CN115049992A

CN115049992A - Logistics monitoring system and method based on big data

Info

Publication number: CN115049992A
Application number: CN202210823325.4A
Authority: CN
Inventors: 丁壮
Original assignee: Individual
Current assignee: Individual
Priority date: 2022-07-14
Filing date: 2022-07-14
Publication date: 2022-09-13

Abstract

The invention discloses a logistics monitoring system and method based on big data, belonging to the technical field of logistics monitoring; the method comprises the steps that various data of standard transport capacity and actual operation conditions of different trucks during logistics transportation are counted and combined to obtain a target evaluation coefficient of the trucks, and a target evaluation grade corresponding to the logistics transportation of the trucks can be obtained according to the target evaluation coefficient, so that corresponding rotation duration can be implemented, and dynamic monitoring and evaluation of the rotation state of a main driving seat and a secondary driving seat are achieved based on the target evaluation coefficient; the invention is used for solving the technical problems that the existing scheme can not carry out targeted monitoring on the states of trucks with different transportation conditions and drivers before driving, can not carry out monitoring and evaluation on the alternate situation of driving by a plurality of drivers in turn, and can not carry out monitoring and automatic alarm on the safety of an oil tank and goods, so that the overall effect of logistics monitoring is poor.

Description

Logistics monitoring system and method based on big data

Technical Field

The invention relates to the technical field of logistics monitoring, in particular to a logistics monitoring system and method based on big data.

Background

The logistics operation monitoring comprises customer service, operation quality and operation cost, related measurable related indexes can be designed and selected for statistics and analysis according to customer requirements and enterprise operation needs in the logistics operation monitoring, and a comprehensive logistics information network is used for real-time monitoring, so that a basis is provided for decision and operation.

When the existing logistics monitoring scheme is implemented, most of the existing logistics monitoring schemes are used for shooting in real time through a camera to obtain the external driving state of a driver, real-time positioning is carried out through a positioning device to realize logistics monitoring, the situation that whether fatigue driving exists or not is judged through psychological data monitoring the eye movement and the head movement of the driver in a matched mode, but the situations that the states of a truck and the driver cannot be monitored in different degrees according to the operation situation and the cargo situation of logistics in a self-adaptive mode, the alternate situation that a plurality of drivers drive in turn cannot be monitored and evaluated, and finally the safety of an oil tank and cargos cannot be monitored and automatically alarmed, so that the overall logistics monitoring effect is poor.

Disclosure of Invention

The invention aims to provide a logistics monitoring system and method based on big data, which are used for solving the technical problems that the prior scheme can not be used for carrying out targeted monitoring on the states of trucks with different transportation conditions and drivers before driving, can not be used for monitoring and evaluating the alternate driving condition of a plurality of drivers in turn, and can not be used for monitoring and automatically alarming the safety of an oil tank and goods, so that the overall effect of logistics monitoring is poor.

The purpose of the invention can be realized by the following technical scheme:

the logistics monitoring system based on big data comprises a carriage module, a vehicle body module, an early warning module, a server, a database and a monitoring platform;

the carriage module is used for shooting the real-time states of a primary driver and a secondary driver in a carriage in the running process of the truck to obtain a driving shooting set;

carrying out feature extraction and processing analysis on the driving camera set:

before monitoring, analyzing and matching, obtaining values of standard load weight, standard load volume, real load weight, real load object volume and type weight coefficient defined by the truck, and calculating in parallel to obtain a target evaluation coefficient of truck driving, wherein the target evaluation coefficient is a value for integrally evaluating the driving alternation condition of the truck;

matching the target evaluation coefficient with a preset target evaluation range to obtain a corresponding target evaluation grade, and respectively performing characteristic matching on a third characteristic set and a fourth characteristic set corresponding to a main driving position and a copilot with a first characteristic set and a second characteristic set according to a rotation duration corresponding to the target evaluation grade to obtain first driving information;

