CN117111100B - Electric bicycle anti-theft tracking system based on Internet of things - Google Patents

Abstract

The invention discloses an electric bicycle anti-theft tracking system based on the Internet of things, which relates to the technical field of electric bicycle anti-theft, and comprises an information acquisition module, a server, a judging module, a comprehensive analysis module, a prompting module and a positioning module; the information acquisition module acquires a plurality of data information including time sequence parameter information and geographic parameter information when a GPS tracker in the electric bicycle anti-theft tracking system based on the Internet of things operates. According to the invention, through monitoring the operation state of the GPS tracker in real time, when the operation state of the GPS tracker has abnormal hidden danger, an early warning prompt is sent out, the operation of the related GPS tracker is positioned, related personnel are prompted to know the situation in time, the operation of the GPS tracker with the abnormal hidden danger is maintained and managed in advance, the GPS tracker is ensured to provide accurate historical track data to cope with possible theft situations, and the stolen vehicle is conveniently tracked and retrieved with high efficiency.

Description

Electric bicycle anti-theft tracking system based on Internet of things

Technical Field

The invention relates to the technical field of electric bicycle burglary prevention, in particular to an electric bicycle burglary prevention tracking system based on the Internet of things.

Background

The electric bicycle anti-theft tracking system based on the Internet of things is an intelligent solution, is used for protecting the electric bicycle from theft and helping to track and recover the stolen bicycle when the vehicle is stolen, and is widely applied to the shared electric bicycle industry; currently, there are various technologies for anti-theft tracking of electric bicycles, and a built-in GPS tracker is one of the most common methods, and the GPS tracker uses a global positioning system to track the position of the bicycle in real time and transmits the data to a cloud server or a mobile application program through an internet connection for monitoring by a user.

The prior art has the following defects: when the GPS tracker is out of operation but the system is not timely aware, the abnormally operated GPS tracker may not provide accurate historical track data, which is critical to understanding the movement pattern of the bicycle and possible theft events, and if the historical data is inaccurate or unavailable, investigation and tracking of the theft events will be effected, which will make it more difficult for the stolen vehicle to track and retrieve.

The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

The invention aims to provide an electric bicycle anti-theft tracking system based on the Internet of things, which monitors the running state of a GPS tracker in real time, when the running state of the GPS tracker has abnormal hidden danger, gives out an early warning prompt, positions the running of the related GPS tracker, prompts related personnel to know the situation in time, maintains and manages the running of the GPS tracker with the abnormal hidden danger in advance, ensures that the GPS tracker provides accurate historical track data to cope with possible theft situations, and is convenient for a stolen vehicle to track and recover efficiently, so as to solve the problems in the background technology.

In order to achieve the above object, the present invention provides the following technical solutions: an electric bicycle anti-theft tracking system based on the Internet of things comprises an information acquisition module, a server, a judging module, a comprehensive analysis module, a prompting module and a positioning module;

the information acquisition module acquires a plurality of data information including time sequence parameter information and geographic parameter information when a GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, processes the time sequence parameter information and the geographic parameter information when the GPS tracker operates after acquisition, and uploads the processed time sequence parameter information and geographic parameter information to the server;

time sequence parameter information of GPS tracker in electric bicycle anti-theft tracking system based on thing networking when operation includes clock drift deviation coefficient, after gathering, information acquisition module marks clock drift deviation coefficient as；

Geographic parameter information of the GPS tracker in the electric bicycle anti-theft tracking system based on the Internet of things during operation comprises a GPS signal processing duration abnormal concealment coefficient and a GPS signal intensity jitter coefficient, and after acquisition, the information acquisition module respectively calibrates the GPS signal processing duration abnormal concealment coefficient and the GPS signal intensity jitter coefficient intoAnd->；

The server comprehensively analyzes the processed time sequence parameter information and geographic parameter information when the GPS tracker operates to generate a track tracking hidden danger index, and transmits the track tracking hidden danger index to the judging module;

the judging module is used for comparing and judging the track tracking hidden danger index generated during the operation of the GPS tracker with a preset track tracking hidden danger index reference threshold value to generate a first hidden danger signal or a second hidden danger signal and transmitting the signals to the comprehensive analysis module;

the comprehensive analysis module is used for comprehensively analyzing a plurality of track tracking hidden danger indexes generated later through the server after receiving a first hidden danger signal generated when the GPS tracker is operated, generating different types of signals, transmitting the signals to the prompting module and the positioning module, sending early warning prompt through the prompting module and positioning the related GPS tracker through the positioning module.

