Positioning data intelligent management system and method applied to Internet of Things product

Abstract

The invention discloses a positioning data intelligent management system and method applied to an Internet of Things product, and relates to the technical field of positioning data management, and the system comprises a distribution area analysis module, a distribution task distribution module, and a distribution unit management module. The distribution area analysis module is used for acquiring a distribution area image and map data, dividing a distribution area into path nodes and path units, marking the path nodes and the path units, and constructing a distribution path node graph; the distribution task distribution module analyzes the distribution exchange rate of the shortest distribution path of each distribution task, carries out the least grouping decision of each distribution task, and plans the distribution path of the distribution unit of each group. And the distribution unit management module determines the path units where the distribution units are located according to the real-time positioning data of the distribution units, and sets obstacle avoidance strategies when the distribution units advance according to the calculation results of the environment change rates of the path units.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning data management, and in particular to an intelligent management system and method for positioning data applied to Internet of Things products. Background Art

[0002] With the widespread use of robotics technology, delivery robots used in the logistics industry are becoming increasingly popular, and energy consumption management has a significant impact on actual delivery efficiency and sustainable operation capabilities; There are the following technical defects in the energy consumption management of today's delivery robots: on the one hand, the lack of integration of positioning and energy consumption data during operation makes it impossible for the robot to accurately judge the operating energy consumption requirements of the environment based on the positioning data; on the other hand, there is a lack of environmental perception-driven strategy switching in different sections of the delivery route, and it is impossible to determine the use strategy of the attached sensors based on the complexity of obstacle avoidance in the actual environment. The whole process of map reconstruction and environmental recognition has redundant energy consumption in some areas with small changes and can be navigated through preset maps; in addition, when allocating delivery tasks, there is also a lack of analysis of the relationship between delivery paths for different delivery tasks, resulting in unnecessary energy consumption due to repeated path planning during actual delivery; Therefore, there is a need for an intelligent management system and method for positioning data applied to IoT products to make up for the above technical defects. Summary of the invention

[0003] The purpose of the present invention is to provide a positioning data intelligent management system and method applied to Internet of Things products to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for intelligent management of positioning data applied to Internet of Things products, the method comprising the following steps: Step S100: Acquire the delivery area image and map data, divide the delivery area into path nodes and path units, and mark the path nodes and path units to construct a delivery path node graph; Step S200: Obtain system delivery tasks, analyze the shortest delivery path of each delivery task, and analyze and calculate the delivery exchange rate of each delivery task; Step S300: grouping each delivery task according to the maximum load of the delivery unit, recording the locations of all assigned task delivery points in each group, and planning a delivery route for each delivery unit; Step S400: when each delivery unit is delivering, the delivery unit positioning information is obtained in real time, the environment monitoring sensor in the path node adjacent to the path unit is used to perform image recognition on the current path unit, and the environment change rate of the delivery unit in the path of the current path unit is analyzed; Step S500: Optimizing energy consumption of navigation and obstacle avoidance of the delivery unit according to the environmental change rate of each path unit in the delivery path.

[0005] In the above technical solution, step S100 includes the following contents: Analyze the distribution range information under the jurisdiction of the current distribution station, collect the distribution area image data within the distribution range, and obtain the distribution range map information; The distribution area is divided by taking each sub-area as a path unit and the connection points between sub-areas as path nodes. The attributes of each path node and path unit are annotated to construct a distribution path node graph. For any path node n, the path node is recorded as: ;in, and is the path unit connected to both sides of the path node n; for any path unit u, the path unit is recorded as: ; in, is the path node number adjacent to path unit u. Any element in the array It refers to the delivery distance between the path node x adjacent to the path unit u and the path node y adjacent to the path unit u. By accurately dividing the sub-areas in the supervised distribution range, a distribution path node diagram is constructed, and the distribution path of the distribution unit is divided into path units, which provides a data basis for the subsequent analysis of the environmental change rate of different path units and ensures the detailed analysis of energy consumption management.

