A robot moving route intelligent control system based on internet of things

Abstract

The application belongs to the field of robots, relates to a data analysis technique, and aims to solve the problem that the existing intelligent control system for robot moving route cannot timely adjust the planned route of the robot according to the obstacle analysis result, in particular to an intelligent control system for robot moving route based on the Internet of Things, which comprises an intelligent control platform, wherein the intelligent control platform is communicatively connected with a dynamic planning module, a decision analysis module, an environment evaluation module, a route optimization module and a storage module; the starting position and the target position of the robot are acquired, all static planning routes between the starting position and the target position are acquired, a plurality of sub-nodes are set on the static planning routes, and the static planning route with the shortest path distance value is marked as a preliminary selected route; the application can plan and analyze the moving route of the robot in a dynamic environment, and obstacle analysis is performed on each sub-node to realize dynamic adjustment of the moving route of the robot.

Description

Technical Field

[0001] This invention belongs to the field of robotics and relates to data analysis technology, specifically an intelligent control system for robot movement routes based on the Internet of Things. Background Technology

[0002] Mobile robot environments can be categorized into static and dynamic environments. In a static environment, the starting and target positions are fixed, and obstacles do not change position over time. In a dynamic environment, the positions of obstacles and the target may change during the search process. Generally, due to the uncertainty of the environment, path planning in dynamic environments is more complex than path planning in static environments.

[0003] Existing intelligent control systems for robot movement paths cannot perform obstacle analysis on the robot's movement path when controlling the robot to move in a dynamic environment. As a result, they cannot adjust the robot's planned route in a timely manner based on the obstacle analysis results, leading to low robot movement efficiency. Furthermore, the robot may collide with obstacles and cause damage when executing incorrect commands.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control system for robot movement routes based on the Internet of Things, which solves the problem that existing intelligent control systems for robot movement routes cannot adjust the robot's planned route in a timely manner based on obstacle analysis results.

[0006] The technical problem to be solved by this invention is: how to provide an intelligent control system for robot movement routes based on the Internet of Things that can adjust the robot's planned route in a timely manner according to the obstacle analysis results.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An intelligent control system for robot movement routes based on the Internet of Things includes an intelligent control platform, which is communicatively connected to a dynamic planning module, a decision analysis module, an environmental assessment module, a route optimization module, and a storage module.

[0009] The dynamic planning module is used to plan and analyze the robot's movement route in a dynamic environment: obtain the robot's starting position and target position, obtain all static planning routes between the starting position and target position, set several sub-nodes on the static planning routes, mark the static planning route with the shortest path distance value as the initial route, control the robot to run on the initial route, and perform obstacle analysis when the robot runs to the sub-node and determine whether there are obstacles in the sub-node.

[0010] The decision analysis module is used to perform decision analysis on the robot's running path after receiving the decision analysis signal: determining whether other statically planned routes pass through the current sub-node; if yes, marking all statically planned routes other than the initial route that pass through the current sub-node as candidate routes, marking the candidate route with the shortest path distance as the secondary route, and controlling the robot to move on the secondary route; if no, controlling the robot to backtrack to the previous sub-node, marking all statically planned routes other than the initial route that pass through the previous sub-node as candidate routes, marking the candidate route with the shortest path distance as the secondary route, and controlling the robot to move on the secondary route;

[0011] The environmental assessment module is used to evaluate and analyze the robot's mobile environment;

[0012] The route optimization module is used to optimize and analyze the robot's movement route in a dynamic environment.

[0013] In a preferred embodiment of the present invention, the specific process of obstacle analysis when the robot reaches a sub-node includes: capturing images using a camera mounted on the front of the robot and marking the captured images as analysis images; enlarging the analysis images into pixel grid images and performing grayscale transformation; retrieving the unobstructed image corresponding to the sub-node through a storage module; marking the pixel grids within the passable area in both the analysis image and the unobstructed image as passable grids; marking the absolute value of the difference between the grayscale value of the passable grid in the analysis image and the grayscale value of the passable grid in the unobstructed image as the obstacle value of the passable grid; obtaining an obstacle threshold through a storage module; comparing the obstacle value of the passable grid with the obstacle threshold: if the obstacle value is less than the obstacle threshold, the corresponding passable grid is marked as an obstacle-free grid; if the obstacle value is greater than or equal to the obstacle threshold, the corresponding passable grid is marked as an obstacle grid; marking the ratio of the number of obstacle grids to the number of passable grids as a blocking coefficient; obtaining a blocking threshold through a storage module; comparing the blocking coefficient with the blocking threshold; and determining whether there are obstacles in the forward direction based on the comparison result.

