A logistics carrier loss tracking method and system based on the Internet of Things

Abstract

The present application 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, which comprises the following steps: acquiring and sorting the positioning information of multiple positioning modules, and outputting a working time period through continuity analysis; taking the position data in the working time period as measurement data to calculate a measurement deviation value, and judging whether the logistics carrier has deviated based on the measurement deviation value, and if so, calibrating a deviation coordinate point; acquiring the current position of the logistics carrier, and when it is determined that the distance between the current position and the deviation coordinate point exceeds a preset distance threshold, determining the deviation coordinate point as a loss coordinate point and generating a loss alarm; the present application filters out invalid data through continuity analysis, reduces the false positive rate by using a secondary confirmation mechanism, and monitors the module state to ensure the reliability of the source data, thereby improving the accuracy of risk identification.

Description

TECHNICAL FIELD

[0001] The present application 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. BACKGROUND

[0002] With the rapid development of modern logistics and supply chain management, as the core unit of carrying, transferring and storing goods, the efficient and safe flow of logistics carriers is the key to ensuring the smooth operation of the entire logistics system. In order to realize the fine management and real-time tracking of massive carrier assets, applying the Internet of Things technology to carrier monitoring has become the mainstream trend in the industry. By equipping carriers with positioning and communication modules, their position and trajectory information can be obtained.

[0003] However, the current scheme generally relies on monitoring a single positioning signal. Once the signal is interrupted or drifts at a certain time point, a loss alarm will be triggered. This threshold judgment mechanism based on instantaneous state ignores the complexity of logistics transportation scenarios and cannot effectively distinguish between real carrier loss and temporary signal interruption caused by the environment, nor can it identify reasonable position fluctuations of the device. At the same time, the alarm mechanism lacks intelligence and adaptability. Due to the roughness 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 the operation efficiency.

[0004] Therefore, in view of this, the industry urgently needs a new logistics carrier state monitoring method and system to solve the problems existing in the prior art. SUMMARY

[0005] The present application aims to overcome the deficiencies in the prior art and provide a logistics carrier loss tracking method and system based on the Internet of Things.

[0006] To achieve the above-mentioned application purposes, the technical solutions adopted by the present application are as follows: a logistics carrier loss tracking method based on the Internet of Things, when it is determined that the distance between the current position of the logistics carrier and the offset coordinate point exceeds the preset distance threshold, the method comprises:

[0007] determining the offset coordinate point as the loss coordinate point and generating a loss alarm;

[0008] and, in the positioning information of the plurality of positioning modules associated with the logistics carrier, finding the index position corresponding to the loss coordinate point and extracting the data containing the index position to form a monitoring data segment, so as to provide the monitoring data segment to the management terminal.

[0009] Preferably, the determination of the offset coordinate point comprises:

[0010] obtaining positioning information of a plurality of positioning modules, the positioning information comprising time data and position data, and sorting the positioning information based on the time data;

[0011] performing continuity analysis on the sorted positioning information, and outputting a working time period for each positioning module;

[0012] taking the position data in the working time period as measurement data, and calculating a measurement deviation value based on a change in the measurement data;

[0013] judging whether the logistics carrier deviates based on the measurement deviation value, and if the logistics carrier deviates, marking a position information corresponding to a time point when the deviation occurs as a deviation coordinate point.

[0014] Preferably, the continuity analysis on the sorted positioning information comprises:

[0015] calculating a time interval between adjacent positioning information in time;

[0016] if the time interval exceeds a preset allowable time interval, marking a data interruption point at the time interval;

[0017] and determining a time section between two adjacent data interruption points as the working time period.

[0018] Preferably, judging whether the logistics carrier deviates based on the measurement deviation value comprises:

[0019] comparing the measurement deviation value with a preset deviation judgment threshold value;

[0020] and if the measurement deviation value is greater than the preset deviation judgment threshold value, confirming that the logistics carrier is in a deviation state.

[0021] Preferably, obtaining the current position of the logistics carrier comprises:

[0022] obtaining basic positioning data at a current time point, and determining an index position of the basic positioning data in the sorted positioning information;

[0023] retrieving a working time period containing a time point corresponding to the index position;

[0024] forming a reference time period based on an end time point of the working time period;

[0025] and repositioning the logistics carrier in the reference time period to obtain an accurate measurement position as the current position.

