CN114238789B - Urban road traffic safety control system based on eight-stage method - Google Patents
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Abstract
The invention discloses an urban road traffic safety control system based on an eight-stage method; summarizing and classifying all related services, performing eight-stage method full-flow closed-loop application on the functions of each service, wherein each service comprises an eight-stage method closed-loop application data diagram, and the eight steps comprise data acquisition, data processing, situation mastering, serial analysis, machine assessment, man-machine consultation, scheme pushing and solution feedback. The eight-stage method is penetrated to the full flow of the closed-loop application function of each business of the urban road traffic safety control method, so that the problems that the existing informatization system does not realize collaborative construction, data sharing, deep fusion and mining of data among the systems, collaborative treatment is not realized, evaluation and supervision feedback links are lacked and the like are solved. The invention realizes the closed-loop application from full data acquisition to cross-system and cross-department deep collaboration to longitudinal and transverse line deep mining of data to problem solving.
Description
Technical Field
The invention relates to the technical field, in particular to an urban road traffic safety management and control system based on an eight-stage method.
Background
In the prior art, the current urban road traffic informatization system mainly comprises two major lines of public security bureau traffic police stations/teams (or traffic administration stations, public security traffic police for short) and traffic committee (or traffic bureau, traffic commission for short). The traffic information system mainly comprises a traffic signal control system, a road video monitoring system, an electronic police system, a bayonet inspection and control system, a public security traffic management comprehensive application platform (six-in-one platform for short), a public security traffic integrated command platform and the like, and the traffic information system mainly comprises a highway monitoring system, a trunk road monitoring system, a traffic modulation system, a traffic information issuing system, a highway no-stop toll collection system, a key operation vehicle networking joint control system and the like, so that very rich traffic perception facilities are accumulated, and in general, the traffic information system constructed in the development process plays a great role in the road traffic management process.
Along with the gradual aggravation of traffic jam, the complexity of traffic control is gradually improved, and the current urban road traffic safety management and control informatization management system has a plurality of business processes which are not closed loop, so that the problems in traffic control are gradually highlighted, and the problems are mainly expressed in the following aspects.
Firstly, the cooperative construction and data sharing are not realized. Most informationized systems of each traffic related department are constructed in a chimney mode, and operate in an island mode, so that data among the systems are not fully shared, a large number of repeated construction exists, and the systems and the businesses are difficult to cooperate.
And secondly, deep fusion and mining of data among the systems are not realized. The data of the related systems such as the traffic signal control system, the electronic police system, the bayonet inspection and control system, the six-in-one platform and the like are not subjected to deep fusion and excavation, and the control support is lack of refinement, so that partial traffic business management is difficult to refine.
Thirdly, the cooperative treatment is not realized. In the actual traffic management process, a part of business related to cooperative control among a plurality of departments is difficult to solve due to the lack of a cooperative control mechanism.
And fourthly, lack of evaluation and supervision feedback links. Most traffic management causes difficulty in improving traffic management effect because a scientific quantitative evaluation and supervision feedback mechanism is not formed.
Therefore, how to realize the closed-loop application from full data collection to cross-system and cross-department deep collaboration to longitudinal and transverse line deep mining of data to problem solving becomes a technical problem which needs to be solved by the technicians in the field.
Disclosure of Invention
In view of the above-mentioned defects of the prior art, the invention provides an urban road traffic safety management and control system based on an eight-stage method, which aims to realize closed-loop application from full data acquisition to cross-system and cross-department deep collaboration to longitudinal and transverse deep mining of data to problem solving and improve the traffic control refinement level.
