Logistics carrier loss tracking method and system based on Internet of Things

Abstract

The invention belongs to the technical field of logistics carrier loss tracking, and particularly relates to a logistics carrier loss tracking method and system based on the Internet of Things, and the method comprises the steps: obtaining and sorting the positioning information of a plurality of positioning modules, and outputting a working time period through continuous analysis; taking the position data in the working time period as measurement data to calculate a measurement deviation value, judging whether the logistics carrier deviates or not based on the measurement deviation value, and if the logistics carrier deviates, calibrating a deviation coordinate point; the current position of the logistics carrier is obtained, when it is judged that the distance between the current position and the offset coordinate point exceeds a preset distance threshold value, the offset coordinate point is determined as a loss coordinate point, and a loss alarm is generated; according to the method, invalid data are filtered out through continuous analysis, the false alarm rate is reduced by adopting a secondary confirmation mechanism, and the reliability of source data is guaranteed by monitoring the module state, so that the accuracy of risk identification is improved.

Description

Technical Field

[0001] This invention belongs to the field of logistics vehicle loss tracking technology, specifically relating to a logistics vehicle loss tracking method and system based on the Internet of Things. Background Technology

[0002] With the rapid development of modern logistics and supply chain management, logistics vehicles, as the core units for carrying, transferring and storing goods, are crucial for the smooth operation of the entire logistics system through their efficient and safe circulation. In order to achieve refined management and real-time tracking of massive vehicle assets, the application of Internet of Things (IoT) technology to vehicle monitoring has become a mainstream trend in the industry. By equipping vehicles with positioning and communication modules, their location and trajectory information can be obtained.

[0003] However, current solutions generally rely on monitoring a single positioning signal. Once the signal is interrupted or drifts at a certain point in time, a loss alarm is triggered. This threshold judgment mechanism based on instantaneous state ignores the complexity of logistics and transportation scenarios, cannot effectively distinguish between actual vehicle loss and temporary signal interruption caused by the environment, and cannot identify reasonable fluctuations in the device's position. At the same time, the alarm mechanism lacks intelligence and adaptability. Due to the crudeness of the front-end judgment logic, a large number of false alarms are generated, which not only frequently interrupts the normal transportation process but also greatly reduces operational efficiency.

[0004] Therefore, in view of this, the industry urgently needs a new method and system for monitoring the status of logistics vehicles to solve the problems existing in the current technology. Summary of the Invention

[0005] The present invention aims to overcome the shortcomings of the prior art and provide a method and system for tracking lost logistics vehicles based on the Internet of Things.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for tracking lost logistics vehicles based on the Internet of Things, wherein when it is determined that the distance between the current location of the logistics vehicle and the offset coordinate point exceeds a preset distance threshold, the method includes:

[0007] The offset coordinate point is identified as the lost coordinate point, and a loss alarm is generated;

[0008] In addition, among the positioning information of multiple positioning modules associated with the logistics vehicle, the index position corresponding to the lost coordinate point is found, and the data containing the index position is extracted to form a monitoring data segment, so as to provide the monitoring data segment to the management terminal.

[0009] Preferably, determining the offset coordinate point includes:

[0010] The system acquires positioning information from multiple positioning modules, which includes time data and location data, and sorts the positioning information based on the time data.

[0011] Perform continuity analysis on the sorted location information and output the working time period for each location module;

[0012] Location data within the working period is used as measurement data, and measurement deviation is calculated based on changes in the measurement data.

[0013] And based on the measured deviation value, it is determined whether the logistics vehicle has deviated. If the logistics vehicle has deviated, the position information corresponding to the time of the deviation is marked as the deviation coordinate point.

[0014] Preferably, the continuity analysis of the sorted location information includes:

[0015] Calculate the time interval between adjacent location information in time;

[0016] If the time interval exceeds the preset allowable time interval, then mark the data interruption point at that time interval;

[0017] And the time interval between two adjacent data interruption points is defined as the working time period.

