Remote management system for fusion of intelligent robot and Internet of Things

Abstract

The invention discloses a remote management system for fusion of an intelligent robot and the Internet of Things, relates to the technical field of remote management, and is characterized in that task requirements are remotely received and analyzed based on the Internet of Things, a work task is divided into a plurality of sub-tasks, and execution robots are allocated to the sub-tasks according to the capability condition and energy reserve information of the intelligent robot; marking a standby robot; performing remote real-time monitoring in the task execution process, detecting each task handover node, and identifying whether the sub-task of each handover node has an abnormal condition or not; and for the identified abnormal condition, triggering a self-checking or adjusting mechanism, and finally, after the task is completed, integrating the task execution information of the intelligent robot of the Internet of Things platform to generate a task report. Deep fusion of the intelligent robot and the Internet of Things is realized, and efficient division, intelligent distribution, real-time monitoring and safety management of remote tasks and comprehensive report generation of task execution information are realized.

Description

Technical Field

[0001] The present invention relates to the field of remote management technology, and in particular to a remote management system integrating an intelligent robot and the Internet of Things. Background Art

[0002] The Internet of Things (IoT) has made significant progress in recent years, achieving a deep integration of the physical and digital worlds through sensors, network communications, data processing, and other technologies. The widespread adoption of IoT technology provides a solid technical foundation for the remote management of intelligent robots. Furthermore, with the continuous development of technologies such as artificial intelligence, machine learning, and computer vision, intelligent robots have enhanced capabilities for autonomous decision-making, environmental perception, and task execution. In industries such as manufacturing, agriculture, and logistics, the demand for automation and intelligence is growing. Furthermore, as enterprises expand in size and reach, the need for remote monitoring and management is becoming increasingly urgent.

[0003] When faced with complex or sudden tasks, existing systems usually take a long time to divide and schedule tasks, resulting in slow response speed. In addition, many existing systems lack intelligence in robot allocation, which may lead to waste or shortage of resources. Due to the lack of effective real-time monitoring functions, existing technologies are unable to detect abnormal situations in task execution in a timely manner. When dealing with abnormal situations, there is a lack of effective self-inspection and adjustment mechanisms, which increases management risks. In addition, traditional systems often have security risks in the process of data transmission and storage, and are vulnerable to attacks.

[0004] Therefore, in response to the above problems, there is an urgent need for a remote management system that integrates intelligent robots and the Internet of Things. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a remote management system that integrates intelligent robots and the Internet of Things, which solves the problems of the existing system's slow response in processing task requirements, waste or shortage of robot resources due to unintelligent resource allocation methods, and untimely response in abnormal situations.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote management system integrating an intelligent robot and the Internet of Things, comprising a task division module, a robot allocation module, a real-time monitoring module, a security management module, and a task report generation module, wherein: the task division module is used to remotely receive and analyze task requirements based on the Internet of Things, divide the work task into multiple subtasks according to the task requirements, and generate a corresponding subtask completion sequence; the robot allocation module is used to assign an execution robot to each subtask based on the capability status and energy reserve information of the intelligent robot, and mark a backup robot; the remote real-time monitoring module is used to perform real-time monitoring during task execution and detect each task handover node to identify whether there are any abnormal conditions in the subtasks of each handover node; the security management module is used to trigger a self-check or adjustment mechanism in response to the identified abnormal conditions, the self-check or adjustment mechanism including self-checking of the execution robot, calling of the backup robot, and data security encryption adjustment based on the Internet of Things security protocol, and also including determining whether the previous subtask needs to be re-executed based on the leakage of sensitive task data; the task report generation module is used to generate a task report form based on the task execution information of the intelligent robot integrated with the Internet of Things platform after the task is completed.

[0007] Furthermore, the dividing the work task into a plurality of subtasks is specifically: dividing the work task into a first subtask, a second subtask, a third subtask, and so on until the nth subtask, and the order of completing the subtasks is set according to task requirements.