monitoring and evaluating the driving states of drivers in different driving positions to obtain second driving information;

the first driving information and the second driving information form a driving analysis set;

the vehicle body module is used for respectively carrying out overlook camera shooting on monitoring areas preset behind from two sides of the vehicle head to obtain a vehicle body camera shooting set containing first camera shooting data and second camera shooting data and sending the vehicle body camera shooting set to the server;

carrying out person identification and analysis on the car body camera shooting set to obtain a car body analysis set containing a first tracking signal and a second tracking signal, and uploading the car body analysis set to a database;

the early warning module is used for carrying out early warning prompt according to the driving analysis set and the vehicle body analysis set.

The system further comprises a truck module and a positioning module, wherein the truck module is used for counting and numbering different types of trucks, a plurality of trucks are numbered as i, i belongs to {1, 2, 3.., n }, and n is a positive integer;

acquiring the corresponding standard load weight and standard load volume of the truck and respectively defining as BZi and BTi; acquiring the weight and the volume of the real load of the truck and respectively defining the weight and the volume as SZi and STi;

the defined standard load weight, standard load volume, real load weight and real load volume form a truck statistical set and are uploaded to a database; the positioning module is used for positioning the truck in real time.

Further, when feature extraction and processing analysis are carried out on the driving camera set, image sets corresponding to a main driving position and a subsidiary driving position in the driving camera set are respectively set as a main camera set and a subsidiary camera set;

acquiring the face characteristics of a driver based on a recognition algorithm, wherein the face characteristics comprise the face characteristics of the driver; respectively setting the face features corresponding to the main camera shooting set and the auxiliary camera shooting set as a first feature set and a second feature set when the truck starts to drive;

and respectively setting the face features corresponding to the main camera shooting set and the auxiliary camera shooting set as a third feature set and a fourth feature set in the driving process of the truck.

Further, respectively carrying out feature matching on a third feature set and a fourth feature set corresponding to the main driving position and the auxiliary driving position with the first feature set and the second feature set, including:

if the similarity of the matching results is greater than m%, and the value range of m is [99, 100], judging that the driving state of the truck meets the driving requirement, and generating a first driving matching signal;

otherwise, judging that the driving state of the truck does not meet the driving requirement, generating a second driving matching signal, and carrying out alternate warning prompt according to the second driving matching signal until the first driving matching signal is generated;

the first driving matching signal and the second driving matching signal form first driving information.

Further, the distance between the destination of the goods and the sending place is obtained and defined as HJi; acquiring the type of goods transported by a truck and defining the type weight coefficient corresponding to the type of goods as LQi; the expression of the objective evaluation coefficient calculation formula is as follows:

in the formula, a1, a2, a3 and a4 are different proportionality coefficients and 0 < a4 < a3 < 1 < a2 < a1 < 5.

Further, the driving states of drivers in different driving positions are monitored and evaluated, and the method comprises the following steps:

acquiring physiological information of a driver in the previous k hours corresponding to a main driving position and a copilot; wherein the physiological information comprises sleep data, eating data and alcohol data;

acquiring and respectively marking the light sleep time and the deep sleep time in the sleep data;

acquiring and marking the time difference of the interval between the meal ending time and the driving starting time in the meal data; acquiring and marking the alcohol concentration in the alcohol data;

respectively acquiring the light sleep time, the deep sleep time and the interval time difference, and simultaneously acquiring the driving evaluation coefficients of the driver by using the numerical values of the light sleep time, the deep sleep time and the interval time difference;

and matching the driving evaluation coefficient with a preset driving evaluation range to obtain second driving information comprising the first driving evaluation signal, the second driving evaluation signal and the third driving evaluation signal.

Further, the construction step of the monitoring area comprises the following steps:

respectively setting the middle points of two sides of the truck as monitoring original points, constructing a three-dimensional coordinate system according to the preset coordinate axis direction and coordinate point distance, and setting the oil tank as a monitoring sample point;

and constructing a rectangular monitoring area on a three-dimensional coordinate system according to the preset monitoring length, the monitoring width and the monitoring height.

