Robot scheduling system for data security protection

Abstract

The invention discloses a robot scheduling system for data security protection. The robot scheduling system comprises a server in communication connection with a plurality of robots, data security modules arranged on the robots and the server, and a behavior planning module, a real-time monitoring module and a fault diagnosis module which are respectively in bidirectional connection with the server. Through the data security module, the behavior planning module, the real-time monitoring module and the fault diagnosis module, the optimal robot moving path and task allocation can be calculated, so that task conflict and resource waste are avoided, the robot can efficiently complete tasks in a complex environment, and the security of robot scheduling can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV, and in particular to a robot scheduling system with data security protection. Background Art

[0002] In the current digital workshops and automated logistics workshops, AGV carts or AGV robots are a type of automated transportation equipment that can complete specified transportation tasks within a given path and scene information layout. A good path planning solution can not only improve automated production efficiency, but also ensure the utilization rate of production equipment.

[0003] However, the existing AGV scheduling system generally has problems such as low efficiency, task conflict, and poor safety. These problems not only affect the normal operation of AGV, but may also have a negative impact on the entire logistics and warehousing system. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a robot scheduling system with data security protection, which can not only ensure the safety of robot scheduling, but also calculate the optimal robot movement path and task allocation, thereby avoiding task conflicts and waste of resources, and enabling robots to efficiently complete tasks in complex environments.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] A robot scheduling system with data security protection includes a server connected to a plurality of robots, a data security module arranged on the robot and the server, a behavior planning module connected to the server in both directions, a real-time monitoring module and a fault diagnosis module;

[0007] The behavior planning module is used to receive and process the position information and the information of whether the robot is in working state sent by the robot in real time through the server and plan an efficient and feasible travel path for the robot;

[0008] The data security module is used to generate and manage public-private key pairs through the CLA key algorithm to achieve security and integrity of information interaction between the robot and the server and to enable the server to distinguish different robots;

[0009] The real-time monitoring module is used to receive information sent by the robot in real time through the server, including but not limited to its own position coordinates, direction, remaining power, whether it is currently loaded and whether it is in a working state, so as to monitor power information and load information and draw a map and route of the robot in the warehouse or factory;

[0010] The fault diagnosis module is used to analyze the robot's operating status, travel trajectory and connection status with the real-time monitoring module to determine whether the robot has a fault and trigger an early warning mechanism when a fault occurs.

[0011] Preferably, the behavior planning module has several maps built in, and uses point connection technology to organically connect the preset working points of each map in series to build a complete path network.

[0012] Preferably, the behavior planning module has a built-in depth-first search algorithm for calculating the idle robot closest to the task point and calculating the shortest distance between the robot's current location and the task target point and generating multiple navigable planned paths for selection.

[0013] Preferably, the behavior planning module is bidirectionally connected to a storage module for storing the robot's planned path, the travel direction and speed of a specific road section. The behavior planning module intelligently excludes planned paths that are currently occupied or congested based on the stored information and selects the optimal travel path.

[0014] Preferably, during the movement of the robot, the behavior planning module compares the actual position of the robot with the travel path and combines the robot's planned path, the travel direction and speed of a specific section stored in the storage module, and uses a Bayesian filter algorithm to obtain the number of robots distributed on the travel path within the next minute, and predict in advance whether the travel path after the next work point is unobstructed.

[0015] Preferably, when the travel path is blocked by a robot moving in the opposite direction or there is a potential risk, the behavior planning module will immediately activate the emergency mechanism, quickly recalculate the shortest distance path, and instantly send the new travel path to the robot through the server; when the robot cannot bypass the blocked path, the behavior planning module will guide the robot to the nearest work point for avoidance, and after the path is restored to normal, plan the best travel path for the robot again.

[0016] Preferably, a weighing sensor is integrated on the robot.

[0017] Preferably, the server is also communicatively connected to a mobile terminal, the fault information is pushed to the management personnel via the mobile terminal, and a data security module is provided on the mobile terminal.

[0018] Due to the adoption of the above technical scheme, the technical progress achieved by the present invention is as follows.

[0019] The present invention, through the provision of a data security module, a behavior planning module, a real-time monitoring module and a fault diagnosis module, can not only calculate the optimal robot movement path and task allocation, thereby avoiding task conflicts and waste of resources, and enabling the robot to efficiently complete tasks in a complex environment, but also ensure the safety of robot scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a principle block diagram of the present invention;

[0021] Figure 2 The flowchart of the behavior planning module of the present invention during the robot's movement;

[0022] Figure 3 It is a management schematic diagram of the present invention;

[0023] Figure 4 This is a flow chart of the present invention after receiving a task request. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] A robot dispatching system with data security protection, combined with Figure 1 As shown, it includes a server, a data security module, a mobile terminal, a behavior planning module, a real-time monitoring module and a fault diagnosis module, wherein the server is communicatively connected with several robots, thereby realizing the communication between the system and the robots; the mobile terminal is communicatively connected with the server, thereby realizing the communication between the system and the management personnel; the data security module is respectively arranged on the robot, the server and the mobile terminal; the behavior planning module, the real-time monitoring module and the fault diagnosis module are respectively bidirectionally connected with the server.

