Intelligent logistics method and equipment based on Beidou satellite navigation and Bluetooth positioning technology

Abstract

The present application is applicable to the field of intelligent logistics technology, and in particular to an intelligent logistics method and equipment based on Beidou satellite navigation and Bluetooth positioning technology, the method comprising: understanding the route requirements of the logistics vehicle by acquiring the operating status and path parameters of the logistics vehicle; performing initial path planning for the logistics vehicle based on the path parameters, and generating a preliminary path planning signal for the logistics vehicle, so as to control changes in the operating status of the logistics vehicle; obtaining environmental adaptation parameters, and determining the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters, understanding the real-time environmental conditions of the logistics vehicle and optimizing the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning; optimizing the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle, and generating an optimized accurate path planning signal; making the driving path and the planned path highly unified, flexibly performing path planning, and improving logistics efficiency.

Description

Technical Field

[0001] The present application belongs to the field of intelligent logistics technology, and in particular, relates to an intelligent logistics method and equipment based on Beidou satellite navigation and Bluetooth positioning technology. Background Art

[0002] As an important part of the modern economy, the efficiency and cost of the logistics industry directly affect the overall performance of the supply chain. Traditional logistics systems often face problems such as information asymmetry, irrational resource allocation, and low transportation efficiency. In recent years, with the application of advanced technologies such as the Internet of Things, big data, and artificial intelligence, the logistics industry is undergoing a profound transformation, and the concept of intelligent logistics has emerged.

[0003] In the existing technology, since the logistics route planning of logistics parks often relies on static map data and manual route planning, it is unable to reflect sudden situations such as changes in road conditions in real time, resulting in a large deviation between the actual driving path and the planned path, and the route planning is not flexible, which affects logistics efficiency. Summary of the invention

[0004] The embodiment of the present application provides an intelligent logistics method and device based on Beidou satellite navigation and Bluetooth positioning technology, which can solve the problem that the logistics route planning of logistics parks often relies on static map data and manual route planning, resulting in low flexibility in path planning and affecting logistics efficiency.

[0005] In a first aspect, the embodiment of the present application provides an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology, including:

[0006] Obtaining the operation status and path parameters of the logistics vehicle; wherein the path parameters are used to reflect the route requirements of the logistics vehicle;

[0007] Performing initial path planning for the logistics vehicle based on the path parameters, and generating a preliminary path planning signal for the logistics vehicle; wherein the preliminary path planning signal is used to control the change of the operating state of the logistics vehicle;

[0008] Acquire environmental adaptation parameters, and determine the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters; wherein the environmental adaptation parameters are used to reflect the real-time environmental conditions of the logistics vehicle, and the environmental adaptation signal is used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning;

[0009] The preliminary path planning signal is optimized according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal.

[0010] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0011] The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in the embodiment of the present application understands the route requirements of the logistics vehicle by obtaining the operating status and path parameters of the logistics vehicle. Perform initial path planning for the logistics vehicle based on the path parameters, and generate a preliminary path planning signal for the logistics vehicle to control the change in the operating status of the logistics vehicle. Obtain environmental adaptation parameters, and determine the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters to understand the real-time environmental conditions of the logistics vehicle and optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning. Optimize the preliminary path planning signal based on the environmental adaptation signal of the logistics vehicle, generate an optimized accurate path planning signal, make the driving path and the planned path highly unified, flexibly perform path planning, and improve logistics efficiency.

[0012] In a second aspect, the embodiment of the present application provides an intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology, including:

[0013] An acquisition unit, used to acquire the operation status and path parameters of the logistics vehicle; wherein the path parameters are used to reflect the route requirements of the logistics vehicle;

[0014] A planning unit, configured to perform initial path planning for the logistics vehicle based on the path parameters, and generate a preliminary path planning signal for the logistics vehicle; wherein the preliminary path planning signal is used to control the change of the operating state of the logistics vehicle;

[0015] An environmental unit, used to obtain environmental adaptation parameters, and determine the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters; wherein the environmental adaptation parameters are used to reflect the real-time environmental conditions of the logistics vehicle, and the environmental adaptation signal is used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning;

[0016] The optimization unit is used to optimize the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal.

[0017] In the third aspect, an embodiment of the present application provides an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the first aspects above when executing the computer program.

[0018] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology, the intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology executes any one of the methods described in the above first aspect.

[0019] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a partial flow chart of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided by an embodiment of the present application;

[0022] Figure 2 It is a flowchart of step S200 in the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application;

[0023] Figure 3 This is another flowchart of step S200 of the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided by an embodiment of the present application;

[0024] Figure 4 It is a flowchart of step S240 of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application;

[0025] Figure 5 It is a flowchart of step S242 of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application;

[0026] Figure 6 It is a flowchart of step S2424 of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in an embodiment of the present application;

[0027] Figure 7 It is a flowchart of step S300 of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application;

[0028] Figure 8 It is a flowchart of step S400 of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application;

[0029] Fig. 9 It is a structural diagram of an intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology provided by an embodiment of the present application;

[0030] Fig.10 It is a structural diagram of an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0032] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0033] It should also be understood that the term “and / or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0034] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0035] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0037] In the existing technology, since the logistics route planning of logistics parks often relies on static map data and manual route planning, it is unable to reflect sudden situations such as changes in road conditions in real time, resulting in a large deviation between the actual driving path and the planned path, and the route planning is not flexible, which affects logistics efficiency.

[0038] To solve the above problems, the embodiments of the present application provide an intelligent logistics method, system and equipment based on Beidou satellite navigation and Bluetooth positioning technology. In this method, the route requirements of the logistics vehicle are understood by obtaining the operating status and path parameters of the logistics vehicle. The initial path planning of the logistics vehicle is performed based on the path parameters, and a preliminary path planning signal of the logistics vehicle is generated to control the change of the operating status of the logistics vehicle. The environmental adaptation parameters are obtained, and the environmental adaptation signal of the logistics vehicle is determined based on the environmental adaptation parameters to understand the real-time environmental conditions of the logistics vehicle and optimize the primary and secondary relationship of Beidou satellite positioning and Bluetooth positioning. The preliminary path planning signal is optimized based on the environmental adaptation signal of the logistics vehicle, and an optimized accurate path planning signal is generated, so that the driving path and the planned path are highly unified, and the path planning is flexibly performed to improve logistics efficiency.