[0006] In the above technical solution, the step S200 is divided into the following steps: Step S201: Acquire all the delivery task information in the system, extract the delivery destination information of each delivery task, and use the path node closest to the delivery destination of each delivery task as the delivery task path planning end point; Step S202: using a path planning algorithm to analyze the shortest delivery path from the delivery site to the delivery task path planning destination; For any delivery task t, the shortest delivery path of delivery task t Denoted as: ;in, The shortest delivery path for delivery task t Total length, The shortest delivery path for delivery task t The node number of the path passed; Step S203: Calculate the delivery exchange rate of each delivery task. For any delivery tasks a and b, the delivery exchange rate from delivery task a to delivery task b is The calculation formula is as follows: ; in, is the total length of the shortest delivery path for delivery task a, is the total length of the shortest delivery path for delivery task b, The distance between the delivery unit's route planning endpoint for delivery task a and the route planning endpoint for delivery task b; By initially planning the delivery routes for each delivery task in the preset map, calculating the estimated delivery distance for each delivery task, and calculating the delivery exchange rate of the delivery route based on the distance to the delivery destination of each delivery task, a scientific data analysis basis is provided for the subsequent grouping of delivery tasks.

[0007] In the above technical solution, step S300 includes the following contents: Set the delivery exchange rate threshold If there is a delivery task that satisfies the current delivery task and the delivery exchange rate of the delivery task is greater than or equal to the delivery exchange rate threshold , then the delivery task with the smallest delivery exchange rate is selected as the subsequent delivery task; if all remaining delivery tasks satisfy the requirement that the delivery exchange rate between the planned delivery task and the delivery task is less than the delivery exchange rate threshold , it is determined that the current delivery task has no subsequent delivery task; Select any delivery task as the initial planned delivery task of the delivery unit, and use the above method to group the delivery tasks. If the delivery task assigned to the current delivery unit is greater than the maximum load of the delivery unit or there is no subsequent delivery task in the planned delivery task of the delivery unit, the delivery unit task grouping is determined to be completed, and the path planning endpoints of the initial planned delivery task of the delivery unit and all subsequent delivery tasks are recorded; All delivery tasks are grouped using a dynamic programming algorithm, and each delivery task is grouped using a genetic algorithm to obtain the shortest path from the delivery site as the starting point, as the delivery path for the current group of delivery units; The minimum grouping decision is made for all delivery tasks, and the energy consumption of all delivery tasks is maximized and centralized, reducing the inherent energy consumption of redundant delivery units. The shortest delivery path planning is also carried out for the delivery tasks in each group to further reduce the energy consumption of the delivery units.

[0008] In the above technical solution, step S400 includes the following contents: When the distribution unit is delivering, the location information of the distribution unit in the distribution area is monitored in real time, and the path unit and the next path node of the distribution unit are determined according to the preset distribution path; Use the environmental monitoring sensor in the path node adjacent to the path unit to perform image recognition on the current path unit and calculate the environmental change rate of the path unit according to the formula: ; ; ; in, is the static change rate of path unit u, is the dynamic change rate of path unit u, is the environmental change rate of path unit u, α is the static weight, is the length occupied by the static object in the path unit u in the preset delivery path, The preset delivery path length of the delivery unit in the path unit u, β is the dynamic weight, i is the dynamic object number in the path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function; Through trajectory prediction, we analyze whether each dynamic object will intersect with the preset delivery path of the delivery unit. When it is determined that the dynamic object will intersect with the preset delivery path of the delivery unit, the function The output is 1; when it is determined that the dynamic object does not intersect with the preset delivery path of the delivery unit, the function The output is 0; The environment of each path unit is analyzed through image monitoring and recognition, and the algorithm operation energy consumption is transferred from the distribution unit's own computing terminal to a unified computing system, further improving the distribution unit's continuous operation capability and distribution efficiency, and avoiding redundant energy consumption from multiple round trips to distribution sites.

[0009] In the above technical solution, step S500 includes the following contents: Set the path unit environment change rate threshold. When the delivery unit reaches the path node adjacent to the next path unit, calculate the next path unit environment change rate and perform threshold judgment. When the environmental change rate of the next path unit is less than the environmental change rate threshold, it is determined that the current environmental change is small and the probability of the preset path being disturbed is small. When the delivery unit enters the path unit, the orientation of the static object in the preset path and the orientation of the intersection of the dynamic object and the preset path in the image information are obtained as path interference points, and the laser radar is used to perform local obstacle avoidance when reaching each path interference point; When the environmental change rate of the next path unit is greater than or equal to the environmental change rate threshold, it is determined that the current environment is relatively complex and the preset path is likely to be disturbed. When the delivery unit enters the path unit, a multi-sensor fusion method is used to perform real-time environmental monitoring and real-time dynamic obstacle avoidance. Static and dynamic objects in each path unit are identified by comparing image monitoring, and different path navigation planning strategies are adopted according to different environmental characteristics, avoiding the additional energy consumption caused by using multi-sensor fusion navigation and obstacle avoidance throughout the distribution process.

