An intelligent GPS positioning data analysis management system and method

Abstract

The invention discloses an intelligent GPS positioning data analysis and management system and method, which relates to the technical field of GPS positioning data management, and comprises a node confirmation module, a data analysis module and a prediction and reminder module; the node confirmation module is used to determine the road nodes in a plane area model and extract the road node information; the data analysis module is used to analyze the relationship between the vehicle and the intersection node and predict the movement of the vehicle; the prediction and reminder module is used to analyze when a block is about to be congested and remind other vehicles passing through the block to avoid the congested road section. The application analyzes GPS positioning data, combines the road distribution situation, predicts the vehicle's driving route, and combines the prediction situation and uses big data to analyze the blocks and sections that are about to be congested, so that when congestion is about to occur, congestion information can be sent to the driver in advance, so as to realize the prediction of road congestion.

Description

Technical Field

[0001] The present invention relates to the technical field of GPS positioning data management, in particular to an intelligent GPS positioning data analysis management system and method. Background Art

[0002] GPS refers to the global positioning system, which can be used to locate and analyze the position of a vehicle. The existing GPS positioning system is mainly used to locate and determine the position of the vehicle, but does not analyze the GPS positioning data to achieve other functions. In the existing technology, the following problems exist for vehicles traveling on the road:

[0003] When existing vehicles are driving, they will only remind the driver of the congestion ahead when the road is congested, and they need to pay attention to re-planning the driving route to avoid the congested section. There is a problem of timeliness, that is, when the vehicle is about to enter the congested section, it is too late to remind it. How to analyze GPS data, predict the phenomenon that the vehicle is about to enter the congested section, and inform the driver in advance, so as to avoid the vehicle from entering the congested section and plan the driving route to avoid the congestion in advance;

[0004] Therefore, people are in urgent need of an intelligent GPS positioning data analysis management system and method to solve the above-mentioned technical problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent GPS positioning data analysis and management system and method to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: an intelligent GPS positioning data analysis and management method, the management method comprising the following steps:

[0007] S1. According to the vehicle GPS positioning data, a plane area model is established with the current position of the vehicle as the center, and a plane rectangular coordinate system is established on the plane area model to assign coordinate values ​​to any point on the plane area model;

[0008] S2. Divide the plane area model into N*N blocks, and the N*N blocks are equally divided. Add a label to each block. For example, 2-5 means the block in the second row and the fifth column. The real-time position coordinates of the vehicle located by GPS are (X i ,Y i ), the outermost block in the plane domain model forms the outer block function E (x) The purpose is to determine whether the plane area model needs to be rebuilt, because once the vehicle enters the external block, it means that the vehicle will drive out of the current plane area model, so the plane area model needs to be rebuilt, and the remaining blocks form the internal block function F(x) , when the real-time position of the vehicle satisfies the external block function, it returns to S1;

[0009] S3, extract the intersection nodes formed by the road intersections in all blocks, and determine the coordinate values ​​of the intersection nodes (x j ,y j ), the intersection node coordinate values ​​of any block form a set P k-s , the intersection nodes of all blocks in the plane area model form a set P;

[0010] S4, according to the real-time position of the vehicle located by GPS, analyze the distance change between the vehicle and any intersection node in the block, so as to determine the next block that the vehicle is about to enter, and analyze the state of the vehicle in the next block, and the analysis results are transferred to S2 and S5;

[0011] S5. Analyze the traffic volume of the road according to the road distribution and vehicle driving conditions in the block, predict whether congestion will occur according to the analysis results, and remind the driver to avoid the route in advance.

[0012] According to the above technical solution, in S3, the intersection nodes of the ksth block are extracted, where ks represents the block of the kth row and sth column, forming a set P k-s ={(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),...,(x n ,y n )}, where n represents that there are n intersection nodes in the ks-th block, and the intersection node set of the block constitutes a set P = {P 1-1 ,P 1-2 ,P 1-3 ,...,P N-N}, where NN indicates that the plane area model is divided into N*N blocks.