[0039] The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in the embodiment of the present application can be applied to intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology. At this time, the intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology is the executor of the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology.

[0040] For example, intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology can be various types of intelligent detection equipment. Intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology can include image acquisition equipment (cameras or other imaging devices), input and output devices, communication equipment, computing equipment, and display equipment. The computing equipment can be computers, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), desktop computers, smart screens, smart TVs, etc.

[0041] In order to better understand the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in the embodiment of the present application, the specific implementation process of the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in the embodiment of the present application is exemplarily introduced below.

[0042] Figure 1 A schematic flow chart of an intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology provided in an embodiment of the present application is shown. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology includes:

[0043] S100, obtaining the operation status and path parameters of the logistics vehicle; wherein the path parameters are used to reflect the route requirements of the logistics vehicle.

[0044] It can be understood that the operating status can include the real-time location, movement status, direction, fuel or battery status, cargo loading status, etc. of the vehicle. The operating status can be collected by various sensors and IoT devices on the vehicle and transmitted and acquired through wireless communication technology. Path parameters are mainly used to reflect the route requirements of logistics vehicles. Path parameters can include the starting and target positions of the vehicle in the logistics park, intermediate stops, etc. By obtaining these parameters, effective path planning can be carried out in the park to ensure that the vehicle can complete the transportation tasks in the park safely and efficiently. There are many specific ways to obtain path parameters. Path parameters can be obtained by accessing the logistics management system or based on order requirements. The operating status and path parameters are not only the basic data for the initial path planning of logistics vehicles, but also provide necessary support for subsequent dynamic adjustment and optimization.

[0045] S200, performing initial path planning for the logistics vehicle based on the path parameters, and generating a preliminary path planning signal for the logistics vehicle; wherein the preliminary path planning signal is used to control the change of the operating state of the logistics vehicle.

[0046] It can be understood that initial path planning is one of the important functions of the logistics vehicle scheduling system. It calculates based on the acquired path parameters, generates a preliminary path planning signal, and guides the logistics vehicle to travel along the optimal path. The preliminary path planning signal includes the starting position, target position, intermediate stop points, estimated arrival time, and road condition warning information of the vehicle. Path planning can be optimized using a variety of algorithms, such as A* algorithm, Dijkstra algorithm, genetic algorithm, etc. These algorithms can find the shortest path or the optimal path based on various path parameters. The acquired path parameters can be used as input. The path parameters may include the starting position, target position, and intermediate stop points of the vehicle. The path parameters are matched with the preset path planning library to find the path planning scheme closest to the input parameters. If the match is successful, the preliminary path planning information of the logistics vehicle can be determined according to the matching result; if the match is unsuccessful, the path planning calculation can be performed based on the path parameters to generate new path planning information. Based on the preliminary path planning information, a preliminary path planning signal of the logistics vehicle is generated, and the preliminary path planning signal will be transmitted to the vehicle through the vehicle navigation system or other control devices to guide the vehicle to travel along the planned route. The initial path planning signal is an operation instruction used to control the logistics vehicle to switch from one motion state to another. The initial path planning based on path parameters not only improves the efficiency of logistics transportation within the park, but also effectively reduces transportation costs and avoids the waste of time and resources caused by improper path selection.

[0047] In one possible implementation, see Figure 2 The path parameters include the starting position and the target position of the logistics vehicle. S200 performs initial path planning for the logistics vehicle based on the path parameters and generates a preliminary path planning signal for the logistics vehicle, including:

[0048] S210, matching a preset path planning library according to the starting position and target position of the logistics vehicle.

[0049] It can be understood that the path planning library is a database containing preset paths, which stores a large amount of possible path information and historical transportation data. When planning a path, you can quickly find a path plan suitable for the current transportation task by matching the path parameters with the preset paths in the path planning library. The starting position and target position of the logistics vehicle can be used as input parameters and input into the path planning library for matching. Through matching algorithms (such as similarity matching, pattern recognition, etc.), find the preset path that best matches the input parameters.

[0050] S220, when the preset path planning library is matched successfully, the preliminary path planning information of the logistics vehicle is determined according to the matching result.

[0051] It can be understood that when the preset path planning library is matched successfully, the matched path information can be output as preliminary path planning information to utilize existing historical data and preset paths to quickly perform path planning, thereby reducing calculation time and complexity.

[0052] S230, performing initial path planning for the logistics vehicle based on the preliminary path planning information of the logistics vehicle, and generating a preliminary path planning signal for the logistics vehicle.

[0053] It can be understood that after successfully matching the preset path planning library and determining the preliminary path planning information, the logistics vehicle can be initially path planned based on this information to generate a preliminary path planning signal. The preliminary path planning signal will guide the logistics vehicle to travel along the optimal path. The preliminary path planning information matched from the path planning library can be parsed to determine the various nodes and paths for the logistics vehicle to travel. The preliminary path planning information path information can be converted into a signal that controls the change in the vehicle's operating state, that is, the preliminary path planning signal, and the generated path planning signal can be transmitted to the navigation system or other control device of the logistics vehicle to guide the vehicle to travel along the planned route. During the driving process of the logistics vehicle, the driving status and path can be monitored in real time, and dynamic adjustments can be made according to the actual situation to ensure that the logistics vehicle can complete the transportation task in the park safely and on time according to the planned route.

[0054] In one possible implementation, see Figure 3 S200, performing initial path planning for the logistics vehicle based on the path parameters, generating a preliminary path planning signal for the logistics vehicle, further comprising:

[0055] S240, when matching the preset path planning library is unsuccessful, determining preliminary path planning information of the logistics vehicle according to the starting position and target position of the logistics vehicle.

