CN117593169B

CN117593169B - Security monitoring early warning method, system and medium for large-piece transportation process based on big data

Info

Publication number: CN117593169B
Application number: CN202410076656.5A
Authority: CN
Inventors: 张登峰; 陈振宇; 李小村; 康敏; 卢志珊; 刘妍彦
Original assignee: Hangzhou Zcits Technology Co ltd
Current assignee: Hangzhou Zcits Technology Co ltd
Priority date: 2024-01-19
Filing date: 2024-01-19
Publication date: 2024-04-09
Anticipated expiration: 2044-01-19
Also published as: CN117593169A

Abstract

The embodiment of the application provides a security monitoring early warning method, a security monitoring early warning system and a security monitoring early warning medium for a large-piece transportation process based on big data. The method comprises the following steps: obtaining traffic point traffic management responsivity data, a space trafficability index, a road traffic difficulty coefficient, a traffic smoothness index, a path point interference index and a traffic safety index according to various record information data of a large transportation task in combination with the characteristic data of the road condition and the traffic condition, obtaining a path point risk early warning response index of a record path node through post-processing, obtaining a path point risk early warning evaluation index through processing a plurality of relevant indexes, coefficients of the passed path node and coefficient indexes of the record path node extracted by a database, and carrying out early warning and scheme response on the path node according to an early warning grade; therefore, the risk early warning evaluation result of the path node to be passed is obtained by collecting the big data of the big transportation and carrying out processing calculation of the passing characteristics, and the safety supervision early warning technology of the big transportation process is realized.

Description

Security monitoring early warning method, system and medium for large-piece transportation process based on big data

Technical Field

The application relates to the field of big data and big-piece transportation, in particular to a security monitoring early warning method, a security monitoring early warning system and a security monitoring early warning medium for big-piece transportation based on big data.

Background

The large-scale transportation refers to large-scale core accessories which are needed by large-scale construction projects such as construction projects, electric power and bridges, and the like, so that the safety supervision and early warning of the large-scale transportation are related to road safety and influence public resource occupation, and the large-scale transportation is one of important core contents of the large-scale transportation due to the comprehensive influence of vehicles, large-scale parts, roads and traffic conditions, namely, how to realize the safety prediction and risk early warning of the large-scale transportation is realized, and the intelligent means for analyzing and processing the large-scale data according to various acquired information of the large-scale transportation to realize the safety supervision and risk assessment is lacking at present.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The embodiment of the application aims to provide a security monitoring early warning method, a security monitoring early warning system and a security monitoring early warning medium for a large transportation process based on large data, and the security monitoring early warning technology for the large transportation process can be realized by acquiring the large data of the large transportation to process and calculate the traffic characteristics to obtain a risk early warning evaluation result of a path node to be passed.

The embodiment of the application also provides a security monitoring early warning method of the large transportation process based on the big data, which comprises the following steps:

acquiring transportation pre-registration information of a large transportation vehicle for executing a target large transportation task, and extracting vehicle record information data, large record information data, path record information data and time node record information data;

acquiring road condition characteristic information and traffic condition characteristic information of a record path node to be passed by the large transport vehicle, extracting road condition characteristic data and traffic condition characteristic data, inquiring through a preset large transport emergency management database according to the large record information data and the path record information data to obtain a road point risk coefficient of the record path node, and inquiring by combining the time node record information data and the traffic condition characteristic data to obtain road point traffic management responsiveness data;

Processing the road condition characteristic data and the traffic situation characteristic data according to the vehicle record information data and the large record information data in combination with a preset road transportation characteristic evaluation model to respectively obtain a space trafficability index and a road traffic difficulty coefficient;

processing according to the space passing index and the road passing difficulty coefficient in combination with the path record information data, the time node record information data and the traffic condition characteristic data to respectively obtain a passing smoothness index, a path point interference index and a passing safety index of the record path node;

processing according to the traffic smoothness index, the path point interference index, the traffic safety index and the traffic point traffic management response data to obtain a path point risk early warning response index of the record path node;

extracting a plurality of path point risk actual measurement evaluation indexes, a plurality of travel point risk coefficients and a path point risk early warning response index corresponding to a plurality of record path nodes through which the large transport vehicle passes through a preset large transport emergency management database, and then processing the path point risk actual measurement evaluation indexes, the plurality of travel point risk coefficients and the path point risk early warning response index with the travel point risk coefficients and the path point risk early warning response index of the record path nodes to be passed through to obtain the path point risk early warning evaluation index;

And carrying out early warning on the record path node to be passed according to a preset early warning level corresponding to the path point risk early warning evaluation index, and implementing a corresponding emergency response scheme.

