Multi-path security inspection robot intelligent scheduling method, system, equipment and medium

Abstract

The invention provides a multi-path security inspection robot intelligent scheduling method, system and device and a medium thereof, and the method comprises the following steps: installing a plurality of sensors in a target inspection area, and collecting the basic parameter information of inspection robots and the inspection area in real time; according to the invention, multi-dimensional data, including various information of the inspection robot and the inspection area, are acquired through various sensors, and through preprocessing and fusion algorithms, noise is removed and multi-source data are integrated, so that an accurate and comprehensive data basis is provided for subsequent decision making, and the problem of information fragmentation is solved; a plurality of factors such as landforms and obstacles are considered through a path planning algorithm, an optimal path is calculated, updating can be carried out according to real-time conditions, and rapid correction is carried out by using an incremental algorithm, so that the planning precision and flexibility are improved, and the inspection efficiency is improved; the robot and obstacle information is acquired in real time through the sensor, so that the route and the task are optimized according to the real-time information, and the inspection effect and the guarantee capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of security patrol inspection, and in particular to a method, system, equipment and medium for intelligent dispatching of multi-channel security patrol inspection robots. Background Art

[0002] With the accelerated advancement of urbanization, various types of infrastructure are constantly improving, population density continues to increase, and commercial activities are becoming increasingly frequent, which has led to an unprecedented demand for security in various fields. Whether it is large factories and warehouses in the field of industrial production, shopping malls and office buildings in the commercial field, or hospitals, schools, stations and other places in the field of public services, they are facing complex and diverse security challenges. These places have a large flow of people and concentrated materials, and there are many potential problems such as fire hazards, theft risks, and threats to personnel safety. Security inspections, as a proactive safety precaution, can promptly detect and deal with these potential safety hazards, thereby effectively preventing accidents and ensuring the safety of personnel and property. However, the existing scheduling methods have a series of shortcomings, which seriously restrict the effectiveness of security inspection robots; First, in terms of information integration, existing methods often fail to achieve comprehensive, accurate, and timely integration and analysis of information from different robots, sensors, and data sources, which leads to fragmentation and incoherence of information, making it difficult to make optimal scheduling decisions without sufficient data basis. Secondly, the accuracy of path planning is also a major shortcoming of existing scheduling methods. When facing complex actual environments, current path planning algorithms often fail to fully consider various dynamic factors and constraints, such as real-time traffic conditions, crowd flow, and the sudden appearance of obstacles. This makes the planned path less than optimized, and the robot may encounter unnecessary detours, congestion, or even impassable situations during the inspection process, thereby wasting time and energy and reducing inspection efficiency. In addition, the lack of ability to respond to changes in real time is a prominent problem of existing scheduling methods. In the actual security inspection process, the environment is dynamically changing, and sudden security incidents, equipment failures, changes in environmental conditions, etc. may occur. Existing scheduling methods often cannot perceive these changes in time and make corresponding adjustments quickly, which results in the inspection robot being unable to respond to emergencies in time, delaying the best processing time. At the same time, it may also cause waste of resources and unsatisfactory inspection results because of the inability to adjust task allocation and path planning according to real-time conditions; To this end, a multi-channel security inspection robot intelligent scheduling method, system, equipment and medium are proposed. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide a multi-channel security inspection robot intelligent scheduling method, system, device and medium thereof to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial choice.

[0004] In order to solve the above technical problems, a technical solution adopted in this application is: a multi-channel security inspection robot intelligent scheduling method, comprising the following steps: Step 1: Install multiple sensors in the target inspection area to collect basic parameter information of the inspection robot and the inspection area in real time; Step 2: Based on the basic parameter information of the inspection robot and the inspection area, multiple inspection robots are evaluated and screened using an intelligent screening algorithm, the target inspection robot is screened out, and an inspection route is formulated using a path planning algorithm; Step 3: Generate inspection tasks based on the selected target inspection robots and the formulated inspection routes; Step 4: According to the inspection task, the target inspection robot starts the inspection work and feeds back its own position, operation status and collected environmental data information to the dispatch center in real time; Step 5: Based on the sensor, the running status of the target inspection robot and the dynamic information of obstacles are obtained in real time, and the newly appeared unknown obstacles are identified and classified by using image processing and pattern recognition technology; Step 6: Based on the real-time acquired operating status of the target inspection robot and dynamic information of obstacles, the inspection route and inspection task are dynamically optimized using a real-time adjustment algorithm; Step 7: Obtain the operation data of the inspection process, use data analysis technology to mine potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; Step 8. After the inspection task is completed, all data generated during the inspection process will be classified, stored and backed up, and the inspection work will be summarized and evaluated.