[0056] It is understandable that in some cases, matching the preset path planning library may fail, for example, a preset path that highly matches the input parameters cannot be found in the path library. The path planning can be re-performed based on the starting position and target position of the logistics vehicle to determine the preliminary path planning information. The starting position and target position of the logistics vehicle can be input into the path planning system as new path planning parameters. A suitable path planning algorithm (such as A* algorithm, Dijkstra algorithm, etc.) can be selected to recalculate the path. According to the input path parameters, the path calculation is performed to generate new path planning information.

[0057] Optionally, see Figure 4 , S240, determining preliminary path planning information of the logistics vehicle based on the starting position and the target position of the logistics vehicle, including:

[0058] S241, according to the starting position and the target position of the logistics vehicle, determine the first grid position information corresponding to the starting position in the preset map grid and the second grid position information corresponding to the target position in the preset map grid.

[0059] It can be understood that in the process of path planning, in order to improve the calculation efficiency and accuracy, the map can be pre-divided into multiple grid areas, and the preliminary path planning information of the logistics vehicle can be determined by the grid path planning method. The map can be divided into multiple grid areas according to certain rules, and each grid area represents a small area on the map. The grid area can be a rectangle, hexagon, etc., and the appropriate shape can be selected according to actual needs, and the park map can be regarded as a map coordinate system according to the grid area. According to the starting position and target position of the logistics vehicle, the grid where the starting position is located (first grid position information) and the grid where the target position is located (second grid position information) are determined. For example, the first grid position information corresponding to the preset map grid of the starting position can be (x1, y1), and the second grid position information corresponding to the preset map grid of the target position can be (x2, y2), so that any position in the park can find a determined coordinate position (X, Y) on the grid map.

[0060] S242, performing grid path planning based on the first grid location information and the second grid location information to determine preliminary path planning information for the logistics vehicle.

[0061] It can be understood that after determining the grids where the starting position and the target position are located, the optimal path from the first grid to the second grid is calculated through a grid path planning algorithm (such as the A* algorithm, the Dijkstra algorithm, etc.). Factors such as the connection relationship between each grid, the path length, and the estimated time need to be considered during the path calculation process. The calculated grid path information is used as preliminary path planning information to generate the corresponding path planning signal. The path planning method based on the grid map can greatly improve the efficiency and accuracy of path calculation, and is suitable for large-scale logistics park maps and path planning under complex road conditions.

[0062] For example, see Figure 5 S242, performing grid path planning based on the first grid location information and the second grid location information to determine preliminary path planning information for the logistics vehicle, including:

[0063] S2421, searching in a preset path planning library according to the first grid position information and the second grid position information to obtain a plurality of preset path planning information; wherein the preset path planning information includes at least one of the first grid position information and the second grid position information.

[0064] It can be understood that when performing grid path planning, a preset path planning library can be used to search for paths and find multiple preset path planning information that meet the conditions. These preset path planning information include at least one of the first grid location information and the second grid location information. A large number of possible path plans are stored in the path planning library, each of which contains detailed information such as the starting grid, the target grid, the path length, and the estimated time. According to the first grid location information and the second grid location information, a search is performed in the preset path planning library to find multiple preset path planning information that meet the conditions. The search algorithm can use efficient data structures such as hash tables and tree structures to quickly find matching path information. The multiple preset path planning information searched can be screened to remove paths that do not meet the requirements of the first grid location information and the second grid location information, and retain the path information that meets the conditions.

[0065] S2422, clustering the plurality of preset path planning information to obtain a plurality of path planning information groups; wherein the path planning information group includes at least two pieces of preset path planning information having similar path nodes.

[0066] It can be understood that after searching for multiple preset path planning information, these path information can be clustered by a clustering algorithm to obtain multiple path planning information groups. Each path planning information group includes at least two preset path planning information with similar path nodes. Suitable clustering algorithms can be selected, such as K-means algorithm, hierarchical clustering algorithm, etc., which can cluster the preset path planning information with similar path nodes into the same group. It is possible to extract features from the path nodes of each preset path planning information to form a feature vector. The feature vector can include path length, estimated time, distance between nodes, etc. According to the feature vector of the path node, clustering calculation is performed to cluster the preset path planning information of similar path nodes into the same group. In the clustering process, factors such as similarity and distance between path nodes can be used, and each cluster is used as a path planning information group.

[0067] S2423, determining the position information of the first grid position information and the second grid position information in each path planning information group based on the first grid position information, the second grid position information and a plurality of path planning information groups.

[0068] It can be understood that after clustering multiple preset path planning information and generating path planning information groups, the position information of the first grid position information and the second grid position information in each path planning information group can be determined. The position information of the path node in each path planning information group can be determined by parsing the path node in each path planning information group. The first grid position information and the second grid position information are mapped to each path planning information group, and the specific positions of the first grid position information and the second grid position information in each path planning information group are determined to obtain the position information of the first grid position information and the second grid position information in each path planning information group.

[0069] S2424, determining preliminary path planning information of the logistics vehicle based on the location information of the first grid location information and the second grid location information in each path planning information group.

[0070] It is understood that after determining the position information of the first grid position information and the second grid position information in each path planning information group, the preliminary path planning information of the logistics vehicle can be determined based on these position information. The position information in each path planning information group can be integrated to form complete path planning information.

[0071] For example, see Figure 6 S2424, based on the first grid location information and the second grid location information in each path planning information group, determining preliminary path planning information of the logistics vehicle, including:

[0072] S24241, based on the position information of the first grid position information and the second grid position information in each path planning information group, quantify the path association strength of each path planning information group with the first grid position information and the second grid position information.

[0073] It can be understood that based on the position information of the first grid position information and the second grid position information in each path planning information group, those path planning information groups that only contain the first grid position information or the second grid position information can be eliminated. By traversing each path planning information group, the path planning information group containing both the first grid position information and the second grid position information is screened out. For the screened path planning information group containing both the first grid position information and the second grid position information, the association strength of each path planning information in each path planning information group with the first grid position information and the second grid position can be calculated and accumulated to obtain the association strength of each path planning information group with the first grid position information and the second grid position, that is, the closer to the possible target route. The calculation formula for the association strength of each path planning information in each path planning information group can be defined based on a variety of factors, which may include the distance between path nodes, the total length of the path, the expected driving time, etc.