[0010] A method for intelligent management of positioning data applied to an Internet of Things product in the above technical solution is applied to an intelligent management system of positioning data applied to an Internet of Things product, the system comprising: a distribution area analysis module, a distribution task allocation module, and a distribution unit management module; The distribution area analysis module is used to obtain distribution area images and map data, divide the distribution area into path nodes and path units, and mark the path nodes and path units to construct a distribution path node diagram; the distribution task allocation module analyzes the distribution exchange rate of the shortest distribution path of each distribution task, makes the minimum grouping decision for each distribution task, and plans the distribution path of each grouped distribution unit; the distribution unit management module determines the path unit where the distribution unit is located based on the real-time positioning data of each distribution unit, and sets the obstacle avoidance strategy of each distribution unit when it is moving based on the calculation result of the path unit environment change rate.

[0011] In the above technical solution, the delivery area analysis module includes: a data acquisition unit, a delivery area marking unit, and a delivery area analysis unit; The data acquisition unit is used to acquire the distribution area image and map data; the distribution area labeling unit divides the distribution area into path nodes and path units, and labels the path nodes and path units; the distribution area analysis unit constructs a distribution path node graph based on the path node and path unit labeling results.

[0012] In the above technical solution, the delivery task allocation module includes: a delivery task analysis unit, a delivery task allocation unit, and a delivery route planning unit; The distribution task analysis unit is used to analyze and calculate the distribution exchange rate between the shortest distribution paths of each distribution task; the distribution task allocation unit makes the minimum grouping decision for each distribution task based on the shortest distribution path and the maximum load of the distribution unit and allocates the distribution unit; the distribution path planning unit plans the shortest distribution path for each grouped distribution unit.

[0013] In the above technical solution, the distribution unit management module includes: a positioning monitoring unit, a path unit analysis unit, and a travel decision unit; The positioning monitoring unit obtains the positioning information of the delivery unit in real time when each delivery unit is delivering; the path unit analysis unit calculates the environmental change rate of the next path unit in the delivery path through image acquisition and analysis, and performs threshold judgment; the travel decision unit sets the obstacle avoidance strategy when the delivery unit travels to each path unit according to the threshold judgment result of the environmental change rate of each path unit.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, by integrating the distribution unit positioning and distribution area environmental data, a comprehensive analysis is made to decide on the navigation and obstacle avoidance methods of the distribution unit in different path units, thereby realizing refined management of the distribution unit energy consumption and improving the sustainable operation capability and distribution work efficiency of the distribution unit; in the present invention, by carrying out azimuth mapping of each distribution task, analyzing the distribution exchange rate between each distribution task, and making a minimum grouping decision, thereby ensuring the distance correlation when the distribution tasks are grouped, and further avoiding redundant energy consumption caused by path duplication; in addition, in the present invention, by analyzing the environmental change rate of each path unit, the obstacle avoidance and navigation strategy of the distribution unit is flexibly decided, and redundant energy consumption caused by high-frequency use of sensors due to full map reconstruction and global navigation during the distribution process is avoided. At the same time, the operation of environmental monitoring and analysis is transferred to a unified operation system, thereby reducing the operation pressure of the distribution unit during the distribution process and improving the sustainable operation capability of the distribution unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for intelligent management of positioning data applied to Internet of Things products of the present invention; Figure 2 This is an organizational structure diagram of an intelligent management system for positioning data applied to Internet of Things products in the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example: See Figure 1-Figure 2 , the present invention provides a technical solution: like Figure 1 As shown in the figure, the present invention provides a method for intelligent management of positioning data applied to Internet of Things products, the method comprising the following steps: Step S100: Acquire the delivery area image and map data, divide the delivery area into path nodes and path units, and mark the path nodes and path units to construct a delivery path node graph; Step S200: Obtain system delivery tasks, analyze the shortest delivery path of each delivery task, and analyze and calculate the delivery exchange rate of each delivery task; Step S300: grouping each delivery task according to the maximum load of the delivery unit, recording the locations of all assigned task delivery points in each group, and planning a delivery route for each delivery unit; Step S400: when each delivery unit is delivering, the delivery unit positioning information is obtained in real time, the environment monitoring sensor in the path node adjacent to the path unit is used to perform image recognition on the current path unit, and the environment change rate of the delivery unit in the path of the current path unit is analyzed; Step S500: Optimizing energy consumption of navigation and obstacle avoidance of the delivery unit according to the environmental change rate of each path unit in the delivery path.