Preferably, the logic for obtaining the clock drift offset coefficient is as follows:

s101, acquiring an actual GPS time stamp and an internal actual time stamp of a GPS tracker at the same moment in T time when the GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and respectively calibrating the actual GPS time stamp and the internal actual time stamp of the GPS tracker asAnd->X represents the number of an actual GPS timestamp and an internal actual timestamp of the GPS tracker at the same moment in T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, and x=1, 2, 3, 4, … … and m are positive integers;

s102, calculating a clock drift deviation coefficient, wherein the calculated expression is as follows:。

preferably, logic for acquiring the abnormal concealment coefficient of the GPS signal processing time length is as follows:

s201, acquiring an optimal GPS signal processing duration range when a GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and calibrating the optimal GPS signal processing duration range as；

S202, acquiring a plurality of actual GPS signal processing time lengths generated in T time when the GPS tracker operates, and calibrating the actual GPS signal processing time lengths asY represents the number of a plurality of actual GPS signal processing durations generated in the T time when the GPS tracker operates, y=1, 2, 3, 4, … … and n, n is a positive integer, wherein the actual GPS signal processing duration is the sum of the GPS signal receiving duration, the signal processing duration and the data transmission duration;

s203, calculating a GPS signal processing time length abnormal state concealment coefficient, wherein the calculated expression is as follows:wherein->Representing the range of time length of GPS signal processing which is acquired in the time T when the GPS tracker is running and is not in the optimal range of time length of GPS signal processing>Actual betweenGPS signal processing duration,/->Representing the range of time length of GPS signal processing which is acquired in the time T when the GPS tracker is running and is not in the optimal range of time length of GPS signal processing>Number of actual GPS signal processing time length in between,/-for each GPS signal processing time length>，/>Is a positive integer>。

Preferably, the logic for acquiring the GPS signal strength jitter coefficient is as follows:

s301, acquiring actual GPS signal intensity of a plurality of GPS signals received by a GPS tracker in a T time in an electric bicycle anti-theft tracking system based on the Internet of things, and calibrating the actual GPS signal intensity asK represents the number of the actual GPS signal intensity of a GPS tracker receiving a plurality of GPS signals in T time in the electric bicycle anti-theft tracking system based on the Internet of things, and k=1, 2, 3, 4, … … and p are positive integers;

s302, calculating GPS signal intensity standard deviation and GPS signal intensity average value through the GPS tracker receiving the actual GPS signal intensities of a plurality of GPS signals in T time, wherein the calculated expression is as follows:，wherein->Represents the standard deviation of GPS signal intensity, < >>Representing a GPS signal strength average;

s303, GPS signal intensity standard deviation of a plurality of GPS signals received in T time through a GPS trackerAnd GPS signal intensity average>Calculating a GPS signal strength variation coefficient, wherein the calculated expression is as follows: />Wherein->Representing the GPS signal strength variation coefficient;

s304, calculating a GPS signal intensity jitter coefficient, wherein the calculated expression is as follows:。

preferably, the server obtains the processed clock drift deviation coefficient when the GPS tracker operatesAbnormal concealment coefficient of GPS signal processing time length>GPS signal strength jitter coefficient ++>Afterwards, will->、/>And +.>Performing formulated analysisGenerating a track following hidden danger index->The formula according to is:wherein->、/>、/>Clock drift deviation coefficients ∈ ->Abnormal concealment coefficient of GPS signal processing time length>GPS signal strength jitter coefficient->Is a preset proportionality coefficient of>1、/>2、/>3 are all greater than 0.

Preferably, the judging module compares and judges the track tracing hidden danger index generated when the GPS tracker operates with a preset track tracing hidden danger index reference threshold value, and the comparison and judgment result is as follows:

if the track tracking hidden danger index is greater than or equal to the track tracking hidden danger index reference threshold, generating a first hidden danger signal and transmitting the signal to the comprehensive analysis module;

and if the track tracing hidden danger index is smaller than the track tracing hidden danger index reference threshold, generating a second hidden danger signal and transmitting the signal to the comprehensive analysis module.

Preferably, after the comprehensive analysis module receives a first hidden danger signal generated during the operation of the GPS tracker, an analysis set is established for a plurality of track tracking hidden danger indexes generated subsequently by a server, and the analysis set is calibrated as Z, thenV denotes the number of the trace hidden danger index in the analysis set, v=1, 2, 3, 4, … …, u being a positive integer.

Preferably, the standard deviation of the track potential tracking index and the average value of the track potential tracking index are obtained through analyzing the track potential tracking index in the set, and the standard deviation of the track potential tracking index and the average value of the track potential tracking index are respectively compared with a preset standard deviation reference threshold value and a preset track potential tracking index reference threshold value for analysis, and the comparison and analysis result is as follows:

if the average value of the track tracing hidden danger indexes is larger than or equal to the reference threshold value of the track tracing hidden danger indexes, generating a continuous abnormal operation signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending out a high hidden danger early warning prompt through the prompting module, and positioning a related GPS tracker through the positioning module;

if the track tracing hidden danger index average value is smaller than the track tracing hidden danger index reference threshold value and the track tracing hidden danger index standard deviation is larger than or equal to the standard deviation reference threshold value, generating an unstable running signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending an unstable hidden danger early warning prompt through the prompting module, and positioning a related GPS tracker through the positioning module;

if the average value of the track tracing hidden danger indexes is smaller than the reference threshold value of the track tracing hidden danger indexes and the standard deviation of the track tracing hidden danger indexes is smaller than the reference threshold value of the standard deviation, generating a normal running signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, and not sending an early warning prompt through the prompting module and positioning the related GPS tracker through the positioning module.