Further, the person recognition and analysis of the car body camera set comprises:

acquiring first camera data and second camera data in a vehicle body camera set; when drivers are in the cab and the truck does not run, respectively carrying out character recognition and analysis on the camera images in the first camera data and the second camera data;

when a person is identified in the camera image and enters the value monitoring area, a tracking instruction is generated, the person is set as a suspicious person, and the moving time of the suspicious person in the monitoring area is tracked according to the tracking instruction.

Further, when the duration of the suspicious person in the monitored area is not greater than the tracking duration threshold, generating a first tracking signal; when the time length of the suspicious person in the monitoring area is greater than the tracking time length threshold value, generating a second tracking signal, simultaneously carrying out voice prompt on the inside and the outside of the cab according to the second tracking signal, and sending the parking position of the truck to the monitoring platform in real time; the first tracking signal and the second tracking signal form a vehicle body analysis set.

In order to solve the problem, the invention also provides a logistics monitoring method based on big data, which comprises the following steps:

counting and numbering different types of trucks to obtain a truck counting set;

positioning real-time positions of different transported trucks to obtain truck positioning sets;

shooting real-time states of a primary driver and a secondary driver in a carriage in the running process of a truck to obtain a driving shooting set containing first shooting data and second shooting data;

performing feature extraction and processing analysis on the driving camera set to obtain a driving analysis set containing first driving information and second driving information;

respectively carrying out overlook camera shooting on monitoring areas preset at the rear part from two sides of a vehicle head to obtain a vehicle body camera shooting set containing first camera shooting data and second camera shooting data;

carrying out figure recognition and analysis on the car body camera set to obtain a car body analysis set;

and carrying out early warning prompt according to the driving analysis set and the vehicle body analysis set.

Compared with the prior art, the invention has the following beneficial effects:

according to the method, various data of standard transport capacity and actual operation conditions during logistics transportation of different trucks are counted and combined to obtain the target evaluation coefficient of the trucks, the target evaluation grade corresponding to the logistics transportation of the trucks can be obtained according to the target evaluation coefficient, so that corresponding alternation duration can be implemented, and dynamic monitoring and evaluation of the alternation state of the main driving position and the auxiliary driving position can be realized based on the target evaluation coefficient; meanwhile, the driving state of the driver is integrally evaluated by combining the body data of all aspects of the driver before driving, so that the accuracy and the efficiency of monitoring the driving state of the driver can be effectively improved.

According to the invention, the human face characteristics of the drivers at the main driving position and the auxiliary driving position at different time points are matched with the sample characteristics, and the early warning prompt is carried out according to the similarity of the matching results, so that the positions of the personnel at the main driving position and the auxiliary driving position can be alternately monitored, whether the original personnel at the main driving position and the auxiliary driving position are replaced or not can be monitored, and the efficient monitoring and identification analysis can be carried out on the work of the drivers corresponding to the main driving position and the auxiliary driving position.

The monitoring area is arranged to monitor and prompt the external safety state of the truck when the truck is parked, so that a driver and a monitoring platform can find the external abnormality of the truck in time, the behavior of abnormal persons close to the truck can be preliminarily evaluated based on the monitoring area, the behavior purpose of the abnormal persons is tracked according to the time length of the abnormal persons staying in the monitoring area, different prompts are generated, and the safety of monitoring the truck oil tank and goods and the diversity of truck logistics monitoring are improved.

Drawings

The invention will be further described with reference to the accompanying drawings.

Fig. 1 is a block diagram of a logistics monitoring system based on big data according to the present invention.

