Vehicle management method and system based on Beidou satellite

Abstract

The present invention relates to the field of vehicle management, and discloses a vehicle management method and system based on Beidou satellite, the method comprising obtaining vehicle implementation information and road traffic flow information, performing multiple linear regression calculation to obtain vehicle prediction information according to the real-time vehicle information, performing first parameter calculation according to the road traffic flow information to obtain road congestion coefficient, performing second parameter calculation according to the road traffic flow information to obtain accident frequency, performing vehicle driving weight calculation according to the real-time vehicle information and vehicle prediction information to obtain vehicle driving weight, performing road condition weight calculation according to the road congestion coefficient and accident frequency to obtain road weight, performing scheduling weight calculation according to the vehicle driving weight and road weight to obtain scheduling weight. The method can optimize vehicle scheduling and path planning, improve traffic mobility, and enhance traffic management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a vehicle intelligent management method and system based on Beidou satellite. Background Art

[0002] Vehicle management is a core component of the intelligent transportation system, which aims to improve the overall efficiency and safety of the transportation system through effective monitoring and dispatching methods. Traditional vehicle management mainly relies on ground monitoring facilities and wireless communication technology to collect and transmit vehicle location information, driving speed, traffic flow and other data in real time. These data are used to support traffic management departments in vehicle dispatching, traffic flow analysis, accident warning and road maintenance. However, with the rapid growth of the number of vehicles and the increasing complexity of the urban traffic environment, traditional vehicle management methods have gradually exposed their limitations in terms of real-time performance, accuracy and comprehensive management capabilities.

[0003] Although traditional vehicle management methods have achieved traffic flow monitoring and scheduling to a certain extent, they still have many significant shortcomings. First, traditional methods mainly rely on ground monitoring facilities and a single data source, which makes it difficult to obtain the precise location and dynamic status of vehicles in a comprehensive and real-time manner, resulting in insufficient real-time and accuracy of data. Secondly, vehicle scheduling is mostly based on static rules and historical data, lacking the ability to dynamically predict and adjust real-time traffic conditions, making it difficult to effectively respond to sudden traffic congestion and accidents, reducing the response speed and scheduling efficiency of traffic management. In addition, traditional systems are relatively simplified in data analysis and road condition assessment, and are unable to deeply explore key indicators such as traffic flow, congestion level, and accident frequency, limiting the scientific nature and foresight of traffic management decisions. Finally, traditional systems lack comprehensive vehicle safety management functions, such as electronic fences and satellite assistance systems, making it difficult to provide timely vehicle protection and emergency response in emergency situations, affecting the safety and reliability of the overall transportation system.

[0004] In summary, the existing vehicle management technology has significant deficiencies in data comprehensiveness and real-time performance, dynamic management capabilities, in-depth analysis, and safety management functions, and cannot fully meet the requirements of modern intelligent transportation systems for efficiency and safety. Summary of the invention

[0005] The present invention provides a vehicle management method and system based on Beidou satellite, so as to realize the acquisition and analysis of real-time vehicle information, generate vehicle dispatch weights, thereby optimizing vehicle dispatch efficiency, improving the accuracy and safety of traffic management, and solving the problems of insufficient real-time vehicle management, lack of in-depth data analysis, and insufficient dispatch accuracy in the prior art.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a vehicle management method based on Beidou satellite, comprising:

[0007] Acquire the real-time vehicle information and road traffic flow information of the vehicle; wherein the real-time vehicle information includes the actual driving time of the vehicle, the actual driving distance of the vehicle and the actual driving speed of the vehicle, and the road traffic flow information includes the number of vehicles parameter, the driving distance parameter, the driving speed parameter, the number of accident vehicles parameter, the vehicle accident impact index parameter, the driving speed parameter, the driving time parameter and the number of vehicles involved in the accident parameter;

[0008] Performing a multivariate linear regression calculation based on the real-time vehicle information to obtain vehicle prediction information; wherein the vehicle prediction information includes the vehicle prediction travel distance, the vehicle prediction travel time and the vehicle prediction speed;

[0009] Calculating a first parameter based on the road traffic flow information to obtain a road congestion coefficient;

[0010] Calculating a second parameter based on the road traffic flow information to obtain an accident frequency;

[0011] Performing a first weight calculation according to the vehicle real-time information and the vehicle prediction information to obtain a vehicle travel weight;

[0012] Performing a second weight calculation according to the road congestion coefficient and the accident frequency to obtain a road condition weight;

[0013] A third weight calculation is performed based on the vehicle travel weight and the road condition weight to obtain a dispatch weight, so that the traffic management system performs traffic management based on the dispatch weight.