In the technical scheme, the invention has the technical effects and advantages that:

according to the invention, through monitoring the operation state of the GPS tracker in real time, when the operation state of the GPS tracker has abnormal hidden danger, an early warning prompt is sent out, the operation of the related GPS tracker is positioned, related personnel are prompted to know the situation in time, the operation of the GPS tracker with the abnormal hidden danger is maintained and managed in advance, the GPS tracker is ensured to provide accurate historical track data to cope with possible theft situations, and the stolen vehicle is convenient to track and retrieve efficiently;

according to the invention, when the operation of the GPS tracker is perceived to be abnormal, the operation state of the GPS tracker is comprehensively analyzed, if the GPS tracker determines that the hidden danger of the operation abnormality exists, whether the operation of the GPS tracker is continuous or unstable can be determined through the comprehensive analysis of the operation state of the GPS tracker, so that the operation abnormality type of the GPS tracker can be conveniently determined, the GPS tracker can be conveniently and efficiently maintained and managed, and secondly, when the operation of the GPS tracker is abnormal accidentally, an early warning prompt is not sent out, frequent early warning caused by the accidental abnormality of the GPS tracker by a system is avoided, and the monitoring accuracy of the GPS tracker is ensured.

Drawings

For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.

Fig. 1 is a schematic block diagram of an electric bicycle anti-theft tracking system based on the internet of things.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

The invention provides an electric bicycle anti-theft tracking system based on the Internet of things, which is shown in fig. 1, and comprises an information acquisition module, a server, a judging module, a comprehensive analysis module, a prompting module and a positioning module;

the information acquisition module acquires a plurality of data information including time sequence parameter information and geographic parameter information when a GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, processes the time sequence parameter information and the geographic parameter information when the GPS tracker operates after acquisition, and uploads the processed time sequence parameter information and geographic parameter information to the server;

time sequence parameter information of GPS tracker in electric bicycle anti-theft tracking system based on thing networking when operation includes clock drift deviation coefficient, after gathering, information acquisition module marks clock drift deviation coefficient as；

Clock drift of a GPS tracker, which is a key component for recording position and time information, means that the time of an internal clock of the GPS tracker is continuously shifted or drifted and is not synchronous with the real time of a Global Positioning System (GPS) satellite, and clock drift is a common problem, which may cause inaccuracy of a time tag of the GPS tracker and further affect the accuracy of a time stamp and a history track of position data, and when the GPS tracker is connected with the GPS satellite, the GPS tracker receives accurate time information from a satellite signal to synchronize the internal clock, however, due to various factors including hardware problems, temperature variation, vibration and the like, the clock may have tiny time drift;

when the clock drift of the GPS tracker is large, it may cause the GPS tracker to fail to provide accurate historical track data, making it more difficult for a stolen vehicle to track and retrieve, because the clock drift may cause inaccuracy of the time tag, thereby affecting the time stamp of the location data and the accuracy of the historical track, as described in detail below:

time tag inaccuracy: a GPS tracker typically adds a time stamp to determine the time stamp of each data point when recording the position data, and if the clock drift is large, the time stamp will not be synchronized with the real time of the Global Positioning System (GPS) satellite, which may cause deviation of the time stamp of each position data point, resulting in confusion of the time sequence of the historical track data;

time inconsistency of historical trace data: because of clock drift, time inconsistency in the historical track data may cause misalignment of the position data on a time axis, and thus, a user cannot accurately restore an actual moving path of the bicycle, the time inconsistency may negatively affect analysis and playback of the track data, and the action history of the vehicle is more difficult to understand;

it is difficult to restore the event sequence: clock drift can cause the time stamps of events to be incorrectly aligned, making it difficult to determine the order of activity of the vehicle, which can cause difficulties in event reconstruction and vehicle behavior analysis, reducing the accuracy of vehicle tracking;

excavation theft is more difficult: when the time drift is large and the time data is inaccurate, tracking and analyzing the theft activity becomes more complex, which may result in the tracker not providing enough information to determine the exact location, movement path and time period of the stolen vehicle, thereby increasing the difficulty of vehicle recovery;

therefore, the hidden trouble of drift deviation when the GPS tracker runs can be found in time by monitoring the GPS time and the internal time of the GPS tracker in the anti-theft tracking system of the electric bicycle based on the Internet of things;

