High-speed compliance control and intelligent execution cooperation method based on public patrol

Abstract

The invention discloses a high-speed compliance control and intelligent execution cooperation method based on public patrol, and the method is realized through a high-speed compliance control and intelligent execution cooperation system based on public patrol. The collaboration system comprises a sensing and data acquisition module, a data processing and analysis module, a data storage and management module, a control execution module, a decision making module, an intelligent interaction module and a system security management module. The collaboration method comprises the following steps: step 1, constructing a high-speed compliance control technology system; 2, constructing an intelligent execution collaborative architecture; 3, constructing a high-speed compliant control and intelligent execution cooperation mechanism; according to the invention, a robot, automatic control, an artificial intelligence algorithm and Internet of Things communication are combined to construct a high-speed compliance control and intelligent execution cooperation method, an efficient, accurate and flexible intelligent control and execution solution is provided, and the efficiency, quality and safety of public patrol work are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of public safety and relates to a collaborative method for high-speed compliant control and intelligent execution based on public patrols. Background Technology

[0002] In current public security patrol work, traditional patrol methods face numerous challenges and shortcomings. On the one hand, with the continuous expansion of urban scale, the increasing population density, and the growing complexity of social activities, the types and number of public safety risks have increased significantly, placing higher demands on the response speed, coverage, and accuracy of patrol work. However, existing patrol equipment and personnel often lack efficient coordination mechanisms and precise control methods when performing tasks, making it difficult to respond quickly and flexibly to complex and ever-changing patrol scenarios. From a device control perspective, traditional patrol robots or automated equipment generally suffer from rigid control issues in motion control. For example, when patrolling narrow streets or densely populated areas, the inability to achieve compliant control makes it difficult for the equipment to maneuver flexibly in complex environments, easily leading to collisions with obstacles. This not only affects the operational safety of the equipment itself but may also cause damage to surrounding personnel and facilities. Furthermore, when performing delicate tasks, such as close inspection of suspicious items or assisting in the rescue of injured persons, rigid device control cannot meet the requirements for precision and flexibility, resulting in poor task performance. On the other hand, there is also a significant disconnect in the coordination between patrol personnel and equipment. Information gathered by patrol personnel often fails to be transmitted to patrol equipment in a timely and accurate manner, preventing the equipment from responding promptly to the actual situation on site. Conversely, information collected by the equipment is also difficult to efficiently relay back to patrol personnel, affecting their comprehensive understanding of the patrol situation and their decision-making. Furthermore, due to the lack of a unified intelligent execution and coordination mechanism, different types of patrol equipment often experience problems such as uncoordinated actions and wasted resources when performing joint tasks, severely restricting the overall effectiveness of public patrol work. Furthermore, in the face of sudden emergencies, such as violent crimes or mass incidents, traditional patrol systems often respond slowly and are unable to quickly organize effective response forces. This is mainly because the lack of efficient coordination and intelligent control in information transmission, decision-making, and task execution prevents the various patrol elements from forming a closely coordinated organic whole. Summary of the Invention

[0003] In order to overcome at least one deficiency of the prior art, the present invention provides a collaborative method for high-speed compliant control and intelligent execution based on public patrol.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a collaborative method for high-speed compliant control and intelligent execution based on public patrol. This method is implemented through a collaborative system for high-speed compliant control and intelligent execution based on public patrol. The collaborative system includes a sensing and data acquisition module, a data processing and analysis module, a data storage and management module, a control execution module, a decision-making module, an intelligent interaction module, and a system security management module. The collaborative approach includes the following steps: Step 1: Construct a high-speed compliant control technology system; Step 2: Build an intelligent execution collaboration architecture; Step 3: Construct a collaborative mechanism for high-speed compliant control and intelligent execution.

[0005] Furthermore, the method for constructing a high-speed compliant control technology system includes: Step 11: Design a compliant motion control algorithm; Step 12: Design an intelligent adaptive control mechanism; Step 13: Optimize hardware structure.