[0018] The step S100 includes the following contents: Analyze the distribution range information under the jurisdiction of the current distribution station, collect the distribution area image data within the distribution range, and obtain the distribution range map information; The distribution area is divided by taking each sub-area as a path unit and the connection points between sub-areas as path nodes. The attributes of each path node and path unit are annotated to construct a distribution path node graph. For any path node n, the path node is recorded as: ;in, and is the path unit connected to both sides of the path node n; for any path unit u, the path unit is recorded as: ; in, is the path node number adjacent to path unit u. Any element in the array Refers to the delivery distance between the path node x adjacent to the path unit u and the path node y adjacent to the path unit u; During the specific implementation, the external map API can be accessed to obtain the distribution range map information and divide the area according to the map. For a large public area taking public roads as an example, the road intersections can be used as path nodes. For longer roads, they can be segmented to ensure the accuracy of energy consumption management. For narrow areas taking internal roads in buildings as an example, the boundaries of each area can be used as path nodes.

[0019] The step S200 is divided into the following steps: Step S201: Acquire all the delivery task information in the system, extract the delivery destination information of each delivery task, and use the path node closest to the delivery destination of each delivery task as the delivery task path planning end point; Step S202: using a path planning algorithm to analyze the shortest delivery path from the delivery site to the delivery task path planning destination; For any delivery task t, the shortest delivery path of delivery task t Denoted as: ;in, The shortest delivery path for delivery task t Total length, The shortest delivery path for delivery task t The node number of the path passed; Step S203: Calculate the delivery exchange rate of each delivery task. For any delivery tasks a and b, the delivery exchange rate from delivery task a to delivery task b is The calculation formula is as follows: ; in, is the total length of the shortest delivery path for delivery task a, is the total length of the shortest delivery path for delivery task b, The distance between the delivery unit's route planning endpoint for delivery task a and the route planning endpoint for delivery task b; In the specific implementation, for any delivery tasks a and b, when planning the route, when using the same delivery unit for delivery, the minimum distance from the delivery destination of delivery task a to the delivery destination of delivery task b is 0, that is, the delivery destinations coincide, and the maximum distance is , that is, there is no other road between the delivery destinations, and it is necessary to return to the delivery site; therefore, the ratio of the distance between the delivery task destinations to the sum of the shortest delivery path distances of the two delivery tasks is calculated, and the delivery exchange rate value range is limited to At the same time, it can also meet the actual path planning analysis needs.

[0020] The step S300 includes the following contents: Set the delivery exchange rate threshold If there is a delivery task that satisfies the current delivery task and the delivery exchange rate of the delivery task is greater than or equal to the delivery exchange rate threshold , then the delivery task with the smallest delivery exchange rate is selected as the subsequent delivery task; if all remaining delivery tasks satisfy the requirement that the delivery exchange rate between the planned delivery task and the delivery task is less than the delivery exchange rate threshold , it is determined that the current delivery task has no subsequent delivery task; Select any delivery task as the initial planned delivery task of the delivery unit, and use the above method to group the delivery tasks. If the delivery task assigned to the current delivery unit is greater than the maximum load of the delivery unit or there is no subsequent delivery task in the planned delivery task of the delivery unit, the delivery unit task grouping is determined to be completed, and the path planning endpoints of the initial planned delivery task of the delivery unit and all subsequent delivery tasks are recorded; All delivery tasks are grouped using a dynamic programming algorithm, and each delivery task is grouped using a genetic algorithm to obtain the shortest path from the delivery site as the starting point, as the delivery path for the current group of delivery units; In specific implementation, by using fewer delivery units to complete all delivery tasks, the inherent energy consumption of redundant delivery units departing from the distribution site can be further avoided.

