CN117610932A - Public transport operation risk management and control system based on artificial intelligence - Google Patents
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Abstract
The invention discloses a public transport operation risk management and control system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence, comprising a data acquisition module, a data analysis module, an intelligent scheduling module, a scheduling plan evaluation module, a risk behavior monitoring module and a security event processing module; processing and analyzing the original data by using an artificial intelligence technology through a data analysis module, and generating a related report and statistical data; scheduling and path planning of the buses are performed through the intelligent scheduling module, so that the running efficiency of the buses is improved; monitoring and evaluating the implementation effect of the scheduling plan through a scheduling plan evaluation module; monitoring and recording the running state of the vehicle, the behavior of a driver and the behavior of a passenger through a risk behavior monitoring module; and the emergency processing module is used for processing emergency safety events in the running process of the bus, and performing quick response aiming at alarm information so as to ensure the safety of passengers and vehicles.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a public transport operation risk management and control system based on artificial intelligence.
Background
With the acceleration of the urban process, the buses are taken as important components of urban traffic, and the operation safety and efficiency of the buses are critical to the development of cities and the travel of residents.
The traditional bus operation risk management and control system is used for summarizing dangerous factors and possible dangerous accidents existing in the operation of a bus by analyzing the driving line, the direction, the use frequency of a light and a brake system and the like, so that a driver can pay more attention to the operation safety in the driving process; the bus route is scientifically arranged according to the passenger flow volume of the route and the station, and the system uses a scheduling mode of combining scheduling with rolling scheduling, so that the intelligent, real-time and scientific operation scheduling of the vehicle is realized, and the command scheduling of the operation vehicle is enhanced.
However, the traditional public transportation operation risk management and control system has some problems that a large amount of manpower and material resources are required to be input, and artificial omission and mistakes are easy to occur; because of the limitation of information transmission and processing, the system has longer response time to the emergency and can not respond effectively in time; the system has weak processing and analysis capability on big data, and valuable information is difficult to extract from massive data.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the public transport operation risk management and control system based on artificial intelligence, which uploads and stores the collected original data in real time through the data collection module, so that inaccuracy and delay of the traditional manual reporting data are avoided; the data analysis module is used for processing and analyzing the original data by utilizing an artificial intelligence technology, predicting the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops, generating related reports and statistical data, providing scientific basis for bus scheduling and path planning decision, and improving the efficiency and the accuracy; the intelligent scheduling module is used for calculating the departure frequency, the vehicle allocation guide coefficient and the line planning guide coefficient according to the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of bus stops, so that the scheduling and the path planning of the bus are carried out, and the running efficiency of the bus is improved; the scheduling plan evaluation module is used for calculating the implementation effect index of the scheduling plan, monitoring and evaluating the implementation effect of the scheduling plan, screening out data with poor implementation effect, and taking measures for optimizing parameters and setting the index, so that the quality and effect of public transportation service are further improved; the risk behavior monitoring module is used for monitoring and recording the running state of the vehicle, the behavior of a driver and the behavior of a passenger, so that the real-time monitoring of sudden safety events in the running process of the bus is realized, and the safety and emergency response capability are improved; the emergency processing module is used for processing emergency safety events in the running process of the bus, and fast responding is carried out aiming at alarm information, so that the safety of passengers and vehicles is ensured, and the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: public transport operation risk management and control system based on artificial intelligence includes:
and a data acquisition module: the system is used for acquiring GPS track data, passenger flow data, vehicle flow data, accident frequency data, traffic signal lamp state and time sequence information, social media real-time feedback data, residence time and meteorological data in real time by using the sensors and the monitoring equipment, and uploading and storing the acquired original data in real time;
and a data analysis module: the system is used for processing and analyzing the original data by utilizing an artificial intelligence technology, predicting the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops, and generating related reports and statistical data; the road condition complexity index specifically refers to the complexity of road traffic conditions; the population distribution coefficient of the bus stops specifically refers to the population density degree near the bus stops;
and the intelligent scheduling module is used for: the method comprises the steps of calculating departure frequency, a vehicle allocation guide coefficient and a line planning guide coefficient according to the running efficiency of the bus, road condition complexity index and population distribution coefficient of bus stops, and scheduling and path planning of the bus;
a dispatch plan evaluation module: the system comprises an intelligent scheduling module, a scheduling management module and a scheduling management module, wherein the intelligent scheduling module is used for receiving data transmitted by the intelligent scheduling module, calculating a plan implementation effect index, monitoring and evaluating the implementation effect of the scheduling, screening out data with poor implementation effect and taking measures for optimizing parameter and index setting;
the risk behavior monitoring module comprises a vehicle running monitoring unit, a driver behavior monitoring unit and a passenger behavior monitoring unit, and is used for monitoring and recording the running state of the vehicle, the behavior of the driver and the behavior of the passenger, so as to realize real-time monitoring of sudden safety events in the running process of the bus;
a security event processing module: the system is used for receiving the data transmitted by the risk behavior monitoring module, processing sudden safety events in the running process of the bus and rapidly responding to the alarm information.