the logic for obtaining the clock drift deviation coefficient is as follows:

s101, acquiring an actual GPS time stamp and an internal actual time stamp of a GPS tracker at the same moment in T time when the GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and respectively calibrating the actual GPS time stamp and the internal actual time stamp of the GPS tracker asAnd->X represents the number of an actual GPS timestamp and an internal actual timestamp of the GPS tracker at the same moment in T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, and x=1, 2, 3, 4, … … and m are positive integers;

it should be noted that, the GPS tracker has a GPS receiver, which receives signals from GPS satellites and provides actual GPS time stamps, these time stamps are generated based on broadcast time information of the GPS satellites, and the GPS tracker generally has an internal clock for recording the internal time stamps, and this clock is generally a part of the device for recording the time of occurrence of an event, and can provide actual time inside the GPS tracker, and then, at the same time, the actual GPS time stamp subscript is the same as the actual time stamp subscript inside the GPS tracker;

s102, calculating a clock drift deviation coefficient, wherein the calculated expression is as follows:；

the calculation expression of the clock drift deviation coefficient shows that the larger the expression value of the clock drift deviation coefficient generated in the T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things is, the larger the hidden danger that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and recover is, otherwise, the smaller the hidden danger that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and recover is;

geographic parameter information of the GPS tracker in the electric bicycle anti-theft tracking system based on the Internet of things during operation comprises a GPS signal processing duration abnormal concealment coefficient and a GPS signal intensity jitter coefficient, and after acquisition, the information acquisition module respectively calibrates the GPS signal processing duration abnormal concealment coefficient and the GPS signal intensity jitter coefficient intoAnd->；

Longer or shorter processing times of the GPS signals by the GPS tracker may result in the GPS tracker not providing accurate historical track data, making it more difficult to track and retrieve a stolen vehicle, because the processing time may adversely affect the real-time and accuracy of the location data, as will be described in detail below:

longer duration of treatment time: if the processing time of the GPS signal is longer, the tracker will need more time to receive, process, calculate and upload the position data, which may lead to the following problems:

delay of historical trajectories: due to the longer processing time, the real-time nature of the historical track data will be reduced, and the position data may lag behind the actual vehicle activity, which makes the tracker unable to provide timely vehicle position information;

inaccuracy of time-stamping: the longer processing time can cause inaccuracy of the time tag, so that an error occurs in the time stamp of the historical track data, which causes the time sequence of the track data to be disordered, and the actual activity history of the vehicle is difficult to restore;

missing data points: if the processing time is long, some location data points may be lost due to delays, resulting in incoherent trajectory information;

shorter duration processing time: if the processing time of the GPS signal is short, the following problems may be caused:

loss of precision: the shorter processing time may cause the tracker to not fully take into account the satellite signal variations when calculating the position, thereby reducing the accuracy of the position data;

instability: shorter processing times may make the tracker more sensitive to signal interference and noise, thereby introducing more positioning errors;

incomplete data: the shorter processing time may result in insufficient processing and correction of the position information by the tracker prior to uploading the data, thereby providing incomplete or inaccurate historical track data;

therefore, the hidden trouble problem of abnormal GPS signal processing time can be found in time by monitoring the GPS signal processing time when the GPS tracker in the electric bicycle anti-theft tracking system based on the Internet of things runs;

the logic for acquiring the abnormal hidden coefficient of the GPS signal processing time length is as follows:

s201, acquiring an optimal GPS signal processing duration range when a GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and calibrating the optimal GPS signal processing duration range as；

It should be noted that, firstly, clear requirements are needed, different applications may need different GPS signal processing durations, for example, for real-time vehicle tracking, a shorter processing duration is needed, and for historical track recording, a longer processing duration can be tolerated, secondly, experiments and tests are key methods for determining the optimal GPS signal processing duration range, and by performing field tests under different environmental conditions, the influence of the processing duration on the accuracy of data can be evaluated, and an optimal balance point can be determined, so that the optimal GPS signal processing duration range in the operation of a GPS tracker in an electric bicycle anti-theft tracking system based on the internet of things is not specifically limited, and can be adjusted according to actual requirements and test conditions;

s202, acquiring a plurality of actual GPS signal processing time lengths generated in T time when the GPS tracker operates, and calibrating the actual GPS signal processing time lengths asY represents the number of a plurality of actual GPS signal processing durations generated in the T time when the GPS tracker operates, y=1, 2, 3, 4, … … and n, n is a positive integer, wherein the actual GPS signal processing duration is the sum of the GPS signal receiving duration, the signal processing duration and the data transmission duration;

it should be noted that, the GPS tracker may provide an interface or an API to obtain real-time data of the GPS module, including location information, a timestamp, a processing duration, etc., and may obtain actual duration information of GPS signal processing through the interface or the API;

s203, calculating a GPS signal processing time length abnormal state concealment coefficient, wherein the calculated expression is as follows:wherein->Representing the range of time length of GPS signal processing which is acquired in the time T when the GPS tracker is running and is not in the optimal range of time length of GPS signal processing>Actual GPS signal processing duration between +.>Representing the range of time length of GPS signal processing which is acquired in the time T when the GPS tracker is running and is not in the optimal range of time length of GPS signal processing>Number of actual GPS signal processing time length in between,/-for each GPS signal processing time length>，/>Is a positive integer>；

