Intelligent positioning method and system for vehicle rescue state

Abstract

The invention provides an intelligent positioning method and system for a vehicle rescue state, and relates to the technical field of vehicle rescue positioning. The method comprises the steps that after historical position information of a vehicle-mounted end is received, if a curve corresponding to the historical position information is analyzed to be a zigzag curve of which the amplitude and the vibration frequency exceed threshold values within a set time length, it is determined that the vehicle enters a GPS signal failure area. After a rescue request signal is received, a last effective positioning point, vehicle speed data and historical track information before failure are obtained, a predicted driving distance is determined by combining a vehicle speed and effective time, candidate paths are determined by combining historical tracks, the positioning point and a terrain path map, and a candidate position set matched with the predicted distance is determined on the candidate paths; and determining the nearest final to-be-rescued position in combination with the position information of the ground base station. According to the multi-dimensional information fusion positioning method, intelligent prediction positioning of the vehicle position can be realized under the condition of GPS signal failure, and a reliable position basis is provided for carrying out rescue work in time.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle rescue positioning technology, and in particular to an intelligent positioning method and system for vehicle rescue status. Background Art

[0002] With the rapid growth of car ownership, the demand for vehicle rescue is increasing. Traditional vehicle rescue often requires the owner to report the vehicle's location by phone or other means. This method has problems such as inaccurate information transmission and long response time, which seriously affects rescue efficiency, easily causes rescue delays, and causes inconvenience to the owner.

[0003] To address these issues, a GPS-based intelligent vehicle rescue positioning system has been developed. By installing a GPS positioning module on a vehicle, the system automatically obtains the vehicle's latitude and longitude information and sends it to a rescue platform when a vehicle breaks down and requires assistance. This positioning information allows rescuers to quickly reach the rescue site, improving rescue efficiency.

[0004] However, existing technologies present a problem: when a vehicle is located in a mountainous area or canyon, GPS satellite signals are easily blocked or reflected, causing GPS failure and significant positioning errors. In these situations, it's difficult for rescue vehicles to accurately locate the vehicle, delaying rescue efforts, increasing costs, and impacting effectiveness. Summary of the Invention

[0005] The present application provides an intelligent positioning method and system for vehicle rescue status, which is used to accurately locate the vehicle to be rescued in areas where GPS signals fail (such as complex terrain such as valleys and suburbs).

[0006] In the first aspect, the present application provides an intelligent positioning method for vehicle rescue status, the method comprising: after receiving historical location information sent by a vehicle-mounted terminal connected to the communication link, if the curve corresponding to the historical location information is analyzed to be a sawtooth curve with an amplitude exceeding a set amplitude and a vibration frequency exceeding a set frequency within a set time period, it is determined that the vehicle-mounted terminal has entered a GPS signal failure area; after receiving a vehicle rescue request signal sent by the vehicle-mounted terminal immediately after the GPS signal failure area, obtaining the last valid positioning point, vehicle speed data and historical trajectory information within a preset time period of the vehicle to be rescued at a second time before the GPS signal failure area; combining The vehicle speed data and the effective time between the second time and the first time determine the predicted distance the vehicle has traveled after the GPS signal fails; determine a candidate predicted route in combination with the historical trajectory information, the last valid positioning point and the terrain path map within a preset range of the last valid positioning point; determine a set of candidate position information that matches the predicted distance traveled on the candidate predicted route; determine the base station position information of the ground base station that sent the vehicle rescue request signal; determine the final position information that is closest to the base station position information from the candidate position information set, and determine the final position information as the final rescue position.

[0007] By employing this technical solution, GPS signal failure areas are first identified by analyzing the curve characteristics of historical location information, allowing for timely detection of positioning anomalies. Combining the vehicle's last valid location point before entering the signal failure area, speed data, and historical trajectory, the predicted distance the vehicle may have traveled is calculated. A terrain path map further identifies the possible travel path, and the final position is determined by combining base station location information. This multi-dimensional information fusion positioning method not only enables intelligent prediction of vehicle position in the event of GPS signal failure, but also significantly improves positioning accuracy, providing a reliable location basis for timely rescue operations. In combination with some embodiments of the first aspect, in some embodiments, obtaining the last valid positioning point of the vehicle to be rescued at the second time before the GPS signal fails specifically includes: taking the moment before the set time length of the jagged curve as the second time, and determining the historical position information corresponding to the moment before the set time length of the jagged curve as the last valid positioning point.

[0008] By adopting this technical solution, the accuracy of the final valid positioning point is ensured by selecting the moment before the sawtooth curve appears as the last valid time point. This curve-based time point selection method avoids the use of unstable positioning data, ensuring the reliability of the basic data for subsequent position prediction from the source, making the entire positioning prediction process more accurate and reliable, thereby improving the accuracy of the final rescue location determination. In combination with some embodiments of the first aspect, in some embodiments, the moment before the set time length of the sawtooth curve is taken as the second time, and the historical position information corresponding to the moment before the set time length of the sawtooth curve is determined as the last valid positioning point, specifically including: obtaining P consecutive historical position points before the appearance of the sawtooth curve; pairing the P consecutive historical position points in chronological order, and calculating the position offset distance between two adjacent position points; dividing each position offset distance by the corresponding time interval to obtain the position offset rate per unit time; comparing each position offset rate with the actual vehicle speed reported by the vehicle-mounted terminal during the corresponding period, and calculating multiple position drift degrees: when the multiple position drift degrees are less than the preset offset degree threshold, the moment when the last position drift degree is less than the preset threshold is taken as the second time, and the corresponding position point is determined as the last valid positioning point.

