CN113611104A - Risk identification method and device for freight vehicle, storage medium and terminal - Google Patents
Risk identification method and device for freight vehicle, storage medium and terminal Download PDFInfo
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- CN113611104A CN113611104A CN202110765045.8A CN202110765045A CN113611104A CN 113611104 A CN113611104 A CN 113611104A CN 202110765045 A CN202110765045 A CN 202110765045A CN 113611104 A CN113611104 A CN 113611104A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses a risk identification method of a freight vehicle, which comprises the following steps: determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance; acquiring respective historical operating data of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period; performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle; calculating the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, and generating the respective reference value of the plurality of risk factors of each freight vehicle; and determining the high-risk vehicles in the group of freight vehicles to be identified according to the reference values of the risk factors of each freight vehicle. Therefore, the intelligent high-risk vehicle monitoring and early warning system provides all-around real-time monitoring and early warning of the vehicle during driving through intelligent identification of the high-risk vehicle, and can effectively guarantee the safety of a freight driver and the transportation safety of the vehicle.
Description
Technical Field
The invention relates to the technical field of driving safety, in particular to a risk identification method and device for a freight vehicle, a storage medium and a terminal.
Background
With the development of logistics transportation, the number of vehicles in transportation enterprises is increased, the departure frequency is increased, the running time is prolonged, the transportation range is enlarged, and the safety problem of road transportation is severe. Through intelligent recognition high risk vehicle, provide the vehicle and travel in transit omnidirectional real time monitoring early warning, can effectively ensure freight transportation driver's safety and the transportation safety of vehicle.
In the prior art, various acquisition devices are required to be installed to acquire real-time environment image data around a current vehicle, then the acquired real-time environment image data is input into a dangerous vehicle identification model, and finally a high-risk vehicle in the acquired real-time environment image data is identified and obtained. Because a large amount of training data need to be collected for labeling when the model is trained in the prior art, machine learning is carried out through the labeled data, a large amount of manpower and material resources need to be consumed in data labeling, and meanwhile, the recognition accuracy of the trained model is low due to the fact that the deviation of a loss function possibly exists, so that the safety of a freight driver and the transportation safety of a vehicle cannot be guaranteed.
Disclosure of Invention
The embodiment of the application provides a risk identification method and device for a freight vehicle, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a risk identification method for a freight vehicle, where the method includes:
determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
acquiring respective historical operating data of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period;
performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
calculating the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, and generating the respective reference value of the plurality of risk factors of each freight vehicle;
and determining the high-risk vehicles in the group of freight vehicles to be identified according to the reference values of the risk factors of each freight vehicle.
Optionally, before determining a group of freight vehicles to be identified from the plurality of groups of freight vehicles divided in advance, the method further includes:
acquiring a plurality of freight vehicles which are currently operated;
acquiring the provincial names of the affiliations of each freight vehicle in the plurality of freight vehicles, and dividing the freight vehicles with the same provincial names into one group to obtain a plurality of groups of freight vehicles;
the plurality of groups of freight vehicles are determined as the plurality of groups of freight vehicles divided in advance.
Optionally, determining the high-risk vehicle in the group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle includes:
judging whether the reference value of each of the multiple risk factors of each freight vehicle is smaller than the corresponding risk factor or not to obtain multiple judgment results of each freight vehicle;
obtaining a judgment result with a value of 1 from a plurality of judgment results of each freight vehicle;
summing up the judgment results with the numerical value of 1 to generate respective judgment values of a plurality of vehicles;
sorting the respective determination values of the plurality of vehicles in a descending order to generate a plurality of sorted determination values;
and determining the high-risk vehicle in the group of freight vehicles to be identified according to the arranged plurality of determination values.
Optionally, the determining a high-risk vehicle present in the group of freight vehicles to be identified based on the ranked plurality of determination values includes:
intercepting a preset percentage of judgment values from the initial positions of the plurality of arranged judgment values serving as initial points to generate a plurality of intercepted judgment values;
identifying freight vehicles corresponding to the plurality of intercepted judgment values from a group of freight vehicles to be identified;
and determining the freight vehicles corresponding to the identified multiple judgment values as high-risk vehicles.