[0005] As a further preferred embodiment of the present technical solution, in step one, the basic parameter information includes the performance parameters of the inspection robot, the current state, the degree of matching with the inspection task, the number of inspections, the task type and the topography of the inspection area, obstacle distribution, task requirements and environmental information.

[0006] As a further preferred embodiment of the present technical solution, in step 2, the intelligent screening algorithm calculates a comprehensive evaluation score of the inspection robot according to the performance parameters, current state and matching degree of the inspection robot with the inspection task; the performance parameters include movement speed, endurance, detection accuracy and load capacity; the current state includes current power, whether a task is being executed and working time; Set the movement speed to , battery life is , the detection accuracy is , load capacity is 、The current power is , Is the task being executed? , if the task is being executed, then ,otherwise , working hours are , and the matching degree with the inspection task is ; Set the motion speed weight to , the endurance weight is , the detection accuracy weight is , the load capacity weight is , the weight of the previous power is , the task execution status weight is , the weight of working hours is The matching weight is ,and ; Comprehensive evaluation score of the inspection robot The calculation formula is:

[0007] ; in, , , , are the maximum values ​​of the corresponding parameters, The maximum working time is set. The robot with the higher score is more suitable for the current inspection task. The robot with the highest score is selected as the target inspection robot. The path planning algorithm calculates the path cost function according to the topography, obstacle distribution and task requirements of the inspection area; the topography includes slope and terrain height; the obstacle distribution includes static obstacles and dynamic obstacles. If the obstacle is a static obstacle, the obstacle position is marked as an inaccessible area by establishing a map model; if the obstacle is a dynamic obstacle, the prediction model is used to predict the position and movement trajectory of the dynamic obstacle in the future based on its historical movement data, current speed and direction information; the task requirements include urgency and importance level; Set the path cost function to , the distance cost is The energy cost is The time cost is The risk cost is : Path cost function The calculation formula is: ; Among them, the distance cost With path length Related, expressed as: ; in, is the distance cost weight; Energy cost It is related to the terrain slope and the robot motion state and is expressed as: ; in, is the number of nodes on the path, For the The estimated energy consumption at each node is is the energy consumption cost weight; Time Cost It is related to the average speed of the robot and the task time requirement and is expressed as: ; in, is the average speed of the robot, is the maximum allowed inspection time, The estimated time required to complete the current task, and is the time cost weight; Risk Cost It is related to the proximity of the obstacle and the urgency of the task, expressed as: ; in, is the number of points on the path that are close to obstacles, is the risk assessment value of the th point, is the risk cost weight; Through optimization algorithms, including genetic algorithms and simulated annealing algorithms, the path that minimizes the path cost function is found as the planned inspection route.

[0008] As a further preferred embodiment of the present technical solution, in step three, the inspection task includes the inspection area of ​​the target inspection robot (accurate to a specific coordinate range or a specific area identification), the inspection time (start time, end time and time nodes of each stage), the inspection frequency (the number of inspections in different cycles such as hours, days and weeks) and the task focus in different areas (such as focusing on detecting temperature changes in some areas and focusing on monitoring personnel activities and equipment operating status in other areas).

[0009] As a further preferred embodiment of the present technical solution, in step four, the inspection task is sent to the target inspection robot via multi-band communication technology. If communication interference is encountered, it automatically switches to the backup frequency band, monitors the communication quality in real time, and adaptively adjusts the communication parameters. After receiving the inspection task, the target inspection robot sends a confirmation signal to the dispatch center. If no confirmation signal is received within the predetermined time, the dispatch center automatically resends the task information.

[0010] As a further preferred embodiment of the present technical solution, in step five, the image processing and pattern recognition technology utilizes a deep learning algorithm to identify and classify unknown obstacles, and establishes an obstacle database to record and classify the identified obstacles.

[0011] As a further preferred embodiment of the present technical solution, in step eight, the data storage and backup utilize distributed storage technology and redundant backup mechanism to disperse and store the data on multiple storage nodes, and encrypt the stored data, and the encryption algorithm utilizes a dynamic key mechanism that is regularly updated.

