Automobile terminal networking accurate positioning system

Abstract

The invention provides an automobile terminal networking accurate positioning system. The system comprises a channel state information acquisition module, a channel feature acquisition module, a channel feature region definition module, an automobile terminal detection frame acquisition module, a to-be-positioned automobile terminal marking module, an accurate position acquisition and output module, an intersection duration acquisition module, an intersection duration judgment module and a positioning failure judgment module. According to the invention, efficient and accurate positioning of the automobile terminal in the preset area can be realized, the accuracy and reliability of positioning are improved, an early warning function of positioning failure is added, and effective technical support is provided for navigation, monitoring and management of the automobile terminal.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision automobile positioning, and more specifically, to a vehicle terminal networking and precise positioning system. Background Art

[0002] The vehicle positioning system in the existing technology faces many challenges in achieving high-precision positioning. First, GNSS signals are easily blocked in scenes such as urban canyons, under viaducts, indoors or in tunnels, affecting positioning accuracy. Secondly, single positioning technology, such as sensor-based positioning, is limited by high cost and sensitivity to the environment. In addition, although positioning accuracy can be improved by integrating multiple positioning technologies, this also brings higher costs and system complexity. In the Internet of Vehicles environment, the vehicle high-precision positioning system needs to meet the positioning requirements in different application scenarios, including positioning accuracy, communication latency and network deployment. Although the combination of GNSS+RTK+IMU can provide centimeter-level positioning accuracy, this method still faces challenges in areas with poor GNSS signal coverage. At the same time, the drawing and updating of high-precision maps are expensive and need to be done regularly to ensure that positioning performance and business needs are met. In addition, although cellular network positioning is crucial to improving positioning performance, the accuracy of cellular positioning based on 4G is usually around tens of meters, which is difficult to meet the needs of high-precision positioning. With the development of 5G technology, although the positioning accuracy can be significantly improved, this also puts higher requirements on the synchronization system.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: Although current vehicle positioning technologies such as the Global Positioning System (GPS), the Differential Global Positioning System (DGPS), the Beidou Satellite Navigation System (BDS), the Inertial Navigation System (INS), the Dead Reckoning (DR) technology, the visual sensor positioning, the LiDAR positioning and the combined positioning methods have been widely used, they have limitations to varying degrees. For example, although GPS can provide all-weather positioning services, its signals are easily interfered by environmental factors such as tall buildings, trees, and tunnels, affecting the positioning accuracy and reliability. In addition, a single positioning technology is difficult to meet the requirements of autonomous driving for high precision and high reliability, especially in complex urban environments, and more advanced positioning technologies are needed to overcome these challenges. Summary of the invention

[0004] The invention provides a vehicle terminal networking and precise positioning system.

[0005] In a first aspect of the present invention, a vehicle terminal networking precise positioning system is provided, comprising:

[0006] A channel state information acquisition module, used to acquire channel state information of all vehicle terminals in the preset area;

[0007] A channel feature acquisition module is used to acquire the channel features of each vehicle terminal respectively through a preset strategy;

[0008] A channel characteristic region definition module, used to define a channel characteristic region based on the channel characteristics of each automobile terminal at a preset ratio, wherein the center of the current channel characteristic region coincides with the midpoint of the current automobile terminal;

[0009] An automobile terminal detection frame acquisition module is used to respond to a network connection request signal of the automobile terminal and acquire an automobile terminal detection frame matching the network connection request signal based on a signal detection algorithm;

[0010] The to-be-located vehicle terminal marking module is used to obtain the positioning requirements of the vehicle terminal and mark the vehicle terminal to be located as the to-be-located vehicle terminal according to the positioning requirements;

[0011] An accurate position acquisition and output module, used to acquire the accurate positions of all automobile terminals to be located through a channel feature matching algorithm and output them to the automobile terminals;

[0012] An intersection duration acquisition module, used to obtain a single intersection duration of the automobile terminal detection frame with each channel feature area, and a total intersection duration with all channel feature areas;

[0013] An intersection duration judgment module, used to judge whether the current single intersection duration exceeds a first preset threshold and whether the total intersection duration exceeds a second preset threshold;

[0014] The positioning failure judgment module is used to judge whether the current single intersection duration exceeds the first preset threshold or whether the total intersection duration exceeds the second preset threshold, and then judge it as positioning failure, generate a warning signal and send it to the vehicle terminal.

[0015] In a second aspect of the present invention, a method for accurately positioning a vehicle terminal through networking is provided, comprising:

[0016] Step S1, obtaining channel status information of all automobile terminals in the preset area;

[0017] Step S2, obtaining the channel characteristics of each vehicle terminal respectively through a preset strategy;

[0018] Step S3, defining a channel characteristic region at a preset ratio based on the channel characteristics of each vehicle terminal, wherein the center of the current channel characteristic region coincides with the midpoint of the current vehicle terminal;

[0019] Step S4, responding to the networking request signal of the automobile terminal and acquiring the automobile terminal detection frame matching the networking request signal based on a signal detection algorithm;

[0020] Step S5, obtaining the positioning requirement of the vehicle terminal, and marking the vehicle terminal to be positioned as a vehicle terminal to be positioned according to the positioning requirement;

[0021] Step S6, obtaining the precise positions of all the automobile terminals to be located through a channel feature matching algorithm and outputting them to the automobile terminals;

[0022] Step S7, obtaining a single intersection duration of the automobile terminal detection frame with each channel feature area, and a total intersection duration with all channel feature areas;

[0023] Step S8, respectively judging whether the current single intersection duration exceeds the first preset threshold value and whether the total intersection duration exceeds the second preset threshold value, if the current single intersection duration exceeds the first preset threshold value or the total intersection duration exceeds the second preset threshold value, executing step S9;

[0024] Step S9: determine that the positioning is invalid, generate a warning signal and send it to the vehicle terminal.

[0025] Further, step S3, based on the channel characteristics of each automobile terminal, a channel characteristic area is defined at a preset ratio, including:

[0026] Step S31, sorting the channel characteristics of all vehicle terminals from small to large;

[0027] Step S32, obtaining the minimum and maximum values ​​of the channel characteristics to establish a channel characteristic interval;

[0028] Step S33, equally dividing the channel characteristic interval to form at least two sub-intervals, and the sub-intervals at both ends are the minimum sub-interval and the maximum sub-interval respectively;

[0029] Step S34, sequentially numbering each sub-interval from small to large, and defining the number of the current sub-interval as the proportional constant of the current sub-interval, the proportional constant of the minimum sub-interval is the minimum proportional constant, and the proportional constant of the maximum sub-interval is the maximum proportional constant;

[0030] Step S35, defining the minimum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the minimum value subinterval multiplied by the minimum proportional constant, and the maximum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the maximum value subinterval multiplied by the maximum proportional constant;

[0031] Step S36, based on the sub-interval where each vehicle terminal is located and the corresponding proportional constant, respectively obtain the channel characteristic area of ​​each vehicle terminal.

