An intelligent management system and method for positioning data applied to Internet of Things products

Abstract

The present invention discloses an intelligent management system and method for positioning data applied to Internet of Things products, relating to the technical field of positioning data management. The system includes: a delivery area analysis module, a delivery task allocation module, and a delivery unit management module. The delivery area analysis module is used to obtain delivery area images and map data, divide the delivery area into path nodes and path units, label the path nodes and path units, and construct a delivery path node graph. The delivery task allocation module analyzes the delivery exchange rate of the shortest delivery path of each delivery task, makes a decision on the least grouping of each delivery task, and plans the delivery path of the delivery unit for each group. The delivery unit management module determines the path unit where the delivery unit is located according to the real-time positioning data of each delivery unit, and sets the obstacle avoidance strategy when each delivery unit moves according to the calculation result of the path unit environment change rate.

Description

Technical Field

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

[0002] With the wide application of robot technology, distribution robots used in the logistics industry have gradually become popular, and energy consumption management has a particularly significant impact on the actual distribution efficiency and continuous operation ability;

[0003] There are the following technical defects in the current energy consumption management of distribution robots: on the one hand, there is insufficient integration of positioning and energy consumption data during operation, resulting in the robot being unable to accurately judge the energy consumption requirements of the operating environment based on positioning data; on the other hand, in different sections of the distribution path, there is a lack of strategy switching driven by environmental perception, and the usage strategy of attached sensors cannot be determined according to the obstacle avoidance complexity in the actual environment. There is redundant energy consumption in the whole process of map reconstruction and environmental recognition in some areas with small changes and where navigation can be carried out through a preset map; in addition, when allocating distribution tasks, there is also a lack of analysis of the correlation between the distribution paths of different distribution tasks, resulting in unnecessary energy consumption caused by repeated path planning during actual distribution;

[0004] Therefore, an intelligent management system and method for positioning data applied to Internet of Things products are needed to make up for the above technical defects. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent management system and method for positioning data applied to Internet of Things products to solve the problems raised in the prior art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An intelligent management method for positioning data applied to Internet of Things products, the method comprising the following steps:

[0008] Step S100: Obtain the distribution area image and map data, divide the distribution area into path nodes and path units, and label the path nodes and path units to construct a distribution path node graph;

[0009] Step S200: Obtain the system distribution tasks, analyze the shortest distribution paths of each distribution task, and analyze and calculate the distribution exchange rate of each distribution task;

[0010] Step S300: Group each distribution task according to the maximum load capacity of the distribution unit, record the azimuths of all distribution points of the distribution tasks in each group, and plan a distribution path for each distribution unit;

[0011] Step S400: When each delivery unit is making a delivery, obtain the positioning information of the delivery unit in real time, use the environmental monitoring sensors in the path nodes adjacent to the current path unit to perform image recognition on the current path unit, and analyze the environmental change rate of the travel path of the delivery unit in the current path unit;

[0012] Step S500: Optimize the energy consumption of the navigation and obstacle avoidance of the delivery unit according to the environmental change rates of the path units in the delivery path.

[0013] In the above technical solution, the step S100 includes the following contents:

[0014] Analyze the delivery scope information under the jurisdiction of the current delivery site, collect the image data of the delivery areas within the delivery scope, and obtain the delivery scope map information;

[0015] Take each sub-region in the delivery area as a path unit, take the connection points between the sub-regions as path nodes, divide the delivery area, and label the attributes of each path node and path unit to construct a delivery path node map;

[0016] For any path node n, denote this path node as: ; where and are the path units connected to both sides of the path node n; for any path unit u, denote this path unit as: ;

[0017] where is the path node number adjacent to the path unit u, and 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; by precisely dividing each sub-region in the supervised delivery scope, constructing a delivery path node map, and splitting the delivery path of the delivery unit into path units, it provides a data basis for the subsequent analysis of the environmental change rates of different path units and also ensures the detailed analysis of energy consumption management.