[0012] To solve the above technical problems, another technical solution adopted by the present application is: a multi-channel security patrol robot intelligent dispatching system, including: a sensor network module, an intelligent screening and path planning module, a patrol task generation module, a real-time communication and feedback module, an obstacle recognition and processing module, a data analysis and warning module and a data storage and management module; The sensor network module is used to install multiple sensors in the target inspection area to collect basic parameter information of the inspection robot and the inspection area in real time. The basic parameter information includes the performance parameters of the inspection robot, the current state, the matching degree with the inspection task, the number of inspections, the task type, and the topography of the inspection area, obstacle distribution, task requirements, and environmental information; The intelligent screening and path planning module is used to evaluate and screen multiple inspection robots based on the basic parameter information collected by the sensor network module using an intelligent screening algorithm, screen out the target inspection robot, and use a path planning algorithm to formulate an inspection route according to the topography, obstacle distribution and task requirements of the inspection area; The inspection task generation module is used to generate specific inspection tasks according to the selected target inspection robots and the formulated inspection routes. The inspection tasks include inspection areas, inspection time, inspection frequency, and task priorities in different areas. The real-time communication and feedback module is used to send the inspection task to the target inspection robot and receive the position, operation status and collected environmental data information fed back by the target inspection robot in real time; The obstacle recognition and processing module is used to obtain the operating status of the target inspection robot and the dynamic information of obstacles in real time based on sensors, and use image processing and pattern recognition technology to identify and classify newly appeared unknown obstacles, and use real-time adjustment algorithms to dynamically optimize inspection routes and inspection tasks based on the operating status of the target inspection robot and the dynamic information of obstacles obtained in real time; The data analysis and early warning module is used to obtain the operation data of the inspection process, use data analysis technology to mine potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; The data storage and management module is used to classify, store and back up all data generated during the inspection process after the inspection task is completed, and summarize and evaluate the inspection work.

[0013] To solve the above technical problems, another technical solution adopted in the present application is: a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor implements the steps of the method for intelligent scheduling of multi-channel security patrol robots when executing the computer program.

[0014] To solve the above technical problems, another technical solution adopted in the present application is: a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the steps of the multi-channel security patrol robot intelligent scheduling method.

[0015] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: The present invention collects multi-dimensional data through multiple sensors, including various types of information about the inspection robot and the inspection area. After preprocessing and fusion algorithms, it removes noise and integrates multi-source data to provide an accurate and comprehensive data basis for subsequent decision-making and solve the problem of information fragmentation. The present invention considers multiple factors such as topography, obstacles (static and dynamic respectively) and task requirements through the path planning algorithm, calculates the optimal path, and can update it according to the real-time situation and quickly correct it with an incremental algorithm, thereby improving planning accuracy and flexibility and improving inspection efficiency; The present invention uses sensors to obtain real-time information about robots and obstacles, and uses image processing and pattern recognition technology to identify unknown obstacles, thereby optimizing routes and tasks based on real-time information, thereby improving inspection effects and security capabilities.

[0016] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a process of an intelligent dispatching method for multi-channel security inspection robots according to the present invention; Figure 2 This is a functional module schematic diagram of a multi-channel security inspection robot intelligent dispatching system of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0019] In the figure: 100, computer equipment; 102, processor; 104, memory; 106, transmission device. DETAILED DESCRIPTION