Optionally, in the safety supervision early warning method for a large-piece transportation process based on large data in the embodiment of the present application, the acquiring transportation pre-registration information of a large-piece transportation vehicle for executing a target large-piece transportation task, and extracting vehicle record information data, large-piece record information data, path record information data and time node record information data includes:

acquiring transportation pre-registration information of a large transportation vehicle for executing a target large transportation task;

the transportation pre-registration information comprises vehicle record information, major part record information, path record information and time node record information;

extracting loading full-size data and loading weight data according to the vehicle record information, and extracting large article attribute type data, large article body quantity series and large article outline data according to the large article record information;

extracting path node record data and corresponding path point importance coefficients according to the path record information, and extracting path node arrival time data and path node traffic duration data according to the time node record information.

Optionally, in the security monitoring and early warning method for a large transportation process based on large data in this embodiment of the present application, the obtaining the road condition feature information and the traffic condition feature information of a record path node to be passed by the large transportation vehicle, extracting the road condition feature data and the traffic condition feature data, and obtaining the traffic point risk coefficient of the record path node by querying a preset large transportation emergency management database according to the large record information data and the path record information data, and obtaining the traffic point responsiveness data by querying in combination with the time node record information data and the traffic condition feature data includes:

acquiring road condition characteristic information and traffic situation characteristic information of a record path node to be passed by the large transport vehicle;

extracting road condition characteristic data according to the road condition characteristic information, wherein the road condition characteristic data comprises road space dimension data and road bearing data;

extracting traffic live feature data according to the traffic live feature information, wherein the traffic live feature data comprises a road condition complexity coefficient and a traffic real-time crowding coefficient;

inquiring through a preset large transportation emergency management database according to the large object attribute type data and the route point importance coefficient to obtain a route point risk coefficient of the record path node;

And inquiring through a preset large transportation emergency management database according to the road point risk coefficient, the road node passing duration data and the traffic real-time crowdedness coefficient to obtain road point traffic management responsiveness data of the record-keeping road node.

Optionally, in the safety supervision early warning method for a large-piece transportation process based on large data according to the embodiment of the present application, the processing according to the vehicle record information data and the large-piece record information data in combination with the road condition feature data and the traffic condition feature data through a preset road transportation characteristic evaluation model, to respectively obtain a space passing index and a road passing difficulty coefficient includes:

calculating according to the loading full-size data, the mass series of the large articles, the outline data of the large articles and the road space dimension data through a preset road transportation characteristic evaluation model to obtain a space passing index;

calculating according to the loading weight data, road load data and road condition complexity coefficient through a preset road transportation characteristic evaluation model to obtain a road traffic difficulty coefficient;

the calculation formula of the space trafficability index and the road traffic difficulty coefficient is as follows:

；

Wherein,is a space passing index>Is the road traffic difficulty coefficient->、/>、/>、/>Respectively loading full-size data, large article outline data, large article volume level and loading weight data,>、/>、respectively is road space dimension data, road bearing data and road condition complexity coefficient, +.>、/>Is a preset characteristic coefficient.

Optionally, in the safety supervision early warning method for a big-piece transportation process based on big data according to the embodiment of the present application, the processing according to the space trafficability index and the road traffic difficulty coefficient in combination with the path record information data, the time node record information data and the traffic live feature data, to obtain a traffic smoothness index, a path point interference index and a traffic safety index of the record path node respectively includes:

calculating according to the space trafficability index and the road traffic difficulty coefficient and combining the traffic real-time congestion degree coefficient to obtain a traffic smoothness index of the recorded path nodes;

calculating according to the space trafficability index, combining the route point importance coefficient, route node passing duration data and traffic real-time crowding degree coefficient to obtain a route point interference degree index of the recorded route node;

And calculating according to the road traffic difficulty coefficient and the road traffic point risk coefficient and combining the road condition complexity coefficient to obtain a traffic safety index of the record path node.