[0032] Further, step S4, in response to the networking request signal of the automobile terminal, and based on the signal detection algorithm, obtaining the automobile terminal detection frame matching the networking request signal, includes:

[0033] Step S41, in response to the networking request signal, acquiring communication data matching the networking request signal through an external preset communication terminal;

[0034] Step S42, dividing the communication data evenly into a preset number of grids;

[0035] Step S43, predicting a plurality of boundary boxes for the automobile terminal in the communication data according to the signal detection algorithm based on all grids;

[0036] Step S44, respectively obtaining the confidence of each bounding box, and obtaining the bounding box with the largest confidence and marking it as a first-order bounding box;

[0037] Step S45, calculating the intersection-over-union ratio of each other bounding box with the first-order bounding box;

[0038] Step S46, selecting a bounding box whose intersection-over-union ratio is greater than or equal to a preset threshold as a second-order bounding box;

[0039] Step S47, obtaining a second-order bounding box with the highest confidence and defining it as the automobile terminal detection box.

[0040] Furthermore, step S6, obtaining the precise positions of all automobile terminals to be located by a channel feature matching algorithm and outputting them to the automobile terminals, includes:

[0041] Step S61, obtaining a geographic information model of the preset area and outputting all automobile terminals to be located to the geographic information model;

[0042] Step S62, dividing the geographic information model by horizontal grids of preset density;

[0043] Step S63, detecting and acquiring all horizontal grids having all automobile terminals to be located by a signal detection algorithm, and defining them as a positioning candidate grid set;

[0044] Step S64, defining each grid in the positioning candidate grid set as a node, and defining all vehicle terminals as starting points and end points;

[0045] Step S65, defining one of all the end points as the positioning end point, defining the other end points as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point through the A* algorithm;

[0046] Step S66, obtaining all horizontal grids corresponding to the minimum number of grids, and sequentially connecting them to form the precise position;

[0047] Step S67, outputting the precise location to a receiving end of the automobile terminal.

[0048] Further, in step S65, one of all the end points is defined as the positioning end point, the other end points are defined as waypoints, and the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point is calculated by the A* algorithm, including:

[0049] Step S651, creating a grid set to be checked;

[0050] Step S652, adding the starting point, all the waypoints, and the end point to the grid set to be checked, and setting the priority of the starting point to the highest, setting the priority of all the waypoints to the middle, and setting the priority of the end point to the lowest;

[0051] Step S653, determining whether the to-be-checked grid set is empty, if the to-be-checked grid set is not empty, executing step S654;

[0052] Step S654, selecting a node with the highest priority from the set of grids to be checked;

[0053] Step S655, determining whether the node is one of the waypoints, if the node is one of the waypoints, executing step S656;

[0054] Step S656, starting from the current waypoint, gradually tracking the parent node until reaching the starting point;

[0055] Step S657, respectively obtaining the number of grids required for each waypoint to reach the starting point, and defining the waypoint with the least number of grids required as the starting point for the next path search;

[0056] Step S658, iterating steps S651 to S657 for several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position;

[0057] Step S659, obtaining all grids covered by the precise position to obtain the minimum number of grids.

[0058] Further, step S653, determining whether the grid set to be checked is empty, then comprising:

[0059] Step S10, if the grid set to be checked is empty, traverse all neighboring nodes of the node and add all neighboring nodes to the grid set to be checked;

[0060] Step S20, obtaining the neighboring node with the highest priority among all neighboring nodes as the parent node for the next traversal;

[0061] Step S30, starting from the current parent node, gradually tracing the parent node of the next traversal until reaching one of the waypoints;

[0062] Step S40, defining one of the waypoints reached as the starting point of the next path search;

[0063] Step S50, iterating steps S10 to S40 several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position;

[0064] Step S60, acquiring all grids covered by the precise position to obtain the minimum number of grids.

[0065] Furthermore, step S7, obtaining the single intersection duration of the automobile terminal detection frame with each channel feature area, and the total intersection duration with all channel feature areas, includes:

[0066] Step S71, defining a world coordinate system according to any horizontal plane and any vertical plane of the preset area;

[0067] Step S72, mapping the vehicle terminal detection frame to the world coordinate system through a homography matrix to form a two-dimensional vehicle terminal detection frame, and the two-dimensional vehicle terminal detection frame is parallel to or coplanar with the arbitrary vertical plane;

[0068] Step S73, obtaining the symmetry axis of the two-dimensional automobile terminal detection frame in the vertical direction;

[0069] Step S74, rotating the two-dimensional automobile terminal detection frame based on the symmetry axis;

[0070] Step S75, obtaining a complete rotation trajectory of the two-dimensional vehicle terminal detection frame, and defining the complete rotation trajectory as a three-dimensional vehicle terminal detection frame;

[0071] Step S76, obtaining the Euclidean distance between the three-dimensional vehicle terminal detection frame and all channel feature areas based on the world coordinate system;

[0072] Step S77, determining whether the Euclidean distance is less than or equal to zero, if the Euclidean distance is less than or equal to zero, executing step S78;

[0073] Step S78, obtaining a first intersection duration of a channel feature area intersecting with the three-dimensional automobile terminal detection frame to obtain the single intersection duration;

[0074] Step S79, obtaining the second intersection durations of the three-dimensional automobile terminal detection frame and each channel feature area respectively, and adding up all the second intersection durations to obtain the total intersection duration.

[0075] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0076] In a fourth aspect of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes any one of the methods in the first aspect.

[0077] According to the above embodiments of the present invention, at least the following beneficial effects are achieved: the automobile terminal networking precise positioning system of the present invention can effectively improve the positioning accuracy and reliability of automobiles in a preset area by comprehensively applying a variety of technical modules. The system uses a channel state information acquisition module and a channel feature acquisition module to collect necessary channel data, laying the foundation for precise positioning. At the same time, through the channel feature area definition module, a channel feature area centered on the midpoint of the automobile terminal is defined for each automobile terminal, which can further enhance the accuracy of positioning. In addition, the system adopts a signal detection algorithm and a channel feature matching algorithm to achieve accurate position acquisition and output of the automobile terminal, meeting the requirements of high-precision positioning. In addition, the system of the present invention also includes an intersection duration acquisition module and a judgment module, which can intelligently judge the effectiveness of positioning by evaluating the intersection duration of the automobile terminal detection frame and the channel feature area. If positioning failure is detected, the system will promptly generate and send a warning signal to the automobile terminal, thereby avoiding possible risks. This method can not only improve the accuracy of positioning, but also enhance the robustness and safety of the system. Through this comprehensive technical solution, the present invention can provide strong support for automatic driving and intelligent transportation systems, and has important practical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0079] Figure 1 A schematic diagram of the structure of a vehicle terminal networking precise positioning system provided by an embodiment of the present invention;

[0080] Figure 2 A schematic diagram of a flow chart of a method for accurately positioning a vehicle terminal through networking according to an embodiment of the present invention;