The calculation expression of the abnormal concealment coefficient of the GPS signal processing time length can show that the larger the expression value of the abnormal concealment coefficient of the GPS signal processing time length generated in the T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things is, the larger the hidden trouble that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and retrieve is, otherwise, the smaller the hidden trouble that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and retrieve is;

the high jitter in GPS signal strength can affect the performance of the GPS tracker, making it difficult to provide accurate historical track data, making it more difficult for a stolen vehicle to track and retrieve, as explained in more detail below:

position accuracy decreases: GPS trackers typically use signals from multiple satellites to determine their position, and if GPS signal strength is severely dithered, the device may receive signals from different satellites at different times, resulting in unstable position data, which may cause significant inconsistencies in the trajectory data, making accurate determination of vehicle position difficult;

loss of signal: when the GPS signal strength jitter is serious, the situation of signal loss can occur, if the GPS tracker loses all satellite signals at a certain moment, any position information can not be provided, and the interruption occurs in track data, so that the activity of the stolen vehicle can not be tracked;

clock synchronization problem: jitter in the strength of the GPS signal may cause clock synchronization problems for the GPS tracker, and the device typically uses the GPS signal to synchronize the internal clock to ensure accuracy of the timestamp, and signal jitter may cause clock instability, thereby affecting the accuracy of the timestamp;

loss of precision: a GPS signal with severe jitter may result in reduced accuracy of signal resolution, meaning that the calculated position may contain greater errors, reducing the accuracy of the historical trajectory data;

it is difficult to analyze and reconstruct the trajectory: when the trajectory data is unstable and inconsistent, it becomes more difficult to analyze and reconstruct the action history of the vehicle, and the tracking and understanding of the activities of the vehicle becomes ambiguous, thereby reducing the efficiency of vehicle tracking;

therefore, the hidden trouble problem of abnormal GPS signal intensity can be found in time by monitoring the GPS signal intensity when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things;

the logic for acquiring the GPS signal strength jitter coefficient is as follows:

s301, acquiring anti-theft tracking of electric bicycle based on Internet of thingsThe GPS tracker in the tracking system receives the actual GPS signal intensity of a plurality of GPS signals in the T time, and the actual GPS signal intensity is calibrated asK represents the number of the actual GPS signal intensity of a GPS tracker receiving a plurality of GPS signals in T time in the electric bicycle anti-theft tracking system based on the Internet of things, and k=1, 2, 3, 4, … … and p are positive integers;

it should be noted that the GPS module and the chip provide an API to allow access to various parameters of the GPS signal, including signal strength, and may obtain strength information of the GPS signal by calling a corresponding API function;

s302, calculating GPS signal intensity standard deviation and GPS signal intensity average value through the GPS tracker receiving the actual GPS signal intensities of a plurality of GPS signals in T time, wherein the calculated expression is as follows:，wherein->Represents the standard deviation of GPS signal intensity, < >>Representing a GPS signal strength average;

s303, GPS signal intensity standard deviation of a plurality of GPS signals received in T time through a GPS trackerAnd GPS signal intensity average>Calculating a GPS signal strength variation coefficient, wherein the calculated expression is as follows: />Wherein->Representing the GPS signal strength variation coefficient;

from the GPS signal strength coefficient of variationIt can be seen that the GPS tracker receives GPS signal intensity variation coefficient generated when a plurality of GPS signals are received in T time +.>The larger the expression value of the GPS tracker is, the larger the jitter of the strength of a plurality of actual GPS signals received by the GPS tracker in the T time is, otherwise, the smaller the jitter of the strength of a plurality of actual GPS signals received by the GPS tracker in the T time is;

s304, calculating a GPS signal intensity jitter coefficient, wherein the calculated expression is as follows:；

the calculation expression of the GPS signal intensity jitter coefficient shows that the larger the expression value of the GPS signal intensity jitter coefficient generated in the T time when a GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things is, the larger the hidden danger that the GPS tracker cannot accurately provide historical track data to cause the stolen vehicle to be difficult to track and recover is, otherwise, the smaller the hidden danger that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and recover is;

the server comprehensively analyzes the processed time sequence parameter information and geographic parameter information when the GPS tracker operates to generate a track tracking hidden danger index, and transmits the track tracking hidden danger index to the judging module;

