Intelligent chemical industry park risk management and control system and method

Abstract

The invention relates to the technical field of park management, in particular to an intelligent chemical park risk management and control system and method. The method comprises the following steps: acquiring an entrance monitoring image; analyzing the entering monitoring image, and determining the type of a vehicle to be entered; determining a vehicle danger coefficient according to the type of the to-be-entered vehicle; obtaining a vehicle reservation condition, and determining a lane queuing condition according to the vehicle reservation condition and the entrance monitoring image; and determining lane characteristics according to the danger coefficient and the lane queuing condition, and determining a lane queuing suggestion according to the lane characteristics. A safety priority basis is provided for lane distribution, and the probability of safety accidents caused by retention of high-risk vehicles is reduced from the source. The defect of queuing extension caused by non-reserved vehicles is eliminated, a complex scene is quantified through double data sources, and the queuing evaluation accuracy is improved. The balance between safety and efficiency is realized, the problem of lane feature staticization is eliminated, and the vehicle is prevented from entering a high-risk lane by mistake.

Description

Technical Field

[0001] The present application relates to the field of park management technology, and in particular to a risk management and control system and method for a smart chemical park. Background Art

[0002] With the increasing importance of safety management in chemical parks, smart chemical park risk management systems have become crucial for the industry, enabling comprehensive, closed-loop management and efficient access for personnel and vehicles. Existing technologies, typically based on artificial intelligence and the Internet of Things (IoT), capture entry surveillance images through license plate recognition, speed monitoring, and other means, and implement categorized vehicle management through an appointment and approval mechanism.

[0003] However, existing technologies typically rely on reservation information and real-time surveillance images, which can result in high-risk vehicles being assigned to lanes with longer queues, leading to increased detention times and significantly increasing safety risks at park entrances. Furthermore, this static or one-dimensional scheduling strategy limits further improvements in overall traffic efficiency, making it difficult to achieve an optimal balance between risk and efficiency. Summary of the Invention

[0004] This application provides a smart chemical park risk management system and method to solve the above problems.

[0005] In a first aspect, the present application provides a risk management method for a smart chemical park, the method comprising: Acquire an entry monitoring image; analyze the entry monitoring image to determine the type of vehicle to be admitted; Determining a vehicle risk factor based on the type of vehicle to be admitted; Obtaining vehicle reservation status, and determining lane queue status based on the vehicle reservation status and the entry surveillance image; A lane characteristic is determined based on the risk factor and the lane queuing condition, and a lane queuing suggestion is determined based on the lane characteristic.

[0006] Through this solution, the entrance monitoring images are obtained to ensure that the perception ability of the current incoming vehicles is consistent with the functions of the entrance monitoring images, and to avoid response delays caused by relying on static data. The entrance monitoring images are analyzed to determine the types of incoming vehicles, eliminating the one-sidedness problem of relying only on registration information. According to the types of incoming vehicles, the vehicle risk coefficients are determined, providing a basis for safety priorities in lane allocation and reducing the probability of safety accidents caused by the detention of high-risk vehicles at the source. The vehicle reservation situation is obtained, and based on the vehicle reservation situation and the entrance monitoring images, the lane queue situation is determined, eliminating the defect of queue extension caused by unreserved vehicles, quantifying complex scenarios through dual data sources, and improving the accuracy of queue assessment. According to the risk coefficients and the lane queue situation, the lane characteristics are determined, and based on the lane characteristics, lane queue suggestions are determined, achieving a balance between safety and efficiency, eliminating the problem of static lane characteristics, avoiding vehicles from entering high-risk lanes by mistake, and simultaneously ensuring "being able to seal off" and "being able to pass quickly".

[0007] Optionally, the determining the vehicle risk coefficient according to the type of incoming vehicle includes: Determining the license plate numbers of the vehicles already in the queue according to the entrance monitoring images; Obtaining the vehicle registration information according to the type of incoming vehicle and the license plate numbers of the vehicles already in the queue; Determining the basic volume of the goods carried and the freight bill according to the vehicle registration information; Determining the vehicle risk coefficient according to the basic volume of the goods carried and the freight bill.

[0008] Through this solution, according to the type of incoming vehicle, the vehicle registration information is obtained to ensure the complete registration details of the visiting vehicles, laying a data foundation for determining the goods-carrying attributes and risk quantification, and avoiding the interruption of risk assessment due to information loss. According to the vehicle registration information, the basic volume of the goods carried and the freight bill are determined, reflecting the actual state of the goods, and providing a basis for calculating the vehicle risk coefficient. According to the basic volume of the goods carried and the freight bill, the vehicle risk coefficient is determined, dynamically identifying high-risk vehicles, supporting queue optimization and lane guidance decisions, and improving the coordinated management and control of park safety and efficiency.

[0009] Optionally, the determining the lane queue situation according to the vehicle reservation situation and the entrance monitoring images includes: Analyzing the vehicle reservation situation to determine the license plate numbers of the reserved vehicles; Determining the lane queue situation according to the license plate numbers of the vehicles already in the queue and the license plate numbers of the reserved vehicles.

[0010] Through this solution, based on the entry monitoring images, the license plate numbers of the queued vehicles are determined to reflect the license plate number information of the vehicles currently queuing in the lane, and the on-site status of the queuing vehicles is grasped in real time. The vehicle reservation situation is analyzed to determine the reserved license plate numbers, providing a decision-making basis for dynamically adjusting the lane functions. According to the license plate numbers of the queued vehicles and the reserved license plate numbers, the lane queuing situation is determined to realize the dynamic adjustment of the lane functions and support the efficiency goal of "smooth passage".

[0011] Optionally, the determining the lane characteristics according to the risk coefficient and the lane queuing situation includes: Determining the difficulty of cargo inspection for unreserved vehicles according to the risk coefficient; Determining the vehicle proportion of the unreserved vehicles according to the lane queuing situation; Predicting the entry delay time according to the cargo inspection difficulty and the vehicle proportion; Determining the lane characteristics according to the entry delay time.