Optionally, in the security monitoring early warning method for a large transportation process based on big data in the embodiment of the present application, the processing according to the traffic smoothness index, the path point interference index, the traffic safety index, and the traffic point traffic management response data to obtain the path point risk early warning response index of the record path node includes:

calculating according to the traffic smoothness index, the path point interference index and the traffic safety index and combining the traffic point traffic management response data through a preset transportation traffic management response model to obtain a path point risk early warning response index of the record path node;

the calculation formula of the path point risk early warning response index is as follows:

；

wherein,for the risk early warning response index of the route point, +.>For the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>For the traffic point traffic tube responsiveness data, +.>、/>、/>Is a preset characteristic coefficient.

Optionally, in the security monitoring early warning method for a big-data-based big-piece transportation process according to the embodiment of the present application, the extracting, by a preset big-piece transportation emergency management database, a plurality of path point risk actual measurement evaluation indexes, a plurality of road point risk coefficients, and a path point risk early warning response index corresponding to a plurality of record path nodes through which the big-piece transportation vehicle passes, and then processing the path point risk actual measurement evaluation indexes, the road point risk coefficients, and the path point risk early warning response index with the road point risk coefficients and the path point risk early warning response indexes of the record path nodes to be passed, to obtain a path point risk early warning evaluation index includes:

extracting a plurality of path point risk actual measurement evaluation indexes corresponding to a plurality of record path nodes through which the large transport vehicle executes the target large transport task through a preset large transport emergency management database;

extracting a plurality of path point risk coefficients and path point risk early warning response indexes corresponding to the plurality of recorded path nodes;

processing according to the path point risk actual measurement evaluation indexes, combining the path point risk coefficients, the path point risk early warning response indexes and the path point risk coefficient and the path point risk early warning response index of the record path node to obtain the path point risk early warning evaluation index of the record path node to be passed;

The calculation formula of the path point risk early warning evaluation index is as follows:

；

wherein,evaluating index for risk early warning of route points, +.>、/>、/>The method comprises the steps of obtaining a path point risk coefficient, a path point risk early warning response index and a path point risk actual measurement evaluation index of an ith node in n recorded path nodes which pass through, wherein the path point risk coefficient, the path point risk early warning response index and the path point risk actual measurement evaluation index are +.>For the risk factor of the waypoints, < > for>For the risk early warning response index of the route point, +.>、/>、/>、/>、/>Is a preset characteristic coefficient.

In a second aspect, embodiments of the present application provide a security monitoring and early warning system for large-scale transportation based on big data, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a safety supervision early warning method of a large-piece transportation process based on big data, and the program of the safety supervision early warning method of the large-piece transportation process based on big data realizes the following steps when being executed by the processor:

Optionally, in the safety supervision early warning system for a large-piece transportation process based on large data in the embodiment of the present application, the acquiring transportation pre-registration information of a large-piece transportation vehicle for executing a target large-piece transportation task, and extracting vehicle record information data, large-piece record information data, path record information data and time node record information data includes:

In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a security monitoring early warning method program based on a big data big part transportation process, where when the security monitoring early warning method program based on the big data big part transportation process is executed by a processor, the steps of the security monitoring early warning method based on the big data big part transportation process described in any one of the foregoing are implemented.

As can be seen from the above, the security monitoring early warning method, system and medium for large transportation process based on large data provided in the embodiments of the present application extracts vehicle, large part, path and time record information data according to transportation preregistration information of large transportation tasks, obtains road condition and traffic situation feature data and road point risk coefficient query, obtains road traffic management responsiveness data, obtains space passing index and road passing difficulty coefficient through model processing, obtains passing smoothness index, path point interference index and passing security index through model processing, and then obtains path point risk early warning response index of record path node through post-processing, and obtains path point risk early warning response index, path point risk early warning response index and path point risk early warning response index of record path node through path point risk actual measurement evaluation index, path point risk early warning response index and path point risk response index of record path node extracted by database, and pre-warns and scheme response is performed on record path node according to corresponding preset early warning level of evaluation index; therefore, the risk early warning evaluation result of the path node to be passed is obtained by collecting the big data of the big transportation and carrying out processing calculation of the passing characteristics, and the safety supervision early warning technology of the big transportation process is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objects and other advantages of the present application may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a security monitoring early warning method for a large-piece transportation process based on big data provided by an embodiment of the application;

fig. 2 is a flowchart of a security monitoring early warning method for a large-piece transportation process based on big data, provided in an embodiment of the present application, for obtaining pre-registration information of transportation and extracting data;

fig. 3 is a flowchart of obtaining a road point risk coefficient and road point traffic management responsivity data according to an embodiment of the present application.