[0008] Furthermore, according to the capability status and energy reserve information of the intelligent robots, an execution robot is assigned to each subtask, and the specific analysis of the standby robots is marked as follows: based on the Internet of Things platform, the demand information of each subtask is detected, and the demand information of each subtask specifically includes the geographical location of each subtask, the execution time of each subtask, and the execution capacity required for each subtask; the geographical location, energy reserve information and capability status of each intelligent robot are obtained, and the energy reserve information is specifically the remaining operating time, and the capability status of each intelligent robot is specifically the equipment capacity of each intelligent robot; the remaining operating time and equipment capacity of each intelligent robot are matched with the execution time and execution capacity required for each subtask, and the intelligent robots whose equipment capacity does not cover the execution capacity required for any subtask, as well as the remaining operating time and equipment capacity of each intelligent robot are screened out. an intelligent robot for a subtask whose execution time is less than the shortest execution time; for the intelligent robots remaining after screening out, based on the geographical location of each subtask and the geographical location of each intelligent robot remaining after screening out, obtain the position distance between each subtask and each intelligent robot remaining after screening out, and then compare the position distance with the distance threshold, mark the intelligent robots with a position distance greater than the distance threshold as backup robots, and mark the intelligent robots with a position distance less than or equal to the distance threshold as execution robots; for the execution robots matched with each subtask, pair them with the execution time and execution capability of each subtask with the remaining running time and equipment capability of the execution robot, and pair the execution robots and mark them as the first subtask execution robot, the second subtask execution robot, the third subtask execution robot, and so on until the nth subtask execution robot.

[0009] Furthermore, remote real-time monitoring is carried out during the execution of the task, and each task handover node is detected to identify whether there are abnormal conditions in the subtasks of each handover node. The specific analysis is as follows: obtaining data leakage detection information and task completion quality information of each task handover node; the data leakage detection information specifically includes actual data transmission volume, unauthorized access operations and data modification operations; the task completion quality information specifically includes execution result accuracy and completion time difference; obtaining the data scheduled transmission volume of each subtask and the allowed difference in data transmission, comparing the actual data transmission volume of each subtask handover node and the corresponding data scheduled transmission volume with the allowed difference in data transmission, and when the actual data is transmitted, the data transmission quality information is obtained. When the difference between the data transmission amount and the corresponding data scheduled transmission amount is greater than the allowable difference in data transmission, or there is an unauthorized access operation, or there is a data modification operation, the previous subtask of the handover node is marked as having a data anomaly; the quality requirements for task execution are obtained, and the quality requirements specifically include the accuracy requirements of the task execution results and the difference requirements of the task completion timeliness. The execution result accuracy and completion timeliness difference of each subtask are compared with the task execution result accuracy requirements and the task completion timeliness difference requirements respectively. When the execution result accuracy is lower than the task execution result accuracy requirement, or the completion timeliness difference is lower than the task completion timeliness difference requirement, the previous subtask of the handover node is marked as having a quality anomaly.

[0010] Furthermore, the self-inspection or adjustment mechanism includes the self-inspection of the execution robot, the calling of the backup robot, and the specific analysis of the data security encryption adjustment based on the Internet of Things security protocol: for the data abnormality of the previous subtask, the data security encryption adjustment function based on the Internet of Things security protocol is triggered, and the data security encryption adjustment function based on the Internet of Things security protocol is specifically: switching to the backup encryption protocol according to the security protocol strategy of the Internet of Things, and generating and distributing the latest encryption key; for the quality abnormality of the previous subtask, the self-inspection function of the execution robot is triggered, and the self-inspection function of the execution robot is specifically: obtaining the self-inspection information of the execution robot, and the execution robot The self-test information specifically includes the sensor data acquisition difference value of the executing robot and the system log crash record of the executing robot. The sensor data acquisition difference value of the executing robot is compared with the sensor data acquisition difference threshold. When the sensor data acquisition difference value of the executing robot is greater than the sensor data acquisition difference threshold, or there is a system log crash record of the executing robot, the call strategy of the backup robot is used to call the backup robot to re-execute the previous subtask; when the sensor data acquisition difference value of the executing robot is less than or equal to the sensor data acquisition difference threshold, and there is no system log crash record of the executing robot, feedback is given on the quality abnormality of the previous subtask.

[0011] Furthermore, the calling strategy of the backup robot is specifically as follows: the backup robots are preliminarily screened based on the fact that the remaining running time of the backup robots is greater than the execution time of the subtasks to be completed by the call, and the equipment capabilities of the backup robots cover the capabilities required for the execution of the subtasks to be completed by the call, and then the preliminarily screened backup robots are called based on the position distance between the backup robots and the subtasks to be completed by the call.

[0012] Furthermore, the specific analysis of determining whether it is necessary to re-execute the previous subtask based on the sensitive data leakage status of the task is as follows: if there is a data anomaly in the previous subtask, identify whether the sensitive data of the previous subtask is leaked; when there is a sensitive data situation in the previous task, re-execute the previous subtask based on the latest encryption key generated and distributed.

[0013] Furthermore, the task report sheet specifically includes intelligent robot task execution information, and the intelligent robot task execution information specifically includes the subtasks completed by each intelligent robot, the subtask execution results, and the subtask completion time.