[0026] The behavior planning module, data security module, real-time monitoring module and fault diagnosis module are described in detail below.

[0027] (1) Behavior Planning Module:

[0028] The behavior planning module is one of the core components of this scheduling system. It can receive and process the location information and working status information sent by the robot through the server in real time, and is responsible for planning an efficient and feasible travel path for the robot in a complex working environment. The behavior planning module has a built-in depth-first search (DFS) algorithm and several maps. The behavior planning module uses advanced point connection technology to organically connect the preset working points of each map, thereby constructing a complete path network.

[0029] Whenever the robot receives a task request, the behavior planning module will quickly start its built-in depth-first search (DFS) algorithm. This algorithm can accurately calculate the idle robot closest to the task point and the shortest distance between the robot's current location and the task target point, and generate multiple passable planned paths for selection. The behavior planning module is bidirectionally connected to the storage module, which is used to store the robot's planned path, the direction of travel and the speed of a specific section of the road. In order to ensure that the robot can complete the task efficiently, the behavior planning module will also intelligently exclude those planned paths that are currently occupied or congested based on the stored information, thereby ensuring that the selected travel path is the best.

[0030] like Figure 2 As shown in the figure, during the robot's movement, the behavior planning module will receive and process the position information sent by the robot in real time. The behavior planning module compares the actual position of the robot with the travel path, and combines the robot's planned path stored in the storage module, the travel direction and speed of a specific section, and uses the Bayesian filter algorithm to obtain the number of robots distributed on the travel path in the next minute, and predict in advance whether the travel path after the next work point is unobstructed.

[0031] Specifically, the Bayesian filter algorithm is as follows:

[0032] The Bayesian filter is used to estimate the distribution of robots on the road, including the number of robots, their locations, their speeds, etc. By continuously updating these estimates, it is possible to predict the distribution of the number of robots within the next minute.

[0033] The basic formula of the Bayesian filter is as follows:

[0034]

[0035] Among them, X represents the state variable (such as the position and speed of the robot on the road section); Y represents the observed variable (such as the real-time robot position data); P(X) is the prior probability; P(Y|X) is the likelihood function; P(X|Y) is the posterior probability.

[0036] By continuously updating the prior probability and likelihood function, the posterior probability can be obtained under given real-time data, thereby realizing the prediction of the number distribution of robots and the judgment of path smoothness in the next minute.

[0037] If it is found that the path is blocked by a robot moving in the opposite direction or there is a potential risk, the behavior planning module will immediately activate the emergency mechanism, quickly recalculate the shortest distance path, and send the new path to the robot through the server in real time.

[0038] In some special cases, if the robot cannot bypass the blocked path, the behavior planning module will guide the robot to the nearest work point for avoidance. This can ensure the safety of the robot and minimize the task delay caused by the blocked path. When the path is restored, the module will plan the best path for the robot again so that it can successfully complete the remaining tasks.

[0039] In general, the behavior planning module provides a strong guarantee for the stable operation of the robot system with its precise computing power, flexible adaptability and efficient path planning strategy. It not only ensures that the robot can complete tasks efficiently in complex environments, but also greatly improves the intelligence level of the entire system and user experience.

[0040] (2) Data security module:

[0041] The data security module has a built-in CLA key algorithm. When the robot goes online, it will first generate a temporary public-private key pair through the CLA key algorithm. This temporary public key and the robot's system unique ID will be sent to the server. After receiving this information, the server will generate the robot's second temporary public-private key pair according to a certain algorithm, and calculate the user's secondary identifier at the same time. This secondary identifier is usually a unique identifier used to distinguish different robots.

[0042] Next, the robot will synthesize its own complete public-private key pair based on the second temporary public-private key pair and the auxiliary identifier. In this process, only the robot has its own private key, ensuring the security and privacy of the private key. The private key is the key for the robot to perform encryption and decryption operations. Only with the private key can the robot decrypt the received information and verify the signature.

[0043] When the system needs to send a message to the robot, it will encrypt and sign the content using the robot's public key. Encryption can ensure that the information is not stolen or tampered with during transmission, while the signature can verify the source and integrity of the information. In this way, even if the information is tampered with or forged during transmission, the robot can detect the problem by verifying the signature, thereby ensuring the authenticity of the information.

