CN-121982936-A - Port vehicle traffic management's safety control monitored control system based on big data

CN121982936ACN 121982936 ACN121982936 ACN 121982936ACN-121982936-A

Abstract

The invention relates to the technical field of port automation and discloses a port vehicle traffic management safety management monitoring system based on big data, which comprises a data acquisition module, an event extraction module, an event system modeling module, a safety standard modeling module, a controllable observable event boundary identification module, a standard projection module, a supervision and permission control module and a runtime verification and control execution module. By carrying out event modeling on the multi-source traffic data, projecting the safety specification onto a controllable and observable event subset, constructing an executable safety specification and a corresponding admission control strategy, and realizing the specification consistency check and dynamic admission control on traffic control events such as gate release, road section entry, intersection traffic and the like. The invention avoids the control mismatch problem caused by directly participating in safety judgment of uncontrollable events or unobservable events by introducing the controllable observable event boundary recognition module and the standard projection module.

Inventors

CUI JIAN
Zhang kanglong
WANG FATIAN
XIAO JIANHUA
MA YIJUN

Assignees

湛江港（集团）股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260209

Claims (10)

1. A security management monitoring system for port vehicle traffic management based on big data, comprising: the data acquisition module is used for acquiring port vehicle positioning data, barrier gate state data, intersection control data and operation scheduling event data, and performing time alignment and space alignment on the data; the event extraction module is used for generating discrete traffic events according to preset resource boundaries and state judgment rules based on the aligned data and attaching confidence marks to each discrete traffic event; The event system modeling module is used for constructing a discrete event system model according to the discrete traffic events and establishing an occupation state relation between vehicles and port resources; The safety specification modeling module is used for expressing the port traffic safety rule as a safety specification set formed by event sequence constraint and resource mutual exclusion constraint; The controllable observable event boundary recognition module is used for performing controllable classification and observable classification on the events in the discrete event system to obtain a controllable event set, an uncontrollable event set, an observable event set and an unobservable event set; the specification projection module is used for mapping the safety specification set to an event subset which belongs to the controllable event set and the observable event set at the same time, and generating an executable safety specification; A supervisory permission control module for constructing a set of permissions for a controllable event based on the executable security specification and the discrete event system model; and the run-time verification and control execution module is used for judging the standard consistency of the real-time event sequence and carrying out permission or blocking processing on the gate release event, the road section entry event and the intersection release event according to the permission set.
2. The big data based port vehicle traffic management security management monitoring system of claim 1, wherein in the data acquisition module, the steps of acquiring port vehicle positioning data, barrier status data, intersection control data and job scheduling event data, and performing time alignment and space alignment on the data comprise: respectively establishing a time stamp sequence for vehicle positioning data, barrier gate state data, intersection control data and job scheduling event data according to a data source; converting each time stamp sequence to a unified time axis based on a preset time mapping relation; carrying out coordinate conversion on the data in different coordinate representation forms according to the port unified coordinate system; rearranging the multisource data after completing time mapping and coordinate conversion according to a unified time sequence; An alignment data record set is generated that contains the vehicle identification, the resource identification, the time information, and the spatial location information.
3. The security management and monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the event extraction module, the step of generating discrete traffic events according to the preset resource boundary and state decision rule and attaching a confidence level identifier to each discrete traffic event comprises: according to the topological relation between the aligned vehicle track data and the port resource space boundary, judging the time when the vehicle enters the resource and leaves the resource; Generating an entry event at the time of determining entry and generating a departure event at the time of determining departure; Generating a release event and a closing event according to the state change moment of the barrier gate; Recording event type, vehicle identification, resource identification and occurrence time for each event; Consistency matching is carried out on the same events from different data sources; Calculating and attaching a corresponding confidence level identifier for each event based on the consistency matching result; A sequence of discrete traffic events is formed in a time sequence.
4. The security management and monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the event system modeling module, the step of constructing a discrete event system model according to the discrete traffic event and establishing an occupancy state relationship between a vehicle and a port resource comprises: defining the set of event types for the discrete traffic event as an event alphabet; Constructing a system state set by using the occupied combined state of the vehicle and the resource; constructing a state transition relation according to the resource occupation change relation before and after the occurrence of the event; establishing an occupancy mapping table of the vehicle and the resource for each state; updating an occupancy mapping table of a corresponding state when an event is triggered, wherein the occupancy mapping table is used for indicating the state change of occupied or released resources; a discrete event system model is formed that includes a set of states, a set of events, and a state transition relationship.