[0074] For example, the path association strength can be expressed as follows: Rs=α / Ds+β / Ts+γ / Cs. Rs is the path association strength, Ds is the total distance of the path, Ts is the estimated travel time of the path, Cs is the connection complexity of the path, and α, β, and γ are weight coefficients used to adjust the influence of each factor in the total strength calculation. The total distance of the Ds path can be calculated by calculating the sum of the distances between all nodes in the path between the first grid location information and the second grid location information. , where d i,i+1 is the distance between adjacent nodes i and i+1 in the path, and n is the total number of nodes in the path. The shorter the path distance Ds, the greater the path association strength. The estimated travel time Ts of the calculated path can be queried or estimated based on historical traffic data. , where v i is the estimated speed of the vehicle between nodes i and i+1 in the path. The shorter the estimated time Ts, the greater the path association strength. The connection complexity Cs of the path can calculate, for example, the number of turns, traffic lights, and intersections in the path. , where c j is the value of the jth complexity factor (for example, the number of turns, the number of traffic lights, etc.), and m is the total number of complexity factors. The weight coefficients α, β, and γ can be preset according to different needs. According to the above calculation formula, the inverse of the total distance of the path 1 / Ds, the inverse of the estimated driving time of the path 1 / Ts, and the inverse of the connection complexity of the path 1 / Cs can be obtained, and the path association strength Rs is obtained, that is, Rs=α / Ds+β / Ts+γ / Cs. By using the inverse, shorter distances, less time, and lower complexity can obtain higher path association strengths. For each path planning information group, all Rs are accumulated to obtain the path association strength R of the path planning information group, which is , where R si is the path association strength of the i-th path planning information in the path planning information group.

[0075] S24242, comparing the path association strength of each path planning information group with the first grid position information and the second grid position information to obtain the path planning information group with the largest path association strength.

[0076] It can be understood that after calculating the path association strength between each path planning information group and the first grid location information and the second grid location information, the path association strengths can be compared to screen out the path planning information group with the largest path association strength. The calculated path association strengths can be sorted from large to small, and the path planning information group with the largest path association strength can be screened out.

[0077] S24243, calculating the straight line equation for the first grid position information and the second grid position information to obtain the basic path function of the logistics vehicle.

[0078] It can be understood that the first grid location information and the second grid location information can be regarded as the two-dimensional spatial coordinates of the starting position and the target position of the logistics vehicle in the park grid. After obtaining the specific coordinates of the first grid location information and the second grid location information, the equation of the straight line between the first grid location information and the second grid location information can be calculated. The basic form of the straight line equation is y=mx+b, where m is the slope of the straight line and b is the intersection of the straight line and the y-axis (ie, the intercept). For example, the first grid location information is (x1, y1) and the second grid location information is (y1, y2). Calculate the slope m: m=x2−x1 / y2−y1, calculate the intercept b: b=y1−m*x1, and you can get the straight line equation y=mx+b, which is the basic path function of the logistics vehicle.

[0079] S24244, fit the path planning information group with the largest path association intensity with the basic path function of the logistics vehicle to obtain preliminary path planning information of the logistics vehicle.

[0080] It can be understood that after obtaining the basic path function, the path planning information group with the largest path correlation strength can be fitted with the basic path function to obtain the preliminary path planning information of the logistics vehicle. Each path planning information in the path planning information group with the largest path correlation strength can be fitted with the basic path function to obtain the fitted path planning information. For each node P in the selected path planning information i (x i ,y i ), calculate the vertical distance d between the node and the basic path function i , if d i If the path exceeds a certain threshold, the node position is adjusted to be closer to the basic path function. An optimization algorithm (such as gradient descent) can be used to minimize the total sum of squared vertical distances so that the path is as smooth as possible and close to the basic path function. Assume that a path planning information G has been selected, which contains a series of nodes P i (x i ,y i )(i=1,2,…,n). Let the basic path function be y=mx+b, for each node P i (x i ,y i ), calculate its vertical distance d to the basic path function i ,d i =|y i −(mx i +b)∣, if d i If it is greater than the threshold, adjust Pi position to reduce d i Gradient descent or other optimization methods can be used to adjust P i The position of the node is optimized until the convergence condition is met or the maximum number of iterations is reached, until the positions of all nodes have been optimized. The final path planning information group is the preliminary path planning information after fitting.

[0081] S250, performing initial path planning for the logistics vehicle based on the preliminary path planning information of the logistics vehicle, and generating a preliminary path planning signal for the logistics vehicle.

[0082] It can be understood that after the preliminary path planning information of the logistics vehicle is determined, the preliminary path planning information can be transmitted to the control device of the logistics vehicle through wireless communication, navigation system, etc., and the corresponding path planning signal can be generated according to the preliminary path planning information. The preliminary path planning signal may include detailed information such as path nodes, driving direction, estimated time, etc. The preliminary path planning signal can be applied to the navigation system of the logistics vehicle to guide the vehicle to travel along the planned route. During the driving process of the vehicle, the preliminary path planning signal can be adjusted in real time according to real-time traffic information, vehicle status and other dynamic data to ensure that the vehicle can travel along the optimal path.

[0083] S300, obtaining environmental adaptation parameters, and determining environmental adaptation signals of the logistics vehicle based on the environmental adaptation parameters; wherein the environmental adaptation parameters are used to reflect the real-time environmental conditions of the logistics vehicle, and the environmental adaptation signals are used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning.

[0084] It can be understood that the parameters of the environment in which the logistics vehicle is located, that is, the environmental adaptation parameters, can be collected in real time through sensors, cameras and other equipment. The environmental adaptation parameters may include the current position, speed, direction, terrain of the surrounding environment, weather, etc. of the vehicle. The collected environmental adaptation parameters can be processed to extract key information, such as the characteristic values ​​of the current environment, environmental change trends, etc. This information can be used to analyze the complexity and changes of the vehicle's environment. According to the processed environmental adaptation parameters, a corresponding environmental adaptation signal is generated. The environmental adaptation signal is used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning, and improve positioning accuracy and adaptability of path planning.