[0020] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0021] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0022] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0023] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0024] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0025] Figure 1 1 is a flowchart of a multi-channel security patrol robot intelligent dispatching method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not based on Figure 1 The process sequence shown is limited. Figure 1 As shown: A multi-channel security inspection robot intelligent scheduling method includes the following steps: Step 1: Install multiple sensors in the target inspection area to collect basic parameter information of the inspection robot and the inspection area in real time; Among them, the sensors include laser radar, camera, infrared sensor and other sensors that can be used to obtain relevant information of the inspection area; step one also includes real-time preprocessing and data fusion of the basic parameter information of the inspection robot and the inspection area collected. For the data collected by different types of sensors, the corresponding filtering algorithm is used to remove noise interference, such as median filtering for laser radar data and Gaussian filtering for camera image data. Then, the multi-source data is fused through the data fusion algorithm to obtain more accurate and comprehensive environmental information. The fusion algorithm can adopt a fusion method based on Kalman filtering or a feature fusion method based on deep learning to improve the reliability and effectiveness of the data and provide better basic data for subsequent robot screening and path planning; Step 2: Based on the basic parameter information of the inspection robot and the inspection area, multiple inspection robots are evaluated and screened using an intelligent screening algorithm, the target inspection robot is screened out, and an inspection route is formulated using a path planning algorithm; Among them, the weight parameters in the intelligent screening algorithm are dynamically adjusted according to the real-time inspection task requirements and environmental changes. When the environmental complexity of the inspection area increases (such as an increase in obstacles, large changes in terrain) or the urgency of the task increases, the weights of factors related to coping with these changes are increased accordingly, such as increasing the weight of detection accuracy and endurance, to ensure that inspection robots that are more suitable for the current complex environment and emergency tasks are screened out. At the same time, an adaptive model for weight adjustment is established to automatically learn and adjust weight parameters based on historical data and real-time feedback information, so that the intelligent screening algorithm can better adapt to different inspection scenarios and improve the accuracy and adaptability of robot screening; After the inspection route is formulated, the path planning algorithm is updated in real time according to the real-time environmental information and robot operation status. When new obstacles are found or the robot power changes, the path cost function is recalculated in time, and the inspection route is optimized locally or globally. The incremental path planning algorithm is used to make rapid corrections based on the original path, reduce the amount of calculation, and improve the real-time and flexibility of path planning. At the same time, the updated results of path planning are fed back to the target inspection robot in real time to ensure that the robot can inspect according to the latest optimal route, thereby improving inspection efficiency and safety. Step 3: Generate inspection tasks based on the selected target inspection robots and the formulated inspection routes; The generated inspection tasks are displayed and managed through a visual interface, which presents the inspection area in the form of a map, marking the inspection route, inspection area, task time node, task focus and other information of each inspection robot. Operators can intuitively understand the arrangement of the entire inspection task through this interface, and can perform interactive operations, such as manually adjusting the inspection route, setting task priority, and viewing the real-time status of the robot. At the same time, the visual interface has a real-time data update function, which can display the robot's position movement, task completion progress and other information in real time as the inspection work progresses, so as to facilitate operators to monitor and dispatch the inspection work in real time, and provide High work efficiency and decision-making accuracy; regularly back up the generated inspection tasks and store them in multiple independent storage media. When the inspection task information is damaged due to system failure, data loss or other unexpected situations, the task data can be quickly restored from the backup to ensure the continuity and stability of the inspection work. At the same time, data encryption and integrity verification technology are used in the backup process to ensure the security and reliability of the backup data. When restoring the task data, the integrity and accuracy of the restored tasks are verified to ensure that the restored inspection tasks can be executed normally, avoiding inspection work abnormalities caused by data damage or errors, and providing strong guarantees for the reliable execution of security inspection tasks; Step 4: According to the inspection task, the target inspection robot starts the inspection work and feeds back its own position, operation status and collected environmental data information to the dispatch center in real time; Among them, when issuing inspection tasks to the target inspection robot, the priority is set according to the urgency and importance of the task, and the urgent tasks are issued first, and the timely delivery of the task information is ensured through multi-channel communication. At the same time, after receiving the task, the robot sends detailed task receipt confirmation information to the dispatch center, including the analysis results of the task content, the estimated execution time, etc. The dispatch center reviews the robot's confirmation information. If any abnormality or non-compliance with the requirements is found, it will interact and communicate with the robot in a timely manner to ensure the accuracy and completeness of the task issuance. In addition, a task issuance log recording system is established to record the time, content, and receipt status of each task issuance in detail for subsequent query and analysis, providing data support for task management and scheduling optimization; Step 5: Based on the sensor, the running status of the target inspection robot and the dynamic information of obstacles are obtained in real time, and the newly appeared unknown obstacles are identified and classified by using image processing and pattern recognition technology; Among them, for the real-time acquisition of the target inspection robot's operating status information, in addition to basic parameters such as position and speed, the robot's mechanical parts vibration frequency and temperature are also monitored through sensors, and the normal vibration frequency range and temperature range are set. When the detected vibration frequency or temperature exceeds the normal range, it is judged that the robot may have a hidden danger of mechanical failure, and an early warning signal is immediately sent to the dispatch center, and the fault diagnosis program is started to conduct preliminary diagnosis and analysis on the robot's mechanical parts, and the diagnosis results are fed back to the dispatch center in real time; In addition to the shape, size and other features, image processing and pattern recognition technologies are used to identify and classify newly emerged unknown obstacles. Deep learning algorithms are also introduced to improve the recognition accuracy of complex obstacles by learning a large amount of obstacle image data. At the same time, obstacles are classified according to their type and degree of danger. For example, obstacles are divided into low-risk, medium-risk and high-risk levels. Different levels of obstacles correspond to different processing strategies and warning levels, so as to more efficiently adjust inspection routes and allocate tasks. Step 6: Based on the real-time acquired operating status of the target inspection robot and dynamic information of obstacles, the inspection route and inspection task are dynamically optimized using a real-time adjustment algorithm; Among them, when the real-time adjustment algorithm is used to dynamically optimize the inspection route and inspection tasks, for the scenario of multi-robot collaborative inspection, the communication delay and cooperation relationship between robots are considered. If multiple robots encounter obstacles or other abnormal situations in a similar area, the distributed negotiation algorithm is used to coordinate the path adjustment and task allocation between the robots to avoid conflicts and repeated operations. For example, when a robot needs to change its task due to insufficient power, the nearby robots decide whether to take over its task through negotiation based on their own status and task priority, and adjust their respective inspection routes at the same time to ensure the continuity and efficiency of the overall inspection task; When encountering an emergency (such as a sudden security incident), the algorithm is adjusted in real time to prioritize inspection tasks in key areas and increase the priority weight of emergency tasks. According to the location and severity of the incident, the robot's inspection route is quickly replanned, resources are concentrated to deploy to key areas, and other related security systems (such as surveillance cameras, alarm devices, etc.) are notified to work together to achieve a full range of security response. In addition, during the emergency handling process, the robot's operation log and related data are recorded in real time to facilitate subsequent evaluation and analysis of the emergency handling process, summarize lessons learned, and further optimize emergency handling strategies; Step 7: Obtain the operation data of the inspection process, use data analysis technology to mine potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; Among them, when using data analysis technology to mine potential safety hazards and abnormal situations, in addition to analyzing based on real-time data, trend analysis is also combined with historical data. For example, by analyzing the trend of temperature data changes over a period of time, possible temperature anomalies can be predicted and early warning information can be issued in advance. At the same time, for the analysis of personnel activities, not only the abnormal gathering of personnel is paid attention to, but also the abnormal behavior patterns of personnel (such as running, fighting, etc.) are identified through behavioral analysis algorithms to timely discover potential safety threats, and the warning information is sent to the mobile terminals of relevant security personnel and the monitoring center at the same time, so that timely and effective response measures can be taken; When optimizing and adjusting subsequent inspection tasks based on early warning information, in addition to adjusting the inspection frequency and route planning, corresponding emergency plans are automatically generated based on different types of early warning information. For example, when a fire hazard warning is detected, the robot is automatically planned to go to the nearby fire equipment point for inspection and notify relevant personnel to make fire preparations; when an abnormal behavior warning is found, the robot is dispatched to approach the area for closer monitoring and the nearby voice broadcasting system is linked to prompt or warn. At the same time, the execution of the emergency plan is included in the evaluation index system of the inspection work, so as to continuously improve and optimize the emergency plan and enhance the emergency response capability of the security inspection system. Step 8: After the inspection task is completed, all data generated during the inspection process will be classified, stored and backed up, and the inspection work will be summarized and evaluated; Among them, in order to facilitate the subsequent analysis and processing of inspection data, a data index and metadata management system is established to classify and index the stored data to improve data query efficiency; at the same time, the source of the data, collection time, related tasks and other metadata are recorded and managed in detail to provide richer contextual information for data analysis. In addition, data interfaces are reserved to facilitate data interaction and integration with other security systems or third-party data analysis platforms, to achieve wider security data applications and collaborative work, and have good scalability and compatibility.