[0026] The application also provides a logistics carrier loss tracking system based on the Internet of Things, comprising:

[0027] a positioning information obtaining module configured to obtain positioning information of a plurality of positioning modules associated with the logistics carrier, the positioning information comprising time data and position data, and the positioning information being sorted according to the time data;

[0028] a carrier loss determining module configured to analyze the sorted positioning information to determine a deviation coordinate point, and obtain a current position of the logistics carrier, and determine whether a distance between the current position and the deviation coordinate point exceeds a preset distance threshold;

[0029] and a tracking information processing module configured to determine the deviation coordinate point as a loss coordinate point, generate a loss alarm, and extract a monitoring data segment to provide to a management terminal when the distance exceeds the preset distance threshold.

[0030] Preferably, the carrier loss determining module is configured to:

[0031] perform continuity analysis on the sorted positioning information, and output a working time period for each positioning module;

[0032] use position data in the working time period as measurement data, and calculate a measurement deviation value according to a change in the measurement data;

[0033] and determine whether the logistics carrier deviates based on the measurement deviation value, and if the logistics carrier deviates, mark position information corresponding to a time when the deviation occurs as the deviation coordinate point.

[0034] Preferably, the carrier loss determining module, when performing continuity analysis, is configured to:

[0035] calculate a time interval between adjacent positioning information in time;

[0036] if the time interval exceeds a preset allowable time interval, mark a data breakpoint at the time interval;

[0037] and determine a time zone between two adjacent data breakpoints as the working time period.

[0038] Advantages

[0039] The application analyzes the acquired positioning information for continuity, identifies data breakpoints by calculating the time interval between adjacent information, determines continuous time segments as working time segments, further calculates the length of each working time segment, compares it with a preset stable length threshold, distinguishes fluctuation data regions from normal fluctuation regions, and determines the starting point of risk assessment; therefore, discrete invalid data generated due to unstable or interrupted signals can be effectively filtered out, ensuring that subsequent offset analysis is based on high-quality continuous trajectory data, avoiding misjudgment of transient signal noise as actual movement of the logistics carrier, and at the same time, through analysis of the length of the working time segment, the starting time of abnormal offset can be more accurately locked, improving the accuracy of risk identification.

[0040] The application adopts a secondary confirmation mechanism when judging the loss of the logistics carrier, calculates a measurement deviation value based on the change of the measurement data, preliminarily determines an offset coordinate point when the value exceeds a preset offset judgment threshold, independently acquires the current position of the logistics carrier, compares the distance between the current position and the aforementioned determined offset coordinate point, and only when the distance exceeds a preset distance threshold, the offset coordinate point is finally confirmed as a loss coordinate point and a loss alarm is triggered. Through comparison and verification of the offset coordinate point and the current position, the real loss of the logistics carrier can be effectively distinguished from temporary path deviation, greatly reducing the false positive rate of the tracking system and ensuring the reliability and practicality of the alarm.

[0041] In addition, the application also introduces a monitoring function of the state of the positioning module itself, calculates the time interval between adjacent sampling points in the sorted positioning information, and statistically processes all time intervals in a specific time period to form an evaluation parameter, compares the evaluation parameter with a set damage judgment standard to judge whether the positioning module has a damage risk, so that the application can actively find hardware failure hidden dangers in the timeliness dimension of data reporting before data content anomalies, prevent individual module failure from outputting incorrect positioning information and further polluting the entire tracking decision-making process, and ensure the reliability of the source data. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a method flowchart of the application. DETAILED DESCRIPTION

[0043] Embodiment one: please refer to Figure 1 The embodiment provides a logistics carrier loss tracking method based on the Internet of Things, which is used for real-time monitoring, risk warning and loss path backtracking of the carrier state in the logistics transportation process. The method comprises 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 determined as the loss coordinate point, and a loss alarm is generated.

[0045] Step S200: In the positioning information of the plurality of positioning modules associated with the logistics vehicle, the index position corresponding to the loss 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.