[0014] Preferably, the vehicle prediction information is calculated by performing a multiple linear regression calculation based on the vehicle real-time information, and the multiple linear regression calculation formula is:

[0015] ,

[0016] in, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the time variable, , , is the regression coefficient;

[0017] ,

[0018] in, is the predicted travel time of the vehicle, is the actual distance travelled by the vehicle, is the vehicle speed variable, , , is the regression coefficient;

[0019] ,

[0020] in, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the actual driving time of the vehicle, is the actual speed of the vehicle, , , is the regression coefficient;

[0021] Preferably, the first parameter is calculated based on the road traffic flow information, and the formula for calculating the first parameter is:

[0022] ,

[0023] in, is the road congestion coefficient, is the number of vehicles, For the The distance traveled by the vehicle in section L, For the The speed of the car, For the number of days.

[0024] Preferably, the second parameter is calculated based on the road traffic flow information, and the formula for calculating the second parameter is:

[0025] ,

[0026] in is the accident frequency, is the number of vehicles involved in the accident; For the Vehicle accident impact index parameters, For the The speed of the accident vehicle, For the The driving time of the accident vehicles, Z is the total number of vehicles involved in the accident;

[0027] Preferably, the first weight calculation is performed according to the vehicle real-time information and the vehicle prediction information, and the first weight calculation formula is:

[0028] ,

[0029] in, is the vehicle driving weight, is the actual speed of the vehicle, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the predicted travel time of the vehicle, , , They are the weight coefficients of speed, distance and time when calculating driving weight.

[0030] Preferably, the second weight calculation is performed according to the road congestion coefficient and the accident frequency, and the second weight calculation formula is:

[0031] ,

[0032] in, is the road weight, which indicates the comprehensive condition of the road. is the road congestion coefficient, is the accident frequency, , is the weight factor;

[0033] Preferably, the third weight calculation is performed according to the vehicle travel weight and the road condition weight, and the third weight calculation formula is:

[0034] ,

[0035] in, is the vehicle dispatch weight, is the vehicle driving weight, is the road weight, , are all scheduling coefficients.

[0036] In a second aspect, the present invention provides a vehicle management system based on Beidou satellite, comprising:

[0037] A data acquisition module, used to acquire real-time vehicle information and road traffic flow information of the vehicle;

[0038] A prediction information calculation module, used to perform multivariate linear regression calculation based on the real-time vehicle information to obtain vehicle prediction information;

[0039] A road condition calculation module, used to calculate the first and second parameters according to the road traffic flow information, and obtain a congestion coefficient and an accident frequency respectively;

[0040] A vehicle travel weight calculation module, used for performing a first weight calculation according to the vehicle real-time information and the vehicle prediction information to obtain a vehicle travel weight;

[0041] A road condition weight calculation module, used to perform a second weight calculation according to the road congestion coefficient and the accident frequency to obtain a road condition weight;

[0042] The dispatch weight calculation module is used to perform a third weight calculation based on the vehicle travel weight and the road condition weight to obtain the dispatch weight.

[0043] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a Beidou satellite-based vehicle management method as described in any one of the above.

[0044] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the Beidou satellite-based vehicle management method as described in any one of the above.

[0045] Compared with the prior art, the present invention has the following beneficial effects: an embodiment of the present invention provides a vehicle management method and system based on Beidou satellite. The method comprises: obtaining real-time information of a vehicle and road traffic flow information; the real-time information of the vehicle comprises actual driving time of the vehicle, actual driving distance of the vehicle and actual driving speed of the vehicle, and the road traffic flow information comprises vehicle quantity parameter, vehicle driving distance parameter, vehicle driving speed parameter, accident vehicle quantity parameter, vehicle accident impact index parameter, accident vehicle driving speed parameter, accident vehicle driving time parameter and accident involved vehicle quantity parameter; performing multivariate linear regression calculation according to the real-time information of the vehicle to obtain vehicle prediction information; wherein the vehicle prediction information comprises predicted vehicle driving distance, predicted vehicle driving time and predicted vehicle speed; performing first parameter calculation according to the road traffic flow information to obtain a road congestion coefficient; performing second parameter calculation according to the road traffic flow information to obtain an accident frequency; performing vehicle driving weight calculation according to the real-time information of the vehicle and the predicted information of the vehicle to obtain a vehicle driving weight; performing road condition weight calculation according to the road congestion coefficient and the accident frequency to obtain a road condition weight; performing dispatch weight calculation according to the vehicle driving weight and the road condition weight to obtain a final dispatch weight, and optimizing traffic management and vehicle dispatch according to the dispatch weight.