In order to achieve the aim, the invention discloses an urban road traffic safety control system based on an eight-stage method; the system summarizes and classifies all related businesses according to responsibilities of the served departments, extracts business function modules according to requirements, performs eight-stage method full-flow closed-loop application on each business function module, and comprises eight-stage method closed-loop application data graphs, wherein the eight-stage method closed-loop application data graphs comprise the following steps:
step 1, data acquisition; the collected data comprise four elements of traffic in urban road traffic, namely static data and dynamic data related to people, vehicles, roads and environments;
Step 2, data processing; checking and correcting all the collected static data, and cleaning, converting and integrating all the dynamic data;
After completion, loading all the static data and all the dynamic data into a data warehouse or a data mart to serve as a basis for subsequent online analysis processing and/or data mining;
after the completion, according to the management and control requirements of each service, the corresponding static data or the corresponding dynamic data are processed by combining a related algorithm model and an engine to form a data base;
Step 3, situation mastering; calculating macroscopic, mesoscopic and microscopic situations related to each business according to the data base, and presenting the macroscopic, mesoscopic and microscopic situations through a human-computer interface;
Step 4, serial analysis; clicking any service to be analyzed in a corresponding computer system to obtain each static data and each dynamic data associated with the person, the vehicle, the road and/or the environment in the four traffic elements of the corresponding service, and performing serial analysis to form serial data;
Step 5, machine evaluation; analyzing related evaluation indexes through a built-in model algorithm of computer software according to the serial data of any business to be analyzed, and primarily evaluating the existing problems;
Step 6, man-machine consultation; aiming at the problems in the preliminary evaluation, the expert team carries out consultation on line and confirms the final solution; meanwhile, continuously training the accuracy of the built-in model algorithm in the step 5 according to a large number of actual cases of man-machine consultation results;
step 7, pushing a scheme; according to the final solution, the final solution is sent to each corresponding department, and is solved according to the responsibility of each department, and cross-department linkage is carried out to jointly advance;
Step 8, solving feedback; the progress of each final solution is tracked in time, supervision is carried out according to the needs, feedback is carried out on the final implementation condition of each final solution, and the process is repeated until the problem is finally solved.
Preferably, the four elements of traffic, namely the static data and the dynamic data related to people, vehicles, roads and environments, include the following three major categories:
the first type is the static data and the dynamic data acquired by the current sensing equipment;
the second category is the static data and the dynamic data generated by the existing urban road traffic informatization system;
The third type is the static data and the dynamic data acquired by sensing equipment reconstruct required by key management and control requirements of each service;
The current situation sensing equipment and the existing urban road traffic informatization system comprise sensing equipment and informatization systems established by public security traffic police, traffic commission, living building bureau, urban capacity greening bureau, urban management bureau, urban ecological environment bureau and/or urban weather bureau.
Preferably, the macroscopic situation, the mesoscopic situation and the microscopic situation related to each business comprise the macroscopic situation, the mesoscopic situation and the microscopic situation of the urban road network in different time spans of the running vehicle and the parking vehicle, the macroscopic situation, the mesoscopic situation and the microscopic situation of equipment in the urban road traffic are the macroscopic situation, the mesoscopic situation and the microscopic situation of different types of implementation equipment, the quantity, the proportion, the damage rate and the repair rate of the equipment are the mirror image simulation presentation of each element in the urban road traffic;
The data of the macro situation, the mesoscopic situation and the micro situation related to each business can be counted or searched in a zoning or self-defined range.
Preferably, the series analysis in the step 4 includes clicking on any vehicle in a road network to connect in series to all paths travelled by the vehicle, all driver's licenses and all violation information;
The series analysis comprises all congestion, all direction queuing length and overflow times and durations which can occur since the history of the crossing can be series by clicking any crossing
Preferably, in the step 5, the built-in model algorithm includes a sensing device data continuity analysis model, a sensing device data delay analysis model, a sensing device data type asynchronous analysis model, and a traffic event handling comprehensive evaluation sub-model;
The sensing equipment data continuity analysis model, the sensing equipment data delay analysis model and the asynchronous analysis model in the sensing equipment data are compared and analyzed by the sensing equipment such as an electronic police, a bayonet and a video monitor, and the data uploaded to the system, so that the system can acquire abnormal conditions of the sensing equipment, and the discovered abnormal equipment conditions including data transmission interruption, no-flow and data transmission delay are pushed to a service manager or an operation and maintenance manager for timely repair, thereby further improving the equipment integrity rate and the data accuracy rate;
The sensing equipment data continuity analysis model is used for generating a time stamp and data return warehouse-in time information based on data and judging whether the return data of each equipment in the current time period are continuous and stable.