[0012] Through this solution, according to the risk coefficient, the difficulty of cargo inspection for unreserved vehicles is determined to reflect the actual inspection needs of unreserved vehicles. According to the lane queuing situation, the vehicle proportion of unreserved vehicles is determined to quantify the impact of unreserved factors on the queuing dynamics. According to the cargo inspection difficulty and the vehicle proportion, the entry delay time is predicted to estimate the additional waiting time caused by unreserved vehicles. According to the entry delay time, the lane characteristics are determined, and the lane functions are optimized according to the real-time delay to achieve the balance between safety and efficiency.

[0013] Optionally, the predicting the entry delay time according to the cargo inspection difficulty and the vehicle proportion includes: Determining the type of inspection personnel for the unreserved vehicles according to the type of vehicle to be entered and the risk coefficient; Obtaining the personnel information of the chemical industrial park; determining the candidate inspection personnel according to the personnel information and the type of inspection personnel; Obtaining the work data of the candidate inspection personnel and determining the current available number of personnel; Predicting the entry delay time according to the cargo inspection difficulty, the current available number of personnel and the vehicle proportion.

[0014] Through this solution, based on the type of vehicle to enter the venue and the risk coefficient, determine the type of inspection personnel for unreserved vehicles, ensure that personnel screening targets the special needs of unreserved vehicles, define the type requirements of inspection resources, and lay a foundation for determining candidate inspection personnel. Obtain the personnel information of the chemical industrial park to ensure that the process of determining candidate inspection personnel has comprehensive data support and avoid resource omission. Based on the personnel information and the type of inspection personnel, determine candidate inspection personnel, narrow down the resource scope, and provide input for evaluating the current available number of personnel. Obtain the work data of candidate inspection personnel, determine the current available number of personnel, and quantify the degree of personnel shortage or sufficiency. Based on the difficulty of cargo inspection, the current available number of personnel, and the vehicle occupancy rate, predict the entry delay time, provide a quantitative prediction, and improve the traffic efficiency.

[0015] Optionally, the determining the vehicle risk coefficient according to the cargo base volume and the cargo waybill includes: Determine the actual cargo volume according to the cargo waybill; Determine the vehicle load limit according to the type of vehicle to enter the venue; Determine the vehicle safety factor according to the actual cargo volume and the vehicle load limit; Compare the vehicle safety factor with a preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, determine the load deviation according to the actual cargo volume and the cargo base volume; Determine the vehicle risk coefficient according to the load deviation.

[0016] Through this solution, determine the actual cargo volume according to the cargo waybill to ensure that the risk calculation is based on real cargo information. Determine the vehicle load limit according to the type of vehicle to enter the venue to provide a standard reference value for evaluating whether the vehicle's cargo is overweight. Determine the vehicle safety factor according to the actual cargo volume and the vehicle load limit, indicating the relative safety level of the vehicle's cargo. Compare the vehicle safety factor with a preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, determine the load deviation according to the actual cargo volume and the cargo base volume to quantify the difference between the actual cargo and the standard cargo, and provide risk deviation data for the vehicle risk coefficient. Determine the vehicle risk coefficient according to the load deviation to complete the closed-loop of vehicle risk assessment.

[0017] Optionally, the determining the lane queuing suggestion according to the lane characteristics includes: Determine the suitable docking type for each lane according to the lane characteristics; Determine the estimated passing time for each lane according to the entry delay time and the lane queuing situation; Determine the lane queuing suggestion according to the suitable docking type and the estimated passing time.

[0018] Through this solution, according to the lane characteristics, the suitable parking type for each lane is determined, enhancing the pertinence and safety of lane management, and avoiding congestion and security loopholes caused by mixed queuing. According to the entry delay time and the lane queuing situation, the estimated passing time for each lane is determined, improving the overall passing speed, alleviating the congestion caused by queuing delays, ensuring the goal of "fast passage", and at the same time providing time data for generating lane queuing suggestions. According to the suitable parking type and the estimated passing time, lane queuing suggestions are determined, reducing the detention time of high-risk vehicles and lowering the accident probability.

[0019] Optionally, the determining the vehicle risk coefficient according to the load deviation includes: Obtaining the cargo compartment scan data; analyzing the cargo compartment scan data to determine the cargo stacking form; Determining the vehicle risk coefficient according to the load deviation and the cargo stacking form.

[0020] Through this solution, the cargo compartment scan data is obtained, eliminating the one-sidedness of single-factor evaluation and ensuring that risk quantification is based on multi-dimensional data. Analyzing the cargo compartment scan data to determine the cargo stacking form, realizing the automation and standardization of risk calculation, ensuring that the output value is objective, repeatable, and quantifying the overall safety risk level of the vehicle. Determining the vehicle risk coefficient according to the load deviation and the cargo stacking form, providing an immediate and quantified risk index.

[0021] Optionally, the determining the lane queuing suggestions according to the suitable parking type and the estimated passing time includes: Obtaining the vehicle entry data and the park monitoring data of the chemical industrial park; Analyzing the park monitoring data to determine the vehicle loading and unloading situation; Parsing the vehicle entry data to determine the types of vehicles that have entered the park; Determining the vehicle access information of the chemical industrial park according to the types of vehicles that have entered the park and the vehicle loading and unloading situation; Determining the lane queuing suggestions according to the vehicle access information, the suitable parking type and the estimated passing time.

[0022] Through this solution, vehicle entry data and park monitoring data of the chemical industrial park are obtained to ensure decision-making based on real and dynamic data, and avoid analysis delays or errors caused by data missing. Analyze the park monitoring data to determine the vehicle loading and unloading situation, and avoid rigid suggestions regarding the static nature of lane characteristics and queuing suggestions. Parse the vehicle entry data to determine the types of vehicles that have entered the park, ensure the consistency and availability of vehicle type information, and mitigate part of the risk of the disconnection between the danger coefficient and queuing dynamics. Based on the types of vehicles that have entered the park and the vehicle loading and unloading situation, determine the vehicle access information for the chemical industrial park, reduce the accident risk caused by mixed queuing, and optimize resource allocation. Based on the vehicle access information, suitable docking types, and estimated travel time, determine lane queuing suggestions, eliminate the static nature of lane characteristics and queuing suggestions, and the queuing extension problem of unreserved vehicles and cargo verification, and improve traffic efficiency and safety.