Fig. 2 is a schematic flow chart of the logistics monitoring method based on big data according to the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example one

As shown in fig. 1, the present invention is a logistics monitoring system based on big data, which comprises a truck module, a positioning module, a carriage module, a body module, an early warning module, a server, a database and a monitoring platform;

the truck module is used for counting and numbering different types of trucks to obtain a truck counting set and transmitting the truck counting set to the database; the method comprises the following steps:

numbering a plurality of trucks as i, wherein i belongs to {1, 2, 3,. and n } and n is a positive integer and represents the total number;

acquiring the corresponding standard load weight and standard load volume of the truck and respectively defining as BZi and BTi;

acquiring the weight and the volume of the real load of the truck and respectively defining the weight and the volume as SZi and STi;

the standard load weight, the standard load volume, the real load weight and the real load volume which are numbered and defined form a truck statistical set.

In the embodiment of the invention, the transport capacities corresponding to different types of trucks are different, and the different trucks are numbered, and meanwhile, various data of the standard transport capacity and the actual operation condition during the logistics transportation of the different trucks are counted and defined, so that data support can be provided for the subsequent logistics transportation analysis of the trucks.

The positioning module is used for positioning the real-time positions of different transported trucks to obtain a truck positioning set and transmitting the truck positioning set to the database;

the positioning of the real-time position can be realized based on the existing truck positioning means; in addition, another purpose of positioning in the embodiment of the present invention is to acquire a corresponding road type based on the real-time positioning coordinates, so that a corresponding monitoring scheme can be performed.

The carriage module is used for shooting the real-time states of a primary driver and a secondary driver in a carriage in the running process of the truck to obtain a driving shooting set and sending the driving shooting set to the server;

it should be noted that, because logistics transportation needs, such as long-distance transportation, or transportation of goods is important, two drivers often need to drive in turn to improve the efficiency of logistics transportation, but in the existing truck transportation monitoring scheme, monitoring analysis and early warning prompt for the states of alternative driving of different drivers are not performed, so that a blind area exists in monitoring of the driving states of multiple drivers; the embodiment of the invention realizes timely and efficient monitoring and prompting of alternate driving conditions of different drivers by analyzing and evaluating the driving characteristics of the main driving position, thereby achieving the purpose of improving the driving safety.

Carrying out feature extraction and processing analysis on the driving camera set to obtain a driving analysis set and uploading the driving analysis set to a database; the method comprises the following steps:

respectively setting image sets corresponding to a main driving position and a subsidiary driving position in the driving camera shooting set as a main camera shooting set and a subsidiary camera shooting set;

acquiring the face characteristics of a driver based on a recognition algorithm, wherein the face characteristics comprise a plurality of face characteristic points; the recognition algorithm is the existing face recognition algorithm, and the specific steps are not described herein;

respectively setting the face features corresponding to the main camera shooting set and the auxiliary camera shooting set as a first feature set and a second feature set when the truck starts to drive;

respectively setting the face features corresponding to the main camera shooting set and the auxiliary camera shooting set as a third feature set and a fourth feature set in the driving process of the truck;

before monitoring, analyzing and matching, obtaining a target evaluation coefficient of truck driving, comprising the following steps:

obtaining a distance between the destination of the goods and the delivery site and defining as HJi;

acquiring the cargo types transported by the truck, setting different cargo types to correspond to different type weight coefficients, matching the acquired cargo types with all the cargo types to acquire corresponding type weight coefficients, and defining the type weight coefficients as LQi;

extracting and connecting the defined values of the standard carrying weight, the standard carrying volume, the real carrying weight, the real carrying object volume and the type weight coefficient, and calculating and obtaining a target evaluation coefficient MPX of the truck driving through a formula; the expression of this formula is:

in the formula, a1, a2, a3 and a4 are different proportionality coefficients, 0 is more than a4 and more than a3 and more than 1 is more than a2 and more than a1 and less than 5, a1 can be 4.265, a2 can be 2.463, a3 can be 0.684, and a4 can be 0.361;