[0013] According to the above technical solution, in S4, the real-time position of the vehicle is located once every time interval t to obtain the position coordinates (X i ,Y i ), calculate the distance between the vehicle's real-time position and the intersection node according to the following formula

[0014]

[0015] Among them, i represents the i-th real-time position of the vehicle, and j represents the j-th intersection node;

[0016] Get the distance set between the vehicle's real-time position and each intersection node in any block m represents the m real-time positions of the vehicle;

[0017] With time as the horizontal axis, the distance set L j The distance value in is the ordinate, a plane rectangular coordinate system is established, and the distance set L j The numerical values ​​in are located in the plane rectangular coordinate system, and the positioning points in the plane rectangular coordinate system are fitted to obtain the line segment. The slope of the line segment is solved to obtain the slope K j ;

[0018] When K j When ≥0, the analysis of GPS positioning data is abandoned, because this indicates that the distance between the vehicle and the j-th intersection node is getting farther and farther, and the vehicle is very likely not to pass through the intersection node;

[0019] When K j When <0, continue to analyze the position relationship between the real-time position of the vehicle and the position of the j-th intersection node, and find the inflection point of the slope change, which is the point where the slope changes from negative to positive;

[0020] Determine the slope before the inflection point of the change is K e , the slope after the inflection point changes is K f ;

[0021] Compare|K e |with|K f |The size of |K e |=|K f |, it indicates that the vehicle has passed the intersection node and has not turned into other roads; when |K e |≠|K f |, indicating that the vehicle has turned into other roads at the intersection node; because when a vehicle turns into other roads at an intersection node, it must reduce its speed, and the reduction in speed will lead to a change in the slope;

[0022] When the vehicle passes through all the intersection nodes and does not turn into other roads, it is determined that the vehicle is about to enter the next block connected to the current road;

[0023] When the vehicle turns into another road at a certain intersection, re-analysis is performed using the analysis method of S4.

[0024] According to the above technical solution, in S4, when it is predicted that the vehicle is about to enter the next block, it is determined whether the next block belongs to an external block. If it belongs to an external block, it returns to S1. Otherwise, the terminal block of the vehicle is calculated and predicted according to the following formula:

[0025]

[0026] Among them, w represents the probability that the vehicle’s terminal block is the next block, a 0 、a 1 、a 2 Represent the influencing factors respectively. represents the error factor, u 1 Indicates the proportion of other vehicles’ historical destination blocks as the next block, u 2 Indicates the percentage of the historical destination block of the current vehicle that is the next block;

[0027] If w>w', the vehicle's terminal block is determined to be the next block, and the determination result is uploaded to the cloud;

[0028] When the end block of the vehicle is the next block, a certain point in the next block is determined to be the destination of the vehicle, and at this time, it can be determined that the vehicle will stay in the next block;

[0029] If w≤w', it is determined that the terminal block of the vehicle is not the next block, but is about to pass through the next block, and the determination result is uploaded to the cloud. The purpose of uploading the determination result to the cloud at this time is to determine whether the current vehicle will cause congestion on the road of the next block when passing through the next block. When the vehicle enters the next block, the analysis process of S4 is repeated, where w' represents the set probability threshold.

[0030] According to the above technical solution, in S5, the determination result of the vehicle in the terminal block is uploaded to the cloud, and the cloud integrates and analyzes all the uploaded data and compares it with the rated vehicle flow of the terminal block;

[0031] When the comparison results show that the traffic flow in the terminal block is about to exceed the rated traffic flow, congestion is determined to occur and the determination result is sent to the drivers of vehicles that are about to pass the terminal block to remind them to take evasive routes and avoid traffic congestion in any block. Even if traffic congestion is about to occur, drivers passing through the congested block can be reminded to re-plan their routes in advance.