[0023] In a second aspect, the present application provides a risk control system for an intelligent chemical industrial park. The system includes: An image analysis module, configured to obtain entry monitoring images; analyze the entry monitoring images to determine the types of vehicles to enter the park; A danger determination module, configured to determine the vehicle danger coefficient according to the types of vehicles to enter the park; A queuing analysis module, configured to obtain vehicle reservation situations, and determine lane queuing situations according to the vehicle reservation situations and the entry monitoring images; A suggestion determination module, configured to determine lane characteristics according to the danger coefficient and the lane queuing situations, and determine lane queuing suggestions according to the lane characteristics.

[0024] Optionally, when the danger determination module determines the vehicle danger coefficient according to the types of vehicles to enter the park, it is configured to: Determine the license plate numbers of vehicles already in the queue according to the entry monitoring images; Obtain vehicle registration information according to the types of vehicles to enter the park and the license plate numbers of vehicles already in the queue; Determine the basic volume of goods carried and the cargo waybill according to the vehicle registration information; Determine the vehicle danger coefficient according to the basic volume of goods carried and the cargo waybill.

[0025] Optionally, when the queuing analysis module determines the lane queuing situation according to the vehicle reservation situations and the entry monitoring images, it is configured to: Parse the vehicle reservation situations to determine the license plate numbers of vehicles already reserved; Determine the lane queuing situation according to the license plate numbers of vehicles already in the queue and the license plate numbers of vehicles already reserved.

[0026] Optionally, when the recommendation determination module determines the lane characteristics based on the risk coefficient and the lane queuing situation, it is used for: Determine the difficulty of cargo inspection for unreserved vehicles according to the risk coefficient; Determine the vehicle occupancy ratio of the unreserved vehicles according to the lane queuing situation; Predict the entry delay time according to the cargo inspection difficulty and the vehicle occupancy ratio; Determine the lane characteristics according to the entry delay time.

[0027] Optionally, when the recommendation determination module predicts the entry delay time according to the cargo inspection difficulty and the vehicle occupancy ratio, it is used for: Determine the type of inspection personnel for the unreserved vehicles according to the type of vehicle to enter and the risk coefficient; Obtain the personnel information of the chemical industrial park; determine the candidate inspection personnel according to the personnel information and the type of inspection personnel; Obtain the work data of the candidate inspection personnel and determine the current available number of personnel; Predict the entry delay time according to the cargo inspection difficulty, the current available number of personnel and the vehicle occupancy ratio.

[0028] Optionally, when the risk determination module determines the vehicle risk coefficient according to the basic cargo volume and the cargo waybill, it is used for: Determine the actual cargo volume according to the cargo waybill; Determine the vehicle load limit according to the type of vehicle to enter; Determine the vehicle safety factor according to the actual cargo volume and the vehicle load limit; Compare the vehicle safety factor with a preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, determine the load deviation according to the actual cargo volume and the basic cargo volume; Determine the vehicle risk coefficient according to the load deviation.

[0029] Optionally, when the recommendation determination module determines the lane queuing recommendation according to the lane characteristics, it is used for: Determine the suitable docking type for each lane according to the lane characteristics; Determine the estimated passing time for each lane according to the entry delay time and the lane queuing situation; Determine the lane queuing recommendation according to the suitable docking type and the estimated passing time.

[0030] Optionally, when the risk determination module determines the vehicle risk coefficient according to the load deviation, it is used for: Obtain the cargo compartment scan data; analyze the cargo compartment scan data to determine the stacking pattern of the goods; Determine the vehicle risk coefficient according to the load deviation and the stacking pattern of the goods.

[0031] Optionally, when the recommended determination module determines the lane queuing recommendation according to the suitable docking type and the estimated passing time, it is used for: Obtain the vehicle entry data and the park monitoring data of the chemical industrial park; Analyze the park monitoring data to determine the vehicle loading and unloading situation; Parse the vehicle entry data to determine the types of vehicles that have entered the park; Determine the vehicle access information of the chemical industrial park according to the types of vehicles that have entered the park and the vehicle loading and unloading situation; Determine the lane queuing recommendation according to the vehicle access information, the suitable docking type and the estimated passing time. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application; Figure 2 It is a flowchart of a risk control method for a smart chemical industrial park provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of a risk control system for a smart chemical industrial park provided by an embodiment of the present application. Detailed Embodiments

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0035] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0036] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0037] Existing technologies typically rely on reservation information and real-time surveillance images, which can result in high-risk vehicles being assigned to lanes with longer queues, leading to increased detention times and significantly increasing safety risks at park entrances. Furthermore, this static or one-dimensional scheduling strategy limits further improvements in overall traffic efficiency, making it difficult to achieve an optimal balance between risk and efficiency.

[0038] Based on this, the present application provides a risk management system and method for a smart chemical park, which obtains entry monitoring images, ensures that the perception capability of the current entry vehicle is consistent with the entry monitoring image function, and avoids response delays caused by reliance on static data. Analyze the entry monitoring image to determine the type of vehicle to be entered, and eliminate the one-sided problem of relying solely on registration information. According to the type of vehicle to be entered, determine the vehicle risk factor, provide a safety priority basis for lane allocation, and reduce the probability of safety accidents caused by high-risk vehicle detention from the source. Obtain the vehicle reservation status, and determine the lane queuing status based on the vehicle reservation status and the entry monitoring image, eliminate the defect of extended queues caused by unreserved vehicles, quantify complex scenarios through dual data sources, and improve the accuracy of queuing assessment. Determine the lane characteristics based on the risk factor and lane queuing status, and determine the lane queuing suggestions based on the lane characteristics to achieve a balance between safety and efficiency, eliminate the problem of static lane characteristics, avoid vehicles from mistakenly entering high-risk lanes, and simultaneously ensure "sealing" and "fast passage".

[0039] Figure 1 This is a schematic diagram of an application scenario provided by this application. The method provided by this application is applied when conducting risk management in chemical parks.

