CN116206263B - Logistics vehicle monitoring method and system - Google Patents
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Abstract
The invention discloses a logistics vehicle monitoring method and a system, which belong to the technical field of logistics monitoring, and the method comprises the following steps: s1: collecting vibration data of a lock tongue on a vehicle door; s2: judging whether the electronic lock state of the logistics vehicle is normal, if so, entering a step S3, otherwise, entering a step S4; s3: preprocessing the real-time in-vehicle image to obtain the latest in-vehicle image, and uploading the latest in-vehicle image to a server to complete logistics vehicle monitoring; s4: and preprocessing the real-time in-vehicle image, the real-time out-of-vehicle image and the real-time position information to respectively obtain the latest in-vehicle image, the latest out-of-vehicle image and the latest running information, and uploading the latest in-vehicle image, the latest out-of-vehicle image and the latest running information to a server to complete logistics vehicle monitoring. According to the logistics vehicle monitoring method, the vibration data of the lock tongue is collected, and the state of the electronic lock is determined through analysis of the vibration data of the lock tongue, so that the safety condition of a logistics vehicle carriage can be known in advance, and the monitoring efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of logistics monitoring, and particularly relates to a logistics vehicle monitoring method and system.
Background
The logistics vehicle monitoring is to feed back the position and image data of the vehicle to the display end of a vehicle manager by utilizing the combination of a terminal data acquisition technology, a mobile communication technology and an internet technology, and the vehicle manager checks the condition of the vehicle.
In the logistics transportation industry, particularly the long-distance logistics transportation industry crossing multiple provinces and cities, due to the fact that the goods are transported to multiple areas, and the goods are stopped and loaded and unloaded for multiple times in the middle, the goods in the carriage are required to be monitored in real time, the phenomenon that the goods are lost or stolen in the middle of the transportation of the goods can be timely found, and the safety of the goods in the carriage is guaranteed.
The existing logistics vehicle information monitoring is generally carried out for monitoring the vehicle interior image, and the vehicle running information and the vehicle exterior image cannot be acquired and processed. Therefore, conventional logistics vehicles have far from meeting the requirements of economic development on logistics transportation.
Disclosure of Invention
The invention provides a logistics vehicle monitoring method and system for solving the problems.
The technical scheme of the invention is as follows: the logistics vehicle monitoring method comprises the following steps:
s1: collecting bolt vibration data on a door of a logistics vehicle;
s2: judging whether the electronic lock state of the logistics vehicle is normal or not according to the vibration data of the lock tongue, if so, entering a step S3, otherwise, entering a step S4;
s3: collecting real-time in-vehicle images of logistics vehicles, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to a server, and completing logistics vehicle monitoring;
s4: and acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of the logistics vehicle, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to a server to complete logistics vehicle monitoring.
Further, step S2 comprises the sub-steps of:
s21: in a two-dimensional coordinate system, a bolt vibration curve is constructed by taking sampling time of bolt vibration data as an abscissa and taking bolt vibration data of each sampling time as an ordinate;
s22: in the spring bolt vibration curve, calculating the difference value of spring bolt vibration data corresponding to every two adjacent inflection points to obtain a vibration difference value group;
s23: clustering the vibration difference value group by using a k-means clustering algorithm to generate contour coefficients corresponding to each type;
s23: calculating a vibration state coefficient according to the contour coefficient corresponding to each type;
s24: and judging whether the vibration state coefficient is larger than or equal to a set state threshold value, if so, enabling the electronic lock to be in a normal state, and entering a step S3, otherwise, enabling the electronic lock to be in an abnormal state, and entering a step S4.
Further, in step S24, the calculation formula of the vibration state coefficient c is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein x is max Represents the maximum value of the vibration data of the lock tongue, x min Representing the minimum value of the vibration data of the lock tongue, y n Representing the difference value, z, of the bolt vibration data corresponding to the nth inflection point and the next adjacent inflection point m The profile coefficient corresponding to the m-th class is represented, and epsilon represents the minimum value.
Further, in step S3 and step S4, the method for preprocessing the real-time in-vehicle image is the same, specifically: and carrying out smoothing processing on the real-time in-vehicle image, and correcting the smoothed real-time in-vehicle image by utilizing an image correction coefficient.