[0018] Preferably, determining whether a logistics vehicle has deviated based on the measured deviation value includes:

[0019] The measured deviation value is compared with the preset offset judgment threshold;

[0020] If the measured deviation value is greater than the preset offset judgment threshold, the logistics vehicle is confirmed to be in an offset state.

[0021] Preferably, obtaining the current location of the logistics vehicle includes:

[0022] Obtain the basic location data at the current time point and determine the index position of this basic location data in the sorted location information;

[0023] Retrieve working time periods containing time points corresponding to index positions;

[0024] The baseline time period is formed based on the end time of the work period;

[0025] And to reposition the logistics vehicle within a reference time period to obtain a precise measurement location as the current location.

[0026] This application also provides an Internet of Things-based logistics vehicle loss tracking system, including:

[0027] The location information acquisition module is used to acquire location information from multiple location modules associated with the logistics vehicle. The location information includes time data and location data, and the location information is sorted based on the time data.

[0028] The vehicle loss determination module is used to analyze the sorted positioning information to determine the offset coordinate point and obtain the current position of the logistics vehicle in order to determine whether the distance between the current position and the offset coordinate point exceeds the preset distance threshold.

[0029] The system also includes a tracking information processing module, which determines the offset coordinate point as the lost coordinate point when the distance exceeds a preset distance threshold, generates a loss alarm, and extracts monitoring data segments to provide to the management terminal.

[0030] Preferably, the vehicle loss determination module is used for:

[0031] Perform continuity analysis on the sorted location information and output the working time period for each location module;

[0032] Location data within the working period is used as measurement data, and measurement deviation is calculated based on changes in the measurement data.

[0033] And based on the measured deviation value, it is determined whether the logistics vehicle has deviated. If the logistics vehicle has deviated, the position information corresponding to the time of the deviation is marked as the deviation coordinate point.

[0034] Preferably, the vehicle loss determination module, when performing continuity analysis, is used for:

[0035] Calculate the time interval between adjacent location information in time;

[0036] If the time interval exceeds the preset allowable time interval, then mark the data interruption point at that time interval;

[0037] And the time interval between two adjacent data interruption points is defined as the working time period.

[0038] Beneficial effects

[0039] This invention performs continuity analysis on the acquired positioning information, identifies data interruption points by calculating the time interval between adjacent information, and thus determines continuous time segments as working time periods. Furthermore, it calculates the duration of each working time period and compares it with a preset stable duration threshold to distinguish between fluctuating data areas and normal fluctuation areas, and determines the starting point for risk assessment. Therefore, it can effectively filter out discrete and invalid data caused by signal instability or interruption, ensuring that subsequent offset analysis is based on high-quality continuous trajectory data, avoiding misjudging instantaneous signal noise as the actual movement of logistics vehicles. Simultaneously, by analyzing the duration of working time periods, it can more accurately pinpoint the starting time of abnormal offsets, improving the accuracy of risk identification.

[0040] This invention employs a secondary confirmation mechanism when determining the loss of a logistics vehicle. Based on changes in measurement data, a measurement deviation value is calculated. When this value exceeds a preset offset judgment threshold, an offset coordinate point is initially determined. The current position of the logistics vehicle is independently obtained, and the distance between this current position and the previously determined offset coordinate point is compared. Only when the distance between the two exceeds a preset distance threshold is the offset coordinate point finally confirmed as the lost coordinate point and a loss alarm is triggered. By comparing and verifying the offset coordinate point with the current position, it is possible to effectively distinguish between a genuine loss of the logistics vehicle and a temporary path deviation, greatly reducing the false alarm rate of the tracking system and ensuring the reliability and practicality of the alarm.

