A method and system for objective railway public security zero-manual full-AI police call and police case disposal

Abstract

The application discloses a kind of railway public security zero artificial full AI police receiving and police situation disposal objectivization method and system, belong to police informationization and artificial intelligence technical field.The technical problem to be solved by the present application is to fundamentally eliminate the information deviation caused by human subjective consciousness in the whole link of police receiving and handling.The core discovery of the present application is that AI large model is the only existence that can meet the two conditions of "no subjective consciousness" and "have complex police situation understanding ability".The technical scheme includes: collecting police situation signals through the multi-modal access hardware devices of police command center, parallel processing through the AI large model inference engine deployed on GPU computing cluster, objective processing without subjective consciousness through AI large model, including objective collection based on multi-modal fusion, lossless recording based on slot filling, objective determination based on knowledge graph constraint, objective scheduling based on digital twinning, and finally outputting police instructions through police communication dispatch gateway.The present application realizes zero artificial intervention in the whole link of police receiving and handling, fundamentally eliminates the seven types of systematic biases of human cognition, and ensures the objectivity and consistency of police information.

Description

Technical Field

[0001] This invention relates to the fields of police informatization and artificial intelligence technology, specifically to an objective method and system for railway police to receive and handle police calls with zero human intervention and full AI, which is particularly applicable to the scenario of railway police handling police calls with zero human intervention and full AI intervention. Background Technology

[0002] Receiving and handling emergency calls is the first line of defense for public security organs in maintaining social order. When an emergency call rings in the command center, every step from the collection of information to its processing directly relates to the safety of people's lives and property. Receiving and handling emergency calls is always a closed loop of human-to-human interaction. The dispatcher's ears, brain, language, emotions, and judgment constitute every node in the process from the collection of information to its processing. However, the existing emergency response system suffers from a fundamental problem that cannot be solved through improvement: subjectivity. Due to human subjectivity, seven types of systemic biases exist in cognition, causing attenuation, distortion, and even errors in the transmission of emergency information. These seven biases include: individual differences in sensory thresholds, selective and filtered attention, perceptual organization and Gestalt mismatch, memory encoding distortion and retrieval deformation, perspective-based language expression and framing effects, heuristics and biases in judgment, and habitual deviations in action. More importantly, these deviations are not accidental errors by individual dispatchers, but rather inherent properties of the human cognitive system. Currently, there are three main types of solutions in the industry that attempt to eliminate subjectivity in emergency response, but all of them have insurmountable flaws: The first category is institutional constraints, such as standardized operating procedures and double-blind review systems. These systems constrain the subjective judgment of dispatchers through strict operating procedures and mutual review by multiple personnel. However, their fundamental dilemma lies in the fact that the implementers of the system are still human, and subjectivity can seep back in through the gaps in its implementation. Different people have different understandings of the same system, and the reviewers themselves also carry subjective biases, making it impossible to fundamentally eliminate deviations. The second category is traditional rule engine solutions, such as automatic classification systems based on preset rules. These solutions involve programmers pre-writing rule bases, and the system classifies incidents based on keyword matching. However, the problem lies in the fact that the rule base is written by programmers, and the limitations of programmers' predictions about incident types are themselves a new source of subjectivity. Rule engines can only handle a limited number of preset cases and cannot make reasonable judgments when faced with the ambiguity, polysemy, and suddenness of real incidents. The third category is human-machine collaborative solutions, such as AI-assisted suggestions combined with human confirmation and decision-making. In this type of solution, the AI ​​system provides suggestive judgments about the emergency, which are ultimately confirmed or modified by humans. However, the problem is that it still involves "humans" making decisions at key points, and human subjectivity still permeates every stage of confirmation, modification, and final decision-making. Cognitive biases, emotional fluctuations, and fatigue can still affect the final outcome of the emergency response. The common flaw of these three types of solutions is that they all attempt to constrain "conscious existence" with "conscious existence," thus the problem of subjectivity remains unresolved. Institutions cannot eliminate consciousness, and traditional procedures cannot solve the problem of understanding. Therefore, there is an urgent need in this field for a technical solution that can fundamentally eliminate information bias caused by human subjective consciousness in the entire emergency response chain. Summary of the Invention