Preferably, the workflow of the perception device data continuity analysis model is as follows:
step 5.A1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
A2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
a3, analyzing data continuity; calculating the generation time difference of the generation time series adjacent record data, acquiring and comparing the continuity time threshold value of the current time period, and marking the related data if the continuity time threshold value exceeds the threshold value;
Marking data of which the generated time sequence is inconsistent with the warehousing time sequence in time sequence arrangement;
The input of the perception device data continuity analysis model comprises device basic information such as device ID, device type, device longitude and latitude, and data information such as data ID, device ID, data generation time stamp, data return warehouse-in time, and the like;
And the output result of the perception device data continuity analysis model is a device ID with the problem of data discontinuity, a discontinuous time period and related data.
Preferably, the sensing device data transmission delay analysis model is used for analyzing the returned data delay of each device in the current time period based on the information such as the data generation time stamp, the data returned warehouse-in time and the like, and comprises the following steps:
Step 5.B1, data preprocessing; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
B2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
b3, analyzing data transmission delay; calculating the data transmission time delay of each record of the generated time sequence, namely returning the difference value between the warehouse-in time and the data generation time, acquiring and comparing the data transmission time delay threshold value of the current time period, and marking the related data if the data transmission time delay threshold value exceeds the threshold value;
The sensing equipment data transmission delay analysis model analyzes each piece of recorded data transmission delay in the current time period, and marks related data for the deviation of the transmission delay and the average transmission delay in the current time period exceeding a threshold value;
The input data of the perception device data transmission delay analysis model comprises device basic information such as device ID, device type, device longitude and latitude and the like, and data information such as data ID, device ID, data generation time stamp, data return warehouse-in time and the like;
The perception device data transmission delay analysis model outputs device ID with data transmission delay problem, problem time period and related data.
Preferably, the asynchronous analysis model in the sensing device data type is used for data generation time stamp, data feedback warehouse-in time and other information, analyzing the reference clock time of each device, comparing the output data time of the same device with the reference clock time, and judging the data clock synchronism, and comprises the following steps:
Step 5, C1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
Acquiring longitude and latitude information of the generated data equipment, and carrying out matching association with a computable road network map;
Step 5, C2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence; based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
step 5, C3, analyzing the time synchronism of the data; analyzing and generating the transmission delay of each piece of recorded data of the time sequence, and marking related data if the transmission delay is negative;
The asynchronous analysis model in the sensing equipment data type comprises equipment basic information such as equipment ID, equipment type, equipment longitude and latitude, and the like, and data information such as data ID, equipment ID, data generation time stamp, data return warehouse-in time, and the like; the model output contains the device ID, the problem period, and the associated data, where the data clock is not synchronized.
Preferably, the traffic event handling comprehensive evaluation score model is used for comprehensively considering congestion, traffic police, emergency verification and handling speed, including average verification/handling time, maximum verification/handling time, verification/handling amount and weighting verification and handling, and building a comprehensive score model, and comprises the following steps:
step 5.D1, formulating weights of various alarm categories;
The class weight of the i-th class of alarms is marked as w i, the class weights of congestion, traffic conditions and emergencies are respectively marked as w 1、w2 and w 3, the total number of alarm classes is m, and the requirements are met And the event number of the ith alarm in the statistical period is recorded as N i, and then 100 minutes of total scores are adopted, and the calculation formula of the score S i of the ith alarm is obtained by further weighting by combining the event number is as follows:
Step 5.D2, formulating assessment standards for checking and disposing time of various alarm events;
For each single alarm event, the processing time of verification and treatment is divided into three assessment grades from fast to slow, namely A, B, C;
setting the quick verification time threshold of the type i alarm verification according to the service requirement as follows Slow verify time threshold is/>Then when the class event verification time is less than/>Rated A when the verification time is between/>And/>Rating B when the verification time is greater than/>The time rating is C;
The comprehensive evaluation score model for traffic event handling adopts a deduction system, and the evaluation rating A, B, C corresponds to the rating coefficients of 0, 0.5 and 1 respectively;
the evaluation coefficient verified by the j-th event of the i-th type alarm is recorded as Similarly, for event handling, the fast handling time threshold for class i alarm verification is also set to/>Slow treatment time threshold is/>Then when the class event handling time is less than/>Rated a when treatment time is between/>And/>Rating B when in between, when treatment time is greater than/>Time rating is C, and the evaluation coefficient of the treatment of the j-th event of the i-th type alarm is recorded as/>
Step 5.D3, formulating the weight of the verification and treatment links;
The weights of the verification and treatment links are respectively set to alpha and beta according to the examination requirements, and the alpha+beta=1 is satisfied, and the rating coefficient of the j-th event of the i-th alarm is Satisfies P i,j epsilon [0,1];
step 5.D4, calculating comprehensive scores;
According to the verification and disposition conditions of various alarms in each administrative district, the comprehensive scoring formula is calculated as follows:
The invention has the beneficial effects that:
The application of the invention realizes the closed-loop application from full data acquisition to cross-system and cross-department deep collaboration to longitudinal and transverse line deep mining of data to problem solving, can be used as an effective reference for the construction of an urban road traffic safety management and control informatization system, is beneficial to realizing quality improvement and efficiency improvement of traffic safety management and control and has good tendency of urban management.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
Fig. 1 shows a schematic structural diagram of an embodiment of the present invention.
