Railway public security interface police decision execution method and system of full AI autonomous closed loop

Abstract

This invention discloses a fully AI-driven autonomous closed-loop method and system for railway police emergency response decision-making and execution, belonging to the field of police information technology. In existing AI-assisted emergency response systems, incident assessment and dispatch decisions still require human confirmation before execution. To address this issue, this invention entrusts the entire emergency response process to a large AI model: the AI ​​model automatically answers emergency calls and conducts multiple rounds of dialogue, automatically extracts key elements of the incident and fills in the necessary slots; automatically determines the incident type, level, and jurisdiction; automatically generates and selects the best police force dispatch plan; and automatically sends the dispatch plan to the officers' terminals. All steps of receiving, assessing, dispatching, and issuing instructions are autonomously completed by the AI ​​model without human intervention, with humans acting as supervisors in a non-interventional manner. This invention achieves a fully automated closed-loop emergency response system where "responding immediately upon call connection," transforming the decision-making body for emergency response from humans to a large AI model for the first time.

Description

Technical Field

[0001] This invention relates to the field of police information technology, specifically to a railway police emergency response method and system in which the entire process of receiving and handling emergency calls is autonomously completed by an AI model without human intervention. Background Technology

[0002] The existing railway police emergency response system mainly operates in two modes. The first is the traditional manual mode. From answering calls, recording information, determining the type and severity of the emergency, to dispatching officers, everything is done by human dispatchers and commanders. The drawback of this mode is that response speed is limited by human reaction time, and human dispatchers cannot handle a large number of concurrent calls during peak periods. The second is the existing AI-assisted mode. These systems use technologies such as voice recognition and emergency classification to assist human dispatchers, but the final emergency assessment and dispatch decisions are still confirmed and executed by humans. For example, an AI-assisted emergency response platform can automatically transcribe alarm voice into text and recommend emergency categories, but human confirmation is still required before it takes effect. The common characteristic of both modes is that the key decision-making processes—emergency assessment and dispatch decisions—still require human intervention. While introducing AI assistance improves information processing efficiency, it fails to fundamentally change the structure where "humans are the decision-makers." To address the aforementioned issues, this invention proposes a fully AI-powered, zero-human-interventional method and system for handling emergency calls. The entire process is autonomously completed by a large AI model, achieving an automated closed loop of "responding immediately upon call connection." Existing technologies have implemented solutions that ensure objectivity in the emergency call handling process through large AI models, resolving information bias caused by human subjectivity. However, these solutions still fail to address the technical challenge of end-to-end autonomous closed-loop execution of the entire AI-driven emergency call handling process. In the complete chain of call reception, judgment, scheduling, and instruction issuance, there are still process interruptions requiring human intervention, preventing the achievement of a fully automated closed loop of "responding immediately upon call connection." To address these unresolved technical issues, this invention proposes a fully AI-powered, zero-human-interventional method and system for handling emergency calls. The entire process is autonomously completed by a large AI model, achieving an automated closed loop of "responding immediately upon call connection." Summary of the Invention

[0003] The purpose of this invention is to provide a fully AI-driven, closed-loop railway police emergency response decision-making and execution method and system, to solve the technical problem that existing emergency response systems still rely on human intervention in the emergency situation determination and dispatch decision-making stages. To achieve the above objective, this invention provides the following technical solutions. Firstly, this invention provides a fully AI-driven, zero-human-interventional railway police emergency response method, comprising: Step S1, an AI big data model automatically answers emergency calls, conducts multiple rounds of dialogue, and extracts key elements of the emergency situation; Step S2, the AI ​​big data model automatically determines the type, level, and jurisdiction of the emergency situation; Step S3, the AI ​​big data model automatically generates a police force dispatch plan and selects the optimal plan; Step S4, the AI ​​big data model automatically sends the dispatch plan to the duty terminals of the corresponding dispatching personnel. Each step from S1 to S4 is autonomously completed by the AI ​​big data model without human intervention, with humans acting as supervisors of the AI's decision-making results in a non-interventional manner. Secondly, this invention provides a fully AI-driven, zero-human-interventional railway police emergency response system, comprising an AI emergency response module, an AI determination module, an AI dispatch module, and an instruction issuance module, all of which require no human intervention during operation. Thirdly, this invention provides a computer-readable storage medium storing a computer program implementing the above-described method. Compared to existing technologies, the core difference of this invention lies in the following: In existing AI-assisted emergency response systems, humans are the decision-makers—AI is responsible for information processing and suggestion generation, while humans are responsible for confirmation and execution; in this invention, the large AI model is the decision-maker—AI is responsible for all decision-making and execution stages, from receiving the alarm and determining its outcome to dispatching and issuing instructions. This fundamental shift in human-machine roles enables the emergency response system to achieve a fully automated closed-loop process of "responding immediately upon call connection" for the first time.