In a preferred embodiment, the specific processing procedure of the data analysis module is as follows:
a1, cleaning and preprocessing the collected original data, including removing abnormal values and filling missing values;
a2, extracting features from the original data based on feature engineering of different indexes for subsequent index calculation and analysis; the characteristics include average speed of the vehicle, total distance travelled, total time travelled, residence time, temperature, humidity, precipitation, traffic flow, accident frequency, green light duration, red light duration, number of passengers per station, number of passengers liked, and number of passengers disliked;
a3, calculating the running efficiency GYx of the bus according to the average speed sp of the vehicle, the running total distance L, the running total time T and the stay time T,
a4, comprehensively analyzing the road condition complexity index LFC, LFC= [ c ] in terms of traffic flow JL, accident occurrence frequency Fs, green light duration tl, red light duration th, temperature w, humidity s and precipitation j (e) JL+Fs +tl-th)×(w+s+j)dt；
A5, combining the number Nm of passengers, the number Nh of good scores of passengers and the number Nc of poor scores of passengers at each station, calculating a population distribution coefficient GRf of the bus station,
a6, taking the characteristics as independent variables, taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables, and analyzing, modeling and predicting the data by applying an artificial intelligence technology;
a7, predicting and analyzing future data by using the model, and predicting bus running efficiency, road condition complexity index and bus stop crowd distribution coefficient target variables;
and A8, generating a related report and statistical data according to the data analysis result.
In a preferred embodiment, the application of artificial intelligence techniques analyzes, models and predicts data; the treatment process is as follows:
a61, acquiring data related to the features as a data set, and dividing the data set into a training set and a testing set by adopting a cross-validation method;
a62, taking the characteristics as independent variables, and taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables;
a63, selecting a deep learning algorithm, and establishing a prediction model;
a64, training the model by using a training set, and calculating the mean square error of the model for evaluating the performance of the model; the calculation formula of the mean square error tau is specifically as follows:wherein Σ represents the sum formula, y represents the actual observation value, +.>Representing a model predictive value, n representing the number of samples;
a65, performing tuning operation on the model according to the performance index of the model on the training set; the tuning operation comprises the steps of adjusting super parameters and changing a model structure;
and A66, verifying the optimized model by using the test set.
In a preferred embodiment, the specific processing procedure of the intelligent scheduling module is as follows:
b1, calculating departure frequency Cf according to bus running efficiency GYx and bus stop crowd distribution coefficient GRf, wherein Cf=mu×e -ω×GYx +phi x ln (1+GRf), where μ, ω, phi represent parameters set according to the actual situation;
b2, calculating a vehicle distribution guide coefficient Cfz according to the running efficiency GYx of the bus and the road condition complexity index LFC,wherein v and b represent the value range in the integral range, and r and p represent indexes set according to actual conditions;
b3, calculating a route planning guidance coefficient Xgz by combining the road condition complexity index and the population distribution coefficient of the bus stops,wherein n represents the total number of stations, lfci represents the road condition complexity index of the ith station, and GRfi represents the population distribution coefficient of the bus station of the ith station;
b4, making an adjustment plan according to the calculated departure frequency, the vehicle allocation guide coefficient and the line planning guide coefficient; the adjustment scheme comprises the steps of adjusting departure times and vehicle quantity in different time periods and road sections, and adjusting line trend and station positions;
b5, conveying the dispatch plan to related vehicles and drivers, and implementing dispatch and path planning.
