High-speed service area intelligent safety management method and system

Abstract

The invention discloses an intelligent safety management method and system for a highway service area, and the method comprises the steps: recognizing the number of vehicles in the service area, and obtaining historical flow data and analysis data corresponding to the historical flow data; identifying the types of vehicles entering the service area, obtaining parking space data of parking areas corresponding to different types of vehicles, and guiding the vehicles to parking spots; safety monitoring is carried out on the vehicles of the focus type in the recognized vehicle types; and pushing the data to a manager. Through accurate identification of the number and types of vehicles entering a service area and combination of historical vehicle conditions of the service area, dynamic analysis of vehicle flow data can be realized, optimal distribution of parking resources in advance is realized, congestion caused by too many vehicles is avoided, and meanwhile, real-time monitoring and early warning of key vehicle types are realized, so that the service area is more intelligent. Safety accidents can be found in time at the beginning, and expansion of the accidents is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle flow control, and in particular to a method and system for intelligent safety management of a highway service area. Background Art

[0002] Highway service areas play an important role in ensuring driving safety and optimizing travel experience, especially in the process of cross-regional personnel and material flow. However, existing technologies mainly rely on video surveillance for simple video recording, which makes it difficult to achieve real-time statistics and dynamic analysis of vehicle flow, resulting in the inability of service areas to formulate effective management strategies based on real-time flow and historical flow patterns, which can easily lead to traffic congestion and resource waste during peak hours. In addition, the existing system cannot accurately identify the types of vehicles entering the service area, and parking resources are difficult to allocate efficiently according to different types of vehicles such as passenger cars, ordinary trucks, and dangerous goods transport vehicles. At the same time, there is a lack of emergency diversion and dynamic guidance mechanisms. For key vehicles such as dangerous goods transport vehicles, current technical means have significant deficiencies in real-time monitoring and safety warnings, and service area managers cannot obtain potential safety hazard information in a timely manner and respond quickly. In addition, due to the lack of a unified data analysis and decision support platform, traffic and safety data in the service area are difficult to efficiently integrate and push, management decision-making efficiency is low, and it cannot meet the needs of intelligent management of service areas. Summary of the invention

[0003] In view of this, the present invention proposes a method and system for intelligent safety management of high-speed service areas, which can significantly improve the management efficiency and safety level of service areas through accurate identification of the number and types of vehicles and dynamic analysis of traffic data. The present invention provides the following technical solutions:

[0004] A method for intelligent safety management of a highway service area, the method comprising: identifying the number of vehicles in the service area, and simultaneously obtaining historical flow data and analysis data of the corresponding historical flow data, so as to formulate flow management decisions; identifying the types of vehicles entering the service area, and obtaining parking space data of parking areas corresponding to different types of vehicles, so as to guide the vehicles to target parking spots; conducting safety monitoring of vehicles of key concern types among the identified types of vehicles; and pushing flow management decisions, vehicle type information, key vehicle information and their safety monitoring data to management personnel.

[0005] Optionally, the method for identifying the number of vehicles in the service area includes: setting up recording equipment at the entrance and exit of the service area to capture vehicle information entering and leaving the service area; if the exit of the service area does not capture the vehicle information recorded at the entrance, the judgment status of the current vehicle is marked as still in the service area, otherwise the vehicle is not counted; and counting the number of vehicles in the judgment status of being in the service area to obtain the number of vehicles in the service area.

[0006] Optionally, the method of identifying the number of vehicles in the service area and obtaining historical traffic data and corresponding analysis data of the historical traffic data to make traffic management decisions includes: obtaining vehicle data entering and exiting the service area every day, month, and year in the past; parsing the vehicle data to obtain vehicle types and entry and exit time data of different types of vehicles; analyzing time period traffic data, vehicle type traffic distribution data, and specific time period traffic data based on vehicle types and corresponding entry and exit time data; generating a traffic prediction model based on time period traffic data, vehicle type traffic distribution data, and specific time period traffic data; inputting the current real-time number of vehicles into the traffic prediction model, and outputting predicted traffic data to make traffic management decisions.

[0007] Optionally, the traffic management decision includes: when the predicted traffic data shows that the traffic flow on the day exceeds the historical peak traffic flow, generating a decision to restrict the entry of new vehicles and speed up vehicle diversion; when the predicted traffic data shows that the traffic flow on the day does not exceed the historical peak traffic flow, generating a decision to optimize vehicle guidance in combination with parking space data.