[0081] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0083] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0084] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0085] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of a vehicle terminal networking precise positioning system provided by an embodiment of the present invention. Figure 1 As shown, a vehicle terminal networking precise positioning system 100 includes:

[0086] The channel state information acquisition module 101 is used to acquire the channel state information of all vehicle terminals in the preset area;

[0087] The channel characteristic acquisition module 102 is used to respectively acquire the channel characteristics of each vehicle terminal through a preset strategy;

[0088] The channel characteristic region definition module 103 is used to define a channel characteristic region based on the channel characteristics of each automobile terminal at a preset ratio, and the center of the current channel characteristic region coincides with the midpoint of the current automobile terminal;

[0089] The vehicle terminal detection frame acquisition module 104 is used to respond to the network connection request signal of the vehicle terminal and acquire the vehicle terminal detection frame matching the network connection request signal based on the signal detection algorithm;

[0090] The to-be-located vehicle terminal marking module 105 is used to obtain the positioning requirements of the vehicle terminal and mark the vehicle terminal to be located as the to-be-located vehicle terminal according to the positioning requirements;

[0091] The precise position acquisition and output module 106 is used to acquire the precise positions of all automobile terminals to be located through a channel feature matching algorithm and output them to the automobile terminals;

[0092] The intersection duration acquisition module 107 is used to obtain the single intersection duration of the vehicle terminal detection frame with each channel feature area, and the total intersection duration with all channel feature areas;

[0093] The intersection duration determination module 108 is used to determine whether the current single intersection duration exceeds a first preset threshold and whether the total intersection duration exceeds a second preset threshold;

[0094] The positioning failure judgment module 109 is used to judge that positioning is failed if the current single intersection duration exceeds the first preset threshold or the total intersection duration exceeds the second preset threshold, and generate a warning signal and send it to the vehicle terminal.

[0095] It should be noted that the channel state information may include key indicators such as the signal-to-noise ratio (SNR), time delay, and Doppler shift of the signal. These parameters can be regularly reported to the positioning system through the communication interface of the vehicle terminal, or automatically acquired by the positioning system by monitoring the received signal. For example, the signal-to-noise ratio can be determined by measuring the power ratio of the received signal to the power ratio of the background noise, and the time delay can be calculated by the timestamps of the sent and received signals.

[0096] Preferably, the channel state information acquisition module can use an adaptive algorithm to optimize the frequency and content of information collection to adapt to different communication environments and network conditions. For example, in an environment with large signal changes, the frequency of information collection can be increased to obtain a more accurate channel state; while in an environment with stable signals, the collection frequency can be reduced to reduce the consumption of system resources.

[0097] It should be noted that the channel feature acquisition module is set up to extract characteristic parameters that can represent the position of the vehicle terminal from the collected channel state information. These parameters will be used for subsequent channel feature area definition and matching algorithms.

[0098] Specifically, channel characteristics may include signal propagation model parameters, characteristic values ​​of multipath effects, etc. These characteristics may be obtained by statistically analyzing channel state information and processing signals. For example, by analyzing the delay distribution of a signal, the number of multipaths and the path length through which the signal passes may be estimated, thereby extracting characteristic values ​​of the multipath effects.

[0099] More specifically, the channel feature acquisition module can be designed as a multi-step processing flow, including signal preprocessing, feature extraction, and feature selection. In the feature extraction stage, a variety of signal processing techniques, such as Fourier transform and wavelet transform, can be used to analyze the frequency domain characteristics of the signal; in the feature selection stage, machine learning algorithms can be used to identify the most useful feature subsets for positioning.

[0100] It should be noted that the channel feature area definition module is set to define a specific area according to the channel characteristics of each vehicle terminal. The center of the area coincides with the midpoint of the vehicle terminal. This area will be used for subsequent positioning algorithms.

[0101] Specifically, the channel characteristic area can be a circular area, a square area or an area of ​​other shapes, and its size and shape can be determined according to the distribution characteristics of the channel characteristics. For example, if the channel characteristics indicate that the signal propagation is relatively concentrated, a smaller area can be defined; otherwise, a larger area needs to be defined to cover the possible signal propagation range.

[0102] Preferably, the definition of the channel feature area can be dynamically adjusted to automatically adjust the size and shape of the area according to the real-time channel state information and historical data. For example, an adaptive algorithm can be set to dynamically adjust the radius of the area according to the signal-to-noise ratio and Doppler shift changes of the signal to achieve a more accurate positioning effect. In addition, the driving status of the car and environmental factors such as traffic flow, weather conditions, etc. can also be considered to further optimize the definition of the area.

[0103] like Figure 2 As shown, in some embodiments, a method 200 for accurately positioning a vehicle terminal through networking is provided, and the method 200 includes:

[0104] Step S1, obtaining channel status information of all automobile terminals in the preset area;

[0105] Step S2, obtaining the channel characteristics of each vehicle terminal respectively through a preset strategy;

[0106] Step S3, defining a channel characteristic region at a preset ratio based on the channel characteristics of each vehicle terminal, wherein the center of the current channel characteristic region coincides with the midpoint of the current vehicle terminal;

[0107] Step S4, responding to the networking request signal of the automobile terminal and acquiring the automobile terminal detection frame matching the networking request signal based on a signal detection algorithm;

[0108] Step S5, obtaining the positioning requirement of the vehicle terminal, and marking the vehicle terminal to be positioned as a vehicle terminal to be positioned according to the positioning requirement;

[0109] Step S6, obtaining the precise positions of all the automobile terminals to be located through a channel feature matching algorithm and outputting them to the automobile terminals;

[0110] Step S7, obtaining a single intersection duration of the automobile terminal detection frame with each channel feature area, and a total intersection duration with all channel feature areas;

[0111] Step S8, respectively judging whether the current single intersection duration exceeds the first preset threshold value and whether the total intersection duration exceeds the second preset threshold value, if the current single intersection duration exceeds the first preset threshold value or the total intersection duration exceeds the second preset threshold value, executing step S9;

[0112] Step S9: determine that the positioning is invalid, generate a warning signal and send it to the vehicle terminal.

[0113] It should be noted that obtaining the channel status information of all vehicle terminals in a preset area is the first step to achieve accurate positioning. This step involves real-time monitoring of the signals received by the vehicle terminals to collect key data such as signal strength and signal quality.

[0114] Specifically, the channel state information in step S1 may include, but is not limited to, signal-to-noise ratio (SNR), time delay, Doppler shift and other parameters. The collection of these parameters can be achieved through a communication module built into the vehicle terminal, which can analyze the received signal in real time and extract key information reflecting the signal characteristics. For example, the SNR can be determined by measuring the ratio of the power of the received signal to the power of the background noise, and the Doppler shift can be obtained by analyzing the change in the signal frequency.

[0115] Preferably, step S1 may further include a filtering process to remove possible noise and interference to ensure the accuracy of the acquired channel state information. In addition, a dynamic threshold may be set to determine whether the signal meets the minimum signal-to-noise ratio required for positioning, thereby improving the robustness of the positioning process.