[0046] Specifically, a plurality of positioning modules are provided for the logistics vehicle, and a unique association between the positioning modules and the logistics vehicle is established in the Internet of Things platform. The positioning information of the plurality of positioning modules associated with the logistics vehicle is obtained, each piece of positioning information containing time data and position data. The obtained positioning information is sorted based on the time data to form sorted positioning information, which is used to ensure the integrity, order and traceability of the data.

[0047] After obtaining the sorted positioning information, the sorted positioning information is analyzed for continuity to identify and segment the working time period and exclude data interruptions caused by signal loss, equipment shutdown and the like. Specifically, the time interval between adjacent positioning information in time is calculated, and the time interval is 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, which is a pre-set time length threshold used to determine whether the time gap between consecutive positioning data points constitutes an effective data interruption. The value is usually determined according to 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 at this time interval. A data interruption point is marked at this time interval, and the time interval of the two adjacent positioning information before and after the point exceeds the preset allowable time interval. The time segment between the two adjacent data interruption points is determined as the working time period, and all subsequent risk analysis will be based on the working time period determined to be continuous.

[0048] From the working time period, further filter out the area worthy of attention, and optimize it to improve the accuracy of judgment. Specifically, the working time period duration of each working time period is calculated and compared with the preset stable duration threshold. The preset stable duration threshold is set to filter out data segments with too short duration and no analysis value, that is, to distinguish between long-term activities with analysis value (fluctuation data region) and short-term and negligible signal fluctuations (normal fluctuation region). If the working time period duration is greater than the preset stable duration threshold, it is considered that the vehicle activity in the period has analysis significance, and the working time period is identified as a fluctuation data region. The region is considered to be a section containing sufficient information for effective risk analysis, and the starting time of the fluctuation data region is set as the risk assessment starting point. If the working time period duration is less than or equal to the preset stable duration threshold, it is marked as a normal fluctuation region. The region is considered to be a normal, short-term signal recovery or movement, and is not an independent focus of analysis. In order to avoid too many unnecessary analysis tasks due to frequent short-term signal recovery, the end time of the normal fluctuation region is extended by a preset duration to form an extended time period, and the starting time of the normal fluctuation region 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 end time of the normal fluctuation region by a preset duration, which is used to merge adjacent short-term fluctuations and avoid analysis fragmentation.

[0049] Further, in some transportation scenarios, multiple risk assessment starting points may be generated in a short period of time. In order to avoid misjudging a single continuous event as multiple independent events, the multiple risk assessment starting points are input into an estimation model based on linear interpolation. The estimation model analyzes the distribution trend of these starting points in time and space, outputs a predicted starting point position, and generates a predicted time period based on the predicted starting point position. The predicted starting point position represents the starting point of the most likely risk event determined by the model, and is the predicted single and unified risk event starting position after integrating a group of adjacent risk assessment starting points in time and space. The predicted starting point position and the predicted time period are used to replace the risk assessment starting point to perform subsequent offset judgment steps, thereby effectively improving the robustness of risk identification. The predicted time period is a time interval generated based on the predicted starting point position, which is used to replace the original multiple and dispersed risk assessment time periods for unified offset analysis.

[0050] The estimation model is a calculation model for integrating multiple adjacent risk assessment starting points in time and space into a unified predicted starting point position. The specific formula is as follows:

[0051]

[0052] where the parameters of the linear model are determined by least squares to minimize the prediction error: and

[0053]

[0054] is set to the earliest timestamp in the input set, i.e. .

[0055] where the input is a set of risk assessment origins , where is the timestamp, is the geographic coordinate, and the output is a single predicted origin location and its corresponding timestamp ; represents the predicted origin coordinate, which means the model's estimate of the most likely starting geographic location for the integrated risk event; represents the predicted origin timestamp, which means the starting time of the integrated risk event, usually the earliest time in the input point set; represents the coordinate of the i-th risk assessment origin, which means the geographic location of the i-th discrete risk assessment event in the input; represents the timestamp of the i-th risk assessment origin, which means the time of the i-th discrete risk assessment event in the input; represents the total number of risk assessment origins, which means the number of discrete risk assessment events that the model integrates; represents the linear model parameters, which means the linear trend of the risk point in the x and y directions over time, respectively, which can be understood as the estimated values of speed and initial position.

