cleaned by the cleaning robot is compared with the initial pollutant content of the corresponding water area. If the pollutant content of the water area to be cleaned by the cleaning robot is greater than the initial pollutant content of the corresponding water area, it is determined that the cleaning problem is caused by a change in pollutant content. The pollutant types exceeding the initial pollutant content are extracted. The presentation characteristics corresponding to the pollutant types are further compared with the reference pollutant type feature set corresponding to each influencing factor in the database to obtain the influencing factors corresponding to the pollutant type content exceeding the standard. Based on the change characteristic data of the influencing factors between the initial time period and the current time period, including the excess amount and the time taken, the final impact presentation value of the influencing factors on the corresponding pollutants in each area of ​​the water area to be cleaned is predicted.

[0040] It should be noted that, based on understanding the changes in pollutants at different time periods under the influence of affected factors, and according to the development characteristics of the changes and the subsequent performance characteristics of the influencing factors, namely intensity and duration, the final impact values ​​of the affected factors on pollutants in each area of ​​the water to be cleaned are obtained, thus obtaining the coverage content of the affected factors on pollutants in each area of ​​the water to be cleaned, providing optimization direction for subsequent path optimization; and the pollutant content is obtained based on instruments such as multispectral cameras and oil analyzers.

[0041] In a specific embodiment, the path optimization of the cleaning robot for the water area to be cleaned is performed as follows: The final impact value of the factors affecting the pollutants in each area of ​​the water area to be cleaned is compared with the reference pollutant impact offset value range corresponding to each combination of pollutants in the problem offset feature library. If the final impact value of the factors affecting the pollutants in a certain area is included in the reference pollutant impact offset value range corresponding to a certain combination of pollutants in the problem offset feature library, then the combination is taken as a new cleaning association combination for the water area to be cleaned. In this way, each new cleaning association combination for the water area to be cleaned is obtained. The reference quality value corresponding to each new cleaning association combination is extracted from the library. The reference quality values ​​of each new cleaning association combination are arranged in descending order. The new cleaning association combination with the highest quality value is taken as the first cleaning area. The path optimization of the cleaning robot for the water area to be cleaned is obtained in this order.

[0042] It should be noted that the reference dirt impact offset value range is a combined cleaning effect value preset by the cleaning management personnel for the cleaning robot. It is used to directly provide feedback on whether the combined cleaning operation of the cleaning robot is in efficient cleaning when it is cleaning the water area according to the cleaning path, so as to achieve the execution of the efficient cleaning path and ensure that the dirt in the area to be cleaned is cleaned quickly and completely.

[0043] In a specific embodiment, the evaluation process for assessing the cleaning coverage of the cleaning robot in the water area to be cleaned is as follows: the cleaning robot is monitored along an optimized path for cleaning the water area to be cleaned, and performance data of the water area under cleaning operation is obtained. The performance data is then imported into the water surface cleaning path full coverage evaluation model, and cleaning performance feature values ​​are output. The cleaning performance feature values ​​include data of 1 and 0.

[0044] If the cleaning performance characteristic value of the cleaning robot for the water area to be cleaned is 1, then the cleaning coverage of the water area to be cleaned by the cleaning robot is deemed to be qualified. If the cleaning performance characteristic value of the cleaning robot for the water area to be cleaned is 0, then the cleaning coverage of the water area to be cleaned by the cleaning robot is deemed to be unqualified.

[0045] It should be noted that the cleanup data includes the amount of residue after the area is cleaned and the number of uncleaned areas.

[0046] Step 4: Early Warning: An early warning will be issued when there are problems with the cleaning robot's cleaning of the water area to be cleaned, or when the cleaning robot's cleaning coverage of the water area to be cleaned is not up to standard.

[0047] Please see Figure 2 As shown, the full-area coverage path optimization system applied to water surface cleaning operations includes a cleaning data acquisition module, a water surface cleaning path analysis module, a water surface cleaning path optimization module, an early warning terminal, and a database.

[0048] The pollution data acquisition module is connected to the water surface pollution path analysis module and the database, the water surface pollution path analysis module is connected to the water surface pollution path optimization module and the database, and the water surface pollution path optimization module is connected to the early warning terminal and the database.