[0040] Specifically, the method provided in this application is applied to any server. The server interacts with the mobile monitoring device to obtain the entrance monitoring image through the mobile monitoring device. Analyze the entrance monitoring image to determine the type of vehicle to enter. According to the type of vehicle to enter, determine the vehicle risk coefficient. Obtain the vehicle reservation situation. According to the vehicle reservation situation and the entrance monitoring image, determine the lane queuing situation, eliminate the defect of queue extension caused by unreserved vehicles, quantify complex scenarios through dual data sources, and improve the accuracy of queue evaluation. According to the risk coefficient and the lane queuing situation, determine the lane characteristics, and according to the lane characteristics, determine the lane queuing suggestion, achieve the balance between safety and efficiency, eliminate the problem of static lane characteristics, avoid vehicles from entering high-risk lanes by mistake, and synchronously ensure "sealing" and "fast passage".

[0041] The specific implementation method can refer to the following embodiments.

[0042] Figure 2 The flowchart of a risk control method for an intelligent chemical industrial park provided by an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes: S201. Obtain the entrance monitoring image; analyze the entrance monitoring image to determine the type of vehicle to enter; The entrance monitoring image can be image data including the license plate number, vehicle body appearance, and cargo loading status of the vehicle to enter.

[0043] The type of vehicle to enter can be the classification result of the vehicle to enter.

[0044] Specifically, the entrance monitoring image is obtained in real time through the mobile monitoring device deployed at the entrance of the park. Then, a pre-trained computer vision algorithm is used to detect the vehicle area in the entrance monitoring image, identify vehicle features such as license plate color, vehicle body identification, and cargo appearance; furthermore, according to the vehicle features, match the predefined classification rules established based on the industry risk control standard to determine the type of vehicle to enter.

[0045] S202. According to the type of vehicle to enter, determine the vehicle risk coefficient; The vehicle risk coefficient can be a predefined numerical parameter used to quantify the safety risk level of the vehicle's cargo.

[0046] Specifically, according to the type of vehicle to enter, query the risk coefficient mapping table constructed based on the industry risk control standard to determine the corresponding vehicle risk coefficient.

[0047] S203. Obtain the vehicle reservation situation. According to the vehicle reservation situation and the entrance monitoring image, determine the lane queuing situation; The vehicle reservation situation can be the vehicle reservation information stored in the reservation database.

[0048] The lane queuing situation can be a lane status indicator, such as the total number of queuing vehicles in the lane, whether there are reservations among the queuing vehicles in the lane, etc.

[0049] Specifically, a reservation database is constructed according to the reservation management data framework, and the vehicle reservation situation is obtained through API query; then, the entrance monitoring images are analyzed, and the license plate numbers of the currently queuing vehicles are detected using image recognition algorithms and the reservation information is matched; furthermore, the number of queuing vehicles in each lane is counted, and according to the vehicle type and vehicle reservation situation, the lane queuing situation is determined.

[0050] S204. Determine the lane characteristics according to the risk coefficient and the lane queuing situation, and determine the lane queuing suggestion according to the lane characteristics.

[0051] The lane characteristics can be descriptions of lane attributes such as the suitable vehicle types for docking and the estimated passing time.

[0052] The lane queuing suggestion can be a vehicle guiding instruction.

[0053] Specifically, based on the risk coefficient and the lane queuing situation, a rule engine is used to determine the lane characteristics such as the suitable docking type and the estimated passing time for each lane; then, the lane characteristics are converted into visual instructions; finally, the recommended lane information is displayed through an electronic sign to generate the final lane queuing suggestion.

[0054] Through this solution, the entrance monitoring images are obtained to ensure that the perception ability of the currently entering vehicles is consistent with the function of the entrance monitoring images, and the response delay caused by relying on static data is avoided. The entrance monitoring images are analyzed to determine the types of vehicles to enter, and the one-sidedness problem of relying only on registration information is eliminated. According to the types of vehicles to enter, the vehicle risk coefficient is determined, providing a basis for safety priority in lane allocation and reducing the probability of safety accidents caused by the detention of high-risk vehicles from the source. The vehicle reservation situation is obtained, and according to the vehicle reservation situation and the entrance monitoring images, the lane queuing situation is determined, eliminating the defect of the queue extension caused by unreserved vehicles, quantifying complex scenarios through dual data sources, and improving the accuracy of queue evaluation. According to the risk coefficient and the lane queuing situation, the lane characteristics are determined, and according to the lane characteristics, the lane queuing suggestion is determined, achieving a balance between safety and efficiency, eliminating the problem of static lane characteristics, preventing vehicles from entering high-risk lanes by mistake, and synchronously ensuring "being able to seal off" and "being able to pass quickly".

[0055] In some embodiments, according to the entrance monitoring images, the queued license plate numbers are determined; according to the types of vehicles to enter and the queued license plate numbers, the vehicle registration information is obtained; according to the vehicle registration information, the basic volume of goods carried and the freight bill of goods carried are determined; according to the basic volume of goods carried and the freight bill of goods carried, the vehicle risk coefficient is determined.

[0056] The queued license plate number can be the license plate number of the vehicle currently queuing in the lane.

[0057] Vehicle registration information can be the basic registration data record of the vehicle.

[0058] The basic volume of goods carried can be the basic physical measurement of the goods.

[0059] The goods transport waybill can be the transport document information of the goods.

[0060] Specifically, apply the license plate recognition algorithm to locate the license plate area in the entrance monitoring image, extract the license plate character features, and identify the queued license plate number. Subsequently, based on the type of vehicle to enter the park and the queued license plate number, retrieve the corresponding vehicle registration information according to the query interface of the park vehicle management database. Then, parse different fields in the vehicle registration information to extract the basic volume of goods carried and the goods transport waybill. Subsequently, establish a preset risk assessment rule based on the theoretical basis of the risk level, and combine the goods category carried in the goods transport waybill and the basic volume to determine the vehicle risk coefficient.