[0056] ​​After determining the working time period to be analyzed, the position data in the working time period is taken as a continuous sequence of carrier position coordinates for performing deviation analysis as measurement data; according to the continuous change of the measurement data, a measurement deviation value is calculated, which can be a comprehensive index for describing the deviation degree of the carrier movement trajectory reflected by the measurement data from its normal or expected mode, such as the change rate of the heading angle of the carrier in a unit time, or the distance between its position and the preset electronic fence or planned path. The measurement deviation value is compared with a preset deviation judgment threshold value, if the measurement deviation value is greater than the preset deviation judgment threshold value, it is confirmed that the logistics carrier is in a deviation state, indicating a state in which the carrier has deviated significantly from its normal trajectory, and the position information corresponding to the moment of deviation is marked as a deviation coordinate point, indicating the geographic position coordinates of the carrier when it is first determined to enter the deviation state; at the same time, the size of the measurement deviation value is determined as the deviation amount, which is the specific value of the measurement deviation value at the moment of triggering the deviation judgment, used to represent the severity of the deviation, if the measurement deviation value is less than or equal to the preset deviation judgment threshold value, it is confirmed that the carrier is not in a deviation state.

[0057] When the carrier is determined to deviate and the deviation coordinate point is determined in the above steps, it is necessary to further confirm whether it constitutes a loss event, indicating that the carrier has deviated from the control or normal operation range, and the current position of the logistics carrier needs to be obtained. In order to obtain a high-precision current position and avoid misjudgment due to single-point positioning error, the following operations are performed: obtaining the basic positioning data at the current time point, which is the latest raw positioning information point obtained without further processing, and determining the index position of the basic positioning data in the sorted positioning information; retrieving the working time period containing the time point corresponding to the index position, taking the end time point of the working time period as the reference, forming a reference time period by increasing or decreasing a preset time length, which is used to collect more positioning data to calculate a more accurate position; positioning the carrier at a higher frequency or performing weighted average on multiple positioning points within the reference time period, thereby obtaining an accurate measurement position, and taking the accurate measurement position as the current position of the carrier.

[0058] Further, after obtaining the current position, it is compared with the previously determined deviation coordinate point, and the straight-line geographic distance between the two is calculated, if the distance exceeds a preset distance threshold value, it indicates that the carrier has not returned to the original area after deviation, but has continued to move away, meeting the judgment standard of the loss event, at this time, the deviation coordinate point is determined as the loss coordinate point, and a loss alarm is generated. The preset distance threshold value is a preset geographic distance, and the loss coordinate point refers to the final marker converted from the deviation coordinate point after the loss event is confirmed, representing the starting position of the carrier when it is confirmed to be lost.

[0059] After the loss alarm is generated, in order to facilitate the management personnel to quickly understand the event and track, an automatic report containing context information is prepared. Specifically, in the sorted positioning information, the index position corresponding to the loss coordinate point is found, and the data containing a predetermined number of data points or covering a predetermined time length, such as the trajectory data of a specific time range before and after the event, is extracted around the index position. After the extracted data is merged, a monitoring data segment is formed, which not only contains the accurate information of the loss point, but also shows the movement trajectory of the carrier before and after the loss event. The monitoring data segment is provided to the management terminal for visual presentation in the form of a map trajectory, a data list, and the like. Further, the monitoring data segment is a subset of data extracted from the complete positioning information sequence around the loss coordinate point, which contains the trajectory information before and after the event, and is used for post-event analysis and tracking.