FIG. 2 illustrates a workflow diagram of a model of perceived device data continuity analysis in one embodiment of the present invention.
Fig. 3 is a flowchart illustrating the operation of the perceived device data transmission delay analysis model in an embodiment of the present invention.
FIG. 4 illustrates a flowchart of the operation of the perceived device data clock synchronization analysis model in one embodiment of the present invention.
Detailed Description
Examples
As shown in fig. 1, an urban road traffic safety control system based on an eight-stage method; the system summarizes and classifies all related businesses according to responsibilities of the served departments, extracts business function modules according to requirements, performs eight-stage method full-flow closed-loop application on the function modules of each business, and comprises eight-stage method closed-loop application data graphs, wherein the eight-stage method closed-loop application data graphs comprise the following steps:
step 1, data acquisition; the collected data comprise four elements of traffic in urban road traffic, namely static data and dynamic data related to people, vehicles, roads and environments;
Step 2, data processing; checking and correcting all the collected static data, and cleaning, converting and integrating all the dynamic data;
After completion, all static data and all dynamic data are loaded into a data warehouse or a data mart to serve as the basis for subsequent online analysis processing and/or data mining;
after the completion, according to the management and control requirements of each service, the corresponding static data or the corresponding dynamic data are processed by combining the related algorithm model and the engine to form a data base;
step 3, situation mastering; according to the data base, calculating macroscopic, mesoscopic and microscopic situations related to each service, and presenting the macroscopic, mesoscopic and microscopic situations through a human-computer interface;
Step 4, serial analysis; clicking any service to be analyzed in a corresponding computer system to obtain each static data and each dynamic data associated with people, vehicles, roads and/or environments in traffic four elements of the corresponding service, and performing serial analysis to form serial data;
Step 5, machine evaluation; according to serial data of any business to be analyzed, analyzing related evaluation indexes through a built-in model algorithm of computer software, and primarily evaluating the existing problems;
Step 6, man-machine consultation; aiming at the problems in preliminary evaluation, the expert team carries out consultation on line and confirms the final solution; meanwhile, continuously training the accuracy of the built-in model algorithm in the step 5 according to a large number of actual cases of man-machine consultation results;
step 7, pushing a scheme; according to the final solution, the solution is sent to each corresponding department, and the solution is solved according to the responsibility of each department, and cross-department linkage is carried out to jointly advance;
Step 8, solving feedback; the progress of each final solution is tracked in time, supervision is carried out according to the needs, feedback is carried out on the final implementation condition of each final solution, and the process is repeated until the problem is finally solved.
The principle of the invention is as follows:
The invention solves the problems that the existing informatization system does not realize collaborative construction, data sharing, deep fusion and excavation of data among the systems, collaborative treatment, lack of evaluation and supervision feedback links and the like by penetrating an eight-stage method to the full flow of each business closed-loop application function of the urban road traffic safety control method.
Preferably, the four elements of traffic, namely static data and dynamic data related to people, vehicles, roads and environments, include the following three general categories:
the first is static data and dynamic data acquired by the current sensing equipment;
The second category is static data and dynamic data generated by the existing urban road traffic informatization system;
the third type is static data and dynamic data acquired by sensing equipment reconstruct required by key management and control requirements of each service;
the current situation sensing equipment and the existing urban road traffic informatization system comprise sensing equipment and informatization systems established by public security traffic police, traffic commission, building bureau, urban capacity greening bureau, urban management bureau, urban ecological environment bureau and/or urban meteorological bureau.