[0014] The present invention has the following beneficial effects:

[0015] This remote management system, which integrates intelligent robots and the Internet of Things, can quickly adapt to different task requirements and optimize resource utilization through intelligent task division and robot allocation, thereby improving overall work efficiency; it can track task progress at any time, identify and handle abnormal situations in a timely manner, reduce potential risks, and ensure the smooth progress of tasks; it can respond to identified abnormal situations in a timely manner, trigger self-checks and backup robot calls, and implement security measures such as data encryption to ensure system security and data integrity; it automatically adjusts task execution strategies based on real-time data analysis to support remote managers in making more scientific and effective decisions; the task report generation module integrates detailed information on task execution, facilitates subsequent analysis, evaluation, and optimization, and improves the transparency and traceability of management; the scheduling mechanism of backup robots can quickly respond to emergencies and ensure the continuity and stability of tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural diagram of a remote management system that integrates an intelligent robot and the Internet of Things according to the present invention.

[0017] Figure 2 This is a flow chart of a method for remote management system integrating intelligent robots and the Internet of Things in the present invention. DETAILED DESCRIPTION

[0018] The embodiment of the present application realizes the deep integration of intelligent robots and the Internet of Things through a remote management system that integrates intelligent robots and the Internet of Things, and realizes the efficient division, intelligent allocation, real-time monitoring, security management of remote tasks and generation of comprehensive reports on task execution information.

[0019] The overall approach to the problems in the embodiments of this application is as follows:

[0020] First, task requirements are remotely received and analyzed through IoT technology, and complex work tasks are broken down into multiple subtasks to ensure that the system can quickly respond and adapt to various working environments and requirements. Based on the results of task division, the robot's capability status and energy reserve information are utilized to intelligently assign a suitable execution robot to each subtask, and mark the backup robot at the same time. During the task execution process, the remote real-time monitoring module continuously tracks the progress of the task and detects the task handover nodes in real time to ensure that each subtask is successfully completed in the predetermined order. An automated self-check and adjustment mechanism is provided to ensure that the system can quickly respond to abnormal situations and encrypt and protect the data. Finally, the task report generation capability is provided to integrate execution information to provide a basis for subsequent task evaluation and optimization.

[0021] See also Figure 1 An embodiment of the present invention provides a technical solution: a remote management system integrating an intelligent robot and the Internet of Things, comprising a task division module, a robot allocation module, a real-time monitoring module, a security management module, and a task report generation module, wherein: the task division module is used to remotely receive and analyze task requirements based on the Internet of Things, divide the work task into multiple subtasks according to the task requirements, and generate a corresponding subtask completion sequence; the robot allocation module is used to assign an execution robot to each subtask based on the capability status and energy reserve information of the intelligent robot, and mark a backup robot; the remote real-time monitoring module is used to perform real-time monitoring during task execution and detect each task handover node to identify whether there are any abnormal conditions in the subtasks of each handover node; the security management module is used to trigger a self-check or adjustment mechanism for identified abnormal conditions, the self-check or adjustment mechanism including self-checking of the execution robot, calling of the backup robot, and data security encryption adjustment based on the Internet of Things security protocol, and also including determining whether the previous subtask needs to be re-executed based on the leakage of sensitive task data; and the task report generation module is used to generate a task report form based on the task execution information of the intelligent robot integrated with the Internet of Things platform after the task is completed.

[0022] Specifically, the work task is divided into a plurality of subtasks as follows: the work task is divided into a first subtask, a second subtask, a third subtask, and so on until the nth subtask, and the order of completing the subtasks is set according to the task requirements.

[0023] According to the capability status and energy reserve information of the intelligent robot, an execution robot is assigned to each subtask, and the specific analysis of the backup robots is marked as follows: based on the Internet of Things platform, the demand information of each subtask is detected. The demand information of each subtask specifically includes the geographical location of each subtask, the execution time of each subtask, and the execution capacity of each subtask; the geographical location, energy reserve information and capability status of each intelligent robot are obtained. The energy reserve information is specifically the remaining running time, and the capability status of each intelligent robot is specifically the equipment capacity of each intelligent robot; the remaining running time and equipment capacity of each intelligent robot are matched with the execution time and execution capacity of each subtask, and the intelligent robots whose equipment capacity does not cover the execution capacity of any subtask are screened out, as well as the intelligent robots whose remaining running time is less than the shortest An intelligent robot that executes a subtask that takes the required time to execute; for the intelligent robots remaining after screening, based on the geographical location of each subtask and the geographical location of each intelligent robot remaining after screening, obtain the position distance between each subtask and each intelligent robot remaining after screening, and then compare the position distance with the distance threshold, mark the intelligent robots with a position distance greater than the distance threshold as backup robots, and mark the intelligent robots with a position distance less than or equal to the distance threshold as execution robots; for the execution robots matched with each subtask, pair them with the remaining running time and equipment capacity of the execution robots through the execution time and execution capacity of each subtask, and pair the execution robots and mark them as the first subtask execution robot, the second subtask execution robot, the third subtask execution robot, and so on until the nth subtask execution robot.