5. The security management monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the security specification modeling module, the step of representing the port traffic security rule as a security specification set of event precedence constraint and resource exclusion constraint comprises: establishing a resource mutual exclusion relation table for port road sections, intersections, narrow channels and operation area entrances; Converting the resource mutual exclusion relation into a constraint which does not allow the resources occupied at the same time; establishing an event pre-relationship table for rules related to release, entry, check and pass operation sequences; converting the event prepositions into event sequence constraints; Uniformly encoding the resource mutual exclusion constraint and the event sequence constraint into a structured safety specification item; a security specification set is formed that includes a plurality of security specification entries.
6. The security management monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the controllable observable event boundary identifying module, the step of controllably classifying and observably classifying the events in the discrete event system comprises: Establishing a corresponding relation table between a port control interface and event types; identifying the events which can be triggered or forbidden through a control interface as controllable events according to the corresponding relation table; identifying the rest events as uncontrollable events; Identifying the event which can be reliably perceived as an observable event according to the event confidence level identification and the sensor coverage range; identifying the rest events as unobservable events; a joint classification table of events and controllability categories and observability categories is formed.
7. The big data based harbour vehicle traffic management security management monitoring system of claim 1, wherein in the specification projection module, the step of mapping the security specification set to a subset of events belonging to both the controllable event set and the observable event set, the step of generating an executable security specification comprises: Extracting event types belonging to a controllable event set and an observable event set simultaneously to form a target event subset; Traversing event sequence constraint and resource mutual exclusion constraint related in the safety specification set; Reserving security specification entries containing only events in the subset of target events; Establishing a corresponding relation between the security specification items containing uncontrollable events or unobservable events and agent events in a target event subset; replacing the uncontrollable event or the unobservable event in the original safety specification item with a corresponding proxy event based on the corresponding relation; The replaced security specification entry is combined with the retained security specification entry to form an executable security specification set containing only the target event subset events.
8. The big data based harbour vehicle traffic management security management monitoring system of claim 1, wherein in a supervisory license control module, the step of constructing a set of licenses for a controllable event based on the executable security specification and the discrete event system model comprises: acquiring a mapping relation between a vehicle corresponding to a current system state in a discrete event system model and resource occupation; Enumerating all controllable events in the current system state; Simulating a state transition result of each controllable event in the discrete event system model; judging whether the state transition result violates any specification item in an executable safety specification set; adding a set of permissions to controllable events that do not cause violation of the specification entry; a set of controllable event permissions corresponding to a current system state is formed.
9. The security management and monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the runtime verification and control execution module, the step of performing the specification consistency judgment on the real-time event sequence comprises: Receiving discrete traffic events arriving in time sequence; mapping the discrete traffic event to a target event subset according to the classification result output by the controllable observable event boundary recognition module; Constructing a projection event sequence based on the target event subset; matching the projection event sequence with an executable safety specification set item by item; Identifying whether the current projection event sequence meets all event sequence constraints and resource exclusion constraints in the executable safety specification set; And generating a specification consistency judgment result.
10. The security management and monitoring system for port vehicle traffic management based on big data according to claim 1, wherein in the run-time verification and control execution module, the step of allowing or blocking gate clearance events, road section entry events and intersection clearance events according to the permission set comprises: Acquiring a controllable event permission set in a corresponding system state before triggering a controllable event; Judging whether a controllable event to be triggered exists in the permission set; When the controllable event to be triggered exists in the permission set, issuing an execution instruction to the corresponding control interface; When the controllable event to be triggered does not exist in the permission set, a blocking instruction is issued to the corresponding control interface; triggering a proxy event flow associated with the blocked event according to the proxy event corresponding relation established by the standard projection module while issuing the blocking instruction; and inputting the execution result of the agent event flow as a new discrete traffic event to an event extraction module and an event system modeling module so as to update the subsequent system state.