[0032] Through the above technical solution, the next movement of the vehicle can be analyzed, and then the sections and blocks where congestion will occur can be determined. Therefore, the analysis results can be used to warn vehicles that are about to enter and pass through congested sections, thereby facilitating vehicles that are about to pass through congested blocks or sections to re-plan their routes and avoid passing through congested sections.

[0033] An intelligent GPS positioning data analysis and management system, the data analysis and management system includes a digital modeling module, a function establishment module, a node confirmation module, a data analysis module and a prediction reminder module;

[0034] The digital modeling module is used to establish a plane area model with the current position of the vehicle as the center and perform digital processing; the function establishment module is used to establish a resolution function, which includes an external block function and an internal block function, and is used to determine whether the plane area model needs to be re-established; the node confirmation module is used to determine the road nodes in the plane area model and extract road node information; the data analysis module is used to analyze the relationship between the vehicle and the intersection node and predict the movement of the vehicle; the prediction reminder module is used to analyze when a block is about to be congested, and remind other vehicles passing through the block to re-plan their routes to avoid congested sections.

[0035] According to the above technical solution, the digital modeling module includes a model building unit, a coordinate system building unit, a coordinate value assigning unit and a block division unit;

[0036] The model building unit is used to build a plane area model centered on the vehicle; the coordinate system building unit is used to build a plane rectangular coordinate system on the plane area model; the coordinate value assigning unit is used to assign a coordinate value to each point on the plane area model; and the block division unit is used to divide the established plane area model into a number of equally divided blocks.

[0037] According to the above technical solution, the node confirmation module includes a block determination unit, a node extraction unit and a coordinate integration unit;

[0038] The block determination unit is used to determine the block label of the extracted node; the node extraction unit is used to extract the coordinate value of the intersection node in the block, and the coordinate integration unit is used to integrate the extracted node coordinates.

[0039] According to the above technical solution, the data analysis module includes a distance calculation unit, a slope analysis unit, an inflection point determination unit, a block prediction unit and an end point prediction unit;

[0040] The distance calculation unit is used to calculate the distance between the current position of the vehicle and each intersection node in the block; the slope analysis unit is used to determine the slope change of the distance over time based on the calculated distance; the inflection point determination unit is used to determine and find the inflection point of the slope; the block prediction unit is used to predict and analyze the next block that the vehicle is about to enter; the end point prediction unit is used to predict whether the end point block of the vehicle is the next block.

[0041] According to the above technical solution, the prediction and reminder module includes a vehicle flow comparison unit and a congestion reminder unit;

[0042] The traffic flow comparison unit is used to determine whether congestion will occur in the next block according to the analysis results of the data analysis module, and the congestion reminder unit is used to remind vehicles that are about to pass through the next block to re-plan their routes to avoid the congested block.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows: the present application analyzes GPS positioning data, combines the road distribution situation, predicts the vehicle's driving route, and combines the prediction situation with big data to analyze the blocks and sections where congestion is about to occur, so that when congestion is about to occur, congestion information can be sent to the driver in advance, and road congestion can be predicted, so that the driver can avoid the route that is about to be congested in advance, reduce the degree of congestion, avoid wasting time on congested roads, and make travel more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the steps of an intelligent GPS positioning data analysis and management method of the present invention;

[0045] Figure 2 This is a schematic diagram of a plane area model of an intelligent GPS positioning data analysis management system and method of the present invention;

[0046] Figure 3 The block diagram is a smart GPS positioning data analysis and management system and method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] like Figure 1 to Figure 3 As shown, the present invention provides the following technical solution, an intelligent GPS positioning data analysis and management method, the management method comprising the following steps:

[0049] S1. According to the vehicle GPS positioning data, a plane area model is established with the current position of the vehicle as the center, and a plane rectangular coordinate system is established on the plane area model to assign coordinate values ​​to any point on the plane area model;