Claims

1. Independent claim (method): A fully AI-driven autonomous closed-loop railway police emergency response decision-making and execution method, characterized in that, Includes the following steps: Step S1: The AI ​​big data model automatically answers emergency calls, conducts multiple rounds of dialogue to guide the caller in describing the incident, and automatically extracts key elements of the incident during the dialogue. Step S2: Based on the extracted key elements of the incident, the AI ​​big data model automatically determines the type, level, and jurisdiction of the incident. Step S3: Based on the type, level, and jurisdiction of the incident, the AI ​​big data model automatically generates a police dispatch plan, which includes the dispatching unit, dispatching personnel, and dispatch route. Step S4: The AI ​​big data model automatically distributes the generated police dispatch plan to the duty terminals of the corresponding dispatching personnel. Each step from S1 to S4 is completed autonomously by the AI ​​big data model without human intervention, with humans playing a non-interventional supervisory role overseeing the AI's decision-making results.

2. Dependent claim: The method according to claim 1, characterized in that, Step S1 includes: when the AI ​​big data model answers an alarm call, it manages the real-time state of the dialogue process through a dialogue state machine, which includes an initial state, an information collection state, and a confirmation state; when in the information collection state, the AI ​​big data model dynamically fills in the dialogue content, automatically extracting six core elements required for the closed-loop execution of the alarm: time, location, people, events, items, and consequences; when a key slot is detected to be missing, the AI ​​big data model automatically generates neutral follow-up questions and asks the caller until the completeness of the slots reaches a preset threshold, without the need for manual intervention or guidance throughout the process.

3. Dependent claim: The method according to claim 1, characterized in that, Step S2 includes: the AI ​​big data model inputs the extracted key elements of the police incident into a pre-trained police incident classification model and outputs the police incident type label and the corresponding confidence level; the AI ​​big data model automatically calculates the police incident response level based on the three dimensions of urgency, severity and complexity; the AI ​​big data model automatically determines the jurisdictional unit based on the police incident location information.

4. Dependent claim: The method according to claim 1, characterized in that, Step S3 includes: the AI ​​big data model matching a preset emergency response plan template according to the type and level of the incident; the AI ​​big data model retrieving the status of currently available police resources, including police location and task status, according to jurisdiction; and the AI ​​big data model calculating the estimated arrival time of each candidate police force based on the location of the incident and the spatial distribution of available police resources, and generating the optimal dispatch plan.

5. Dependent claim: The method according to claim 4, characterized in that, The process of generating the optimal scheduling scheme includes: generating multiple candidate scheduling schemes that conform to the emergency response specifications, and performing parallel simulation and deduction of the entire emergency response process of each candidate scheme in a digital twin sandbox. Based on the expected arrival time and on-site handling matching degree in the simulation results, the globally optimal execution scheme is automatically selected without the need for manual intervention in the entire process.

6. Independent claim: A fully AI-driven autonomous closed-loop railway police emergency response decision-making and execution system, characterized in that, include: The AI ​​alarm receiving module automatically answers emergency calls using a large AI model, conducts multi-round dialogues to guide the caller in describing the incident, and automatically extracts key elements of the incident during the dialogue. The AI ​​judgment module automatically determines the type, severity level, and jurisdiction of the emergency based on the extracted key elements. The AI ​​dispatch module automatically generates a police dispatch plan based on the incident type, severity level, and jurisdiction, including the dispatching unit, personnel, and route. The command issuance module automatically issues the generated dispatch plan to the corresponding personnel's terminals. All four modules operate without human intervention; humans supervise the AI ​​decisions in a non-interventional manner.

7. Independent claim: A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the fully AI-based, zero-human-operated railway police emergency response method as described in any one of claims 1 to 8.