Optionally, the method further comprises:
subscribing the position of the high-risk vehicle and generating position subscription information;
pushing the position subscription information to a message queue, and informing a vehicle-mounted terminal of a high-risk vehicle of reporting the position;
receiving the longitude and latitude of the high-risk vehicle reported by the vehicle-mounted terminal;
calculating in real time according to the longitude and latitude of the high-risk vehicle to generate the running speed and running duration of the high-risk vehicle;
carrying out risk early warning according to the running speed and the running duration of the high-risk vehicle;
alternatively, the first and second electrodes may be,
and carrying out visual analysis according to the running speed and the running duration of the high-risk vehicle.
Optionally, the risk early warning is performed according to the running speed and the running duration of the high-risk vehicle, and the risk early warning method includes:
when the running speed and the running duration of the high-risk vehicle are greater than preset values, generating first early warning information and second early warning information;
sending the first early warning information to a vehicle-mounted terminal of a high-risk vehicle for warning and reminding;
and the number of the first and second groups,
and pushing the second early warning information to a high-risk vehicle pool of the customer service system.
Optionally, the step of performing big data offline calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle includes:
determining historical operating data for a first one of the plurality of cargo vehicles from historical operating data for each of the plurality of cargo vehicles;
loading a risk factor calculation rule table;
identifying the required parameter values of each risk factor calculation rule in the risk factor calculation rule table one by one from historical operating data of the first freight vehicle;
calculating the risk factors one by one based on the required parameter values of each risk factor calculation rule to generate the risk factor of the first freight vehicle;
and processing according to the steps according to the historical operation data of the freight vehicles to generate a plurality of risk factors of each freight vehicle.
In a second aspect, an embodiment of the present application provides a risk identification device for a freight vehicle, where the device includes:
the freight vehicle determining module is used for determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
the historical operation data acquisition module is used for acquiring the historical operation data of each of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period;
the risk factor calculation module is used for performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
the benchmark value calculation module is used for calculating the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle and generating the benchmark value of each risk factor of each freight vehicle;
and the high-risk vehicle identification module is used for judging high-risk vehicles in a group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the risk identification device of the freight vehicle firstly determines a group of freight vehicles to be identified from a plurality of groups of freight vehicles divided in advance, then obtains respective historical operation data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period, then performs big data off-line calculation according to the respective historical operation data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle, secondly calculates the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, generates a respective reference value of the plurality of risk factors of each freight vehicle, and finally determines a high-risk vehicle in the group of freight vehicles to be identified according to the respective reference value of the plurality of risk factors of each freight vehicle. According to the method and the device, the risk factors are calculated through big data in an off-line mode, and the high-risk vehicles are comprehensively judged by calculating the upper quartile of each risk factor to determine the reference value, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, and the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic flow chart diagram illustrating a risk identification method for a freight vehicle according to an embodiment of the present application;
FIG. 2 is a system block diagram of a risk identification system for a freight vehicle according to an embodiment of the present disclosure;
fig. 3 is a schematic device diagram of a risk identification device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The application provides a risk identification method, a risk identification device, a storage medium and a risk identification terminal of a freight vehicle, which are used for solving the problems in the related technical problems. In the technical scheme provided by the application, the risk factors are calculated through big data offline, and the reference value is determined through calculating the upper quartile of each risk factor to comprehensively judge the existing high-risk vehicles, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed, and the detailed description is carried out by adopting an exemplary embodiment.
The risk identification method for a freight vehicle according to the embodiment of the present application will be described in detail below with reference to fig. 1-2. The method may be implemented by means of a computer program, which is executable on a risk identification device for a freight vehicle based on the von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application. The risk identification device for the freight vehicle in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a schematic flow chart of a risk identification method for a freight vehicle is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
the plurality of groups of freight vehicles divided in advance are freight vehicles of a plurality of provinces divided according to different vehicle attribution provinces.
In the embodiment of the application, when the division is performed, firstly, a plurality of freight vehicles which are currently running are obtained, then, the provincial affiliation name of each freight vehicle in the plurality of freight vehicles is obtained, the freight vehicles with the same provincial affiliation name are divided into one group, a plurality of groups of freight vehicles are obtained, and finally, the plurality of groups of freight vehicles are determined to be the plurality of groups of freight vehicles which are divided in advance.