[0014] As a preferred embodiment of the present invention, the specific process of comparing the obstruction coefficient with the obstruction threshold includes: if the obstruction coefficient is less than the obstruction threshold, it is determined that there is no obstacle in the forward direction, and the robot is controlled to continue running on the initially selected route; if the obstruction coefficient is greater than or equal to the obstruction threshold, it is determined that there is an obstacle in the forward direction, the dynamic planning module sends a decision analysis signal to the intelligent control platform, and the intelligent control platform sends the decision analysis signal to the decision analysis module after receiving the decision analysis signal.

[0015] As a preferred embodiment of the present invention, the specific process of the environmental evaluation module to evaluate and analyze the robot's mobile environment includes: after the robot completes the movement from the starting position to the target position, the number of sub-nodes traversed in the actual route of the robot is marked as the movement value, the number of decision analysis signals received by the decision analysis module is marked as the decision value, the ratio of the decision value to the movement value is marked as the environmental coefficient, the environmental threshold is obtained through the storage module, the environmental coefficient is compared with the environmental threshold, and the robot's mobile environment is judged to meet the requirements based on the comparison result.

[0016] As a preferred embodiment of the present invention, the specific process of comparing the environmental coefficient with the environmental threshold includes: if the environmental coefficient is less than the environmental threshold, it is determined that the robot's movement environment meets the requirements; if the environmental coefficient is greater than or equal to the environmental threshold, it is determined that the robot's movement environment does not meet the requirements, the environmental evaluation module sends an environmental abnormality signal to the intelligent control platform, and the intelligent control platform sends the environmental abnormality signal to the mobile terminal of the management personnel after receiving the environmental abnormality signal.

[0017] As a preferred embodiment of the present invention, the specific process of the route optimization module to optimize the robot's movement route in a dynamic environment includes: generating an optimization period; marking the movement process in which the starting position and the target position are the same within the optimization period as the optimization process; marking the sub-nodes traversed by the robot in the actual path during the optimization process as actual nodes; marking the number of sub-nodes on the statically planned route that overlap with the actual nodes as actual values ​​SJ; marking the path distance value of the statically planned route as path value JL; obtaining the optimization coefficient YH of the statically planned route in the optimization process by numerically calculating the actual value SJ and the path value; summing and averaging the optimization coefficient YH of the statically planned route in all optimization processes to obtain the optimization value; sorting the statically planned routes in descending order of optimization values ​​to obtain the optimization order; sending the optimization order to the intelligent control platform; and the intelligent control platform sending the optimization order to the storage module for storage after receiving the optimization order.

[0018] As a preferred embodiment of the present invention, the working method of the IoT-based intelligent control system for robot movement routes includes the following steps:

[0019] Step 1: Plan and analyze the robot's movement route in a dynamic environment: Obtain all static planned routes between the starting position and the target position, set several sub-nodes on the static planned routes, mark the static planned route with the shortest path distance value as the initial route, control the robot to run on the initial route, and perform obstacle analysis when the robot runs to the sub-node;

[0020] Step 2: After receiving the decision analysis signal, perform decision analysis on the robot's running path: determine whether other statically planned routes pass through the current child node and obtain the secondary route, and control the robot to move on the secondary route;

[0021] Step 3: Evaluate and analyze the robot's movement environment: After the robot completes its movement from the starting position to the target position, obtain the environmental coefficients during the robot's movement, and determine whether the robot's movement environment meets the requirements based on the values ​​of the environmental coefficients.