Fig. 4 is a flowchart of a method for obtaining a space passing index and a road passing difficulty coefficient of an safety supervision early warning method of a large transportation process based on big data according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an security monitoring early warning system based on big data in a big transportation process according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of an early warning method for security monitoring of a large-piece transportation process based on big data in some embodiments of the present application. The security monitoring early warning method based on the big data in the big transportation process is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The security monitoring early warning method for the large transportation process based on the big data comprises the following steps:

s11, acquiring transportation pre-registration information of a large transportation vehicle for executing a target large transportation task, and extracting vehicle record information data, large record information data, path record information data and time node record information data;

s12, acquiring road condition characteristic information and traffic condition characteristic information of a record path node to be passed by the large transport vehicle, extracting road condition characteristic data and traffic condition characteristic data, inquiring through a preset large transport emergency management database according to the large record information data and the path record information data to obtain a road point risk coefficient of the record path node, and inquiring by combining the time node record information data and the traffic condition characteristic data to obtain road point traffic management responsiveness data;

s13, processing the road condition characteristic data and the traffic situation characteristic data according to the vehicle record information data and the large record information data by combining the road condition characteristic data and the traffic situation characteristic data through a preset road transportation characteristic evaluation model to respectively obtain a space trafficability index and a road traffic difficulty coefficient;

S14, processing the path record information data, the time node record information data and the traffic condition characteristic data according to the space trafficability index and the road traffic difficulty coefficient to respectively obtain a traffic smoothness index, a path point interference index and a traffic safety index of the record path node;

s15, processing the traffic smoothness index, the path point interference index, the traffic safety index and the traffic point traffic management response data to obtain a path point risk early warning response index of the record path node;

s16, extracting a plurality of path point risk actual measurement evaluation indexes, a plurality of travel point risk coefficients and path point risk early warning response indexes corresponding to a plurality of record path nodes through which the large transport vehicle passes through a preset large transport emergency management database, and then processing the path point risk actual measurement evaluation indexes, the plurality of travel point risk coefficients and the path point risk early warning response indexes with the travel point risk coefficients and the path point risk early warning response indexes of the record path nodes to be passed through to obtain the path point risk early warning evaluation indexes;

and S17, carrying out early warning on the record path nodes to be passed according to preset early warning grades corresponding to the path point risk early warning evaluation indexes, and implementing a corresponding emergency response scheme.

In order to realize the processing calculation of the traffic characteristics by collecting the big data of the big transportation to obtain the risk early warning evaluation result of the route node to be passed, realize the safety monitoring early warning of the big transportation process, extract the information data of the vehicle, the big part, the route and the time record by the transportation pre-registration information of the big transportation vehicle to execute the target big transportation task, obtain the characteristic information of the road condition and the traffic condition of the route node to be passed by the vehicle transportation record and extract the corresponding characteristic data, then obtain the risk coefficient of the route point by the inquiry of the preset big transportation emergency management database, namely the risk evaluation coefficient of the route node to be passed, and obtain the traffic point traffic response data, namely the traffic excitation response degree data of the route node, obtaining space passing index and road passing difficulty coefficient through preset road transportation characteristic evaluation model processing according to the recorded data of vehicles and large pieces and the characteristic data of road conditions and traffic conditions, namely calculating to obtain an evaluation index coefficient reflecting road space passing and passing difficulty of the path nodes, further calculating according to the index coefficient of the evaluation result and the recorded data and the characteristic data to respectively obtain a passing smoothness index, a path point interference index and a passing safety index of the recorded path nodes, further combining with the road point traffic management response data processing to obtain a response index reflecting risk early warning when the recorded path nodes pass, further processing a plurality of path point actual measurement evaluation indexes corresponding to a plurality of recorded path nodes passed by the large piece of transportation vehicles and a plurality of road point risk coefficient and a road point risk early warning response index to obtain a path point risk early warning evaluation index, and finally, carrying out early warning and response of a corresponding emergency scheme on the recorded path node according to a preset early warning level corresponding to the index.

