Traffic operation management system based on multi-dimensional data and Internet of Things fusion

Abstract

The invention discloses a traffic operation management system based on multi-dimensional data and Internet of Things fusion, and relates to the technical field of traffic management. Comprising a front-end sensing device, the front-end sensing device comprises a vehicle-mounted sensor, a roadside sensor and a video monitoring device and is used for collecting traffic operation data in real time, the traffic operation data comprise the vehicle position, the speed, the driving track, the cargo type and the driver behavior, and the data are transmitted to a communication integration module through the Internet of Things technology; the communication integration module is responsible for transmitting and integrating data acquired by the front-end sensing equipment, and real-time communication between vehicles, between vehicles and roadside equipment and between vehicles and a central cloud is realized through 5G and Internet of Vehicles technologies; and a central cloud computing power integration module. According to the invention, through the architecture based on front-end sensing, terminal processing and rear-end supervision, and multi-dimensional data are fused at the same time, so that the'data isolated island 'of supervision is broken, the multi-dimensional data can be comprehensively evaluated, and the supervision precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a traffic operation management system based on the integration of multi-dimensional data and the Internet of Things. Background Art

[0002] With the development of the Internet of Things and big data, it is widely used in the field of intelligent control identification and scheduling, such as traffic operation management.

[0003] The Internet of Things (IoT) is a network system that uses information sensing devices such as radio frequency identification (RFID), infrared sensors, global positioning systems, laser scanners, etc. to connect any object to the Internet in accordance with agreed protocols to exchange and communicate information in order to achieve intelligent identification, positioning, tracking, monitoring and management.

[0004] Multidimensional data fusion is a technology that aims to integrate data from multiple sources and formats and extract valuable information through analysis and processing.

[0005] In the existing technology, the intelligent traffic management still has the phenomenon of "data islands". The data in each dimension are independent of each other and cannot be comprehensively identified, which leads to certain limitations in the supervision of vehicles or drivers. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a traffic operation management system based on the integration of multidimensional data and the Internet of Things.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0009] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0010] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0011] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0012] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0013] Preferably, the communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0014] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0015] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0016] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0017] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0018] Preferably: in the step S3, the method for calculating the optimal communication vehicle comprises the following steps:

[0019] S31: Calculate the communication distance d between the vehicle and the road test equipment or the central cloud;

[0020] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0021] Preferably, the communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0022] Preferably, the positioning verification function of the communication integration module specifically includes the following steps:

[0023] A1: Select the target vehicle and obtain its real-time position (x, y) through communication;

[0024] A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area;

[0025] A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained;

[0026] A4: Set the error coefficient k;

[0027] A5: According to the formula Calculate the error distance if ΔS >k, the evaluation result of the position of the target vehicle by the i-th vehicle is “inaccurate”, otherwise it is “accurate”;

[0028] A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

[0029] Preferably: the supervision logic of the statistical analysis module includes the following steps:

[0030] B1: Establishment of functional relationship, based on the functional relationship between the probability of vehicle violation and monitoring dimension y=f i (x), where x represents the monitoring data of the monitoring dimension, and f i represents the functional relationship between the i-th monitoring dimension data and the probability of vehicle violation, and y is the probability of vehicle violation;

[0031] B2: Multi-dimensional data fusion, which integrates multi-dimensional data to obtain expressions of multi-dimensional data and the probability of vehicle violations;

[0032] B3: Determine the trustworthiness a of the vehicle based on its past violation records, and set a probability threshold σ for violation;

[0033] B4: For the target vehicle, after obtaining its multi-dimensional data, the probability y of the vehicle violating traffic rules is obtained using the function expression, and then the vehicle is judged whether it needs on-site inspection and supervision based on y, a, and σ;

[0034] B5: If y / a>σ, the vehicle is judged to have violated traffic rules and regulations, and dispatch supervisors to intercept the vehicle and conduct on-site inspection; otherwise, the vehicle is normal.

[0035] Preferably: in step B2, the formula for multidimensional data fusion is: The multi-dimensional data is fused, k′ is the weight of each dimension, n represents n dimensions, and the function expression of the vehicle violation probability with respect to n-dimensional data is obtained.

[0036] Preferably: in step B3, the vehicle trustworthiness is determined according to the following logic:

[0037] B31: Set initial trust level a 初始 ;

[0038] B32: For each violation type, set a trust level Δa i , which represents the number of trust levels of the violation in the i-th one is Δa;

[0039] B33: Then calculate the current trust a based on the comprehensive algorithm based on frequency and type 实际 .

[0040] Preferably: In step B33, the calculation formula of the comprehensive algorithm based on frequency and type is:

[0041] Preferably: in step B33, p is the frequency of violations.