[0041] Furthermore, this invention introduces a monitoring function for the positioning module's own module status. By calculating the time interval between adjacent sampling points in the sorted positioning information and statistically processing all time intervals within a specific time period, an evaluation parameter is formed. This evaluation parameter is compared with a set damage judgment standard to determine whether the positioning module is at risk of damage. Therefore, this invention can proactively detect potential hardware failures from the perspective of the timeliness of data reporting, before data content anomalies occur. This prevents the output of incorrect positioning information due to the failure of individual modules, thereby contaminating the entire tracking decision-making process and ensuring the reliability of the source data. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0043] Example 1: Please refer to Figure 1 This embodiment provides an IoT-based method for tracking lost logistics vehicles, used for real-time monitoring of vehicle status, risk warning, and loss path retrieval during logistics transportation. The method includes the following steps:

[0044] Step S100: When it is determined that the distance between the current position of the logistics vehicle and the offset coordinate point exceeds the preset distance threshold, the offset coordinate point is identified as the lost coordinate point and a loss alarm is generated.

[0045] Step S200: In the positioning information of multiple positioning modules associated with the logistics vehicle, find the index position corresponding to the lost coordinate point, and extract the data containing the index position to form a monitoring data segment, so as to provide the monitoring data segment to the management terminal.

[0046] Specifically, multiple positioning modules are equipped on logistics vehicles, and a unique association between the positioning modules and the logistics vehicles is established in the Internet of Things platform. The positioning information of multiple positioning modules associated with the logistics vehicles is obtained. Each piece of positioning information contains time data and location data. The obtained positioning information is sorted based on the time data to form sorted positioning information, which is used to ensure the integrity, orderliness and traceability of the data.

[0047] After obtaining the sorted location information, a continuity analysis is performed to identify and segment working time periods, eliminating data interruptions caused by signal loss, equipment shutdown, or other reasons. Specifically, the time interval between adjacent location information is calculated and compared with a preset allowable time interval. The preset allowable time interval can be set according to the normal reporting frequency of the positioning module. It is a pre-set time length threshold used to determine whether the time gap between consecutive location data points constitutes a valid data interruption. Its value is usually determined based on the expected reporting frequency of the positioning module. If the time interval is less than or equal to the preset allowable time interval, the data is considered continuous. If the time interval exceeds the preset allowable time interval, it indicates that a data interruption has occurred. The data interruption point is marked at this time interval. The time interval between the two adjacent location information before and after this point exceeds the preset allowable time interval. The time segment between two adjacent data interruption points is determined as the working time period. All subsequent risk analyses will be based on the determined continuous working time period.

[0048] From the working hours, further filter out areas that deserve special attention and optimize them to improve the accuracy of the judgment. Specifically, the duration of each working period is calculated and compared with a preset stable duration threshold. The preset stable duration threshold is set to filter out data segments that are too short to be of analytical value, that is, to distinguish between long-term activities with analytical value (fluctuation data area) and short-term, negligible signal fluctuations (normal fluctuation area). If the working period is longer than the preset stable duration threshold, the vehicle activity within that period is considered to be of analytical significance, and the working period is identified as a fluctuation data area. This area is considered to contain sufficient information for effective risk analysis, and the start time of this fluctuation data area is set as the starting point for risk assessment. If the working period is shorter than or equal to the preset stable duration threshold, it is marked as a normal fluctuation area. This area is considered to be a normal, short-term signal recovery or movement and is not an independent focus of analysis. To avoid generating too many unnecessary analysis tasks due to frequent short-term signal recovery, the preset duration is extended based on the end time of the normal fluctuation area to form an extended time period. At the same time, the start time of the normal fluctuation area is output as the corresponding risk assessment starting point, and the end time of the extended time period is output as the corresponding end time point. It should be noted that the extended time period is a time window formed by extending the normal fluctuation range by a preset duration. This is used to merge adjacent short-term fluctuations and avoid fragmented analysis.