[0021] The step S400 includes the following contents: When the distribution unit is delivering, the location information of the distribution unit in the distribution area is monitored in real time, and the path unit and the next path node of the distribution unit are determined according to the preset distribution path; Use the environmental monitoring sensor in the path node adjacent to the path unit to perform image recognition on the current path unit and calculate the environmental change rate of the path unit according to the formula: ; ; ; in, is the static change rate of path unit u, is the dynamic change rate of path unit u, is the environmental change rate of path unit u, α is the static weight, is the length occupied by the static object in the path unit u in the preset delivery path, The preset delivery path length of the delivery unit in the path unit u, β is the dynamic weight, i is the dynamic object number in the path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function; Through trajectory prediction, we analyze whether each dynamic object will intersect with the preset delivery path of the delivery unit. When it is determined that the dynamic object will intersect with the preset delivery path of the delivery unit, the function The output is 1; when it is determined that the dynamic object does not intersect with the preset delivery path of the delivery unit, the function The output is 0; In the specific implementation, since there will be new obstacles in the distribution area environment compared to the initially collected images, and different obstacles have different interference effects on the delivery unit's movement, static objects and dynamic objects are analyzed separately. For static objects, if they are not in the travel route, obstacle avoidance analysis is not performed; for dynamic objects, if the predicted travel route does not intersect with the delivery unit's travel route, it can be considered that it will not interfere with the delivery unit's preset path.

[0022] The step S500 includes the following contents: Set the path unit environment change rate threshold. When the delivery unit reaches the path node adjacent to the next path unit, calculate the next path unit environment change rate and perform threshold judgment. When the environmental change rate of the next path unit is less than the environmental change rate threshold, it is determined that the current environmental change is small and the probability of the preset path being disturbed is small. When the delivery unit enters the path unit, the orientation of the static object in the preset path and the orientation of the intersection of the dynamic object and the preset path in the image information are obtained as path interference points, and the laser radar is used to perform local obstacle avoidance when reaching each path interference point; When the environmental change rate of the next path unit is greater than or equal to the environmental change rate threshold, it is determined that the current environment is relatively complex and the preset path is likely to be disturbed. When the delivery unit enters the path unit, a multi-sensor fusion method is used to perform real-time environmental monitoring and real-time dynamic obstacle avoidance.

[0023] like Figure 2 As shown, the present invention also provides a positioning data intelligent management system applied to Internet of Things products, the system comprising: a distribution area analysis module, a distribution task allocation module, and a distribution unit management module; The distribution area analysis module is used to obtain distribution area images and map data, divide the distribution area into path nodes and path units, and mark the path nodes and path units to construct a distribution path node diagram; the distribution task allocation module analyzes the distribution exchange rate of the shortest distribution path of each distribution task, makes the minimum grouping decision for each distribution task, and plans the distribution path of each grouped distribution unit; the distribution unit management module determines the path unit where the distribution unit is located based on the real-time positioning data of each distribution unit, and sets the obstacle avoidance strategy of each distribution unit when it is moving based on the calculation result of the path unit environment change rate.

[0024] The delivery area analysis module includes: a data acquisition unit, a delivery area marking unit, and a delivery area analysis unit; The data acquisition unit is used to acquire the distribution area image and map data; the distribution area labeling unit divides the distribution area into path nodes and path units, and labels the path nodes and path units; the distribution area analysis unit constructs a distribution path node graph based on the path node and path unit labeling results.

[0025] The delivery task allocation module includes: a delivery task analysis unit, a delivery task allocation unit, and a delivery route planning unit; The distribution task analysis unit is used to analyze and calculate the distribution exchange rate between the shortest distribution paths of each distribution task; the distribution task allocation unit makes the minimum grouping decision for each distribution task based on the shortest distribution path and the maximum load of the distribution unit and allocates the distribution unit; the distribution path planning unit plans the shortest distribution path for each grouped distribution unit.

[0026] The distribution unit management module includes: a positioning monitoring unit, a path unit analysis unit, and a travel decision unit; The positioning monitoring unit obtains the positioning information of the delivery unit in real time when each delivery unit is delivering; the path unit analysis unit calculates the environmental change rate of the next path unit in the delivery path through image acquisition and analysis, and performs threshold judgment; the travel decision unit sets the obstacle avoidance strategy when the delivery unit travels to each path unit according to the threshold judgment result of the environmental change rate of each path unit.