[0050] S2. Divide the plane area model into N*N blocks, and the N*N blocks are equally divided. Add a label to each block. For example, 2-5 means the block in the second row and the fifth column. The real-time position coordinates of the vehicle located by GPS are (X i ,Yi ), the outermost block in the plane domain model forms the outer block function E (x) The purpose is to determine whether the plane area model needs to be rebuilt, because once the vehicle enters the external block, it means that the vehicle will drive out of the current plane area model, so the plane area model needs to be rebuilt, and the remaining blocks form the internal block function F (x) , when the real-time position of the vehicle satisfies the external block function, it returns to S1;

[0051] S3, extract the intersection nodes formed by the road intersections in all blocks, and determine the coordinate values ​​of the intersection nodes (x j ,y j ), the intersection node coordinate values ​​of any block form a set P k-s , the intersection nodes of all blocks in the plane area model form a set P;

[0052] Extract the intersection nodes of the ksth block, where ks represents the block in the kth row and sth column, forming a set P k-s ={(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),...,(x n ,y n )}, where n represents that there are n intersection nodes in the ks-th block, and the intersection node set of the block constitutes a set P = {P 1-1 ,P 1-2 ,P 1-3 ,...,P N-N}, where NN indicates that the plane area model is divided into N*N blocks.

[0053] S4, according to the real-time position of the vehicle located by GPS, analyze the distance change between the vehicle and any intersection node in the block, so as to determine the next block that the vehicle is about to enter, and analyze the state of the vehicle in the next block, and the analysis results are transferred to S2 and S5;

[0054] The real-time position of the vehicle is located once every time interval t to obtain the vehicle's position coordinates (X i ,Y i ), calculate the distance between the vehicle's real-time position and the intersection node according to the following formula

[0055]

[0056] Among them, i represents the i-th real-time position of the vehicle, and j represents the j-th intersection node;

[0057] Get the distance set between the vehicle's real-time position and each intersection node in any block m represents the m real-time positions of the vehicle;

[0058] With time as the horizontal axis, the distance set L j The distance value in is the ordinate, a plane rectangular coordinate system is established, and the distance set L j The numerical values ​​in are located in the plane rectangular coordinate system, and the positioning points in the plane rectangular coordinate system are fitted to obtain the line segment. The slope of the line segment is solved to obtain the slope K j ;

[0059] When K j When ≥0, the analysis of GPS positioning data is abandoned, because this indicates that the distance between the vehicle and the j-th intersection node is getting farther and farther, and the vehicle is very likely not to pass through the intersection node;

[0060] When K j When <0, continue to analyze the position relationship between the real-time position of the vehicle and the position of the j-th intersection node, and find the inflection point of the slope change, which is the point where the slope changes from negative to positive;

[0061] Determine the slope before the inflection point of the change is K e , the slope after the inflection point changes is K f ;

[0062] Compare|K e |with|K f |The size of |K e |=|K f |, it indicates that the vehicle has passed the intersection node and has not turned into other roads; when |K e |≠|K f |, indicating that the vehicle has turned into other roads at the intersection node; because when a vehicle turns into other roads at an intersection node, it must reduce its speed, and the reduction in speed will lead to a change in the slope;

[0063] When the vehicle passes through all the intersection nodes and does not turn into other roads, it is determined that the vehicle is about to enter the next block connected to the current road;

[0064] When the vehicle turns into another road at a certain intersection, re-analysis is performed using the analysis method of S4.

[0065] In S4, when it is predicted that the vehicle is about to enter the next block, it is determined whether the next block belongs to an external block. If it belongs to an external block, it returns to S1. Otherwise, the terminal block of the vehicle is calculated and predicted according to the following formula:

[0066]

[0067] Among them, w represents the probability that the vehicle’s terminal block is the next block, a 0 、a 1 、a 2 Represent the influencing factors respectively. represents the error factor, u 1 Indicates the proportion of other vehicles’ historical destination blocks as the next block, u 2 Indicates the percentage of the historical destination block of the current vehicle that is the next block;