Specifically, in the dividing, each freight vehicle in the national freight vehicles is acquired first, then the province of the residence of each freight vehicle is acquired, and finally the vehicles of the same province are classified, so that the freight vehicles of each province are obtained, and the freight vehicles of each province are regarded as a group.
Further, a plurality of groups of freight vehicles are obtained after each provincial freight vehicle is considered as one group, and a group of freight vehicles to be identified is determined from the plurality of groups of freight vehicles.
In one possible implementation, after obtaining the freight vehicles of each of the plurality of provinces, a certain province needing risk identification is determined, and then all the freight vehicles of the certain province needing risk identification are determined as a group of freight vehicles to be identified.
S102, acquiring respective historical operation data of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period;
the preset period is a historical time period which is set by a user and needs to be analyzed, the historical time period can be one week in the past or one month in the past, and the specific time period can be set by the user according to an actual application scene.
In the embodiment of the application, a preset period is determined, then a big data center is connected, a query rule of historical operation data of each freight vehicle is created according to the preset period and the identification of each freight vehicle, and finally the query historical operation data is traversed according to the query rule of the historical operation data of each freight vehicle, and the historical operation data of each freight vehicle is obtained.
Preferably, because the number of freight vehicles in a group is very large, a conventional one-by-one traversal query mode is very time-consuming, and the data loading speed is greatly reduced, so that the identification efficiency of high-risk vehicles is greatly reduced.
In a possible implementation manner, after determining a group of freight vehicles to be identified according to step S101, historical operation data of each freight vehicle in the group of freight vehicles to be identified in a preset time period needs to be obtained from a big data center.
S103, performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
in the embodiment of the application, historical operating data of a first freight vehicle is determined from respective historical operating data of a plurality of freight vehicles, then a risk factor calculation rule table is loaded, required parameter values of each risk factor calculation rule in the risk factor calculation rule table are identified one by one from the historical operating data of the first freight vehicle, then the risk factors are calculated one by one based on the required parameter values of each risk factor calculation rule to generate the risk factors of the first freight vehicle, and finally the risk factors of each freight vehicle are generated by processing according to the steps according to the respective historical operating data of the plurality of freight vehicles.
For example, as shown in table 1, table 1 is a risk factor calculation rule table provided in the present application, and each risk factor in the table corresponds to a specific calculation rule.
TABLE 1
When the average daily mileage of the first cargo vehicle needs to be calculated, it is known that the corresponding rule is "total monthly mileage/monthly operating days", and the "total monthly mileage/monthly operating days" of the first cargo vehicle can be obtained from the historical operating data of the first cargo vehicle, so that the average daily mileage of the first cargo vehicle can be obtained after calculation.
S104, calculating the upper quartile of each risk factor in the multiple risk factors of each freight vehicle, and generating the respective reference value of the multiple risk factors of each freight vehicle;
the Quartile (Quartile) is also called a Quartile, and means that all numerical values are arranged from small to large in statistics and divided into four equal parts at the positions of three dividing points.
In one possible implementation, after calculating the plurality of risk factors for each of the freight vehicles, a reference value may be obtained for each of the plurality of risk factors, so as to obtain a respective reference value for the plurality of risk factors for each of the freight vehicles.
And S105, judging high-risk vehicles in a group of freight vehicles to be identified according to the reference values of the risk factors of each freight vehicle.
In a possible implementation manner, when determining a high-risk vehicle existing in a group of freight vehicles to be identified, first determining whether a reference value of each of a plurality of risk factors of each freight vehicle is smaller than a risk factor corresponding to the reference value, obtaining a plurality of determination results of each freight vehicle, then obtaining a determination result with a value of 1 from the plurality of determination results of each freight vehicle, then summing the determination results with the value of 1, generating a determination value of each of the plurality of vehicles, then performing descending order arrangement on the determination values of each of the plurality of vehicles, generating a plurality of arranged determination values, and finally determining the high-risk vehicle existing in the group of freight vehicles to be identified according to the plurality of arranged determination values.