[0061] Through this solution, according to the type of vehicle to enter the park, obtain the vehicle registration information, ensure the complete registration details of the accessed vehicle, lay a data foundation for determining the goods-carrying attribute and risk quantification, and avoid the interruption of risk assessment due to missing information. According to the vehicle registration information, determine the basic volume of goods carried and the goods transport waybill, reflect the actual state of the goods, and provide a basis for calculating the vehicle risk coefficient. According to the basic volume of goods carried and the goods transport waybill, determine the vehicle risk coefficient, dynamically identify high-risk vehicles, support queuing optimization and lane guidance decisions, and improve the collaborative management and control of park safety and efficiency.

[0062] In some embodiments, parse the vehicle reservation situation to determine the reserved license plate number; based on the queued license plate number and the reserved license plate number, determine the lane queuing situation.

[0063] The reserved license plate number can be the license plate number of the vehicle that has passed the reservation approval.

[0064] Specifically, access the vehicle reservation database, retrieve the vehicle reservation situation, extract the license plate number field, and thus determine the reserved license plate number. Furthermore, compare the queued license plate number with the reserved license plate number to identify the reserved vehicles in the queue; then, calculate the vehicles whose queued license plate numbers do not appear in the reserved license plate number list and mark them as unreserved queuing vehicles; finally, based on the comparison results, count the total number of lane queuing vehicles, the proportion of reserved vehicles, and the proportion of unreserved vehicles to determine the final lane queuing situation.

[0065] Through this solution, based on the entrance monitoring images, the license plate numbers of the queued vehicles are determined to reflect the license plate information of the currently queued vehicles in the lane, and the on-site status of the queued vehicles is grasped in real time. The vehicle reservation situation is analyzed to determine the reserved license plate numbers, providing a decision-making basis for dynamically adjusting the lane functions. Based on the license plate numbers of the queued vehicles and the reserved license plate numbers, the lane queuing situation is determined to realize the dynamic adjustment of the lane functions, supporting the efficiency goal of "fast passage".

[0066] In some embodiments, according to the risk coefficient, the difficulty of cargo inspection for unreserved vehicles is determined; according to the lane queuing situation, the vehicle proportion of unreserved vehicles is determined; according to the cargo inspection difficulty and the vehicle proportion, the predicted entrance delay time is predicted; according to the entrance delay time, the lane characteristics are determined.

[0067] The unreserved vehicles can be the vehicles not in the reservation approval system.

[0068] The cargo inspection difficulty can be the complexity level of cargo inspection.

[0069] The vehicle proportion can be the proportion of the number of unreserved vehicles in the total queued vehicles in the lane.

[0070] The entrance delay time can be the predicted additional time for a vehicle to pass from queuing to entering the lane.

[0071] Specifically, retrieve the vehicle list in the lane queuing situation and identify the unreserved vehicles; then, extract the risk coefficient corresponding to the unreserved vehicles; subsequently, based on the risk coefficient, convert the risk coefficient into the cargo inspection difficulty through a preset mapping rule. Then, count the total number of queued vehicles in the lane queuing situation; furthermore, compare the queued license plate numbers with the reserved license plate numbers, perform a data difference set operation, determine the unmatched queued license plate numbers as unreserved vehicles, and count the number of unreserved vehicles; then, match the number of unreserved vehicles with the total number of queued vehicles to determine the vehicle proportion of unreserved vehicles. Subsequently, based on the cargo inspection difficulty and the vehicle proportion, predict the entrance delay time through a rule engine. Finally, based on the entrance delay time, query the preset dynamic rule library to determine the corresponding lane characteristics.

[0072] Through this solution, according to the risk coefficient, the cargo inspection difficulty of unreserved vehicles is determined to reflect the actual inspection requirements of unreserved vehicles. According to the lane queuing situation, the vehicle proportion of unreserved vehicles is determined to quantify the impact of unreserved factors on the queuing dynamics. According to the cargo inspection difficulty and the vehicle proportion, the entrance delay time is predicted to estimate the additional waiting time caused by unreserved vehicles. According to the entrance delay time, the lane characteristics are determined, and the lane functions are optimized according to the real-time delay to achieve the balance between safety and efficiency.

[0073] In some embodiments, according to the type and risk coefficient of the vehicle to enter the venue, determine the type of inspection personnel for unreserved vehicles; obtain the personnel information of the chemical industrial park; determine the candidate inspection personnel according to the personnel information and the type of inspection personnel; obtain the work data of the candidate inspection personnel, and determine the current available number of personnel; predict the entry delay time according to the difficulty of cargo inspection, the current available number of personnel, and the vehicle occupancy rate.

[0074] The type of inspection personnel can be the category of inspection personnel determined based on the type and risk coefficient of the vehicle to enter the venue.

[0075] The chemical industrial park can be a park environment implementing risk control of an intelligent chemical industrial park.

[0076] The personnel information can be a dataset containing attributes such as personnel roles, skill types, and on-the-job status.

[0077] The candidate inspection personnel can be a set of personnel meeting the conditions of the type of inspection personnel.

[0078] The work data can be data reflecting the work status of the candidate inspection personnel.

[0079] The current available number of personnel can be the number of available candidate inspection personnel.

[0080] Specifically, based on the type and risk coefficient of the vehicle to enter the venue, query the predefined rule mapping table established according to industry standards to determine the type of inspection personnel for unreserved vehicles. Then, query the central personnel database of the chemical industrial park and obtain the personnel information through API calls. Subsequently, use a data matching algorithm to screen out the candidate inspection personnel whose skill attributes in the personnel information meet the type of inspection personnel. Then, query the work data of the candidate inspection personnel from the real-time task monitoring device, check the status field in the work data, and count the current available number of personnel with the status of being idle. Finally, assign impact factors to the cargo inspection difficulty level, the current available number of personnel, and the vehicle occupancy rate, and apply the weight superposition rule to predict the entry delay time.