Further, the calculation formula of the image correction coefficient d is:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The brightness average value of the real-time in-vehicle image after the smoothing is represented, M represents the length of the real-time in-vehicle image after the smoothing, N represents the width of the real-time in-vehicle image after the smoothing, and g (k) represents the number of pixels with the gray value of k in the real-time in-vehicle image after the smoothing.
Further, in step S4, preprocessing the real-time outside image includes the following sub-steps:
a41: performing edge processing on the real-time vehicle exterior image by utilizing a Sobel edge detection operator to obtain a first vehicle exterior image;
a42: performing feature processing on the first vehicle exterior image to obtain a second vehicle exterior image;
a43: and carrying out enhancement processing on the second outside image to obtain the latest outside image.
Further, in step a42, the specific method for performing feature processing on the first out-of-vehicle image is as follows: extracting a first out-of-vehicle imageCalculating the linear equation of each edge direction from the central pixel point to the edge direction of each other pixel point, determining the slope of each linear equation, generating a slope characteristic matrix, constructing a characteristic processing model according to the slope characteristic matrix, and carrying out characteristic processing on the first vehicle exterior image by utilizing the characteristic processing model to obtain a second vehicle exterior image; the expression of the feature processing model G is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein H represents a slope feature matrix, H T Represents the transpose of the slope feature matrix, λ represents the regularization coefficient, I represents the identity matrix, ++>Indicating the correction amount.
Further, in step S4, preprocessing the real-time location information includes the following sub-steps:
b41: generating a current transportation track line of the logistics vehicle according to the GPS coordinate points at all moments in the real-time position information;
and B42: constructing a track deviation correction model;
b43: and inputting the current transportation track line and the standard transportation track line of the logistics vehicle into a track deviation correction model, judging whether the current transportation track line and the standard transportation track line are overlapped, if so, taking the current transportation track line as the latest running information and uploading the latest running information to a server, otherwise, generating an error transportation track line, taking the current transportation track line and the error transportation track line as the latest running information and uploading the latest running information to the server.
Further, the expression of the trajectory deviation correction model F is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein dist (α, P) represents the distance between the GPS abscissa f (α) in the standard transportation track line and the GPS abscissa P in the current transportation track line, dist (f (β), q) represents the distance between the GPS ordinate f (β) in the standard transportation track line and the GPS ordinate q in the current transportation track line, and P represents the current transportation track lineThe set of GPS abscissas in the transportation track line, Q represents the set of GPS abscissas in the current transportation track line.
The beneficial effects of the invention are as follows:
(1) According to the logistics vehicle monitoring method, the vibration data of the lock tongue is collected, and the state of the electronic lock is determined through analysis of the vibration data of the lock tongue, so that the safety condition of a logistics vehicle carriage can be known in advance, different data can be collected in subsequent steps conveniently, algorithm flows are saved, and monitoring efficiency is improved;
(2) The logistics vehicle monitoring method is used for collecting different types of data according to different electronic lock states; when the electronic lock is normal, only the in-vehicle images are collected and processed and then transmitted to the server side, so that operation and maintenance personnel can monitor the in-vehicle images conveniently; when the electronic lock is abnormal, not only the in-vehicle image, but also the out-vehicle image and the position information are acquired, the out-vehicle image is convenient for operation and maintenance personnel to observe the out-vehicle environment of the logistics vehicle, judge whether abnormal conditions occur, and the position information is convenient for the operation and maintenance personnel to observe whether route deviation occurs, so that potential safety hazards possibly existing in the logistics vehicle are timely perceived;
(3) The logistics vehicle monitoring method is used for carrying out smoothing and correction on the dim in-vehicle images, so that the interference caused by insufficient in-vehicle light sources and shielding of cargoes is reduced as much as possible, and the quality of images received by operation and maintenance personnel is ensured; edge processing, feature processing and enhancement processing are carried out on the external image with complex external environment, so that interference caused by external environment factors is reduced as much as possible, and definition of the uploaded image is ensured;
(4) The logistics vehicle monitoring method monitors real-time position information of logistics vehicles, realizes effective monitoring of a transportation route, judges whether deviation occurs between a transportation track line and a standard transportation track line at the moment, and then feeds back the deviation to operation and maintenance personnel for timely intervention, and ensures safe driving and logistics transportation efficiency of drivers.