[0026] In one embodiment, specifically, in step 1, the basic parameter information includes the performance parameters of the inspection robot, the current state, the matching degree with the inspection task, the number of inspections, the task type, and the topography of the inspection area, the obstacle distribution, the task requirements, and the environmental information; Among them, the performance parameters of the inspection robot include the robot's moving speed, endurance, sensor accuracy, data processing capabilities, etc. These parameters directly determine whether the robot can effectively perform tasks in a specific environment; Current status refers to the robot's immediate state before performing a task, such as power level, sensor status, and the integrity of mechanical parts. This information helps determine whether the robot is suitable for immediate task execution or whether it needs maintenance or charging first. The matching degree with the inspection task refers to evaluating whether the robot has the specific functions or capabilities required to complete the task according to the nature and requirements of the task. For example, some tasks may require the robot to have high-definition camera functions or be able to move stably in complex terrain. Inspection times refers to the number of times the robot needs to perform inspections, which is usually related to the periodicity of the inspection and the importance of the task; Task types include equipment inspection, environmental monitoring, security patrols, etc. Different types of tasks have different requirements for robots; The topography of the inspection area includes the flatness, slope, soil type, etc. This information is crucial for planning the robot's movement path and avoiding potential risks; Obstacle distribution refers to the location and type of obstacles (such as trees, buildings, equipment, etc.) in the inspection area, which helps the robot plan obstacle avoidance strategies in advance; Task requirements include inspection accuracy, speed, and the type of data that needs to be collected, which directly guide the robot's operating strategy; Environmental information includes weather conditions, light intensity, temperature, humidity, etc. This information is very important for evaluating the robot's working environment and ensuring the safe execution of tasks.

[0027] In one embodiment, specifically, in step 2, the intelligent screening algorithm calculates the comprehensive evaluation score of the inspection robot according to the performance parameters, current state and matching degree of the inspection robot with the inspection task; the performance parameters include movement speed, endurance, detection accuracy and load capacity; the current state includes the current power, whether the task is being executed and the working time; Set the movement speed to , battery life is , the detection accuracy is , load capacity is 、The current power is , Is the task being executed? , if the task is being executed, then ,otherwise , working hours are , and the matching degree with the inspection task is ; Set the motion speed weight to , the endurance weight is , the detection accuracy weight is , the load capacity weight is , the weight of the previous power is , the task execution status weight is , the weight of working hours is The matching weight is ,and ; Comprehensive evaluation score of the inspection robot The calculation formula is:

[0028] ; in, , , , are the maximum values ​​of the corresponding parameters, The maximum working time is set. The robot with the higher score is more suitable for the current inspection task. The robot with the highest score is selected as the target inspection robot. The path planning algorithm calculates the path cost function based on the topography, obstacle distribution and task requirements of the inspection area; the topography includes slope and terrain height; the obstacle distribution includes static obstacles and dynamic obstacles. If the obstacle is a static obstacle, the obstacle location is marked as an inaccessible area by establishing a map model; if the obstacle is a dynamic obstacle, the prediction model is used to predict its location and movement trajectory in the future based on the historical movement data, current speed and direction information of the dynamic obstacle; the task requirements include the degree of urgency and importance level; Set the path cost function to , the distance cost is The energy cost is The time cost is The risk cost is : Path cost function The calculation formula is: ; Among them, the distance cost With path length Related, expressed as: ; in, is the distance cost weight; Energy cost It is related to the terrain slope and the robot motion state and is expressed as: ; in, is the number of nodes on the path, For the The estimated energy consumption at each node is is the energy consumption cost weight; Time Cost It is related to the average speed of the robot and the task time requirement and is expressed as: ; in, is the average speed of the robot, is the maximum allowed inspection time, The estimated time required to complete the current task, and is the time cost weight; Risk Cost It is related to the proximity of the obstacle and the urgency of the task, expressed as: ; in, is the number of points on the path that are close to obstacles, is the risk assessment value of the th point, is the risk cost weight; Through optimization algorithms, including genetic algorithms and simulated annealing algorithms, the path that minimizes the path cost function is found as the planned inspection route.

[0029] In one embodiment, specifically, in step three, the inspection task includes the inspection area of ​​the target inspection robot (accurate to a specific coordinate range or a specific area identifier), the inspection time (start time, end time, and time nodes of each stage), the inspection frequency (the number of inspections in different cycles such as hours, days, and weeks), and the task focus in different areas (such as focusing on detecting temperature changes in certain areas and focusing on monitoring personnel activities and equipment operating status in other areas). By comprehensively considering factors such as the inspection area, time, frequency, and task focus, a detailed and feasible inspection plan can be formulated, which helps the robot to perform tasks efficiently while ensuring the accuracy and reliability of the inspection results. In addition, according to actual needs, the inspection plan can also be dynamically adjusted and optimized to adapt to the ever-changing working environment and task requirements.

[0030] In one embodiment, specifically, in step four, the inspection task is sent to the target inspection robot through multi-band communication technology. If communication interference is encountered, it will automatically switch to the backup frequency band, monitor the communication quality in real time, and adaptively adjust the communication parameters. After receiving the inspection task, the target inspection robot sends a confirmation signal to the dispatch center. If the confirmation signal is not received within the predetermined time, the dispatch center automatically resends the task information. By adopting multi-band communication technology, real-time monitoring of communication quality and adaptive adjustment of communication parameters, the stability and reliability of inspection task transmission can be significantly improved. At the same time, by requiring the target inspection robot to send a confirmation signal and automatically resending the task information when no confirmation is received, the accurate communication of task information and the smooth execution of tasks can be further ensured. These measures together constitute an efficient and reliable inspection task transmission and confirmation mechanism.

[0031] In one embodiment, specifically, in step five, image processing and pattern recognition technology utilizes deep learning algorithms to identify and classify unknown obstacles, and establishes an obstacle database to record and classify the identified obstacles. By applying image processing and pattern recognition technology, the inspection robot can effectively identify and classify unknown obstacles, which not only improves the robot's environmental perception ability, but also provides important data support for its subsequent path planning and obstacle avoidance strategies. At the same time, by establishing an obstacle database, the robot can achieve long-term tracking and management of obstacles, providing more accurate and reliable decision-making basis for future inspection tasks.

[0032] In one embodiment, specifically, in step eight, data storage and backup utilize distributed storage technology and redundant backup mechanism to disperse and store data on multiple storage nodes, and encrypt the stored data. The encryption algorithm utilizes a dynamic key mechanism that is updated regularly. By applying distributed storage technology, redundant backup mechanism, and data encryption processing and other measures, the inspection robot can achieve comprehensive protection of the inspection data. These measures together constitute an efficient and secure data storage and backup mechanism, ensuring the integrity, availability, and confidentiality of the inspection data, which not only helps to improve the efficiency and quality of the inspection tasks, but also provides strong support for subsequent data analysis and decision-making.

[0033] The multi-channel security patrol robot intelligent scheduling method provided in this embodiment realizes efficient scheduling and monitoring of the patrol robots. This method improves the execution efficiency and safety of patrol tasks, reduces the cost and risk of manual intervention, and at the same time, through data mining and early warning mechanisms, can timely discover potential safety hazards and abnormal situations, providing strong support for security work.