[0042] The beneficial effects of the present invention are:

[0043] 1. The present invention integrates multi-dimensional data through an architecture based on front-end perception, terminal processing, and back-end supervision, thereby breaking the "data island" of supervision, allowing data from multiple dimensions to be comprehensively evaluated, thereby increasing the accuracy of supervision.

[0044] 2. With regard to the positioning dimension, the present invention utilizes communication positioning on the one hand and mutual voting on the other hand, thereby improving the positioning accuracy. At the same time, vehicle positioning failures can be discovered and maintained in a timely manner.

[0045] 3. The present invention uses a combination of multi-dimensional data and trustworthiness to determine abnormal vehicles, thereby enabling a more accurate assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is an architecture diagram of a traffic operation management system based on the integration of multi-dimensional data and the Internet of Things proposed by the present invention;

[0047] Figure 2 This is a communication logic diagram of a traffic operation management system based on the integration of multi-dimensional data and the Internet of Things proposed by the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0049] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0050] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0051] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0052] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0053] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0054] Embodiment 2:

[0055] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0056] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0057] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0058] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0059] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0060] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0061] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0062] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0063] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0064] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0065] Embodiment 3:

[0066] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0067] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0068] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0069] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0070] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0071] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0072] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0073] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0074] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0075] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0076] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0077] S31: Calculate the communication distance d between the vehicle and the road test equipment or the central cloud;

[0078] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0079] Embodiment 4:

[0080] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0081] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0082] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0083] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0084] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0085] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0086] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0087] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0088] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0089] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0090] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0091] S31: Calculate the communication distance between the vehicle and the road test equipment or the central cloud d ;

[0092] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0093] The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0094] Embodiment 5:

[0095] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0096] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0097] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0098] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0099] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0100] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0101] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0102] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0103] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0104] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0105] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0106] S31: Calculate the communication distance between the vehicle and the road test equipment or the central cloud d ;

[0107] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0108] The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0109] The communication integration module positioning verification function specifically includes the following steps:

[0110] A1: Select the target vehicle and obtain its real-time position (x, y) through communication;

[0111] A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area;

[0112] A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained;

[0113] A4: Set the error coefficient k;

[0114] A5: According to the formula Calculate the error distance. If ΔS>k, the position evaluation result of the i-th vehicle to the target vehicle is "inaccurate", otherwise it is "accurate";

[0115] A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

[0116] Embodiment 6:

[0117] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0118] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0119] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0120] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0121] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0122] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0123] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0124] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0125] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0126] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0127] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0128] S31: Calculate the communication distance between the vehicle and the road test equipment or the central cloud d ;

[0129] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0130] The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0131] The communication integration module positioning verification function specifically includes the following steps:

[0132] A1: Select the target vehicle and obtain its real-time position (x, y) through communication;

[0133] A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area;

[0134] A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained;

[0135] A4: Set the error coefficient k;

[0136] A5: According to the formula Calculate the error distance. If ΔS>k, the position evaluation result of the i-th vehicle to the target vehicle is "inaccurate", otherwise it is "accurate";

[0137] A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

[0138] The supervision logic of the statistical analysis module includes the following steps:

[0139] B1: Establishment of functional relationship, based on the functional relationship between the probability of vehicle violation and monitoring dimension y=f i (x), where x represents the monitoring data of the monitoring dimension, and f i represents the functional relationship between the i-th monitoring dimension data and the probability of vehicle violation, and y is the probability of vehicle violation;

[0140] B2: Multi-dimensional data fusion, which integrates multi-dimensional data to obtain expressions of multi-dimensional data and the probability of vehicle violations;

[0141] B3: Determine the trustworthiness a of the vehicle based on its past violation records, and set a probability threshold σ for violation;

[0142] B4: For the target vehicle, after obtaining its multi-dimensional data, the probability y of the vehicle violating traffic rules is obtained using the function expression, and then the vehicle is judged whether it needs on-site inspection and supervision based on y, a, and σ;

[0143] B5: If y / a>σ, the vehicle is judged to have violated traffic rules and regulations, and dispatch supervisors to intercept the vehicle and conduct on-site inspection; otherwise, the vehicle is normal.

