Business rule-based port automatic driving safety supervision system and supervision method thereof

Abstract

The invention relates to the technical field of port automatic driving safety supervision, in particular to a port automatic driving safety supervision system based on business rules and a supervision method thereof, and the supervision system comprises the following modules: a data acquisition module, a rule engine module, a safety supervision module, a safety early warning module, an instruction interaction module and an equipment module. The supervision method comprises the following steps: collecting data; calling the rule; the early warning threshold is used for identifying risks; the method has the beneficial effects that in a port operation environment, the system identifies operation risks through a rule engine in the system based on abnormal state data during equipment operation, and sends an optimal instruction to a vehicle-end equipment end, so that risks caused by limited sensing and decision-making capabilities are reduced; through fusion of sensing data of the container truck and the equipment and close cooperation of the rule engine and the safety early warning module, the operation risk is directly, efficiently and automatically identified, and a corresponding optimal instruction is issued, so that the operation efficiency is improved, and the safety risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of port autonomous driving safety supervision, and specifically to a port autonomous driving safety supervision system and a supervision method thereof based on business rules. Background Art

[0002] Autonomous container trucks are an important application scenario for autonomous driving in ports, and are the first to be commercialized. Unlike cities and highways, ports are closed operation parks with well-developed infrastructure, simple traffic signs, low interference from vehicles and pedestrians, and easy to control.

[0003] Port autonomous driving refers to the application of autonomous driving technology to port operation scenarios, using unmanned vehicles to complete tasks such as cargo transportation and loading and unloading. Port autonomous driving uses advanced autonomous driving technology, combined with the port's unique operating environment and needs, to achieve unmanned operation of vehicles. The technical applications involved mainly include AGV automatic guided transport vehicles, ASC unmanned straddle carriers, and unmanned container trucks. These vehicles use advanced sensor fusion positioning technologies, such as lidar, Beidou navigation, 5G communication, V2X vehicle-to-vehicle communication, etc., to achieve accurate perception and efficient navigation of the complex environment of the port.

[0004] Since the operation process of autonomous driving in ports involves three links: shore operations, road driving, and yard operations, and requires interaction with external container trucks, bridge cranes, gantry cranes, and flow machines, there are safety risks in each link, and it is necessary to strengthen safety supervision of the operation process of autonomous driving vehicles.

[0005] The existing technology for port autonomous driving safety supervision has the following technical defects:

[0006] 1. Limited perception and decision-making capabilities:

[0007] Existing autonomous driving technology still has certain limitations in perception and decision-making, especially in complex port environments, where the ability to perceive and recognize information such as obstacles and traffic signals is limited.

[0008] 2. Imperfect security mechanism:

[0009] In port operations, due to the complex environment, frequent movement of equipment and container trucks, and frequent cross-operation between equipment, these problems not only reduce operational efficiency but also increase operational risks. Summary of the invention

[0010] In order to overcome the technical defects in the prior art and effectively solve the technical problems in the background technology, the present invention provides the following technical solutions:

[0011] The port autonomous driving safety supervision system based on business rules includes the following modules: data collection module, rule engine module, safety supervision module, safety warning module, command interaction module and equipment module;

[0012] The data acquisition module is used to collect data from FMS, remote driving system, TOS, port machinery system and roadside system; the FMS is used to collect vehicle location, speed and driving route data in real time and analyze driver behavior; the remote driving system is used to transmit vehicle video and sound status in real time and report throttle status and brake status data; the TOS is used to manage ship entry and exit time and berth allocation, and track cargo loading and unloading, storage and transshipment; the port machinery system is used to monitor the location, speed, load and running time of equipment; the roadside system provides real-time traffic congestion, accidents and road conditions by communicating with vehicles, pedestrians and other traffic facilities;

[0013] The rule engine module processes data based on preset rules and is composed of a rule database, a configuration parameter library, a basic database and a security rule library. The rule database stores and manages all preset rules, the configuration parameter library stores various parameters required to execute business rules, and the basic database stores the core data supporting the rule engine;

[0014] The safety supervision module is used to establish clear safety supervision rules, collect and analyze real-time data of autonomous driving vehicles and environments, identify potential risks, conduct safety assessments, and ensure safe and efficient operations;

[0015] The safety warning module is responsible for warning potential safety hazards in port operations and taking advance measures to avoid accidents. It defines warning levels according to safety risks and impact scopes and supports hierarchical warning strategies. It analyzes monitoring data, predicts possible safety issues, issues warning signals, and guides relevant personnel to take corresponding measures.