[0003] I. Technical problems to be solved The core technical problem that this invention aims to solve is: how to fundamentally eliminate information bias caused by human subjective consciousness in the entire chain of receiving and handling police reports, and ensure that the collection, recording, judgment and dispatch of police information all achieve verifiable objectivity. To eliminate subjectivity, two conditions must be met simultaneously: Condition 1 (Unconscious Constraint): The person receiving and handling the emergency must be free from subjective consciousness; otherwise, subjectivity will be transferred from the person to the subject. Condition 2 (Cognitive Ability Constraint): The personnel handling the emergency call must have the cognitive ability to understand complex emergency situations and be able to handle complex situations such as ambiguous descriptions of real emergency situations, incomplete information, and cross-type complex situations. Conscious humans satisfy condition two but not condition one. Unconscious traditional tools (institutions, rule engines, traditional procedures) satisfy condition one but not condition two. II. Technical Solution The core discovery of this invention is that the AI ​​large-scale model is the only one that can simultaneously satisfy both of the above conditions. It has no motivation for self-protection, no stereotypes about different groups, and its judgment standards are not altered by fatigue or emotions. Each of its outputs is essentially a probability calculation of trillions of parameters on the input data; it is not "my opinion," but "data-driven." Simultaneously, it possesses the ability to understand complex police situations—not taught by programmers, but learned autonomously from massive amounts of real-world data. Based on this core discovery, this invention provides an objectification method for railway police's zero-human, fully AI-based alarm reception and incident handling, comprising: collecting alarm signals through multimodal access hardware devices in the police command center, the multimodal access hardware devices including voice acquisition gateways, video surveillance access devices, and IoT sensor access devices; processing the alarm signals in parallel through an AI large-scale model inference engine deployed on a GPU computing power cluster, the AI ​​large-scale model inference engine running on NVIDIA H100 or Huawei Ascend 910C heterogeneous computing power nodes; performing subjective-conscious objectification processing on the processed alarm signals through the AI ​​large-scale model, including: objective acquisition based on multimodal fusion, lossless recording based on slot filling, objective judgment based on knowledge graph constraints, and objective scheduling based on digital twins, wherein the AI ​​large-scale model is naturally immune to the seven types of systematic biases of the human cognitive system because it lacks subjective consciousness; and outputting dispatch instructions to the on-site police terminal through a police communication dispatch gateway. Accordingly, the present invention also provides an objectification system for railway public security's zero-human, fully AI-based alarm reception and incident handling, comprising: a multimodal access hardware device deployed in the police command center for collecting alarm signals; a GPU computing power cluster device, including multiple NVIDIA H100 or Huawei Ascend 910C heterogeneous computing power nodes, for running an AI large-scale model inference engine; an AI large-scale model objectification processing device deployed on the GPU computing power cluster for performing subjective objectification processing on alarm signals; and a police communication dispatch gateway device for outputting dispatch instructions to on-site police terminals. This invention, based on the aforementioned core ideas, proposes the following seven levels of specific technical solutions, collectively constituting a "full-link, subjective-free" emergency response system: 1. An objective emergency information collection method based on multimodal fusion: Through full voice transcription, video behavior detection, and joint analysis of sensor data, three independent information collection channels are constructed. The signals from the three channels are cross-verified to eliminate selective bias caused by single human perception. 2. A lossless emergency information recording method based on slot filling and hash chains: Real-time slot filling is performed on emergency response dialogues, automatically extracting six elements: time, location, person, event, item, and consequence. Hash chain technology is used to lock the integrity of the original dialogue and structured records, ensuring that the entire link from collection to archiving is tamper-proof. 3. An objective emergency judgment method based on knowledge graph constraints and confidence accumulation: A railway domain knowledge graph (line topology, jurisdiction, and legal provisions) is constructed to provide precise factual constraints for emergency judgment. A dynamic confidence accumulation model is adopted to avoid anchoring effects and confirmation bias in human judgment. 4. An objective police force dispatching method based on digital twin simulation and multi-objective optimization: Multiple candidate dispatching schemes are simulated in parallel within a digital twin sandbox. Dispatch decisions are generated through a global optimal algorithm, eliminating path dependence and proximity preference in human dispatching. 5. An AI judgment alignment method based on reinforcement learning and fairness auditing: Human preference bias in labeled data is eliminated through RLHF training. Multi-dimensional fairness auditing (dialect, region, gender, ethnicity) is introduced to eliminate systemic intergroup bias. 6. A system objectivity protection method based on multi-layered adversarial defense: Three lines of defense are deployed: prompt injection detection, jailbreak attack identification, and multi-modal adversarial sample detection, to prevent malicious input from inducing the system to produce non-objective judgments. 7. A decision traceability method based on complete inference chain archiving and blockchain evidence storage: For each emergency response decision in the system, the complete inference chain from the original emergency input to the final dispatch instruction is stored. Blockchain evidence storage ensures that the audit log is tamper-proof, achieving full-link traceability. III. Technical Effects Compared with existing technologies, this invention has the following substantial technical effects: 1. It achieves zero human intervention in the entire emergency response chain for the first time: from answering the phone to dispatching police forces, all links are completed independently by the AI ​​big data model, without any infiltration of human subjective consciousness. 2. It achieves an objective closed loop for emergency information: from perception, recording, judgment, dispatch to archiving, a complete objective information chain is formed, and any link is traceable and auditable. 3. It fundamentally eliminates seven types of human cognitive biases: the mathematical nature and unconscious characteristics of the AI ​​big data model make it naturally immune to the systematic biases of the human cognitive system—differences in sensory thresholds, selective filtering of attention, mismatch of perceptual organization, distortion of memory encoding, framing effect of language expression, heuristic bias of judgment, and habitual deviation of behavior. 4. System consistency across time domains: the judgment criteria output by the same model are completely consistent at different times, locations, and to different callers, without the fluctuations in the state of human duty.