Preferably, the macroscopic situation, the mesoscopic situation and the microscopic situation related to each business comprise the macroscopic situation, the mesoscopic situation and the microscopic situation of the urban road network in different time spans of the running vehicle and the parking vehicle, the macroscopic situation, the mesoscopic situation and the microscopic situation of equipment in urban road traffic, the quantity, the proportion, the damage rate and the repair rate of different types of implementation equipment, and the mirror image simulation presentation of each element in the urban road traffic;
The data of the macro situation, the mesoscopic situation and the micro situation related to each service can be counted or searched in a zoned or custom range.
Preferably, the series analysis in step 4 includes clicking any vehicle in the road network to connect in series to all paths travelled by the vehicle, all driver's licenses and all violation information;
The tandem analysis includes all congestion, all direction queuing length and number of overflows and duration that can occur since the tandem intersection has history by clicking on any one intersection
Preferably, in step 5, the built-in model algorithm includes a sensing device data continuity analysis model, a sensing device data delay analysis model, a sensing device data type asynchronous analysis model, and a traffic event handling comprehensive evaluation sub-model;
The sensing equipment data continuity analysis model, the sensing equipment data delay analysis model and the asynchronous analysis model in the sensing equipment data are compared and analyzed by the sensing equipment such as electronic police, a bayonet and video monitoring to the data of the system, the system can acquire the abnormal running condition of the sensing equipment, and the discovered abnormal equipment conditions including data transmission interruption, no-flow and data transmission delay are pushed to a service manager or an operation and maintenance manager for timely repair, so that the equipment integrity rate and the data accuracy rate are further improved;
The sensing equipment data continuity analysis model is used for generating a time stamp and data return warehouse-in time information based on data and judging whether the return data of each equipment in the current time period are continuous and stable.
Preferably, the workflow of the perception device data continuity analysis model is as follows:
step 5.A1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
A2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
a3, analyzing data continuity; calculating the generation time difference of the generation time series adjacent record data, acquiring and comparing the continuity time threshold value of the current time period, and marking the related data if the continuity time threshold value exceeds the threshold value;
Marking data of which the generated time sequence is inconsistent with the warehousing time sequence in time sequence arrangement;
The input of the perception device data continuity analysis model comprises device basic information such as device ID, device type, device longitude and latitude and data information such as data ID, device ID, data generation time stamp, data return warehouse-in time and the like;
The output result of the perception device data continuity analysis model is the device ID with the data discontinuity problem, the discontinuity time period and the related data.
Preferably, the sensing device data transmission delay analysis model is used for analyzing the returned data delay of each device in the current time period based on the information such as the data generation time stamp, the data returned warehouse-in time and the like, and comprises the following steps:
Step 5.B1, data preprocessing; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
B2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
b3, analyzing data transmission delay; calculating the data transmission time delay of each record of the generated time sequence, namely returning the difference value between the warehouse-in time and the data generation time, acquiring and comparing the data transmission time delay threshold value of the current time period, and marking the related data if the data transmission time delay threshold value exceeds the threshold value;
The data transmission delay analysis model of the sensing equipment analyzes each record data transmission delay in the current time period, and marks related data for the deviation of the transmission delay and the average transmission delay in the current time period exceeding a threshold value;
The input data of the perception device data transmission delay analysis model comprises device ID, device type, device longitude and latitude and other device basic information, and data ID, device ID, data generation time stamp, data return warehouse-in time and other data information;
the perceived device data transmission delay analysis model outputs a device ID with data transmission delay problems, a problem period, and related data.
Preferably, the asynchronous analysis model in the sensing equipment data is used for data generation time stamp, data feedback warehouse-in time and other information, analyzing the reference clock time of each equipment, comparing the output data time of the same equipment with the reference clock time, and judging the synchronism of the data clock, and comprises the following steps:
Step 5, C1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
Acquiring longitude and latitude information of the generated data equipment, and carrying out matching association with a computable road network map;
Step 5, C2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence; based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
step 5, C3, analyzing the time synchronism of the data; analyzing and generating the transmission delay of each piece of recorded data of the time sequence, and marking related data if the transmission delay is negative;
The asynchronous analysis model in the sensing equipment data type comprises equipment basic information such as equipment ID, equipment type, equipment longitude and latitude, and the like, and data information such as data ID, equipment ID, data generation time stamp, data return warehouse-in time, and the like; the model output contains the device ID, the problem period, and the associated data, where the data clock is not synchronized.