[0081] Through this solution, according to the type and risk coefficient of the vehicle to enter the venue, determine the type of inspection personnel for unreserved vehicles, ensure that the personnel screening targets the special needs of unreserved vehicles, limit the type requirements of inspection resources, and lay a foundation for determining candidate inspection personnel. Obtain the personnel information of the chemical industrial park to ensure that the process of determining candidate inspection personnel has comprehensive data support and avoid resource omission. Determine the candidate inspection personnel according to the personnel information and the type of inspection personnel, narrow down the resource scope, and provide input for evaluating the current available number of personnel. Obtain the work data of the candidate inspection personnel and determine the current available number of personnel to quantify the degree of personnel shortage or sufficiency. Predict the entry delay time according to the difficulty of cargo inspection, the current available number of personnel, and the vehicle occupancy rate, provide a quantitative prediction, and improve the traffic efficiency.

[0082] In some embodiments, the actual load is determined according to the freight bill; the vehicle load limit is determined according to the type of vehicle to enter the site; the vehicle safety factor is determined according to the actual load and the vehicle load limit; the vehicle safety factor is compared with a preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, the load deviation is determined according to the actual load and the basic load volume; and the vehicle risk factor is determined according to the load deviation.

[0083] The actual load can be a quantified value of the goods currently loaded on the vehicle.

[0084] The vehicle load limit can be the maximum load capacity value allowed for different vehicle types.

[0085] The vehicle safety factor can be a quantified index representing the cargo safety level.

[0086] The preset safety threshold can be a predefined fixed value used to determine whether the vehicle safety factor is in a risk critical state. It is pre-stored in the server and called when in use.

[0087] The load deviation can be a quantified value of the difference between the actual load and the basic load volume.

[0088] Specifically, based on the freight bill, the weight or volume data of the goods is extracted to determine the actual load. Then, based on the type of vehicle to enter the site, a predefined vehicle type database established based on the technical specifications provided by the vehicle manufacturer and industry safety standards is queried; furthermore, the corresponding maximum load capacity is matched to determine the vehicle load limit. Subsequently, the actual load is compared with the vehicle load limit to calculate a ratio value; furthermore, according to the calculation result, the vehicle safety factor is determined. Subsequently, the preset safety threshold is set through statistical analysis of historical accident data and industry safety specifications; then, the vehicle safety factor is compared with the preset safety threshold; if the vehicle safety factor is lower than the preset safety threshold, the absolute difference is calculated according to the actual load and the basic load volume; furthermore, according to the calculation result, the load deviation is determined. Finally, based on the load deviation, through the predefined mapping rules formulated by expert experience combined with the risk grading model, the load deviation is converted into a risk level, thereby determining the final vehicle risk factor.

[0089] Through this solution, based on the cargo waybill, the actual cargo volume is determined to ensure that the risk calculation is based on the real cargo information. According to the type of the vehicle to enter the site, the vehicle load limit is determined to provide a standard reference value for evaluating whether the vehicle's cargo is overweight. According to the actual cargo volume and the vehicle load limit, the vehicle safety factor is determined, which represents the relative safety level of the vehicle's cargo. The vehicle safety factor is compared with the preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, then according to the actual cargo volume and the basic cargo volume, the load deviation is determined to quantify the difference between the actual cargo and the standard cargo, providing risk deviation data for the vehicle risk coefficient. According to the load deviation, the vehicle risk coefficient is determined to complete the closed-loop of vehicle risk assessment.

[0090] In some embodiments, according to the lane characteristics, the suitable docking type for each lane is determined; according to the entry delay time and the lane queuing situation, the estimated passing time for each lane is determined; according to the suitable docking type and the estimated passing time, the lane queuing suggestion is determined.

[0091] A lane can be a dedicated passage for vehicle queuing and passing.

[0092] The suitable docking type can be the vehicle type suitable for docking in the designated lane.

[0093] The estimated passing time can be the estimated time for the vehicle to pass through the lane.

[0094] Specifically, the lane characteristics are parsed by the internal rule engine to extract the safety level attribute in the lane characteristics; then, according to the safety level attribute, the suitable docking type for each lane is determined. Subsequently, the lane queuing situation is parsed to extract the current number of queuing vehicles and the average service time; furthermore, according to the current number of queuing vehicles and the average service time, the estimated waiting time is calculated; then, combined with the entry delay time, the estimated passing time for each lane is determined. Subsequently, according to the type of the vehicle to enter the site, the candidate lanes with matching suitable docking types are screened from several lanes; then, the candidate lanes are sorted in ascending order of the estimated passing time; furthermore, the lane with the shortest estimated passing time is selected as the recommended lane; thus, the final lane queuing suggestion is determined.

[0095] Through this solution, according to the lane characteristics, the suitable docking type for each lane is determined, enhancing the pertinence and safety of lane management and avoiding congestion and safety loopholes caused by mixed queuing. According to the entry delay time and the lane queuing situation, the estimated passing time for each lane is determined, improving the overall passing speed, alleviating the congestion caused by queuing delay, ensuring the goal of "fast passing", and at the same time providing time data for generating lane queuing suggestions. According to the suitable docking type and the estimated passing time, the lane queuing suggestion is determined, reducing the residence time of high-risk vehicles and reducing the accident probability.

[0096] In some embodiments, obtain cargo compartment scan data; analyze the cargo compartment scan data to determine the cargo stacking pattern; and determine the vehicle risk coefficient based on the load deviation and the cargo stacking pattern.

[0097] The cargo compartment scan data can be the original data of the vehicle's cargo compartment.

[0098] The cargo stacking pattern can be a description of the cargo stacking characteristics.

[0099] Specifically, use a scanning device to perform non-contact scanning on the vehicle's cargo compartment to generate cargo compartment scan data. Then, use an image processing algorithm to analyze the cargo compartment scan data to identify the stacking characteristics of the cargo in the cargo compartment; furthermore, according to predefined form classification rules, determine the cargo stacking pattern. Finally, combine the load deviation and the cargo stacking pattern, and apply a preset risk coefficient calculation rule to determine the vehicle risk coefficient.

[0100] Through this solution, obtain the cargo compartment scan data, eliminate the one-sidedness of single-factor evaluation, and ensure that risk quantification is based on multi-dimensional data. Analyze the cargo compartment scan data to determine the cargo stacking pattern, realize the automation and standardization of risk calculation, ensure that the output value is objective, repeatable, and quantify the overall safety risk level of the vehicle. Determine the vehicle risk coefficient based on the load deviation and the cargo stacking pattern, and provide an immediate and quantified risk indicator.