[0027] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for intelligent management of positioning data applied to IoT products, characterized in that , the method comprises the following steps: Step S100: Acquire the delivery area image and map data, divide the delivery area into path nodes and path units, and mark the path nodes and path units to construct a delivery path node graph; Step S200: Obtain system delivery tasks, analyze the shortest delivery path of each delivery task, and analyze and calculate the delivery exchange rate of each delivery task; Step S300: grouping each delivery task according to the maximum load of the delivery unit, recording the locations of all assigned task delivery points in each group, and planning a delivery route for each delivery unit; Step S400: when each delivery unit is delivering, the delivery unit positioning information is obtained in real time, the environment monitoring sensor in the path node adjacent to the path unit is used to perform image recognition on the current path unit, and the environment change rate of the delivery unit in the path of the current path unit is analyzed; Step S500: Optimizing energy consumption of navigation and obstacle avoidance of the delivery unit according to the environmental change rate of each path unit in the delivery path.

2. According to the method for intelligent management of positioning data applied to Internet of Things products described in claim 1, it is characterized in that: The step S100 includes the following contents: Analyze the distribution range information under the jurisdiction of the current distribution station, collect the distribution area image data within the distribution range, and obtain the distribution range map information; The distribution area is divided by taking each sub-area as a path unit and the connection points between sub-areas as path nodes. The attributes of each path node and path unit are annotated to construct a distribution path node graph. For any path node n, the path node is recorded as: ;in, and is the path unit connected to both sides of the path node n; for any path unit u, the path unit is recorded as: ; in, is the path node number adjacent to path unit u. Any element in the array Refers to the delivery distance between the path node x adjacent to the path unit u and the path node y adjacent to the path unit u.

3. According to the method for intelligent management of positioning data applied to Internet of Things products as described in claim 2, it is characterized in that: The step S200 is divided into the following steps: Step S201: Acquire all the delivery task information in the system, extract the delivery destination information of each delivery task, and use the path node closest to the delivery destination of each delivery task as the delivery task path planning end point; Step S202: using a path planning algorithm to analyze the shortest delivery path from the delivery site to the delivery task path planning destination; For any delivery task t, the shortest delivery path of delivery task t Denoted as: ;in, The shortest delivery path for delivery task t Total length, The shortest delivery path for delivery task t The node number of the path passed; Step S203: Calculate the delivery exchange rate of each delivery task. For any delivery tasks a and b, the delivery exchange rate from delivery task a to delivery task b is The calculation formula is as follows: ； in, is the total length of the shortest delivery path for delivery task a, is the total length of the shortest delivery path for delivery task b, The distance between the delivery unit's path planning endpoint of delivery task a and the path planning endpoint of delivery task b.

4. According to the method for intelligent management of positioning data applied to Internet of Things products as described in claim 2, it is characterized in that: The step S300 includes the following contents: Set the delivery exchange rate threshold If there is a delivery task that satisfies the current delivery task and the delivery exchange rate of the delivery task is greater than or equal to the delivery exchange rate threshold , then the delivery task with the smallest delivery exchange rate is selected as the subsequent delivery task; if all remaining delivery tasks satisfy the requirement that the delivery exchange rate between the planned delivery task and the delivery task is less than the delivery exchange rate threshold , it is determined that the current delivery task has no subsequent delivery task; Select any delivery task as the initial planned delivery task of the delivery unit, and use the above method to group the delivery tasks. If the delivery task assigned to the current delivery unit is greater than the maximum load of the delivery unit or there is no subsequent delivery task in the planned delivery task of the delivery unit, the delivery unit task grouping is determined to be completed, and the path planning endpoints of the initial planned delivery task of the delivery unit and all subsequent delivery tasks are recorded; All delivery tasks are grouped using a dynamic programming algorithm to make the minimum grouping decision, and the delivery tasks are grouped using a genetic algorithm to obtain the shortest path from the delivery site as the starting point, which is used as the delivery path for the delivery unit of the current group.