[0009] By adopting this technical solution and introducing a position drift calculation and determination mechanism, the reliability of positioning data can be more accurately determined by comparing the position offset rate with the actual vehicle speed. The final valid positioning point is determined only when the position drift of multiple consecutive points is within a reasonable range. This multi-point verification method greatly improves the reliability of the final valid positioning point and provides a more accurate starting reference point for subsequent position predictions. In combination with some embodiments of the first aspect, in some embodiments, the step of determining a candidate predicted path in combination with the historical trajectory information, the last valid positioning point, and a terrain path map within a preset range of the last valid positioning point specifically includes: obtaining road information of the last valid positioning point within a preset range according to the terrain path map, the road information including feasible road paths and road direction information; determining the N trajectory points before the last valid positioning point according to the historical trajectory information, and determining the trajectory direction according to the trajectory points; taking the angle between the trajectory direction and the road direction information, eliminating feasible road paths whose angle is greater than a preset angle threshold, and determining the remaining feasible road paths as candidate predicted paths.

[0010] By employing this technical solution and comparing historical trajectory directions with road direction information, roads with significant deviations from the actual driving direction are eliminated, significantly reducing the number of possible routes. This historical trajectory-based path screening method not only significantly improves the accuracy of candidate routes but also reduces the computational complexity of subsequent location prediction, ensuring that the final rescue location is more consistent with the vehicle's actual driving trajectory. In combination with some embodiments of the first aspect, in some embodiments, before the step of determining the predicted distance the vehicle has traveled after the GPS signal fails in combination with the vehicle speed data and the effective time between the second time and the first time, it also includes: obtaining driving record information sent from the vehicle-mounted end; determining whether the vehicle to be rescued has stopped between the second time and the first time based on the driving record information, and determining the duration of the stop; and subtracting the total time between the second time and the first time from the stop duration to obtain the effective time.

[0011] By employing this technical solution, the vehicle's actual travel time can be accurately determined by analyzing driving record information to identify vehicle stop states and calculate their duration. This time calculation method, which factors in vehicle stops, avoids distance prediction errors caused by including stop time in driving time, significantly improving the accuracy of predicted distance traveled and providing a more precise distance reference for subsequent position determination. In combination with some embodiments of the first aspect, in some embodiments, after the step of subtracting the stay time from the total time between the second time and the first time to obtain the effective time, it also includes: after determining that the vehicle to be rescued has stayed between the second time and the first time, determining the real-time speed data of the vehicle within the effective time according to the driving record information; and determining the predicted distance traveled according to the real-time speed data and the effective time.

[0012] By employing this technical solution, the predicted distance traveled is calculated based on the effective travel time corrected for dwell time and combined with real-time vehicle speed data. This dynamic speed-time integral calculation method accounts for actual vehicle speed variations and avoids the potential calculation bias associated with using average speed. This makes the predicted distance calculation more accurate and provides a reliable distance basis for ultimately determining the rescue location. In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the final location information that is closest to the base station location information from the candidate location information set and determining the final location information as the final rescue location, it also includes: sending a signal strength query request to multiple target base stations within a set range of the final rescue location; receiving the signal strength data and target base station location information for the vehicle-mounted terminal returned by each target base station; and calibrating the final rescue location using a weighted triangulation positioning algorithm based on the signal strength data returned by at least three target base stations and combined with the target base station location information to obtain the calibrated final rescue location.

[0013] By adopting this technical solution, the preliminary rescue location is calibrated using multi-base station signal strength data and a weighted triangulation algorithm. This multi-base station collaborative positioning method, based on actual signal strength, effectively eliminates the errors inherent in single-base station positioning. This multi-dimensional position calibration significantly improves positioning accuracy, ultimately resulting in more accurate and reliable rescue location coordinates. In a second aspect, the present application provides an intelligent positioning system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the intelligent positioning system to perform the method described in the first aspect and any possible implementation of the first aspect.

[0014] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a smart positioning system, enable the smart positioning system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0015] In a fourth aspect, the present application provides a computer program product, which, when executed on an intelligent positioning system, enables the intelligent positioning system to execute the method described in the first aspect and any possible implementation of the first aspect. One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the above technical solution, the technical means of identifying GPS signal failure based on the curve characteristics of historical position information, combining the last valid positioning point with the vehicle speed data for distance prediction, and using the terrain path map and base station location information for multi-dimensional fusion positioning are adopted. Therefore, the technical problem of the inability to accurately locate the position of the rescue vehicle in the area where the GPS signal fails in the existing technology is effectively solved, thereby realizing the intelligent prediction and positioning of the vehicle position in the case of GPS signal failure, and significantly improving the technical effect of positioning accuracy.