Preferably, the traffic event handling comprehensive evaluation score model is used for comprehensively considering congestion, traffic police, emergency verification and handling speed, including average verification/handling time, maximum verification/handling time, verification/handling amount and weighting verification and handling, and establishing a comprehensive score model, and comprises the following steps:
step 5.D1, formulating weights of various alarm categories;
The class weight of the i-th class of alarms is marked as w i, the class weights of congestion, traffic conditions and emergencies are respectively marked as w 1、w2 and w 3, the total number of alarm classes is m, and the requirements are met And the event number of the ith alarm in the statistical period is recorded as N i, and then 100 minutes of total scores are adopted, and the calculation formula of the score S i of the ith alarm is obtained by further weighting by combining the event number is as follows:
Step 5.D2, formulating assessment standards for checking and disposing time of various alarm events;
For each single alarm event, the processing time of verification and treatment is divided into three assessment grades from fast to slow, namely A, B, C;
setting the quick verification time threshold of the type i alarm verification according to the service requirement as follows Slow verify time threshold is/>Then when the class event verification time is less than/>Rated A when the verification time is between/>And/>Rating B when the verification time is greater than/>The time rating is C;
the comprehensive evaluation score model for traffic event handling adopts a deduction method, and the evaluation rating A, B, C corresponds to the rating coefficients of 0, 0.5 and 1 respectively;
the evaluation coefficient verified by the j-th event of the i-th type alarm is recorded as Similarly, for event handling, the fast handling time threshold for class i alarm verification is also set to/>Slow treatment time threshold is/>Then when the class event handling time is less than/>Rated a when treatment time is between/>And/>Rating B when in between, when treatment time is greater than/>Time rating is C, and the evaluation coefficient of the treatment of the j-th event of the i-th type alarm is recorded as/>
Step 5.D3, formulating the weight of the verification and treatment links;
The weights of the verification and treatment links are respectively set to alpha and beta according to the examination requirements, and the alpha+beta=1 is satisfied, and the rating coefficient of the j-th event of the i-th alarm is Satisfies P i,j epsilon [0,1];
step 5.D4, calculating comprehensive scores;
According to the verification and disposition conditions of various alarms in each administrative district, the comprehensive scoring formula is calculated as follows:
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (5)
1. Urban road traffic safety control system based on eight-stage method; the system is characterized in that the system summarizes and classifies all related businesses according to responsibilities of a service department, extracts business function modules according to requirements, performs eight-stage method full-flow closed-loop application on each business function module, and comprises eight-stage method closed-loop application data graphs, and specifically comprises the following steps:
step 1, data acquisition; the collected data comprise four elements of traffic in urban road traffic, namely static data and dynamic data related to people, vehicles, roads and environments;
Step 2, data processing; checking and correcting all the collected static data, and cleaning, converting and integrating all the dynamic data;
After completion, loading all the static data and all the dynamic data into a data warehouse or a data mart to serve as a basis for subsequent online analysis processing and/or data mining;
after the completion, according to the management and control requirements of each service, the corresponding static data or the corresponding dynamic data are processed by combining a related algorithm model and an engine to form a data base;
Step 3, situation mastering; calculating macroscopic, mesoscopic and microscopic situations related to each business according to the data base, and presenting the macroscopic, mesoscopic and microscopic situations through a human-computer interface;
Step 4, serial analysis; clicking any service to be analyzed in a corresponding computer system to obtain each static data and each dynamic data associated with the person, the vehicle, the road and/or the environment in the four traffic elements of the corresponding service, and performing serial analysis to form serial data;
Step 5, machine evaluation; analyzing related evaluation indexes through a built-in model algorithm of computer software according to the serial data of any business to be analyzed, and primarily evaluating the existing problems;
the built-in model algorithm comprises a sensing equipment data continuity analysis model, a sensing equipment data delay analysis model, a sensing equipment data type asynchronous analysis model and a traffic event handling comprehensive evaluation sub-model;
The sensing equipment data continuity analysis model, the sensing equipment data delay analysis model and the asynchronous analysis model in the sensing equipment data are compared and analyzed by the electronic police, the bayonet and the video monitoring sensing equipment to the data of the system, the system can acquire abnormal conditions of the sensing equipment operation, the discovered abnormal equipment conditions including data transmission interruption, no-flow and data transmission delay are pushed to a service manager or an operation and maintenance manager for timely repair, and the equipment integrity rate and the data accuracy rate are further improved;