[0046] In the present invention, the method can calculate vehicle prediction information based on vehicle real-time information, the method can calculate a first parameter based on road traffic flow information to obtain a road congestion coefficient, the method can calculate a second parameter based on road traffic flow information to obtain an accident frequency, the method can calculate a vehicle driving weight based on vehicle real-time information and vehicle prediction information to obtain a vehicle driving weight, the method can calculate a road condition weight based on a road congestion coefficient and an accident frequency to obtain a road weight, the method can calculate a scheduling weight based on a vehicle driving weight and a road weight to obtain a scheduling weight. In summary, by accurately predicting the driving status of a vehicle, timely grasping the road congestion situation and accident frequency, dynamically adjusting the scheduling strategy, optimizing traffic flow, and improving road traffic efficiency, the present invention effectively improves traffic safety, can timely warn of accidents and make emergency responses, ensure the efficient operation of the transportation system and the rational use of resources, and reduce congestion and delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flow chart of a Beidou satellite vehicle management method provided by a first embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the structure of a Beidou satellite vehicle management system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Reference Figure 1 The first embodiment of the present invention provides a vehicle management method based on Beidou satellite, comprising the following steps:

[0051] S11, obtaining real-time vehicle information and road traffic flow information;

[0052] S12, performing a multivariate linear regression calculation based on the real-time vehicle information to obtain vehicle prediction information;

[0053] S13, according to the road traffic flow information, calculate the first parameter to obtain the road congestion coefficient;

[0054] S14, calculating a second parameter based on the road traffic flow information to obtain an accident frequency.

[0055] S15, calculating the vehicle travel weight according to the vehicle real-time information and the vehicle prediction information to obtain the vehicle travel weight;

[0056] S16, calculating the road condition weight according to the road congestion coefficient and the accident frequency to obtain a road weight;

[0057] S17, calculating the dispatch weight according to the vehicle travel weight and the road weight to obtain the dispatch weight

[0058] In step S11, it is necessary to obtain real-time vehicle information and road traffic flow information.

[0059] It is worth noting that the real-time information of the vehicle includes three key aspects: the actual driving time, driving distance and real-time driving speed of the vehicle. These data are collected through the GPS tracker installed on the vehicle. The GPS tracker can accurately capture every position change of the vehicle during driving, and record the driving time from the starting point to the current time, the total distance traveled and the instantaneous speed at each moment in real time. These data are transmitted to the cloud database through the Beidou satellite communication system to achieve seamless real-time transmission and ensure the accuracy and timeliness of the data.

[0060] Road traffic flow information is also collected through a variety of equipment, mainly including the number of vehicles on the road, the speed of vehicles, and the number of accident vehicles. In particular, when a traffic accident occurs, relevant parameters of the accident vehicle, such as the speed and driving time of the accident vehicle, will also be collected. These data are obtained through sensors and cameras in the road monitoring system, and then combined with the vehicle's own GPS tracker and accident sensor system, all information is integrated and transmitted to the traffic management center. These data provide raw data support for subsequent traffic condition assessment and vehicle scheduling optimization.

[0061] In step S12, a multivariate linear regression calculation is performed based on the real-time vehicle information to obtain vehicle prediction information, wherein the vehicle prediction information includes the vehicle predicted driving distance, the vehicle predicted driving time and the vehicle predicted speed.

[0062] Specifically, the multivariate linear regression calculation formula is: vehicle predicted driving distance :

[0063] ,

[0064] in, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the time variable, , , is the regression coefficient;

[0065] Vehicle predicted travel time :

[0066] ,

[0067] in, is the predicted travel time of the vehicle, is the actual distance travelled by the vehicle, is the vehicle speed variable, , , is the regression coefficient;

[0068] Vehicle predicted speed :

[0069] ,

[0070] in, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the actual driving time of the vehicle, is the actual speed of the vehicle, , , is the regression coefficient.

[0071] It is worth noting that the multivariate linear regression is a statistical technique used to analyze the relationship between multiple independent variables and dependent variables. In the vehicle management system, the prediction model is mainly established by collecting historical data and real-time data of the vehicle such as driving time, driving distance and speed. The core purpose of multivariate linear regression is to use these known input variables (independent variables) to predict the target output variable (dependent variable). Specifically, it performs a weighted combination of multiple independent variables to determine the degree of influence of these variables on the dependent variable, thereby deriving a linear equation. The regression coefficients in this equation are as follows: , , They are obtained by fitting historical data and reflect the contribution of each independent variable to the dependent variable.

[0072] In the present invention, multiple linear regression is used to predict the future driving state of the vehicle, such as driving distance, driving time and driving speed. First, by collecting the current real-time data of the vehicle (such as the actual driving time, distance and speed), the system inputs these data into the regression model as independent variables. Then, the model uses these independent variables for calculation, and combines the independent variables into a linear equation through the previously fitted regression coefficients, and finally predicts the future driving distance, time and speed of the vehicle.

[0073] The advantage of this method is that it can dynamically combine historical data and real-time data to accurately reflect the trend of vehicle driving status. As the data is continuously updated, the regression model will also continuously adjust its regression coefficient according to the new data to ensure the accuracy and timeliness of the prediction results. In this way, multivariate linear regression provides strong data support for the system's traffic scheduling and vehicle management, helping the system to predict potential problems in advance and develop optimization plans.

[0074] In step S13, a first parameter calculation is performed according to the road traffic flow information to obtain a road congestion coefficient, including:

[0075] According to the road traffic flow information, a first parameter is calculated to obtain a road congestion coefficient. The specific formula is:

[0076] ,

[0077] in, is the road congestion coefficient, is the number of vehicles, For the The distance traveled by the vehicle in section L, For the The speed of the car, For the number of days.