[0060] In order to ensure the reliability of the entire tracking system, a parallel module health state evaluation mechanism is also included, which is used to distinguish whether the carrier has a real abnormality or the positioning module itself has a fault, that is, it is used to analyze the time regularity of the data reported by the positioning module to determine whether the module is working normally. Specifically, for the sorted positioning information, the time difference between each current sampling point and the adjacent previous sampling point is calculated, and the time difference is taken as the sampling time difference, that is, the time interval between two positioning information points reported by the same positioning module in time; all sampling time differences generated by a certain positioning module within a certain monitoring time period are statistically processed, for example, the average and variance are calculated, so as to form an evaluation parameter, which reflects the stability of the data reported by the module; the evaluation parameter is compared with the set damage judgment standard, if the evaluation parameter exceeds the damage judgment standard, it is judged that the corresponding positioning module has a damage risk, and a module damage alarm is triggered, if the evaluation parameter does not exceed the damage judgment standard, it is judged that the corresponding positioning module is in a normal deviation state, and a normal state signal is output. The damage judgment standard is a threshold or rule preset for the evaluation parameter, which is used to judge whether the positioning module has a fault.

[0061] The method of the embodiment can be applied to city logistics, industrial park distribution, port container turnover, express delivery and other scenes, not only suitable for real-time positioning of a single carrier, but also can effectively identify risk nodes, respond to deviation paths and provide flexible abnormal warning mechanism in a large-scale logistics network, which helps to improve the operation safety and scheduling efficiency of the logistics link.

[0062] Embodiment two: the embodiment provides a logistics carrier loss tracking system based on Internet of Things, which is used for the logistics carrier loss tracking method based on Internet of Things in embodiment one, and specifically includes the following modules:

[0063] The information preprocessing module is configured to acquire positioning information of a plurality of positioning modules associated with the logistics carrier, the positioning information comprising time data and position data, and sort the positioning information based on the time data.

[0064] The continuity monitoring module is configured to perform continuity analysis on the sorted positioning information and output an operating time period for each positioning module, by calculating a time interval between adjacent positioning information in time, comparing the time interval with a preset allowable time interval, marking a data interruption point at the time interval if the time interval exceeds the preset allowable time interval, and determining a time section between the adjacent two data interruption points as the operating time period.

[0065] The risk identification module is configured to take the position data in the operating time period as measurement data, calculate a measurement deviation value based on a change in the measurement data, determine whether the carrier has deviated based on the measurement deviation value, and determine a deviation coordinate point when the carrier deviates.

[0066] The carrier loss determination module is configured to acquire a current position of the logistics carrier, compare the current position with the deviation coordinate point, and determine the deviation coordinate point as a loss coordinate point and generate a loss alarm when a distance between the current position and the deviation coordinate point exceeds a preset distance threshold.

[0067] The tracking information processing module is configured to find an index position corresponding to the loss coordinate point in the sorted positioning information, extract data containing the index position to form a monitoring data section, and provide the monitoring data section to a management terminal.

[0068] In summary, the logistics carrier loss tracking system based on the Internet of Things has data acquisition and processing capabilities, and supports construction of a dynamic risk area based on historical data, and realizes automatic logic of evolving deviation analysis into tracking path generation.

[0069] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present 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 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, which is then provided to the management terminal. The determination of offset coordinate points includes: acquiring positioning information from multiple positioning modules, which includes time data and location data, and sorting the positioning information based on the time data; performing continuity analysis on the sorted positioning information to output a working time period for each positioning module; using the location data within the working time period as measurement data and calculating the measurement deviation value based on the changes in the measurement data; and determining whether the logistics vehicle has deviated based on the measurement deviation value. If the logistics vehicle has deviated, the location information corresponding to the time of deviation is marked as the offset coordinate point. The continuity analysis of the sorted location information includes: calculating the time interval between adjacent location information; marking a data interruption point at the time interval if the time interval exceeds the preset allowable time interval; and determining the time segment between two adjacent data interruption points as the working time segment. The method of determining whether a logistics vehicle has deviated based on the measured deviation value includes: comparing the measured deviation value with a preset deviation judgment threshold; and if the measured deviation value is greater than the preset deviation judgment threshold, then confirming that the logistics vehicle is in a deviated state.

2. 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.

3. An Internet of Things-based logistics vehicle loss tracking system, applied to the method described in claim 1, 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.

4. The IoT-based logistics vehicle loss tracking system according to claim 3, 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.

5. A logistics vehicle loss tracking system based on the Internet of Things according to claim 4, 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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