CN117314281A - Cargo transportation safety situation awareness method, device and computer equipment - Google Patents
Cargo transportation safety situation awareness method, device and computer equipment Download PDFInfo
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Abstract
The invention discloses a cargo transportation security situation awareness method, a cargo transportation security situation awareness device and computer equipment. The method comprises the following steps: acquiring dynamic basic data acquired by the vehicle-mounted internet of things safety equipment in each cargo transportation process; performing quality inspection on the dynamic basic data; judging whether a safety event occurs in each cargo transportation process by utilizing dynamic basic data; in a statistical period, calculating the occurrence times of all safety events of each driver, and calculating the deduction value of each driver according to a pre-customized deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list. The invention fully utilizes the data collected by various vehicle-mounted safety devices to monitor whether safety events occur in the cargo transportation process, comprehensively monitors and reflects the driving behavior and the cargo condition in the transportation process, and can timely discover drivers with larger potential safety hazards, thereby realizing early warning in advance and reducing the occurrence rate of safety accidents.
Description
Technical Field
The present disclosure relates to the field of logistics technologies, and in particular, to a method and an apparatus for sensing a cargo transportation security situation, and a computer device.
Background
With the rapid development of economy and the increase of logistics demands, road transportation occupies an important position in a transportation system in China, and is one of the main transportation modes in China. However, trucks are used as main vehicles for road transportation, and due to the characteristics of large volume, heavy load, many dead zones and the like, traffic accidents are easy to occur, and accident consequences are often serious. Therefore, improving the safety of highway freight is an urgent task.
The safety factor of road freight is complex and multiple, and according to the data of the 'white paper for safety of Chinese road freight 2021', the driving behavior problem of drivers is the most important factor of road freight accidents in China, and the driving behavior problem accounts for 37 percent. Among them, fatigue driving and aggressive driving are the most common driving behavior problems of the driver. In addition, equipment factors (blind areas, etc.) are also an important aspect of causing accidents.
At present, the popularization and application of the active safety equipment provide basic data for the perception of the safety situation of the cargo transportation, and can provide comprehensive support for the improvement of the safety of the transportation. For example, A Driver Assistance System (ADAS) may collect data from radar, video, and lidar sensors to monitor conditions around the vehicle in real time. However, at present, data linkage is lacking among various active safety devices, so that driving behaviors and cargo conditions in the transportation process cannot be comprehensively recorded and reflected, and in-transit supervision of a driver is difficult.
Disclosure of Invention
Based on the above, the method, the device and the computer equipment for sensing the security situation of the cargo transportation are provided to solve the technical problems that the prior active security equipment lacks data linkage and the driving behavior and the cargo situation in the transportation process can not be comprehensively recorded and reflected.
In order to achieve the above object, the present application provides the following technical solutions:
in a first aspect, a method for sensing a security situation of cargo transportation includes:
s1, acquiring dynamic basic data acquired by vehicle-mounted internet of things safety equipment in each cargo transportation process;
s2, performing quality inspection on the dynamic basic data to ensure that the data normal rate reaches a preset normal rate threshold;
s3, judging whether a safety event occurs in each cargo transportation process by utilizing the dynamic basic data;
s4, in a statistical period, calculating the occurrence times of all safety events of each driver, and calculating the deduction value of each driver according to a predefined deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
Optionally, the vehicle-mounted internet of things safety device comprises a vehicle positioning device, a range radar, a cab monitoring device, a container monitoring device, a load sensor and an advanced driving assistance system; the dynamic basis data includes vehicle positioning data, driver driving behavior data, and cargo state data.
Optionally, the quality checking of the dynamic basic data includes checking for missing, duplicate, outlier conditions of the dynamic basic data.
Optionally, step S2 further includes:
if the data normal rate does not reach the preset normal rate threshold, a prompt instruction is sent to the monitoring terminal, and the prompt instruction is used for reminding a responsible person to check and maintain the corresponding vehicle-mounted internet of things safety equipment.
Optionally, the preset normal rate threshold is 80%.
Optionally, the safety event comprises a driving behavior violation event, a vehicle transition anomaly event, and a cargo anomaly event.
Further optionally, the driving behavior violation event includes fatigue driving, using a mobile phone, and smoking; the abnormal event of the vehicle state transition comprises deviation from a preset route, abnormal parking, rapid acceleration and deceleration, frequent lane change and traffic violation; the abnormal cargo events include cargo shed, illegal loading and unloading and overload.
Optionally, the determining the training list includes:
determining the drivers with the deduction scores ranked in the top 20% as key education objects, and carrying out offline safe driving training on the drivers;
determining drivers with the score ranking of the deduction being 20% -50% of the first as common educational objects, and performing online special training on the drivers;
and pushing a safe driving behavior analysis report to other drivers.
In a second aspect, a cargo transportation security posture awareness apparatus includes:
the data acquisition module is used for acquiring dynamic basic data acquired by the vehicle-mounted internet of things safety equipment in each cargo transportation process;
the data processing module is used for carrying out quality inspection on the dynamic basic data and ensuring that the data normal rate reaches a preset normal rate threshold;
the safety event judging module is used for judging whether a safety event occurs in each cargo transportation process or not by utilizing the dynamic basic data;
the analysis module is used for calculating the occurrence times of all safety events of each driver in a statistical period and calculating the deduction value of each driver according to a predefined deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
In a third aspect, a computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the first aspects when the computer program is executed.