[0049] The pollution data acquisition module is used to acquire pollution images of the pollution cleaning robot in each historical water surface pollution cleaning operation, build a problem database for the pollution cleaning robot's corresponding water surface pollution cleaning operation, acquire pollutant data for the current water area to be cleaned, and analyze the pollution problem set of the corresponding water area to be cleaned by the pollution cleaning robot.

[0050] The water surface cleaning path analysis module is used to analyze the optimal cleaning combinations that the cleaning robot can perform cleaning operations on the water area to be cleaned, based on the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot, and then analyze the cleaning operation path of the cleaning robot on the water area to be cleaned.

[0051] The water surface cleaning path optimization module is used to monitor the cleaning robots performing cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the corresponding water area to be cleaned, analyze the influencing factors of the cleaning path of the cleaning robots on the corresponding water area to be cleaned when there are problems in the cleaning of the corresponding water area to be cleaned, and perform path optimization of the cleaning robots on the corresponding water area to be cleaned, and evaluate the cleaning coverage of the cleaning robots on the corresponding water area to be cleaned.

[0052] The early warning terminal is used to issue early warnings when there are problems with the cleaning of the water area to be cleaned by the cleaning robot or when the cleaning coverage of the water area to be cleaned by the cleaning robot is not up to standard.

[0053] The database is used to store cleaning and decontamination anomaly characteristic data, basic characteristic parameters, problem characteristic parameters, pollutant data, abnormal characteristic data, complementary characteristic data, comprehensive characteristic data, combined high-quality values, cleaning and decontamination feedback data, pollutant content, initial pollutant content, final impact presentation value, reference pollution impact offset value range, and cleaning data.

[0054] This invention constructs a problem database for water surface cleaning operations corresponding to cleaning robots, analyzes the set of cleaning problems existing in the water areas to be cleaned by the cleaning robots, and then constructs an efficient and optimized cleaning combination for the cleaning robots. The cleaning operation path is analyzed, and the cleaning results of the water areas to be cleaned by the cleaning robots are analyzed. When problems exist, the influencing factors are understood, and path optimization is performed. Finally, the cleaning coverage of the water areas to be cleaned by the cleaning robots is evaluated. This enables a comprehensive analysis of the water surface cleaning situation by the cleaning robots, achieving effective cleaning under full-area coverage, improving water surface cleaning efficiency, reducing serious ecological damage caused by untimely removal of surface pollutants, and further protecting the ecological health of the water area. Simultaneously, based on the monitoring and analysis of the cleaning situation under the cleaning paths of the cleaning robots, the system intelligently avoids incomplete or missed cleaning of the water surface, achieving intelligent and efficient water surface cleaning.

[0055] The examples described in this invention are not limited to the specific embodiments listed above. The examples are merely illustrative to facilitate understanding of the invention and do not constitute a limitation on the scope of protection of this invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of this invention should be included within the scope of protection.

[0056] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing the path of full-area coverage in water surface cleaning operations, characterized in that, Includes the following steps: Step 1: Data Acquisition of Cleaning Data: Acquire images of the cleaning robot in each historical water surface cleaning operation, construct a problem database for the cleaning robot's corresponding water surface cleaning operation, acquire pollutant data for the current water area to be cleaned, and analyze the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot. Step 2, Analysis of Water Surface Cleaning Path: Based on the cleaning problem set of the water area to be cleaned corresponding to the cleaning robot, analyze the optimal cleaning combinations that can be associated for the cleaning robot to perform cleaning operations in the water area to be cleaned, and then analyze the cleaning operation path of the cleaning robot in the water area to be cleaned. Step 3: Optimization of water surface cleaning path: Monitor the cleaning robots that perform cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the water area to be cleaned, analyze the factors affecting the coverage path of the cleaning robots on the water area to be cleaned when there are problems in the cleaning of the water area to be cleaned, and perform path optimization of the cleaning robots on the water area to be cleaned, and evaluate the cleaning coverage of the water area to be cleaned by the cleaning robots. Step 4: Early Warning: An early warning will be issued when there are problems with the cleaning robot's cleaning of the water area to be cleaned, or when the cleaning robot's cleaning coverage of the water area to be cleaned is not up to standard.

2. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 1, characterized in that, The specific construction process for building the problem library corresponding to water surface cleaning operations for the cleaning robot is as follows: Based on the extraction of problem images showing abnormal cleaning status of the cleaning robot from various cleaning images, and the extraction of cleaning abnormality feature data from each problem image, a cleaning abnormality feature map is constructed. At the same time, the specific abnormal manifestations of each type of dirt corresponding to the cleaning abnormality feature map are identified. Thus, the cleaning problems that exist when the cleaning robot cleans each type of dirt and the abnormal feature data when the problems occur are obtained, thereby forming a problem library for the cleaning robot when cleaning the water surface.

3. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 2, characterized in that, The analysis process for the set of cleaning problems corresponding to the water area to be cleaned by the cleaning robot is as follows: The system employs both submersible and surface-mounted monitoring devices to perform dual monitoring of the water area to be cleaned, acquiring surface and bottom images of the water area. Pollutant identification is then performed on both images to determine the pollutant types in the water area. These pollutant types are then compared with the pollutant sets corresponding to various cleaning problems in a problem database. If a pollutant type in the water area is included in the pollutant set corresponding to a specific cleaning problem in the problem database, the cleaning problem corresponding to that pollutant type is considered a cleaning problem for that pollutant type in the water area. This process yields the cleaning problem set for the water area to be cleaned, corresponding to the cleaning robot's capabilities.

4. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 3, characterized in that, The analysis process is as follows: The analysis process involves identifying optimal cleaning combinations of the cleaning robot performing cleaning operations on the water area to be cleaned. Based on the set of cleaning problems existing in the water area to be cleaned by the cleaning robot, the abnormal performance characteristics of the cleaning robot when cleaning various types of pollutants are problematic are extracted. The hierarchical relationship between pollution type, typical anomaly and feature data is obtained. Based on the hierarchical relationship, a problem association library for each type of pollutant corresponding to the cleaning robot is constructed. The hierarchical relationship of pollutant problems for each type of pollutant in the water area to be cleaned by the current cleaning robot is extracted. At the same time, the similar feature data between the hierarchical relationship of pollutant problems for each type of pollutant in the water area to be cleaned by the current cleaning robot is extracted. A problem cancellation feature library between specific anomalies of each type of pollutant is established. When the cleaning problem between two types of pollutants constitutes anomaly problem parameter cancellation, the cleaning association combination of the two types of pollutants is performed. In this way, the optimal cleaning combination that can be associated for the cleaning robot to perform cleaning operations in the water area to be cleaned is obtained.

5. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 4, characterized in that, The analysis process for determining the cleaning operation path of the cleaning robot for the water area to be cleaned is as follows: Based on the comprehensive feature data of the cleaning robots performing cleaning operations in the waters to be cleaned, the comprehensive feature data is imported into the quality evaluation model of the waste combination. The quality value of each associated high-quality cleaning combination is output. Then, the quality values ​​of each associated high-quality cleaning combination are arranged in descending order. The cleaning area of ​​the waters to be cleaned is divided according to the order from the first to the last quality value. The cleaning operation path of the corresponding cleaning operation in the waters to be cleaned is executed in ascending order of the quality values.

6. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 5, characterized in that, The analysis of the cleaning results of the cleaning robot for the water area to be cleaned is as follows: The cleaning feedback data of the cleaning robot for the water area to be cleaned is compared with the preset reference cleaning feedback data range. If one or more pieces of the cleaning feedback data of the cleaning robot for the water area to be cleaned are included in the preset reference cleaning feedback data range, it is determined that there is a problem with the cleaning of the water area to be cleaned by the cleaning robot, and the cleaning path is not in full coverage of the area. If all the cleaning feedback data of the cleaning robot for the water area to be cleaned are included in the preset reference cleaning feedback data range, it is determined that there is no problem with the cleaning of the water area to be cleaned by the cleaning robot, and the cleaning path is in full coverage of the area. The remaining water area cleaning will continue to be performed according to the path.

7. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 1, characterized in that, The analysis of the factors influencing the coverage path of the cleaning robot for the water area to be cleaned is as follows: The system obtains the pollutant content of the water area to be cleaned when there are problems with the cleaning robot. It compares the pollutant content of the water area to be cleaned with the initial pollutant content of the corresponding water area. If the pollutant content of the water area to be cleaned with the cleaning robot is greater than the initial pollutant content of the corresponding water area, it is determined that the cleaning problem is caused by the change in pollutant content. The pollutant types that exceed the initial pollutant content are extracted. The presentation characteristics of the pollutant types are compared with the reference pollutant type feature set of each influencing factor in the database to obtain the influencing factors of the pollutant type content exceeding the standard. Based on the change feature data of the influencing factors between the initial time period and the current time period, including the excess amount and the time taken, the final impact presentation value of the pollutants in each area of ​​the water area to be cleaned is predicted.

8. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 1, characterized in that, The path optimization for the cleaning robot corresponding to the water area to be cleaned is performed as follows: The final impact values ​​of the factors affecting the contaminants in each area of ​​the water to be cleaned are compared with the reference contaminant impact offset value ranges for each combination of contaminants in the problem offset feature library. If the final impact value of the factors affecting the contaminants in a certain area is included in the reference contaminant impact offset value range for a certain combination of contaminants in the problem offset feature library, then that combination is taken as a new cleaning association combination for the water area. In this way, each new cleaning association combination for the water area is obtained, and the reference quality value corresponding to each new cleaning association combination is extracted from the library. The reference quality values ​​of each new cleaning association combination are arranged in descending order, and the new cleaning association combination with the highest quality value is taken as the first cleaning area. This order is used to form the path optimization for the cleaning robot for the water area to be cleaned.

9. The method for optimizing the full-area coverage path for surface cleaning operations according to claim 1, characterized in that, The assessment process for evaluating the cleaning coverage of the cleaning robot in the water area to be cleaned is as follows: The cleaning robot is monitored along the optimized path of the water area to be cleaned. Performance data of the water area is obtained under the cleaning operation. The performance data is imported into the full coverage evaluation model of the water surface cleaning path and the cleaning performance feature value is output. The cleaning performance feature value includes data of 1 and 0. If the cleaning performance characteristic value of the cleaning robot for the water area to be cleaned is 1, then the cleaning coverage of the water area to be cleaned by the cleaning robot is deemed to be qualified. If the cleaning performance characteristic value of the cleaning robot for the water area to be cleaned is 0, then the cleaning coverage of the water area to be cleaned by the cleaning robot is deemed to be unqualified.

10. A full-area coverage path optimization system for implementing the full-area coverage path optimization method for water surface cleaning operations as described in any one of claims 1-9, characterized in that, include: The pollution data acquisition module is used to acquire pollution images of the pollution cleaning robot in each historical water surface pollution cleaning operation, build a problem database for the pollution cleaning robot corresponding to the water surface pollution cleaning operation, acquire pollutant data corresponding to the current water area to be cleaned, and analyze the pollution problem set of the pollution cleaning robot corresponding to the water area to be cleaned. The surface cleaning path analysis module is used to analyze the optimal cleaning combinations that the cleaning robot can perform cleaning operations on the corresponding water area based on the cleaning problem set of the cleaning robot. Then, it analyzes the cleaning operation path of the cleaning robot on the corresponding water area. The water surface cleaning path optimization module is used to monitor the cleaning robots that perform cleaning operations on the water area to be cleaned according to the cleaning path, obtain cleaning feedback data, analyze the cleaning results of the cleaning robots on the water area to be cleaned, analyze the influencing factors of the coverage path of the cleaning robots on the water area to be cleaned when there are problems in the cleaning of the water area to be cleaned, and perform path optimization of the cleaning robots on the water area to be cleaned, and evaluate the cleaning coverage of the cleaning robots on the water area to be cleaned. The early warning terminal is used to issue early warnings when there are problems with the cleaning of the water area to be cleaned by the cleaning robot or when the cleaning coverage of the water area to be cleaned by the cleaning robot is not up to standard.

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