the server obtains the clock drift deviation coefficient processed during the operation of the GPS trackerAbnormal concealment coefficient of GPS signal processing time length>GP and GPS signal intensity dither coefficient->Afterwards, will->、/>And +.>Carrying out formulated analysis to generate a track tracing hidden danger index +.>The formula according to is:wherein->、/>、/>Clock drift deviation coefficients ∈ ->Abnormal concealment coefficient of GPS signal processing time length>GPS signal strength jitter coefficient->Is a preset proportionality coefficient of>1、/>2、/>3 are all greater than 0;

according to a calculation formula, the larger the clock drift deviation coefficient generated in the T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, the larger the GPS signal processing time length abnormal concealing coefficient and the larger the GPS signal intensity jitter coefficient are, namely the track tracking hidden danger index generated in the T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of thingsThe larger the expression value of the system is, the larger the hidden danger that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and recover is, otherwise, the smaller the hidden danger that the GPS tracker cannot accurately provide the historical track data to cause the stolen vehicle to be difficult to track and recover is;

it should be noted that, the selection of the time T is a time period with a short time, the time in the time period is not limited specifically herein, and can be set according to practical situations, so as to monitor the situation of the GPS tracker in the anti-theft tracking system of the electric bicycle based on the internet of things in the time T when the GPS tracker operates, thereby monitoring the running state of the GPS tracker in the anti-theft tracking system of the electric bicycle based on the internet of things in real time in different time periods (in the time T);

the judging module is used for comparing and judging the track tracking hidden danger index generated during the operation of the GPS tracker with a preset track tracking hidden danger index reference threshold value to generate a first hidden danger signal or a second hidden danger signal and transmitting the signals to the comprehensive analysis module;

the judging module compares and judges the track tracing hidden danger index generated when the GPS tracker operates with a preset track tracing hidden danger index reference threshold value, and the comparison and judgment result is as follows:

if the track tracking hidden danger index is greater than or equal to the track tracking hidden danger index reference threshold, generating a first hidden danger signal, transmitting the signal to the comprehensive analysis module, and when the GPS tracker generates the first hidden danger signal during operation, indicating that the GPS tracker cannot accurately provide historical track data to cause greater hidden danger that the stolen vehicle is difficult to track and recover, and further analyzing is needed;

if the track tracking hidden danger index is smaller than the track tracking hidden danger index reference threshold, generating a second hidden danger signal, and transmitting the signal to the comprehensive analysis module, wherein when the GPS tracker generates the second hidden danger signal during operation, the GPS tracker can not accurately provide historical track data, so that hidden danger that a stolen vehicle is difficult to track and retrieve is smaller, namely the GPS tracker can accurately provide the historical track data;

the comprehensive analysis module is used for comprehensively analyzing a plurality of track tracking hidden danger indexes generated later through a server after receiving a first hidden danger signal generated when the GPS tracker operates, generating signals of different types, transmitting the signals to the prompting module and the positioning module, sending out early warning prompts through the prompting module, and positioning the related GPS tracker through the positioning module;

after the comprehensive analysis module receives a first hidden danger signal generated during the operation of the GPS tracker, an analysis set is established for a plurality of track tracking hidden danger indexes generated subsequently by a server, and the analysis set is calibrated as ZV represents the number of the track trace hidden danger indexes in the analysis set, v=1, 2, 3, 4, … … and u, and u is a positive integer;

track potential tracking index standard deviation and track potential tracking index average value are obtained through analyzing the track potential tracking index in the set, and are respectively compared with a preset standard deviation reference threshold value and a preset track potential tracking index reference threshold value, and the comparison analysis results are as follows:

if the average value of the track tracking hidden danger indexes is larger than or equal to the reference threshold value of the track tracking hidden danger indexes, generating a continuous abnormal operation signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending out a high hidden danger early warning prompt through the prompting module, positioning a related GPS tracker through the positioning module, and prompting related personnel that the hidden danger that the stolen vehicle is difficult to track and recover due to the fact that the GPS tracker cannot accurately provide historical track data is larger, and timely maintenance and management are needed;

if the average value of the track tracing hidden danger indexes is smaller than the reference threshold value of the track tracing hidden danger indexes and the standard deviation of the track tracing hidden danger indexes is larger than or equal to the reference threshold value of the standard deviation, generating an unstable operation signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending an unstable hidden danger early warning prompt through the prompting module, positioning a related GPS tracker through the positioning module, prompting related personnel that the operation stability of the GPS tracker is poor, and the hidden danger that the stolen vehicle is difficult to trace and recover due to the fact that historical track data cannot be accurately provided is larger, so that timely maintenance and management are needed;