Specifically, when a high-risk vehicle in a group of freight vehicles to be identified is judged according to a plurality of arranged judgment values, a preset percentage of judgment values are intercepted from the starting positions of the plurality of arranged judgment values as starting points to generate a plurality of intercepted judgment values, then the freight vehicles corresponding to the plurality of intercepted judgment values are identified from the group of freight vehicles to be identified, and finally the freight vehicles corresponding to the plurality of identified judgment values are determined as the high-risk vehicles.
Further, after the high-risk vehicle is determined, risk early warning and visual analysis are needed, the position of the high-risk vehicle is subscribed at first, position subscription information is generated, then the position subscription information is pushed to a message queue, a vehicle-mounted terminal of the high-risk vehicle is informed of reporting the position, the longitude and latitude of the high-risk vehicle reported by the vehicle-mounted terminal are received, then real-time calculation is carried out according to the longitude and latitude of the high-risk vehicle, the running speed and the running duration of the high-risk vehicle are generated, finally risk early warning is carried out according to the running speed and the running duration of the high-risk vehicle, and visual analysis is carried out according to the running speed and the running duration of the high-risk vehicle.
Specifically, when risk early warning is carried out according to the running speed and the running duration of a high-risk vehicle, first early warning information and second early warning information are generated when the running speed and the running duration of the high-risk vehicle are larger than preset values, and then the first early warning information is sent to a vehicle-mounted terminal of the high-risk vehicle for warning and reminding; and pushing the second early warning information to a high-risk vehicle pool of the customer service system.
For example, for an identified high-risk vehicle, such as a vehicle generating a dangerous behavior, the vehicle is pushed to a high-risk vehicle pool of a customer service system in real time, and customer service personnel perform positioning and tracking on the vehicle in real time and make a call to perform manual intervention reminding, so that the occurrence of dangerous accidents is reduced. The high-risk vehicles are identified through offline analysis, the high-risk vehicle proportion is calculated through statistical analysis of enterprises to which the vehicles belong, 50 fleet data before the proportion are taken, output is provided in an open API mode, and organization security personnel conduct offline education through offline security return visits, and therefore fleet safety awareness is improved.
In the embodiment of the application, the risk identification device of the freight vehicle firstly determines a group of freight vehicles to be identified from a plurality of groups of freight vehicles divided in advance, then obtains respective historical operation data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period, then performs big data off-line calculation according to the respective historical operation data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle, secondly calculates the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, generates a respective reference value of the plurality of risk factors of each freight vehicle, and finally determines a high-risk vehicle in the group of freight vehicles to be identified according to the respective reference value of the plurality of risk factors of each freight vehicle. According to the method and the device, the risk factors are calculated through big data in an off-line mode, and the high-risk vehicles are comprehensively judged by calculating the upper quartile of each risk factor to determine the reference value, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, and the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed.
Referring to fig. 2, a schematic diagram of a risk identification system for a freight vehicle is provided according to an embodiment of the present application. As shown in fig. 2, the system is composed of a dynamic high risk identification module, a real-time risk early warning processing module and a data evaluation application module.
The dynamic high-risk identification module mainly analyzes 8 risk factors influencing the running risk of the vehicle by means of big data offline analysis, and identifies the high-risk vehicle.
And the real-time risk early warning processing module is used for calculating the reported position of the vehicle-mounted terminal in real time for the high-risk vehicle, pushing early warning information for alarm data of high-risk behaviors such as overspeed, fatigue and the like in the driving process, and meanwhile pushing the alarm data to a high-risk vehicle pool for manual intervention processing.
The data evaluation application module mainly analyzes and counts high-risk vehicles of an enterprise fleet.