[0085] The generated environmental adaptation signal is applied to the vehicle's navigation system and path planning system to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning, ensuring that the vehicle can be accurately positioned and driven in various environments.

[0086] In one possible implementation, see Figure 7 , S300, obtaining environmental adaptation parameters, and determining an environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters, including:

[0087] S310, obtaining environmental adaptation parameters, and determining environmental identification information and real-time environmental information of the logistics vehicle based on the environmental adaptation parameters.

[0088] It can be understood that the process of obtaining environmental adaptation parameters can be achieved by various sensors and camera devices installed on logistics vehicles. These sensors include but are not limited to temperature sensors, humidity sensors, light intensity sensors, ultrasonic sensors, infrared sensors, etc. The sensors can collect data about the vehicle's surrounding environment in real time and transmit these data for processing. The images and three-dimensional environmental information around the vehicle can be captured by camera devices installed on the vehicle, such as cameras and lidar. The data collected by the sensor can be preprocessed, including data denoising, filtering and standardization to remove noise and outliers. Then, image processing technology can be used to analyze the images collected by the camera device. For example, through edge detection, shape recognition and texture analysis, key information in the environment, such as road signs, obstacles, pedestrians and other vehicles, can be extracted. After extracting the key information, the information can be further analyzed to generate environmental recognition information. Environmental recognition information is used to identify the type of environment in which the vehicle is currently located, such as an indoor environment or an outdoor environment. This recognition process can be achieved through pattern recognition and machine learning technology. For example, a classifier can be trained to use historical data to identify the characteristics of different environments, and the data of the current environment can be input into the classifier for recognition. Real-time environmental information can be generated by further processing the environmental adaptation parameters. Real-time environmental information reflects the real-time changes in the vehicle's current environment, such as obstacles on the road, changes in light, weather conditions, etc. This information can be obtained by fusing multi-source data, such as fusing the data of the light intensity sensor with the image collected by the camera to identify light changes; using the data of the ultrasonic sensor and the infrared sensor to identify obstacles on the road. The generated environmental recognition information and real-time environmental information can help logistics vehicles better adapt to the current environment and improve the safety and efficiency of vehicle operation. For example, when it is recognized that the vehicle is in an indoor environment, the vehicle's navigation strategy can be adjusted to make it more suitable for driving in an indoor environment; when obstacles are recognized on the road, obstacle avoidance measures can be taken in time to ensure the safe driving of the vehicle.

[0089] S320, determining a primary positioning operation and a secondary positioning operation of the logistics vehicle according to the environmental identification information of the logistics vehicle.

[0090] It can be understood that when determining the primary positioning operation and the secondary positioning operation of the logistics vehicle, it can be judged based on the environmental identification information. The environmental identification information is used to identify the type of environment in which the vehicle is located, and the primary positioning operation and the secondary positioning operation are determined based on the type of environment. The primary positioning operation refers to the positioning method that is preferentially used in the current environment, while the secondary positioning operation is an auxiliary positioning method.

[0091] Optionally, the environment identification information is used to identify whether the logistics vehicle is in an indoor environment or an outdoor environment. S320, according to the environment identification information of the logistics vehicle, determines a primary positioning operation and a secondary positioning operation of the logistics vehicle, including:

[0092] S321, when it is identified that the logistics vehicle is in an outdoor environment according to the environmental identification information of the logistics vehicle, Beidou satellite positioning is determined as the primary positioning operation of the logistics vehicle, and Bluetooth positioning is determined as the secondary positioning operation of the logistics vehicle.

[0093] It can be understood that when the logistics vehicle is identified in an outdoor environment based on the environmental identification information of the logistics vehicle, Beidou satellite positioning can be used as the main positioning operation due to its wide coverage and high accuracy. Bluetooth positioning is used as a secondary positioning operation, as a backup and auxiliary when the Beidou satellite positioning signal is weak or unavailable. This ensures that the vehicle can always obtain accurate positioning information in an outdoor environment. When the environmental identification information identifies that the vehicle is in an outdoor environment, it can automatically switch to Beidou satellite positioning as the main positioning operation. At this time, the location information provided by Beidou satellite positioning is processed first, and path planning and navigation are performed based on this information. At the same time, Bluetooth positioning is still in working condition, but its location information is only used as a reference, not as the main basis for positioning.

[0094] Optionally, the environment identification information is used to identify whether the logistics vehicle is in an indoor environment or an outdoor environment. S320, according to the environment identification information of the logistics vehicle, determines a primary positioning operation and a secondary positioning operation of the logistics vehicle, including:

[0095] S322, when the logistics vehicle is identified as being in an indoor environment based on the environmental identification information of the logistics vehicle, Bluetooth positioning is determined as the primary positioning operation of the logistics vehicle, and Beidou satellite positioning is determined as the secondary positioning operation of the logistics vehicle.

[0096] It can be understood that when the vehicle is in an indoor environment, Bluetooth positioning becomes a more suitable choice because the reception effect of Beidou satellite signals in indoor environments is poor. Bluetooth positioning provides location information through Bluetooth beacons installed indoors. Its advantages are stable signals, low cost, and suitability for short-distance positioning. When the environmental identification information identifies that the vehicle is in an indoor environment, it can automatically switch to Bluetooth positioning as the main positioning operation. At this time, the location information provided by Bluetooth positioning is processed first, and path planning and navigation are performed based on this information. Beidou satellite positioning is used as a secondary positioning operation and as a backup when the Bluetooth positioning signal is weak or unavailable. This method of determining the main positioning operation and the secondary positioning operation based on environmental identification information can ensure that the vehicle can always obtain accurate positioning information in different environments, improving the reliability and stability of positioning.