[0006] Furthermore, the method for constructing an intelligent execution collaborative architecture includes... Step 21: Construct a multi-source information fusion and sharing platform to enable efficient information exchange between patrol personnel, equipment, and the command center; Step 22: Develop an intelligent task allocation and scheduling system to scientifically and rationally allocate tasks based on the priority, urgency, resource requirements, and status and capabilities of each execution unit of the patrol tasks. Step 23: Design a multi-agent collaborative mechanism: Abstract patrol personnel and equipment into intelligent agents with autonomous decision-making and collaborative capabilities.

[0007] Furthermore, the method for constructing a collaborative mechanism between high-speed compliant control and intelligent execution includes... Step 31: Establish a collaborative control strategy library and formulate corresponding high-speed compliant control and intelligent execution collaborative strategies for different patrol scenarios and task types; Step 32: Design a real-time feedback and dynamic adjustment mechanism to enable the high-speed compliant control and intelligent execution collaborative system to optimize and adjust itself in a timely manner according to the execution of patrol tasks and environmental changes; Step 33: Design a system safety system: Employ multiple redundancy technologies and fault diagnosis mechanisms to ensure that the high-speed compliant control and intelligent execution collaborative system can still operate normally even if some devices or communication links fail.

[0008] Furthermore, the compliant motion control algorithm adopts a hybrid control strategy based on force feedback and position control, enabling the device to sense the forces exerted by the external environment in real time during movement and automatically adjust its motion trajectory and intensity based on feedback information. The intelligent adaptive control mechanism uses machine learning algorithms to analyze historical patrol data, establish motion models and control strategy libraries for different scenarios, and the device collects environmental information in real time through sensors, automatically identifies the current scenario, selects the optimal control parameters from the strategy library, and adaptively adjusts the motion control.

[0009] Furthermore, the multi-source information fusion and sharing platform collects and aggregates video surveillance data, sensor monitoring data, and personnel location information from the patrol site in real time, and transmits them to a unified information processing platform. The platform then uses data fusion algorithms to integrate and analyze the multi-source information, eliminating information silos.

[0010] Furthermore, the intelligent task allocation and scheduling system uses optimization algorithms from operations research to establish a task allocation model and optimize resource allocation under the premise of meeting task constraints. The intelligent agents interact and collaborate with each other through a communication network to jointly complete complex patrol tasks.

[0011] Furthermore, through the analysis of numerous historical cases and simulation experiments, the optimal collaborative schemes under various conditions are summarized and stored in the collaborative control strategy library. Based on real-time perceived scene information and task requirements, the system automatically matches and calls appropriate collaborative strategies from the strategy library, enabling seamless integration of high-speed compliant control and intelligent execution. During task execution, each execution unit feeds back real-time execution information to the collaborative control system. By analyzing this feedback information, the system determines whether the current collaborative strategy is effective. If the execution effect is found to be unsatisfactory or the environment has undergone significant changes, the system immediately activates a dynamic adjustment mechanism to reassess task requirements and resource status, and makes corresponding adjustments to control parameters, task allocation schemes, and collaborative strategies.

[0012] Furthermore, the system security system backs up critical equipment and data, and when the main equipment fails, the backup equipment can be quickly switched on and put into use; by monitoring the system's operating status in real time, potential fault hazards are promptly identified using fault diagnosis algorithms, and corresponding repair measures are taken; multiple communication methods are used for redundancy backup, and when one communication method is interrupted, the system automatically switches to other communication methods.

[0013] Furthermore, the sensing and data acquisition module is connected to the data processing and analysis module and interacts with the control execution module. The data processing and analysis module is connected to the data storage and management module and the decision-making module, respectively. The decision-making module is connected to the control execution module and interacts with the intelligent interaction module. The intelligent interaction module interacts with the control execution module.