[0024] In this implementation scheme, the geographical location of each subtask is obtained through sensors and GPS positioning technology; the time required for execution is obtained by analyzing the complexity and time consumption of each subtask based on historical data or estimation methods; the ability to execute is analyzed based on the specific requirements of the subtask and the required skills or equipment capabilities; the geographical location of each intelligent robot is also obtained in real time through sensors or GPS positioning technology; the remaining running time is based on the robot's battery management system (BMS), which monitors the robot's current power and remaining running time in real time; the equipment capability is specifically obtained by analyzing the capability parameters set when the robot is designed, including information such as the type of task that can be executed, the required tools and the load capacity; the geographical location indicates the specific physical location of the task or robot, which is used for task scheduling and allocation; the remaining running time indicates the time the robot can continue to work at the current power level, which helps to determine whether the assigned subtask can be completed; the equipment capability indicates that the robot has the ability to perform specific tasks, ensuring that it can meet the requirements of the subtask.

[0025] The distance threshold is used to judge the effective distance between the robot and the task location to determine the allocation of execution robots and backup robots. It can be set based on historical task execution data, task type and time requirements. Specifically, by analyzing historical task data, the average task execution distance and time requirements are determined, and then a reasonable threshold is set. The required reasonable distance is evaluated according to different types of tasks and environmental factors to ensure the efficiency and safety of the system during execution.

[0026] By breaking down work tasks into multiple subtasks and intelligently assigning execution robots, the system can improve the efficiency and accuracy of task execution and reduce the overall task completion time; based on the robot's capabilities and energy status, it can ensure that each subtask is executed by an appropriate robot, avoiding waste of resources and unnecessary scheduling; by marking backup robots, it can quickly switch to backup robots when the main execution robot fails or is delayed, thereby improving task reliability; based on real-time robot status information, task allocation can be flexibly adjusted to enhance the ability to respond to emergencies; through intelligent task division and resource allocation, the need for human intervention is reduced, work efficiency is improved, and the possibility of human error is reduced.

[0027] Specifically, real-time monitoring is carried out during the task execution process, and each task handover node is detected to identify whether there are abnormal conditions in the subtasks of each handover node. The specific analysis is as follows: obtaining data leakage detection information and task completion quality information of each task handover node; data leakage detection information specifically includes actual data transmission volume, unauthorized access operations and data modification operations; task completion quality information specifically includes execution result accuracy and completion time difference; obtaining the data scheduled transmission volume of each subtask and the allowed difference in data transmission, comparing the actual data transmission volume of each subtask handover node and the corresponding data scheduled transmission volume with the allowed difference in data transmission, and when the actual data transmission is completed, the difference between the actual data transmission volume and the corresponding data scheduled transmission volume is compared with the allowed difference in data transmission. When the difference between the amount and the corresponding scheduled data transmission amount is greater than the allowable difference in data transmission, or there is an unauthorized access operation, or there is a data modification operation, the previous subtask of the handover node is marked as having a data anomaly; the quality requirements for task execution are obtained, and the quality requirements specifically include the accuracy requirements of task execution results and the difference requirements of task completion timeliness. The execution result accuracy and completion timeliness difference of each subtask are compared with the task execution result accuracy requirements and task completion timeliness difference requirements respectively. When the execution result accuracy is lower than the task execution result accuracy requirement, or the completion timeliness difference is lower than the task completion timeliness difference requirement, the previous subtask of the handover node is marked as having a quality anomaly.

[0028] In this implementation plan, the actual data transmission volume refers to the actual data volume transmitted during the task execution process, reflecting the data flow situation, and is obtained by real-time recording and statistically counting the data during the data transmission process through the data monitoring system; unauthorized access operation refers to the access behavior of users or systems that have not obtained legal authorization to the data during the task execution process, and is obtained by monitoring and recording all access attempts through security audit logs, intrusion detection systems (IDS) or access control systems; data modification operation refers to any unauthorized changes to the data content during the task execution process, and all modification records of the data are tracked through the log management system and compared with the original data to detect and identify unauthorized modification operations; execution result accuracy refers to the degree of consistency between the task execution results and the expected results, which is used to evaluate the success rate of the task, and is obtained by comparing with the preset results and statistically calculating the accuracy of the actual execution results; completion time difference refers to the difference between the actual completion time of the task and the expected completion time, which is used to evaluate the efficiency of the task, and is obtained by recording the start time and end time of the task, calculating the actual completion time, and comparing it with the preset completion time.