[0016] The instruction interaction module receives and parses instructions from different systems, executes corresponding business logic, and returns processing results;

[0017] The equipment module includes various equipment used for loading, unloading, transportation and auxiliary operations in the port; it is used to receive instructions sent by the forwarding rule engine and report equipment operation status and operation instruction information.

[0018] As a preferred technical solution of the present invention, the instruction interaction module includes instruction reception, instruction parsing and business logic processing, the instruction reception is used to receive instructions from different systems or interfaces, the instruction parsing is used to parse the received instructions into executable commands, and the business logic processing is used to execute specific business logic according to the commands after instruction parsing.

[0019] The port autonomous driving safety supervision method based on business rules includes the following steps:

[0020] S1: Data collection: In the port, data is collected from multiple dimensions, and the dispatching plan is optimized and the safety of personnel and equipment is ensured through real-time monitoring and data analysis;

[0021] S2: rule invocation;

[0022] S3: Warning threshold and risk identification.

[0023] As a preferred technical solution of the present invention, the warning threshold and identification risk in S4 include: speed limit, obstacle detection and traffic signal recognition;

[0024] The speed limit means that when the vehicle speed exceeds 50% of the speed limit inside the port, the cloud control system will issue an early warning after receiving the reported data;

[0025] Obstacle detection means that when the distance between a vehicle and an obstacle is less than 5 meters inside the port, the cloud control system will issue an early warning warning;

[0026] The traffic signal recognition means that when a vehicle approaches a traffic signal inside the port but does not slow down, the cloud control system issues an early warning.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The supervision dimension of the vehicle operation process is more comprehensive, which is a full-chain supervision process from beginning to end. The impact on the business and the existing safety hazards can be identified in advance to avoid safety accidents caused by the vehicle's unfamiliarity with the business process.

[0029] 2. Regulatory rules can be configured according to actual business needs, making them more flexible and efficient.

[0030] 3. Through data collection and historical backtracking, a rule library can be established to support the analysis and formulation of new business rules.

[0031] Specifically, in the port operation environment, the system uses the rule engine in the system to identify operation risks based on the abnormal status data of container trucks, gantry cranes, bridge cranes, and mobile cranes during operation, and sends the best instructions to the vehicle-side equipment to reduce the risks caused by limited perception and decision-making capabilities. At the same time, the system can plan the operation process and driving route in advance to improve the operating efficiency of the equipment in the port.

[0032] By integrating the sensory data of container trucks and equipment, and closely cooperating with the rule engine and safety warning module, operational risks can be directly and efficiently identified automatically, and the corresponding optimal instructions can be issued. This highly integrated automation solution not only improves operational efficiency, but also reduces safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a framework diagram of the supervision system of the present invention.

[0034] Figure 2 This is a schematic diagram of charging rule calling of the present invention.

[0035] Figure 3 It is a schematic diagram of the movement judgment rules of the gantry bridge crane of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1-3 , the present invention provides a technical solution:

[0038] The port autonomous driving safety supervision system based on business rules includes the following modules: data acquisition module, rule engine module, safety supervision module, safety warning module, command interaction module and equipment module.

[0039] Data acquisition module: used to collect data from FMS, remote driving system, TOS, port machinery system and roadside system.

[0040] Vehicle / FMS system (Fleet Management System): used to collect vehicle location, speed and driving route data in real time, and analyze driver behavior, such as speeding, sudden braking, etc. Specifically, the autonomous driving fleet management system (Fleet Management System, FMS) refers to the autonomous driving vehicle dispatch management platform, which mainly connects with the TOS system to obtain the target business data of the autonomous driving vehicle, and the autonomous driving vehicle dynamically navigates to the target destination. During the operation of the autonomous driving vehicle, the platform conducts real-time supervision and remote control of vehicle status events.

[0041] Remote driving system: used to transmit vehicle video and sound status in real time, and report data such as throttle status and brake status.