Based on the method, the invention also provides a logistics vehicle monitoring system which comprises a vibration data acquisition unit, an electronic lock state generation unit, a first logistics vehicle monitoring unit and a second logistics vehicle monitoring unit;
the vibration data acquisition unit is used for acquiring the vibration data of the lock tongue on the door of the logistics vehicle;
the electronic lock state generating unit is used for determining the electronic lock state of the logistics vehicle according to the vibration data of the lock tongue;
the first logistics vehicle monitoring unit is used for acquiring real-time in-vehicle images of logistics vehicles when the electronic lock state is normal, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to the server, and completing logistics vehicle monitoring;
the second physical distribution vehicle monitoring unit is used for acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of the physical distribution vehicle when the electronic lock state is abnormal, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to the server to complete physical distribution vehicle monitoring.
The beneficial effects of the invention are as follows: the logistics vehicle monitoring system determines the state of the electronic lock by collecting the vibration data of the lock tongue, and collects different image information and position information according to the state of the electronic lock to complete logistics vehicle monitoring. The whole monitoring system analyzes the internal and external images and the position information of the logistics vehicle in real time, improves the reliability of data, is convenient for operation and maintenance personnel to check the logistics information, ensures the real-time controllability of the cargo state in the logistics process when the electronic lock is abnormal, and further improves the safety of the cargo in the logistics transportation process.
Drawings
FIG. 1 is a flow chart of a method of logistics vehicular monitoring;
fig. 2 is a block diagram of a logistics vehicle monitoring system.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a logistics vehicle monitoring method, which comprises the following steps:
s1: collecting bolt vibration data on a door of a logistics vehicle;
s2: judging whether the electronic lock state of the logistics vehicle is normal or not according to the vibration data of the lock tongue, if so, entering a step S3, otherwise, entering a step S4;
s3: collecting real-time in-vehicle images of logistics vehicles, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to a server, and completing logistics vehicle monitoring;
s4: and acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of the logistics vehicle, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to a server to complete logistics vehicle monitoring.
In an embodiment of the present invention, step S2 comprises the sub-steps of:
s21: in a two-dimensional coordinate system, a bolt vibration curve is constructed by taking sampling time of bolt vibration data as an abscissa and taking bolt vibration data of each sampling time as an ordinate;
s22: in the spring bolt vibration curve, calculating the difference value of spring bolt vibration data corresponding to every two adjacent inflection points to obtain a vibration difference value group;
s23: clustering the vibration difference value group by using a k-means clustering algorithm to generate contour coefficients corresponding to each type;
s23: calculating a vibration state coefficient according to the contour coefficient corresponding to each type;
s24: and judging whether the vibration state coefficient is larger than or equal to a set state threshold value, if so, enabling the electronic lock to be in a normal state, and entering a step S3, otherwise, enabling the electronic lock to be in an abnormal state, and entering a step S4.
k-means clustering algorithm: an iterative solving cluster analysis algorithm, a data point set and the required number of clusters k are given, k is designated by a user, and the k-means algorithm repeatedly divides data into k clusters according to a certain distance function.
In the embodiment of the present invention, in step S24, the calculation formula of the vibration state coefficient c is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein x is max Represents the maximum value of the vibration data of the lock tongue, x min Representing the minimum value of the vibration data of the lock tongue, y n Representing the difference value, z, of the bolt vibration data corresponding to the nth inflection point and the next adjacent inflection point m The profile coefficient corresponding to the m-th class is represented, and epsilon represents the minimum value.
In the embodiment of the present invention, in step S3 and step S4, the method for preprocessing the real-time in-vehicle image is the same, specifically: and carrying out smoothing processing on the real-time in-vehicle image, and correcting the smoothed real-time in-vehicle image by utilizing an image correction coefficient.
In the embodiment of the invention, the calculation formula of the image correction coefficient d is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The brightness average value of the real-time in-vehicle image after the smoothing is represented, M represents the length of the real-time in-vehicle image after the smoothing, N represents the width of the real-time in-vehicle image after the smoothing, and g (k) represents the number of pixels with the gray value of k in the real-time in-vehicle image after the smoothing.