[0097] S330, determining the real-time positioning information of the logistics vehicle based on the primary positioning operation and the secondary positioning operation of the logistics vehicle.

[0098] It can be understood that when determining the real-time positioning information of the logistics vehicle, the location information provided by the primary positioning operation and the secondary positioning operation can be integrated. Real-time positioning information is used to guide the navigation and path planning of the vehicle, and is the basis for the vehicle to drive accurately. For example, when the vehicle is in an outdoor environment, Beidou satellite positioning is the primary positioning operation, and Bluetooth positioning is the secondary positioning operation; when the vehicle is in an indoor environment, Bluetooth positioning is the primary positioning operation, and Beidou satellite positioning is the secondary positioning operation. When Beidou satellite positioning is the primary positioning operation, the location information provided by Beidou satellites can be received in real time, including longitude, latitude, altitude, speed and other data. After these data are processed and calibrated, the real-time positioning information of the vehicle is generated. At the same time, backup location information can be obtained from the secondary positioning operation. For example, when Bluetooth positioning is the secondary positioning operation, the location information provided by Bluetooth positioning will be received. Although this information is not used as the main positioning basis under normal circumstances, it can be used as a supplement when the signal of the primary positioning operation is weak or unavailable. The location information provided by the primary positioning operation and the secondary positioning operation can be fused. The fusion processing includes data comparison, calibration and synthesis to ensure that the final real-time positioning information has high accuracy and high reliability. For example, when the location information of the primary positioning operation and the secondary positioning operation differ greatly, historical data and environmental information can be used to make corrections to ensure the accuracy of the final positioning information. Finally, the processed real-time positioning information is applied to the vehicle's navigation and path planning. The real-time positioning information is used to guide the vehicle to drive along the planned route and adjust the path in time when it deviates from the route or encounters obstacles, ensuring the safety and efficient operation of the vehicle.

[0099] S340, obtaining an environmental adaptation signal of the logistics vehicle based on the real-time positioning information and real-time environmental information of the logistics vehicle.

[0100] It can be understood that by analyzing and processing the real-time positioning information and real-time environmental information of the logistics vehicle, an environmental adaptation signal of the logistics vehicle can be generated. The environmental adaptation signal is used to reflect the real-time adaptation of the vehicle's current environment and guide the vehicle to make adaptive adjustments in a complex environment. The real-time positioning information and real-time environmental information can be fused and processed. For example, the location information provided by Beidou satellite positioning and Bluetooth positioning is comprehensively analyzed with the environmental information provided by the camera and sensor to obtain the current position, speed, direction and changes in the surrounding environment of the vehicle. The fused data is modeled and analyzed to generate an environmental adaptation signal. The environmental adaptation signal includes information from multiple aspects, such as the characteristic values ​​of the current environment, environmental change trends, road conditions, traffic flow, etc. The environmental adaptation signal can be used to determine the complexity and changes of the vehicle's current environment. For example, when it is identified that the road ahead of the vehicle is congested, the environmental adaptation signal can reflect the degree and duration of the road congestion; when it is identified that the vehicle is driving in an area with large light changes, the environmental adaptation signal can reflect the frequency and intensity of light changes. After the environmental adaptation signal is generated, it can be used to guide the vehicle to make adaptive adjustments in a complex environment. For example, when a road obstacle is identified, the vehicle's speed can be adjusted according to the environmental adaptation signal to avoid long waits due to congestion; when a large change in light is identified, the camera's exposure parameters can be adjusted to improve image clarity and recognition accuracy. In this way, the environmental adaptation signal can help the vehicle better adapt to the current environment and improve the safety and efficiency of vehicle operation. Especially in complex and dynamically changing environments, the application of environmental adaptation signals can significantly improve the vehicle's adaptive ability and the flexibility of path planning, ensuring the smooth completion of transportation tasks.

[0101] S400, optimizing the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal.

[0102] It can be understood that the preliminary path planning signal can be analyzed and evaluated based on the environmental adaptation signal. The preliminary path planning signal is an initial path planning scheme generated based on the path parameters, but in the actual operation process, due to changes in the environment, the preliminary path planning signal may need to be adjusted. The environmental adaptation signal can provide an adaptability assessment of the current path, such as whether the road is unobstructed, whether there are obstacles, whether the driving speed needs to be adjusted, etc. In this way, potential problems and optimization needs in the preliminary path planning can be identified, and real-time path optimization information can be generated based on the environmental adaptation signal. Real-time path optimization information reflects the optimization needs of the current path and is an important basis for adjusting and optimizing the preliminary path planning signal. Real-time path optimization information can include multiple aspects, such as avoiding obstacles, adjusting driving speed, etc. This information is generated through data fusion and analysis processing to ensure that the optimized path is more in line with actual operation needs.

[0103] In one possible implementation, see Figure 8 , S400, optimizing the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal, including:

[0104] S410, determining the real-time path optimization information of the logistics vehicle according to the environmental adaptation signal of the logistics vehicle; wherein the real-time path optimization information is used to reflect the optimization requirements of the real-time road conditions.

[0105] It can be understood that after obtaining the environmental adaptation signal, the real-time path optimization information of the logistics vehicle can be determined according to the environmental adaptation signal. The real-time path optimization information is an important basis for adjusting and optimizing the preliminary path planning signal, and reflects the optimization requirements of the current road conditions. For example, when congestion is detected on the road ahead, the real-time path optimization information can indicate the path that needs to be detoured; when obstacles are detected on the road, the real-time path optimization information can indicate the route that needs to be avoided. The key optimization information can be extracted by analyzing and processing the environmental adaptation signal. The analysis and processing of the environmental adaptation signal can be achieved by setting a mapping relationship in advance or by machine learning. The optimization process can be achieved by a path optimization algorithm. For example, classic path optimization algorithms such as the A* algorithm and the Dijkstra algorithm can be used, combined with real-time environmental information, to generate an optimized accurate path planning signal. For example, by analyzing the road congestion information, the congestion degree and duration of the current road can be determined; by analyzing the location and size of the obstacle, the route to be avoided and the path for safe driving can be determined; by analyzing the light change information, the light conditions that need to be adjusted during driving can be determined. The generated real-time path optimization information will be used to guide the vehicle's path adjustment and optimization. Real-time path optimization information can help the vehicle better adapt to the current environment and improve the flexibility and accuracy of path planning.