[0022] Step 4: Optimize and analyze the robot's movement path in the dynamic environment: generate an optimization cycle, obtain the optimization value within the static optimization cycle, sort the static planned routes in descending order of optimization value, and obtain the optimization order.

[0023] The present invention has the following beneficial effects:

[0024] 1. The dynamic programming module can plan and analyze the robot's movement route in a dynamic environment. By performing obstacle analysis on the selected sub-nodes of the initial route, feedback can be provided on the feasibility of continuing the initial route. Obstacle analysis is performed at each sub-node to achieve dynamic adjustment of the robot's movement route and improve the robot's movement efficiency.

[0025] 2. The decision analysis module can perform decision analysis on the robot's running path. When the robot runs to a sub-node with obstacles, the decision analysis method can quickly screen the candidate routes and select the secondary route from the candidate routes. Then, the robot can be controlled to move directly according to the remaining path of the selected route, which further improves the robot's movement efficiency.

[0026] 3. The environmental assessment module can evaluate and analyze the robot's mobile environment. By analyzing and calculating the number of sub-nodes in the robot's actual route and the number of times it encounters obstacles, an environmental coefficient is obtained. The environmental coefficient is then used to provide feedback on the robot's overall mobile environment status, allowing for timely adjustments when the mobile environment is harsh.

[0027] 4. The route optimization module can optimize and analyze the robot's movement route in a dynamic environment. By analyzing the sub-nodes traversed in the actual path of all optimization processes within the optimization cycle, the optimization value of the statically planned route is obtained. The optimization value is then used to prioritize the statically optimized routes, thereby improving the movement efficiency of the robot when moving to the same starting position and target position in subsequent operations. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0030] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, an intelligent control system for robot movement routes based on the Internet of Things includes an intelligent control platform, which is communicatively connected to a dynamic planning module, a decision analysis module, an environmental assessment module, a route optimization module, and a storage module.

[0034] The dynamic programming module is used to plan and analyze the robot's movement path in a dynamic environment: It obtains the robot's starting and target positions, acquires all statically planned routes between them, sets several child nodes on these routes, marks the route with the shortest path distance as the initial selection route, controls the robot to run on the initial selection route, and performs obstacle analysis when the robot reaches a child node: It captures images using a camera positioned on the front of the robot and marks the captured images as analysis images. The analysis images are enlarged to pixel-level images and subjected to grayscale transformation. The module retrieves the corresponding unobstructed image for each child node, marks the pixels within the passable areas in both the analysis image and the unobstructed image as passable cells, and marks the absolute value of the difference between the grayscale value of the passable cell in the analysis image and the grayscale value of the passable cell in the unobstructed image as the obstacle value of the passable cell. The module obtains the obstacle threshold and compares the obstacle value of the passable cell with the obstacle threshold: if the obstacle value is less than the obstacle threshold... If the obstacle value is greater than or equal to the obstacle threshold, the corresponding passable grid is marked as an obstacle-free grid; if the obstacle value is greater than or equal to the obstacle threshold, the corresponding passable grid is marked as an obstacle grid. The ratio of the number of obstacle grids to the number of passable grids is marked as the barrier coefficient. The barrier threshold is obtained through the storage module, and the barrier coefficient is compared with the barrier threshold: if the barrier coefficient is less than the barrier threshold, it is determined that there is no obstacle in the forward direction, and the robot continues to run on the initially selected route; if the barrier coefficient is greater than or equal to the barrier threshold, it is determined that there is an obstacle in the forward direction, and the dynamic planning module sends a decision analysis signal to the intelligent control platform. After receiving the decision analysis signal, the intelligent control platform sends the decision analysis signal to the decision analysis module. The robot's movement route in the dynamic environment is planned and analyzed. By performing obstacle analysis on the robot at the selected sub-nodes of the initially selected route, feedback is provided on the feasibility of continuing to execute the initially selected route. Obstacle analysis is performed at each sub-node to realize dynamic adjustment of the robot's movement route and improve the robot's movement efficiency.