if the average value of the track tracing hidden danger indexes is smaller than the reference threshold value of the track tracing hidden danger indexes and the standard deviation of the track tracing hidden danger indexes is smaller than the reference threshold value of the standard deviation, generating a normal operation signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending out early warning prompt without the prompting module, and positioning a related GPS tracker without the positioning module, wherein when the situation occurs, the situation that the GPS tracker is accidentally abnormal during operation possibly occurs;

according to the invention, through monitoring the operation state of the GPS tracker in real time, when the operation state of the GPS tracker has abnormal hidden danger, an early warning prompt is sent out, the operation of the related GPS tracker is positioned, related personnel are prompted to know the situation in time, the operation of the GPS tracker with the abnormal hidden danger is maintained and managed in advance, the GPS tracker is ensured to provide accurate historical track data to cope with possible theft situations, and the stolen vehicle is convenient to track and retrieve efficiently;

according to the invention, when the operation of the GPS tracker is perceived to be abnormal, the operation state of the GPS tracker is comprehensively analyzed, if the GPS tracker determines that the hidden danger of the operation abnormality exists, whether the operation of the GPS tracker is continuous or unstable can be determined through the comprehensive analysis of the operation state of the GPS tracker, so that the operation abnormality type of the GPS tracker can be conveniently determined, the GPS tracker can be conveniently and efficiently maintained and managed, and secondly, when the operation of the GPS tracker is abnormal accidentally, an early warning prompt is not sent out, frequent early warning caused by the accidental abnormality of the GPS tracker by a system is avoided, and the monitoring accuracy of the GPS tracker is ensured.

The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.

It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (8)

1. The electric bicycle anti-theft tracking system based on the Internet of things is characterized by comprising an information acquisition module, a server, a judging module, a comprehensive analysis module, a prompting module and a positioning module;

the information acquisition module acquires a plurality of data information including time sequence parameter information and geographic parameter information when a GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, processes the time sequence parameter information and the geographic parameter information when the GPS tracker operates after acquisition, and uploads the processed time sequence parameter information and geographic parameter information to the server;

time sequence parameter information of GPS tracker in electric bicycle anti-theft tracking system based on thing networking when operation includes clock drift deviation coefficient, after gathering, information acquisition module marks clock drift deviation coefficient as；

Geographic parameter information of the GPS tracker in the electric bicycle anti-theft tracking system based on the Internet of things during operation comprises a GPS signal processing duration abnormal concealment coefficient and a GPS signal intensity jitter coefficient, and after acquisition, the information acquisition module respectively calibrates the GPS signal processing duration abnormal concealment coefficient and the GPS signal intensity jitter coefficient intoAnd->；

The server comprehensively analyzes the processed time sequence parameter information and geographic parameter information when the GPS tracker operates to generate a track tracking hidden danger index, and transmits the track tracking hidden danger index to the judging module;

the judging module is used for comparing and judging the track tracking hidden danger index generated during the operation of the GPS tracker with a preset track tracking hidden danger index reference threshold value to generate a first hidden danger signal or a second hidden danger signal and transmitting the signals to the comprehensive analysis module;

the comprehensive analysis module is used for comprehensively analyzing a plurality of track tracking hidden danger indexes generated later through the server after receiving a first hidden danger signal generated when the GPS tracker is operated, generating different types of signals, transmitting the signals to the prompting module and the positioning module, sending early warning prompt through the prompting module and positioning the related GPS tracker through the positioning module.

2. The electric bicycle anti-theft tracking system based on the internet of things according to claim 1, wherein the logic for obtaining the clock drift deviation coefficient is as follows:

s101, acquiring an actual GPS time stamp and an internal actual time stamp of a GPS tracker at the same moment in T time when the GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and respectively calibrating the actual GPS time stamp and the internal actual time stamp of the GPS tracker asAnd->X represents the number of an actual GPS timestamp and an internal actual timestamp of the GPS tracker at the same moment in T time when the GPS tracker operates in the electric bicycle anti-theft tracking system based on the Internet of things, and x=1, 2, 3, 4, … … and m are positive integers;

s102, calculating a clock drift deviation coefficient, wherein the calculated expression is as follows:。

3. the electric bicycle anti-theft tracking system based on the internet of things according to claim 2, wherein logic for acquiring the abnormal concealment coefficient of the GPS signal processing time length is as follows:

s201, acquiring an optimal GPS signal processing duration range when a GPS tracker operates in an electric bicycle anti-theft tracking system based on the Internet of things, and calibrating the optimal GPS signal processing duration range as；

S202, acquiring a plurality of actual GPS tracker operation time generated in T timeGPS signal processing time length, and calibrating the actual GPS signal processing time length asY represents the number of a plurality of actual GPS signal processing durations generated in the T time when the GPS tracker operates, y=1, 2, 3, 4, … … and n, n is a positive integer, wherein the actual GPS signal processing duration is the sum of the GPS signal receiving duration, the signal processing duration and the data transmission duration;

s203, calculating a GPS signal processing time length abnormal state concealment coefficient, wherein the calculated expression is as follows:wherein->Representing a range of non-optimal GPS signal processing durations acquired during a time T while a GPS tracker is operatingActual GPS signal processing duration between +.>Representing the range of time length of GPS signal processing which is acquired in the time T when the GPS tracker is running and is not in the optimal range of time length of GPS signal processing>The number of actual GPS signal processing durations in between,，/>is a positive integer>。