the sensing equipment data continuity analysis model is used for generating a time stamp and data return warehouse-in time information based on data and judging whether the return data of each equipment in the current time period are continuous and stable or not;
the workflow of the perception device data continuity analysis model is as follows:
step 5.A1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
A2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
a3, analyzing data continuity; calculating the generation time difference of the generation time series adjacent record data, acquiring and comparing the continuity time threshold value of the current time period, and marking the related data if the continuity time threshold value exceeds the threshold value;
Marking data of which the generated time sequence is inconsistent with the warehousing time sequence in time sequence arrangement;
The input of the perception equipment data continuity analysis model comprises equipment ID, equipment type, equipment longitude and latitude equipment basic information, data ID, equipment ID, data generation time stamp and data return warehouse-in time data information;
the output result of the perception device data continuity analysis model is a device ID with a data discontinuity problem, a discontinuous time period and related data;
The sensing equipment data transmission delay analysis model is used for generating a time stamp and data return warehouse-in time information based on data and analyzing the return data delay of each equipment in the current time period, and comprises the following steps:
Step 5.B1, data preprocessing; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
B2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence;
b3, analyzing data transmission delay; calculating the data transmission time delay of each record of the generated time sequence, namely returning the difference value between the warehouse-in time and the data generation time, acquiring and comparing the data transmission time delay threshold value of the current time period, and marking the related data if the data transmission time delay threshold value exceeds the threshold value;
The sensing equipment data transmission delay analysis model analyzes each piece of recorded data transmission delay in the current time period, and marks related data for the deviation of the transmission delay and the average transmission delay in the current time period exceeding a threshold value;
The input data of the perception device data transmission delay analysis model comprises device ID, device type, device longitude and latitude device basic information, data ID, device ID, data generation time stamp and data return warehouse-in time data information;
The perception device data transmission delay analysis model outputs a device ID with a data transmission delay problem, a problem time period and related data;
the asynchronous analysis model in the sensing equipment data type is used for generating a time stamp and returning data to the warehouse-in time information, analyzing the reference clock time of each equipment, comparing the data output time of the same equipment with the reference clock time, and judging the synchronism of the data clock, and comprises the following steps:
Step 5, C1, preprocessing data; grouping the accessed data according to the equipment ID interval, and marking the data with missing and incomplete key field information;
Acquiring longitude and latitude information of the generated data equipment, and carrying out matching association with a computable road network map;
Step 5, C2, constructing a data time sequence; based on the data generation time stamp, sorting the new access data in groups, inserting the new access data into the established sequence, and constructing a new data generation time sequence; based on the data warehousing time, sorting the new access data in groups to construct a data warehousing time sequence;
step 5, C3, analyzing the time synchronism of the data; analyzing and generating the transmission delay of each piece of recorded data of the time sequence, and marking related data if the transmission delay is negative;
The asynchronous analysis model in the sensing equipment data type comprises equipment ID, equipment type, equipment longitude and latitude basic information, data ID, equipment ID, data generation time stamp and data return warehouse-in time data information; the model output contains a device ID with a data clock asynchronous problem, a problem time period and related data;
Step 6, man-machine consultation; aiming at the problems in the preliminary evaluation, the expert team carries out consultation on line and confirms the final solution; meanwhile, continuously training the accuracy of the built-in model algorithm in the step 5 according to a large number of actual cases of man-machine consultation results;
step 7, pushing a scheme; according to the final solution, the final solution is sent to each corresponding department, and is solved according to the responsibility of each department, and cross-department linkage is carried out to jointly advance;
Step 8, solving feedback; the progress of each final solution is tracked in time, supervision is carried out according to the needs, feedback is carried out on the final implementation condition of each final solution, and the process is repeated until the problem is finally solved.