[0035] The decision analysis module is used to perform decision analysis on the robot's running path after receiving the decision analysis signal: it determines whether other statically planned routes pass through the current sub-node; if so, it marks all statically planned routes other than the initial route that pass through the current sub-node as candidate routes, marks the candidate route with the shortest path distance as the secondary route, and controls the robot to move on the secondary route; if not, it controls the robot to back up to the previous sub-node, marks all statically planned routes other than the initial route that pass through the previous sub-node as candidate routes, marks the candidate route with the shortest path distance as the secondary route, and controls the robot to move on the secondary route; it performs decision analysis on the robot's running path, and when the robot runs to a sub-node with obstacles, it quickly filters candidate routes through decision analysis, selects the secondary route from the candidate routes, and then controls the robot to move directly according to the remaining path of the self-selected route, further improving the robot's movement efficiency.

[0036] The environmental evaluation module is used to evaluate and analyze the robot's mobile environment. After the robot completes its movement from the starting position to the target position, the number of sub-nodes traversed in the actual route is marked as the movement value, the number of decision analysis signals received by the decision analysis module is marked as the decision value, and the ratio of the decision value to the movement value is marked as the environmental coefficient. The environmental threshold is obtained through the storage module, and the environmental coefficient is compared with the environmental threshold. If the environmental coefficient is less than the environmental threshold, the robot's mobile environment is deemed to meet the requirements; if the environmental coefficient is greater than or equal to the environmental threshold, the robot's mobile environment is deemed to not meet the requirements, and the environmental evaluation module sends an environmental anomaly signal to the intelligent control platform. After receiving the environmental anomaly signal, the intelligent control platform sends the signal to the mobile terminal of the management personnel. The environmental evaluation module evaluates and analyzes the robot's mobile environment by analyzing and calculating the number of sub-nodes in the robot's actual route and the number of times obstacles are encountered to obtain the environmental coefficient. The environmental coefficient is then used to provide feedback on the overall mobile environment status of the robot, allowing for timely adjustments when the mobile environment is unfavorable.

[0037] The route optimization module is used to optimize and analyze the robot's movement route in a dynamic environment. It generates optimization cycles, marks movements where the starting and target positions are the same within the optimization cycle as optimization processes, marks the sub-nodes traversed by the robot's actual path as actual nodes, marks the number of sub-nodes on the statically planned route that overlap with actual nodes as the actual value SJ, and marks the path distance value of the statically planned route as the path value JL. The optimization coefficient YH of the statically planned route in the optimization process is obtained using the formula YH = (α1*SJ) / (α2*JL). The optimization coefficient is a value reflecting the priority of the statically planned route in the robot's movement process; the larger the optimization coefficient, the higher the priority of the statically planned route in the robot's movement process. The higher the priority, the better; where α1 and α2 are both proportional coefficients, and α1 > α2 > 1; the optimization coefficients YH of the static planning route in all optimization processes are summed and averaged to obtain the optimization value. The static planning routes are sorted in descending order of optimization value to obtain the optimization order. The optimization order is sent to the intelligent control platform. After receiving the optimization order, the intelligent control platform sends the optimization order to the storage module for storage; the robot's movement route in the dynamic environment is optimized and analyzed. By analyzing the sub-nodes passed in the actual path of all optimization processes within the optimization cycle, the optimization value of the static planning route is obtained. The optimization value is then used to prioritize the static optimization routes, thereby improving the movement efficiency of the robot when moving to the same starting position and target position in subsequent processes.

[0038] Example 2

[0039] like Figure 2 As shown, an intelligent control method for robot movement routes based on the Internet of Things includes the following steps:

[0040] Step 1: Plan and analyze the robot's movement route in a dynamic environment: Obtain all static planned routes between the starting position and the target position, set several sub-nodes on the static planned routes, mark the static planned route with the shortest path distance value as the initial route, control the robot to run on the initial route, and perform obstacle analysis when the robot runs to the sub-node;

[0041] Step 2: After receiving the decision analysis signal, perform decision analysis on the robot's running path: determine whether other statically planned routes pass through the current child node and obtain the secondary route, and control the robot to move on the secondary route;

[0042] Step 3: Evaluate and analyze the robot's movement environment: After the robot completes its movement from the starting position to the target position, obtain the environmental coefficients during the robot's movement, and determine whether the robot's movement environment meets the requirements based on the values ​​of the environmental coefficients.