[0068] If w>w', the vehicle's terminal block is determined to be the next block, and the determination result is uploaded to the cloud;

[0069] When the end block of the vehicle is the next block, a certain point in the next block is determined to be the destination of the vehicle, and at this time, it can be determined that the vehicle will stay in the next block;

[0070] If w≤w', it is determined that the terminal block of the vehicle is not the next block, but is about to pass through the next block, and the determination result is uploaded to the cloud. The purpose of uploading the determination result to the cloud at this time is to determine whether the current vehicle will cause congestion on the road of the next block when passing through the next block. When the vehicle enters the next block, the analysis process of S4 is repeated, where w' represents the set probability threshold.

[0071] S5. Analyze the traffic volume of the road according to the road distribution and vehicle driving conditions in the block, predict whether congestion will occur according to the analysis results, and remind the driver to avoid the route in advance.

[0072] The determination result of the vehicles in the terminal block will be uploaded to the cloud, which will integrate and analyze all the uploaded data and compare it with the rated traffic volume of the terminal block;

[0073] When the comparison results show that the traffic flow in the terminal block is about to exceed the rated traffic flow, congestion is determined to occur and the determination result is sent to the drivers of vehicles that are about to pass the terminal block to remind them to take evasive routes and avoid traffic congestion in any block. Even if traffic congestion is about to occur, drivers passing through the congested block can be reminded to re-plan their routes in advance.

[0074] Through the above technical solution, the next movement of the vehicle can be analyzed, and then the sections and blocks where congestion will occur can be determined. Therefore, the analysis results can be used to warn vehicles that are about to enter and pass through congested sections, thereby facilitating vehicles that are about to pass through congested blocks or sections to re-plan their routes and avoid passing through congested sections.

[0075] An intelligent GPS positioning data analysis and management system, the data analysis and management system includes a digital modeling module, a function establishment module, a node confirmation module, a data analysis module and a prediction reminder module;

[0076] The digital modeling module is used to establish a plane area model with the current position of the vehicle as the center and perform digital processing; the function establishment module is used to establish a resolution function, which includes an external block function and an internal block function, and is used to determine whether the plane area model needs to be re-established; the node confirmation module is used to determine the road nodes in the plane area model and extract road node information; the data analysis module is used to analyze the relationship between the vehicle and the intersection node and predict the movement of the vehicle; the prediction reminder module is used to analyze when a block is about to be congested, and remind other vehicles passing through the block to re-plan their routes to avoid congested sections.

[0077] The digital modeling module includes a model building unit, a coordinate system building unit, a coordinate value assigning unit and a block dividing unit;

[0078] The model building unit is used to build a plane area model centered on the vehicle; the coordinate system building unit is used to build a plane rectangular coordinate system on the plane area model; the coordinate value assigning unit is used to assign a coordinate value to each point on the plane area model; and the block division unit is used to divide the established plane area model into a number of equally divided blocks.

[0079] The node confirmation module includes a block determination unit, a node extraction unit and a coordinate integration unit;

[0080] The block determination unit is used to determine the block label of the extracted node; the node extraction unit is used to extract the coordinate value of the intersection node in the block, and the coordinate integration unit is used to integrate the extracted node coordinates.

[0081] The data analysis module includes a distance calculation unit, a slope analysis unit, an inflection point determination unit, a block prediction unit and an endpoint prediction unit;

[0082] The distance calculation unit is used to calculate the distance between the current position of the vehicle and each intersection node in the block; the slope analysis unit is used to determine the slope change of the distance over time based on the calculated distance; the inflection point determination unit is used to determine and find the inflection point of the slope; the block prediction unit is used to predict and analyze the next block that the vehicle is about to enter; the end point prediction unit is used to predict whether the end point block of the vehicle is the next block.

[0083] The prediction and reminder module includes a vehicle flow comparison unit and a congestion reminder unit;

[0084] The traffic flow comparison unit is used to determine whether congestion will occur in the next block according to the analysis results of the data analysis module, and the congestion reminder unit is used to remind vehicles that are about to pass through the next block to re-plan their routes to avoid the congested block.