In a preferred embodiment, the specific processing procedure of the scheduling plan evaluation module is as follows:
the method comprises the steps of C1, implementing a scheduling plan, and calculating a plan implementation effect index through real-time monitoring and data collection, wherein the plan implementation effect index is used for monitoring and evaluating the implementation effect of the scheduling plan; the calculation formula of the plan implementation effect index JQ specifically comprises the following steps:wherein Zmy denotes an overall satisfaction index, wf denotes the number of service, szd denotes the number of actual quasi-shift times, and ZBc denotes the number of total shift times; the total satisfaction index can be obtained through passenger feedback investigation, complaint rate and scoring mode, the actual quasi-point shift number can be obtained through comparing the vehicle arrival time recorded by the real-time monitoring system with the estimated arrival time, and the total shift number is the number of all shifts in a specified time period;
and C2, judging and comparing the planned implementation effect index JQ with a preset implementation effect threshold JQ, if JQ is larger than the JQ threshold, the planned implementation effect is good, and if JQ is smaller than or equal to the JQ threshold, the planned implementation effect is poor, and parameters and index settings are required to be optimized so as to further improve the running efficiency and the service quality.
In a preferred embodiment, the vehicle operation monitoring unit is configured to analyze vehicle operation state data, calculate a risk index of vehicle operation state, and detect abnormal behavior; such abnormal behavior includes, but is not limited to, overspeed, sudden braking and sudden acceleration; the treatment process is as follows:
d1, monitoring the running state of the vehicle and acquiring the running state data of the vehicle; the vehicle running state data comprise speed, acceleration and braking conditions;
d2, analyzing the vehicle running state data, and calculating a vehicle running state risk index epsilon, epsilon=ln1+ (0, k) (h1×v) 2 +h2×o+h3×S)dk]Wherein v represents the vehicle speed, o represents the absolute value of the vehicle acceleration, S represents an indicator variable of the braking condition, the available value is 1 or 0, k represents a time interval, k represents a calculated time range, c represents an integration operation, each time segment in the time interval is accumulated, and h1, h2 and h3 represent proportionality coefficients of the items;
d3, judging and comparing the risk index epsilon of the vehicle running state with a preset risk threshold epsilon threshold of the vehicle running state, if epsilon is larger than the epsilon threshold, indicating that the vehicle running state has higher risk, and triggering an alarm mechanism; otherwise, the running state of the vehicle is relatively safe, and the risk degree is low.
In a preferred embodiment, the driver behavior monitoring unit is configured to analyze and process the driver behavior data by using a computer vision technology and a pattern recognition algorithm, and recognize abnormal behaviors of the driver; the method comprises the steps that driver behavior data including facial expressions, eye activities and hand actions are obtained through driver monitoring equipment; judging whether the driver is tired or not by utilizing a facial expression recognition algorithm; detecting whether the driver is focused by using an eye-tracking technology; detecting whether dangerous actions exist in a driver by using a hand action recognition algorithm; if the fatigue, the lack of concentration or dangerous actions of the driver are found, an alarm mechanism is immediately triggered; including but not limited to making a call, smoking a cigarette.
In a preferred embodiment, the passenger behavior monitoring unit is configured to analyze the passenger behavior and identify abnormal passenger behavior by using a computer vision technology and a pattern recognition algorithm; specifically, passenger behaviors are monitored in real time through camera equipment in a carriage, and image or video data are collected; detecting standing, sitting or leaning conditions of passengers by using a human body posture estimation algorithm; judging whether dangerous actions exist or not by utilizing a behavior recognition algorithm, including but not limited to pushing, quarrying and holding dangerous goods; if dangerous actions exist, triggering an alarm mechanism and recording related video or image data.
In a preferred embodiment, the safety event processing module is used for performing deceleration and displaying warning information on an in-vehicle display screen or an instrument panel aiming at the situation that the running state of the vehicle has higher risk; aiming at the abnormal behavior of the driver, reminding the driver of the standard behavior in a sound, light or vibration mode; and carrying out broadcast notification or calling security personnel aiming at the abnormal behavior of passengers.