[0016] 2. By adopting the above technical solution, due to the use of technical means based on continuous multi-point position drift calculation and actual vehicle speed comparison verification, and dynamically determining the last valid positioning point by setting a preset threshold, it effectively solves the technical problem in the existing technology that the GPS signal begins to fail and causes inaccurate starting reference position, thereby achieving the technical effect of accurately identifying the last valid positioning point and providing a reliable reference point for subsequent position prediction.

[0017] 3. By adopting the above technical solution, since the vehicle stop status is identified based on driving record information and the actual effective driving time is calculated by correcting the stop duration, the technical problem of the existing technology that does not consider the vehicle stop factor and causes the driving distance prediction deviation is effectively solved, thereby achieving a more accurate vehicle driving distance prediction and providing a technical effect of accurate distance reference basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of an intelligent positioning method for a vehicle rescue state in an embodiment of the present application; Figure 2 This is a schematic diagram of an application scenario of the intelligent positioning method for a vehicle rescue state in an embodiment of the present application; Figure 3 This is another application scenario diagram of the intelligent positioning method for a vehicle rescue state in an embodiment of the present application; Figure 4 This is a schematic diagram of the physical device structure of the intelligent positioning system in the embodiment of the present application. DETAILED DESCRIPTION

[0019] The terms used in the following examples of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and encompasses any or all possible combinations of one or more of the listed items.

[0020] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more. For ease of understanding, the following describes the process of the method provided by this implementation. Figure 1 , which is a flow chart of the intelligent positioning method for the vehicle rescue status in an embodiment of the present application.

[0021] S101. After receiving historical location information from a vehicle-mounted terminal connected to the communication link, if a curve corresponding to the historical location information is analyzed to be a sawtooth curve with an amplitude exceeding a set amplitude and a vibration frequency exceeding a set frequency within a set time period, it is determined that the vehicle-mounted terminal has entered a GPS signal failure area; Among them, historical location information refers to the sequence of GPS positioning coordinates collected by the vehicle-mounted terminal at preset time intervals; the vehicle-mounted terminal refers to an intelligent terminal device with GPS positioning and communication functions installed on the vehicle; a sawtooth curve refers to a curve that shows irregular fluctuations in a coordinate system with time as the horizontal axis and position coordinates as the vertical axis. Its shape is like a sawtooth, reflecting the unstable changes in location information. The set amplitude is used to indicate the maximum allowable range of position fluctuations. For example, it is set to 50 meters. If the position change exceeds this distance, it is considered an abnormal fluctuation. The set frequency refers to the threshold number of position changes per unit time. For example, frequent changes exceeding 8 times per minute may indicate an abnormality. The GPS signal failure area refers to the area where the quality of GPS signal reception is significantly reduced due to factors such as terrain and buildings. In mountainous areas and canyons, mountain obstructions make it difficult for satellite signals to be transmitted stably.

[0022] The user can bind the vehicle-mounted terminal to the platform of the intelligent positioning system in advance. The intelligent positioning system can be a towing company platform with intelligent positioning function. Specifically, when the user binds the vehicle-mounted terminal to the platform of the intelligent positioning system in advance, when driving into non-plain areas such as mountainous areas and canyons, the vehicle-mounted terminal combines the vehicle positioning information and map data to determine that it has entered a non-plain area, and then sends historical location information to the intelligent positioning system. After receiving the information, the intelligent positioning system processes the historical location information in the form of a sliding window, and the window length is the set length. In each window, the system calculates the distance between adjacent location points, determines the amplitude of the position fluctuation, and counts the number of position changes to obtain the vibration frequency. If the amplitude continues to exceed the set amplitude and the vibration frequency is higher than the set frequency in multiple consecutive sliding windows, the position-time curve drawn at this time will appear jagged, and the system can determine that the vehicle-mounted terminal has entered the GPS signal failure area. In addition, the system will also combine the surrounding terrain data for auxiliary judgment. If the vehicle is located in an area with terrain features such as tall mountains and dense forests that easily block signals, it will further enhance the basis for determining signal failure and reduce misjudgments caused by normal vehicle driving bumps or short-term signal interference.

[0023] S102: After receiving the vehicle rescue request signal sent by the vehicle-mounted terminal immediately after the GPS signal fails in the area, obtain the last valid positioning point, vehicle speed data, and historical trajectory information of the vehicle to be rescued within a preset time period immediately before the GPS signal fails in the area; The vehicle rescue request signal refers to an emergency request sent to the intelligent positioning system via the vehicle's onboard terminal when the vehicle encounters an accident, breakdown, or other predicament and is in an area where the GPS signal fails. The signal contains key information such as the vehicle's identification and the time the rescue request was sent. The first time represents the specific moment when the vehicle owner triggers the rescue request signal. The second time refers to the last moment before the GPS signal fails when the vehicle's location information remains stable and reliable. The last valid positioning point is the vehicle's latitude and longitude coordinates corresponding to the second time, which serves as the starting reference point for subsequent positioning calculations. Vehicle speed data refers to the vehicle's speed over a period of time before and after the second time point. It can be derived from data collected by the vehicle's speed sensor or calculated using location information. Historical trajectory information within a preset time period refers to the continuous record of the vehicle's position changes within a fixed period of time (e.g., 5 minutes) before the second time point, used to analyze vehicle driving trends and behavioral habits.