[0116] It should be noted that obtaining the channel characteristics of each vehicle terminal through a preset strategy is a key step in the positioning method. The purpose of this step is to extract features that are helpful in determining the location of the vehicle terminal from the collected channel state information.

[0117] Specifically, the channel characteristics in step S2 may include propagation characteristics of the signal, characteristic values ​​of the multipath effect, frequency offset of the signal, etc. These characteristics may be obtained by in-depth analysis of the channel state information. For example, the propagation characteristics of the signal may be inferred by measuring the arrival time of the signal, and the characteristic value of the multipath effect may be determined by analyzing the reflection and scattering of the signal.

[0118] More specifically, step S2 can use a machine learning algorithm to identify and extract channel features. The training model can be used to identify which channel state information has the strongest correlation with the location of the vehicle terminal, thereby improving the accuracy and efficiency of feature extraction. In addition, a feature weight system can be designed to assign different weights to different channel features to reflect their importance in the positioning process.

[0119] It should be noted that defining a channel characteristic area based on the channel characteristics of each car terminal at a preset ratio is the key to achieving regional positioning. This step involves associating channel characteristics with geographic locations and demarcating a specific area for each car terminal for more accurate positioning.

[0120] Specifically, the channel characteristic region in step S3 can be a circular, square or any other shaped region, and its size and shape can be determined according to the distribution characteristics of the channel characteristics. For example, if the channel characteristics indicate that the signal propagation is relatively concentrated, a smaller circular region can be defined; otherwise, a larger square region needs to be defined to cover a wider signal propagation range.

[0121] Preferably, step S3 may include an adaptive adjustment mechanism to dynamically adjust the size and shape of the channel characteristic region according to the channel state information updated in real time. For example, an algorithm may be designed to automatically adjust the radius or side length of the region according to the changes in the signal-to-noise ratio and Doppler frequency shift of the signal to adapt to changes in the signal propagation environment. In addition, the speed and direction of the car, as well as the influence of the surrounding environment, may be considered to further optimize the definition of the channel characteristic region.

[0122] In some embodiments, step S3, based on the channel characteristics of each automobile terminal, defines a channel characteristic area with a preset ratio, including:

[0123] Step S31, sorting the channel characteristics of all vehicle terminals from small to large;

[0124] Step S32, obtaining the minimum and maximum values ​​of the channel characteristics to establish a channel characteristic interval;

[0125] Step S33, equally dividing the channel characteristic interval to form at least two sub-intervals, and the sub-intervals at both ends are the minimum sub-interval and the maximum sub-interval respectively;

[0126] Step S34, sequentially numbering each sub-interval from small to large, and defining the number of the current sub-interval as the proportional constant of the current sub-interval, the proportional constant of the minimum sub-interval is the minimum proportional constant, and the proportional constant of the maximum sub-interval is the maximum proportional constant;

[0127] Step S35, defining the minimum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the minimum value subinterval multiplied by the minimum proportional constant, and the maximum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the maximum value subinterval multiplied by the maximum proportional constant;

[0128] Step S36, based on the sub-interval where each vehicle terminal is located and the corresponding proportional constant, respectively obtain the channel characteristic area of ​​each vehicle terminal.

[0129] It should be noted that a channel characteristic area is defined based on the channel characteristics of each automobile terminal at a preset ratio. This step is to divide and define the channel characteristic area in more detail to achieve more accurate positioning.

[0130] Specifically, the channel characteristic area referred to in step S3 refers to a geographical space range determined according to the channel characteristics of the automobile terminal, such as signal propagation loss, multipath effect, etc. The center of the area coincides with the midpoint of the automobile terminal, which means that the area is defined with the current position of the automobile terminal as the center point. The preset ratio can be a fixed value or a parameter dynamically adjusted according to the signal characteristics to determine the size of the area.

[0131] Preferably, step S3 may further include the following detailed operations:

[0132] Step S31, sorting the channel characteristics of all automobile terminals from small to large, which can be achieved by comparing the channel loss or signal strength of each terminal;

[0133] Step S32, obtaining the minimum and maximum values ​​of the channel characteristics to establish a channel characteristic interval, which provides a range for subsequent sub-interval division;

[0134] Step S33, equally dividing the channel characteristic interval to form at least two sub-intervals, which can ensure that the channel characteristics in each sub-interval have similar characteristics;

[0135] Step S34, sequentially numbering each sub-interval from small to large, and defining the number of the current sub-interval as the proportional constant of the current sub-interval, which provides a basis for the size of the channel characteristic area of ​​the automobile terminal in each sub-interval;

[0136] Step S35, defining the minimum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the minimum value subinterval multiplied by the minimum proportional constant, which ensures accurate positioning even in poor channel conditions;

[0137] Step S36, based on the sub-interval where each automobile terminal is located and the corresponding proportional constant, the channel characteristic area of ​​each automobile terminal is obtained respectively, which allows personalized positioning of each terminal.

[0138] More specifically, the preset ratio in step S3 can be adjusted according to the type of automobile terminal, driving environment or communication requirements. For example, in areas with severe signal obstruction such as urban canyons or tunnels, it is necessary to increase the proportional constant to expand the channel characteristic area to ensure the accuracy of positioning. In addition, an adaptive algorithm can be used to dynamically adjust the proportional constant according to real-time channel state information to adapt to changes in channel conditions. It is also possible to combine the historical positioning data and behavior patterns of automobile terminals to optimize the definition of channel characteristic areas and improve the intelligence level of the positioning system.

[0139] In some embodiments, step S4, in response to a network connection request signal of a vehicle terminal, and based on a signal detection algorithm, obtaining a vehicle terminal detection frame matching the network connection request signal, includes:

[0140] Step S41, in response to the networking request signal, acquiring communication data matching the networking request signal through an external preset communication terminal;

[0141] Step S42, dividing the communication data evenly into a preset number of grids;

[0142] Step S43, predicting a plurality of boundary boxes for the automobile terminal in the communication data according to the signal detection algorithm based on all grids;

[0143] Step S44, respectively obtaining the confidence of each bounding box, and obtaining the bounding box with the largest confidence and marking it as a first-order bounding box;

[0144] Step S45, calculating the intersection-over-union ratio of each other bounding box with the first-order bounding box;

[0145] Step S46, selecting a bounding box whose intersection-over-union ratio is greater than or equal to a preset threshold as a second-order bounding box;

[0146] Step S47, obtaining a second-order bounding box with the highest confidence and defining it as the automobile terminal detection box.

[0147] It should be noted that responding to the networking request signal of the automobile terminal and obtaining the automobile terminal detection frame matching the networking request signal based on the signal detection algorithm is a step to determine the physical location of the automobile terminal and provide a basis for further precise positioning.