4. The electric bicycle anti-theft tracking system based on the internet of things according to claim 3, wherein the logic for acquiring the GPS signal strength jitter coefficient is as follows:

s301, acquiring actual GPS signal intensity of a plurality of GPS signals received by a GPS tracker in a T time in an electric bicycle anti-theft tracking system based on the Internet of things, and calibrating the actual GPS signal intensity asK represents the number of the actual GPS signal intensity of a GPS tracker receiving a plurality of GPS signals in T time in the electric bicycle anti-theft tracking system based on the Internet of things, and k=1, 2, 3, 4, … … and p are positive integers;

s302, calculating GPS signal intensity standard deviation and GPS signal intensity average value through the GPS tracker receiving the actual GPS signal intensities of a plurality of GPS signals in T time, wherein the calculated expression is as follows:，wherein->Represents the standard deviation of GPS signal intensity, < >>Representing a GPS signal strength average;

s303, GPS signal intensity standard deviation of a plurality of GPS signals received in T time through a GPS trackerAnd GPS signal intensity average>Calculating a GPS signal strength variation coefficient, wherein the calculated expression is as follows: />Wherein->Representing the GPS signal strength variation coefficient;

s304, calculating a GPS signal intensity jitter coefficient, wherein the calculated expression is as follows:。

5. the electric bicycle anti-theft tracking system based on the internet of things according to claim 4, wherein the server obtains the processed clock drift deviation coefficient when the GPS tracker is operatedAbnormal concealment coefficient of GPS signal processing time length>GPS signal strength jitter coefficient ++>Afterwards, will->、/>And +.>Carrying out formulated analysis to generate a track tracing hidden danger index +.>The formula according to is: />Wherein->、/>、/>Clock drift deviation coefficients ∈ ->Abnormal concealment coefficient of GPS signal processing time length>GPS signal strength jitter coefficient->Is a preset proportionality coefficient of>1、/>2、/>3 are all greater than 0.

6. The electric bicycle anti-theft tracking system based on the internet of things according to claim 5, wherein the judging module compares a track tracking hidden danger index generated when the GPS tracker is operated with a preset track tracking hidden danger index reference threshold value, and the comparison judging result is as follows:

if the track tracking hidden danger index is greater than or equal to the track tracking hidden danger index reference threshold, generating a first hidden danger signal and transmitting the signal to the comprehensive analysis module;

and if the track tracing hidden danger index is smaller than the track tracing hidden danger index reference threshold, generating a second hidden danger signal and transmitting the signal to the comprehensive analysis module.

7. The electric bicycle anti-theft tracking system based on the Internet of things according to claim 6, wherein after the comprehensive analysis module receives a first hidden danger signal generated during the operation of the GPS tracker, an analysis set is established for a plurality of track tracking hidden danger indexes generated subsequently through a server, and the analysis set is calibrated as Z, thenV denotes the number of the trace hidden danger index in the analysis set, v=1, 2, 3, 4, … …, u being a positive integer.

8. The electric bicycle anti-theft tracking system based on the internet of things according to claim 7, wherein the standard deviation of the track potential hazard index and the average value of the track potential hazard index are calculated by analyzing the track potential hazard indexes in the collection, and the standard deviation of the track potential hazard index and the average value of the track potential hazard index are respectively compared with a preset standard deviation reference threshold value and a preset track potential hazard index reference threshold value, and the comparison analysis results are as follows:

if the average value of the track tracing hidden danger indexes is larger than or equal to the reference threshold value of the track tracing hidden danger indexes, generating a continuous abnormal operation signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending out a high hidden danger early warning prompt through the prompting module, and positioning a related GPS tracker through the positioning module;

if the track tracing hidden danger index average value is smaller than the track tracing hidden danger index reference threshold value and the track tracing hidden danger index standard deviation is larger than or equal to the standard deviation reference threshold value, generating an unstable running signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, sending an unstable hidden danger early warning prompt through the prompting module, and positioning a related GPS tracker through the positioning module;

if the average value of the track tracing hidden danger indexes is smaller than the reference threshold value of the track tracing hidden danger indexes and the standard deviation of the track tracing hidden danger indexes is smaller than the reference threshold value of the standard deviation, generating a normal running signal through the comprehensive analysis module, transmitting the signal to the prompting module and the positioning module, and not sending an early warning prompt through the prompting module and positioning the related GPS tracker through the positioning module.