[0014] In summary, the advantages of this invention are: This invention integrates robotics, automated control, artificial intelligence algorithms, and IoT communication to construct a collaborative method for high-speed compliant control and intelligent execution. It provides efficient, precise, and flexible intelligent control and execution solutions for various public patrol scenarios, including urban street patrols, community security patrols, large-scale event security, and public place order maintenance. By achieving high-speed compliant control of patrol equipment and personnel, and collaborative optimization of intelligent task execution, it significantly improves the efficiency, quality, and safety of public patrol work, providing strong technical support for maintaining public safety and stability. Attached Figure Description

[0015] Figure 1 This is a flowchart of the collaborative method for high-speed compliant control and intelligent execution of the present invention.

[0016] Figure 2 This is a diagram illustrating the architecture of the collaborative system for high-speed compliant control and intelligent execution according to the present invention. Detailed Implementation

[0017] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0018] Example: like Figures 1-2As shown, a collaborative method for high-speed compliant control and intelligent execution based on public patrol is proposed. This method is implemented through a collaborative system for high-speed compliant control and intelligent execution based on public patrol. The collaborative system includes a sensing and data acquisition module, a data processing and analysis module, a data storage and management module, a control execution module, a decision-making module, an intelligent interaction module, and a system safety management module. The sensing and data acquisition module is connected to the data processing and analysis module and interacts with the control execution module. The data processing and analysis module is connected to both the data storage and management module and the decision-making module. The decision-making module is connected to the control execution module and interacts with the intelligent interaction module. The intelligent interaction module also interacts with the control execution module.

[0019] The collaborative approach includes the following steps: Step 1: Construct a high-speed compliant control technology system; Step 2: Build an intelligent execution collaboration architecture; Step 3: Construct a collaborative mechanism for high-speed compliant control and intelligent execution; Methods for constructing a high-speed compliant control technology system include Step 11: Design a compliant motion control algorithm; The compliant motion control algorithm employs a hybrid control strategy combining force feedback and position control, enabling the device to sense the forces exerted by the external environment in real time during movement and automatically adjust its trajectory and force based on feedback information. For example, when a patrol robot encounters a pedestrian obstructing its path in a narrow passage, it acquires contact force information through force sensors mounted on the robot's shell. The system, based on a pre-set compliant control algorithm, automatically reduces the robot's forward speed and fine-tunes its direction of movement to achieve safe and smooth obstacle avoidance, preventing collisions and providing precise and flexible motion control capabilities for patrol equipment. Step 12: Design an intelligent adaptive control mechanism; Machine learning algorithms are used to analyze a large amount of historical patrol data to establish motion models and control strategy libraries for different scenarios. In actual operation, the equipment collects environmental information in real time through sensors, automatically identifies the current scene, and selects the optimal control parameters from the strategy library to achieve adaptive adjustment of motion control. For example, when patrolling in low-light areas at night, the equipment automatically adjusts the camera's exposure parameters and movement speed to improve image acquisition quality and patrol safety; when patrolling in densely populated commercial areas, it speeds up the recognition of people's behavior while reducing its own movement speed to enhance sensitivity to the surrounding environment. Through an intelligent adaptive control mechanism, the patrol equipment can automatically optimize control parameters according to different patrol scenarios and task requirements. Step 13: Optimize hardware structure; By employing technologies such as flexible joints and elastic materials, rigid impacts between moving parts of the equipment are reduced, improving the equipment's adaptability to complex environments. For example, flexible buffer devices are designed for the joints of patrol robots. When the robot walks on uneven surfaces or has minor collisions with obstacles, the flexible joints can act as a buffer, reducing damage to the internal structure of the equipment while ensuring the robot's motion stability, thereby improving the mechanical compliance of the patrol equipment.

[0020] Methods for building intelligent execution collaboration architectures include: Step 21: Construct a multi-source information fusion and sharing platform to enable efficient information exchange between patrol personnel, equipment, and the command center; The system collects and aggregates various multi-source information from patrol sites, including video surveillance data, sensor monitoring data, and personnel location information, and transmits it to a unified information processing platform. Data fusion algorithms are used to integrate and analyze this multi-source information, eliminating information silos and providing comprehensive and accurate data support for intelligent execution and collaboration. For example, in security for large-scale events, video footage from multiple surveillance cameras, data collected by crowd density sensors, and reports from patrol personnel are fused and analyzed. This allows the command center to grasp the overall situation at the event site in real time, providing a basis for the rational allocation of patrol resources. The multi-source information fusion and sharing platform enables efficient information exchange between patrol personnel, equipment, and the command center.