The invention has the technical effects and advantages that:
according to the invention, the data acquisition module is used for uploading and storing the acquired original data in real time, so that inaccuracy and delay of the traditional manual reporting data are avoided; the data analysis module is used for processing and analyzing the original data by utilizing an artificial intelligence technology, predicting the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops, generating related reports and statistical data, providing scientific basis for bus scheduling and path planning decision, and improving the efficiency and the accuracy; the intelligent scheduling module is used for calculating the departure frequency, the vehicle allocation guide coefficient and the line planning guide coefficient according to the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of bus stops, so that the scheduling and the path planning of the bus are carried out, and the running efficiency of the bus is improved; the scheduling plan evaluation module is used for calculating the implementation effect index of the scheduling plan, monitoring and evaluating the implementation effect of the scheduling plan, screening out data with poor implementation effect, and taking measures for optimizing parameters and setting the index, so that the quality and effect of public transportation service are further improved; the risk behavior monitoring module is used for monitoring and recording the running state of the vehicle, the behavior of a driver and the behavior of a passenger, so that the real-time monitoring of sudden safety events in the running process of the bus is realized, and the safety and emergency response capability are improved; and the emergency processing module is used for processing emergency safety events in the running process of the bus, and performing quick response aiming at alarm information so as to ensure the safety of passengers and vehicles.
Drawings
Fig. 1 is a block diagram showing the overall structure of the present invention.
Fig. 2 is a block diagram of a risk behavior monitoring module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a public transport operation risk management and control system based on artificial intelligence as shown in fig. 1-2, which comprises a data acquisition module, a data analysis module, an intelligent scheduling module, a scheduling plan evaluation module, a risk behavior monitoring module and a security event processing module;
the data acquisition module is used for acquiring GPS track data, passenger flow data, vehicle flow data, accident frequency data, traffic signal lamp state and time sequence information, social media real-time feedback data, residence time and meteorological data in real time by utilizing the sensor and the monitoring equipment, and uploading and storing the acquired original data in real time;
the data analysis module is used for processing and analyzing the original data by utilizing an artificial intelligence technology, predicting the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops, and generating a related report and statistical data; the road condition complexity index specifically refers to the complexity of road traffic conditions; the population distribution coefficient of the bus stops specifically refers to the population density near the bus stops, and is helpful for optimizing route planning and departure frequency;
the implementation needs to specifically explain that the specific processing procedure of the data analysis module is as follows:
a1, cleaning and preprocessing the collected original data, including removing abnormal values and filling missing values so as to ensure the accuracy and the integrity of the data;
a2, extracting features from the original data based on feature engineering of different indexes for subsequent index calculation and analysis; the characteristics include average speed of the vehicle, total distance travelled, total time travelled, residence time, temperature, humidity, precipitation, traffic flow, accident frequency, green light duration, red light duration, number of passengers per station, number of passengers liked, and number of passengers disliked;
extracting features from the original data, specifically calculating the average speed, the total distance and the total time of running of the bus according to GPS track data; counting traffic flow on the road based on the traffic flow data; calculating accident frequency on the road according to the accident frequency data; acquiring green light duration and red light duration through the state and time sequence information of the traffic signal lamp; recording the number of passengers at each station according to the passenger flow data; acquiring temperature, humidity and precipitation from meteorological data; acquiring the good evaluation quantity of the passengers and the poor evaluation quantity of the passengers from the social media real-time feedback data;
a3, calculating the running efficiency GYx of the bus according to the average speed sp of the vehicle, the running total distance L, the running total time T and the stay time T,
a4, comprehensively analyzing the road condition complexity index LFC, LFC= [ c ] in terms of traffic flow JL, accident occurrence frequency Fs, green light duration tl, red light duration th, temperature w, humidity s and precipitation j (e) JL+Fs +tl-th)×(w+s+j)dt；
A5, combining the number Nm of passengers, the number Nh of good scores of passengers and the number Nc of poor scores of passengers at each station, calculating a population distribution coefficient GRf of the bus station,
a6, taking the characteristics as independent variables, taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables, and analyzing, modeling and predicting the data by applying an artificial intelligence technology; the treatment process is as follows:
a61, acquiring data related to the features as a data set, and dividing the data set into a training set and a testing set by adopting a cross-validation method; the cross-validation method belongs to the prior art means, so the embodiment does not make a specific description;
a62, taking the characteristics as independent variables, and taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables;
a63, selecting a deep learning algorithm, and establishing a prediction model;
a64, training the model by using a training set, and calculating the mean square error of the model for evaluating the performance of the model; the calculation formula of the mean square error tau is specifically as follows:wherein Σ represents the sum formula, y represents the actual observation value, +.>Representing a model predictive value, n representing the number of samples;