[0148] Specifically, step S4 involves receiving and analyzing the network request signal sent by the automobile terminal, and the signal detection algorithm includes signal acquisition, filtering, feature extraction and matching processes. For example, signal acquisition can be achieved through the communication module of the automobile terminal, filtering is used to remove noise and interference, feature extraction is used to obtain key information from the signal, such as the frequency and amplitude of the signal, and the matching process is used to compare these features with known signal patterns to determine the source of the signal.

[0149] Preferably, step S4 may further include the following detailed operations:

[0150] Step S41, in response to the networking request signal, obtaining communication data matching the networking request signal through an external preset communication terminal, which involves interaction with a base station or other communication infrastructure;

[0151] Step S42, evenly dividing the communication data into a preset number of grids, which can be achieved by dividing the signal coverage area into a number of units of equal size, each unit corresponding to a grid;

[0152] Step S43, predicting a number of boundary boxes for the automobile terminal in the communication data based on all grids according to the signal detection algorithm, which can predict the possible source range of the signal through the algorithm;

[0153] Step S44, respectively obtaining the confidence of each bounding box, and obtaining the bounding box with the largest confidence and marking it as a first-order bounding box, which helps to identify the area most likely to contain the car terminal;

[0154] Step S45, calculating the intersection-over-union ratio of each other bounding box with the first-order bounding box, which helps to evaluate the similarity between the other bounding boxes and the best matching bounding box;

[0155] Step S46, selecting a bounding box whose intersection-over-union ratio is greater than or equal to a preset threshold as a second-order bounding box, which can further refine the positioning candidate area;

[0156] Step S47, obtaining the second-order bounding box with the highest confidence and defining it as the automobile terminal detection box, which ultimately determines the detection area of ​​the automobile terminal.

[0157] More specifically, the signal detection algorithm in step S4 can adopt a variety of existing signal processing technologies, such as fast Fourier transform (FFT), wavelet transform, etc., to adapt to different signal characteristics and environmental conditions. The preset threshold can be set according to the actual communication quality requirements and positioning accuracy requirements. In addition, machine learning technology can be combined to optimize the performance of the signal detection algorithm through training data sets to improve the accuracy and efficiency of positioning. It is also possible to consider introducing multi-sensor data fusion technology, combining GPS, IMU, LiDAR and other sensor data to achieve more comprehensive signal detection and more accurate positioning.

[0158] In some embodiments, step S6, obtaining the precise positions of all automobile terminals to be located by a channel feature matching algorithm and outputting them to the automobile terminals, includes:

[0159] Step S61, obtaining a geographic information model of the preset area and outputting all automobile terminals to be located to the geographic information model;

[0160] Step S62, dividing the geographic information model by horizontal grids of preset density;

[0161] Step S63, detecting and acquiring all horizontal grids having all automobile terminals to be located by a signal detection algorithm, and defining them as a positioning candidate grid set;

[0162] Step S64, defining each grid in the positioning candidate grid set as a node, and defining all vehicle terminals as starting points and end points;

[0163] Step S65, defining one of all the end points as the positioning end point, defining the other end points as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point through the A* algorithm;

[0164] Step S66, obtaining all horizontal grids corresponding to the minimum number of grids, and sequentially connecting them to form the precise position;

[0165] Step S67, outputting the precise location to a receiving end of the automobile terminal.

[0166] It should be noted that the step of obtaining the precise location of all vehicle terminals to be located through the channel feature matching algorithm and outputting it to the vehicle terminal is the core link in achieving accurate positioning. It involves combining the collected channel features with the geographic information model to calculate the exact location of the vehicle terminal.

[0167] Specifically, the channel feature matching algorithm in step S6 includes sub-steps such as feature extraction, feature comparison and position calculation. Feature extraction is to identify key parameters that are helpful for positioning from the channel state information, such as signal delay, frequency offset, etc. Feature comparison is to match these parameters with known features in the geographic information model to narrow the possible location range. Position calculation is to determine the precise location of the vehicle terminal based on the matching results, combined with other sensor data, using positioning algorithms such as least squares or Kalman filter.

[0168] Preferably, step S6 may further include the following detailed operations:

[0169] Step S61, obtaining a geographic information model of the preset area and outputting all automobile terminals to be located to the geographic information model, which involves using high-precision maps and real-time traffic data to construct a geographic information model;

[0170] Step S62, dividing the geographic information model by horizontal grids of a preset density, which can be achieved by setting a grid size parameter to ensure that the entire preset area is covered;

[0171] Step S63, detecting and acquiring all horizontal grids with all automobile terminals to be located by a signal detection algorithm, and defining them as a positioning candidate grid set, which involves analyzing signal characteristics and determining grids that may contain the target automobile terminal;

[0172] Step S64, defining each grid in the positioning candidate grid set as a node, and defining all vehicle terminals as starting points and end points, respectively, which provides a basis for using a path planning algorithm;

[0173] Step S65, defining one of all the end points as the positioning end point, and defining the other end points as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point through the A* algorithm, which is a key step in determining the terminal position of the vehicle;

[0174] Step S66, obtaining all horizontal grids corresponding to the minimum number of grids, and sequentially connecting them to form the precise position, which provides precise position information for the final output;

[0175] Step S67, outputting the precise location to the receiving end of the automobile terminal, which involves sending the location data to the automobile terminal via a wireless communication network.

[0176] More specifically, the A* algorithm in step S65 can use a heuristic function to estimate the cost from the current node to the target node to optimize the path search process. The heuristic function can be designed based on the distance between the grids, the signal strength difference, or other relevant factors. In addition, multipath and non-line-of-sight (NLOS) error compensation techniques can be introduced to improve positioning accuracy. The dynamic characteristics of the automobile terminal can also be considered, and the grid division and path planning can be dynamically adjusted by updating its position and speed information in real time to adapt to its movement.

[0177] In some embodiments, step S65, defining one of all the endpoints as the positioning endpoint and the other endpoints as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning endpoint by using the A* algorithm, includes:

[0178] Step S651, creating a grid set to be checked;

[0179] Step S652, adding the starting point, all the waypoints, and the end point to the grid set to be checked, and setting the priority of the starting point to the highest, setting the priority of all the waypoints to the middle, and setting the priority of the end point to the lowest;

[0180] Step S653, determining whether the to-be-checked grid set is empty, if the to-be-checked grid set is not empty, executing step S654;

[0181] Step S654, selecting a node with the highest priority from the set of grids to be checked;

[0182] Step S655, determining whether the node is one of the waypoints, if the node is one of the waypoints, executing step S656;

[0183] Step S656, starting from the current waypoint, gradually tracking the parent node until reaching the starting point;

[0184] Step S657, respectively obtaining the number of grids required for each waypoint to reach the starting point, and defining the waypoint with the least number of grids required as the starting point for the next path search;

[0185] Step S658, iterating steps S651 to S657 for several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position;

[0186] Step S659, obtaining all grids covered by the precise position to obtain the minimum number of grids.

[0187] It should be noted that step S65 uses the A* algorithm to calculate the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point, which is one of the key steps to achieve accurate positioning. The A* algorithm is an effective path search algorithm that evaluates the cost from the starting point to the end point and selects the path with the minimum cost.