[0049] Furthermore, in certain transportation scenarios, multiple risk assessment starting points may arise within a short period. To avoid misclassifying a single continuous event as multiple independent events, these multiple risk assessment starting points are input into an estimation model built using linear interpolation. The estimation model analyzes the temporal and spatial distribution trends of these starting points and outputs predicted starting point locations. These predicted starting point locations represent the most likely starting points for the series of risk events, as determined by the model. They are the single, unified starting point location predicted after integrating a group of temporally and spatially adjacent risk assessment starting points. Based on these predicted starting point locations, a predicted time period is generated. This predicted starting point location and the predicted time period are then used to replace the original multiple, dispersed risk assessment time periods for subsequent offset analysis, effectively improving the robustness of risk identification. The predicted time period, generated based on the predicted starting point location, replaces the original multiple, dispersed risk assessment time periods for unified offset analysis.

[0050] The estimation model is a computational model used to integrate multiple spatiotemporally adjacent risk assessment starting points into a unified predicted starting point location. Its specific formula is as follows:

[0051]

[0052] Among them, the parameters of the linear model and Determined using the least squares method to minimize prediction error:

[0053]

[0054] and Set as the earliest timestamp in the input set, i.e. .

[0055] In the formula, the input is a set containing A set of risk assessment starting points ,in For timestamps, Given geographic coordinates, the output is a single predicted starting point location. and its corresponding timestamp ; This represents the predicted starting point coordinates, which means the model's estimate of the most likely starting geographical location for the integrated risk event; This indicates the prediction start time timestamp, which represents the start time of the integrated risk event. It is usually the earliest time in the set of input points. The coordinates of the i-th risk assessment starting point represent the geographical location of the i-th discrete risk assessment event. The timestamp representing the starting point of the i-th risk assessment means the time when the i-th discrete risk assessment event occurs. This represents the total number of risk assessment starting points, which means the number of discrete risk assessment events that are integrated into the input model. These represent the parameters of the linear model, which describe the linear trends of the risk point in the x and y directions over time, respectively. They can be understood as estimates of velocity and initial position.

[0056] After determining the working time period to be analyzed, the location data within that period is used as measurement data, which is a continuous sequence of vehicle position coordinates for offset analysis. Based on the continuous changes in the measurement data, a measurement deviation value is calculated. This deviation value can be a comprehensive index describing the degree of deviation of the vehicle's trajectory from its normal or expected pattern, as reflected in the measurement data. Examples include the rate of change of the vehicle's heading angle per unit time, or its distance from a preset electronic fence or planned path. The measurement deviation value is compared with a preset offset judgment threshold. If the measurement deviation value is greater than the threshold, the logistics vehicle is confirmed to be in an offset state, indicating a significant deviation from its normal trajectory. The location information corresponding to the moment of offset is marked as the offset coordinate point, indicating the geographical coordinates of the vehicle when it is first determined to be in an offset state. Simultaneously, the magnitude of the measurement deviation value is determined as the offset amount, representing the specific value of the measurement deviation at the moment the offset judgment is triggered, characterizing the severity of the offset. If the measurement deviation value is less than or equal to the preset offset judgment threshold, the vehicle is confirmed not to be in an offset state.

[0057] After determining that the vehicle has deviated and identifying the offset coordinates in the above steps, it is necessary to further confirm whether this constitutes a loss event, indicating that the vehicle has left control or the normal operating range. Therefore, it is necessary to obtain the current location of the logistics vehicle. To obtain a high-precision current location and avoid misjudgments due to single-point positioning errors, the following operations are performed: Obtain the basic positioning data at the current time point, which is the latest, unprocessed raw positioning information point, and determine the index position of this basic positioning data in the sorted positioning information; retrieve the working time period containing the time point corresponding to the index position, and use the end time of this working time period as a benchmark. By increasing or decreasing the preset time length, a benchmark time period is formed, which is used to collect more positioning data to calculate a more accurate location; within the benchmark time period, the vehicle is positioned more frequently or multiple positioning points are weighted and averaged to obtain a precise measurement position, which is then used as the vehicle's current location.

[0058] Furthermore, after obtaining the current location, it is compared with the previously determined offset coordinates, and the straight-line geographical distance between the two is calculated. If this distance exceeds a preset distance threshold, it indicates that the vehicle has not returned to the original area after the offset, but continues to move away, meeting the criteria for a loss event. At this time, the offset coordinates are determined as the loss coordinates, and a loss alarm is generated. Here, the preset distance threshold is a preset geographical distance, and the loss coordinates refer to the final marker converted from the offset coordinates after the loss event is confirmed, representing the starting position of the confirmed loss of the vehicle.