When the electronic lock is in a normal state, the logistics vehicle runs normally, no emergency exists, and only the in-vehicle image is required to be collected and transmitted to the server after being preprocessed, so that operation and maintenance personnel can monitor the in-vehicle condition in real time. The commodity circulation vehicle can appear the interference condition that the goods sheltered from owing to the goods is numerous in the car, and can't guarantee in the car that the light source is sufficient, for example some goods need keep away from the light, and consequently the image in the car probably appears that imaging quality is poor, signal to noise ratio is low and the colour information loss is serious possibility, leads to transmitting to the image of server side unclear, so need carry out smoothing and correction to the real-time image in the car, reduce the interference.
The method corrects the real-time in-vehicle image by using a square root method, and the calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In the formula, I (x, y) represents the real-time in-vehicle image after the smoothing process, and I' (x, y) represents the real-time in-vehicle image after the correction process.
In the embodiment of the present invention, in step S4, preprocessing the real-time outside image includes the following sub-steps:
a41: performing edge processing on the real-time vehicle exterior image by utilizing a Sobel edge detection operator to obtain a first vehicle exterior image;
a42: performing feature processing on the first vehicle exterior image to obtain a second vehicle exterior image;
a43: and carrying out enhancement processing on the second outside image to obtain the latest outside image.
When the state of the electronic lock is abnormal, the condition that the goods in the logistics vehicle are possibly lost is indicated, so that the real-time image acquisition is required to be carried out on the external condition of the logistics vehicle. The real-time image acquisition outside the vehicle is realized by cameras arranged around the carriage of the logistics vehicle. During the whole transportation process, the logistics vehicle may be in a driving state or a parking state, so that the interference of the surrounding environment on the acquired image needs to be considered. The image acquisition when the state of the electronic lock is abnormal ensures the definition and accuracy of the image, so that the real-time vehicle exterior image is carried out
In the logistics vehicle transportation process, extreme weather (such as storm and the like) can be possibly encountered, so that edge processing is required to be carried out through a Sobel edge detection operator, and the influence of the extreme weather on the image definition is reduced. And further processing the edge pixel points extracted by edge detection by constructing a feature processing model, so that the influence of weather factors on the imaging quality of the image is weakened.
Sobel edge detection operator: an important processing method in the field of computer vision is to weight the gray value in the four fields of up, down, left and right of each pixel in an image to reach an extreme value at an edge so as to detect the edge.
In an embodiment of the present invention, in the present invention,in step a42, the specific method for performing feature processing on the first off-vehicle image includes: extracting edge directions from a central pixel point to other pixel points in a first vehicle exterior image, calculating linear equations of all edge directions, determining slopes of all linear equations, generating slope feature matrixes, constructing a feature processing model according to the slope feature matrixes, and carrying out feature processing on the first vehicle exterior image by utilizing the feature processing model to obtain a second vehicle exterior image; the expression of the feature processing model G is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein H represents a slope feature matrix, H T Represents the transpose of the slope feature matrix, λ represents the regularization coefficient, I represents the identity matrix, ++>Indicating the correction amount. In the coordinate position of the real-time vehicle exterior image, a linear equation of each edge direction is calculated.
In the embodiment of the present invention, in step S4, preprocessing the real-time location information includes the following sub-steps:
b41: generating a current transportation track line of the logistics vehicle according to the GPS coordinate points at all moments in the real-time position information; and sequentially connecting the GPS coordinate points according to the sequence of each moment to generate the current transportation track line of the logistics vehicle.
And B42: constructing a track deviation correction model;
b43: and inputting the current transportation track line and the standard transportation track line of the logistics vehicle into a track deviation correction model, judging whether the current transportation track line and the standard transportation track line are overlapped, if so, taking the current transportation track line as the latest running information and uploading the latest running information to a server, otherwise, generating an error transportation track line, taking the current transportation track line and the error transportation track line as the latest running information and uploading the latest running information to the server.
The standard transportation track line can be a route navigated by a mobile terminal (mobile phone) or an optimal transportation route established by transportation personnel.
In the embodiment of the present invention, the expression of the track deviation correction model F is:
the method comprises the steps of carrying out a first treatment on the surface of the Where dist (α, P) represents a distance between the Global Position System (GPS) abscissa f (α) in the standard transport track line and the Global Position System (GPS) abscissa P in the current transport track line, dist (f (β, Q) represents a distance between the Global Position System (GPS) ordinate f (β) in the standard transport track line and the Global Position System (GPS) ordinate Q in the current transport track line, P represents a Global Position System (GPS) abscissa set in the current transport track line, and Q represents a Global Position System (GPS) ordinate set in the current transport track line.