5. According to the method for intelligent management of positioning data applied to Internet of Things products as described in claim 2, it is characterized in that: The step S400 includes the following contents: When the distribution unit is delivering, the location information of the distribution unit in the distribution area is monitored in real time, and the path unit and the next path node of the distribution unit are determined according to the preset distribution path; Use the environmental monitoring sensor in the path node adjacent to the path unit to perform image recognition on the current path unit and calculate the environmental change rate of the path unit according to the formula: ； ； ； in, is the static change rate of path unit u, is the dynamic change rate of path unit u, is the environmental change rate of path unit u, α is the static weight, is the length occupied by the static object in the path unit u in the preset delivery path, The preset delivery path length of the delivery unit in the path unit u, β is the dynamic weight, i is the dynamic object number in the path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function; Through trajectory prediction, we analyze whether each dynamic object will intersect with the preset delivery path of the delivery unit. When it is determined that the dynamic object will intersect with the preset delivery path of the delivery unit, the function The output is 1; when it is determined that the dynamic object does not intersect with the preset delivery path of the delivery unit, the function The output is 0.

6. According to the method for intelligent management of positioning data applied to Internet of Things products as described in claim 5, it is characterized in that: The step S500 includes the following contents: Set the path unit environment change rate threshold. When the delivery unit reaches the path node adjacent to the next path unit, calculate the next path unit environment change rate and perform threshold judgment. When the environmental change rate of the next path unit is less than the environmental change rate threshold, it is determined that the current environmental change is small and the probability of the preset path being disturbed is small. When the delivery unit enters the path unit, the orientation of the static object in the preset path and the orientation of the intersection of the dynamic object and the preset path in the image information are obtained as path interference points, and the laser radar is used to perform local obstacle avoidance when reaching each path interference point; When the environmental change rate of the next path unit is greater than or equal to the environmental change rate threshold, it is determined that the current environment is relatively complex and the preset path is likely to be disturbed. When the delivery unit enters the path unit, a multi-sensor fusion method is used to perform real-time environmental monitoring and real-time dynamic obstacle avoidance.

7. An intelligent management system for positioning data applied to Internet of Things products using the intelligent management method for positioning data applied to Internet of Things products according to any one of claims 1 to 6, characterized in that: The system includes: a distribution area analysis module, a distribution task allocation module, and a distribution unit management module; The distribution area analysis module is used to obtain distribution area images and map data, divide the distribution area into path nodes and path units, and mark the path nodes and path units to construct a distribution path node diagram; the distribution task allocation module analyzes the distribution exchange rate of the shortest distribution path of each distribution task, makes the minimum grouping decision for each distribution task, and plans the distribution path of each grouped distribution unit; the distribution unit management module determines the path unit where the distribution unit is located based on the real-time positioning data of each distribution unit, and sets the obstacle avoidance strategy of each distribution unit when it is moving based on the calculation result of the path unit environment change rate.

8. According to the intelligent management system for positioning data applied to Internet of Things products as described in claim 7, it is characterized in that: The delivery area analysis module includes: a data acquisition unit, a delivery area marking unit, and a delivery area analysis unit; The data acquisition unit is used to acquire the distribution area image and map data; the distribution area labeling unit divides the distribution area into path nodes and path units, and labels the path nodes and path units; the distribution area analysis unit constructs a distribution path node graph based on the path node and path unit labeling results.

9. According to the intelligent management system for positioning data applied to Internet of Things products as described in claim 7, it is characterized in that: The delivery task allocation module includes: a delivery task analysis unit, a delivery task allocation unit, and a delivery route planning unit; The distribution task analysis unit is used to analyze and calculate the distribution exchange rate between the shortest distribution paths of each distribution task; the distribution task allocation unit makes the minimum grouping decision for each distribution task based on the shortest distribution path and the maximum load of the distribution unit and allocates the distribution unit; the distribution path planning unit plans the shortest distribution path for each grouped distribution unit.

10. According to the intelligent management system for positioning data applied to Internet of Things products as described in claim 7, it is characterized in that: The distribution unit management module includes: a positioning monitoring unit, a path unit analysis unit, and a travel decision unit; The positioning monitoring unit obtains the positioning information of the delivery unit in real time when each delivery unit is delivering; the path unit analysis unit calculates the environmental change rate of the next path unit in the delivery path through image acquisition and analysis, and performs threshold judgment; the travel decision unit sets the obstacle avoidance strategy when the delivery unit travels to each path unit according to the threshold judgment result of the environmental change rate of each path unit.

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