[0085] Embodiment 1:

[0086] S1. Based on the vehicle GPS positioning data, a plane area model with a side length of 5 km is established with the current position of the vehicle as the center, and a plane rectangular coordinate system is established on the plane area model;

[0087] S2. Divide the plane area model into 5*5 blocks, each block has a side length of 1km, and use GPS to locate the current position coordinate value of the vehicle to be (2500,2500);

[0088] S3, extract the coordinate values ​​of the intersection nodes in the block where the vehicle is currently located, and form a set P 3-3 ={(2508,2800),(2497,2650),(2508,2360)};

[0089] S4, the coordinate values ​​of the positioning vehicle at different time points are t 1 =(2500,2500),t 2 =(2500,2430),t 3 =(2500,2370),t 4 =(2500,2310),t 5 =(2500,2250);

[0090] Calculate the vehicle at t 1 ~t 5 The distance change between the coordinate value of the time point and the coordinate value of the intersection node forms a set:

[0091] The set corresponding to the coordinate value of the first intersection node (2508, 2800) is {300.11, 370.1, 430, 490.1, 550.1};

[0092] The set corresponding to the coordinate value of the second intersection node (2497, 2650) is {150, 220, 280, 340, 400};

[0093] The set corresponding to the coordinate value of the third intersection node (2508, 2360) is {140.23, 70.46, 12.81, 50.64, 110.3};

[0094] In t 1 ~t 5In the data analysis collected at the time point, the distance between the first intersection node and the second intersection node and the real-time position of the vehicle gradually increases, indicating that the vehicle has passed the first intersection node and the second intersection node and will not turn into other roads through the first intersection node and the second intersection node. The distance between the third intersection node and the vehicle position gradually decreases and then gradually increases, indicating that at t 1 ~t 5 At this time point, the vehicle passes the third intersection node, but does not enter other roads through the third intersection node. The third intersection node is the last intersection node of the current road in the current block, indicating that the vehicle will definitely enter the next block next;

[0095] Continue to call the intersection node data of the next block that the vehicle is about to enter, and use the same method as above to analyze and process it, so as to predict and analyze the vehicle's route in a short time;

[0096] S5. The data analysis results of all vehicles in the Internet of Vehicles will be uploaded to the cloud. The cloud will integrate and manage all uploaded data and analyze the traffic conditions on the road. When it is predicted that a large number of vehicles will enter a certain road, which will cause congestion on the road, early warning reminders will be given to vehicles that have not yet entered the road but are about to enter the road, reminding drivers to change their driving routes and avoid congested sections.

[0097] Embodiment 2:

[0098] When it is predicted that the vehicle is about to enter the next block, it is determined whether the next block belongs to an external block. If it belongs to an external block, it returns to S1. Otherwise, the terminal block of the vehicle is calculated and predicted according to the following formula:

[0099]

[0100]

[0101]

[0102] Among them, w represents the probability that the vehicle’s terminal block is the next block, a 0 、a 1 、a 2 Represent the influencing factors respectively. represents the error factor, u 1 Indicates the proportion of other vehicles’ historical destination blocks as the next block, u 2 Indicates the percentage of the historical destination block of the current vehicle that is the next block;

[0103] If w=0.74>w'=0.65, the vehicle's terminal block is determined to be the next block, and the determination result is uploaded to the cloud;

[0104] If w=0.66>w'=0.65, the vehicle's terminal block is determined to be the next block, and the determination result is uploaded to the cloud;

[0105] When w=0.5≤w'=0.66, it is determined that the terminal block of the vehicle is not the next block, but is about to pass the next block. The determination result is uploaded to the cloud. When the vehicle enters the next block, the analysis process of S4 is repeated, where w' represents the set probability threshold.