Referring to fig. 2, fig. 2 is a flowchart of a method for acquiring pre-registration information of transportation and extracting data in an early warning method for safety supervision of a large transportation process based on big data in some embodiments of the present application. According to the embodiment of the invention, the method for acquiring the transportation pre-registration information of the large transportation vehicle for executing the target large transportation task and extracting the vehicle record information data, the large record information data, the path record information data and the time node record information data specifically comprises the following steps:

s21, acquiring transportation pre-registration information of a large transportation vehicle for executing a target large transportation task;

s22, the transportation pre-registration information comprises vehicle record information, large part record information, path record information and time node record information;

s23, extracting loading full-size data and loading weight data according to the vehicle record information, and extracting large article attribute type data, large article body quantity series and large article outline data according to the large article record information;

and S24, extracting path node record data and corresponding path point importance coefficients according to the path record information, and extracting path node arrival time data and path node traffic duration data according to the time node record information.

In order to evaluate the risk evaluation early warning result of the route point of the large transport vehicle to pass through in the transport route, firstly, related record information of the large transport is required to be acquired, corresponding data is extracted according to the record information, so as to evaluate the safety supervision early warning of the large transport, transport preregistration information of the large transport vehicle for executing the target large transport task is acquired, wherein the transport preregistration information comprises the record information of the items of the vehicle, the large object, the route and time nodes, and the record information is respectively extracted to load full-size data, load weight data, attribute type data of the large object, the number of stages of the large object, outline data of the large object, route node record data, importance coefficient of the route point, node arrival time data and traffic duration data of the route nodes.

Referring to fig. 3, fig. 3 is a flowchart of obtaining a road point risk coefficient and road point traffic management responsiveness data according to an safety supervision early warning method of a large transportation process based on big data in some embodiments of the present application. According to the embodiment of the invention, the road condition characteristic information and the traffic condition characteristic information of the record path node to be passed by the large transport vehicle are obtained, the road condition characteristic data and the traffic condition characteristic data are extracted, the road condition risk coefficient of the record path node is obtained by inquiring the large record information data and the path record information data through a preset large transport emergency management database, and the road condition risk coefficient of the record path node is obtained by inquiring the time node record information data and the traffic condition characteristic data, specifically, the road condition risk coefficient is obtained by inquiring the time node record information data and the traffic condition characteristic data:

S31, acquiring road condition characteristic information and traffic situation characteristic information of a record path node to be passed by the large transport vehicle;

s32, extracting road condition characteristic data according to the road condition characteristic information, wherein the road condition characteristic data comprises road space size data and road bearing data;

s33, extracting traffic live characteristic data according to the traffic live characteristic information, wherein the traffic live characteristic data comprises a road condition complexity coefficient and a traffic real-time crowding coefficient;

s34, inquiring through a preset large-piece transportation emergency management database according to the large-piece object attribute type data and the route point importance coefficient to obtain a route point risk coefficient of the record path node;

and S35, inquiring through a preset large transportation emergency management database according to the road point risk coefficient, the road node traffic duration data and the traffic real-time crowdedness coefficient to obtain road point traffic management responsivity data of the record-keeping road node.

The method comprises the steps of acquiring information of road conditions and real-time traffic conditions of a large transportation path node, extracting road condition characteristic data comprising size data of a road space and design bearing data of a road according to the road condition characteristic information, extracting traffic condition characteristic data comprising real-time complexity coefficients of road conditions and real-time traffic congestion coefficients of the road of the path node according to the traffic condition characteristic information, acquiring traffic point risk coefficients of the record path node according to large article attribute type data and the importance coefficients of the path node through inquiring a preset large transportation emergency management database, namely acquiring risk coefficients of corresponding traffic path nodes through dangerous attributes of large articles and the importance of the type and the path node through inquiring a database, and acquiring traffic point traffic management response data of the record path node through inquiring the database according to the traffic point risk coefficients and the traffic duration data and the traffic real-time congestion coefficients of the path node.

Referring to fig. 4, fig. 4 is a flowchart of a method for obtaining a space passing index and a road passing difficulty coefficient of an safety supervision early warning method for a large transportation process based on big data in some embodiments of the present application. According to the embodiment of the invention, the vehicle record information data and the large record information data are combined with the road condition characteristic data and the traffic condition characteristic data to be processed through a preset road transportation characteristic evaluation model, so that a space passing index and a road passing difficulty coefficient are respectively obtained, and the method specifically comprises the following steps:

s41, calculating according to the loading full-size data, the mass series of the large articles and the outline data of the large articles and the road space dimension data through a preset road transportation characteristic evaluation model to obtain a space passing index;

s42, calculating according to the loading weight data, road bearing data and road condition complexity coefficients through a preset road transportation characteristic evaluation model to obtain road traffic difficulty coefficients;

；

wherein,is a space passing index>Is the road traffic difficulty coefficient- >、/>、/>、/>Respectively loading full-size data, large article outline data, large article volume level and loading weight data,>、/>、respectively is road space dimension data, road bearing data and road condition complexity coefficient, +.>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through inquiring a preset large transportation emergency management database platform).