[0029] The specific data transmission volume of each subtask can be preset according to the task requirements and historical data during the task design phase, and set through demand analysis, project planning or past experience; the allowable difference in data transmission is set according to the execution of previous tasks and the data transmission characteristics, to ensure that the system can tolerate fluctuations in data transmission within a certain range; the accuracy of task execution results needs to be communicated with relevant stakeholders during the task design phase to clarify the success criteria of the task, and set based on industry standards or project requirements; the demand for difference in task completion time is set through project planning and historical data analysis, and the expected completion time limit for each task is set, taking into account possible delay factors.

[0030] By real-time monitoring of abnormal conditions such as data leakage, unauthorized access and data modification, potential security risks can be identified in a timely manner, so that corresponding protective measures can be taken to ensure the integrity and security of the data; by real-time monitoring of the quality of task completion, problems in the task execution process can be discovered in a timely manner, so that adjustments can be made to ensure that the final result meets the predetermined quality standards; monitoring of handover nodes can identify and handle abnormal tasks in a timely manner, reduce resource waste and unnecessary delays, and improve overall work efficiency; by collecting and analyzing task execution data, managers can obtain comprehensive task execution status, which is convenient for subsequent analysis and optimized decision-making; real-time monitoring and analysis of data can automatically adjust the execution strategy according to the current task status, improving the overall intelligence level of management.

[0031] Specifically, the self-inspection or adjustment mechanism includes self-inspection of the executing robot, calling of the backup robot, and data security encryption adjustment based on the Internet of Things security protocol. The specific analysis is as follows: if there is a data anomaly in the previous subtask, the data security encryption adjustment function based on the Internet of Things security protocol is triggered. The data security encryption adjustment function based on the Internet of Things security protocol is specifically: switching to the backup encryption protocol according to the Internet of Things security protocol policy, and generating and distributing the latest encryption key; if there is a quality anomaly in the previous subtask, the self-inspection function of the executing robot is triggered. The self-inspection function of the executing robot is specifically: obtaining the self-inspection information of the executing robot. The self-inspection information of the executing robot specifically includes the sensor data acquisition difference value of the executing robot and the system log crash record of the executing robot, comparing the sensor data acquisition difference value of the executing robot with the sensor data acquisition difference threshold. When the sensor data acquisition difference value of the executing robot is greater than the sensor data acquisition difference threshold, or there is a system log crash record of the executing robot, the backup robot is called by the calling strategy of the backup robot to re-execute the previous subtask; when the sensor data acquisition difference value of the executing robot is less than or equal to the sensor data acquisition difference threshold, and there is no system log crash record of the executing robot, feedback on the quality anomaly of the previous subtask is provided.

[0032] In this implementation, by triggering data encryption adjustments based on the Internet of Things security protocol, data leakage or unauthorized access can be effectively prevented, thereby improving data security; executing the robot self-check function can promptly detect anomalies in the robot hardware or software, avoid mission failures due to faults, and enhance system reliability; utilizing the backup robot call strategy, problematic robots can be replaced in a timely manner to ensure the smooth progress of tasks and reduce delays caused by robot failures; when the robot is performing normal work, the system can further optimize the quality of task execution based on the feedback mechanism to reduce false alarms or unnecessary interruptions.

[0033] The security protocol strategy of the Internet of Things refers to the encryption, authentication and access control rules and standards developed in IoT devices and networks to ensure the confidentiality, integrity and availability of data. It covers multiple aspects from data encryption, identity authentication to network security protection, ensuring the security of communication between IoT devices, including: Data encryption strategy: Encrypt data in transmission to prevent data leakage or tampering. Common encryption protocols include TLS, DTLS, and AES; Identity authentication strategy: Ensure that communication between devices is trustworthy, usually using authentication methods based on public key infrastructure (PKI) or shared keys; Access control strategy: Control which devices and users can access specific data or functions; Data integrity verification: Ensure that data has not been tampered with during transmission, and use hash functions or digital signatures to verify data integrity; IoT security protocol update strategy: Regularly evaluate and update protocols to ensure that the protocols remain effective as security threats evolve; Intrusion detection and protection: Monitor the network in real time, detect and prevent malicious attacks, and use firewalls, IDS / IPS and other technologies for protection.