[0024] Specifically, after receiving a rescue request signal from the vehicle and confirming that the vehicle is in a GPS signal failure zone, the intelligent positioning system needs to backtrack to determine a reliable positioning starting point before the signal failure. The system first plots a time-position curve based on historical location information. Through waveform analysis, it identifies the start time of the jagged curve and uses a time period (e.g., 10 minutes) forward of that time as a candidate data interval. Within this interval, the system extracts P consecutive historical location points and arranges them in chronological order into an ordered sequence. P can be a positive integer pre-set by the platform. For each pair of adjacent location points, the system converts the geographic coordinates (latitude and longitude) into plane coordinates (e.g., Mercator projection) and calculates the Euclidean distance between the two points as the position offset. The system also obtains the acquisition timestamps of the adjacent points and calculates the time interval. The offset distance is divided by the time interval to obtain the position drift rate per unit time. The system then retrieves the actual vehicle speed data for the corresponding time period from the vehicle's historical data. If the actual vehicle speed for a period is zero (e.g., the vehicle is stationary), the drift calculation for that period is skipped to avoid invalid comparisons. For periods of time when the actual vehicle speed is non-zero, the system calculates the absolute difference between the offset rate and the actual vehicle speed, then divides it by the actual vehicle speed to obtain the drift percentage. When multiple consecutive drift degrees (e.g., three) are less than a preset threshold, the system determines that the position data for that period is reliable and determines the moment of the last position point that meets the conditions as the second time, with its coordinates becoming the last valid positioning point. During this process, the system must handle data anomalies. For example, if the acquisition timestamp of a location point is missing, it can be supplemented by interpolating the previous and next time points. If the actual vehicle speed data is interrupted, the average of the adjacent time periods is used as a replacement.

[0025] S103, determining a predicted distance traveled by the vehicle after the GPS signal fails by combining the vehicle speed data and the effective time between the second time and the first time; The vehicle speed data represents the vehicle's speed, collected in real time by the vehicle's speed sensor or calculated through position offset. The effective time between the second time and the first time refers to the actual travel time, expressed in seconds or minutes, from the last reliable moment before the GPS signal failed (the second time) to the moment the driver triggered a rescue request (the first time), after deducting the vehicle's dwell time. The predicted distance traveled is an estimate of the distance the vehicle would have traveled from its last valid location after the GPS signal failed, based on the speed data and effective time.

[0026] This step is performed after the intelligent positioning system receives the rescue request signal from the vehicle and has acquired relevant data such as vehicle speed, second time, first time, and driving log information. This scenario involves a vehicle in an area with no GPS signal, requiring additional data to estimate its travel distance in order to determine the rescue location. Specifically, the intelligent positioning system first obtains the driving log information from the vehicle and analyzes it to determine whether the vehicle being rescued stopped between the second and first times. If so, the system further determines the duration of the stop. This can be achieved by identifying periods in the driving log where the vehicle's speed is zero for a certain period of time. The system then subtracts the stop duration from the total time between the second and first times to determine the effective time. After determining the effective time, the system uses the vehicle speed data to determine the vehicle's speed during the effective time. This speed data may be a constant value or may change in real time. If the speed data changes in real time, the system integrates the speed during the effective time to accurately determine the distance traveled. For example, if a vehicle travels at 60 km / h for 3 minutes and then at 40 km / h for 4 minutes within the valid time, the distances for both segments need to be calculated separately and then added together. If the speed data is constant, the distance traveled can be simply calculated by multiplying the speed by the valid time. This process allows the system to more accurately determine the predicted distance traveled after the GPS signal fails, providing a reliable basis for subsequent position determination. In some embodiments, the predicted distance traveled can be determined by combining vehicle speed data and effective time in a variety of ways: Optionally, the intelligent positioning system first obtains driving record information from the vehicle terminal, extracts the vehicle speed data from the second time to the first time, analyzes the vehicle's stop status during this time period, calculates the stop duration, subtracts the stop duration from the total time to obtain the effective time, and then determines whether the vehicle speed data is a constant value. If it is a constant value, the vehicle speed is multiplied by the effective time to obtain the predicted distance; if the vehicle speed data is variable, the effective time is divided into multiple time periods, and the vehicle speed in each time period is assumed to be the average value within the time period. The travel distance of each time period is calculated separately, and the distances of all time periods are added together to obtain the predicted distance. Optionally, the system first determines the effective time based on the driving record information, then obtains the real-time vehicle speed data from the vehicle terminal during the effective time, converts the effective time into hours, and then integrates the real-time vehicle speed data, that is, through mathematical integration methods, multiplies the vehicle speed at each time point by the corresponding time element, and then accumulates the total travel distance.