[0021] Step 22: Develop an intelligent task allocation and scheduling system to scientifically and rationally allocate tasks based on the priority, urgency, resource requirements, and status and capabilities of each execution unit (personnel and equipment) of patrol tasks.

[0022] The intelligent task allocation and scheduling system utilizes optimization algorithms from operations research to establish a task allocation model, achieving optimal resource allocation while meeting task constraints. For example, upon receiving an emergency alarm, the system quickly analyzes the location, status, and task execution of surrounding patrol personnel and equipment, prioritizing the allocation of tasks to the nearest, best-equipped, and currently least-loaded execution unit, ensuring timely and effective task processing.

[0023] Step 23: Design a multi-agent collaborative mechanism: Abstract patrol personnel and equipment into intelligent agents with autonomous decision-making and collaborative capabilities; The various intelligent agents interact and collaborate through a communication network to jointly complete complex patrol tasks. Each agent possesses a certain level of intelligent decision-making ability, capable of autonomously formulating action strategies based on acquired information and task requirements, and coordinating with other agents when necessary. For example, during a suspect pursuit, the patrol car agent utilizes its speed advantage for rapid tracking, while simultaneously transmitting the suspect's location information in real time to nearby patrol personnel agents and surveillance camera agents. The patrol personnel agents use this information to set up ambushes at appropriate locations, while the surveillance camera agents continuously track the suspect's movements, providing comprehensive information support for the pursuit operation. This close collaboration among the agents increases the success rate of the pursuit.

[0024] Methods for constructing a collaborative mechanism between high-speed compliant control and intelligent execution include: Step 31: Establish a collaborative control strategy library and formulate corresponding high-speed compliant control and intelligent execution collaborative strategies for different patrol scenarios and task types; Through analysis of numerous historical cases and simulation experiments, the optimal collaborative solutions for various scenarios are summarized and stored in a strategy library. In actual patrol operations, the system automatically matches and invokes appropriate collaborative strategies from the strategy library based on real-time perceived scene information and task requirements, achieving seamless integration of high-speed compliant control and intelligent execution. For example, when handling patrol tasks at fire scene locations, the collaborative strategy library pre-sets equipment motion control methods for fire scenarios (such as using a low-speed, stable motion mode to approach the fire source to avoid secondary disasters) and personnel-equipment collaborative processes (such as fire-fighting robots responsible for firefighting operations, and patrol personnel responsible for evacuating the public and setting up warning areas). The system can quickly invoke these strategies to ensure the orderly conduct of all tasks.

[0025] Step 32: Design a real-time feedback and dynamic adjustment mechanism to enable the high-speed compliant control and intelligent execution collaborative system to optimize and adjust itself in a timely manner according to the execution of patrol tasks and environmental changes; During task execution, each execution unit feeds back real-time execution information to the collaborative control system. The system analyzes this feedback to determine the effectiveness of the current collaborative strategy. If the execution effect is unsatisfactory or the environment undergoes significant changes, the system immediately activates a dynamic adjustment mechanism to reassess task requirements and resource status, adjusting control parameters, task allocation schemes, and collaborative strategies accordingly to ensure the patrol mission can proceed continuously and efficiently. For example, during patrol, if a large number of people suddenly gather in an area, potentially posing a safety hazard, the system, based on on-site feedback, promptly adjusts the movement paths of nearby patrol equipment to monitor and manage the area, while simultaneously deploying more patrol personnel to assist, ensuring stability at the scene.