[0034] An encryption protocol is a set of rules and algorithms used to ensure the security of communication data. It is used to encrypt data transmission to prevent unauthorized access and tampering. It can be understood as a set of mechanisms that define how data is encrypted from the sender and decrypted after transmission to the receiver. Common encryption protocols include: TLS (Transport Layer Security) and DTLS (Datagram Transport Layer Security).

[0035] The specific steps for generating and distributing the latest encryption keys are as follows: when the system detects data leakage or anomalies, it triggers the backup encryption protocol, selects a new encryption algorithm (such as AES, RSA, etc.) according to the IoT security protocol, generates a new symmetric or asymmetric encryption key to replace the existing protocol, and then uses the public key infrastructure of the IoT device to encrypt the newly generated encryption key, and distributes the encrypted key to each subtask and execution robot through a secure channel (such as TLS); after each device receives the encryption key, it ensures the correctness of the key through a digital signature and authentication mechanism. The system verifies whether the new encryption key is effective by monitoring data transmission and ensures secure data transmission.

[0036] The difference value of the robot's sensor data is monitored in real time by the robot's internal sensor system and compared with the standard sensor reading value. The data difference is obtained by calculating the difference between the sensor's real-time data and the preset or historical normal data; the system log crash record is recorded in real time by the robot's built-in operating system or task execution software, and monitors whether a crash or error event occurs for identification; the sensor data acquisition difference threshold is the range set by the system to determine whether the sensor data is normal. By analyzing the robot's sensor data during normal operation, the normal range of its data fluctuations is obtained, and a reasonable error range is set. The sensor can also be calibrated and tested in a laboratory environment, and its error under various conditions is recorded and a tolerance range is set. In actual application, the sensor data difference threshold can be dynamically adjusted according to the actual environment and execution of the task to adapt to different task requirements.

[0037] Specifically, the calling strategy of the backup robot is as follows: the backup robots are preliminarily screened based on the fact that the remaining running time of the backup robots is greater than the execution time of the subtasks to be completed by the call, and the equipment capabilities of the backup robots cover the capabilities required for the execution of the subtasks to be completed by the call, and then the preliminarily screened backup robots are called based on the position distance between the backup robots and the subtasks to be completed by the call.

[0038] In this implementation plan, through a reasonable calling strategy for backup robots, when the executing robot fails or performs poorly, it can respond quickly, reduce the risk of task interruption, and ensure the continuity of the task; prioritize the screening of qualified backup robots, and evaluate them from three dimensions: remaining running time, equipment capacity, and location distance, to ensure that the backup robots can complete the task efficiently and avoid waste of resources; by prioritizing the selection of backup robots based on location distance, the delay in task switching and robot scheduling is reduced, and the task response speed is improved, especially in emergency situations, the task can be completed faster; only backup robots whose equipment capacity and running time meet the task requirements are selected to avoid task failure due to insufficient backup robot capacity or insufficient running time, thereby improving the success rate of task execution.

[0039] Specifically, the specific analysis of whether it is necessary to re-execute the previous subtask based on the sensitive data leakage status of the task is as follows: if there is a data anomaly in the previous subtask, identify whether the sensitive data of the previous subtask is leaked. When there is a sensitive data situation in the previous task, re-execute the previous subtask based on the latest encryption key generated and distributed.

[0040] In this implementation plan, by promptly identifying and responding to sensitive data leakage, the risks brought by data leakage can be effectively prevented, ensuring that sensitive information is not accessed or abused without authorization; when sensitive data leakage is identified, immediately re-executing the previous subtask can ensure the integrity and correctness of the data, reducing economic losses and reputation damage caused by data leakage; promptly handling data leakage incidents and taking remedial measures can enhance users' trust in the system and demonstrate the system's attention to data security; by re-executing the previous subtask, it is ensured that the task is completed in accordance with the set security standards, ultimately improving the success rate and quality of the overall task.

[0041] The specific method for identifying whether the sensitive data of the previous subtask has been leaked is to monitor data flow in real time, record data access and transmission, detect unauthorized access or abnormal data operations, and use log analysis tools to audit data access records during task execution to identify suspicious operations. Abnormal traffic specifically uses traffic analysis tools to monitor network traffic anomalies. If the data transmission volume is found to be outside the normal range, it indicates a data leak. Sensitive data refers to information that, if leaked, tampered with, or accessed without authorization, can cause damage to individuals or businesses and affect their privacy or security. It includes personal identity information, financial information, medical records, and other types of information that require protection. The scope of sensitive data can also be set according to different task requirements.