[0144] Embodiment 7:

[0145] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0146] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0147] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0148] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0149] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0150] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0151] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0152] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0153] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0154] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0155] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0156] S31: Calculate the communication distance d between the vehicle and the road test equipment or the central cloud;

[0157] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0158] The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0159] The communication integration module positioning verification function specifically includes the following steps:

[0160] A1: Select the target vehicle and obtain its real-time position (x, y) through communication;

[0161] A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area;

[0162] A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained;

[0163] A4: Set the error coefficient k;

[0164] A5: According to the formula Calculate the error distance. If ΔS>k, the position evaluation result of the i-th vehicle to the target vehicle is "inaccurate", otherwise it is "accurate";

[0165] A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

[0166] The supervision logic of the statistical analysis module includes the following steps:

[0167] B1: Establishment of functional relationship, based on the functional relationship between the probability of vehicle violation and monitoring dimension y=f i (x), where x represents the monitoring data of the monitoring dimension, and f i represents the functional relationship between the i-th monitoring dimension data and the probability of vehicle violation, and y is the probability of vehicle violation;

[0168] B2: Multi-dimensional data fusion, which integrates multi-dimensional data to obtain expressions of multi-dimensional data and the probability of vehicle violations;

[0169] B3: Determine the trustworthiness a of the vehicle based on its past violation records, and set a probability threshold σ for violation;

[0170] B4: For the target vehicle, after obtaining its multi-dimensional data, the probability y of the vehicle violating traffic rules is obtained using the function expression, and then the vehicle is judged whether it needs on-site inspection and supervision based on y, a, and σ;

[0171] B5: If y / a>σ, the vehicle is judged to have violated traffic rules and regulations, and dispatch supervisors to intercept the vehicle and conduct on-site inspection; otherwise, the vehicle is normal.

[0172] In step B2, the formula for multidimensional data fusion is: The multi-dimensional data is fused, k′ is the weight of each dimension, n represents n dimensions, and the function expression of the vehicle violation probability with respect to n-dimensional data is obtained.

[0173] Embodiment 8:

[0174] A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, comprising:

[0175] Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology;

[0176] The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud.

[0177] The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information.

[0178] Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

[0179] The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps:

[0180] S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ;

[0181] S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road;

[0182] S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle;

[0183] S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

[0184] In the step S3, the method for calculating the optimal communication vehicle includes the following steps:

[0185] S31: Calculate the communication distance d between the vehicle and the road test equipment or the central cloud;

[0186] S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

[0187] The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

[0188] The communication integration module positioning verification function specifically includes the following steps:

[0189] A1: Select the target vehicle and obtain its real-time position (x, y) through communication;

[0190] A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area;

[0191] A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained;

[0192] A4: Set the error coefficient k;

[0193] A5: According to the formula Calculate the error distance. If ΔS>k, the position evaluation result of the i-th vehicle to the target vehicle is "inaccurate", otherwise it is "accurate";

[0194] A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

[0195] The supervision logic of the statistical analysis module includes the following steps:

[0196] B1: Establishment of functional relationship, based on the functional relationship between the probability of vehicle violation and monitoring dimension y=f i (x), where x represents the monitoring data of the monitoring dimension, and f i represents the functional relationship between the i-th monitoring dimension data and the probability of vehicle violation, and y is the probability of vehicle violation;

[0197] B2: Multi-dimensional data fusion, which integrates multi-dimensional data to obtain expressions of multi-dimensional data and the probability of vehicle violations;

[0198] B3: Determine the trustworthiness a of the vehicle based on its past violation records, and set a probability threshold σ for violation;

[0199] B4: For the target vehicle, after obtaining its multi-dimensional data, the probability y of the vehicle violating traffic rules is obtained using the function expression, and then the vehicle is judged whether it needs on-site inspection and supervision based on y, a, and σ;

[0200] B5: If y / a>σ, the vehicle is judged to have violated traffic rules and regulations, and dispatch supervisors to intercept the vehicle and conduct on-site inspection; otherwise, the vehicle is normal.

[0201] In step B2, the formula for multidimensional data fusion is: The multi-dimensional data is fused, k′ is the weight of each dimension, n represents n dimensions, and the function expression of the vehicle violation probability with respect to n-dimensional data is obtained.

[0202] In step B3, the vehicle trustworthiness is determined according to the following logic:

[0203] B31: Set initial trust level a 初始 ;

[0204] B32: For each violation type, set a trust level Δa i , which represents the number of trust levels of the violation in the i-th one is Δa;

[0205] B33: Then calculate the current trust a based on the comprehensive algorithm based on frequency and type 实际 .

[0206] In step B33, the calculation formula of the comprehensive algorithm based on frequency and type is:

[0207] In step B33, p is the frequency of traffic violations.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things, characterized in that: include: Front-end sensing equipment, including vehicle-mounted sensors, roadside sensors, and video surveillance equipment, is used to collect real-time traffic operation data, including vehicle location, speed, driving trajectory, cargo type, and driver behavior. The data is transmitted to the communication integration module through the Internet of Things technology; The communication integration module is responsible for transmitting and integrating the data collected by the front-end sensing devices. It uses 5G and Internet of Vehicles technologies to achieve real-time communication between vehicles, vehicles and roadside equipment, and vehicles and the central cloud. The central cloud computing power integration module is responsible for processing and analyzing the data transmitted by the communication integration module. It uses big data processing, cloud computing and other technologies to clean, mine and analyze the data to form valuable traffic operation information. Statistical analysis module: The statistical analysis module integrates the data processed by the central cloud computing power integration module to form auxiliary decision-making support capabilities, including illegal operation supervision module, dangerous goods transportation supervision module, etc., which are used for real-time monitoring and early warning of illegal operation behaviors and dangerous goods transportation risks.