[0043] Step 4: Optimize and analyze the robot's movement path in the dynamic environment: generate an optimization cycle, obtain the optimization value within the static optimization cycle, sort the static planned routes in descending order of optimization value, and obtain the optimization order.

[0044] An IoT-based intelligent control system for robot movement routes, during operation, acquires all statically planned routes between the starting position and the target position, sets several sub-nodes on the statically planned routes, marks the statically planned route with the shortest path distance as the initial route, controls the robot to run on the initial route, and performs obstacle analysis when the robot runs to a sub-node; determines whether other statically planned routes pass through the current sub-node and acquires a secondary route, controls the robot to move on the secondary route; after the robot completes the movement from the starting position to the target position, acquires the environmental coefficients during the robot's movement, and determines whether the robot's movement environment meets the requirements based on the values ​​of the environmental coefficients; generates an optimization cycle, acquires the optimization values ​​within the optimization cycle of the static optimization route, and sorts the statically planned routes in descending order of optimization values ​​to obtain the optimization order.

[0045] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0046] The above formulas are all derived from software simulation using a large amount of data, and are selected to be close to the true values. The coefficients in the formulas are set by those skilled in the art according to the actual situation; for example, the formula YH=(α1*SJ) / (α2*JL); those skilled in the art collect multiple sets of sample data and set corresponding optimization coefficients for each set of sample data; substitute the set optimization coefficients and the collected sample data into the formulas, and any three formulas form a system of three linear equations in three variables. The calculated coefficients are filtered and the average value is taken, and the values ​​of α1, α2 and α3 are 3.74, 2.97 and 2.65 respectively.

[0047] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the optimization coefficient initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, such as the optimization coefficient being proportional to the actual value.

[0048] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0049] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control system for robot movement routes based on the Internet of Things, characterized in that, It includes an intelligent control platform, which is communicatively connected to a dynamic planning module, a decision analysis module, an environmental assessment module, a route optimization module, and a storage module; The dynamic planning module is used to plan and analyze the robot's movement route in a dynamic environment: obtain the robot's starting position and target position, obtain all static planning routes between the starting position and target position, set several sub-nodes on the static planning routes, mark the static planning route with the shortest path distance value as the initial route, control the robot to run on the initial route, and perform obstacle analysis when the robot runs to the sub-node and determine whether there are obstacles in the sub-node. The decision analysis module is used to perform decision analysis on the robot's running path after receiving the decision analysis signal: determining whether other statically planned routes pass through the current sub-node; if yes, marking all statically planned routes other than the initial route that pass through the current sub-node as candidate routes, marking the candidate route with the shortest path distance as the secondary route, and controlling the robot to move on the secondary route; if no, controlling the robot to backtrack to the previous sub-node, marking all statically planned routes other than the initial route that pass through the previous sub-node as candidate routes, marking the candidate route with the shortest path distance as the secondary route, and controlling the robot to move on the secondary route; The environmental assessment module is used to evaluate and analyze the robot's mobile environment; The route optimization module is used to optimize and analyze the robot's movement route in a dynamic environment; The specific process of the route optimization module to optimize the robot's movement route in a dynamic environment includes: generating an optimization cycle, marking the movement process where the starting position and the target position are the same within the optimization cycle as the optimization process, marking the sub-nodes traversed by the robot in the actual path during the optimization process as actual nodes, marking the number of sub-nodes that overlap with actual nodes on the statically planned route as the actual value SJ, marking the path distance value of the statically planned route as the path value JL, and obtaining the optimization coefficient YH of the statically planned route in the optimization process by numerically calculating the actual value SJ and the path value. The optimization coefficients YH of the static planning route in all optimization processes are summed and averaged to obtain the optimization value. The static planning routes are sorted in descending order of optimization value to obtain the optimization order. The optimization order is sent to the intelligent control platform. After receiving the optimization order, the intelligent control platform sends the optimization order to the storage module for storage.