[0008] Optionally, the method of identifying the type of vehicle entering the service area and obtaining parking space data of parking areas corresponding to different types of vehicles to guide the vehicle to the target parking point includes: identifying the number of empty parking spaces in parking areas corresponding to multiple different types of vehicles; identifying the type of vehicle entering the service area in real time, and matching it with the empty parking spaces corresponding to the corresponding vehicle types; if there is an unallocated empty parking space, guiding the vehicle to the empty parking space, otherwise prompting the vehicle to leave.

[0009] Optionally, the method of identifying the types of vehicles entering the service area and obtaining parking space data of parking areas corresponding to different types of vehicles to guide vehicles to target parking points also includes: obtaining predicted traffic and predicted peak traffic of dangerous vehicle types based on the output predicted traffic data; if the predicted traffic exceeds the empty parking spaces in the special parking area for dangerous vehicle types, restricting entry or guiding vehicles to temporary parking areas; if the predicted traffic exceeds the set monitoring threshold, a dangerous vehicle alarm can be generated and monitored in real time; if the predicted peak traffic exceeds the set guidance threshold, a call to management personnel can be made to guide parking on site and monitor in real time.

[0010] Optionally, the method for conducting safety monitoring on vehicles of key concern categories among the identified vehicle types includes: determining whether there are vehicles of key concern categories based on the identified vehicle types; if so, automatically triggering an early warning, and sending the early warning information to management personnel; conducting hazard monitoring on vehicles of key concern categories, including one or more of fire point detection, temperature detection, and smoke and fire detection; if hazard monitoring finds an abnormality, triggering an early warning, and sending the early warning information to the service area management system for timely processing by management personnel.

[0011] The present invention further discloses an intelligent safety management system for a high-speed service area, comprising: an identification and analysis module, used to identify the number of vehicles in the service area, and simultaneously obtain historical flow data and analysis data of the corresponding historical flow data, so as to make flow management decisions; a vehicle guidance module, used to identify the types of vehicles entering the service area, and obtain parking space data of parking areas corresponding to different types of vehicles, so as to guide the vehicles to the target parking points; a safety monitoring module, used to perform safety monitoring on vehicles of key focus types among the identified vehicle types; and a data processing module, used to push flow management decisions, vehicle type information, key vehicle information and their safety monitoring data to management personnel.

[0012] The present invention further discloses a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned high-speed service area intelligent safety management method is implemented.

[0013] The present invention further discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned high-speed service area intelligent safety management method when executing the program.

[0014] According to the technical solution of the present invention, by accurately identifying the number and types of vehicles entering the service area and combining the historical vehicle conditions of the service area, it is possible to dynamically analyze the vehicle flow data and optimize the allocation of parking resources in advance to avoid congestion caused by too many vehicles. At the same time, real-time monitoring and early warning of key vehicle types can be used to promptly discover safety accidents at the beginning and avoid the expansion of accidents. Unified data push and management decision support can also be used to significantly improve the management efficiency and safety level of service areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For the purpose of illustration and not limitation, the present invention is now described in conjunction with the embodiments of the present invention and the accompanying drawings, in which:

[0016] Figure 1 It is a flowchart of a high-speed service area intelligent safety management method in an embodiment of the present invention;

[0017] Figure 2 It is a structural schematic diagram of the intelligent safety management system of a high-speed service area in an embodiment of the present invention;

[0018] Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its implementation methods, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0022] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to the specific circumstances. In addition, the term "plurality" should mean two or more than two.

[0023] It should be noted that, in the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0024] refer to Figure 1 This embodiment discloses a method for intelligent safety management of a high-speed service area, the method comprising:

[0025] S100: Identify the number of vehicles in the service area, and simultaneously obtain historical traffic data and analysis data corresponding to the historical traffic data to make traffic management decisions.

[0026] For the identification of the number of vehicles, this embodiment is implemented by recording the license plate information of each vehicle and the status of the vehicle entering and exiting the service area corresponding to the license plate information, that is, recording the vehicle with a certain license plate entering the service area. If the vehicle is identified to be leaving the service area, it means that the vehicle is not in the service area, otherwise it means that the vehicle is still in the service area. In one or more embodiments, the number of vehicles in the parking lot can also be calculated by recording the number of vehicles entering the service area and the number of vehicles leaving the service area through image recognition technology, which is not limited in this embodiment. If vehicle identification is to be achieved, this embodiment is implemented by providing intelligent capture equipment at the entrance and exit of the service area to record the relevant data of each vehicle, such as license plate data, vehicle model data, vehicle type data, etc.