[0027] S104: Determine a candidate predicted route by combining the historical trajectory information, the last valid positioning point, and a terrain path map within a preset range of the last valid positioning point; The historical trajectory information represents the continuous position change record of the rescue vehicle within a preset time period before the second time, reflecting the vehicle's driving trends and behavioral habits before entering the GPS signal failure area. The last valid positioning point refers to the latitude and longitude coordinates corresponding to the last moment before the GPS signal failure when the vehicle's position information was still stable and reliable, serving as the starting reference point for subsequent path determination. The terrain path map represents the terrain features and road distribution within the preset range of the last valid positioning point, including information on feasible road paths and road directions. For example, the preset range could be an area within 5 kilometers of the last valid positioning point, and the terrain path map would display all roads within that area and their directions.

[0028] This step is performed after the intelligent positioning system has determined the last valid location, obtained historical trajectory information, and acquired a terrain path map. It then needs to predict the vehicle's path after GPS signal failure to identify possible candidate routes. This scenario involves a vehicle entering a GPS signal failure area, unable to obtain real-time GPS location information. Historical driving trajectories and terrain maps are then used to infer possible routes. Specifically, the intelligent positioning system first uses the terrain path map to obtain road information within a preset range of the last valid location. This road information includes all feasible road paths and the direction of each road. For example, if the preset range is 3 kilometers around the last valid location, the system extracts all roads within this range from the terrain path map, such as main roads, secondary roads, and branch roads, and determines the direction of each road, such as east-west or north-south. The system then determines the N trajectory points preceding the last valid location based on the historical trajectory information. N can be set based on actual conditions, such as 5 or 10. Using these trajectory points, the system calculates the vehicle's direction of travel before entering the last valid location, known as the trajectory direction. Next, the system compares the trajectory direction with the direction information for each feasible road and calculates the angle between them. If the angle exceeds a preset angle threshold, it indicates that the road's direction deviates significantly from the vehicle's historical direction and the vehicle is unlikely to continue traveling along it, so the road is eliminated. The remaining feasible road paths become the candidate predicted routes. Through this process, the system can eliminate some unreasonable roads, narrow the candidate paths, and improve the accuracy of subsequent position determination.

[0029] In some embodiments, the candidate predicted path can be determined by combining historical trajectory information, the last valid positioning point and the terrain path map in a variety of ways: Optionally, the intelligent positioning system first extracts all road information within a preset range of the last valid positioning point from the terrain path map, including feasible road paths and road direction information, and then obtains the N trajectory points before the last valid positioning point from the historical trajectory information, connects these trajectory points in chronological order, and determines the trajectory direction by calculating the slope of this connecting line. Then, the angle between the trajectory direction and the direction of each road is calculated, and roads with angles greater than a preset threshold are eliminated. The remaining roads are used as candidate paths. Optionally, the system first smoothes the historical trajectory information to eliminate the influence of some noise points, and then determines the N trajectory points before the last valid positioning point, fits a curve through these trajectory points, obtains the trajectory direction, and then compares it with the road direction in the terrain path map, eliminates roads with excessively large angles, and obtains the candidate path.

[0030] S105: Determine a candidate location information set that matches the predicted traveled distance on the candidate predicted travel path; The candidate predicted path represents the possible road path that the vehicle will travel after the GPS signal fails, determined by combining historical trajectory information, the last valid positioning point, and a terrain path map. The predicted distance traveled refers to the distance the vehicle will travel after the GPS signal fails, calculated by combining vehicle speed data and validity time. The candidate location information set represents the set of all possible vehicle locations on the candidate predicted path that match the predicted distance traveled. For example, if the candidate predicted path is a 10-kilometer road and the predicted distance traveled is 5 kilometers, then the candidate location information set is all possible locations on this road within 5 kilometers of the last valid positioning point.

[0031] This step is performed after the intelligent positioning system has determined candidate predicted routes and predicted distances traveled. It then needs to find possible locations along these routes that match the predicted distances, allowing for the subsequent determination of the final rescue location based on base station location information. This scenario involves a vehicle traveling in an area with a GPS signal failure, making it impossible to determine its specific location. By identifying possible locations along the candidate routes based on the predicted distances, this provides a reference for rescue efforts. Specifically, the intelligent positioning system first processes each candidate predicted route. For each route, the system determines its starting point—the projection point or closest point on the route of the last valid positioning point. The system then calculates distance from the starting point along the route to find a location that matches the predicted distance traveled. Because roads may curve or branch, the distance calculation must take into account the actual length and direction of the road. For example, a curved road may have an actual length greater than the straight-line distance, requiring the distance to be calculated based on the actual trajectory of the road. Furthermore, it must account for potential vehicle turns and lane changes during travel. However, due to a GPS signal failure, real-time driving information is unavailable, so possible locations can only be determined based on the candidate routes and predicted distances. When determining a location point, the system ensures that it is on a candidate path and that its distance from the starting point is equal to the predicted distance traveled. For each candidate path, one or more locations may meet these criteria, forming the candidate location information set. This process allows the system to identify all possible vehicle locations on the candidate path, providing a rich set of candidate information for subsequent location determination.