In order to evaluate the trafficability of a road and space passing through a to-be-recorded path node and the road passing difficulty, the space passing index is obtained by calculating according to loading full-size data, the mass series of the large article, the outline data of the large article and the road space dimension data through a preset formula contained in a preset road transportation characteristic evaluation model, and the road passing difficulty coefficient is obtained by calculating according to loading weight data, road bearing data and road condition complexity coefficient through a preset formula contained in the model.

According to the embodiment of the invention, the path record information data, the time node record information data and the traffic live characteristic data are combined according to the space trafficability index and the road traffic difficulty coefficient to be processed, so as to respectively obtain a traffic smoothness index, a path point interference index and a traffic safety index of the record path node, wherein the traffic smoothness index, the path point interference index and the traffic safety index are specifically as follows:

After the passing performance and the difficulty evaluation result of the passing of the record path node are obtained, further respectively calculating the passing smoothness of the large transportation passing through the record path node to be passed, the road traffic interference degree caused by the passing of the large transportation and the related index data of the passing safety degree according to a preset calculation formula, wherein the passing smoothness is the constraint degree reflecting the smoothness of the large transportation by the road space and the real-time traffic condition when the large transportation passes through the path node, the interference degree is the road interference degree reflecting the road busy condition, the passing efficiency and the blocking condition of the record path node when the large transportation passes through the node, and the passing safety degree is the safety degree reflecting the passing difficulty and the risk caused by the road condition, the topography, the risk degree and the road condition complex condition of the path node, wherein the calculation formula of the passing smoothness index, the path point interference degree index and the passing safety degree index is as follows:

；

Wherein,for the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>Is a space passing index>Is the road traffic difficulty coefficient->Risk coefficient of the waypoints, < >>、/>The traffic duration data of the path nodes and the importance coefficient of the path points are respectively +.>、/>Respectively are roadsCondition complexity factor, traffic real-time congestion factor, < ->、/>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through inquiring a preset large transportation emergency management database platform).

According to the embodiment of the invention, the processing is performed according to the traffic smoothness index, the path point interference index, the traffic safety index and the traffic point traffic management response data to obtain the path point risk early warning response index of the record path node, specifically:

；

wherein,for the risk early warning response index of the route point, +. >For the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>For the traffic point traffic tube responsiveness data, +.>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through inquiring a preset large transportation emergency management database platform).

In order to realize early warning response to traffic risks generated when large pieces are transported through the recorded path nodes, road vehicles, pedestrians and environmental facilities are reminded and announced to take evading measures, potential safety hazards are reduced, and calculation processing is carried out according to traffic smoothness indexes, path point interference indexes and traffic safety indexes and by combining with traffic point traffic management response data through a preset calculation formula, so that the path point risk early warning response indexes of the recorded path nodes are obtained.

According to the embodiment of the invention, the preset large transportation emergency management database is used for extracting a plurality of path point risk actual measurement evaluation indexes corresponding to a plurality of record path nodes through which the large transportation vehicle passes, a plurality of traveling point risk coefficients and path point risk early warning response indexes, and then processing the plurality of path point risk actual measurement evaluation indexes, the plurality of traveling point risk coefficients and the plurality of path point risk early warning response indexes with the traveling point risk coefficients and the path point risk early warning response indexes of the record path nodes to be passed to obtain the path point risk early warning evaluation indexes, wherein the method comprises the following steps:

；

wherein,evaluating index for risk early warning of route points, +.>、/>、/>The method comprises the steps of obtaining a path point risk coefficient, a path point risk early warning response index and a path point risk actual measurement evaluation index of an ith node in n recorded path nodes which pass through, wherein the path point risk coefficient, the path point risk early warning response index and the path point risk actual measurement evaluation index are +.>For the risk factor of the waypoints, < > for>For the risk early warning response index of the route point, +.>、/>、/>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through inquiring a preset large transportation emergency management database platform).