a65, performing tuning operation on the model according to the performance index of the model on the training set; the tuning operation comprises the steps of adjusting super parameters and changing the model structure so as to improve the prediction capability and generalization capability of the model;
a66, verifying the optimized model by using the test set;
a7, predicting and analyzing future data by using the model, and predicting bus running efficiency, road condition complexity index and bus stop crowd distribution coefficient target variables;
a8, generating a related report and statistical data according to the data analysis result so that a decision maker and related personnel can monitor, evaluate and make decisions;
it should be noted that, the data preprocessing and the calculation of the target variable are to prepare the data required by modeling, and the modeling analysis and the prediction are performed on the basis of the preprocessed data, so that the flow can help better understand the data, find rules, and predict and analyze future conditions;
the intelligent scheduling module is used for calculating departure frequency, vehicle allocation guide coefficient and line planning guide coefficient according to the running efficiency of the buses, the road condition complexity index and the population distribution coefficient of bus stops, and scheduling and path planning of the buses so as to improve the running efficiency and reduce congestion;
the implementation needs to specifically explain that the specific processing procedure of the intelligent scheduling module is as follows:
b1, calculating departure frequency Cf according to bus running efficiency GYx and bus stop crowd distribution coefficient GRf, wherein Cf=mu×e -ω×GYx +phi x ln (1+GRf), where μ, ω, phi represent parameters set according to the actual situation; for example, μmay be set according to the policies and requirements of the bus operator to reflect preferences for departure frequency; omega can be set according to the relation between the running efficiency and the demand of the bus so as to enable the departure frequency to be inversely related to the running efficiency of the bus; phi can be set according to the importance of the population distribution coefficient of the bus station;
b2, calculating a vehicle distribution guide coefficient Cfz according to the running efficiency GYx of the bus and the road condition complexity index LFC,wherein v and b represent the value range in the integral range, and r and p represent indexes set according to actual conditions; for example, r may be set according to the importance of the operating efficiency of the bus; p can be set according to the relationship between the road condition complexity index and the vehicle distribution;
b3, calculating a route planning guidance coefficient Xgz by combining the road condition complexity index and the population distribution coefficient of the bus stops,wherein n represents the total number of stations, lfci represents the road condition complexity index of the ith station, and GRfi represents the population distribution coefficient of the bus station of the ith station;
b4, making an adjustment plan according to the calculated departure frequency, the vehicle allocation guide coefficient and the line planning guide coefficient; the adjustment scheme comprises the steps of adjusting departure times and vehicle quantity in different time periods and road sections, and adjusting line trend and station positions;
b5, conveying the dispatch plan to related vehicles and drivers, and implementing dispatch and path planning;
the scheduling plan evaluation module is used for receiving the data transmitted by the intelligent scheduling module, calculating a plan implementation effect index, monitoring and evaluating the implementation effect of the scheduling plan, screening out the data with poor implementation effect and taking measures for optimizing parameter and index setting;
the implementation needs to specifically explain that the specific processing procedure of the scheduling plan evaluation module is as follows:
the method comprises the steps of C1, implementing a scheduling plan, and calculating a plan implementation effect index through real-time monitoring and data collection, wherein the plan implementation effect index is used for monitoring and evaluating the implementation effect of the scheduling plan; the calculation formula of the plan implementation effect index JQ specifically comprises the following steps:wherein Zmy denotes an overall satisfaction index, wf denotes the number of service, szd denotes the number of actual quasi-shift times, and ZBc denotes the number of total shift times; the total satisfaction index can be obtained through passenger feedback investigation, complaint rate and scoring mode, the actual quasi-point shift number can be obtained through comparing the vehicle arrival time recorded by the real-time monitoring system with the estimated arrival time, and the total shift number is the number of all shifts in a specified time period;
c2, judging and comparing the planned implementation effect index JQ with a preset implementation effect threshold JQ, if JQ is larger than the JQ threshold, the planned implementation effect is good, and if JQ is smaller than or equal to the JQ threshold, the planned implementation effect is poor, and parameters and index settings are required to be optimized so as to further improve the operation efficiency and the service quality; the preset implementation effect threshold JQ can be specifically set according to specific situations, and specific data is not specifically limited in this embodiment;
the risk behavior monitoring module comprises a vehicle running monitoring unit, a driver behavior monitoring unit and a passenger behavior monitoring unit, and is used for monitoring and recording the running state of a vehicle, the behavior of a driver and the behavior of a passenger, so that real-time monitoring of sudden safety events in the running process of the bus is realized;
the vehicle running monitoring unit is used for analyzing the vehicle running state data and calculating the risk index of the vehicle running state to detect abnormal behaviors; such abnormal behavior includes, but is not limited to, overspeed, sudden braking and sudden acceleration;
the implementation needs to specifically explain that the specific processing procedure of the vehicle operation monitoring unit is as follows:
d1, monitoring the running state of the vehicle and acquiring the running state data of the vehicle; the vehicle running state data comprise speed, acceleration and braking conditions;