[0026] Step 33: Design a system safety system: Employ multiple redundancy technologies and fault diagnosis mechanisms to ensure that the high-speed compliant control and intelligent execution collaborative system can still operate normally even if some devices or communication links fail. Key equipment and data are backed up, ensuring that backup equipment can quickly switch on and be put into use when the main equipment fails, guaranteeing uninterrupted system operation. Simultaneously, by monitoring the system's operational status in real time, fault diagnosis algorithms are used to promptly identify potential faults and implement corresponding remedial measures. For example, in the patrol robot's control system, a dual-processor redundancy design is employed. When one processor fails, the other processor can immediately take over control tasks, ensuring the robot's normal operation. Regarding the communication network, multiple communication methods (such as 4G, 5G, Wi-Fi, etc.) are used for redundancy backup. When one communication method is interrupted, the system automatically switches to another available communication method, ensuring real-time information transmission.

[0027] The perception and data acquisition module is used to collect various information from the patrol site, including but not limited to video images, sound, environmental parameters (such as temperature, humidity, and concentration of harmful gases), and the location and movement of personnel and vehicles. Utilizing high-definition cameras, microphones, various sensors, and positioning devices, it achieves comprehensive, real-time perception of the patrol area. The acquired data undergoes preliminary preprocessing, such as data filtering and format conversion, to improve data quality and usability, and the preprocessed data is then transmitted to the data processing and analysis module. The perception and data acquisition module provides raw data support to the data processing and analysis module; the accuracy and completeness of its collected data improve the decision-making and control effectiveness of subsequent modules. It interacts with the control execution module, receiving control commands to adjust its acquisition parameters and operating mode. For example, when the control execution module requires focused monitoring of a specific area, the perception and data acquisition module adjusts the camera's focal length and shooting angle to improve the data acquisition accuracy for that area; upon receiving a system warning about severe weather, it automatically adjusts the sensor sensitivity to adapt to the impact of environmental changes on data acquisition.

[0028] The data processing and analysis module employs big data processing technologies and artificial intelligence algorithms to perform in-depth analysis and mining of data transmitted from the perception and data acquisition module. Through techniques such as target recognition, behavior analysis, and anomaly detection, it extracts valuable information from massive amounts of data, such as identifying the behavioral patterns of suspicious individuals and vehicles, and detecting potential safety hazards. Based on the analysis results, it provides decision-making support to the decision-making module, and stores the processed data in the data storage and management module for subsequent querying and historical data analysis. It works closely with the sensing and data acquisition module to obtain the data needed for analysis; it provides data support and analysis results to the decision-making module, helping it to make scientific and reasonable decisions; and it interacts with the data storage and management module to store and retrieve data. Simultaneously, it receives feedback from the decision-making module and adjusts the focus and direction of data analysis based on the decisions made. For example, if the decision-making module requires a specific analysis of a certain type of security incident, the data processing and analysis module will, based on this requirement, re-screen and analyze relevant data to provide more targeted support for decision-making.

[0029] The decision-making module, based on information provided by the data processing and analysis module, and combined with preset rules, strategies, and real-time task requirements, formulates execution decisions for patrol tasks. Execution decisions include determining the movement paths of patrol equipment, task allocation schemes, and collaborative operation processes. The decision results are sent to the control execution module in the form of control commands and communicate with the intelligent interaction module to provide feedback on decision information and task execution plans to patrol personnel and the command center. It works in conjunction with the data processing and analysis module to make decisions based on data analysis results; it issues control commands to the control execution module to guide it in carrying out patrol tasks; and it interacts with the intelligent interaction module to achieve information communication with patrol personnel and the command center. Simultaneously, it receives feedback from the control execution module on task execution status and on-site changes, adjusting and optimizing decisions in real time. For example, when the control execution module reports that the actual situation in a certain area does not match expectations, the decision-making module reassesses the task requirements, adjusts the movement path of the patrol equipment and the task allocation plan, ensuring the smooth execution of the task.