And finally, in order to further improve the accuracy of risk early warning response of the large transport vehicle to the record path node, processing the data of the path node to be passed according to the actually measured risk data and related data of the path node to be passed of the large transport vehicle, obtaining risk early warning evaluation indexes, extracting a plurality of actually measured path point risk evaluation indexes corresponding to a plurality of record path nodes of the large transport vehicle to which the large transport vehicle is subjected to execute the target large transport task through a preset large transport emergency management database, namely, extracting a plurality of traveling path point risk coefficients and path point risk early warning response indexes of a plurality of the passing path nodes, calculating the corresponding path point risk early warning evaluation indexes with the traveling path point risk coefficients and the path point risk early warning response indexes of the record path node to be passed, obtaining a corresponding grade early warning response scheme through the early warning evaluation indexes, and carrying out the pre-plan early warning on the path node to be passed through a large data processing technology, thereby realizing the safety early warning of the large transport process.

As shown in fig. 5, the invention also discloses a security monitoring early warning system 5 for large-scale transportation process based on big data, which comprises a memory 51 and a processor 52, wherein the memory comprises a security monitoring early warning method program for large-scale transportation process based on big data, and the following steps are realized when the security monitoring early warning method program for large-scale transportation process based on big data is executed by the processor to correct the abnormal sign data:

According to the embodiment of the invention, the method for acquiring the transportation pre-registration information of the large transportation vehicle for executing the target large transportation task and extracting the vehicle record information data, the large record information data, the path record information data and the time node record information data specifically comprises the following steps:

According to the embodiment of the invention, the road condition characteristic information and the traffic condition characteristic information of the record path node to be passed by the large transport vehicle are obtained, the road condition characteristic data and the traffic condition characteristic data are extracted, the road condition risk coefficient of the record path node is obtained by inquiring the large record information data and the path record information data through a preset large transport emergency management database, and the road condition risk coefficient of the record path node is obtained by inquiring the time node record information data and the traffic condition characteristic data, specifically, the road condition risk coefficient is obtained by inquiring the time node record information data and the traffic condition characteristic data:

According to the embodiment of the invention, the vehicle record information data and the large record information data are combined with the road condition characteristic data and the traffic condition characteristic data to be processed through a preset road transportation characteristic evaluation model, so that a space passing index and a road passing difficulty coefficient are respectively obtained, and the method specifically comprises the following steps:

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wherein,is a space passing index>Is the road traffic difficulty coefficient->、/>、/>、/>Respectively loading full-size data, large article outline data, large article volume level and loading weight data,>、/>、respectively is road space dimension data, road bearing data and road condition complexity coefficient, +. >、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through inquiring a preset large transportation emergency management database platform).

；/>

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Wherein,for the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>Is a space passing index>Is the road traffic difficulty coefficient->Risk coefficient of the waypoints, < >>、/>The traffic duration data of the path nodes and the importance coefficient of the path points are respectively +.>、/>The road condition complexity coefficient and the traffic real-time congestion coefficient are respectively +.>、/>、/>、/>For a predetermined characteristic factor (characteristic factor transported by predetermined large pieceAnd (5) obtaining an emergency management database platform query).

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The third aspect of the present invention provides a readable storage medium, wherein the readable storage medium includes a large-data-based large-scale transportation process safety monitoring and early warning method program, and when the large-data-based large-scale transportation process safety monitoring and early warning method program is executed by a processor, the steps of the large-data-based large-scale transportation process safety monitoring and early warning method are implemented.

According to the safety supervision early warning method, system and medium for the large-piece transportation process based on large data, vehicle, large-piece, path and time record information data are extracted according to transportation preregistration information of a large-piece transportation task, road condition and traffic condition characteristic data and road point risk coefficient inquiry are combined to obtain road point traffic management responsiveness data, space passing index and road passing difficulty coefficient are obtained through model processing, passing smoothness index, path point interference index and passing safety index are obtained through model processing, path point risk early warning response index of record path nodes is obtained through post-processing, and then the path point risk early warning response index, the path point risk coefficient and the path point risk early warning response index of the record path nodes are obtained through processing of the path point risk actual measurement evaluation index, the path point risk coefficient and the path point risk early warning response index of the record path nodes extracted by a database, and the path point risk early warning response index of the record path nodes are subjected to early warning and scheme response according to preset early warning grades corresponding to the evaluation indexes; therefore, the risk early warning evaluation result of the path node to be passed is obtained by collecting the big data of the big transportation and carrying out processing calculation of the passing characteristics, and the safety supervision early warning technology of the big transportation process is realized.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The safety monitoring and early warning method for the large transportation process based on the big data is characterized by comprising the following steps of:

early warning is carried out on the record path nodes to be passed according to preset early warning grades corresponding to the path point risk early warning evaluation indexes, and a corresponding emergency response scheme is implemented;