[0034] Figure 2Schematic diagram of the functional modules of a multi-channel security inspection robot intelligent dispatching system according to an embodiment of the present application. Figure 2 As shown, a multi-channel security inspection robot intelligent dispatching system includes: a sensor network module, an intelligent screening and path planning module, an inspection task generation module, a real-time communication and feedback module, an obstacle recognition and processing module, a data analysis and warning module, and a data storage and management module; The sensor network module is used to install multiple sensors in the target inspection area to collect basic parameter information of the inspection robot and the inspection area in real time. The basic parameter information includes the performance parameters of the inspection robot, the current state, the matching degree with the inspection task, the number of inspections, the task type, and the topography of the inspection area, obstacle distribution, task requirements, and environmental information; The intelligent screening and path planning module is used to evaluate and screen multiple inspection robots based on the basic parameter information collected by the sensor network module using an intelligent screening algorithm, select the target inspection robot, and use the path planning algorithm to formulate an inspection route according to the topography, obstacle distribution and task requirements of the inspection area; The inspection task generation module is used to generate specific inspection tasks based on the selected target inspection robots and the formulated inspection routes. The inspection tasks include inspection areas, inspection time, inspection frequency, and task priorities in different areas. The real-time communication and feedback module is used to send the inspection task to the target inspection robot and receive the position, operation status and collected environmental data information fed back by the target inspection robot in real time; The obstacle recognition and processing module is used to obtain the operating status of the target inspection robot and the dynamic information of obstacles in real time based on sensors, and use image processing and pattern recognition technology to identify and classify new unknown obstacles. According to the operating status of the target inspection robot and the dynamic information of obstacles obtained in real time, the inspection route and inspection task are dynamically optimized using real-time adjustment algorithms; The data analysis and early warning module is used to obtain the operation data of the inspection process, use data analysis technology to explore potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; The data storage and management module is used to classify, store and back up all data generated during the inspection process after the inspection task is completed, and to summarize and evaluate the inspection work.

[0035] The present embodiment provides a multi-channel security patrol robot intelligent scheduling system, which realizes efficient scheduling and intelligent management of patrol robots by integrating multiple modules. The system can collect basic parameter information of patrol robots and patrol areas in real time, intelligently screen and plan patrol robots and patrol routes, generate specific patrol tasks, and receive and process feedback information from patrol robots in real time. At the same time, the system can also identify and handle obstacles, explore potential safety hazards and abnormal situations, and generate early warning information to optimize and adjust subsequent patrol tasks. Finally, the system classifies, stores and backs up all data generated during the patrol process, and summarizes and evaluates the patrol work. These functions together constitute an efficient, intelligent and reliable security patrol system.

[0036] For other details about the technical solutions for implementing each module in the system of the above embodiment, please refer to the description of a multi-channel security inspection robot intelligent scheduling method in the above embodiment, which will not be repeated here.

[0037] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0038] like Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure is shown, which is a schematic diagram of the structure of a computer device suitable for implementing the embodiment of the present disclosure. Figure 3 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0039] like Figure 3 As shown, a computer device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a fire emergency virtual fire extinguishing actual combat simulation method provided in the above method embodiment.

[0040] Figure 3 The hardware structure diagram of a device for implementing a multi-channel security inspection robot intelligent dispatching method provided in an embodiment of the present application is shown. The device can participate in or include the device or system provided in an embodiment of the present application. Figure 3As shown, the computer device 100 may include one or more processors 102 (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0041] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer device 100 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is controlled as a processor.

[0042] The memory 104 can be used to store software programs and modules of application software, such as a program instruction / data storage device corresponding to a multi-channel security patrol robot intelligent scheduling method in an embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer device 100 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0043] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer device 100. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0044] The display device may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer device 100 (or mobile device).

[0045] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0046] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0047] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0048] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0049] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0050] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0051] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0052] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A multi-channel security inspection robot intelligent dispatching method, characterized in that: The following steps are involved: Acquire sensor data, install multiple sensors in the target inspection area, and collect basic parameter information of the inspection robot and the inspection area in real time; Based on the basic parameter information of the inspection robot and the inspection area, the intelligent screening algorithm is used to evaluate and screen multiple inspection robots, select the target inspection robot, and use the path planning algorithm to formulate the inspection route; Determine and generate inspection tasks based on the selected target inspection robots and the established inspection routes; According to the inspection task, the target inspection robot starts the inspection work and feeds back its own position, operation status and collected environmental data information to the dispatch center in real time; Based on sensors, the target inspection robot's operating status and dynamic information of obstacles are acquired in real time, and new unknown obstacles are identified and classified using image processing and pattern recognition technology; According to the real-time acquired operating status of the target inspection robot and dynamic information of obstacles, the inspection route and inspection task are dynamically optimized using real-time adjustment algorithms; Obtain the operation data of the inspection process, use data analysis technology to mine potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; After the inspection task is completed, all data generated during the inspection process will be classified, stored and backed up, and the inspection work will be summarized and evaluated.