[0042] Specifically, the task report sheet includes the intelligent robot task execution information, and the intelligent robot task execution information includes the subtasks completed by each intelligent robot, the subtask execution results, and the subtask completion time.

[0043] In this implementation plan, the task report sheet provides comprehensive execution information, making the task execution process transparent, and relevant parties can clearly understand the execution status and results of each subtask; by analyzing the task execution information of the intelligent robot, the performance of the robot in different tasks can be effectively evaluated, providing a basis for subsequent optimization and decision-making; when problems arise, the task report sheet can be used as a basis for traceability, helping to identify bottlenecks and abnormal situations in task execution, and facilitating troubleshooting; detailed execution information provides decision-making support for management, and can adjust resource allocation or optimize task arrangements based on task completion status.

[0044] In summary, this application has at least the following effects:

[0045] Intelligently decompose complex tasks into multiple manageable subtasks and generate a reasonable execution order, thus avoiding inefficiency and resource waste when executing a single task and improving the efficiency of overall task completion; allocation is based on the real-time capabilities and energy reserve information of the robot to ensure that each subtask can be executed by the most suitable robot, and at the same time, backup robots are preset to deal with emergencies, which greatly optimizes the use and allocation of resources and reduces task delays caused by insufficient or improper resource allocation; remote real-time monitoring can monitor the task execution process in real time and conduct detection at the task handover node to promptly discover and identify abnormal conditions, reduce the risk of task failure, and enhance task efficiency. The reliability and stability of task execution are ensured; a series of security measures are taken for identified abnormal conditions, including self-inspection of the execution robot, calling of backup robots, and adjustment of data security encryption, which effectively prevent data leakage and security risks. At the same time, whether to re-execute the previous subtask is determined based on the leakage of sensitive task data, further ensuring data security and compliance during task execution; after the task is completed, the intelligent robot task execution information of the Internet of Things platform is integrated to generate a detailed task report, which provides managers with a comprehensive basis for task execution evaluation, helps to understand various situations during task execution, and provides strong support for future task planning and decision-making.

[0046] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0047] The present invention is described with reference to a block diagram of a system according to an embodiment of the present invention. It should be understood that the combination of each structure in the block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions, executed by the processor of the computer or other programmable data processing device, produce a device for implementing the functions specified in each structure in the block diagram.

[0048] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in each structure of the structural diagram.

[0049] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in each structure in the structural diagram.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A remote management system integrating intelligent robots and the Internet of Things, characterized in that: It includes task division module, robot allocation module, real-time monitoring module, safety management module, and task report generation module, among which: The task division module is used to remotely receive and analyze task requirements based on the Internet of Things, divide the work task into multiple subtasks according to the task requirements, and generate the corresponding subtask completion order; The robot allocation module is used to allocate execution robots to each subtask according to the capabilities and energy reserve information of the intelligent robots, and mark spare robots; The real-time monitoring module is used to perform remote real-time monitoring during the task execution process, and detect each task handover node to identify whether there are any abnormal conditions in the subtasks of each handover node; The security management module is used to trigger a self-check or adjustment mechanism based on the identified abnormal conditions. The self-check or adjustment mechanism includes self-checking of the execution robot, calling a backup robot, and adjusting data security encryption based on the Internet of Things security protocol. It also includes determining whether to re-execute the previous subtask based on the leakage of sensitive task data; The task report generation module is used to generate a task report sheet by integrating the task execution information of the intelligent robot of the Internet of Things platform after the task is completed.

2. A remote management system integrating intelligent robots and the Internet of Things according to claim 1, characterized in that: The work task is divided into a plurality of subtasks specifically as follows: the work task is divided into a first subtask, a second subtask, a third subtask, and so on until the nth subtask, and the order of completing the subtasks is set according to the task requirements.

3. The remote management system integrating intelligent robots and the Internet of Things according to claim 1 is characterized in that: Based on the capabilities and energy reserve information of the intelligent robots, robots are assigned to each subtask and the specific analysis of the backup robots is marked as follows: Detecting the demand information of each subtask based on the Internet of Things platform, wherein the demand information of each subtask specifically includes the geographical location of each subtask, the time required for execution of each subtask, and the capacity required for execution of each subtask; Obtaining the geographic location, energy reserve information, and capability status of each intelligent robot, wherein the energy reserve information specifically includes the remaining operating time, and the capability status of each intelligent robot specifically includes the equipment capability of each intelligent robot; Match the remaining running time and equipped capacity of each intelligent robot with the execution time and required capacity of each subtask, and filter out intelligent robots whose equipped capacity does not cover the execution capacity required for any subtask, as well as intelligent robots whose remaining running time is less than the shortest execution time; For the remaining intelligent robots, based on the geographical location of each subtask and the geographical location of each remaining intelligent robot, the location distance between each subtask and each remaining intelligent robot is obtained, and then the location distance is compared with the distance threshold. The intelligent robots with a location distance greater than the distance threshold are marked as backup robots, and the intelligent robots with a location distance less than or equal to the distance threshold are marked as execution robots; For the execution robots matched with each subtask, the execution time and capacity required for each subtask are matched with the remaining running time and equipment capacity of the execution robots, and the execution robots are paired and marked as the first subtask execution robot, the second subtask execution robot, the third subtask execution robot, and so on until the nth subtask execution robot.