2. A traffic operation management system based on the integration of multidimensional data and the Internet of Things according to claim 1, characterized in that: The communication integration module implements a real-time communication networking method between vehicles, between vehicles and road test equipment, and between vehicles and a central cloud, including the following steps: S1: Assign a unique identifier M to each vehicle's communication device and the vehicle's communication capability P M ; S2: Select the communication area S according to the communication position of the vehicle, where S = D × L, where D is the width of the one-way lane of the road on which the vehicle is traveling, and L is the length of the road; S3: In the communication area S, interconnect the vehicles and calculate the optimal communication vehicle; S4: Transmit the remaining vehicle information in each area S to the optimal communication vehicle, which then packages the information and transmits it to the road test equipment or the central cloud. Different areas S have different transmission threads.

3. A traffic operation management system based on the integration of multidimensional data and the Internet of Things according to claim 2, characterized in that: In the step S3, the method for calculating the optimal communication vehicle includes the following steps: S31: Calculate the communication distance d between the vehicle and the road test equipment or the central cloud; S32: Then according to the formula Calculate the priority of each vehicle, where Z is the vehicle priority, k1 and k2 are d and P respectively M The weights are preset by the administrator.

4. A traffic operation management system based on the integration of multidimensional data and the Internet of Things according to claim 3, characterized in that: The communication integration module also utilizes vehicle-to-vehicle communication to implement a vehicle positioning verification function.

5. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 4, characterized in that: The communication integration module positioning verification function specifically includes the following steps: A1: Select the target vehicle and obtain its real-time position (x, y) through communication; A2: Select other vehicles in its area S and obtain the communication position (x i ,y i ), which represents the position of the i-th vehicle in the S area; A3: Based on the mutual communication between the target vehicle and the i-th vehicle, the relative position difference (Δx, Δy) between the two vehicles is obtained; A4: Set the error coefficient k; A5: According to the formula Calculate the error distance. If ΔS>k, the position evaluation result of the i-th vehicle to the target vehicle is "inaccurate", otherwise it is "accurate"; A6: Follow steps A1-A5 to traverse other vehicles in area S and count the voting results. If the frequency of "accurate" is greater than that of "inaccurate", it means that the target vehicle is accurately located. If the frequency of "accurate" is less than that of "inaccurate", the communication positioning of the target vehicle fails and needs to be repaired.

6. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 5, characterized in that: The supervision logic of the statistical analysis module includes the following steps: B1: Establishment of functional relationship, based on the functional relationship between the probability of vehicle violation and monitoring dimension y=f i (x), where x represents the monitoring data of the monitoring dimension, and f i represents the functional relationship between the i-th monitoring dimension data and the probability of vehicle violation, and y is the probability of vehicle violation; B2: Multi-dimensional data fusion, which integrates multi-dimensional data to obtain expressions of multi-dimensional data and the probability of vehicle violations; B3: Determine the trustworthiness a of the vehicle based on its past violation records, and set a probability threshold σ for violation; B4: For the target vehicle, after obtaining its multi-dimensional data, use the function expression to obtain the probability y of the vehicle violating traffic rules, and then judge whether the vehicle needs on-site inspection and supervision based on y, a, and σ; B5: If y / a>σ, the vehicle is judged to have violated traffic rules and regulations, and dispatch supervisors to intercept the vehicle and conduct on-site inspection; otherwise, the vehicle is normal.

7. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 6, characterized in that: In step B2, the formula for multidimensional data fusion is: The multi-dimensional data is fused, k′ is the weight of each dimension, n represents n dimensions, and the function expression of the vehicle violation probability with respect to n-dimensional data is obtained.

8. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 7, characterized in that: In step B3, the vehicle trustworthiness is determined according to the following logic: B31: Set initial trust level a 初始 ; B32: For each violation type, set a trust level Δa i , which represents the number of trust levels of the violation in the i-th one is Δa; B33: Then calculate the current trust a based on the comprehensive algorithm based on frequency and type 实际 .

9. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 8, characterized in that: In step B33, the calculation formula of the comprehensive algorithm based on frequency and type is:

10. A traffic operation management system based on the integration of multi-dimensional data and the Internet of Things according to claim 9, characterized in that: In step B33, p is the frequency of traffic violations.

Images