In the embodiment of the application, the risk identification device of the freight vehicle firstly determines a group of freight vehicles to be identified from a plurality of groups of freight vehicles divided in advance, then obtains respective historical operation data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period, then performs big data off-line calculation according to the respective historical operation data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle, secondly calculates the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, generates a respective reference value of the plurality of risk factors of each freight vehicle, and finally determines a high-risk vehicle in the group of freight vehicles to be identified according to the respective reference value of the plurality of risk factors of each freight vehicle. According to the method and the device, the risk factors are calculated through big data in an off-line mode, and the high-risk vehicles are comprehensively judged by calculating the upper quartile of each risk factor to determine the reference value, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, and the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 3, a schematic structural diagram of a risk identification device for a cargo vehicle according to an exemplary embodiment of the present invention is shown. The risk identification means of the freight vehicle may be implemented as all or part of a terminal by software, hardware or a combination of both. The device 1 comprises a freight vehicle determination module 10, a historical operation data acquisition module 20, a risk factor calculation module 30, a benchmark value calculation module 40 and a high-risk vehicle identification module 50.
A freight vehicle determination module 10 for determining a group of freight vehicles to be identified from among a plurality of groups of freight vehicles divided in advance;
a historical operation data obtaining module 20, configured to obtain historical operation data of each of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period;
the risk factor calculation module 30 is configured to perform big data offline calculation according to respective historical operating data of the plurality of freight vehicles, and generate a plurality of risk factors for each freight vehicle;
a reference value calculation module 40, configured to calculate an upper quartile of each risk factor in the multiple risk factors of each freight vehicle, and generate a reference value of each of the multiple risk factors of each freight vehicle;
and the high-risk vehicle identification module 50 is used for judging the high-risk vehicles in the group of freight vehicles to be identified according to the reference values of the risk factors of each freight vehicle.
It should be noted that, when the risk identification device for a freight vehicle provided in the foregoing embodiment executes the risk identification method for a freight vehicle, only the division of the functional modules is taken as an example, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the equipment may be divided into different functional modules so as to complete all or part of the functions described above. In addition, the risk identification device for the freight vehicle provided by the above embodiment and the risk identification method embodiment for the freight vehicle belong to the same concept, and the detailed implementation process is shown in the method embodiment, which is not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the risk identification device of the freight vehicle firstly determines a group of freight vehicles to be identified from a plurality of groups of freight vehicles divided in advance, then obtains respective historical operation data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period, then performs big data off-line calculation according to the respective historical operation data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle, secondly calculates the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, generates a respective reference value of the plurality of risk factors of each freight vehicle, and finally determines a high-risk vehicle in the group of freight vehicles to be identified according to the respective reference value of the plurality of risk factors of each freight vehicle. According to the method and the device, the risk factors are calculated through big data in an off-line mode, and the high-risk vehicles are comprehensively judged by calculating the upper quartile of each risk factor to determine the reference value, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, and the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed.
The present invention also provides a computer readable medium having stored thereon program instructions which, when executed by a processor, implement the method of risk identification for a freight vehicle provided by the various method embodiments described above. The invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to carry out the method of risk identification of freight vehicles of the various method embodiments described above.
Please refer to fig. 4, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 4, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 4, the memory 1005, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a risk identification application for freight vehicles.
In the terminal 1000 shown in fig. 4, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke the risk identification application for the freight vehicle stored in the memory 1005 and specifically perform the following operations:
determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
acquiring respective historical operating data of a plurality of freight vehicles in a group of freight vehicles to be identified in a preset period;
performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
calculating the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, and generating the respective reference value of the plurality of risk factors of each freight vehicle;
and determining the high-risk vehicles in the group of freight vehicles to be identified according to the reference values of the risk factors of each freight vehicle.
In one embodiment, the processor 1001, before performing the determination of the one group of freight vehicles to be identified from the plurality of groups of freight vehicles divided in advance, further performs the following operations:
acquiring a plurality of freight vehicles which are currently operated;
acquiring the provincial names of the affiliations of each freight vehicle in the plurality of freight vehicles, and dividing the freight vehicles with the same provincial names into one group to obtain a plurality of groups of freight vehicles;
the plurality of groups of freight vehicles are determined as the plurality of groups of freight vehicles divided in advance.