[0059] After a loss alarm is generated, a report containing contextual information is automatically prepared to facilitate quick understanding and tracking by management personnel. Specifically, in the sorted location information, the index position corresponding to the lost coordinate point is located. Centered on this index position, data containing a predetermined number of data points or covering a predetermined time period is extracted, such as trajectory data within a specific time range before and after the event. The extracted data is merged to form a monitoring data segment. The monitoring data segment not only contains precise information about the lost point but also displays the vehicle's movement trajectory before and after the loss event. The monitoring data segment is provided to the management terminal for visualization in the form of map trajectory, data list, etc. To further clarify, the monitoring data segment is a subset of data extracted from the complete location information sequence, surrounding the lost coordinate point, containing trajectory information before and after the event, for post-event analysis and tracking.

[0060] To ensure the reliability of the entire tracking system, a parallel module health status assessment mechanism is included. This mechanism distinguishes between a genuine vehicle malfunction and a fault in the positioning module itself. Specifically, it analyzes the temporal regularity of the data reported by the positioning module to determine whether the module is functioning correctly. For the sorted positioning information, the time difference between each current sampling point and its preceding adjacent sampling point is calculated and used as the sampling time difference—the time interval between two consecutive positioning information points reported by the same positioning module. All sampling time differences generated by a positioning module within a specific monitoring period are statistically processed, such as calculating their average and variance, to form evaluation parameters. These parameters reflect the stability of the module's reported data. The evaluation parameters are compared with a set damage judgment standard. If the evaluation parameters exceed the standard, the corresponding positioning module is considered to be at risk of damage, and a module damage alarm is triggered. If the evaluation parameters do not exceed the standard, the corresponding positioning module is considered to be in a normal deviation state, and a normal status signal is output. The damage judgment standard is a preset threshold or rule for the evaluation parameters, used to determine whether the positioning module is faulty.

[0061] The method in this embodiment can be applied to scenarios such as urban logistics, industrial park distribution, port container turnover and express delivery. It is not only suitable for real-time positioning of a single vehicle, but also can effectively identify risk nodes, respond to deviation paths and provide a flexible anomaly warning mechanism in large-scale logistics networks, which helps to improve the operational safety and scheduling efficiency of logistics links.

[0062] Example 2: This example provides an IoT-based logistics vehicle loss tracking system. This system is used in the IoT-based logistics vehicle loss tracking method of Example 1, and specifically includes the following modules:

[0063] The information preprocessing module is used to acquire the positioning information of multiple positioning modules associated with the logistics vehicle. The positioning information includes time data and location data, and the positioning information is sorted based on the time data.

[0064] The continuous monitoring module is used to perform continuous analysis on the sorted positioning information and output the working time period for each positioning module. The specific implementation steps are as follows: calculate the time interval between adjacent positioning information, compare the time interval with the preset allowable time interval, and if the time interval exceeds the preset allowable time interval, mark the data interruption point at the time interval and determine the time segment between two adjacent data interruption points as the working time period.

[0065] The risk identification module uses location data during the working period as measurement data, calculates the measurement deviation value based on the changes in the measurement data, determines whether the vehicle has deviated based on the measurement deviation value, and determines the offset coordinate point when the vehicle deviates.

[0066] The vehicle loss determination module is used to obtain the current position of the logistics vehicle, compare the current position with the offset coordinate point, and when the distance between the two exceeds a preset distance threshold, the offset coordinate point is determined as the lost coordinate point and a loss alarm is generated.

[0067] The tracking information processing module is used to find the index position corresponding to the lost coordinate point in the sorted location information, extract the data containing the index position to form a monitoring data segment, and provide the monitoring data segment to the management terminal.