it should be noted that the objective evaluation coefficient is a numerical value for overall evaluation of the driving rotation condition of the truck; dynamically monitoring and evaluating the alternate states of the main driving position and the auxiliary driving position based on the target evaluation coefficient; the target evaluation grade corresponding to the freight logistics transportation can be obtained according to the target evaluation coefficient so that the corresponding alternation duration can be implemented, a plurality of different target evaluation grades are preset in different target evaluation ranges, and the different target evaluation grades correspond to different alternation durations;

in the embodiment of the invention, the target evaluation grades can be three and respectively correspond to a primary target evaluation grade, a middle target evaluation grade and a high target evaluation grade, and the values of the corresponding grade ranges are sequentially increased, for example, the grade ranges corresponding to the primary target evaluation grade, the middle target evaluation grade and the high target evaluation grade are [ D1, D2], (D2, D3] and (D3, D4 ]; D1 < D2 < D3 < D4; the corresponding alternation duration is sequentially decreased, and the smaller alternation duration indicates that the alternation times are more in the same time;

matching the target evaluation coefficient with a preset target evaluation range to obtain a corresponding target evaluation grade, and respectively performing feature matching on a third feature set and a fourth feature set corresponding to the main driving position and the auxiliary driving position with the first feature set and the second feature set according to the rotation duration corresponding to the target evaluation grade;

if the similarity of the matching results is greater than m%, the value range of m is [99, 100], the value of m can be 99, the driving state of the truck is judged to meet the driving requirement, and a first driving matching signal is generated; the purpose that the similarity of the matching results is greater than the standard similarity is that the positions of the personnel in the main and auxiliary driving positions can be alternately monitored, whether the original personnel in the main and auxiliary driving positions are replaced or not can be monitored, and efficient monitoring, identification and analysis of the work of the drivers corresponding to the main and auxiliary driving positions can be realized;

the first driving matching signal and the second driving matching signal form first driving information;

it is to be noted that the differential rotation alarm monitoring and prompting can be performed based on the road type corresponding to the real-time position coordinate of the truck, for example, the driving rotation condition monitoring and evaluation is performed when the road type corresponding to the real-time position coordinate is a national road or a provincial road;

and when the road type corresponding to the real-time position coordinates is the expressway, monitoring and evaluating the driving alternation condition of the service area parked on the expressway is carried out, because the practical operation can not be carried out when the monitoring and evaluating scheme is implemented, and because the alternation can not be carried out on the expressway.

Monitoring and evaluating the driving states of drivers in different driving positions to obtain second driving information; the method comprises the following steps:

acquiring physiological information of a driver in the previous k hours corresponding to a main driving position and a copilot position, wherein k is a positive integer; wherein the physiological information comprises sleep data, eating data and alcohol data;

acquiring a light sleep time length and a deep sleep time length in the sleep data and respectively marking the light sleep time length and the deep sleep time length as QSi and SSi; monitoring statistics can be performed based on existing intelligent monitoring equipment, such as an intelligent bracelet;

acquiring the time difference of the interval between the meal ending time and the driving starting time in the meal data and marking as SCi; the units corresponding to the light sleep time, the deep sleep time and the interval time difference are all hours;

the alcohol concentration in the alcohol data is obtained and labeled JNi; the vehicle can be detected and obtained by an alcohol detector before driving;

respectively acquiring a light sleep time QSi, a deep sleep time SSi and an interval time difference SCi, combining the values of the light sleep time QSi, the deep sleep time SSi and the interval time difference SCi, and calculating by a formula to acquire a driving evaluation coefficient JPX of a driver; the expression of this formula is:

in the formula, b1, b2, b3 and b4 are different proportionality coefficients and have value ranges of (0, 5), b1 can take a value of 0.365, b2 can take a value of 0.735, b3 can take a value of 1.432, b4 can take a value of 4.361, QSi0 is a preset standard light sleep time, SSi0 is a preset standard deep sleep time, and SCi0 is a preset standard interval time difference;

the driving evaluation coefficient is a numerical value for evaluating the driving state of the driver before driving as a whole; the embodiment of the invention can be used for integrally evaluating the driving state of the driver by combining body data of all aspects of the driver before driving, so that the accuracy and the efficiency of monitoring the driving state of the driver can be effectively improved, and the driver avoids fatigue driving by monitoring the driver alternately in the driving process, thereby realizing the all-round monitoring of logistics.