[0030] The control execution module, based on control commands sent by the decision-making module, performs high-speed, compliant control of the patrol equipment, enabling precise movement and task execution. Simultaneously, it coordinates the collaborative work between patrol personnel and equipment, ensuring that each execution unit operates according to the predetermined task allocation plan and collaborative process. It monitors the equipment's operating status and task execution progress in real time and feeds relevant information back to the decision-making module and the intelligent interaction module. It receives and executes control commands from the decision-making module, transmitting the execution results and feedback information to both the decision-making and intelligent interaction modules. It also interacts with the perception and data acquisition module, adjusting the working status of the sensing devices according to task requirements. During task execution, it maintains real-time communication and collaboration with patrol personnel to ensure effective coordination between personnel and equipment. For example, during patrol missions, the control execution module precisely controls the patrol robot's speed and direction according to the route planned by the decision-making module, while simultaneously maintaining information exchange with surrounding patrol personnel to jointly complete security checks of the patrol area.

[0031] The intelligent interaction module provides an interface for human-computer interaction, enabling convenient communication and information exchange between patrol personnel and the system. Through mobile terminal applications, command center consoles, and other devices, it displays task information, on-site situation, and decision-making results to patrol personnel, and receives feedback and command input from them. Simultaneously, it pushes important system information and early warnings to the command center, facilitating macro-level decision-making and dispatching by commanders. It interacts with the decision-making and control execution modules to transmit personnel instructions and feedback information; it also displays the results of the data processing and analysis module and the decision-making information of the decision-making module to patrol personnel and the command center. For example, it can push information on suspicious persons identified by the data processing and analysis module to nearby patrol personnel via mobile terminals to facilitate their investigation; it can also receive reports of new situations discovered by patrol personnel on site and promptly transmit the information to the decision-making module to provide a basis for subsequent decisions.

[0032] The data storage and management module employs distributed database and big data storage technologies to store and manage various types of data generated during patrols. This includes historical patrol data, real-time collected data, analysis results data, and system configuration data. The module establishes data indexing and backup mechanisms to ensure rapid data retrieval and security. Based on the importance and timeliness of the data, it performs hierarchical storage and cleanup, optimizing the data storage structure and reducing storage costs. The data storage and management module provides historical data support for the data processing and analysis module, storing its analysis results; it receives and stores real-time data transmitted from the sensing and data acquisition module; and it quickly retrieves and provides relevant data based on query requests from the decision-making and intelligent interaction modules. Simultaneously, it collaborates with the system's security management module to ensure secure data storage and access. For example, when the data processing and analysis module performs historical data analysis, it retrieves years of patrol data from the data storage and management module; when the decision-making module needs to query historical event information for a specific area within a certain time period, the data storage and management module quickly locates and provides the relevant data through indexing. The system security management module ensures the security and stability of the entire public patrol high-speed compliant control and intelligent execution collaborative system. This includes employing network security technologies such as firewalls, intrusion detection systems, and encrypted communication to prevent external network attacks and data leaks. It also performs user authentication and access control to ensure that only authorized personnel can access and operate the system. Simultaneously, it monitors the system's operational status in real time, provides early warnings and preventative measures against potential security risks, develops contingency plans, and enables rapid recovery and handling in the event of system failures or attacks. The system security management module collaborates with the data storage and management module to protect data security by encrypting and controlling access to data during storage, transmission, and processing. It also provides a secure operating environment for other modules, ensuring secure communication and reliable data interaction between them. For example, during data transmission from the sensing and data acquisition module to the data processing and analysis module, the system security management module encrypts the data to prevent theft or tampering. Furthermore, it performs identity authentication and access control checks on user operations in critical modules such as the decision-making and control execution modules to ensure the secure operation of the system.

[0033] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

Claims

1. A collaborative method for high speed compliance control and intelligent execution based on public patrol, characterized in that: The method is realized by a collaborative system of high-speed compliant control based on public patrol and intelligent execution, which includes a perception and data acquisition module, a data processing and analysis module, a data storage and management module, a control execution module, a decision-making module, an intelligent interaction module, and a system security management module, The collaborative method includes the following steps: Step 1: Build a high-speed compliant control technology system; Step 2: Build an intelligent execution collaborative architecture; Step 3: Build a high-speed compliant control and intelligent execution collaborative mechanism.