[0027] The service area in this embodiment has a data center for storing and processing data acquired by the intelligent capture device. The data center stores historical vehicle data of the current service area, including historical daily traffic data, monthly traffic data, annual traffic data, etc. The specific data statistical period can be freely set. By analyzing the historical vehicle data, it is possible to predict the traffic data of the service area in the current period. And according to the prediction results, traffic management decisions for vehicle management are made, specifically:

[0028] S110: Obtain vehicle data that has entered and exited the service area every day, month, and year in the past. The intelligent traffic high-definition cameras installed at the entrances and exits of the service area capture and record the license plate information and timestamps of all vehicles entering and exiting the service area. The collected vehicle data is uploaded to the data center and stored in the database according to the time dimensions of day, month, and year. The historical data is regularly deduplicated, classified, and outliers are removed to ensure data accuracy and completeness.

[0029] S120: Analyze vehicle data to obtain vehicle types and entry and exit time data of different types of vehicles. Use license plate recognition technology and intelligent classification algorithms to classify vehicles into passenger cars, ordinary trucks, and dangerous goods transport vehicles based on the color, number rules, or size characteristics of the vehicle license plates. Pair the entry and exit times of each vehicle, calculate its stay time in the service area, and store it in association with the license plate and vehicle type.

[0030] S130: Analyze the time-divided traffic data, vehicle type traffic distribution data and specific time period traffic data based on the vehicle type and the corresponding entry and exit time data. According to the timestamp of the vehicle entering and exiting the service area, the traffic of each vehicle is counted by hour, day or other time units, and a time-divided traffic curve is generated. The proportion of different vehicle types in the total traffic in a specific time period is calculated. For example, passenger cars account for 40%, ordinary trucks account for 50%, and dangerous goods transport vehicles account for 10%. Focus on analyzing the vehicle traffic characteristics during holidays, peak hours or abnormal events (such as bad weather), and generate corresponding data reports.

[0031] S140: Generate a traffic prediction model based on traffic data by time period, traffic distribution data by vehicle type, and traffic data for a specific time period. Extract key features from traffic data by time period and historical traffic patterns, including peak traffic, valley traffic, vehicle type ratio, etc., and use machine learning algorithms (such as time series analysis or regression models) to establish a traffic prediction model to predict vehicle traffic conditions in future time periods. Use part of the historical data as a test set to verify the accuracy of the prediction model, and continuously optimize the model parameters.

[0032] S150: Input the current real-time number of vehicles into the traffic prediction model, and output the predicted traffic data to make traffic management decisions. Through the real-time collected vehicle quantity data, the current traffic information is input into the prediction model, and the model outputs the predicted traffic data for a certain time period in the future. For example, based on an hourly cycle, the vehicle traffic data predicted for the next 3 hours is output, including the traffic change trend in different time periods, the traffic ratio of vehicle types, and potential congestion periods. Combined with the predicted traffic data, corresponding management decisions are made, such as traffic diversion, optimal allocation of parking spaces, parking resource adjustment, etc., and pushed to the service area management personnel.

[0033] In order to avoid problems with vehicle management in the service area, the traffic management decision of this implementation includes: when the predicted traffic data shows that the traffic volume on the day exceeds the historical peak traffic volume, a decision to restrict the entry of new vehicles and speed up vehicle diversion is generated. The current decision is sent to the on-site management personnel to assist them in handling vehicle management to avoid invalid queues caused by vehicle congestion. When the predicted traffic data shows that the traffic volume on the day does not exceed the historical peak traffic volume, a decision to optimize vehicle guidance is generated in combination with parking space data. Through the reasonable allocation of parking spaces for vehicles, vehicles can enter quickly and exit smoothly, further improving the efficiency of vehicle management in the service area.

[0034] In the service area, different types of vehicles have separate parking areas to improve the management efficiency and safety of vehicles in the service area. Specifically, the safety management method of this embodiment further includes:

[0035] S200: Identify the type of vehicle entering the service area and obtain parking space data of parking areas corresponding to different types of vehicles, so as to guide the vehicle to the target parking spot.