[0078] It is worth mentioning that Represents the Vehicles on a specific road section The specific road section L here refers to a specific road section or interval that is designated for analyzing traffic flow or congestion. For example, this can be a section of a highway, a city trunk road, or a specific traffic monitoring area.

[0079] It is worth noting that the road congestion coefficient Used to evaluate the degree of traffic congestion on the road. By calculating the driving distance and speed of each vehicle, a comprehensive congestion index can be obtained. This index reflects the traffic conditions of the road. The larger the value, the more congested the road is and the more difficult it is for vehicles to drive; the smaller the value, the smoother the road and the smoother the vehicles. This calculation process takes into account the combined impact of the number of vehicles, driving distance and speed, and provides real-time traffic condition information for the traffic management system. In system applications, the congestion coefficient can be used in the following aspects: Traffic scheduling optimization: By real-time monitoring of the congestion coefficient, the system can identify sections with severe congestion, give priority to traffic scheduling, recommend vehicles to avoid the section, or adjust the signal time to optimize traffic flow. Traffic warning: When the congestion coefficient reaches a certain threshold, the system can issue a warning in advance to notify the management department to take measures to prevent the congestion from worsening. Path planning: When planning a driving route for a vehicle, the congestion coefficient can be used as a key parameter to help the system select a smoother path for the vehicle and improve traffic efficiency.

[0080] By calculating the congestion coefficient, the traffic management system can have a quantitative assessment of road congestion, thereby making more intelligent scheduling and optimization decisions.

[0081] In step S14, a second parameter is calculated based on the road traffic flow information to obtain the accident frequency. The specific formula is:

[0082] ,

[0083] in is the accident frequency, is the number of vehicles involved in the accident; For the Vehicle accident impact index parameters, For the The speed of the accident vehicle, For the is the driving time of the accident vehicles, and Z is the total number of vehicles involved in the accident.

[0084] It is worth noting that Z represents the total number of vehicles involved in the accident, specifically the total number of vehicles actually involved in a traffic accident. This total number includes not only vehicles that directly collide, but also vehicles that are affected by the accident, such as chain rear-end collisions, collisions, or forced stops caused by the accident. The data of these vehicles are usually used to measure the scale and severity of the accident, thereby providing more accurate parameters when calculating the accident frequency.

[0085] It is worth noting that the accident frequency is an indicator used to measure the frequency of traffic accidents on a certain road within a specific period of time. It reflects the number of traffic accidents that occur within a certain period of time and the impact of these accidents on road traffic. When calculating the accident frequency, multiple factors are usually combined, including the number of vehicles at the time of the accident, the speed of the accident vehicle, the driving time of the accident vehicle, and other parameters, to comprehensively evaluate the frequency and severity of the accident.

[0086] By calculating the accident frequency, it can play the following roles: Evaluating road safety Traffic management systems can quantify the frequency and impact of accidents, thereby evaluating the safety of roads. If a section of road has a high accident frequency, it indicates that there are potential safety hazards in that section, and measures may need to be taken to reduce the occurrence of accidents, such as speed limits or enhanced traffic monitoring. Accident warning and prevention: Accident frequency can be used in early warning systems. If the accident frequency of a road reaches a certain threshold, the system can issue a warning in advance to remind traffic management personnel or drivers to be vigilant and take necessary prevention and control measures. This can reduce future accidents to a certain extent. Optimizing vehicle scheduling and route planning: By analyzing accident frequency data, the system can identify high-risk sections and provide vehicles with safer driving routes. Accident frequency is also an important reference factor when scheduling vehicles. The system can give priority to avoiding accident-prone sections to reduce delays and traffic congestion. Traffic management and decision support: Accident frequency can provide decision support for urban traffic management departments and help formulate relevant policies and measures, such as installing monitoring equipment on sections with high accident frequencies, increasing police patrols, or implementing road reconstruction projects. These measures can effectively improve road safety and reduce accidents.

[0087] By calculating the accident frequency, the system can fully understand the safety conditions of the road, thereby optimizing traffic management strategies, improving road traffic efficiency and ensuring traffic safety.

[0088] In step S15, a first weight calculation is performed based on the vehicle real-time information and the vehicle prediction information to obtain the vehicle driving weight, and the specific formula is:

[0089] ,

[0090] in, is the vehicle driving weight, is the actual speed of the vehicle, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the predicted travel time of the vehicle, , , They are the weight coefficients of speed, distance and time when calculating driving weight.

[0091] It is worth noting that the vehicle driving weight is a parameter that comprehensively measures the difference between the vehicle's current driving state and the predicted state. It compares the vehicle's speed, driving distance and time to determine whether the vehicle's actual state deviates from the system's prediction. Specifically: when the vehicle's real-time speed is close to the predicted speed, the speed ratio is When the actual driving distance of the vehicle is close to the predicted distance, and the actual driving time is close to the predicted time, the two ratios are and It will also be close to 1.