2. The intelligent dispatching method of a multi-channel security inspection robot according to claim 1, characterized in that: The basic parameter information includes the performance parameters of the inspection robot, the current state, the matching degree with the inspection task, the number of inspections, the task type and the topography of the inspection area, the obstacle distribution, the task requirements and the environmental information.

3. The intelligent dispatching method of a multi-channel security inspection robot according to claim 2 is characterized in that: The intelligent screening algorithm calculates the comprehensive evaluation score of the inspection robot based on the performance parameters, current status and matching degree of the inspection robot with the inspection task; the performance parameters include movement speed, endurance, detection accuracy and load capacity; The path planning algorithm calculates the path cost function according to the topography, obstacle distribution and task requirements of the inspection area; the topography includes slope and terrain height; the obstacle distribution includes static obstacles and dynamic obstacles. If the obstacle is a static obstacle, the obstacle position is marked as an inaccessible area by establishing a map model; if the obstacle is a dynamic obstacle, the prediction model is used to predict the future position and movement trajectory of the dynamic obstacle based on its historical motion data, current speed and direction information; the task requirements include urgency and importance levels.

4. The intelligent dispatching method of a multi-channel security inspection robot according to claim 1, characterized in that: The inspection task includes the inspection area, inspection time, inspection frequency and task focus in different areas of the target inspection robot.

5. The intelligent dispatching method of multiple security inspection robots according to claim 1, characterized in that: The inspection task is sent to the target inspection robot through multi-band communication technology. If communication interference is encountered, it will automatically switch to the backup frequency band, monitor the communication quality in real time, and adaptively adjust the communication parameters. After receiving the inspection task, the target inspection robot sends a confirmation signal to the dispatch center. If the confirmation signal is not received within the predetermined time, the dispatch center automatically resends the task information.

6. The intelligent dispatching method of multiple security inspection robots according to claim 1, characterized in that: The image processing and pattern recognition technology utilizes a deep learning algorithm to identify and classify unknown obstacles, and establishes an obstacle database to record and classify identified obstacles.

7. The intelligent dispatching method of multiple security inspection robots according to claim 1, characterized in that: The data storage and backup utilizes distributed storage technology and redundant backup mechanism to disperse and store data on multiple storage nodes, and encrypts the stored data. The encryption algorithm utilizes a dynamic key mechanism that is regularly updated.

8. A multi-channel security inspection robot intelligent dispatching system, characterized in that: include: Sensor network module, intelligent screening and path planning module, inspection task generation module, real-time communication and feedback module, obstacle identification and processing module, data analysis and early warning module, and data storage and management module; The sensor network module is used to install multiple sensors in the target inspection area to collect basic parameter information of the inspection robot and the inspection area in real time. The basic parameter information includes the performance parameters of the inspection robot, the current state, the matching degree with the inspection task, the number of inspections, the task type, and the topography of the inspection area, obstacle distribution, task requirements, and environmental information; The intelligent screening and path planning module is used to evaluate and screen multiple inspection robots based on the basic parameter information collected by the sensor network module using an intelligent screening algorithm, screen out the target inspection robot, and use a path planning algorithm to formulate an inspection route according to the topography, obstacle distribution and task requirements of the inspection area; The inspection task generation module is used to generate specific inspection tasks according to the selected target inspection robots and the formulated inspection routes. The inspection tasks include inspection areas, inspection time, inspection frequency, and task priorities in different areas. The real-time communication and feedback module is used to send the inspection task to the target inspection robot and receive the position, operation status and collected environmental data information fed back by the target inspection robot in real time; The obstacle recognition and processing module is used to obtain the operating status of the target inspection robot and the dynamic information of obstacles in real time based on sensors, and use image processing and pattern recognition technology to identify and classify newly appeared unknown obstacles, and use real-time adjustment algorithms to dynamically optimize inspection routes and inspection tasks based on the operating status of the target inspection robot and the dynamic information of obstacles obtained in real time; The data analysis and early warning module is used to obtain the operation data of the inspection process, use data analysis technology to mine potential safety hazards and abnormal situations, generate early warning information, and optimize and adjust subsequent inspection tasks based on the early warning information; The data storage and management module is used to classify, store and back up all data generated during the inspection process after the inspection task is completed, and summarize and evaluate the inspection work.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, the steps of the intelligent scheduling method for multi-channel security inspection robots described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the multi-channel security inspection robot intelligent scheduling method described in any one of claims 1 to 7 are implemented.

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