[0042] TOS (Terminal Operating System): It is used to manage the time of ship entry and exit and berth allocation, and track cargo loading and unloading, storage and transshipment. Specifically, the terminal TOS system refers to the terminal business management system, which mainly implements yard planning, ship loading, vehicle business scheduling, operation control, port machinery remote control and other functions for terminal business.

[0043] ECS (Equipment Control System) / Port Machinery System: used to monitor the position, speed, load and running time of equipment such as gantry cranes, bridge cranes, forklifts and trailers.

[0044] Roadside system: Provides real-time information on traffic congestion, accidents and road conditions by communicating with vehicles, pedestrians and other traffic facilities.

[0045] Rule engine module: processes data based on preset rules, and consists of a rule database, a configuration parameter library, a basic database, and a security rule library. The rule database stores and manages all preset rules, the configuration parameter library stores various parameters required to execute business rules (including system parameters and business parameters), and the basic database stores the core data that supports the rule engine. Specifically, rule engine technology is the core of implementing flexible business logic. It allows for quick adjustment and optimization of scheduling rules without modifying the underlying code. Modern rule engines not only support complex conditional judgments and action execution, but also greatly improve the ability of business personnel to participate in rule formulation and adjustment.

[0046] Safety supervision module: used to establish clear safety supervision rules, collect and analyze real-time data on autonomous driving vehicles and the environment; identify potential risks, conduct safety assessments, and ensure safe and efficient operations.

[0047] Safety warning module: responsible for warning of potential safety hazards in port operations and taking advance measures to avoid accidents; it defines warning levels according to safety risks and impact scope, and supports hierarchical warning strategies; by analyzing monitoring data, it predicts possible safety issues, issues warning signals, and guides relevant personnel to take corresponding measures.

[0048] Instruction interaction module: receives and parses instructions from different systems, executes corresponding business logic, and returns processing results.

[0049] Equipment module: Contains various equipment used for loading, unloading, transportation and auxiliary operations in the port, such as vehicles, gantry cranes, bridge cranes, forklifts, etc. It is used to receive instructions from the forwarding rule engine and report information such as equipment operation status and operation instructions.

[0050] Furthermore, the instruction interaction module includes instruction reception, instruction parsing and business logic processing. Specifically:

[0051] Command reception: used to receive commands from different systems or interfaces.

[0052] Instruction parsing: used to parse received instructions into executable commands.

[0053] Business logic processing: used to execute specific business logic according to the commands after instruction parsing, such as truck dispatching.

[0054] The port autonomous driving safety supervision method based on business rules includes the following steps:

[0055] S1: Data collection: In the port, data is collected from multiple dimensions. Through real-time monitoring and data analysis, the dispatching plan is optimized and the safety of personnel and equipment is ensured. Specifically, common data collection in the port includes the following aspects:

[0056] (1) Equipment status monitoring:

[0057] a. Sensor data: sensor data such as temperature, pressure, collision, etc., to monitor the health status of the equipment.

[0058] b. Operating parameters: equipment operating time, load and other parameters.

[0059] c. Fault record: equipment fault type, time and location of occurrence.

[0060] (2) Location:

[0061] a.GPS positioning: The location information of the device is used for real-time tracking and scheduling.

[0062] b. Heading angle: used to calculate key parameters such as device safety distance.

[0063] (3) Dispatch records:

[0064] a. Fleet dispatch records: The dispatch tasks of other vehicles are collected through the FMS system for tracking and assigning dispatch tasks.

[0065] b. Vessel data: The TOS system collects voyage number, departure port, departure time, destination port, destination arrival time, etc. for dispatching vehicles and arranging vessel berthing operations.

[0066] S2: rule calling; including charging rule calling and gantry crane movement judgment rule, etc. Specifically, its workflow is as follows Figure 2 and 3 shown.

[0067] S3: Warning threshold and risk identification.