Based on the method, the invention also provides a logistics vehicle monitoring system, as shown in fig. 2, which comprises a vibration data acquisition unit, an electronic lock state generation unit, a first logistics vehicle monitoring unit and a second logistics vehicle monitoring unit;
the vibration data acquisition unit is used for acquiring the vibration data of the lock tongue on the door of the logistics vehicle;
the electronic lock state generating unit is used for determining the electronic lock state of the logistics vehicle according to the vibration data of the lock tongue;
the first logistics vehicle monitoring unit is used for acquiring real-time in-vehicle images of logistics vehicles when the electronic lock state is normal, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to the server, and completing logistics vehicle monitoring;
the second physical distribution vehicle monitoring unit is used for acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of the physical distribution vehicle when the electronic lock state is abnormal, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to the server to complete physical distribution vehicle monitoring.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (9)
1. The logistics vehicle monitoring method is characterized by comprising the following steps of:
s1: collecting bolt vibration data on a door of a logistics vehicle;
s2: judging whether the electronic lock state of the logistics vehicle is normal or not according to the vibration data of the lock tongue, if so, entering a step S3, otherwise, entering a step S4;
s3: collecting real-time in-vehicle images of logistics vehicles, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to a server, and completing logistics vehicle monitoring;
s4: acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of a logistics vehicle, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to a server to complete logistics vehicle monitoring;
said step S2 comprises the sub-steps of:
s21: in a two-dimensional coordinate system, a bolt vibration curve is constructed by taking sampling time of bolt vibration data as an abscissa and taking bolt vibration data of each sampling time as an ordinate;
s22: in the spring bolt vibration curve, calculating the difference value of spring bolt vibration data corresponding to every two adjacent inflection points to obtain a vibration difference value group;
s23: clustering the vibration difference value group by using a k-means clustering algorithm to generate contour coefficients corresponding to each type;
s23: calculating a vibration state coefficient according to the contour coefficient corresponding to each type;
s24: and judging whether the vibration state coefficient is larger than or equal to a set state threshold value, if so, enabling the electronic lock to be in a normal state, and entering a step S3, otherwise, enabling the electronic lock to be in an abnormal state, and entering a step S4.
2. The method for monitoring a logistics vehicle according to claim 1, wherein in the step S24, the calculation formula of the vibration state coefficient c is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein x is max Represents the maximum value of the vibration data of the lock tongue, x min Representing the minimum value of the vibration data of the lock tongue, y n Representing the difference value, z, of the bolt vibration data corresponding to the nth inflection point and the next adjacent inflection point m The profile coefficient corresponding to the m-th class is represented, and epsilon represents the minimum value.
3. The logistic vehicle monitoring method according to claim 1, wherein in the step S3 and the step S4, the method for preprocessing the real-time in-vehicle image is the same, specifically: and carrying out smoothing processing on the real-time in-vehicle image, and correcting the smoothed real-time in-vehicle image by utilizing an image correction coefficient.
4. The logistic vehicle monitoring method according to claim 3, wherein the calculation formula of the image correction coefficient d is:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The brightness average value of the real-time in-vehicle image after the smoothing is represented, M represents the length of the real-time in-vehicle image after the smoothing, N represents the width of the real-time in-vehicle image after the smoothing, and g (k) represents the number of pixels with the gray value of k in the real-time in-vehicle image after the smoothing.
5. The logistical vehicle monitoring method according to claim 1, wherein in the step S4, the preprocessing of the real-time outside image includes the sub-steps of:
a41: performing edge processing on the real-time vehicle exterior image by utilizing a Sobel edge detection operator to obtain a first vehicle exterior image;
a42: performing feature processing on the first vehicle exterior image to obtain a second vehicle exterior image;
a43: and carrying out enhancement processing on the second outside image to obtain the latest outside image.