[0188] Specifically, the A* algorithm in step S65 needs to define a cost function that comprehensively considers the actual cost from the starting point to the current node and the estimated cost from the current node to the end point (heuristic function). The heuristic function usually uses Euclidean distance or Manhattan distance to estimate the distance between nodes. During the execution of the algorithm, two lists are maintained: a list of visited nodes and a list of nodes to be checked, and the next node is selected by comparing the costs of the nodes.

[0189] Preferably, step S65 may further include the following detailed operations:

[0190] Step S651, creating a grid set to be checked, which is the initial step of the algorithm. All nodes are initially located in the grid set to be checked;

[0191] Step S652, adding the starting point, all the waypoints, and the end point to the set of grids to be checked, and setting the priority of the starting point to the highest, the priority of all the waypoints to the middle, and the priority of the end point to the lowest, so as to ensure that the algorithm considers the starting point first, and gradually searches to the end point according to the priority order;

[0192] Step S653, determining whether the grid set to be checked is empty, if it is empty, it means that the path from the starting point to the end point has been found.

[0193] More specifically, the A* algorithm in step S65 may adopt the following refinements or alternatives:

[0194] 1. Use different heuristic functions, such as selecting the heuristic function of the A* algorithm according to the actual scenario. For example, the A* (Euclidean distance) variant of the A* algorithm may be used in an urban environment, while the A* (Manhattan distance) variant may be used on a gridded road.

[0195] 2. Introduce a dynamic weight adjustment mechanism to dynamically adjust the weight of the heuristic function according to real-time traffic information or signal strength changes to optimize the path search process.

[0196] 3. Combined with machine learning technology, the optimal heuristic function is learned through training data to improve the performance and efficiency of the algorithm in a specific environment.

[0197] 4. Consider multi-objective optimization. In addition to minimizing the path length, you can also consider the safety, cost or other factors of the path for comprehensive evaluation and path selection.

[0198] 5. During the execution of the algorithm, an interrupt condition can be set, such as exceeding the maximum search depth or search time, to avoid the algorithm falling into an infinite loop and ensure the real-time performance of the algorithm.

[0199] In some embodiments, step S653, determining whether the grid set to be checked is empty, then includes:

[0200] Step S10, if the grid set to be checked is empty, traverse all neighboring nodes of the node and add all neighboring nodes to the grid set to be checked;

[0201] Step S20, obtaining the neighboring node with the highest priority among all neighboring nodes as the parent node for the next traversal;

[0202] Step S30, starting from the current parent node, gradually tracing the parent node of the next traversal until reaching one of the waypoints;

[0203] Step S40, defining one of the waypoints reached as the starting point of the next path search;

[0204] Step S50, iterating steps S10 to S40 several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position;

[0205] Step S60, acquiring all grids covered by the precise position to obtain the minimum number of grids.

[0206] It should be noted that step S7 involves obtaining the intersection time between the vehicle terminal detection frame and the channel feature area, which is a key step in determining the accuracy of positioning. Obtaining the intersection time helps to evaluate the stay of the vehicle terminal in a specific channel feature area, and then to determine the effectiveness of the positioning result.

[0207] Specifically, the vehicle terminal detection frame in step S7 refers to the range of possible vehicle locations determined by the signal detection algorithm, and the channel feature area is an area defined based on the channel features of the vehicle terminal. A single intersection duration refers to the overlap time between the vehicle terminal detection frame and each channel feature area, and the total intersection duration is the sum of the overlap times with all channel feature areas.

[0208] Preferably, step S7 may further include the following detailed operations:

[0209] Step S71, defining a world coordinate system according to any horizontal and vertical planes of a preset area, which provides a reference for subsequent geometric calculations;

[0210] Step S72, mapping the vehicle terminal detection frame to the world coordinate system through a homography matrix to form a two-dimensional vehicle terminal detection frame that is parallel to or coplanar with the vertical plane;

[0211] Step S73, obtaining the symmetry axis of the two-dimensional automobile terminal detection frame in the vertical direction, which is helpful for subsequent rotation and translation operations;

[0212] Step S74, rotating the two-dimensional automobile terminal detection frame based on the symmetry axis to analyze its spatial relationship with the channel feature area;

[0213] Step S75, obtaining the complete rotation trajectory of the two-dimensional vehicle terminal detection frame and defining it as a three-dimensional vehicle terminal detection frame, which helps to more accurately calculate the intersection with the channel feature area;

[0214] Step S76, obtaining the Euclidean distance between the three-dimensional vehicle terminal detection frame and all channel feature areas based on the world coordinate system, which provides necessary information for calculating the intersection duration;

[0215] Step S77, determining whether the Euclidean distance is less than or equal to zero, if so, it indicates that the detection frame and the channel feature area have an intersection;

[0216] Step S78, obtaining a first intersection duration of a channel feature area intersecting with the three-dimensional automobile terminal detection frame, that is, a single intersection duration;

[0217] Step S79, obtaining the second intersection duration between the three-dimensional automobile terminal detection frame and each channel feature area respectively, and adding up all the second intersection durations to obtain the total intersection duration.

[0218] More specifically, the operation in step S7 may adopt the following refinements or alternatives:

[0219] 1. In step S71, the definition of the world coordinate system can be adjusted according to the requirements of different application scenarios. For example, the influence of buildings needs to be considered in an urban canyon environment.

[0220] 2. The homography matrix in step S72 can be obtained through computer vision technology, or can be implemented through other geometric transformation methods.

[0221] 3. The rotation operation in step S74 can adopt different rotation angles and rotation centers to adapt to different detection frame shapes and positions.

[0222] 4. The three-dimensional vehicle terminal detection frame in step S75 can be generated by three-dimensional modeling software, or directly converted from a two-dimensional detection frame through an algorithm.

[0223] 5. In step S76, the calculation of the Euclidean distance may adopt various mathematical formulas or geometric algorithms, such as the Pythagorean theorem or the spatial distance formula.

[0224] 6. In steps S77 and S78, the determination and acquisition of the intersection duration can be comprehensively evaluated in combination with other factors such as signal strength and quality to improve the accuracy of positioning failure determination.