The invention has at least the following beneficial effects:
the embodiment of the invention provides a cargo transportation safety situation sensing method, which comprises the steps of acquiring dynamic basic data acquired by vehicle-mounted internet of things safety equipment in each cargo transportation process, performing quality inspection on the dynamic basic data, and judging whether a safety event occurs in each cargo transportation process by utilizing the dynamic basic data; in a statistical period, calculating the occurrence times of all safety events of each driver, and calculating the deduction value of each driver according to a pre-customized deduction rule; generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list; the cargo transportation safety situation awareness method fully utilizes the data acquired by various vehicle-mounted safety devices to monitor whether safety events occur in the cargo transportation process, plays the value of the data, comprehensively monitors and reflects the driving behavior and the cargo situation in the transportation process, and greatly improves the supervision dimension of the driving behavior and the cargo state of a driver in the transportation process; meanwhile, a driver list needing training can be determined according to the deduction value of the driver in one period, so that the driver with larger potential safety hazard can be found timely, targeted preventive measures can be conveniently taken, pre-warning can be achieved, the occurrence rate of safety accidents is reduced, and finally driving safety and cargo safety are improved.
Drawings
Fig. 1 is a schematic flow chart of a cargo transportation security situation awareness method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a module architecture of a cargo transportation security situation awareness apparatus according to an embodiment of the present invention;
fig. 3 is an internal structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for sensing a security situation of cargo transportation, comprising the steps of:
s1, acquiring dynamic basic data acquired by vehicle-mounted internet of things safety equipment in each cargo transportation process.
That is, dynamic basic data collection is performed by means of vehicle-mounted internet of things safety devices including, but not limited to, vehicle positioning devices, range radars, cab monitoring devices, cargo box monitoring devices, load sensors, and Advanced Driving Assistance Systems (ADAS).
Through multiple on-vehicle thing networking safety device, the dynamic basic data that can acquire includes vehicle positioning data, driver's driving behavior data, goods state data. And the data of each device are gathered to a data receiving and transmitting terminal. Because the data volume is large, in order to lighten the data storage and transmission pressure, the data receiving and transmitting terminal can set the uploading frequency of each data, and the requirements of security situation awareness and early warning are met by the minimum data volume. In addition, static data such as road network data, vehicles, driver information, etc. are stored in the database.
S2, performing quality inspection on the dynamic basic data, and ensuring that the normal rate of the data reaches a preset normal rate threshold.
Further, the quality inspection of the dynamic basic data comprises the detection of the missing, repeated and abnormal value conditions of the dynamic basic data. The preset normal rate threshold is 80%.
In addition, if the data normal rate does not reach the preset normal rate threshold, a prompt instruction is sent to the monitoring terminal, and the prompt instruction is used for reminding a responsible person to check and maintain the corresponding vehicle-mounted internet of things safety equipment.
That is, when data uploaded by the data transceiver terminal is received, quality inspection needs to be performed on the data to ensure reliability of state sensing. Checking the missing, repeated and abnormal values of the data, wherein the data normal rate is more than 80%. And for unqualified data, reminding relevant responsible persons to check maintenance data acquisition equipment in time.
S3, judging whether a safety event occurs in each cargo transportation process by utilizing dynamic basic data.
Further, the safety events are mainly classified into three types, namely driving behavior violation events, including fatigue driving, mobile phone use, smoking and the like; secondly, abnormal vehicle state events, including deviation from a preset route, abnormal parking, rapid acceleration and deceleration, frequent lane changing, traffic violations and the like; and thirdly, abnormal events of the goods, including goods throwing, illegal loading and unloading, overload and the like.
S4, in a statistical period, calculating the occurrence times of all safety events of each driver, and calculating the deduction value of each driver according to a predefined deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
Corresponding scores are assigned to each security event based on the severity of the outcome that may be caused by the respective security event. The criteria and scores for trucking security events are shown in table 1.
TABLE 1 judgment basis and score for freight transportation safety event
Analyzing and early warning, calculating the occurrence times of the safety events of each driver in a statistical period, and deducting corresponding scores. The calculation formula is as follows:
wherein S is D The deduction score of the driver is N which is the index number of the safety event, A i The score for a single indicator is shown in table 1.
Further, determining the training list includes:
determining the drivers with the deduction scores ranked in the top 20% as key education objects, and carrying out offline safe driving training on the drivers;
determining drivers with the score ranking of the deduction being 20% -50% of the first as common educational objects, and performing online special training on the drivers;
and pushing a safe driving behavior analysis report to other drivers.
That is, the drivers who withhold the top 20% of the rank are important educational objects for which offline safe driving training is required; the drivers who withhold the top 20% -50% of the ranking need to participate in special training online; and other drivers push safe driving behavior reports for the drivers according to the deduction condition to remind the safety problems to be noticed.