d2, analyzing the vehicle running state data, and calculating a vehicle running state risk index epsilon, epsilon=ln1+ (0, k) (h1×v) 2 +h2×o+h3×S)dk]Wherein v represents the vehicle speed, o represents the absolute value of the vehicle acceleration, S represents an indicator variable of the braking condition, the value 1 or 0 can be taken to represent whether braking action exists, k represents a time interval, k represents a calculated time range, +.;
d3, judging and comparing the risk index epsilon of the vehicle running state with a preset risk threshold epsilon threshold of the vehicle running state, if epsilon is larger than the epsilon threshold, indicating that the vehicle running state has higher risk, and triggering an alarm mechanism; otherwise, the running state of the vehicle is relatively safe, and the risk degree is low; the preset risk threshold epsilon threshold of the running state of the vehicle can be specifically set according to specific conditions, and specific data are not specifically limited in the embodiment;
the driver behavior monitoring unit is used for analyzing and processing the driver behavior data by utilizing a computer vision technology and a pattern recognition algorithm and recognizing the abnormal behavior of the driver so as to prevent possible dangerous situations;
the implementation needs to specifically explain that the specific processing mode of the driver behavior monitoring unit is as follows: acquiring, by a driver monitoring device, driver behavior data including facial expressions, eye movements, and hand movements; judging whether the driver is tired or not by utilizing a facial expression recognition algorithm; detecting whether the driver is focused by using an eye-tracking technology; detecting whether dangerous actions exist in a driver by using a hand action recognition algorithm; if the fatigue, the lack of concentration or dangerous actions of the driver are found, an alarm mechanism is immediately triggered; the dangerous actions include, but are not limited to, making a call and smoking; the facial expression recognition algorithm, eye tracking technology and hand motion recognition algorithm belong to the prior art means, so the embodiment does not make a specific description;
the passenger behavior monitoring unit is used for analyzing the passenger behavior by utilizing a computer vision technology and a pattern recognition algorithm and recognizing the abnormal behavior of the passenger so as to maintain the safety order in the vehicle;
the implementation needs to specifically explain that the specific processing mode of the passenger behavior monitoring unit is as follows: the passenger behavior is monitored in real time through camera equipment in a carriage, and image or video data are collected; detecting standing, sitting or leaning conditions of passengers by using a human body posture estimation algorithm; judging whether dangerous actions exist or not by utilizing a behavior recognition algorithm, including but not limited to pushing, quarrying and holding dangerous goods; if dangerous actions exist, triggering an alarm mechanism and recording related video or image data; the human body posture estimation algorithm and the behavior recognition algorithm belong to the prior art means, so the embodiment does not make a specific description;
the safety event processing module is used for receiving the data transmitted by the risk behavior monitoring module, processing sudden safety events in the running process of the bus and rapidly responding to the alarm information so as to ensure the safety of passengers and drivers; when a safety accident occurs, the safety accident is treated in time, so that the influence of the accident is reduced;
the implementation needs to specifically explain that the safety event processing module is used for carrying out deceleration and displaying warning information on an in-vehicle display screen or an instrument panel aiming at the situation that the running state of the vehicle has higher risk; aiming at the abnormal behavior of the driver, reminding the driver of the standard behavior in a sound, light or vibration mode; and carrying out broadcast notification or calling security personnel aiming at the abnormal behavior of passengers.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. Public transit operation risk management and control system based on artificial intelligence, its characterized in that: comprising the following steps:
and a data acquisition module: the system is used for acquiring GPS track data, passenger flow data, vehicle flow data, accident frequency data, traffic signal lamp state and time sequence information, social media real-time feedback data, residence time and meteorological data in real time by using the sensors and the monitoring equipment, and uploading and storing the acquired original data in real time;
and a data analysis module: the system is used for processing and analyzing the original data by utilizing an artificial intelligence technology, predicting the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops, and generating related reports and statistical data; the road condition complexity index specifically refers to the complexity of road traffic conditions; the population distribution coefficient of the bus stops specifically refers to the population density degree near the bus stops;
and the intelligent scheduling module is used for: the method comprises the steps of calculating departure frequency, a vehicle allocation guide coefficient and a line planning guide coefficient according to the running efficiency of the bus, road condition complexity index and population distribution coefficient of bus stops, and scheduling and path planning of the bus;
a dispatch plan evaluation module: the system comprises an intelligent scheduling module, a scheduling management module and a scheduling management module, wherein the intelligent scheduling module is used for receiving data transmitted by the intelligent scheduling module, calculating a plan implementation effect index, monitoring and evaluating the implementation effect of the scheduling, screening out data with poor implementation effect and taking measures for optimizing parameter and index setting;
the risk behavior monitoring module comprises a vehicle running monitoring unit, a driver behavior monitoring unit and a passenger behavior monitoring unit, and is used for monitoring and recording the running state of the vehicle, the behavior of the driver and the behavior of the passenger, so as to realize real-time monitoring of sudden safety events in the running process of the bus;
a security event processing module: the system is used for receiving the data transmitted by the risk behavior monitoring module, processing sudden safety events in the running process of the bus and rapidly responding to the alarm information.
2. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the specific processing procedure of the data analysis module is as follows:
a1, cleaning and preprocessing the collected original data, including removing abnormal values and filling missing values;
a2, extracting features from the original data based on feature engineering of different indexes for subsequent index calculation and analysis; the characteristics include average speed of the vehicle, total distance travelled, total time travelled, residence time, temperature, humidity, precipitation, traffic flow, accident frequency, green light duration, red light duration, number of passengers per station, number of passengers liked, and number of passengers disliked;
a3, calculating the running efficiency GYx of the bus according to the average speed sp of the vehicle, the running total distance L, the running total time T and the stay time T,
a4, comprehensively analyzing the road condition complexity index LFC, LFC= [ c ] in terms of traffic flow JL, accident occurrence frequency Fs, green light duration tl, red light duration th, temperature w, humidity s and precipitation j (e) JL+Fs +tl-th)×(w+s+j)dt；
A5, combining the number Nm of passengers, the number Nh of good scores of passengers and the number Nc of poor scores of passengers at each station, calculating a population distribution coefficient GRf of the bus station,
a6, taking the characteristics as independent variables, taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables, and analyzing, modeling and predicting the data by applying an artificial intelligence technology;
a7, predicting and analyzing future data by using the model, and predicting bus running efficiency, road condition complexity index and bus stop crowd distribution coefficient target variables;
and A8, generating a related report and statistical data according to the data analysis result.
3. The public transportation operation risk management and control system based on artificial intelligence according to claim 2, wherein: the application of artificial intelligence technology analyzes, models and predicts the data; the treatment process is as follows:
a61, acquiring data related to the features as a data set, and dividing the data set into a training set and a testing set by adopting a cross-validation method;
a62, taking the characteristics as independent variables, and taking the running efficiency of the bus, the road condition complexity index and the population distribution coefficient of the bus stops as target variables;
a63, selecting a deep learning algorithm, and establishing a prediction model;
a64, training the model by using a training set, and calculating the mean square error of the model for evaluating the performance of the model; the calculation formula of the mean square error tau is specifically as follows:wherein Σ represents the sum formula, y represents the actual observation value, +.>Representing a model predictive value, n representing the number of samples;
a65, performing tuning operation on the model according to the performance index of the model on the training set; the tuning operation comprises the steps of adjusting super parameters and changing a model structure;
and A66, verifying the optimized model by using the test set.
4. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the specific processing procedure of the intelligent scheduling module is as follows:
b1, calculating departure frequency Cf according to bus running efficiency GYx and bus stop crowd distribution coefficient GRf, wherein Cf=mu×e -ω×GYx +phi x ln (1+GRf), where μ, ω, phi represent parameters set according to the actual situation;
b2, calculating a vehicle distribution guide coefficient Cfz according to the running efficiency GYx of the bus and the road condition complexity index LFC,wherein v and b represent the value range in the integral range, and r and p represent indexes set according to actual conditions;
b3, calculating a line by combining the road condition complexity index and the population distribution coefficient of the bus stopsThe routing guide coefficients Xgz are set,wherein n represents the total number of stations, lfci represents the road condition complexity index of the ith station, and GRfi represents the population distribution coefficient of the bus station of the ith station;
b4, making an adjustment plan according to the calculated departure frequency, the vehicle allocation guide coefficient and the line planning guide coefficient; the adjustment scheme comprises the steps of adjusting departure times and vehicle quantity in different time periods and road sections, and adjusting line trend and station positions;
b5, conveying the dispatch plan to related vehicles and drivers, and implementing dispatch and path planning.
5. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the specific processing procedure of the scheduling plan evaluation module is as follows:
the method comprises the steps of C1, implementing a scheduling plan, and calculating a plan implementation effect index through real-time monitoring and data collection, wherein the plan implementation effect index is used for monitoring and evaluating the implementation effect of the scheduling plan; the calculation formula of the plan implementation effect index JQ specifically comprises the following steps:wherein Zmy denotes an overall satisfaction index, wf denotes the number of service, szd denotes the number of actual quasi-shift times, and ZBc denotes the number of total shift times; the total satisfaction index can be obtained through passenger feedback investigation, complaint rate and scoring mode, the actual quasi-point shift number can be obtained through comparing the vehicle arrival time recorded by the real-time monitoring system with the estimated arrival time, and the total shift number is the number of all shifts in a specified time period;
and C2, judging and comparing the planned implementation effect index JQ with a preset implementation effect threshold JQ, if JQ is larger than the JQ threshold, the planned implementation effect is good, and if JQ is smaller than or equal to the JQ threshold, the planned implementation effect is poor, and parameters and index settings are required to be optimized so as to further improve the running efficiency and the service quality.
6. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the vehicle running monitoring unit is used for analyzing the vehicle running state data and calculating the risk index of the vehicle running state to detect abnormal behaviors; such abnormal behavior includes, but is not limited to, overspeed, sudden braking and sudden acceleration; the treatment process is as follows:
d1, monitoring the running state of the vehicle and acquiring the running state data of the vehicle; the vehicle running state data comprise speed, acceleration and braking conditions;
d2, analyzing the vehicle running state data, and calculating a vehicle running state risk index epsilon, epsilon=ln1+ (0, k) (h1×v) 2 +h2×o+h3×S)dk]Wherein v represents the vehicle speed, o represents the absolute value of the vehicle acceleration, S represents an indicator variable of the braking condition, the available value is 1 or 0, k represents a time interval, k represents a calculated time range, c represents an integration operation, each time segment in the time interval is accumulated, and h1, h2 and h3 represent proportionality coefficients of the items;
d3, judging and comparing the risk index epsilon of the vehicle running state with a preset risk threshold epsilon threshold of the vehicle running state, if epsilon is larger than the epsilon threshold, indicating that the vehicle running state has higher risk, and triggering an alarm mechanism; otherwise, the running state of the vehicle is relatively safe, and the risk degree is low.
7. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the driver behavior monitoring unit is used for analyzing and processing the driver behavior data by using a computer vision technology and a pattern recognition algorithm to recognize the abnormal behavior of the driver; the method comprises the steps that driver behavior data including facial expressions, eye activities and hand actions are obtained through driver monitoring equipment; judging whether the driver is tired or not by utilizing a facial expression recognition algorithm; detecting whether the driver is focused by using an eye-tracking technology; detecting whether dangerous actions exist in a driver by using a hand action recognition algorithm; if the fatigue, the lack of concentration or dangerous actions of the driver are found, an alarm mechanism is immediately triggered; including but not limited to making a call, smoking a cigarette.
8. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the passenger behavior monitoring unit is used for analyzing the passenger behavior by using a computer vision technology and a pattern recognition algorithm and recognizing the abnormal behavior of the passenger; specifically, passenger behaviors are monitored in real time through camera equipment in a carriage, and image or video data are collected; detecting standing, sitting or leaning conditions of passengers by using a human body posture estimation algorithm; judging whether dangerous actions exist or not by utilizing a behavior recognition algorithm, including but not limited to pushing, quarrying and holding dangerous goods; if dangerous actions exist, triggering an alarm mechanism and recording related video or image data.
9. The public transportation operation risk management and control system based on artificial intelligence according to claim 1, wherein: the safety event processing module is used for carrying out deceleration and displaying warning information on an in-vehicle display screen or an instrument panel according to the situation that the running state of the vehicle has higher risk; aiming at the abnormal behavior of the driver, reminding the driver of the standard behavior in a sound, light or vibration mode; and carrying out broadcast notification or calling security personnel aiming at the abnormal behavior of passengers.
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