[0106] The traffic volume on the road is analyzed according to the road distribution and vehicle driving conditions in the block, and whether congestion will occur is predicted based on the analysis results, so as to remind the driver to avoid the route in advance.

[0107] The determination result of the vehicles in the terminal block will be uploaded to the cloud, which will integrate and analyze all the uploaded data and compare it with the rated traffic volume of the terminal block;

[0108] When the comparison results show that the traffic flow of the terminal block is about to exceed the rated traffic flow, it is determined that congestion will occur, and the judgment result is sent to the driver of the vehicle that is about to pass the terminal block to remind him to avoid the route.

[0109] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent GPS positioning data analysis and management method, characterized in that: The management method includes the following steps: S1. According to the vehicle GPS positioning data, a plane area model is established with the current position of the vehicle as the center, and a plane rectangular coordinate system is established on the plane area model to assign coordinate values ​​to any point on the plane area model; S2, divide the plane area model into N*N blocks, and use GPS to locate the real-time position coordinates of the vehicle as (X i ,Y i ), the outermost block in the plane domain model forms the outer block function E (x) , the remaining blocks form the internal block function F (x) , when the real-time position of the vehicle satisfies the external block function, it returns to S1; S3, extract the intersection nodes formed by the road intersections in all blocks, and determine the coordinate values ​​of the intersection nodes (x j ,y j ), the intersection node coordinate values ​​of any block form a set P k-s , the intersection nodes of all blocks in the plane area model form a set P; S4, according to the real-time position of the vehicle located by GPS, analyze the distance change between the vehicle and any intersection node in the block, so as to determine the next block that the vehicle is about to enter, and analyze the state of the vehicle in the next block, and the analysis results are transferred to S2 and S5; S5. Analyze the traffic volume of the road according to the road distribution and vehicle driving conditions in the block, predict whether congestion will occur according to the analysis results, and remind the driver to avoid the route in advance; In S4, the real-time position of the vehicle is located once every time interval t to obtain the position coordinates (X i ,Y i ), calculate the distance between the vehicle's real-time position and the intersection node according to the following formula Among them, i represents the i-th real-time position of the vehicle, and j represents the j-th intersection node; Get the distance set between the vehicle's real-time position and each intersection node in any block m represents the m real-time positions of the vehicle; With time as the horizontal axis, the distance set L j The distance value in is the ordinate, a plane rectangular coordinate system is established, and the distance set L j The numerical values ​​in are located in the plane rectangular coordinate system, and the positioning points in the plane rectangular coordinate system are fitted to obtain the line segment. The slope of the line segment is solved to obtain the slope K j ; When K j When ≥0, the analysis of GPS positioning data is abandoned; When K j <0, continue to analyze the relationship between the real-time position of the vehicle and the position of the j-th intersection node to find the inflection point of the slope change; Determine the slope before the inflection point of the change is K e , the slope after the inflection point changes is K f ; Compare|K e |with|K f |The size of |K e |=|K f |, it indicates that the vehicle has passed the intersection node and has not turned into other roads; when |K e |≠|K f |, indicating that the vehicle has turned into other roads at the intersection node; when the vehicle passes through all intersection nodes and does not turn into other roads, it is determined that the vehicle is about to enter the next block connected to the current road; When the vehicle turns into another road at a certain intersection, reanalysis is performed using the analysis method of S4; In S4, when it is predicted that the vehicle is about to enter the next block, it is determined whether the next block belongs to an external block. If it belongs to an external block, it returns to S1. Otherwise, the terminal block of the vehicle is calculated and predicted according to the following formula: Among them, w represents the probability that the vehicle's terminal block is the next block, a0, a1, and a2 represent the influencing factors, respectively. represents the error factor, u1 represents the proportion of the historical endpoint block of other vehicles that is the next block, and u2 represents the proportion of the historical endpoint block of the current vehicle that is the next block; If w>w′, the vehicle’s destination block is determined to be the next block, and the determination result is uploaded to the cloud; If w≤w′, it is determined that the terminal block of the vehicle is not the next block, but is about to pass the next block. The determination result is uploaded to the cloud. When the vehicle enters the next block, the analysis process of S4 is repeated, where w′ represents the set probability threshold.