[0036] Identify the number of empty parking spaces in parking areas corresponding to multiple types of vehicles. Divide the parking lot in the service area into multiple areas (such as passenger car parking area, ordinary truck parking area, and dangerous goods transport vehicle parking area), assign a unique identifier to each area, and install geomagnetic sensors, ultrasonic parking space detectors or video surveillance equipment in the parking spaces of each parking area to monitor the status of parking spaces (free or occupied) in real time, and dynamically update the number of free parking spaces for each type of vehicle in the parking area based on this, upload the data of empty parking spaces in each parking area to the data center, and achieve real-time synchronization.

[0037] Identify the types of vehicles entering the service area in real time and match them with the empty parking spaces corresponding to the corresponding vehicle types. If there are unallocated empty parking spaces, guide the vehicle into the empty parking spaces, otherwise prompt it to leave. Specifically: The system queries the number of empty parking spaces in the corresponding parking area based on the vehicle type. If there are empty parking spaces in the parking area that matches the vehicle type, the nearest free parking space is allocated and the parking space status is locked to avoid conflicts. When the vehicle enters the allocated parking space, the parking space monitoring device updates the parking space status to "occupied". If the vehicle does not occupy the parking space within the specified time, the system will release the parking space and re-match other vehicles. If there are no empty parking spaces in the matching parking area, the optimal path from the entrance to the target parking area is generated based on the parking area layout, and dynamic electronic signs are set at key intersections inside the service area and the entrance of the parking area to display guidance information in real time, or the administrator guides the vehicle to leave.

[0038] Due to the particularity of dangerous vehicles, for the parking of this type of vehicle, the predicted flow and predicted peak flow of the dangerous vehicle type are obtained according to the predicted flow data output by the flow prediction model in step S100. The predicted flow is the number of dangerous vehicles predicted to enter the service area in the next cycle, and the predicted peak flow refers to the maximum flow of dangerous vehicles concentrated in the service area at a certain moment. Due to the particularity of dangerous vehicles, it is necessary to pay attention to the status of the vehicle at all times. If the predicted flow exceeds the empty parking spaces in the special parking area for dangerous vehicles, entry will be restricted or guided to the temporary parking area. If the predicted flow exceeds the set monitoring threshold, a dangerous vehicle alarm can be generated and monitored in real time. If the predicted peak flow exceeds the set guidance threshold, a call to the management staff can be superimposed to guide parking on site and monitor in real time.

[0039] S300: Conduct safety monitoring on vehicles of key concern types among the identified vehicle types. When a vehicle enters a service area, determine whether there are vehicles of key concern types based on the identified vehicle types. If so, an early warning is automatically triggered, and the early warning information is sent to the management personnel. Danger monitoring is performed on vehicles of key concern types, including one or more of fire point detection, temperature detection, and smoke and fire detection. If the danger monitoring finds an abnormality, an early warning is triggered, and the early warning information is sent to the service area management system for timely processing by the management personnel. In order to monitor dangerous vehicle types, the parking area for such vehicles is equipped with safety monitoring equipment, such as thermal imaging devices, to achieve detection effects such as fire point detection, temperature detection, and smoke and fire detection.

[0040] S400: Push the traffic management decision, vehicle type information, key vehicle information and its safety monitoring data to the management personnel. In this embodiment, a management system is built in the service area, and the management system is connected to the data center for communication. The management system displays the data from the data center after obtaining it. After the management system obtains the traffic management decision, it displays it in the control panel of the management system in the form of charts, text, warning signs, etc., and intuitively presents the current traffic status and decision-making plan. Specifically, according to the real-time collected data, the distribution statistics of different types of vehicles (such as passenger cars, ordinary trucks, and dangerous goods transport vehicles) and the corresponding parking occupancy are generated. For key vehicles, the vehicles entering the service area are automatically compared with the key vehicle database through license plate recognition, and special vehicle information such as dangerous goods transport vehicles and key supervision vehicles are marked. When the key vehicle is identified, the system immediately sends a notification to the management platform, including: vehicle type, license plate number, current parking status and location. If the key vehicle does not park in the designated area or behaves abnormally, the system generates a warning message and pushes it to the management personnel.

[0041] In summary, the intelligent safety management method for highway service areas of this embodiment realizes the comprehensive optimization of highway service areas in terms of traffic management, parking guidance, safety monitoring and key vehicle supervision through a series of intelligent technical means. The system uses vehicle quantity and type identification technology combined with historical traffic data analysis to accurately formulate dynamic traffic management decisions; through real-time monitoring of parking areas and intelligent matching mechanisms, efficient allocation of vehicle types and empty parking spaces is achieved; based on the traffic prediction model, the traffic peak of dangerous vehicle types is predicted in advance, and multi-level measures such as restriction, guidance and early warning are taken to ensure the safe operation of the service area; at the same time, the safety monitoring data of traffic decisions, vehicle types and key vehicles are integrated and pushed to management personnel in real time to ensure rapid response and scientific decision-making. Compared with the existing technology, the present invention has significant advantages in data integration, dynamic analysis, precise guidance and safety early warning, which effectively improves the management efficiency and safety level of the service area.