6. The method for monitoring a logistics vehicle of claim 5, wherein in the step a42, the specific method for performing feature processing on the first out-of-vehicle image is as follows: extracting edge directions from a central pixel point to other pixel points in a first vehicle exterior image, calculating linear equations of all edge directions, determining slopes of all linear equations, generating slope feature matrixes, constructing a feature processing model according to the slope feature matrixes, and carrying out feature processing on the first vehicle exterior image by utilizing the feature processing model to obtain a second vehicle exterior image; the expression of the feature processing model G is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein H represents a slope feature matrix, H T Represents the transpose of the slope feature matrix, λ represents the regularization coefficient, I represents the identity matrix, ++>Indicating the correction amount.
7. The method for monitoring a logistics vehicle of claim 1, wherein in the step S4, the preprocessing of the real-time position information comprises the following sub-steps:
b41: generating a current transportation track line of the logistics vehicle according to the GPS coordinate points at all moments in the real-time position information;
and B42: constructing a track deviation correction model;
b43: and inputting the current transportation track line and the standard transportation track line of the logistics vehicle into a track deviation correction model, judging whether the current transportation track line and the standard transportation track line are overlapped, if so, taking the current transportation track line as the latest running information and uploading the latest running information to a server, otherwise, generating an error transportation track line, taking the current transportation track line and the error transportation track line as the latest running information and uploading the latest running information to the server.
8. The logistic vehicle monitoring method according to claim 7, wherein the expression of the trajectory deviation correction model F is:
the method comprises the steps of carrying out a first treatment on the surface of the Where dist (α, P) represents a distance between the Global Position System (GPS) abscissa f (α) in the standard transport track line and the Global Position System (GPS) abscissa P in the current transport track line, dist (f (β, Q) represents a distance between the Global Position System (GPS) ordinate f (β) in the standard transport track line and the Global Position System (GPS) ordinate Q in the current transport track line, P represents a Global Position System (GPS) abscissa set in the current transport track line, and Q represents a Global Position System (GPS) ordinate set in the current transport track line.
9. The logistics vehicle monitoring system is characterized by comprising a vibration data acquisition unit, an electronic lock state generation unit, a first logistics vehicle monitoring unit and a second logistics vehicle monitoring unit;
the vibration data acquisition unit is used for acquiring the vibration data of the lock tongue on the door of the logistics vehicle;
the electronic lock state generating unit is used for determining the electronic lock state of the logistics vehicle according to the vibration data of the lock tongue;
the first logistics vehicle monitoring unit is used for acquiring real-time in-vehicle images of logistics vehicles when the electronic lock is in a normal state, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to the server, and completing logistics vehicle monitoring;
the second physical distribution vehicle monitoring unit is used for acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of the physical distribution vehicle when the electronic lock state is abnormal, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to the server to complete physical distribution vehicle monitoring;
the logistics vehicle monitoring system is realized based on a logistics vehicle monitoring method, and the method comprises the following steps:
s1: collecting bolt vibration data on a door of a logistics vehicle;
s2: judging whether the electronic lock state of the logistics vehicle is normal or not according to the vibration data of the lock tongue, if so, entering a step S3, otherwise, entering a step S4;
s3: collecting real-time in-vehicle images of logistics vehicles, preprocessing the real-time in-vehicle images to obtain latest in-vehicle images, uploading the latest in-vehicle images to a server, and completing logistics vehicle monitoring;
s4: acquiring real-time in-vehicle images, real-time out-of-vehicle images and real-time position information of a logistics vehicle, preprocessing the real-time in-vehicle images, the real-time out-of-vehicle images and the real-time position information to respectively obtain latest in-vehicle images, latest out-of-vehicle images and latest running information, and uploading the latest in-vehicle images, the latest out-of-vehicle images and the latest running information to a server to complete logistics vehicle monitoring;
said step S2 comprises the sub-steps of:
s21: in a two-dimensional coordinate system, a bolt vibration curve is constructed by taking sampling time of bolt vibration data as an abscissa and taking bolt vibration data of each sampling time as an ordinate;
s22: in the spring bolt vibration curve, calculating the difference value of spring bolt vibration data corresponding to every two adjacent inflection points to obtain a vibration difference value group;
s23: clustering the vibration difference value group by using a k-means clustering algorithm to generate contour coefficients corresponding to each type;
s23: calculating a vibration state coefficient according to the contour coefficient corresponding to each type;
s24: and judging whether the vibration state coefficient is larger than or equal to a set state threshold value, if so, enabling the electronic lock to be in a normal state, and entering a step S3, otherwise, enabling the electronic lock to be in an abnormal state, and entering a step S4.
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