Matching the driving evaluation coefficient with a preset driving evaluation range;

if the driving evaluation coefficient is larger than the maximum value of the driving evaluation range, judging that the driving state of the corresponding driver is excellent and generating a first driving evaluation signal;

if the driving evaluation coefficient is not larger than the maximum value of the driving evaluation range and not smaller than the minimum value of the driving evaluation range, judging that the driving state of the corresponding driver is normal and generating a second driving evaluation signal;

if the driving evaluation coefficient is smaller than the minimum value of the driving evaluation range, judging that the driving state of the corresponding driver is abnormal and generating a third driving evaluation signal;

the first driving estimation signal, the second driving estimation signal and the third driving estimation signal form second driving information;

the first driving information and the second driving information constitute a driving analysis set.

In the embodiment of the invention, the driving state of each driver before the truck drives is integrally evaluated based on the driving evaluation coefficient, the driver in the abnormal driving state can be dynamically regulated and controlled in time, dangerous driving can be fundamentally eliminated, and compared with the prior scheme that only monitoring analysis and early warning can be carried out on the driving process, the embodiment of the invention can realize safer monitoring evaluation.

The vehicle body module is used for respectively carrying out overlook camera shooting on monitoring areas preset behind from two sides of the vehicle head to obtain a vehicle body camera shooting set containing first camera shooting data and second camera shooting data and sending the vehicle body camera shooting set to the server; the purpose of performing the top-view image pickup is to analyze and judge the position of the person relative to the truck, for example, when the image pickup is performed horizontally, the position of the person relative to the truck cannot be judged efficiently and quickly;

the construction method of the monitoring area comprises the following steps:

constructing a rectangular monitoring area on a three-dimensional coordinate system according to a preset monitoring length, a preset monitoring width and a preset monitoring height; specific numerical values of the preset monitoring length, the preset monitoring width and the preset monitoring height need to be set according to the actual length and the actual height of the truck;

in the embodiment of the invention, the purpose of setting the monitoring area is to monitor and prompt the external safety state of the truck when the truck parks, so that a driver and a monitoring platform can find the external abnormality of the truck in time, for example, the oil in an oil tank is stolen when the truck parks at night, or goods are stolen when the truck parks in the day; the behavior of the abnormal persons close to the truck can be preliminarily evaluated based on the monitoring area, and the specific purpose of the behavior of the abnormal persons needs to be further tracked.

Carrying out figure recognition and analysis on the car body camera shooting set to obtain a car body analysis set and uploading the car body analysis set to a database; the method comprises the following steps:

acquiring first camera data and second camera data in a vehicle body camera set;

when drivers are in the cab and the truck does not run, respectively carrying out character recognition and analysis on the camera images in the first camera data and the second camera data; the drivers are limited to recognize and analyze characters in the cab to eliminate interference on analysis caused by the drivers in a monitoring area, and the main application scene is that suspicious characters are automatically recognized and alarmed when the drivers are in rest;

when a person is identified in the camera image and enters a value monitoring area, generating a tracking instruction, setting the person as a suspicious person, and tracking the moving time of the suspicious person in the monitoring area according to the tracking instruction;

when the duration of the suspicious person in the monitoring area is not greater than the tracking duration threshold, judging that the behavior of the suspicious person is not suspicious and generating a first tracking signal;

when the duration of the suspicious person in the monitoring area is greater than the tracking duration threshold, judging that the behavior of the suspicious person is suspicious, generating a second tracking signal, simultaneously carrying out voice prompt on the inside and the outside of the cab according to the second tracking signal, and sending the parking position of the truck to the monitoring platform in real time;

the first tracking signal and the second tracking signal form a vehicle body analysis set.