[0042] refer to Figure 2 This embodiment further discloses a high-speed service area intelligent safety management system, the system comprising:

[0043] The identification and analysis module 21 is used to identify the number of vehicles in the service area, and to obtain historical traffic data and analysis data of the corresponding historical traffic data, so as to make traffic management decisions; it is also used to set up video recording equipment at the entrance and exit of the service area to capture the information of vehicles entering and leaving the service area; if the exit of the service area does not capture the vehicle information recorded at the entrance, the judgment status of the current vehicle is marked as still in the service area, otherwise the vehicle is not counted; the number of vehicles judged to be in the service area is counted to obtain the number of vehicles in the service area; it is also used to obtain the vehicle data entering and leaving the service area every day, month, and year in the past; analyze the vehicle data to obtain the type and type of vehicle Entry and exit time data of vehicles of the same type; based on the vehicle type and the corresponding entry and exit time data, analyze the time period traffic data, vehicle type traffic distribution data and specific time period traffic data; generate a traffic prediction model based on the time period traffic data, vehicle type traffic distribution data and specific time period traffic data; input the current real-time number of vehicles into the traffic prediction model, and output the predicted traffic data to make traffic management decisions; specifically, when the predicted traffic data shows that the traffic flow on the day exceeds the historical peak traffic flow, generate a decision to restrict the entry of new vehicles and speed up vehicle diversion; when the predicted traffic data shows that the traffic flow on the day does not exceed the historical peak traffic flow, generate a decision to optimize vehicle guidance in combination with parking space data.

[0044] The vehicle guidance module 22 is used to identify the type of vehicle entering the service area and obtain the parking space data of the parking areas corresponding to different types of vehicles, so as to guide the vehicle to the target parking point; it is also used to identify the number of empty parking spaces in the parking areas corresponding to multiple different types of vehicles; identify the type of vehicle entering the service area in real time and match it with the empty parking space corresponding to the corresponding type of vehicle; if there is an unallocated empty parking space, guide the vehicle to the empty parking space, otherwise prompt it to leave; it is also used to obtain the predicted flow and predicted peak flow of dangerous vehicle types based on the output predicted flow data; if the predicted flow exceeds the empty parking spaces in the special parking area for dangerous vehicles, restrict entry or guide the vehicle to a temporary parking area; if the predicted flow exceeds the set monitoring threshold, a dangerous vehicle alarm can be generated and monitored in real time; if the predicted peak flow exceeds the set guidance threshold, on-site parking guidance by management personnel can be generated and monitored in real time.

[0045] The safety monitoring module 23 is used to perform safety monitoring on vehicles of key concern categories among the identified vehicle types; it is also used to determine whether there are vehicles of key concern categories based on the identified vehicle types; if so, an early warning is automatically triggered and the early warning information is sent to the management personnel; the vehicles of key concern categories are monitored for danger, including one or more of fire point detection, temperature detection, and smoke and fire detection; if the danger monitoring finds an abnormality, an early warning is triggered and the early warning information is sent to the service area management system for timely processing by the management personnel.

[0046] The data processing module 24 is used to push traffic management decisions, vehicle type information, key vehicle information and safety monitoring data to management personnel.

[0047] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device 50 includes: a processor 501 (processor), a memory 502 (memory) and a bus 503;

[0048] The processor 501 and the memory 502 communicate with each other via the bus 503 ; the processor 501 is used to call program instructions in the memory 502 to execute the methods provided by the above-mentioned method implementation methods.

[0049] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the methods provided by the above-mentioned method embodiments.

[0050] A person skilled in the art can understand that all or part of the steps for implementing the above-mentioned method implementation method can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method implementation method; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various storage media that can store program codes.

[0051] The device implementation described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., 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 present implementation scheme. Those of ordinary skill in the art may understand and implement it without creative effort.

[0052] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each implementation method or some parts of the implementation method.