In one embodiment, the processor 1001, when executing the determination of the high risk vehicle existing in the group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle, specifically executes the following operations:
judging whether the reference value of each of the multiple risk factors of each freight vehicle is smaller than the corresponding risk factor or not to obtain multiple judgment results of each freight vehicle;
obtaining a judgment result with a value of 1 from a plurality of judgment results of each freight vehicle;
summing up the judgment results with the numerical value of 1 to generate respective judgment values of a plurality of vehicles;
sorting the respective determination values of the plurality of vehicles in a descending order to generate a plurality of sorted determination values;
and determining the high-risk vehicle in the group of freight vehicles to be identified according to the arranged plurality of determination values.
In one embodiment, the processor 1001, when executing the determination of the high-risk vehicle existing in the group of freight vehicles to be identified based on the ranked plurality of determination values, specifically performs the following operations:
intercepting a preset percentage of judgment values from the initial positions of the plurality of arranged judgment values serving as initial points to generate a plurality of intercepted judgment values;
identifying freight vehicles corresponding to the plurality of intercepted judgment values from a group of freight vehicles to be identified;
and determining the freight vehicles corresponding to the identified multiple judgment values as high-risk vehicles.
In one embodiment, the processor 1001 also performs the following operations:
subscribing the position of the high-risk vehicle and generating position subscription information;
pushing the position subscription information to a message queue, and informing a vehicle-mounted terminal of a high-risk vehicle of reporting the position;
receiving the longitude and latitude of the high-risk vehicle reported by the vehicle-mounted terminal;
calculating in real time according to the longitude and latitude of the high-risk vehicle to generate the running speed and running duration of the high-risk vehicle;
carrying out risk early warning according to the running speed and the running duration of the high-risk vehicle;
alternatively, the first and second electrodes may be,
and carrying out visual analysis according to the running speed and the running duration of the high-risk vehicle.
In one embodiment, when performing the risk pre-warning according to the driving speed and the driving duration of the high-risk vehicle, the processor 1001 specifically performs the following operations:
when the running speed and the running duration of the high-risk vehicle are greater than preset values, generating first early warning information and second early warning information;
sending the first early warning information to a vehicle-mounted terminal of a high-risk vehicle for warning and reminding;
and the number of the first and second groups,
and pushing the second early warning information to a high-risk vehicle pool of the customer service system.
In one embodiment, the processor 1001 performs the following operations when performing big data offline calculation according to the historical operating data of each of the plurality of freight vehicles to generate a plurality of risk factors for each of the plurality of freight vehicles:
determining historical operating data for a first one of the plurality of cargo vehicles from historical operating data for each of the plurality of cargo vehicles;
loading a risk factor calculation rule table;
identifying the required parameter values of each risk factor calculation rule in the risk factor calculation rule table one by one from historical operating data of the first freight vehicle;
calculating the risk factors one by one based on the required parameter values of each risk factor calculation rule to generate the risk factor of the first freight vehicle;
and processing according to the steps according to the historical operation data of the freight vehicles to generate a plurality of risk factors of each freight vehicle.
In the embodiment of the application, the risk identification device of the freight vehicle firstly determines a group of freight vehicles to be identified from a plurality of groups of freight vehicles divided in advance, then obtains respective historical operation data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period, then performs big data off-line calculation according to the respective historical operation data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle, secondly calculates the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, generates a respective reference value of the plurality of risk factors of each freight vehicle, and finally determines a high-risk vehicle in the group of freight vehicles to be identified according to the respective reference value of the plurality of risk factors of each freight vehicle. According to the method and the device, the risk factors are calculated through big data in an off-line mode, and the high-risk vehicles are comprehensively judged by calculating the upper quartile of each risk factor to determine the reference value, so that the high-risk vehicles can be intelligently identified to provide all-around real-time monitoring and early warning during the running of the vehicles, and the safety of freight drivers and the transportation safety of the vehicles can be effectively guaranteed.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware that is related to instructions of a computer program, and the program can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method of risk identification of a freight vehicle, characterised in that the method comprises:
determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
acquiring respective historical operating data of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period;
performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
calculating the upper quartile of each risk factor in the plurality of risk factors of each freight vehicle, and generating a reference value of each risk factor of each freight vehicle;
and determining the high-risk vehicles in the group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle.