2. The method of claim 1, wherein: The method of building a high-speed compliant control technology system includes Step 11: Design a compliant motion control algorithm; Step 12: Design an intelligent adaptive control mechanism; Step 13: Optimize the hardware structure.

3. The method of claim 1, wherein: The method of building an intelligent execution collaborative architecture includes Step 21: Build a multi-source information fusion and sharing platform to enable efficient information exchange between patrol personnel, equipment, and command centers; Step 22: Develop an intelligent task allocation and scheduling system to allocate tasks scientifically and reasonably based on task priority, urgency, resource requirements, and the status and capabilities of each execution unit; Step 23: Design a multi-agent collaborative mechanism: abstract patrol personnel and equipment as agents with autonomous decision-making and collaboration capabilities.

4. The method of claim 1, wherein: The method of building a high-speed compliant control and intelligent execution collaborative mechanism includes Step 31: Establish a collaborative control strategy library to develop appropriate high-speed compliant control and intelligent execution collaborative strategies for different patrol scenarios and task types; Step 32: Design a real-time feedback and dynamic adjustment mechanism to enable the high-speed compliant control and intelligent execution collaborative system to self-optimize and adjust in real time based on the execution of patrol tasks and environmental changes; Step 33: Design a system security system: use multiple redundancy techniques and fault diagnosis mechanisms to enable the high-speed compliant control and intelligent execution collaborative system to function normally even if some equipment or communication links fail.

5. The method of claim 2, wherein: The compliant motion control algorithm uses a hybrid control strategy based on force feedback and position control, enabling the device to perceive external forces in real time during motion and automatically adjust the motion trajectory and force based on feedback information. The intelligent adaptive control mechanism uses machine learning algorithms to analyze historical patrol data, establishing motion models and control strategy libraries for different scenarios. The device automatically identifies the current scenario by real-time environmental information collection through sensors, selects the optimal control parameters from the strategy library, and performs adaptive adjustments for motion control.

6. The method of claim 2, wherein: The multi-source information fusion and sharing platform collects and aggregates video monitoring data, sensor monitoring data, and personnel location information from the patrol site in real time, and transmits them to a unified information processing platform. It uses data fusion algorithms to integrate and analyze multi-source information, eliminating information silos.

7. The method of claim 3, wherein: The intelligent task allocation and scheduling system uses optimization algorithms from operations research to establish a task allocation model, optimally configuring resources while meeting task constraints. Intelligent agents interact and collaborate through a communication network to complete complex patrol tasks together.

8. The method of claim 4, wherein: Through the analysis of a large number of historical cases and simulation experiments, the optimal cooperation scheme under various conditions is summarized and stored in the cooperation control strategy library, and the system automatically matches and calls the appropriate cooperation strategy from the strategy library according to the real-time perceived scene information and task demand, so as to realize the seamless connection of high-speed flexible control and intelligent execution; in the task execution process, each execution unit feeds back the real-time execution information to the cooperation control system, and the system judges whether the current cooperation strategy is effective through the analysis of the feedback information; if it is found that the execution effect is not ideal or the environment has changed significantly, the system immediately starts the dynamic adjustment mechanism, re-evaluates the task demand and resource state, and adjusts the control parameters, task allocation scheme and cooperation strategy accordingly.

9. The method of claim 4, wherein: The system safety system backs up key equipment and data, so that when the main equipment fails, the standby equipment can be quickly switched to use; by monitoring the running state of the system in real time, using fault diagnosis algorithm to find potential hidden trouble in time, and taking corresponding repair measures; a variety of communication methods are used for redundancy backup, and when one communication method is interrupted, the system automatically switches to other communication methods.

10. The method of claim 1, wherein: The perception and data acquisition module is connected with the data processing and analysis module, and interacts with the control execution module; the data processing and analysis module is connected with the data storage and management module and the decision making module respectively; the decision making module is connected with the control execution module and interacts with the intelligent interaction module; the intelligent interaction module interacts with the control execution module.

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