[0018] In the above technical solution, the step S200 is divided into the following steps:

[0019] Step S201: Obtain 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 end point of the delivery task path planning;

[0020] Step S202: Use a path planning algorithm to analyze the shortest delivery path from the delivery site to the end point of the delivery task path planning;

[0021] For any delivery task t, the shortest delivery path Denoted as: ; where is the total length of the shortest delivery path of delivery task t and is the number of the path nodes passed by the shortest delivery path of delivery task t ;

[0022] 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 calculated as follows:

[0023] ;

[0024] where is the total length of the shortest delivery path of delivery task a, is the total length of the shortest delivery path of delivery task b, is the distance between the end point of the path planning of delivery task a and the end point of the path planning of delivery task b for the delivery unit;

[0025] By initially planning the delivery paths of each delivery task in the preset map, estimating the expected delivery distances of each delivery task, and calculating the delivery exchange rate of the delivery paths based on the distances of the delivery end points of each delivery task, a scientific data analysis basis is provided for subsequent grouping of delivery tasks.

[0026] In the above technical solution, the step S300 includes the following content:

[0027] Set the delivery exchange rate threshold . If there is a delivery task that satisfies that the delivery exchange rate between the current delivery task and this delivery task is greater than or equal to the delivery exchange rate threshold , then select the delivery task with the smallest delivery exchange rate as the subsequent delivery task; if all the remaining delivery tasks satisfy that the delivery exchange rate between the planned delivery task and this delivery task is less than the delivery exchange rate threshold , then it is determined that the current delivery task has no subsequent delivery task;

[0028] Select any delivery task as the initially planned delivery task of the delivery unit, and use the above method to group the delivery tasks. If the number of delivery tasks assigned to the current delivery unit is greater than the maximum load of the delivery unit or the planned delivery task of the delivery unit has no subsequent delivery task, it is determined that the grouping of the delivery unit tasks ends, and record the end points of the path planning of the initially planned delivery task of the delivery unit and all subsequent delivery tasks;

[0029] Use the dynamic programming algorithm to make the least grouping decision for all delivery tasks, and use the genetic algorithm for each group of delivery tasks to obtain the shortest path of the whole passing distance starting from the delivery site as the delivery path of the delivery unit for the current group;

[0030] Make the least grouping decision for all distribution tasks, maximize the energy consumption of all distribution tasks for centralized processing, and reduce the inherent energy consumption of redundant distribution units; also plan the shortest distribution path for the distribution tasks in each group to further reduce the energy consumption of the distribution units.

[0031] In the above technical solution, the following content is included in the step S400:

[0032] When the distribution unit is in distribution, the positioning information of the distribution unit in the distribution area is monitored in real time, and according to the preset distribution path, the path unit where the distribution unit is located and the next path node are determined;

[0033] Use the environmental monitoring sensors in the path nodes adjacent to the path unit where it is located to perform image recognition on the current path unit, calculate the environmental change rate of the path unit, and according to the formula:

[0034] ;

[0035] ;

[0036] ;

[0037] Among them, 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 occupied length of the static objects in path unit u in the preset distribution path, is the length of the preset distribution path of the distribution unit in path unit u, β is the dynamic weight, i is the number of the dynamic object in path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function;

[0038] Analyze whether each dynamic object will intersect with the preset distribution path of the distribution unit through trajectory prediction. When it is judged that the dynamic object will intersect with the preset distribution path of the distribution unit, the function outputs 1; when it is judged that the dynamic object does not intersect with the preset distribution path of the distribution unit, the function outputs 0;

[0039] Analyze the environment of each path unit through image monitoring and recognition, transfer the algorithm operation energy consumption from the operation terminal attached to the distribution unit itself to the unified operation system, further improve the continuous operation ability and distribution efficiency of the distribution unit, and avoid the redundant energy consumption of multiple round trips to the distribution site.