[0101] In some embodiments, obtain the vehicle entry data of the chemical industrial park and the park monitoring data; analyze the park monitoring data to determine the vehicle loading and unloading situation; analyze the vehicle entry data to determine the types of vehicles that have entered the park; determine the vehicle access information of the chemical industrial park according to the types of vehicles that have entered the park and the vehicle loading and unloading situation; and determine the lane queuing suggestion according to the vehicle access information, the suitable docking type, and the estimated passing time.

[0102] The vehicle entry data can be data such as the license plate number, reservation information, and entry time of the vehicle entering the park.

[0103] The park monitoring data can be video streams or image data obtained by the internal monitoring devices of the park.

[0104] The vehicle loading and unloading situation can be the vehicle loading and unloading status.

[0105] The types of vehicles that have entered the park can be the vehicle classifications parsed based on the vehicle entry data.

[0106] The vehicle access information can be information on whether the vehicle is allowed to enter.

[0107] Specifically, vehicle entry data is obtained in real time through the API interface of the chemical industrial park; at the same time, park monitoring data is obtained through the monitoring devices inside the park. Then, based on the park monitoring data, the vehicle positions and cargo areas in the video frames are detected; furthermore, the loading and unloading behavior characteristics are identified to determine the vehicle loading and unloading conditions. Then, the reservation information field in the vehicle entry data is parsed, and according to the predefined classification criteria established by the reservation approval mechanism and vehicle registration information, the license plate number is mapped to the corresponding vehicle type that has entered the park. Furthermore, combining the vehicle type that has entered the park and the vehicle loading and unloading conditions, applying the predefined access rules established by queuing theory, the vehicle access information for the chemical industrial park is determined. Finally, the vehicle access information is matched with the suitable docking type, and compatible lanes are screened; combining the estimated travel time, the lane with the shortest travel time is selected to determine the lane queuing suggestion.

[0108] Through this solution, the vehicle entry data and park monitoring data of the chemical industrial park are obtained, ensuring decision-making based on real and dynamic data and avoiding analysis delays or errors caused by data missing. Analyzing the park monitoring data to determine the vehicle loading and unloading conditions, avoiding the rigid suggestions of the static problems of lane characteristics and queuing suggestions. Parsing the vehicle entry data to determine the vehicle type that has entered the park, ensuring the consistency and availability of vehicle type information and alleviating part of the risk of the disconnection between the danger coefficient and queuing dynamics. According to the vehicle type that has entered the park and the vehicle loading and unloading conditions, determining the vehicle access information for the chemical industrial park, reducing the accident risk brought by mixed queuing and optimizing resource allocation. According to the vehicle access information, suitable docking type and estimated travel time, determining the lane queuing suggestion, eliminating the static problems of lane characteristics and queuing suggestions and the queuing extension problems of unreserved vehicles and cargo verification, and improving the traffic efficiency and safety.

[0109] Figure 3 As shown in the following figure, it is a schematic structural diagram of a risk control system for a smart chemical industrial park provided by an embodiment of the present application. Figure 3 As shown, the risk control system 300 for the smart chemical industrial park in this embodiment includes: an image analysis module 301, a danger determination module 302, a queuing analysis module 303, and a suggestion determination module 304.

[0110] The image analysis module 301 is used to obtain the entry monitoring images; analyze the entry monitoring images to determine the types of vehicles to enter the park; The danger determination module 302 is used to determine the vehicle danger coefficient according to the types of vehicles to enter the park; The queuing analysis module 303 is used to obtain the vehicle reservation situation, and determine the lane queuing situation according to the vehicle reservation situation and the entry monitoring images; The suggestion determination module 304 is used to determine the lane characteristics according to the danger coefficient and the lane queuing situation, and determine the lane queuing suggestion according to the lane characteristics.

[0111] Optionally, when determining the vehicle danger coefficient according to the type of vehicle to enter the venue, the danger determination module 302 is configured to: Determine the license plate numbers of the vehicles already in the queue according to the entry monitoring image; Obtain vehicle registration information according to the type of vehicle to enter the venue and the license plate numbers of the vehicles already in the queue; Determine the basic volume of goods carried and the freight bill according to the vehicle registration information; Determine the vehicle danger coefficient according to the basic volume of goods carried and the freight bill.

[0112] Optionally, when determining the lane queue situation according to the vehicle reservation situation and the entry monitoring image, the queue analysis module 303 is configured to: Analyze the vehicle reservation situation to determine the license plate numbers of the vehicles already reserved; Determine the lane queue situation according to the license plate numbers of the vehicles already in the queue and the license plate numbers of the vehicles already reserved.

[0113] Optionally, when determining the lane characteristics according to the danger coefficient and the lane queue situation, the suggestion determination module 304 is configured to: Determine the difficulty of cargo inspection for unreserved vehicles according to the danger coefficient; Determine the vehicle occupancy rate of the unreserved vehicles according to the lane queue situation; Predict the entry delay time according to the difficulty of cargo inspection and the vehicle occupancy rate; Determine the lane characteristics according to the entry delay time.

[0114] Optionally, when predicting the entry delay time according to the difficulty of cargo inspection and the vehicle occupancy rate, the suggestion determination module 304 is configured to: Determine the type of inspection personnel for the unreserved vehicles according to the type of vehicle to enter the venue and the danger coefficient; Obtain the personnel information of the chemical industrial park; determine the candidate inspection personnel according to the personnel information and the type of inspection personnel; Obtain the work data of the candidate inspection personnel and determine the current available number of personnel; Predict the entry delay time according to the difficulty of cargo inspection, the current available number of personnel and the vehicle occupancy rate.

[0115] Optionally, when determining the vehicle danger coefficient according to the basic volume of goods carried and the freight bill, the danger determination module 302 is configured to: Determine the actual cargo volume according to the freight bill; Determine the vehicle load limit according to the type of vehicle to enter the venue; Determine the vehicle safety factor according to the actual load and the vehicle load limit; Compare the vehicle safety factor with a preset safety threshold. If the vehicle safety factor is lower than the preset safety threshold, determine the load deviation according to the actual load and the basic load volume; Determine the vehicle danger coefficient according to the load deviation.