[0068] As a preferred technical solution of the present invention, the warning threshold and identification risk in S4 include: speed limit, obstacle detection and traffic signal recognition; specifically:

[0069] Speed ​​limit: refers to the fact that the speed of autonomous vehicles is usually limited to 20-30 km / h inside the port. When the vehicle speed exceeds 50% of the speed limit, the cloud control system will issue an early warning after receiving the reported data. Specifically:

[0070] Risk level: low risk (speed exceeds the speed limit by 50%); medium risk (speed exceeds the speed limit by 80%); high risk (speed exceeds the speed limit by 100%);

[0071] Obstacle detection: refers to the fact that vehicles must detect and avoid obstacles in front of them, such as pedestrians, other vehicles, and cargo, etc. When the distance between the vehicle and the obstacle is less than 5 meters, the cloud control system will issue an early warning. Specifically:

[0072] Risk level: low risk (deviation within 1 meter); medium risk (deviation 2-4 meters); high risk (deviation more than 5 meters);

[0073] Traffic signal recognition: refers to the ability of vehicles to recognize and obey traffic signals, such as traffic lights and stop signs, inside the port. When a vehicle approaches a traffic signal but does not slow down, the cloud control system issues an early warning.

[0074] Risk level: low risk (approaching a traffic signal but not slowing down); medium risk (ignoring traffic signals and continuing to drive); high risk (running a red light, etc.);

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A port autonomous driving safety supervision system based on business rules, characterized by: It includes the following modules: data acquisition module, rule engine module, safety supervision module, safety warning module, instruction interaction module and equipment module; The data acquisition module is used to collect data from FMS, remote driving system, TOS, port machinery system and roadside system; the FMS is used to collect vehicle position, speed and driving route data in real time and analyze driver behavior; The remote driving system is used to transmit the vehicle video and sound status in real time, and report the throttle status and brake status data; the TOS is used to manage the ship's entry and exit time and berth allocation, and track cargo loading and unloading, storage and transshipment; the port machinery system is used to monitor the equipment's location, speed, load and operating time; the roadside system provides real-time traffic congestion, accidents and road conditions by communicating with vehicles, pedestrians and other traffic facilities; The rule engine module processes data based on preset rules and is composed of a rule database, a configuration parameter library, a basic database and a security rule library. The rule database stores and manages all preset rules, the configuration parameter library stores various parameters required to execute business rules, and the basic database stores the core data supporting the rule engine; The safety supervision module is used to establish clear safety supervision rules and collect and analyze real-time data on the autonomous driving vehicle and the environment; Identify potential risks, conduct safety assessments, and ensure safe and efficient operations; The safety warning module is responsible for warning of potential safety hazards in port operations and taking advance measures to avoid accidents; It defines warning levels according to security risks and impact scope, and supports hierarchical warning strategies; By analyzing monitoring data, we can predict possible safety issues, issue early warning signals, and guide relevant personnel to take corresponding measures; The instruction interaction module receives and parses instructions from different systems, executes corresponding business logic, and returns processing results; The equipment module includes various equipment used for loading, unloading, transportation and auxiliary operations in the port; it is used to receive instructions sent by the forwarding rule engine and report equipment operation status and operation instruction information.

2. The port autonomous driving safety supervision system based on business rules according to claim 1 is characterized by: The instruction interaction module includes instruction reception, instruction parsing and business logic processing. The instruction reception is used to receive instructions from different systems or interfaces, the instruction parsing is used to parse the received instructions into executable commands, and the business logic processing is used to execute specific business logic according to the commands after instruction parsing.

3. A port autonomous driving safety supervision method based on business rules, characterized by: The following steps are involved: S1: Data collection: In the port, data is collected from multiple dimensions, and the dispatching plan is optimized and the safety of personnel and equipment is ensured through real-time monitoring and data analysis; S2: rule calling; S3: Warning threshold and risk identification.

4. The port autonomous driving safety supervision method based on business rules according to claim 3 is characterized by: The warning threshold and identified risks in S4 include: speed limit, obstacle detection and traffic signal recognition; The speed limit means that when the vehicle speed exceeds 50% of the speed limit inside the port, the cloud control system will issue an early warning after receiving the reported data; Obstacle detection means that when the distance between a vehicle and an obstacle is less than 5 meters inside the port, the cloud control system will issue an early warning warning; The traffic signal recognition means that when a vehicle approaches a traffic signal inside the port but does not slow down, the cloud control system issues an early warning.

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