[0040] In the above technical solution, the following content is included in step S500:

[0041] Set the threshold of the environmental change rate of the path unit. When the delivery unit reaches the path node adjacent to the next path unit, calculate the environmental change rate of the next path unit and perform threshold judgment;

[0042] 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 interfered is small. When the delivery unit enters this path unit, obtain the orientation of static objects in the preset path and the orientation of the intersection of dynamic objects and the preset path in the image information as path interference points, and use lidar for local obstacle avoidance when reaching each path interference point;

[0043] 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 probability of the preset path being interfered is large. When the delivery unit enters this path unit, use the multi-sensor fusion method for real-time environmental monitoring and perform real-time dynamic obstacle avoidance;

[0044] Identify static and dynamic objects in each path unit according to the comparison image monitoring, and adopt different path navigation planning strategies according to different environmental characteristics, avoiding the additional energy consumption caused by using the multi-sensor fusion method for navigation and obstacle avoidance throughout the delivery process.

[0045] An intelligent management system for positioning data applied to Internet of Things products, which applies the above technical solution of an intelligent management method for positioning data applied to Internet of Things products. The system includes: a delivery area analysis module, a delivery task allocation module, and a delivery unit management module;

[0046] The delivery area analysis module is used to obtain the delivery area image and map data, divide the delivery area into path nodes and path units, label the path nodes and path units, and construct a delivery path node map; the delivery task allocation module analyzes the delivery exchange rate of the shortest delivery path of each delivery task, makes the least grouping decision for each delivery task, and plans the delivery path of the delivery unit for each group; the delivery unit management module determines the path unit where the delivery unit is located according to the real-time positioning data of each delivery unit, and sets the obstacle avoidance strategy when each delivery unit moves according to the calculation result of the environmental change rate of the path unit.

[0047] In the above technical solution, the delivery area analysis module includes: a data acquisition unit, a delivery area labeling unit, and a delivery area analysis unit;

[0048] The data acquisition unit is used to acquire the distribution area image and map data; the distribution area annotation unit divides the distribution area into path nodes and path units, and annotates the path nodes and path units, and the distribution area analysis unit constructs a distribution path node graph according to the annotation results of the path nodes and path units.

[0049] In the above technical solution, the distribution task allocation module includes: a distribution task analysis unit, a distribution task allocation unit, and a distribution path planning unit;

[0050] 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 a least grouping decision for each distribution task according to 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.

[0051] In the above technical solution, the distribution unit management module includes: a positioning and monitoring unit, a path unit analysis unit, and a travel decision-making unit;

[0052] The positioning and monitoring unit acquires the positioning information of the distribution unit in real time when each distribution unit is in distribution; the path unit analysis unit calculates the environmental change rate of the next path unit in the distribution path through image acquisition and analysis, and makes a threshold judgment; the travel decision-making unit sets the obstacle avoidance strategy when the distribution unit travels to each path unit according to the threshold judgment result of the environmental change rate of each path unit.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] In the present invention, by integrating the positioning of the distribution unit and the environmental data of the distribution area, comprehensively analyzing and making decisions on the navigation and obstacle avoidance methods of the distribution unit in different path units, the refined management of the energy consumption of the distribution unit is realized, and the continuous operation ability and distribution work efficiency of the distribution unit are improved; in the present invention, by mapping the azimuths of each distribution task, analyzing the distribution exchange rate between each distribution task, and making a least grouping decision, the distance correlation during the grouping of distribution tasks is ensured, and the redundant energy consumption caused by repeated paths is further avoided; in addition, in the present invention, by analyzing the environmental change rate of each path unit, the obstacle avoidance navigation strategy of the distribution unit is flexibly determined, the redundant energy consumption caused by the high-frequency use of sensors due to the global map reconstruction and global navigation during the distribution process is avoided, and at the same time, the operation of environmental monitoring and analysis is transferred to a unified operation system, reducing the operation pressure of the distribution unit during the distribution process and improving the continuous operation ability of the distribution unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of a method for intelligent management of positioning data applied to Internet of Things products according to the present invention;

[0056] Figure 2 This is the organizational structure diagram of an intelligent management system for positioning data applied to Internet of Things products according to the present invention. Specific implementation manners

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment: Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution:

[0059] As Figure 1 shown, the present invention provides an intelligent management method for positioning data applied to Internet of Things products. The method includes the following steps:

[0060] Step S100: Obtain the distribution area image and map data, divide the distribution area into path nodes and path units, and label the path nodes and path units to construct a distribution path node graph;

[0061] Step S200: Obtain the system distribution tasks, analyze the shortest distribution paths of each distribution task, and analyze and calculate the distribution exchange rate of each distribution task;

[0062] Step S300: Group each distribution task according to the maximum load capacity of the distribution unit, record the orientations of the distribution points of all assigned tasks in each group, and plan a distribution path for each distribution unit;

[0063] Step S400: When each distribution unit is in distribution, obtain the positioning information of the distribution unit in real time, use the environmental monitoring sensors in the path nodes adjacent to the current path unit to perform image recognition on the current path unit, and analyze the environmental change rate of the traveling path of the distribution unit in the current path unit;

[0064] Step S500: Optimize the energy consumption of the navigation and obstacle avoidance of the distribution unit according to the environmental change rate of each path unit in the distribution path.

[0065] The content included in the step S100 is as follows:

[0066] Analyze the distribution range information under the jurisdiction of the current distribution site, collect the distribution area image data within the distribution range, and obtain the distribution range map information;

[0067] Take each sub-region in the delivery area as a path unit, take the connection points between sub-regions as path nodes, divide the delivery area, and label the attributes of each path node and path unit to construct a delivery path node graph;

[0068] For any path node n, denote this path node as: ; where, and are the path units connected to both sides of path node n; For any path unit u, denote this path unit as: ;

[0069] where, is the path node number adjacent to path unit u, and any element in the array refers to the delivery distance between the path node x adjacent to path unit u and the path node y adjacent to path unit u;

[0070] In specific implementation, an external map API can be accessed to obtain the map information of the delivery scope, and the area can be divided according to the map. Taking a large-scale public area such as a public road as an example, the road intersection points can be used as path nodes. For a long road, it can be segmented to ensure the accuracy of energy consumption management; Taking the narrow area such as the internal road of a building as an example, the boundaries of each area can be used as path nodes.

[0071] The step S200 is divided into the following steps:

[0072] Step S201: Obtain all delivery task information in the system, extract the delivery destination information of each delivery task, and take the path node closest to the delivery destination of each delivery task as the end point of the delivery task path planning;

[0073] Step S202: Use the path planning algorithm to analyze the shortest delivery path from the delivery station to the end point of the delivery task path planning;

[0074] For any delivery task t, denote the shortest delivery path of delivery task t as: ; where, is the total length of the shortest delivery path of delivery task t, is the path node number passed by the shortest delivery path of delivery task t;

[0075] 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 calculated as follows:

[0076] ;

[0077] Among them, 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, is the distance between the end point of the path planning of delivery task a and the end point of the path planning of delivery task b for the delivery unit;

[0078] In specific implementation, for any delivery tasks a and b, when planning the path and using the same delivery unit for delivery, when reaching from the delivery end point of delivery task a to the delivery end point of delivery task b, the minimum distance is 0, that is, the delivery end points coincide, and the maximum distance is , that is, there is no other road between the delivery end points and it is necessary to return to the delivery station; therefore, by calculating the ratio of the distance between the delivery end points of the delivery tasks to the sum of the shortest delivery path distances of the two delivery tasks, the value range of the delivery exchange rate is limited to while also meeting the requirements of actual path planning analysis.

[0079] The step S300 includes the following content:

[0080] Set the delivery exchange rate threshold , if there is a delivery task that satisfies that the delivery exchange rate between the current delivery task and this delivery task is greater than or equal to the delivery exchange rate threshold , then select the delivery task with the smallest delivery exchange rate as the subsequent delivery task; if all the remaining delivery tasks satisfy that the delivery exchange rate between the planned delivery task and this delivery task is less than the delivery exchange rate threshold , then it is determined that the current delivery task has no subsequent delivery task;

[0081] 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 number of delivery tasks assigned to the current delivery unit is greater than the maximum load capacity of the delivery unit or the planned delivery task of the delivery unit has no subsequent delivery task, it is determined that the task grouping of the delivery unit is completed, and record the path planning end points of the initial planned delivery task of the delivery unit and all subsequent delivery tasks;

[0082] Use the dynamic programming algorithm to make the least grouping decision for all delivery tasks, and use the genetic algorithm for each group of delivery tasks to obtain the shortest path with the shortest total passing distance starting from the delivery station as the delivery path of the delivery unit for the current group;

[0083] In specific implementation, by using fewer delivery units to complete all delivery tasks, the inherent energy consumption of redundant delivery units starting from the delivery station is further avoided.