The method for acquiring the transportation pre-registration information of the large transportation vehicle for executing the target large transportation task, extracting the vehicle record information data, the large record information data, the path record information data and the time node record information data, and comprises the following steps:

extracting path node record data and corresponding path point importance coefficients according to the path record information, and extracting path node arrival time data and path node traffic duration data according to the time node record information;

the obtaining the road condition characteristic information and the traffic condition characteristic information of the record path node to be passed by the large transport vehicle, extracting the road condition characteristic data and the traffic condition characteristic data, inquiring through a preset large transport emergency management database according to the large record information data and the path record information data to obtain the travel point risk coefficient of the record path node, and inquiring by combining the time node record information data and the traffic condition characteristic data to obtain the travel point traffic management responsiveness data, comprising the following steps:

inquiring through a preset large transportation emergency management database according to the road point risk coefficient, the road node passing duration data and the traffic real-time crowdedness coefficient to obtain road point traffic management responsivity data of the record-keeping road node;

the processing according to the vehicle record information data and the major record information data in combination with the road condition feature data and the traffic condition feature data through a preset road transportation characteristic evaluation model to respectively obtain a space passing index and a road passing difficulty coefficient comprises the following steps:

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wherein,is a space passing index>Is the road traffic difficulty coefficient->、/>、/>、/>Respectively loading full-size data, large article outline data, large article volume level and loading weight data,>、/>、/>respectively is road space dimension data, road bearing data and road condition complexity coefficient, +.>、/>Is a preset characteristic coefficient;

the processing according to the space trafficability index and the road traffic difficulty coefficient in combination with the path record information data, the time node record information data and the traffic live characteristic data to respectively obtain a traffic smoothness index, a path point interference index and a traffic safety index of the record path node comprises the following steps:

calculating according to the road traffic difficulty coefficient and the road traffic point risk coefficient and combining the road condition complexity coefficient to obtain a traffic safety index of the record path node;

the calculation formulas of the traffic smoothness index, the path point interference index and the traffic safety index are as follows:

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wherein,for the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>Is a space passing index>Is the road traffic difficulty coefficient->Risk coefficient of the waypoints, < >>、/>The traffic duration data of the path nodes and the importance coefficient of the path points are respectively +.>、/>The road condition complexity coefficient and the traffic real-time congestion coefficient are respectively +.>、/>、/>、/>Is a preset characteristic coefficient;

the processing is performed according to the traffic smoothness index, the path point interference index, the traffic safety index and the traffic point traffic management response data to obtain the path point risk early warning response index of the record path node, including:

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wherein,for the risk early warning response index of the route point, +.>For the smooth degree index of traffic->For the pathpoint interference index, +.>For the traffic safety index>For the traffic point traffic tube responsiveness data, +.>、/>、/>Is a preset characteristic coefficient;

extracting a plurality of path point risk actual measurement evaluation indexes, a plurality of travel point risk coefficients and a path point risk early warning response index corresponding to a plurality of record path nodes through which the large transport vehicle passes through a preset large transport emergency management database, and then processing the path point risk actual measurement evaluation indexes, the plurality of travel point risk coefficients and the path point risk early warning response index with the travel point risk coefficients and the path point risk early warning response index of the record path nodes to be passed through to obtain the path point risk early warning evaluation index, wherein the method comprises the following steps:

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2. Safety supervision early warning system based on big data's big transportation process, its characterized in that, this system includes: the system comprises a memory and a processor, wherein the memory comprises a program of a safety supervision early warning method of a large-piece transportation process based on big data, and the program of the safety supervision early warning method of the large-piece transportation process based on big data realizes the following steps when being executed by the processor:

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3. The computer readable storage medium is characterized in that the computer readable storage medium comprises a large-data-based large-piece transportation process safety monitoring and early warning method program, and when the large-data-based large-piece transportation process safety monitoring and early warning method program is executed by a processor, the steps of the large-data-based large-piece transportation process safety monitoring and early warning method are realized.