2. The intelligent GPS positioning data analysis and management method according to claim 1, characterized in that: In S3, the intersection nodes of the ksth block are extracted, where ks represents the block in the kth row and sth column, forming a set P k-s ={(x1, y1), (x2, y2), (x3, y3),..., (x n ,y n )}, where n represents that there are n intersection nodes in the ks-th block, and the intersection node set of the block constitutes a set P = {P 1-1 , P 1-2 , P 1-3 , ..., P N-N }, where NN indicates that the plane area model is divided into N*N blocks.

3. The intelligent GPS positioning data analysis and management method according to claim 2, characterized in that: In S5, the determination result of the vehicles in the terminal block is uploaded to the cloud, which integrates and analyzes all uploaded data and compares them with the rated vehicle flow of the terminal block; When the comparison results show that the traffic flow of the terminal block is about to exceed the rated traffic flow, it is determined that congestion will occur, and the judgment result is sent to the driver of the vehicle that is about to pass the terminal block to remind him to avoid the route.

4. An intelligent GPS positioning data analysis and management system that implements the intelligent GPS positioning data analysis and management method according to any one of claims 1 to 3, characterized in that: The data analysis and management system includes a digital modeling module, a function establishment module, a node confirmation module, a data analysis module and a prediction reminder module; The digital modeling module is used to establish a plane area model with the current position of the vehicle as the center and perform digital processing; The function establishment module is used to establish a resolution function, which includes an external block function and an internal block function, and is used to determine whether the plane area model needs to be re-established; the node confirmation module is used to determine the road nodes in the plane area model and extract the road node information; The data analysis module is used to analyze the relationship between vehicles and intersection nodes and predict the movement of vehicles; the prediction reminder module is used to analyze when a block is about to be congested and remind other vehicles passing through the block to re-plan their routes to avoid congested sections.

5. The intelligent GPS positioning data analysis and management system according to claim 4 is characterized by: The digital modeling module includes a model building unit, a coordinate system building unit, a coordinate value assigning unit and a block dividing unit; The model building unit is used to build a plane area model centered on the vehicle; the coordinate system building unit is used to build a plane rectangular coordinate system on the plane area model; the coordinate value assigning unit is used to assign a coordinate value to each point on the plane area model; and the block division unit is used to divide the established plane area model into a number of equally divided blocks.

6. The intelligent GPS positioning data analysis and management system according to claim 5 is characterized by: The node confirmation module includes a block determination unit, a node extraction unit and a coordinate integration unit; The block determination unit is used to determine the block label of the extracted node; the node extraction unit is used to extract the coordinate value of the intersection node in the block, and the coordinate integration unit is used to integrate the extracted node coordinates.

7. The intelligent GPS positioning data analysis and management system according to claim 6 is characterized by: The data analysis module includes a distance calculation unit, a slope analysis unit, an inflection point determination unit, a block prediction unit and an endpoint prediction unit; The distance calculation unit is used to calculate the distance between the current position of the vehicle and each intersection node in the block; the slope analysis unit is used to determine the slope change of the distance over time based on the calculated distance; the inflection point determination unit is used to determine and find the inflection point of the slope; the block prediction unit is used to predict and analyze the next block that the vehicle is about to enter; the end point prediction unit is used to predict whether the end point block of the vehicle is the next block.

8. The intelligent GPS positioning data analysis and management system according to claim 7 is characterized by: The prediction and reminder module includes a vehicle flow comparison unit and a congestion reminder unit; The traffic flow comparison unit is used to determine whether congestion will occur in the next block according to the analysis results of the data analysis module, and the congestion reminder unit is used to remind vehicles that are about to pass through the next block to re-plan their routes to avoid the congested block.

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