[0084] The step S400 includes the following content:

[0085] When the delivery unit is in the process of delivery, the positioning information of the delivery unit within the delivery area is monitored in real time. According to the preset delivery route, the path unit where the delivery unit is located and the next path node are determined.

[0086] Use the environmental monitoring sensors in the path nodes adjacent to the current path unit to perform image recognition on the current path unit, calculate the environmental change rate of the path unit, according to the formula:

[0087] ;

[0088] ;

[0089] ;

[0090] Where, 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 occupied length of static objects in path unit u in the preset delivery path, is the length of the preset delivery path of the delivery unit in path unit u, β is the dynamic weight, i is the number of the dynamic object in path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function;

[0091] Through trajectory prediction, analyze whether each dynamic object will intersect with the preset delivery path of the delivery unit. When it is judged that a dynamic object will intersect with the preset delivery path of the delivery unit, the function outputs 1; when it is judged that a dynamic object does not intersect with the preset delivery path of the delivery unit, the function outputs 0;

[0092] In specific implementation, since there will be newly added obstacles in the delivery area environment compared to the initially collected images, and different obstacles have different interference effects on the progress of the delivery unit. Therefore, static objects and dynamic objects are analyzed separately. For static objects, if they are not on the travel route, no obstacle avoidance analysis is performed; for dynamic objects, if it is predicted that the travel route does not intersect with the travel route of the delivery unit, it can be considered that there will be no interference to the preset path of the delivery unit.

[0093] The step S500 includes the following content:

[0094] Set the threshold of the environmental change rate of the path unit. When the delivery unit reaches the path node adjacent to the next path unit, calculate the environmental change rate of the next path unit and perform threshold judgment;

[0095] 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 interfered is small. When the delivery unit enters this path unit, obtain the orientation of static objects in the preset path and the orientation of the intersection point of dynamic objects and the preset path in the image information as path interference points, and use lidar for local obstacle avoidance when reaching each path interference point;

[0096] 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 probability of the preset path being interfered is large. When the delivery unit enters this path unit, use the multi-sensor fusion method for real-time environmental monitoring and perform real-time dynamic obstacle avoidance.

[0097] As Figure 2 shown, the present invention also provides an intelligent management system for positioning data applied to Internet of Things products. The system includes: a delivery area analysis module, a delivery task allocation module, and a delivery unit management module;

[0098] The delivery area analysis module is used to obtain the delivery area image and map data, divide the delivery area into path nodes and path units, and label the path nodes and path units to construct a delivery path node graph; the delivery task allocation module analyzes the delivery exchange rate of the shortest delivery paths of each delivery task, makes the least grouping decision for each delivery task, and plans the delivery paths of the delivery units in each group; the delivery unit management module determines the path unit where the delivery unit is located according to the real-time positioning data of each delivery unit, and sets the obstacle avoidance strategy when each delivery unit moves according to the calculation result of the environmental change rate of the path unit.

[0099] The delivery area analysis module includes: a data acquisition unit, a delivery area labeling unit, and a delivery area analysis unit;

[0100] The data acquisition unit is used to obtain the delivery area image and map data; the delivery area labeling unit divides the delivery area into path nodes and path units and labels the path nodes and path units, and the delivery area analysis unit constructs a delivery path node graph according to the labeling results of the path nodes and path units.