In the embodiment of the invention, the behavior of suspicious persons entering the monitoring area is evaluated, and different prompts are generated, so that the safety of monitoring the truck oil tank and goods and the diversity of truck logistics monitoring are improved.

The early warning module is used for carrying out early warning and prompting on the driving states of the primary driver and the secondary driver in the carriage according to the second driving matching signal and the third driving estimation signal in the driving analysis set, and carrying out early warning and prompting on the states around the vehicle body according to the second tracking signal in the vehicle body analysis set.

The above formulas are obtained by collecting a large amount of data and performing software simulation, and the proportion coefficients in the formulas are set by those skilled in the art according to actual conditions.

Example two

As shown in fig. 2, the logistics monitoring method based on big data includes:

In the embodiments provided in the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed.

Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It is obvious to a person skilled in the art that the invention is not restricted to details of the above-described exemplary embodiments, but that it can be implemented in other specific forms without departing from the essential characteristics of the invention.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. The logistics monitoring system based on big data is characterized by comprising a carriage module, a vehicle body module, an early warning module, a server, a database and a monitoring platform;

and (3) performing feature extraction and processing analysis on the driving camera set:

2. The logistics monitoring system based on big data as claimed in claim 1, further comprising a truck module and a positioning module, wherein the truck module is used for counting and numbering different types of trucks, a plurality of trucks are numbered as i, i e {1, 2, 3,.. multidot.,. n }, and n is a positive integer;

the defined standard cargo weight, standard cargo volume, actual cargo weight and actual cargo volume form a truck statistical set and are uploaded to a database; the positioning module is used for positioning the truck in real time.

3. The logistics monitoring system based on big data as claimed in claim 1, wherein when the driving camera set is subjected to feature extraction and processing analysis, image sets corresponding to a main driving seat and a subsidiary driving seat in the driving camera set are respectively set as a main camera set and a subsidiary camera set;

4. The logistics monitoring system based on big data according to claim 1, wherein the feature matching of the third feature set and the fourth feature set corresponding to the main driving position and the auxiliary driving position with the first feature set and the second feature set respectively comprises:

5. The big data based logistics monitoring system of claim 2, wherein the distance between the goods destination and the delivery site is obtained and defined as HJi; acquiring the type of goods transported by a truck and defining the type weight coefficient corresponding to the type of goods as LQi; the expression of the objective evaluation coefficient calculation formula is as follows:

6. The logistics monitoring system based on big data is characterized in that the monitoring and evaluation of the driving states of drivers in different driving positions comprises the following steps:

acquiring physiological information of a driver in the previous k hours corresponding to a main driving position and a secondary driving position; wherein the physiological information comprises sleep data, eating data and alcohol data;

7. The logistics monitoring system based on big data as claimed in claim 1, wherein the step of constructing the monitoring area comprises:

8. The logistics monitoring system based on big data as claimed in claim 1, wherein the human recognition and analysis of the car body camera comprises:

9. The logistics monitoring system based on big data as claimed in claim 1, wherein when the duration that the suspicious person stays in the monitored area is not greater than the tracking duration threshold, a first tracking signal is generated; when the time length of the suspicious person in the monitoring area is greater than the tracking time length threshold value, generating a second tracking signal, simultaneously carrying out voice prompt on the inside and the outside of the cab according to the second tracking signal, and sending the parking position of the truck to the monitoring platform in real time; the first tracking signal and the second tracking signal form a vehicle body analysis set.

10. The logistics monitoring method based on big data is applied to the logistics monitoring system based on big data according to any one of claims 1 to 9, and is characterized by comprising the following steps:

shooting real-time states of primary drivers and secondary drivers in a truck compartment in the running process of the truck to obtain a driving shooting set containing first shooting data and second shooting data;