Description

Port vehicle traffic management's safety control monitored control system based on big data Technical Field The invention relates to the technical field of port automation, in particular to a port vehicle traffic management safety management and monitoring system based on big data. Background Port vehicle traffic management systems typically involve a plurality of subsystems in conjunction with vehicle positioning, road traffic control, gate clearance control, and job scheduling, the operation of which relies on the cooperation of multi-source awareness devices and control devices. In the prior art, all observed traffic events are always integrated into a safety judgment range, and conflict detection or constraint verification is directly carried out based on the events and safety rules. The events include both events that can be directly interfered by the control interface, such as gate opening, crossing signal release, road section entering permission, etc., and events that cannot be directly restrained or triggered by the control interface, such as acceleration and deceleration behavior of the vehicle itself, temporary parking behavior, and sudden state changes caused by external environments. Meanwhile, although the part of events logically belong to important objects of safety judgment, the part of events are limited by sensor layout positions, communication delays or perception precision, and the occurrence time and the occurrence position of the part of events cannot be acquired in a deterministic manner by a system and can be observed only in an incomplete or uncertain manner. However, the inventor finds that in the process of implementing the related technical scheme, at least the technical problem of control mismatch caused by directly taking the complete event set containing the uncontrollable event or the unobservable event as a constraint object in the safety judging process due to the fact that the safety specification does not distinguish the controllability and the observability boundary of the event, is caused by directly taking part in the safety judgment of the uncontrollable event or the unobservable event. Disclosure of Invention In order to make up for the defects, the invention provides a harbour vehicle traffic management safety management monitoring system based on big data, which aims to solve the problem that the control mismatch is caused by directly participating in safety judgment due to uncontrollable events or unobservable events because the safety specification does not distinguish the controllability and observability boundary of traffic events in the prior art. The invention provides a port vehicle traffic management safety management monitoring system based on big data, which comprises: the data acquisition module is used for acquiring port vehicle positioning data, barrier gate state data, intersection control data and operation scheduling event data, and performing time alignment and space alignment on the data; the event extraction module is used for generating discrete traffic events according to preset resource boundaries and state judgment rules based on the aligned data and attaching confidence marks to each discrete traffic event; The event system modeling module is used for constructing a discrete event system model according to the discrete traffic events and establishing an occupation state relation between vehicles and port resources; The safety specification modeling module is used for expressing the port traffic safety rule as a safety specification set formed by event sequence constraint and resource mutual exclusion constraint; The controllable observable event boundary recognition module is used for performing controllable classification and observable classification on the events in the discrete event system to obtain a controllable event set, an uncontrollable event set, an observable event set and an unobservable event set; the specification projection module is used for mapping the safety specification set to an event subset which belongs to the controllable event set and the observable event set at the same time, and generating an executable safety specification; A supervisory permission control module for constructing a set of permissions for a controllable event based on the executable security specification and the discrete event system model; and the run-time verification and control execution module is used for judging the standard consistency of the real-time event sequence and carrying out permission or blocking processing on the gate release event, the road section entry event and the intersection release event according to the permission set. Preferably, in the data acquisition module, the step of acquiring port vehicle positioning data, barrier gate status data, intersection control data and job scheduling event data, and performing time alignment and space alignment on the data includes: respectively establishing a time stamp sequence for