[0068] In summary, the IoT-based logistics vehicle loss tracking system in this embodiment not only has data collection and processing capabilities, but also supports the construction of dynamic risk areas based on historical data, realizing the automated logic of evolving offset analysis into tracking path generation.

[0069] The above are merely preferred embodiments of this application and are not intended to limit this application. For those skilled in the art, this application can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for tracking lost logistics vehicles based on the Internet of Things, characterized in that, When it is determined that the distance between the current position of the logistics vehicle and the offset coordinate point exceeds a preset distance threshold, the method includes: The offset coordinate point is identified as the lost coordinate point, and a loss alarm is generated; In addition, among the positioning information of multiple positioning modules associated with the logistics vehicle, the index position corresponding to the lost coordinate point is found, and the data containing the index position is extracted to form a monitoring data segment, so as to provide the monitoring data segment to the management terminal.

2. The method for tracking lost logistics vehicles based on the Internet of Things according to claim 1, characterized in that, Determining the offset coordinate point includes: The system acquires positioning information from multiple positioning modules, which includes time data and location data, and sorts the positioning information based on the time data. Perform continuity analysis on the sorted location information and output the working time period for each location module; Location data within the working period is used as measurement data, and measurement deviation is calculated based on changes in the measurement data. And based on the measured deviation value, it is determined whether the logistics vehicle has deviated. If the logistics vehicle has deviated, the position information corresponding to the time of the deviation is marked as the deviation coordinate point.

3. The method for tracking lost logistics vehicles based on the Internet of Things according to claim 2, characterized in that, Continuity analysis of the sorted location information includes: Calculate the time interval between adjacent location information in time; If the time interval exceeds the preset allowable time interval, then mark the data interruption point at that time interval; And the time interval between two adjacent data interruption points is defined as the working time period.

4. The method for tracking lost logistics vehicles based on the Internet of Things according to claim 2, characterized in that, Determining whether a logistics vehicle has deviated based on measurement deviation values ​​includes: The measured deviation value is compared with the preset offset judgment threshold; If the measured deviation value is greater than the preset offset judgment threshold, the logistics vehicle is confirmed to be in an offset state.

5. The method for tracking lost logistics vehicles based on the Internet of Things according to claim 1, characterized in that, Obtaining the current location of the logistics vehicle includes: Obtain the basic location data at the current time point and determine the index position of this basic location data in the sorted location information; Retrieve working time periods containing time points corresponding to index positions; The baseline time period is formed based on the end time of the work period; And to reposition the logistics vehicle within a reference time period to obtain a precise measurement location as the current location.

6. A logistics vehicle loss tracking system based on the Internet of Things, characterized in that, include: The location information acquisition module is used to acquire location information from multiple location modules associated with the logistics vehicle. The location information includes time data and location data, and the location information is sorted based on the time data. The vehicle loss determination module is used to analyze the sorted positioning information to determine the offset coordinate point and obtain the current position of the logistics vehicle in order to determine whether the distance between the current position and the offset coordinate point exceeds the preset distance threshold. The system also includes a tracking information processing module, which determines the offset coordinate point as the lost coordinate point when the distance exceeds a preset distance threshold, generates a loss alarm, and extracts monitoring data segments to provide to the management terminal.

7. A logistics vehicle loss tracking system based on the Internet of Things according to claim 6, characterized in that, The vehicle loss detection module is used for: Perform continuity analysis on the sorted location information and output the working time period for each location module; Location data within the working period is used as measurement data, and measurement deviation is calculated based on changes in the measurement data. And based on the measured deviation value, it is determined whether the logistics vehicle has deviated. If the logistics vehicle has deviated, the position information corresponding to the time of the deviation is marked as the deviation coordinate point.

8. A logistics vehicle loss tracking system based on the Internet of Things according to claim 7, characterized in that, The vehicle loss determination module is used for: During continuity analysis. Calculate the time interval between adjacent location information in time; If the time interval exceeds the preset allowable time interval, then mark the data interruption point at that time interval; And the time interval between two adjacent data interruption points is defined as the working time period.

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