2. The method of claim 1, wherein prior to determining a set of freight vehicles to be identified from the pre-divided sets of freight vehicles, further comprising:
acquiring a plurality of freight vehicles which are currently operated;
acquiring the provincial names of the owners of each freight vehicle in the plurality of freight vehicles, and dividing the freight vehicles with the same provincial names into one group to obtain a plurality of groups of freight vehicles;
and determining the plurality of groups of freight vehicles as a plurality of groups of freight vehicles which are divided in advance.
3. The method according to claim 1, wherein the determining high-risk vehicles in the group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle comprises:
judging whether the reference value of each of the multiple risk factors of each freight vehicle is smaller than the corresponding risk factor or not to obtain multiple judgment results of each freight vehicle;
obtaining a judgment result with a value of 1 from a plurality of judgment results of each freight vehicle;
summing the judgment results with the numerical value of 1 to generate respective judgment values of a plurality of vehicles;
sorting the respective determination values of the plurality of vehicles in descending order to generate a plurality of sorted determination values;
and determining the high-risk vehicle in the group of freight vehicles to be identified according to the arranged plurality of determination values.
4. The method according to claim 3, wherein the determination of the high-risk vehicle present in the group of freight vehicles to be identified based on the ranked plurality of determination values includes:
intercepting a preset percentage of judgment values from the initial positions of the plurality of arranged judgment values serving as initial points to generate a plurality of intercepted judgment values;
identifying freight vehicles corresponding to the intercepted judgment values from the group of freight vehicles to be identified;
and determining the freight vehicles corresponding to the plurality of identified judgment values as high-risk vehicles.
5. The method of claim 4, further comprising:
subscribing the position of the high-risk vehicle and generating position subscription information;
pushing the position subscription information to a message queue, and informing a vehicle-mounted terminal of the high-risk vehicle of reporting the position;
receiving the longitude and latitude of the high-risk vehicle reported by the vehicle-mounted terminal;
calculating in real time according to the longitude and latitude of the high-risk vehicle to generate the running speed and running duration of the high-risk vehicle;
carrying out risk early warning according to the running speed and the running duration of the high-risk vehicle;
alternatively, the first and second electrodes may be,
and carrying out visual analysis according to the running speed and the running duration of the high-risk vehicle.
6. The method of claim 5, wherein the risk pre-warning according to the driving speed and the driving duration of the high-risk vehicle comprises:
when the running speed and the running duration of the high-risk vehicle are greater than preset values, generating first early warning information and second early warning information;
sending the first early warning information to a vehicle-mounted terminal of a high-risk vehicle for warning and reminding;
and the number of the first and second groups,
and pushing the second early warning information to a high-risk vehicle pool of a customer service system.
7. The method of claim 1, wherein the performing big data offline calculations based on historical operating data of each of the plurality of cargo vehicles to generate a plurality of risk factors for each cargo vehicle comprises:
determining historical operating data for a first one of the plurality of cargo vehicles from historical operating data for each of the plurality of cargo vehicles;
loading a risk factor calculation rule table;
identifying the required parameter values of each risk factor calculation rule in the risk factor calculation rule table one by one from the historical operating data of the first freight vehicle;
calculating the risk factors one by one based on the required parameter values of each risk factor calculation rule to generate the risk factor of the first freight vehicle;
and processing according to the steps according to the historical operation data of the freight vehicles to generate a plurality of risk factors of each freight vehicle.
8. A risk identification device for a freight vehicle, characterized in that the device comprises:
the freight vehicle determining module is used for determining a group of freight vehicles to be identified from a plurality of groups of freight vehicles which are divided in advance;
the historical operation data acquisition module is used for acquiring the historical operation data of each of a plurality of freight vehicles in the group of freight vehicles to be identified in a preset period;
the risk factor calculation module is used for performing big data off-line calculation according to the respective historical operating data of the plurality of freight vehicles to generate a plurality of risk factors of each freight vehicle;
a reference value calculation module, configured to calculate an upper quartile of each risk factor in the multiple risk factors of each freight vehicle, and generate a reference value of each of the multiple risk factors of each freight vehicle;
and the high-risk vehicle identification module is used for judging the high-risk vehicles in the group of freight vehicles to be identified according to the respective reference values of the plurality of risk factors of each freight vehicle.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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