[0101] The delivery task allocation module includes: a delivery task analysis unit, a delivery task allocation unit, and a delivery path planning unit;

[0102] The delivery task analysis unit is used to analyze and calculate the delivery exchange rate between the shortest delivery paths of each delivery task; the delivery task allocation unit makes the least grouping decision for each delivery task according to the shortest delivery path and the maximum load capacity of the delivery unit and allocates the delivery unit; the delivery path planning unit plans the shortest delivery path for each group of delivery units.

[0103] The delivery unit management module includes: a positioning and monitoring unit, a path unit analysis unit, and a travel decision-making unit;

[0104] When each delivery unit is making a delivery, the positioning and monitoring unit obtains the positioning information of the delivery unit in real time; 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 makes a threshold judgment; the travel decision-making 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.

[0105] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claimed invention.

Claims

1. An intelligent management method for positioning data applied to Internet of Things products, characterized in that , The method includes the following steps: Step S100: Obtain the distribution area image and map data, divide the distribution area into path nodes and path units, label the path nodes and path units, and construct a distribution path node graph; Step S200: Obtain the system distribution tasks, analyze the shortest distribution paths of each distribution task, and analyze and calculate the distribution exchange rate of each distribution task; The step S200 is divided into the following steps: Step S201: Obtain all distribution task information in the system, extract the distribution destination information of each distribution task, and use the path node closest to the distribution destination of each distribution task as the end point of the distribution task path planning; Step S202: Use the path planning algorithm to analyze the shortest distribution path from the distribution site to the end point of the distribution task path planning; For any delivery task t, the shortest delivery path of delivery task t is denoted as: ; where is the total length of the shortest delivery path of delivery task t, and is the serial number of the path node passed by the shortest delivery path Step S203: Calculate the delivery exchange rate for each delivery task. For any delivery tasks a and b, the delivery exchange rate from delivery task a to delivery task b The calculation formula is as follows: ； Among them, 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, is the distance between the end point of the path planning of delivery task a and the end point of the path planning of delivery task b for the delivery unit; Step S300: Group each distribution task according to the maximum load capacity of the distribution unit, record the orientations of the distribution points of all assigned tasks in each group, and plan the distribution path for each distribution unit; Step S400: When each distribution unit is in distribution, obtain the positioning information of the distribution unit in real time, use the environmental monitoring sensors in the path nodes adjacent to the current path unit to perform image recognition on the current path unit, and analyze the environmental change rate of the travel path of the distribution unit in the current path unit; When the distribution unit is in distribution, monitor the positioning information of the distribution unit in the distribution area in real time, and determine the path unit and the next path node where the distribution unit is located according to the preset distribution path; Use the environmental monitoring sensors in the path nodes adjacent to the current path unit to perform image recognition on the current path unit, calculate the environmental change rate of the path unit, according to the formula: ； ； ； Among them, 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, and α is the static weight, is the occupied length of the static object in path unit u in the preset delivery path, is the preset delivery path length of the delivery unit in path unit u, β is the dynamic weight, and i is the number of the dynamic object in path unit u, is the number of dynamic objects in path unit u, is the intersection judgment function; Analyze whether each dynamic object will intersect with the preset delivery path of the delivery unit through trajectory prediction. When it is determined that a dynamic object will intersect with the preset delivery path of the delivery unit, the function outputs 1; when it is determined that a dynamic object does not intersect with the preset delivery path of the delivery unit, the function outputs 0; Step S500: Optimize the energy consumption of the navigation and obstacle avoidance of the distribution unit according to the environmental change rate of each path unit in the distribution path.

2. The intelligent management method for positioning data applied to Internet of Things products according to claim 1, wherein, The following contents are included in the step S100: Analyze the distribution range information under the jurisdiction of the current distribution site, collect the distribution area image data within the distribution range, and obtain the distribution range map information; Take each sub-region in the distribution area as a path unit, take the connection points between the sub-regions as path nodes, divide the distribution area, and label the attributes of each path node and path unit to construct a distribution path node graph; For any path node n, denote this path node as: ; where and are path units connected to both sides of the path node n; for any path unit u, denote this path unit as: ; Among them, is the path node number adjacent to the path unit u, and any element in the array refers to the distribution distance between the path node x adjacent to the path unit u and the path node y adjacent to the path unit u.