[0092] If a ratio deviates too much from 1, it means that the current state of the vehicle deviates greatly from the system prediction, indicating that speed slowdown, delay, etc. may have occurred.

[0093] The role of vehicle driving weight: Real-time evaluation of vehicle driving status: The primary role of vehicle driving weight is to evaluate the current driving status of the vehicle by comparing the actual driving data of the vehicle with the predicted value. The system will calculate the difference between these indicators and the prediction based on the real-time speed, driving distance and driving time of the vehicle to determine whether the vehicle is driving as planned. If the driving weight of the vehicle is close to 1, it means that the driving status of the vehicle is close to the prediction and the operation is good; if the weight value deviates greatly, it means that the vehicle may have been delayed, the driving speed is abnormal, or it has deviated from the planned driving route. The system can identify the vehicle that needs attention based on this. Support intelligent scheduling: In traffic scheduling, vehicle driving weight is an important basis for dynamic scheduling decisions. When the driving weight of some vehicles deviates greatly from the ideal value, it indicates that the driving conditions of these vehicles are abnormal, which may be due to traffic congestion, route problems or vehicle failures, causing them to fail to run at the scheduled time or speed. The system can adjust the vehicle scheduling plan based on this information, such as changing the driving route of these vehicles or increasing the scheduling priority to ensure that they minimize delays. In addition, in sections with traffic congestion or frequent accidents, the system can reasonably arrange vehicle detours based on the vehicle weight information, thereby optimizing the overall traffic flow. Improve the accuracy and efficiency of scheduling: Vehicle driving weights help the system improve the accuracy of scheduling decisions. By monitoring the driving status of vehicles in real time, the system can make personalized scheduling decisions for each vehicle, rather than relying solely on static scheduling rules. For example, some vehicles may deviate from the predicted state more, and the system can prioritize these vehicles for adjustment, optimize their driving paths or give them higher priority scheduling weights. On the contrary, if the driving weights of some vehicles are close to the prediction, the system can not intervene in them, thereby reducing the computational burden and improving scheduling efficiency. Reduce vehicle delays and optimize driving paths: Vehicle driving weights continuously monitor the deviation of vehicles from the predicted state, promptly identify vehicle driving anomalies, and avoid further traffic congestion due to delays. When a vehicle cannot travel as planned due to traffic conditions, accidents or other reasons, the system can respond quickly based on the vehicle driving weights. For example, the system may adjust the route planning, redirect vehicles that deviate from expectations to other routes, or increase priority scheduling on specific sections of the road to help vehicles get out of the congested area as soon as possible. This can effectively reduce vehicle travel time, avoid delays, and improve overall traffic flow efficiency. Dynamically adjust vehicle driving paths and signal control: In large-scale urban traffic management, vehicle driving weights help the system achieve smarter signal control and path planning. By calculating and analyzing the driving weight of each vehicle, the system can dynamically adjust the switching time of the signal light, give priority to vehicles with larger weight deviations, and help them get out of the congested area as soon as possible. Especially in unexpected situations (such as accidents or emergencies), vehicles with large weights can be treated more preferentially, reducing secondary congestion caused by abnormal conditions.Support emergency response and special vehicle dispatch: Vehicle travel weights also play an important role in emergency response. For example, the system can assign higher travel weights to emergency vehicles such as ambulances, fire trucks, and police cars to ensure that they have priority. In an emergency, the system can automatically adjust the duration and priority of traffic lights based on the travel weights of these special vehicles to ensure that emergency vehicles can reach their destination quickly. At the same time, vehicle travel weights can also be used to coordinate other non-emergency vehicles to detour to avoid interfering with the passage of emergency vehicles. Long-term data feedback and optimized prediction model: The calculation of vehicle travel weights not only helps the system make real-time dispatch decisions, but also provides data feedback for the system's prediction model. By continuously monitoring and recording the deviation between the vehicle's travel status and the predicted value, the system can regularly adjust the parameters of the multivariate linear regression model to optimize the prediction accuracy in the future. For example, if the travel weight of a certain type of vehicle deviates from the predicted state for a long time, the system can analyze the cause and make more accurate predictions and adjustments for this type of vehicle in future dispatches. This continuous feedback mechanism ensures the dynamic optimization of the prediction model, allowing the traffic management system to continuously improve itself as traffic conditions change.

[0094] Vehicle driving weight is an indicator that comprehensively measures the real-time and predicted status of a vehicle. It is mainly used to monitor the operation of the vehicle in real time and provide a basis for dynamic scheduling and optimized path planning for the traffic management system.

[0095] In step S16, the road weight is calculated according to the road congestion coefficient and the accident frequency to obtain the road weight. The specific formula is:

[0096] ,

[0097] in, is the road weight, which indicates the comprehensive condition of the road. is the road congestion coefficient, is the accident frequency, , is the weight factor.