[0116] Optionally, when the recommendation determination module 304 determines the lane queuing recommendation according to the lane characteristics, it is used for: Determine the suitable docking type for each lane according to the lane characteristics; Determine the estimated passing time for each lane according to the entry delay time and the lane queuing situation; Determine the lane queuing recommendation according to the suitable docking type and the estimated passing time.

[0117] Optionally, when the danger determination module 302 determines the vehicle danger coefficient according to the load deviation, it is used for: Obtain the cargo compartment scan data; analyze the cargo compartment scan data to determine the cargo stacking form; Determine the vehicle danger coefficient according to the load deviation and the cargo stacking form.

[0118] Optionally, when the recommendation determination module 304 determines the lane queuing recommendation according to the suitable docking type and the estimated passing time, it is used for: Obtain the vehicle entry data and the park monitoring data of the chemical industrial park; Analyze the park monitoring data to determine the vehicle loading and unloading situation; Parse the vehicle entry data to determine the types of vehicles that have entered the park; Determine the vehicle access information of the chemical industrial park according to the types of vehicles that have entered the park and the vehicle loading and unloading situation; Determine the lane queuing recommendation according to the vehicle access information, the suitable docking type and the estimated passing time.

[0119] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

Claims

1. A risk control method for an intelligent chemical industrial park, characterized in that Including: Obtain the entrance monitoring image; Analyze the entrance monitoring image to determine the type of vehicle to enter; Determine the vehicle risk coefficient according to the type of vehicle to enter; Obtain the vehicle reservation situation, and determine the lane queuing situation according to the vehicle reservation situation and the entrance monitoring image; Determine the lane characteristics according to the risk coefficient and the lane queuing situation, and determine the lane queuing suggestion according to the lane characteristics.

2. The method according to claim 1, wherein The determining the vehicle risk coefficient according to the type of vehicle to enter includes: Determine the license plate numbers of the vehicles already in the queue according to the entrance monitoring image; Obtain the vehicle registration information according to the type of vehicle to enter and the license plate numbers of the vehicles already in the queue; Determine the basic volume of the goods carried and the freight bill according to the vehicle registration information; Determine the vehicle risk coefficient according to the basic volume of the goods carried and the freight bill.

3. The method according to claim 2, wherein The determining the lane queuing situation according to the vehicle reservation situation and the entrance monitoring image includes: Analyze the vehicle reservation situation to determine the license plate numbers of the vehicles already reserved; Determine the lane queuing situation according to the license plate numbers of the vehicles already in the queue and the license plate numbers of the vehicles already reserved.

4. The method according to claim 1, characterized in that The determining the lane characteristics according to the risk coefficient and the lane queuing situation includes: Determine the difficulty of inspecting the goods of the unreserved vehicles according to the risk coefficient; Determine the vehicle proportion of the unreserved vehicles according to the lane queuing situation; Predict the entrance delay time according to the difficulty of inspecting the goods and the vehicle proportion; Determine the lane characteristics according to the entrance delay time.

5. The method according to claim 4, wherein The predicting the entrance delay time according to the difficulty of inspecting the goods and the vehicle proportion includes: Determine the type of inspection personnel for the unreserved vehicles according to the type of vehicle to enter and the risk coefficient; Obtain the personnel information of the chemical industrial park; determine the candidate inspection personnel according to the personnel information and the type of inspection personnel; Obtain the work data of the candidate inspection personnel and determine the current available number of personnel; Predict the entrance delay time according to the difficulty of inspecting the goods, the current available number of personnel and the vehicle proportion.

6. The method according to claim 2, characterized in that The determining the vehicle risk coefficient according to the basic volume of the goods carried and the freight bill includes: Determine the actual volume of the goods carried according to the freight bill; Determine the vehicle load limit according to the type of vehicle to enter; Determine the vehicle safety coefficient according to the actual volume of the goods carried and the vehicle load limit; Compare the vehicle safety coefficient with the preset safety threshold. If the vehicle safety coefficient is lower than the preset safety threshold, determine the load deviation according to the actual volume of the goods carried and the basic volume of the goods carried; Determine the vehicle risk coefficient according to the load deviation.

7. The method according to claim 4, wherein The determining the lane queuing suggestion according to the lane characteristics includes: Determine the suitable docking type for each lane according to the lane characteristics; Determine the estimated passing time for each lane according to the entrance delay time and the lane queuing situation; Determine the lane queuing suggestion according to the suitable docking type and the estimated passing time.

8. The method according to claim 6, wherein The determining the vehicle risk coefficient according to the load deviation includes: Obtain the cargo compartment scan data; analyze the cargo compartment scan data to determine the stacking form of the goods; Determine the vehicle risk coefficient according to the load deviation and the cargo stacking pattern.

9. The method according to claim 7, characterized in that, Determine the lane queuing suggestion according to the suitable docking type and the estimated passing time, including: Obtain the vehicle entry data and the park monitoring data of the chemical industrial park; Analyze the park monitoring data to determine the vehicle loading and unloading situation; Analyze the vehicle entry data to determine the types of vehicles that have entered the park; Determine the vehicle access information of the chemical industrial park according to the types of vehicles that have entered the park and the vehicle loading and unloading situation; Determine the lane queuing suggestion according to the vehicle access information, the suitable docking type and the estimated passing time.

10. A risk control system for an intelligent chemical industrial park, characterized in that, Applied to the method according to any one of claims 1-9, including: An image analysis module for obtaining the entry monitoring image; analyzing the entry monitoring image to determine the type of vehicle to enter the park; A risk determination module for determining the vehicle risk coefficient according to the type of vehicle to enter the park; A queuing analysis module for obtaining the vehicle reservation situation and determining the lane queuing situation according to the vehicle reservation situation and the entry monitoring image; A suggestion determination module for determining the lane characteristics according to the risk coefficient and the lane queuing situation, and determining the lane queuing suggestion according to the lane characteristics.

Images