[0106] S420, optimizing the preliminary path planning signal based on the real-time path optimization information of the logistics vehicle to generate an optimized accurate path planning signal.

[0107] It can be understood that after the real-time path optimization information is generated, the preliminary path planning signal can be optimized according to the information. The goal of the optimization process is to adjust the preliminary path planning according to the changes in the current environment and the actual operation requirements, and generate an optimized precise path planning signal. The preliminary path planning signal and the real-time path optimization information can be comprehensively analyzed to determine the path that needs to be adjusted and optimized. For example, when it is identified that the road ahead is congested, the driving route can be adjusted according to the real-time path optimization information, and a detour path can be selected to avoid the congested area; when it is identified that there are obstacles on the road, the driving route can be adjusted according to the real-time path optimization information to avoid obstacles and ensure driving safety. The optimized precise path planning signal will be used to guide the driving of the vehicle. The optimized precise path planning signal can ensure that the vehicle drives along the optimal path and improve the efficiency and safety of the transportation task. For example, when it is identified that the road ahead is congested, the optimized path planning signal can instruct the vehicle to select a detour path to avoid the congested area; when it is identified that there are obstacles on the road, the optimized path planning signal can instruct the vehicle to avoid obstacles and ensure driving safety.

[0108] Optionally, S420, optimizing the preliminary path planning signal according to the real-time path optimization information of the logistics vehicle to generate an optimized accurate path planning signal, including:

[0109] S421, based on the optimized precise path planning signal, control the logistics vehicle to change the operating state at the starting position and the target position.

[0110] It can be understood that the operation state of the logistics vehicle is controlled according to the precise path planning signal to ensure that the vehicle travels along the optimized path. The driving speed, direction and position of the vehicle can be adjusted according to the optimized path planning signal to ensure that the vehicle travels along the optimal path. For example, according to the optimized precise path planning signal, the logistics vehicle is controlled from a stationary operation state to a moving operation state at the starting position, and the logistics vehicle is controlled from a moving operation state to a stationary operation state at the target position. When it is recognized that the road ahead is congested, the optimized path planning signal can instruct the vehicle to slow down and avoid the congested area; when it is recognized that there are obstacles on the road, the optimized path planning signal can instruct the vehicle to adjust the driving direction and avoid the obstacles; when it is recognized that the light changes greatly, the optimized precise path planning signal can instruct the vehicle to adjust the exposure parameters of the camera to improve the clarity and recognition accuracy of the image. By adjusting and controlling the operation state of the vehicle, it can be ensured that the vehicle travels along the optimized path and improves the efficiency and safety of the transportation task. Especially in complex and dynamically changing environments, the driving path is highly consistent with the planned path, flexible path planning, and logistics efficiency are improved to ensure the smooth completion of the transportation task.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] Corresponding to the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology described in the above embodiment, the embodiment of the present application also provides an intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology, and each unit of the system can implement each step of the intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology. Fig. 9 A structural block diagram of an intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0113] Reference Fig. 9 The intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology includes:

[0114] An acquisition unit, used to acquire the operation status and path parameters of the logistics vehicle; wherein the path parameters are used to reflect the route requirements of the logistics vehicle;

[0115] A planning unit, configured to perform initial path planning for the logistics vehicle based on the path parameters, and generate a preliminary path planning signal for the logistics vehicle; wherein the preliminary path planning signal is used to control the change of the operating state of the logistics vehicle;

[0116] An environmental unit, used to obtain environmental adaptation parameters, and determine the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters; wherein the environmental adaptation parameters are used to reflect the real-time environmental conditions of the logistics vehicle, and the environmental adaptation signal is used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning;

[0117] The optimization unit is used to optimize the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal.

[0118] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0119] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0120] The present application also provides an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology. Fig.10 This is a schematic diagram of the structure of an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology provided in an embodiment of the present application. Fig.10 As shown, the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology in this embodiment includes: at least one processor 60 ( Fig.10 Only one is shown), at least one memory 61 ( Fig.10Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology implements the steps in any of the above-mentioned intelligent logistics method embodiments based on Beidou satellite navigation and Bluetooth positioning technology, or enables the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology to implement the functions of each unit in the above-mentioned system embodiments.

[0121] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology.

[0122] The intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology can be a variety of intelligent detection equipment. The intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology can include image acquisition equipment (camera or other imaging equipment), input and output equipment, communication equipment, computing equipment, display equipment, and the computing equipment can be a computer, notebook, PDA, cloud server and other computing equipment. The computing equipment of the intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Fig.10 It is only an example of the intelligent logistics equipment 6 based on Beidou satellite navigation and Bluetooth positioning technology, and does not constitute a limitation of the intelligent logistics equipment 6 based on Beidou satellite navigation and Bluetooth positioning technology. It may include more or less components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access equipment, buses, etc.