[0098] It is worth noting that road weight is a comprehensive indicator used in traffic management systems to quantitatively evaluate the current traffic conditions and safety of a road. and accident frequency The two key parameters reflect the overall traffic capacity and traffic risk of the road. The road congestion coefficient mainly evaluates the traffic flow of the road, measures the smoothness of vehicle driving on the road section and the degree of road congestion; while the accident frequency is used to evaluate the frequency of traffic accidents on the road section, reflecting the safety and risk level of the road. By combining these two key data, the system can quantitatively evaluate the current condition of the road, thereby providing a scientific basis for subsequent traffic management decisions such as vehicle scheduling, path optimization, and traffic signal adjustment. The role of dispatch weights: Optimizing vehicle dispatch decisions: Dispatching weights help the system determine which vehicles need to be dispatched first by combining vehicle driving status and road conditions. For example, when the driving status of a vehicle deviates greatly from the prediction, such as vehicle delays or abnormal speed, the dispatch weight will increase the dispatch priority of the vehicle according to the vehicle driving weight, so that the system can adjust the path or dispatch plan of the vehicle in time. Similarly, if a road is severely congested or accidents occur frequently, the system will avoid the road section through road weights and give priority to other vehicles on smooth driving paths. Dynamically adjust the dispatch strategy: The dispatch weight can dynamically adjust the dispatch strategy according to the real-time vehicle status and road conditions. This means that the system can not only perform personalized path planning for each vehicle, but also perform global dispatch according to the real-time changing traffic environment. Through this dynamic weight calculation, the system can maintain the optimal vehicle dispatch and path selection in a large-scale traffic network, ensuring that vehicles can reach their destinations with the lowest delay and the highest efficiency. Improve traffic flow efficiency: Dispatch weights are crucial in improving overall traffic flow efficiency. By comprehensively considering the road capacity and the driving status of the vehicle, the system can reasonably allocate traffic resources to avoid excessive load on a single road section or certain vehicles. Especially during peak hours or special circumstances (such as traffic accidents, road closures, etc.), dispatch weights can help the system better divert vehicles, reduce traffic congestion, and improve road capacity. Reduce vehicle delays and congestion: The calculation of dispatch weights enables the system to respond quickly to traffic anomalies. If a section of road suddenly becomes congested or an accident occurs, the system can quickly adjust the vehicle's route through the road weight to avoid vehicles getting stuck in congested areas. At the same time, if some vehicles are unable to arrive at their destination on time due to delays, the dispatch weight will increase their priority, and the system will reallocate routes to ensure that the dispatch of these vehicles is optimized. Improving traffic safety: Dispatching weights can also play a role in improving traffic safety. By incorporating accident frequency into the road weight calculation, the system can identify high-risk sections of road and avoid these sections during dispatch, reducing the number of vehicles traveling in accident-prone areas. In addition, if some vehicles are driving abnormally, the dispatch weight will promptly increase the priority of these vehicles, helping the system to quickly adjust the dispatch plan and reduce potential safety hazards. Support emergency response: Dispatching weights can also help the system respond quickly in emergency situations.For example, in the event of sudden natural disasters, major traffic accidents, etc., the system can prioritize emergency vehicles such as rescue vehicles, police cars or ambulances based on dispatch weights to ensure that these vehicles can reach their destinations in the shortest time possible, thereby improving emergency response efficiency.

[0099] The dispatch weight is a dynamic weight that comprehensively reflects the vehicle driving status and road traffic conditions. It helps the traffic management system make the best dispatch decision by weighted calculation of the vehicle driving weight and the road weight. Through the dispatch weight, the system can adjust the vehicle's driving route in real time, avoid congestion and delays, optimize traffic flow, improve traffic safety, and respond quickly in emergency situations. The calculation and application of dispatch weights ensure the efficiency and intelligence of the traffic management system and improve the operating efficiency of the entire traffic network. In step S17, the dispatch weight is calculated according to the vehicle driving weight and the road condition weight to obtain the dispatch weight. The specific formula is:

[0100] ,

[0101] in, is the vehicle dispatch weight, is the vehicle driving weight, is the road weight, , are all scheduling coefficients.

[0102] It is worth mentioning that the dispatch weight is a key indicator used in traffic management systems to optimize vehicle dispatch and route planning. It is obtained by comprehensively calculating the vehicle driving weight and road weight. The dispatch weight is used to measure the dispatch priority of a vehicle under specific road conditions, thereby helping the system make intelligent dispatch decisions.