[0032] In some embodiments, determining a candidate location information set that matches the predicted distance traveled on a candidate predicted path can be achieved in a variety of ways: Optionally, the intelligent positioning system first digitally models each candidate predicted path, represents it as a series of coordinate point sequences, and then calculates the distance from the last valid positioning point to the nearest point on the path as the starting point. Then, starting from the starting point, the distance from each point to the starting point is calculated in sequence along the coordinate point sequence of the path. When the distance is equal to the predicted distance traveled, the coordinates of the point are recorded as a candidate location point until the entire path is traversed and all points that meet the conditions are collected to form a candidate location information set.

[0033] S106, determining the base station location information of the ground base station that sent the vehicle rescue request signal; A ground base station is a wireless communication base station deployed on the ground, used to enable communication between the vehicle-mounted terminal and the intelligent positioning system. Vehicle-mounted terminals within its coverage area can send rescue request signals through the base station. A vehicle rescue request signal is an emergency call signal triggered by the vehicle-mounted terminal in an area where GPS signals are ineffective, containing information such as the vehicle's identity and the time of transmission. Base station location information represents the specific geographic coordinates (such as longitude and latitude) of the ground base station. This information is typically pre-stored in the intelligent positioning system's database or accessible through a communication network. For example, when a vehicle-mounted terminal triggers a rescue request in a mountainous area, the signal is received by the nearest ground base station and forwarded to the intelligent positioning system. The location information of this base station is the base station location information required here.

[0034] This step is performed after the intelligent positioning system receives a rescue request signal from the vehicle's onboard terminal. It immediately determines the location of the ground base station that sent the signal so that it can subsequently determine the rescue location based on the candidate location information. For example, if a vehicle issues a rescue request in an area where GPS signal is lost, the intelligent positioning system must trace the signal's originating base station through the communication link to obtain the base station's location coordinates. Specifically, upon receiving the rescue request signal, the intelligent positioning system first parses the communication protocol field in the signal to extract the identification information of the ground base station through which the signal passed. The system then queries the geographic coordinates corresponding to the base station identifier based on a pre-stored database that maps base station identifiers to location information. If the base station location information is not directly stored in the database, the system sends a query request to the operator's base station positioning server via the communication network, submitting the base station identifier information and receiving and parsing the base station location coordinate data returned by the server. The system also verifies the validity of the obtained base station location information, for example, by checking whether the coordinate values ​​are within a reasonable range and whether they match parameters such as signal strength reported by the vehicle's onboard terminal to ensure the accuracy of the base station location information. Through this series of processing, the intelligent positioning system can quickly and accurately determine the location information of the ground base station that sends the rescue request signal, providing key reference coordinates for subsequent position matching.

[0035] S107: Determine the final location information closest to the base station location information from the candidate location information set, and determine the final location information as the final location to be rescued.

[0036] Among them, the nearest final location information is used to represent the location point in the candidate location set with the smallest Euclidean distance or geographical distance to the base station location coordinates. This point is identified as the most likely location for the vehicle to be rescued.

[0037] This step is performed after the intelligent positioning system has obtained the candidate location information set and the base station location information, and it is necessary to filter out the most likely vehicle location from the candidate locations through spatial distance calculation. The scenario is when the vehicle is driving in an area where the GPS signal fails and cannot be directly located, and the spatial relationship between the base station location and the candidate location is used to infer the actual location of the vehicle. Specifically, the intelligent positioning system first traverses each location point in the candidate location information set, and calculates the distance between the geographic coordinates (such as longitude and latitude) of each location point and the base station location coordinates. The distance calculation method is selected according to the coordinate type: if it is a plane rectangular coordinate, the Euclidean distance formula is used for calculation; if it is a longitude and latitude coordinate, the spherical distance formula is used to calculate the shortest distance between the two points on the earth's surface. After the calculation is completed, the system sorts all the distance values, finds the location point with the smallest distance, and determines it as the final location information.

[0038] In the above embodiment, due to the adoption of technical means of identifying GPS signal failure areas based on historical position information curve characteristics, combining the last valid positioning point with vehicle speed data for distance prediction, and using terrain path map and base station location information for multi-dimensional fusion positioning, it is possible to accurately predict the vehicle's driving trajectory and position when the GPS signal fails, effectively solving the problem in the prior art of GPS signals being blocked by terrain, resulting in large positioning deviations and low rescue efficiency, thereby realizing intelligent predictive positioning of the vehicle's position, providing a reliable position basis for timely rescue work, and significantly improving positioning accuracy and rescue response efficiency. In some embodiments, after the final rescue location is determined in step S107, the location can be further calibrated to eliminate single-base station positioning errors or terrain interference. Specifically, the intelligent positioning system first defines a set range (e.g., 2-5 kilometers based on the base station coverage radius) with the preliminarily determined final rescue location as the center, and screens all target base stations within this range. A signal strength query request is then sent to each target base station, carrying the unique identifier of the vehicle-mounted terminal, allowing the base station to identify and feedback the corresponding signal data. Upon receiving the request, each target base station queries the current signal strength received from the vehicle-mounted terminal and returns the signal strength data and its own location coordinates (latitude and longitude). After receiving data from at least three base stations, the system initiates a weighted triangulation algorithm. First, the system estimates the virtual distance from the vehicle to each base station based on an empirical formula for signal strength and distance (e.g., for every 3dBm decrease in signal strength, the distance approximately doubles). Then, using the absolute or normalized value of signal strength as a weight (e.g., a base station with an RSSI of -60dBm is weighted higher than one with -75dBm), it performs a weighted average of the base station coordinates, or uses geometric triangulation combined with weighted corrections to determine the final, calibrated location for rescue. This process effectively reduces the random errors inherent in single-base station positioning by fusing multi-dimensional signal data, significantly improving positioning reliability, especially in complex terrain. The following is a schematic diagram of an application scenario of an embodiment of the present application. Figure 2 , Figure 2 This is a schematic diagram of an application scenario of the intelligent positioning method for vehicle rescue status in an embodiment of the present application.