2. The eight-phase method-based urban road traffic safety management and control system according to claim 1, characterized in that said four elements of traffic, namely said static data and said dynamic data related to people, vehicles, roads and environment, comprise the following three major classes:
the first type is the static data and the dynamic data acquired by the current sensing equipment;
the second category is the static data and the dynamic data generated by the existing urban road traffic informatization system;
The third type is the static data and the dynamic data acquired by sensing equipment reconstruct required by key management and control requirements of each service;
The current situation sensing equipment and the existing urban road traffic informatization system comprise sensing equipment and informatization systems established by public security traffic police, traffic commission, living building bureau, urban capacity greening bureau, urban management bureau, urban ecological environment bureau and/or urban weather bureau.
3. The urban road traffic safety management and control system based on the eight-stage method according to claim 1, wherein the macroscopic situation, the mesoscopic situation and the microscopic situation related to each business comprise the macroscopic situation, the mesoscopic situation and the microscopic situation of the urban road network in different time spans of the traveling vehicles and the parking vehicles, the macroscopic situation, the mesoscopic situation and the microscopic situation of equipment in the urban road traffic are represented by mirror image simulation of each element in the urban road traffic, and the quantity, the proportion, the damage rate and the repair rate of different types of implementation equipment are represented by the macroscopic situation, the mesoscopic situation and the microscopic situation;
The data of the macro situation, the mesoscopic situation and the micro situation related to each business can be counted or searched in a zoning or self-defined range.
4. The urban road traffic safety management and control system based on the eight-stage method according to claim 1, characterized in that,
The serial analysis in the step 4 comprises the step of clicking any vehicle in a road network to connect all paths, all driver drivers' licenses and all violation information which can be driven by the vehicle in series;
The tandem analysis includes all congestion, all direction queuing lengths and overflow times and durations that can occur since the intersection has history by clicking on any intersection.
5. The eight-phase method-based urban road traffic safety management and control system according to claim 1, wherein the traffic event handling comprehensive evaluation score model is used for comprehensively considering congestion, traffic police, emergency verification, handling speed, including average verification/handling time, maximum verification/handling time, verification/handling amount, and weighting verification, handling, and establishing a comprehensive scoring model, comprising the steps of:
step 5.D1, formulating weights of various alarm categories;
The class weight of the i-th class of alarms is marked as w i, the class weights of congestion, traffic conditions and emergencies are respectively marked as w 1、w2 and w 3, the total number of alarm classes is m, and the requirements are met And the event number of the ith alarm in the statistical period is recorded as N i, and then 100 minutes of total scores are adopted, and the calculation formula of the score S i of the ith alarm is obtained by further weighting by combining the event number is as follows:
Step 5.D2, formulating assessment standards for checking and disposing time of various alarm events;
For each single alarm event, the processing time of verification and treatment is divided into three assessment grades from fast to slow, namely A, B, C;
setting the quick verification time threshold of the type i alarm verification according to the service requirement as follows Slow verify time threshold isThen when the class event verification time is less than/>Rated A when the verification time is between/>And/>Rating B when the verification time is greater than/>The time rating is C;
The comprehensive evaluation score model for traffic event handling adopts a deduction system, and the evaluation rating A, B, C corresponds to the rating coefficients of 0, 0.5 and 1 respectively;
the evaluation coefficient verified by the j-th event of the i-th type alarm is recorded as Similarly, for event handling, the fast handling time threshold for class i alarm verification is also set to/>Slow treatment time threshold is/>Then when the class event handling time is less than/>Rated a when treatment time is between/>And/>Rating B when in between, when treatment time is greater thanTime rating is C, and the evaluation coefficient of the treatment of the j-th event of the i-th type alarm is recorded as/>
Step 5.D3, formulating the weight of the verification and treatment links;
The weights of the verification and treatment links are respectively set to alpha and beta according to the examination requirements, and the alpha+beta=1 is satisfied, and the rating coefficient of the j-th event of the i-th alarm is Satisfies P i,j epsilon [0,1];
step 5.D4, calculating comprehensive scores;
According to the verification and disposition conditions of various alarms in each administrative district, the comprehensive scoring formula is calculated as follows:
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