3. The intelligent management method for positioning data applied to Internet of Things products according to claim 2, characterized in that The following contents are included in the step S300: Set the delivery exchange rate threshold , if there is a delivery task such that the delivery exchange rate between the current delivery task and this delivery task is greater than or equal to the delivery exchange rate threshold , then select the delivery task with the smallest delivery exchange rate as the subsequent delivery task; if all remaining delivery tasks satisfy that the delivery exchange rate between the planned delivery task and this delivery task is less than the delivery exchange rate threshold , then determine that there is no subsequent delivery task for the current delivery task; Select any distribution task as the initial planned distribution task of the distribution unit, group the distribution tasks using the above method. If the distribution tasks assigned to the current distribution unit are greater than the maximum load capacity of the distribution unit or there is no subsequent distribution task for the distribution unit's planned distribution task, determine that the distribution unit task grouping is over, and record the initial planned distribution task of the distribution unit and the path planning end points of all subsequent distribution tasks; Use the dynamic programming algorithm to make the least grouping decision for all distribution tasks, and use the genetic algorithm for each group of distribution tasks to obtain the shortest path of the full route distance starting from the distribution site as the distribution path of the distribution unit for the current group.

4. The intelligent management method for positioning data applied to Internet of Things products according to claim 1, characterized in that The following contents are included in the step S500: Set the environmental change rate threshold of the path unit. When the distribution unit reaches the path node adjacent to the next path unit, calculate the environmental change rate of the next path unit 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 interfered is small. When the delivery unit enters this path unit, the azimuth of static objects in the preset path and the azimuth of the intersection point of dynamic objects and the preset path in the acquired image information are used as path interference points, and lidar is used for 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 probability of the preset path being interfered is large. When the delivery unit enters this path unit, a multi-sensor fusion method is used for real-time environmental monitoring and real-time dynamic obstacle avoidance.

5. A positioning data intelligent management system for Internet of Things products, which applies the positioning data intelligent management method for Internet of Things products described in any one of claims 1-4, characterized in that, The system includes: a delivery area analysis module, a delivery task allocation module, and a delivery unit management module; The delivery area analysis module is used to obtain the delivery area image and map data, divide the delivery area into path nodes and path units, label the path nodes and path units, and construct a delivery path node map; the delivery task allocation module analyzes the delivery exchange rate of the shortest delivery paths of each delivery task, makes the least grouping decision for each delivery task, and plans the delivery paths of the delivery units in each group; the delivery unit management module determines the path unit where the delivery unit is located according to the real-time positioning data of each delivery unit, and sets the obstacle avoidance strategy when each delivery unit moves according to the calculation result of the environmental change rate of the path unit.

6. The intelligent management system for positioning data applied to Internet of Things products according to claim 5, characterized in that, The delivery area analysis module includes: a data acquisition unit, a delivery area labeling unit, and a delivery area analysis unit; The data acquisition unit is used to obtain the delivery area image and map data; the delivery area labeling unit divides the delivery area into path nodes and path units and labels the path nodes and path units, and the delivery area analysis unit constructs a delivery path node map according to the labeling results of the path nodes and path units.

7. The intelligent management system for positioning data applied to Internet of Things products according to claim 5, characterized in that, The delivery task allocation module includes: a delivery task analysis unit, a delivery task allocation unit, and a delivery path planning unit; The delivery task analysis unit is used to analyze and calculate the delivery exchange rate between the shortest delivery paths of each delivery task; the delivery task allocation unit makes the least grouping decision for each delivery task according to the shortest delivery path and the maximum load capacity of the delivery unit and allocates the delivery unit; the delivery path planning unit plans the shortest delivery path for each group of delivery units.

8. The intelligent management system for positioning data applied to Internet of Things products according to claim 5, characterized in that, The delivery unit management module includes: a positioning monitoring unit, a path unit analysis unit, and a movement decision-making 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 makes a threshold judgment; the movement decision-making unit sets the obstacle avoidance strategy when the delivery unit moves to each path unit according to the threshold judgment result of the environmental change rate of each path unit.

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