[0053] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent safety management of a high-speed service area, characterized in that: The method comprises: Identify the number of vehicles in the service area and obtain historical traffic data and corresponding analysis data of historical traffic data to make traffic management decisions; Identify the type of vehicle entering the service area and obtain the parking space data of the parking area corresponding to different types of vehicles, so as to guide the vehicle to the target parking spot; Conduct safety monitoring on vehicles of key concern among the identified vehicle types; Push traffic management decisions, vehicle type information, key vehicle information and safety monitoring data to management personnel.

2. The high-speed service area intelligent safety management method according to claim 1, characterized in that: The method for identifying the number of vehicles in a service area comprises: Video recording equipment is installed at the entrance and exit of the service area to capture information about vehicles entering and leaving the service area; If the exit of the service area does not capture the vehicle information recorded at the entrance, the current vehicle's judgment status is marked as still in the service area, otherwise the vehicle is not counted; The number of vehicles judged to be in the service area is counted to obtain the number of vehicles in the service area.

3. The high-speed service area intelligent safety management method according to claim 2, characterized in that: The method of identifying the number of vehicles in the service area and obtaining historical traffic data and analysis data corresponding to the historical traffic data to formulate traffic management decisions includes: Obtain the vehicle data that entered and left the service area every day, month, and year in the past; Parse vehicle data to obtain vehicle types and entry and exit time data for different types of vehicles; Analyze the traffic data by time period, traffic distribution data by vehicle type and traffic data for a specific time period based on the vehicle type and the corresponding entry and exit time data; Generate a traffic prediction model based on traffic data by time period, traffic distribution data by vehicle type and traffic data for a specific time period; The current real-time vehicle count is input into the traffic prediction model and the predicted traffic data is output to make traffic management decisions.

4. The high-speed service area intelligent safety management method according to claim 3 is characterized in that: The traffic management decisions include: When the predicted traffic data shows that the traffic volume on that day exceeds the historical peak traffic volume, a decision is made to restrict the entry of new vehicles and speed up vehicle diversion; When the predicted traffic data shows that the traffic volume for the day does not exceed the historical peak traffic volume, a decision is generated to optimize vehicle guidance in combination with parking space data.

5. The high-speed service area intelligent safety management method according to claim 3, characterized in that: The method of identifying the type of vehicle entering the service area and obtaining parking space data of parking areas corresponding to different types of vehicles to guide the vehicle to the target parking spot includes: Identify the number of empty parking spaces corresponding to multiple types of vehicles; Identify the type of vehicle entering the service area in real time and match it with the vacant parking spaces corresponding to the vehicle type; If there is an unallocated vacant parking space, the vehicle will be guided into the vacant parking space; otherwise, the vehicle will be prompted to leave.

6. The high-speed service area intelligent safety management method according to claim 5, characterized in that: The method of identifying the type of vehicle entering the service area and obtaining parking space data of parking areas corresponding to different types of vehicles to guide the vehicle to the target parking spot also includes: Obtain predicted flow and predicted peak flow of dangerous vehicle types according to the output predicted flow data; If the forecasted flow exceeds the available parking spaces in the parking area for hazardous vehicle categories, restrict access or direct traffic to temporary parking areas; If the predicted traffic exceeds the set monitoring threshold, a dangerous vehicle alarm can be generated and monitored in real time; If the predicted peak traffic exceeds the set guidance threshold, the management staff can be called on site to guide parking and monitor in real time.

7. The high-speed service area intelligent safety management method according to claim 1, characterized in that: The method for performing safety monitoring on the identified key focus type vehicles includes: Determine whether there are any vehicles of a particular type based on the identified vehicle types; If it exists, an early warning will be automatically triggered and the early warning information will be sent to the management personnel; Conduct hazard monitoring on key types of vehicles, including one or more of fire point detection, temperature detection, and smoke and fire detection; If the danger monitoring finds an abnormality, an early warning will be triggered and the warning information will be sent to the service area management system for timely processing by the management personnel.

8. An intelligent safety management system for a high-speed service area, characterized in that: include: The identification and analysis module is used to identify the number of vehicles in the service area and obtain historical traffic data and corresponding analysis data of historical traffic data to make traffic management decisions; The vehicle guidance module is used to identify the type of vehicle entering the service area and obtain the parking space data of the parking area corresponding to different types of vehicles, so as to guide the vehicle to the target parking spot; A safety monitoring module is used to perform safety monitoring on vehicles of key concern types among the identified vehicle types; The data processing module is used to push traffic management decisions, vehicle type information, key vehicle information and its safety monitoring data to management personnel.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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