[0225] In some embodiments, step S7, obtaining the single intersection duration of the automobile terminal detection frame with each channel feature area, and the total intersection duration with all channel feature areas, includes:

[0226] Step S71, defining a world coordinate system according to any horizontal plane and any vertical plane of the preset area;

[0227] Step S72, mapping the vehicle terminal detection frame to the world coordinate system through a homography matrix to form a two-dimensional vehicle terminal detection frame, and the two-dimensional vehicle terminal detection frame is parallel to or coplanar with the arbitrary vertical plane;

[0228] Step S73, obtaining the symmetry axis of the two-dimensional automobile terminal detection frame in the vertical direction;

[0229] Step S74, rotating the two-dimensional automobile terminal detection frame based on the symmetry axis;

[0230] Step S75, obtaining a complete rotation trajectory of the two-dimensional vehicle terminal detection frame, and defining the complete rotation trajectory as a three-dimensional vehicle terminal detection frame;

[0231] Step S76, obtaining the Euclidean distance between the three-dimensional vehicle terminal detection frame and all channel feature areas based on the world coordinate system;

[0232] Step S77, determining whether the Euclidean distance is less than or equal to zero, if the Euclidean distance is less than or equal to zero, executing step S78;

[0233] Step S78, obtaining a first intersection duration of a channel feature area intersecting with the three-dimensional automobile terminal detection frame to obtain the single intersection duration;

[0234] Step S79, obtaining the second intersection durations of the three-dimensional automobile terminal detection frame and each channel feature area respectively, and adding up all the second intersection durations to obtain the total intersection duration.

[0235] It should be noted that the intersection time between the vehicle terminal detection frame and the channel feature area is obtained. These steps ensure that the residence time of the vehicle in a specific area can be accurately measured, thereby assisting the positioning failure judgment module to make effective judgments.

[0236] Specifically, the operations involved in step S7 include defining a world coordinate system, mapping a detection frame, calculating a symmetry axis, rotating the detection frame, obtaining a rotation trajectory, calculating the Euclidean distance, determining the intersection, and calculating the intersection duration. These operations require precise mathematical models and algorithms to implement, such as using three-dimensional space coordinate transformation and geometric calculation methods.

[0237] Preferably, step S7 may further include the following detailed operations:

[0238] Step S71, when defining the world coordinate system, appropriate reference points and directions may be selected according to actual geographical and environmental characteristics to ensure the accuracy and applicability of the coordinate system;

[0239] Step S72, when mapping the detection frame to the world coordinate system, a mathematical homography matrix may be used to achieve two-dimensional to three-dimensional mapping to ensure the correct position and direction of the detection frame;

[0240] Step S73, obtaining the symmetry axis can be achieved by analyzing the shape and characteristics of the detection frame. For example, for a rectangular frame, the symmetry axis can be the center line of the frame;

[0241] Step S74, when rotating the detection frame, an appropriate rotation angle and rotation center may be selected according to the axis of symmetry to facilitate subsequent calculation of the intersection duration;

[0242] Step S75, obtaining the rotation trajectory can be achieved by recording each position of the detection frame during the rotation process, which helps to build a three-dimensional model of the detection frame;

[0243] Step S76, calculating the Euclidean distance may use the distance formula between two points in three-dimensional space to determine the relative position of the detection frame and the channel feature area;

[0244] Step S77, determining the intersection can be achieved by comparing the relationship between the Euclidean distance and zero. If the distance is less than or equal to zero, it means that the detection frame intersects with the channel feature area;

[0245] Step S78, obtaining a single intersection duration can be calculated by analyzing the start and end positions where the detection frame intersects with the channel feature area in the rotation trajectory;

[0246] Step S79, calculating the total intersection duration can be achieved by accumulating the intersection durations of the detection frame and all channel feature areas.

[0247] More specifically, the operation in step S7 may adopt the following refinements or alternatives:

[0248] 1. In step S71, a local coordinate system or a global coordinate system can be selected according to different application scenarios to meet different positioning requirements.

[0249] 2. The homography matrix in step S72 can be obtained through methods such as camera calibration and sensor data fusion to improve the accuracy of mapping.

[0250] 3. The rotation operation in step S74 can be implemented using quaternions or Euler angles to avoid the universal lock problem and provide more flexible rotation control.

[0251] 4. The rotation trajectory in step S75 can be generated by 3D modeling software or algorithms to facilitate visualization and analysis of the spatial motion of the detection frame.

[0252] 5. In step S76, in addition to the Euclidean distance, the Mahalanobis distance or other distance measurement methods may also be considered to adapt to different data distribution characteristics.

[0253] 6. In steps S77 and S78, the calculation of the intersection duration can be combined with other factors such as signal strength and signal quality for comprehensive evaluation to improve the accuracy and reliability of positioning failure judgment.

[0254] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0255] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 701, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0256] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0257] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0258] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A vehicle terminal networking precise positioning system, It is characterized in that The positioning system comprises: A channel state information acquisition module, used to acquire channel state information of all vehicle terminals in the preset area; A channel feature acquisition module is used to acquire the channel features of each vehicle terminal respectively through a preset strategy; A channel characteristic region definition module, used to define a channel characteristic region based on the channel characteristics of each automobile terminal at a preset ratio, wherein the center of the current channel characteristic region coincides with the midpoint of the current automobile terminal; An automobile terminal detection frame acquisition module is used to respond to a network connection request signal of the automobile terminal and acquire an automobile terminal detection frame matching the network connection request signal based on a signal detection algorithm; The to-be-located vehicle terminal marking module is used to obtain the positioning requirements of the vehicle terminal and mark the vehicle terminal to be located as the to-be-located vehicle terminal according to the positioning requirements; An accurate position acquisition and output module, used to acquire the accurate positions of all automobile terminals to be located through a channel feature matching algorithm and output them to the automobile terminals; An intersection duration acquisition module, used to obtain a single intersection duration of the automobile terminal detection frame with each channel feature area, and a total intersection duration with all channel feature areas; An intersection duration judgment module, used to judge whether the current single intersection duration exceeds a first preset threshold and whether the total intersection duration exceeds a second preset threshold; The positioning failure judgment module is used to judge whether the current single intersection duration exceeds the first preset threshold or whether the total intersection duration exceeds the second preset threshold, and then judge it as positioning failure, generate a warning signal and send it to the vehicle terminal.

2. A method for accurately positioning a vehicle terminal through networking, wherein the vehicle terminal is located in a preset area. It is characterized in that The positioning method comprises: Step S1, obtaining channel status information of all automobile terminals in the preset area; Step S2, obtaining the channel characteristics of each vehicle terminal respectively through a preset strategy; Step S3, defining a channel characteristic region at a preset ratio based on the channel characteristics of each vehicle terminal, wherein the center of the current channel characteristic region coincides with the midpoint of the current vehicle terminal; Step S4, responding to the networking request signal of the automobile terminal and acquiring the automobile terminal detection frame matching the networking request signal based on a signal detection algorithm; Step S5, obtaining the positioning requirement of the vehicle terminal, and marking the vehicle terminal to be positioned as a vehicle terminal to be positioned according to the positioning requirement; Step S6, obtaining the precise positions of all the automobile terminals to be located through a channel feature matching algorithm and outputting them to the automobile terminals; Step S7, obtaining a single intersection duration of the automobile terminal detection frame with each channel feature area, and a total intersection duration with all channel feature areas; Step S8, respectively judging whether the current single intersection duration exceeds the first preset threshold value and whether the total intersection duration exceeds the second preset threshold value, if the current single intersection duration exceeds the first preset threshold value or the total intersection duration exceeds the second preset threshold value, executing step S9; Step S9: determine that the positioning is invalid, generate a warning signal and send it to the vehicle terminal.