[0039] Figure 2 The video presents a scenario where the intelligent positioning system locates and rescues vehicles with failed GPS signals in mountainous areas. When the vehicle is driving normally, it relies on the GPS module to continuously obtain location information. When it reaches the "last valid positioning point", the GPS signal is blocked by mountainous terrain such as mountains and canyons. The vehicle-mounted terminal cannot receive effective satellite signals and GPS positioning becomes invalid. If the owner discovers engine abnormalities, tire failures, and other faults while the vehicle is driving, he will actively operate the vehicle-mounted terminal to send a vehicle rescue request signal to the system. The signal is transmitted wirelessly and is captured by the first base station that is closer to the current position of the vehicle. The first base station forwards the rescue request signal to the intelligent positioning platform based on the communication protocol and link.

[0040] Upon receiving the signal, the intelligent positioning platform immediately initiates the location-based rescue process. First, it retrieves the vehicle's historical trajectory data from its last valid location point. This data records the vehicle's complete route and reveals its driving patterns and direction. Combined with a mountainous terrain map, it analyzes the distribution of roads surrounding the last valid location point and identifies two possible routes the vehicle could have taken after the breakdown (e.g., continuing on the main road or taking a side road to avoid danger). Next, it obtains vehicle speed data, which can be collected in real time by the vehicle's speed sensor. If valid data exists before the GPS failure, the stable speed can be estimated using previous data. The platform also extracts the travel time from the last valid location point to the driver's request for assistance. By correlating speed and time, it calculates the distance traveled during the GPS signal failure period. Subsequently, based on the historical trajectory trends, the baseline location of the last valid location point, the distance traveled during the failure period, and the two selected feasible paths, the platform simulates the vehicle's travel after the GPS failure. It predicts locations on each path that match the travel distance, namely the first and second candidate locations, forming a candidate location set. Afterwards, the platform calls the pre-stored base station location database to obtain the latitude and longitude and other location information of the first base station that receives the signal. Through the spatial distance algorithm (such as calculating the spherical distance by longitude and latitude), the distance between the first candidate position, the second candidate position and the first base station is calculated respectively, and the nearest candidate position is determined as the final rescue position, providing a coordinate basis for the rescue force to rush to the scene accurately, thereby solving the problem of vehicle positioning caused by the failure of GPS signals in mountainous areas and ensuring the efficient implementation of rescue. In some embodiments, the owner of a vehicle that breaks down in a mountainous area or in the suburbs can proactively fill in rescue information using a mobile phone that has been pre-linked to the smart positioning system. Figure 3 , Figure 3 This is another application scenario diagram of the intelligent positioning method for vehicle rescue status in an embodiment of the present application.

[0041] exist Figure 3 In (a), if a car owner suddenly has an accident in the suburbs, and he knows what kind of accident has happened and what type of rescue is needed, he can Figure 3 As shown in (a), fill in the rescue information. In mountainous areas, there may be inaccurate positioning. If the user clicks Figure 3 The location mark corresponding to the incident location in (a) can be displayed as follows Figure 3 The content shown in (b) in the The intelligent positioning system accurately locates the location information currently being used by the owner according to the method in steps S101 to S107, and accurately displays "your location" in the corresponding place. If the user needs to enter a destination, he can click Figure 3The input box corresponding to "Please enter your destination" displayed at the top of (b) in the figure will be displayed as follows: Figure 3 The content shown in (c) above displays the final destination entered by the user. If the user clearly feels that the current location is inaccurate, they can reposition it by moving the "Your Location" icon. This allows drivers to proactively enter rescue information even in mountainous or suburban areas where positioning is difficult. They can also flexibly correct their location through interaction (clicking the location marker or input box), improving operational autonomy and convenience. The following describes the intelligent positioning system in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , which is a schematic diagram of the structure of a physical device of the intelligent positioning system in an embodiment of the present application.