[0044] When the robot receives the encrypted and signed information, it uses its own private key to decrypt the data and verify the signature. The decryption process can restore the information to its original form, allowing the robot to understand and execute the corresponding instructions, while the verification signature can ensure the source and integrity of the information and prevent the information from being tampered with or forged.

[0045] In general, by generating and managing public-private key pairs through the CLA key algorithm, the robot system can ensure the confidentiality and integrity of information. This encryption and signature mechanism not only protects the privacy and security of users, but also improves the reliability and stability of the robot system.

[0046] (3) Real-time monitoring module:

[0047] The robot has the ability to upload its own position coordinates, direction, remaining power, whether it is currently loaded, whether it is in working state, and other information in real time. This information is crucial for building the robot's operating environment model, optimizing task scheduling, and achieving efficient operation and maintenance. By receiving and processing the position and posture information uploaded by the robot, the real-time monitoring module can accurately draw a map and route of the real-time monitoring module in the warehouse or factory. This helps managers understand the real-time location and working status of the real-time monitoring module, and promptly discover and solve problems.

[0048] At the same time, power information monitoring is an important part of the robot's operation. The real-time monitoring module can monitor the robot's power status in real time. When the power is lower than the threshold, the real-time monitoring module will automatically send a charging task to the idle robot through the server. During the charging process, the real-time monitoring module can also monitor the power fullness in real time, and automatically notify the robot to stop charging when the power is full, thereby avoiding overcharging and energy waste.

[0049] In addition, the robot is integrated with a weighing sensor, through which the robot can also detect the weight of the goods being carried in real time and upload the relevant information to the real-time monitoring module. This is of great significance for ensuring the safety of cargo transportation and preventing overloading problems. When the robot is in motion, the real-time monitoring module can also obtain the robot's task execution status in real time, such as the progress of task completion, whether obstacles are encountered, etc., so that the system can adjust task scheduling in time and optimize the robot's operating efficiency.

[0050] In summary, by uploading the position, status information and task execution status in real time, the robot can achieve efficient and autonomous material handling and transportation tasks. At the same time, combined with the system's real-time monitoring and processing of information, the robot's operating efficiency and safety can be further improved.

[0051] (4) Fault diagnosis module:

[0052] The fault diagnosis module determines whether a fault occurs by analyzing the robot's operating status and trajectory, as well as their connection status with the real-time monitoring module, and thus sends information to the mobile terminal in a timely manner to warn the management personnel.

[0053] Specifically, when a robot fails, its operating status and trajectory will usually show abnormalities. For example, the robot may be stranded in a certain location for a long time and unable to continue to complete the task; or the robot may lose connection with the real-time monitoring module, resulting in the inability to receive instructions or upload data. These abnormal situations can be monitored in real time by the real-time monitoring module, and the early warning mechanism will be triggered to push the fault information to the management personnel in a timely manner.

[0054] After receiving the warning information, the manager can quickly check the current location of the robot and go to the site to handle it. In this way, not only can the fault be quickly resolved and the robot can be restored to normal operation, but also the production interruption and cost loss caused by the fault can be avoided.

[0055] In addition to the basic warning and fault handling functions, we can further optimize the robot's work efficiency by analyzing the robot's operating status and trajectory. For example, if the robot is found to be moving slowly in a certain area, it may be because the road layout in the area is not reasonable or the distribution of goods is uneven. At this time, we can adjust the road layout or optimize the storage method of goods to improve the robot's work efficiency.

[0056] In addition, by long-term monitoring and analysis of the robot's operating status, we can also discover some potential problems and hidden dangers. For example, some parts of the robot may be close to the end of their life and need to be replaced in time; or some parameter settings of the robot may not be reasonable and need to be adjusted. Timely discovery and handling of these potential problems can effectively avoid the occurrence of failures and ensure the long-term stable operation of the robot.

[0057] In summary, by real-time monitoring and analysis of the robot's operating status, trajectory, and connection status with the real-time monitoring module, we can effectively determine whether the robot has a fault and promptly warn the management personnel. This can not only quickly handle the fault and restore the robot's normal operation, but also optimize the robot's work efficiency, discover potential problems and hidden dangers, and ensure the long-term stable operation of the robot.

[0058] The following will explain in detail the operation process of the robot and scheduling system, including key links such as map marking, path planning, task allocation, and real-time adjustment, in order to reveal its internal working mechanism.

[0059] like Figure 3 As shown in the figure, before the robot is put into operation, the system will accurately mark the information of each key operation point on all preset maps. These points include but are not limited to operation points, waypoints, charging stations, boundary points, and elevator points, etc., providing the necessary basic data for subsequent path planning and task allocation.