[0103] The role of dispatch weights: Optimizing vehicle dispatch decisions Dispatching weights help the system determine which vehicles need to be dispatched first by combining the vehicle's driving status and road conditions. For example, when a vehicle's driving status deviates greatly from the prediction (such as a vehicle delay or abnormal speed), the dispatch weight will increase the dispatch priority of the vehicle based on the vehicle's driving weight, allowing the system to adjust the vehicle's path or dispatch plan in a timely manner. Similarly, if a road is severely congested or accidents occur frequently, the system will use the road weight to avoid the section and give priority to other vehicles on the smooth driving path. Dynamic adjustment of dispatch strategy: Dispatching weights can dynamically adjust the dispatch strategy based on the real-time vehicle status and road conditions. This means that the system can not only perform personalized path planning for each vehicle, but also perform global dispatch based on the real-time changing traffic environment. Through this dynamic weight calculation, the system can maintain the optimal vehicle dispatch and path selection in a large-scale traffic network, ensuring that vehicles can reach their destination with the lowest delay and the highest efficiency. Improve traffic flow efficiency: Dispatching weights are crucial in improving overall traffic flow efficiency. By comprehensively considering the road's capacity and the vehicle's driving status, the system can reasonably allocate traffic resources to avoid excessive load on a single section or certain vehicles. Especially during peak hours or special circumstances (such as traffic accidents, road closures, etc.), dispatch weights can help the system better divert vehicles, reduce traffic congestion, and improve road capacity. Reduce vehicle delays and congestion: The calculation of dispatch weights enables the system to respond quickly to traffic anomalies. If a section of road suddenly becomes congested or an accident occurs, the system can quickly adjust the vehicle's route through the road weight to avoid vehicles getting stuck in the congested area. At the same time, if some vehicles are unable to arrive at their destination on time due to delays, the dispatch weight will increase their priority, and the system will reallocate routes to ensure that the dispatch of these vehicles is optimized. Improve traffic safety: Dispatch weights can also play a role in improving traffic safety. By incorporating accident frequency into the calculation of road weights, the system can identify high-risk sections and avoid these sections during dispatch, reducing the number of vehicles traveling in accident-prone areas. In addition, if some vehicles are in abnormal driving conditions, the dispatch weights will also promptly increase the priority of these vehicles, helping the system to quickly adjust the dispatch plan and reduce potential safety hazards. Support emergency response: Dispatch weights can also help the system respond quickly in emergency situations. For example, in the event of sudden natural disasters, major traffic accidents, etc., the system can prioritize emergency vehicles such as rescue vehicles, police cars or ambulances based on dispatch weights to ensure that these vehicles can reach their destinations in the shortest time possible, thereby improving emergency response efficiency.

[0104] In summary, the present invention provides a vehicle management method based on Beidou satellite, including: obtaining real-time vehicle information and road traffic flow information, predicting the future state of the vehicle, calculating the congestion coefficient and accident frequency, and comprehensive weight calculation and scheduling optimization. First, through the GPS tracker installed on the vehicle, the system can collect the vehicle's driving data in real time, including the actual driving time, driving distance and real-time speed, and this information is transmitted to the cloud database through the Beidou satellite system. At the same time, the system obtains road traffic flow information through road monitoring equipment such as cameras and sensors, covering the number of vehicles on the road, driving speed and relevant data of accident vehicles (such as the speed of the accident vehicle, driving time, etc.). These raw data provide the basis for subsequent calculations and decisions.

[0105] Next, the system uses the multivariate linear regression method to calculate based on the real-time driving data of the vehicle to obtain the vehicle's prediction information. Specifically, the system predicts the vehicle's future driving distance, driving time and speed through the regression model, which provides an accurate prediction basis for vehicle scheduling. After obtaining the prediction information, the system uses the road traffic flow information to calculate the road congestion coefficient, which is used to quantify the current road congestion level; at the same time, the system also calculates the accident frequency, and measures the safety of the road by evaluating the frequency and impact of traffic accidents on the road. These two parameters are the important basis for the system to optimize scheduling.

[0106] On this basis, the system combines the vehicle's real-time driving data with the predicted information to calculate the vehicle's driving weight, which evaluates the vehicle's current driving status by comparing the difference between the vehicle's actual speed, driving distance and time and the predicted values. The closer the weight is to 1, the closer the vehicle's operating status is to the prediction; if the deviation is large, it may indicate that the vehicle is delayed or deviates from the plan. Then, the system further calculates the road weight based on the congestion coefficient and accident frequency. This weight is a comprehensive assessment of the road's current traffic flow and safety status, and is used to reflect the road's capacity and safety risks.

[0107] Finally, the system calculates the dispatch weight by combining the vehicle driving weight and the road weight. The dispatch weight reflects the basis for intelligent dispatch optimization after comprehensively considering the vehicle operation status and road traffic conditions. The calculation of dispatch weight enables the system to dynamically adjust the allocation of traffic resources, optimize vehicle driving paths and times, reduce delays and improve the efficiency of traffic flow. The whole process is monitored in real time and the data feedback mechanism enables the system to continuously adjust the prediction model to ensure the accuracy of the prediction results and the optimality of the dispatch strategy, thereby effectively improving the efficiency, safety and accuracy of the traffic management system.