In the cargo transportation safety situation awareness method, the data acquired by various vehicle-mounted safety devices are fully utilized, the value of the data is exerted, and the supervision dimension of the driving behaviors and the cargo states of a driver in the transportation process can be greatly improved. The flexibility is high, and the index can be selected according to the installation condition of the equipment. The data receiving and transmitting terminal can set the uploading frequency, so that the data transmission and storage cost is greatly reduced. The early warning can be realized in advance, the targeted preventive measures can be conveniently adopted, and the occurrence rate of safety accidents is reduced.
According to the cargo transportation safety situation awareness method provided by the embodiment of the invention, data acquisition is performed through the active safety equipment, so that cargo transportation safety situation awareness is further analyzed, safety events are judged, a safety early warning model is built, safety problems are found timely, targeted preventive measures are conveniently taken, the occurrence rate of traffic and transportation accidents is reduced, and finally driving safety and cargo safety are improved.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in fig. 1 may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily sequential, but may be performed in rotation or alternatively with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 2, there is provided a cargo transportation security situation awareness apparatus, comprising the following program modules:
the data acquisition module 201 is used for acquiring dynamic basic data acquired by the vehicle-mounted internet of things safety equipment in each cargo transportation process;
the data processing module 202 is configured to perform quality inspection on the dynamic basic data, and ensure that the normal rate of the data reaches a preset normal rate threshold;
the security event judging module 203 is configured to judge whether a security event occurs during each cargo transportation process by using dynamic basic data;
the analysis module 204 is configured to calculate, in a statistical period, the occurrence times of all the safety events of each driver, and calculate the score of each driver according to a predefined score rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
The cargo transportation safety situation sensing device provided by the embodiment of the invention has the advantages of simple structure, low cost and high automation degree, and can improve the driving safety and the cargo transportation safety.
For a specific limitation of the device for sensing the security situation of cargo transportation, reference may be made to the limitation of the method for sensing the security situation of cargo transportation hereinabove, and the description thereof will not be repeated here. The modules in the cargo transportation safety situation awareness apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a cargo transportation security situation awareness method provided by the above embodiments.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, including a memory and a processor, the memory having stored therein a computer program, involving all or part of the flow of the methods of the embodiments described above.
In one embodiment, a computer readable storage medium having a computer program stored thereon is provided, involving all or part of the flow of the methods of the embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static RandomAccess Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for sensing the security situation of cargo transportation, comprising:
s1, acquiring dynamic basic data acquired by vehicle-mounted internet of things safety equipment in each cargo transportation process;
s2, performing quality inspection on the dynamic basic data to ensure that the data normal rate reaches a preset normal rate threshold;
s3, judging whether a safety event occurs in each cargo transportation process by utilizing the dynamic basic data;
s4, in a statistical period, calculating the occurrence times of all safety events of each driver, and calculating the deduction value of each driver according to a predefined deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
2. The method of claim 1, wherein the vehicle-mounted internet of things security devices include a vehicle locating device, a range radar, a cab monitoring device, a cargo box monitoring device, a load sensor, and an advanced driving assistance system; the dynamic basis data includes vehicle positioning data, driver driving behavior data, and cargo state data.
3. The method of claim 1, wherein said quality checking of said dynamic base data includes checking for missing, duplicate, outlier conditions of said dynamic base data.
4. The method of claim 1, wherein step S2 further comprises:
if the data normal rate does not reach the preset normal rate threshold, a prompt instruction is sent to the monitoring terminal, and the prompt instruction is used for reminding a responsible person to check and maintain the corresponding vehicle-mounted internet of things safety equipment.
5. The method of claim 1, wherein the predetermined normal rate threshold is 80%.
6. The method of claim 1, wherein the security event comprises a driving behavior violation event, a vehicle transition anomaly event, a cargo anomaly event.
7. The method of claim 6, wherein the driving behavior violation event includes fatigue driving, use of a cell phone, and smoking; the abnormal event of the vehicle state transition comprises deviation from a preset route, abnormal parking, rapid acceleration and deceleration, frequent lane change and traffic violation; the abnormal cargo events include cargo shed, illegal loading and unloading and overload.
8. The method of claim 1, wherein determining a training list comprises:
determining the drivers with the deduction scores ranked in the top 20% as key education objects, and carrying out offline safe driving training on the drivers;
determining drivers with the score ranking of the deduction being 20% -50% of the first as common educational objects, and performing online special training on the drivers;
and pushing a safe driving behavior analysis report to other drivers.
9. A cargo transportation security posture sensing device, comprising:
the data acquisition module is used for acquiring dynamic basic data acquired by the vehicle-mounted internet of things safety equipment in each cargo transportation process;
the data processing module is used for carrying out quality inspection on the dynamic basic data and ensuring that the data normal rate reaches a preset normal rate threshold;
the safety event judging module is used for judging whether a safety event occurs in each cargo transportation process or not by utilizing the dynamic basic data;
the analysis module is used for calculating the occurrence times of all safety events of each driver in a statistical period and calculating the deduction value of each driver according to a predefined deduction rule; and generating a driver safe driving behavior analysis report according to the deduction value of each driver, and determining a training list.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.
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