[0042] It should be noted that Figure 4 The structure of the smart positioning system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0043] like Figure 4 As shown, the intelligent positioning system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0044] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, push button switches, and the like; an output section 407 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0045] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409 and / or installed from removable media 411. When executed by central processing unit (CPU) 401, the computer program performs the various functions defined in the present invention.

[0046] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0047] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0048] Specifically, the intelligent positioning system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the intelligent positioning method for the vehicle rescue state provided in the above embodiment is implemented.

[0049] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the intelligent positioning system described in the above embodiments, or may exist independently and not incorporated into the intelligent positioning system. The storage medium carries one or more computer programs. When executed by a processor of the intelligent positioning system, the intelligent positioning system implements the intelligent positioning method for vehicle rescue status provided in the above embodiments.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0051] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0052] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An intelligent positioning method for a vehicle rescue state, characterized in that: The method comprises: After receiving historical location information sent by the vehicle-mounted terminal in communication connection, if the curve corresponding to the historical location information is analyzed to be a sawtooth curve with an amplitude exceeding a set amplitude and a vibration frequency exceeding a set frequency within a set time period, it is determined that the vehicle-mounted terminal has entered a GPS signal failure area; After receiving the vehicle rescue request signal sent by the vehicle-mounted terminal immediately after the GPS signal fails in the area, the system obtains the last valid positioning point, vehicle speed data and historical trajectory information within a preset time period of the vehicle to be rescued immediately before the GPS signal fails in the area; Determining a predicted distance traveled by the vehicle after the GPS signal fails by combining the vehicle speed data and the effective time between the second time and the first time; Determining a candidate predicted path by combining the historical trajectory information, the last valid positioning point, and a terrain path map within a preset range of the last valid positioning point; Determining a set of candidate location information that matches the predicted traveled distance on the candidate predicted travel path; Determining base station location information of the ground base station that sent the vehicle rescue request signal; The final location information closest to the base station location information is determined from the candidate location information set, and the final location information is determined as the final location to be rescued.

2. The method according to claim 1, characterized in that The step of obtaining the last valid positioning point of the vehicle to be rescued at the second time before the GPS signal fails specifically includes: The time before the set time period of the sawtooth curve is used as the second time, and the historical position information corresponding to the time before the set time period of the sawtooth curve is determined as the last valid positioning point.

3. The method according to claim 2, characterized in that The step of taking the time before the set time of the sawtooth curve as the second time, and determining the historical position information corresponding to the time before the set time of the sawtooth curve as the last valid positioning point specifically includes: Obtaining P consecutive historical position points before the sawtooth curve appears; P consecutive historical location points are paired in chronological order, and the location offset distance between two adjacent location points is calculated; Divide each position offset distance by the corresponding time interval to obtain the position offset rate per unit time; Compare each position offset rate with the actual vehicle speed reported by the vehicle during the corresponding period, and calculate multiple position drift degrees: When the degree of position drift of multiple positions is less than the preset offset threshold, the moment when the last position drift degree is less than the preset threshold is taken as the second time, and the corresponding position point is determined as the last valid positioning point.

4. The method according to claim 1, wherein The step of determining a candidate predicted path by combining the historical trajectory information, the last valid positioning point, and a terrain path map within a preset range of the last valid positioning point specifically includes: Acquiring road information within a preset range of the last valid positioning point according to the terrain path map, wherein the road information includes feasible road paths and road direction information; Determine the N trajectory points before the last valid positioning point based on the historical trajectory information, and determine the trajectory direction based on the trajectory points; The angle between the trajectory direction and the road direction information is taken, and feasible road paths with an angle greater than a preset angle threshold are eliminated, and the remaining feasible road paths are determined as candidate predicted paths.

5. The method according to claim 1, wherein Before the step of determining the predicted distance traveled by the vehicle after the GPS signal fails by combining the vehicle speed data and the effective time between the second time and the first time, the method further includes: Get driving record information sent by the vehicle terminal; Determine whether the vehicle to be rescued has stopped between the second time and the first time according to the driving record information, and determine the length of the stop; The effective time is obtained by subtracting the stay time from the total time between the second time and the first time.

6. The method according to claim 5, characterized in that After the step of subtracting the stay time from the total time between the second time and the first time to obtain the effective time, the method further includes: After determining that the vehicle to be rescued has stopped between the second time and the first time, determining the real-time speed data of the vehicle within the effective time according to the driving record information; The predicted distance traveled is determined based on the real-time vehicle speed data and the effective time.

7. The method according to claim 1, characterized in that After the step of determining the final location information closest to the base station location information from the candidate location information set and determining the final location information as the final location to be rescued, the method further includes: Sending a signal strength query request to multiple target base stations within a set range of the final rescue location; receiving signal strength data for the vehicle-mounted terminal and target base station location information returned by each target base station; The final position to be rescued is calibrated using a weighted triangulation positioning algorithm according to signal strength data returned by at least three target base stations in combination with the position information of the target base stations to obtain a calibrated final position to be rescued.

8. An intelligent positioning system, characterized in that: The smart positioning system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the smart positioning system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on the smart positioning system, the smart positioning system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a smart positioning system, the smart positioning system is enabled to perform the method according to any one of claims 1 to 7.

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