[0108] Reference Figure 2 The second embodiment of the present invention provides a vehicle management system based on Beidou satellite, including:

[0109] Data acquisition module, used for real-time vehicle information and road traffic flow information;

[0110] A prediction information calculation module, used to calculate vehicle prediction information according to the real-time vehicle information to obtain vehicle prediction information;

[0111] A road condition calculation module, used to calculate the first and second parameters according to the road traffic flow information, and obtain a congestion coefficient and an accident frequency respectively;

[0112] A vehicle travel weight calculation module is used to calculate the vehicle travel weight according to the vehicle real-time information and the vehicle prediction information to obtain the vehicle travel weight;

[0113] A road condition weight calculation module, used to calculate the road condition weight according to the road congestion coefficient and the accident frequency to obtain the road condition;

[0114] The dispatch weight calculation module performs a third weight calculation on the vehicle travel weight and the road condition weight to obtain the dispatch weight;

[0115] It should be noted that the Beidou satellite-based vehicle management system provided in an embodiment of the present invention is used to execute all the process steps of the Beidou satellite-based vehicle management method in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be repeated here.

[0116] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a Beidou satellite vehicle management program. When the processor executes the computer program, the steps in the above-mentioned Beidou satellite-based vehicle management method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented, such as the scheduling weight calculation module.

[0117] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0118] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0119] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0120] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0121] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0122] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0123] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A vehicle management method based on Beidou satellite, characterized in that: include: Acquire real-time vehicle information and road traffic flow information; wherein the real-time vehicle information includes the actual driving time of the vehicle, the actual driving distance of the vehicle and the actual driving speed of the vehicle, and the road traffic flow information includes the number of vehicles parameter, the driving distance parameter, the driving speed parameter, the number of accident vehicles parameter, the vehicle accident impact index parameter, the driving speed parameter, the driving time parameter and the number of vehicles involved in the accident parameter; Performing a multivariate linear regression calculation based on the real-time vehicle information to obtain vehicle prediction information; wherein the vehicle prediction information includes the vehicle prediction travel distance, the vehicle prediction travel time and the vehicle prediction speed; Calculating a first parameter based on the road traffic flow information to obtain a road congestion coefficient; Calculating a second parameter based on the road traffic flow information to obtain an accident frequency; Performing a first weight calculation according to the vehicle real-time information and the vehicle prediction information to obtain a vehicle travel weight; Performing a second weight calculation according to the road congestion coefficient and the accident frequency to obtain a road condition weight; Performing a third weight calculation according to the vehicle travel weight and the road condition weight to obtain a dispatch weight, so that the traffic management system performs traffic management according to the dispatch weight; Wherein, the multivariate linear regression calculation formula is: ， in, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the time variable, , , is the regression coefficient; ， in, is the predicted travel time of the vehicle, is the actual distance travelled by the vehicle, is the vehicle speed variable, , , is the regression coefficient; ， in, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the actual driving time of the vehicle, is the actual speed of the vehicle, , , is the regression coefficient; The formula for calculating the first parameter is: ， in, is the road congestion coefficient, is the number of vehicles, For the The distance traveled by the vehicle in section L, For the The speed of the car, is the number of days; The calculation formula of the second parameter is: ， in is the accident frequency, is the number of vehicles involved in the accident; For the Vehicle accident impact index parameters, For the The speed of the accident vehicle, For the The driving time of the accident vehicles, Z is the total number of vehicles involved in the accident; Wherein, the first weight calculation formula is: ， in, is the vehicle driving weight, is the actual speed of the vehicle, is the predicted vehicle speed, is the actual distance travelled by the vehicle, is the vehicle’s predicted driving distance, is the actual driving time of the vehicle, is the predicted travel time of the vehicle, , , They are the weight coefficients of speed, distance and time when calculating driving weight; Wherein, the second weight calculation formula is: ， in, is the road weight, which indicates the comprehensive condition of the road. is the road congestion coefficient, is the accident frequency, , is the weight factor; Wherein, the third weight calculation formula is: ， in, is the vehicle dispatch weight, is the vehicle driving weight, is the road weight, , are scheduling coefficients.

2. A vehicle management system based on Beidou satellite, characterized in that: Used to implement the Beidou satellite-based vehicle management method as claimed in claim 1, comprising: A data acquisition module, used to acquire real-time vehicle information and road traffic flow information of the vehicle; A prediction information calculation module, used to perform multivariate linear regression calculation based on the real-time vehicle information to obtain vehicle prediction information; A road condition calculation module, used to calculate the first and second parameters according to the road traffic flow information, and obtain a congestion coefficient and an accident frequency respectively; A vehicle travel weight calculation module, used for performing a first weight calculation according to the vehicle real-time information and the vehicle prediction information to obtain a vehicle travel weight; A road condition weight calculation module, used to perform a second weight calculation according to the road congestion coefficient and the accident frequency to obtain a road condition weight; The dispatch weight calculation module is used to perform a third weight calculation based on the vehicle travel weight and the road condition weight to obtain the dispatch weight.

3. An electronic device, characterized in that: The system comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the vehicle management method based on Beidou satellite as claimed in claim 1 is implemented when the processor executes the computer program.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the Beidou satellite-based vehicle management method as claimed in claim 1.

Images