3. The positioning method according to claim 2, It is characterized in that Step S3, defining a channel characteristic area based on the channel characteristics of each automobile terminal at a preset ratio, including: Step S31, sorting the channel characteristics of all vehicle terminals from small to large; Step S32, obtaining the minimum and maximum values ​​of the channel characteristics to establish a channel characteristic interval; Step S33, equally dividing the channel characteristic interval to form at least two sub-intervals, and the sub-intervals at both ends are the minimum sub-interval and the maximum sub-interval respectively; Step S34, sequentially numbering each sub-interval from small to large, and defining the number of the current sub-interval as the proportional constant of the current sub-interval, the proportional constant of the minimum sub-interval is the minimum proportional constant, and the proportional constant of the maximum sub-interval is the maximum proportional constant; Step S35, defining the minimum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the minimum value subinterval multiplied by the minimum proportional constant, and the maximum channel characteristic area as the minimum enclosing area of ​​the vehicle terminal within the maximum value subinterval multiplied by the maximum proportional constant; Step S36, based on the sub-interval where each vehicle terminal is located and the corresponding proportional constant, respectively obtain the channel characteristic area of ​​each vehicle terminal.

4. The positioning method according to claim 2, It is characterized in that Step S4, responding to the networking request signal of the automobile terminal and acquiring the automobile terminal detection frame matching the networking request signal based on the signal detection algorithm, includes: Step S41, in response to the networking request signal, acquiring communication data matching the networking request signal through an external preset communication terminal; Step S42, dividing the communication data evenly into a preset number of grids; Step S43, predicting a plurality of boundary boxes for the automobile terminal in the communication data according to the signal detection algorithm based on all grids; Step S44, respectively obtaining the confidence of each bounding box, and obtaining the bounding box with the largest confidence and marking it as a first-order bounding box; Step S45, calculating the intersection-over-union ratio of each other bounding box with the first-order bounding box; Step S46, selecting a bounding box whose intersection-over-union ratio is greater than or equal to a preset threshold as a second-order bounding box; Step S47, obtaining a second-order bounding box with the highest confidence and defining it as the automobile terminal detection box.

5. The positioning method according to claim 2, It is characterized in that Step S6, obtaining the precise positions of all automobile terminals to be located through a channel feature matching algorithm and outputting them to the automobile terminals, including: Step S61, obtaining a geographic information model of the preset area and outputting all automobile terminals to be located to the geographic information model; Step S62, dividing the geographic information model by horizontal grids of preset density; Step S63, detecting and acquiring all horizontal grids having all automobile terminals to be located by a signal detection algorithm, and defining them as a positioning candidate grid set; Step S64, defining each grid in the positioning candidate grid set as a node, and defining all vehicle terminals as starting points and end points; Step S65, defining one of all the end points as the positioning end point, defining the other end points as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point through the A* algorithm; Step S66, obtaining all horizontal grids corresponding to the minimum number of grids, and sequentially connecting them to form the precise position; Step S67, outputting the precise location to a receiving end of the automobile terminal.

6. The positioning method according to claim 5, It is characterized in that Step S65, defining one of all the end points as the positioning end point, defining the other end points as waypoints, and calculating the minimum number of grids required for the starting point to pass through all the waypoints and finally reach the positioning end point by using the A* algorithm, including: Step S651, creating a grid set to be checked; Step S652, adding the starting point, all the waypoints, and the end point to the grid set to be checked, and setting the priority of the starting point to the highest, setting the priority of all the waypoints to the middle, and setting the priority of the end point to the lowest; Step S653, determining whether the to-be-checked grid set is empty, if the to-be-checked grid set is not empty, executing step S654; Step S654, selecting a node with the highest priority from the set of grids to be checked; Step S655, determining whether the node is one of the waypoints, if the node is one of the waypoints, executing step S656; Step S656, starting from the current waypoint, gradually tracking the parent node until reaching the starting point; Step S657, respectively obtaining the number of grids required for each waypoint to reach the starting point, and defining the waypoint with the least number of grids required as the starting point for the next path search; Step S658, iterating steps S651 to S657 for several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position; Step S659, obtaining all grids covered by the precise position to obtain the minimum number of grids.

7. The positioning method according to claim 6, It is characterized in that Step S653, determining whether the grid set to be checked is empty, and then comprising: Step S10, if the grid set to be checked is empty, traverse all neighboring nodes of the node and add all neighboring nodes to the grid set to be checked; Step S20, obtaining the neighboring node with the highest priority among all neighboring nodes as the parent node for the next traversal; Step S30, starting from the current parent node, gradually tracing the parent node of the next traversal until reaching one of the waypoints; Step S40, defining one of the waypoints reached as the starting point of the next path search; Step S50, iterating steps S10 to S40 several times until the starting point, all the waypoints, and the end point are sequentially connected by a single line to obtain the precise position; Step S60, acquiring all grids covered by the precise position to obtain the minimum number of grids.

8. The positioning method according to claim 2, It is characterized in that Step S7, obtaining the single intersection duration of the automobile terminal detection frame with each channel feature area, and the total intersection duration with all channel feature areas, including: Step S71, defining a world coordinate system according to any horizontal plane and any vertical plane of the preset area; Step S72, mapping the vehicle terminal detection frame to the world coordinate system through a homography matrix to form a two-dimensional vehicle terminal detection frame, and the two-dimensional vehicle terminal detection frame is parallel to or coplanar with the arbitrary vertical plane; Step S73, obtaining the symmetry axis of the two-dimensional automobile terminal detection frame in the vertical direction; Step S74, rotating the two-dimensional automobile terminal detection frame based on the symmetry axis; Step S75, obtaining a complete rotation trajectory of the two-dimensional vehicle terminal detection frame, and defining the complete rotation trajectory as a three-dimensional vehicle terminal detection frame; Step S76, obtaining the Euclidean distance between the three-dimensional vehicle terminal detection frame and all channel feature areas based on the world coordinate system; Step S77, determining whether the Euclidean distance is less than or equal to zero, if the Euclidean distance is less than or equal to zero, executing step S78; Step S78, obtaining a first intersection duration of a channel feature area intersecting with the three-dimensional automobile terminal detection frame to obtain the single intersection duration; Step S79, obtaining the second intersection durations of the three-dimensional automobile terminal detection frame and each channel feature area respectively, and adding up all the second intersection durations to obtain the total intersection duration.

9. An electronic device, It is characterized in that It includes a processor and a memory coupled to the processor, wherein the memory stores program instructions that can be executed by the processor; when the processor executes the program instructions stored in the memory, the method for precise positioning of an automobile terminal connected to the Internet as described in any one of claims 2 to 8 is implemented.

10. A storage medium, It is characterized in that The storage medium stores program instructions, which, when executed by the processor, can implement the method for precise positioning of an automobile terminal connected to the Internet as described in any one of claims 2 to 8.

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