4. The remote management system integrating intelligent robots and the Internet of Things according to claim 1, characterized in that: During the task execution process, remote real-time monitoring is carried out, and each task handover node is detected to identify whether there are any abnormal conditions in the subtasks of each handover node. The specific analysis is as follows: Obtain data leakage detection information and task completion quality information for each task handover node; The data leakage detection information specifically includes actual data transmission volume, unauthorized access operations and data modification operations; The task completion quality information specifically includes the accuracy of execution results and the difference in completion time; Obtain the scheduled data transmission volume and the allowed data transmission difference of each subtask, compare the difference between the actual data transmission volume and the corresponding scheduled data transmission volume of each subtask handover node with the allowed data transmission difference, and when the difference between the actual data transmission volume and the corresponding scheduled data transmission volume is greater than the allowed data transmission difference, or there is an unauthorized access operation or a data modification operation, mark the previous subtask of the handover node as having a data anomaly; Obtain the quality requirements for task execution. The quality requirements specifically include the task execution result accuracy requirements and the task completion time variability requirements. Compare the execution result accuracy and completion time variability of each subtask with the task execution result accuracy requirements and task completion time variability requirements respectively. When the execution result accuracy is lower than the task execution result accuracy requirement, or the completion time variability is lower than the task completion time variability requirement, mark the previous subtask of the handover node as having a quality abnormality.

5. According to claim 4, the remote management system for integrating intelligent robots with the Internet of Things, wherein the self-check or adjustment mechanism includes executing robot self-check, calling a backup robot, and adjusting data security encryption based on the Internet of Things security protocol. Specific analysis is as follows: In response to the data anomaly in the previous subtask, a data security encryption adjustment function based on the IoT security protocol is triggered. The data security encryption adjustment function based on the IoT security protocol specifically switches to an alternative encryption protocol according to the IoT security protocol policy, and generates and distributes the latest encryption key; In response to a quality abnormality in the previous subtask, a self-check function of the executing robot is triggered. The self-check function of the executing robot is specifically as follows: obtaining self-check information of the executing robot, the self-check information of the executing robot specifically including a difference value of sensor data obtained by the executing robot and a crash record of the executing robot in the system log, comparing the difference value of the sensor data obtained by the executing robot with a sensor data obtained difference threshold, and when the difference value of the sensor data obtained by the executing robot is greater than the sensor data obtained difference threshold, or there is a crash record of the executing robot in the system log, calling the standby robot by using the calling strategy of the standby robot to re-execute the previous subtask; When the sensor data acquisition difference value of the executing robot is less than or equal to the sensor data acquisition difference threshold and there is no system log crash record of the executing robot, feedback is given on the quality abnormality of the previous subtask.

6. According to a remote management system for the integration of intelligent robots and the Internet of Things according to claim 5, the calling strategy of the backup robot is specifically as follows: the backup robots are preliminarily screened based on the fact that the remaining running time of the backup robots is greater than the execution time of the subtasks to be completed by the call, and the equipment capabilities of the backup robots cover the capabilities required for the execution of the subtasks to be completed by the call, and then the preliminarily screened backup robots are called based on the position distance between the backup robots and the subtasks to be completed by the call.

7. According to the remote management system for the integration of intelligent robots and the Internet of Things according to claim 5, the specific analysis of judging whether it is necessary to re-execute the previous subtask based on the leakage status of sensitive data of the task is as follows: if there is a data anomaly in the previous subtask, identify whether the sensitive data of the previous subtask is leaked; when there is a sensitive data situation in the previous task, re-execute the previous subtask based on the latest encryption key generated and distributed.

8. According to a remote management system integrating intelligent robots and the Internet of Things according to claim 1, the task report form specifically includes intelligent robot task execution information, and the intelligent robot task execution information specifically includes the subtasks completed by each intelligent robot, the subtask execution results and the subtask completion time.

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