2. The intelligent control system for robot movement routes based on the Internet of Things according to claim 1, characterized in that, The specific process of obstacle analysis when the robot reaches a child node includes: capturing images using a camera positioned at the front of the robot and marking the captured images as analysis images; enlarging the analysis images into pixel-level images and performing grayscale transformation; retrieving the corresponding unobstructed image for the child node through the storage module; marking the pixels within the passable area in both the analysis image and the unobstructed image as passable cells; marking the absolute value of the difference between the grayscale value of the passable cell in the analysis image and the grayscale value of the passable cell in the unobstructed image as the obstacle value of the passable cell; obtaining the obstacle threshold through the storage module; comparing the obstacle value of the passable cell with the obstacle threshold: if the obstacle value is less than the obstacle threshold, the corresponding passable cell is marked as an obstacle-free cell; if the obstacle value is greater than or equal to the obstacle threshold, the corresponding passable cell is marked as an obstacle cell; marking the ratio of the number of obstacle cells to the number of passable cells as the obstruction coefficient; obtaining the obstruction threshold through the storage module; comparing the obstruction coefficient with the obstruction threshold; and determining whether there are obstacles in the forward direction based on the comparison result.

3. The intelligent control system for robot movement routes based on the Internet of Things according to claim 2, characterized in that, The specific process of comparing the barrier coefficient with the barrier threshold includes: if the barrier coefficient is less than the barrier threshold, it is determined that there is no obstacle in the direction of travel, and the robot continues to run on the initially selected route; if the barrier coefficient is greater than or equal to the barrier threshold, it is determined that there is an obstacle in the direction of travel, the dynamic planning module sends a decision analysis signal to the intelligent control platform, and the intelligent control platform sends the decision analysis signal to the decision analysis module after receiving the decision analysis signal.

4. The intelligent control system for robot movement routes based on the Internet of Things according to claim 3, characterized in that, The specific process of the environmental evaluation module to evaluate and analyze the robot's mobile environment includes: after the robot completes the movement from the starting position to the target position, the number of sub-nodes traversed in the actual route of the robot is marked as the movement value, the number of decision analysis signals received by the decision analysis module is marked as the decision value, the ratio of the decision value to the movement value is marked as the environmental coefficient, the environmental threshold is obtained through the storage module, the environmental coefficient is compared with the environmental threshold, and the comparison result is used to determine whether the robot's mobile environment meets the requirements.

5. The intelligent control system for robot movement routes based on the Internet of Things according to claim 4, characterized in that, The specific process of comparing the environmental coefficient with the environmental threshold includes: if the environmental coefficient is less than the environmental threshold, the robot's movement environment is determined to meet the requirements; if the environmental coefficient is greater than or equal to the environmental threshold, the robot's movement environment is determined to not meet the requirements, the environmental evaluation module sends an environmental anomaly signal to the intelligent control platform, and the intelligent control platform sends the environmental anomaly signal to the mobile terminal of the management personnel after receiving the environmental anomaly signal.

6. A robot movement route intelligent control system based on the Internet of Things according to any one of claims 1-5, characterized in that, The working method of this IoT-based intelligent control system for robot movement routes includes the following steps: Step 1: Plan and analyze the robot's movement route in a dynamic environment: Obtain all static planned routes between the starting position and the target position, set several sub-nodes on the static planned routes, mark the static planned route with the shortest path distance value as the initial route, control the robot to run on the initial route, and perform obstacle analysis when the robot runs to the sub-node; Step 2: After receiving the decision analysis signal, perform decision analysis on the robot's running path: determine whether other statically planned routes pass through the current child node and obtain the secondary route, and control the robot to move on the secondary route; Step 3: Evaluate and analyze the robot's movement environment: After the robot completes its movement from the starting position to the target position, obtain the environmental coefficients during the robot's movement, and determine whether the robot's movement environment meets the requirements based on the values ​​of the environmental coefficients. Step 4: Optimize and analyze the robot's movement path in the dynamic environment: generate an optimization cycle, obtain the optimization value within the static optimization cycle, sort the static planned routes in descending order of optimization value, and obtain the optimization order.

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