[0123] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0124] In some embodiments, the memory 61 may be an internal storage unit of the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology, such as a hard disk or memory of the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology. In other embodiments, the memory 61 may also be an external storage device of the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology. Further, the memory 61 may also include both the internal storage unit and the external storage device of the intelligent logistics device 6 based on Beidou satellite navigation and Bluetooth positioning technology. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0125] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0126] An embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology, the intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology implements the steps in any of the above-mentioned method embodiments.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0128] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] In the embodiments provided in the present application, it should be understood that the disclosed intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology / intelligent logistics equipment and methods based on Beidou satellite navigation and Bluetooth positioning technology can be implemented in other ways. For example, the above-described intelligent logistics system based on Beidou satellite navigation and Bluetooth positioning technology / intelligent logistics equipment based on Beidou satellite navigation and Bluetooth positioning technology is merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology, characterized in that: include: Obtaining the operation status and path parameters of the logistics vehicle; wherein the path parameters are used to reflect the route requirements of the logistics vehicle; Performing initial path planning for the logistics vehicle based on the path parameters, and generating a preliminary path planning signal for the logistics vehicle; wherein the preliminary path planning signal is used to control the change of the operating state of the logistics vehicle; Acquire environmental adaptation parameters, and determine the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters; wherein the environmental adaptation parameters are used to reflect the real-time environmental conditions of the logistics vehicle, and the environmental adaptation signal is used to optimize the primary and secondary relationship between Beidou satellite positioning and Bluetooth positioning; Optimizing the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal; The path parameters include the starting position and the target position of the logistics vehicle, and the initial path planning of the logistics vehicle based on the path parameters to generate a preliminary path planning signal of the logistics vehicle includes: According to the starting position and the target position of the logistics vehicle, a preset path planning library is matched; When the preset path planning library is matched successfully, the preliminary path planning information of the logistics vehicle is determined according to the matching result; Performing initial path planning for the logistics vehicle based on the preliminary path planning information of the logistics vehicle, and generating a preliminary path planning signal for the logistics vehicle; The performing initial path planning for the logistics vehicle based on the path parameters to generate a preliminary path planning signal for the logistics vehicle further includes: When matching the preset path planning library is unsuccessful, determining preliminary path planning information of the logistics vehicle according to the starting position and the target position of the logistics vehicle; Performing initial path planning for the logistics vehicle according to the preliminary path planning information of the logistics vehicle, and generating a preliminary path planning signal for the logistics vehicle; Determining preliminary path planning information of the logistics vehicle according to the starting position and the target position of the logistics vehicle includes: Determine, according to the starting position and the target position of the logistics vehicle, first grid position information corresponding to the starting position in a preset map grid and second grid position information corresponding to the target position in a preset map grid; Performing grid path planning based on the first grid location information and the second grid location information to determine preliminary path planning information for the logistics vehicle; The performing grid path planning based on the first grid location information and the second grid location information to determine the preliminary path planning information of the logistics vehicle includes: Searching the preset path planning library according to the first grid location information and the second grid location information to obtain a plurality of preset path planning information; wherein the preset path planning information includes at least one of the first grid location information and the second grid location information; Clustering the plurality of preset path planning information to obtain a plurality of path planning information groups; wherein the path planning information group includes at least two pieces of preset path planning information having similar path nodes; Determine the position information of the first grid position information and the second grid position information in each of the path planning information groups according to the first grid position information, the second grid position information and the plurality of path planning information groups; Based on the position information of the first grid position information and the second grid position information in each of the path planning information groups, preliminary path planning information of the logistics vehicle is determined.

2. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology as claimed in claim 1, characterized in that: The determining of the preliminary path planning information of the logistics vehicle based on the location information of each path planning information group based on the first grid location information and the second grid location information includes: quantifying the path association strength of each of the path planning information groups with the first grid location information and the second grid location information based on the location information of the first grid location information and the second grid location information in each of the path planning information groups; Compare the path association strengths of each of the path planning information groups with the first grid location information and the second grid location information to obtain the path planning information group with the greatest path association strength; Calculating a straight line equation for the first grid position information and the second grid position information to obtain a basic path function for the logistics vehicle; The path planning information group with the largest path association strength is fitted with the basic path function of the logistics vehicle to obtain preliminary path planning information of the logistics vehicle.

3. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology as claimed in claim 1, characterized in that: The acquiring of the environmental adaptation parameters and determining the environmental adaptation signal of the logistics vehicle based on the environmental adaptation parameters includes: Acquire the environmental adaptation parameters, and determine the environmental identification information and real-time environmental information of the logistics vehicle according to the environmental adaptation parameters; Determining a primary positioning operation and a secondary positioning operation of the logistics vehicle according to the environmental identification information of the logistics vehicle; Determining real-time positioning information of the logistics vehicle according to the primary positioning operation and the secondary positioning operation of the logistics vehicle; Based on the real-time positioning information and the real-time environmental information of the logistics vehicle, an environmental adaptation signal of the logistics vehicle is obtained.

4. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology as claimed in claim 3, characterized in that: The environment identification information is used to identify whether the logistics vehicle is in an indoor environment or an outdoor environment, and determining the primary positioning operation and the secondary positioning operation of the logistics vehicle according to the environment identification information of the logistics vehicle includes: When it is identified that the logistics vehicle is in an outdoor environment according to the environmental identification information of the logistics vehicle, Beidou satellite positioning is determined as the primary positioning operation of the logistics vehicle, and Bluetooth positioning is determined as the secondary positioning operation of the logistics vehicle.

5. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology as claimed in claim 3, characterized in that: The environment identification information is used to identify whether the logistics vehicle is in an indoor environment or an outdoor environment, and determining the primary positioning operation and the secondary positioning operation of the logistics vehicle according to the environment identification information of the logistics vehicle includes: When it is identified that the logistics vehicle is in an indoor environment according to the environmental identification information of the logistics vehicle, Bluetooth positioning is determined as the primary positioning operation of the logistics vehicle, and Beidou satellite positioning is determined as the secondary positioning operation of the logistics vehicle.

6. The intelligent logistics method based on Beidou satellite navigation and Bluetooth positioning technology as claimed in claim 1, characterized in that: The step of optimizing the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal includes: Determine the real-time path optimization information of the logistics vehicle according to the environmental adaptation signal of the logistics vehicle; wherein the real-time path optimization information is used to reflect the optimization requirements of the real-time road conditions; Optimizing the preliminary path planning signal according to the real-time path optimization information of the logistics vehicle to generate an optimized accurate path planning signal; And / or, the optimizing processing of the preliminary path planning signal according to the environmental adaptation signal of the logistics vehicle to generate an optimized accurate path planning signal also includes: Based on the optimized precise path planning signal, the logistics vehicle is controlled to change the operating state between the starting position and the target position.

7. An intelligent logistics device based on Beidou satellite navigation and Bluetooth positioning technology, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

Images