[0060] Subsequently, the system will connect all traversable points based on the marked key points through algorithm optimization, thus constructing a path network on the entire map. This network not only shows the direct connection between the points in detail, but also realizes the logical connection between different maps through the association between the boundary points and the boundary points, and the elevator points and the elevator points of the map, so that the robot can complete cross-map operations based on these connections, thereby greatly improving the efficiency and flexibility of the operation.

[0061] After completing the map marking and path planning, the system will pre-arrange various work tasks according to the work points on each map. These tasks cover multiple links such as cargo handling, storage, and sorting, and are the core content of the daily operation of the robot system.

[0062] like Figure 4 As shown in the figure, when the system receives a task request, it will use the built-in behavior planning module to accurately calculate the idle robot closest to the task point based on the requirements of the task point and the robot's location and status. Then, the system will combine the robot's real-time location information to plan the optimal path to ensure that the robot can complete the task safely and efficiently.

[0063] After planning the travel path, the system will start the data security module to encrypt and sign the travel path information to ensure the integrity and security of the data during transmission. Subsequently, the encrypted travel path information will be sent to the target robot. After receiving the command to perform the task, the robot will decrypt the encrypted command and verify whether the signature is valid. After the command is verified, it will start to execute the task strictly according to the planned route.

[0064] The present invention can be widely used in the fields of logistics, warehousing, production line automation, etc., and is of great significance for improving the level of automation and efficiency. Compared with the prior art, the present invention can calculate the optimal robot movement path and task allocation, thereby avoiding task conflicts and waste of resources, enabling robots to efficiently complete tasks in complex environments, having higher reliability and stability, and being able to better meet the needs of practical applications. In addition, the use of the present invention can reduce labor costs, improve work efficiency, and has high practical value and economic value. In the present invention, data transmission is processed using CLA encryption technology to ensure the security of robot scheduling.

Claims

1. A robot dispatching system with data security protection, characterized by: It includes a server connected to several robots, a data security module set on the robots and the server, a behavior planning module, a real-time monitoring module and a fault diagnosis module connected to the server in both directions; The behavior planning module is used to receive and process the position information and the information of whether the robot is in working state sent by the robot in real time through the server and plan an efficient and feasible travel path for the robot; The data security module is used to generate and manage public-private key pairs through the CLA key algorithm to achieve security and integrity of information interaction between the robot and the server and to enable the server to distinguish different robots; The real-time monitoring module is used to receive information sent by the robot in real time through the server, including but not limited to its own position coordinates, direction, remaining power, whether it is currently loaded and whether it is in a working state, so as to monitor power information and load information and draw a map and route of the robot in the warehouse or factory; The fault diagnosis module is used to analyze the robot's operating status, travel trajectory and connection status with the real-time monitoring module to determine whether the robot has a fault and trigger an early warning mechanism when a fault occurs.

2. The data security protection robot scheduling system according to claim 1, characterized in that: The behavior planning module has several maps built in, and uses point connection technology to organically connect the preset working points of each map in series to build a complete path network.

3. The data security protection robot scheduling system according to claim 2 is characterized in that: The behavior planning module has a built-in depth-first search algorithm for calculating the idle robot closest to the task point and calculating the shortest distance between the robot's current location and the task target point and generating multiple navigable planned paths for selection.

4. The data security protection robot scheduling system according to claim 3 is characterized in that: The behavior planning module is bidirectionally connected to a storage module for storing the robot's planned path, the travel direction and speed of a specific road section. The behavior planning module intelligently excludes the planned path that is currently occupied or congested based on the stored information and selects the optimal travel path.

5. The data security protection robot scheduling system according to claim 4 is characterized in that: During the movement of the robot, the behavior planning module compares the actual position of the robot with the travel path and combines the robot's planned path, the travel direction and speed of a specific section stored in the storage module, and uses a Bayesian filter algorithm to obtain the number of robots distributed on the travel path within the next minute, and predict in advance whether the travel path after the next work point is unobstructed.

6. The robot dispatching system with data security protection according to claim 5, characterized in that: When a robot moving in the opposite direction blocks the path or there is a potential risk, the behavior planning module will immediately activate the emergency mechanism, quickly recalculate the shortest distance path, and send the new path to the robot through the server in real time; when the robot cannot bypass the blocked path, the behavior planning module will guide the robot to the nearest work point for avoidance, and after the path is restored, the best path will be planned for the robot again.

7. The data security protection robot scheduling system according to claim 1, characterized in that: A weighing sensor is integrated on the robot.

8. The data security protection robot scheduling system according to claim 1, characterized in that: The server is also communicatively connected to a mobile terminal, and the fault information is pushed to the management personnel via the mobile terminal, and a data security module is arranged on the mobile terminal.

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