Claims

1. An objective method and system for railway police to receive and handle police reports entirely through AI with zero human intervention, characterized in that: Includes the following steps: S1: Multimodal alarm information cross-validation and collection. Through multimodal channels of voice, text, location, and environmental perception, alarm information from all dimensions of the alarmer is collected simultaneously, and invalid interference information is eliminated through multi-channel cross-validation; S2: Lossless structured recording of alarm elements. Based on a preset alarm element slot system, core alarm elements are automatically filled in through large model entity extraction technology, and all original alarm data are stored and locked using hash chain technology. S3: Objective classification and judgment of police incidents. Based on the factual rule constraint system built on the knowledge graph of the public security field, it uses a large model confidence accumulation reasoning mechanism to make a purely objective judgment on the authenticity, urgency and risk level of police incidents, without any human subjective intervention throughout the process. S4: Global optimal police force intelligent dispatch. Based on the real-time situation of the digital twin of the police force in the jurisdiction, a global dispatch plan that meets the requirements of dispatch timeliness, police force matching and optimal route is generated through multi-objective reinforcement learning algorithm and automatically pushed to the corresponding police terminal. S5: Full-link reasoning process traceability and auditability. The large model reasoning chain, data flow and decision basis of the entire process of police situation collection, judgment and dispatch are fully documented and supported for full node backtracking and compliance auditing.

2. The objective method and system for zero-human, fully AI-based alarm reception and incident handling in railway public security, as described in claim 1, is characterized in that... Step S1 further includes at least one of the following: extracting voiceprint features and recognizing emotions from the caller's voice, and simultaneously collecting environmental sound features throughout the call as an auxiliary basis for determining the authenticity of the alarm; automatically acquiring the caller's real-time location information, and combining it with a GIS map to match the location type and historical alarm data of the alarm location to supplement the alarm scene dimension information; supporting the caller to supplement and upload alarm scene information in the form of text, pictures, and short videos, and automatically verifying the authenticity and extracting the structure of the uploaded content; performing conflict verification on information collected from multiple channels, and automatically triggering a secondary information verification process when there are contradictions in information from different channels, marking it as a high-concern alarm.

3. The objective method and system for zero-human, fully AI-based alarm reception and incident handling in railway public security, as described in claim 1, is characterized in that... Step S2 further includes at least one of the following: The police incident element slot system includes seven core slots: basic information of the caller, location of the incident, type of incident, information of the persons involved, information of the items involved, urgency level indicator, and risk indicator. Each slot has a preset standardized enumeration value and a free text supplement field. The large model entity extraction process retains the corresponding mapping relationship between the original text and the extraction results throughout the process, and supports one-click backtracking of the extraction basis. The hash chain evidence storage adopts the national cryptographic SM3 algorithm to generate a unique hash value for each police incident data, which is synchronously uploaded to the public security private chain to ensure that the original data cannot be tampered with or deleted. The system automatically provides intelligent prompts for police incidents that are missing core elements, guiding the caller to supplement key information without the need for manual intervention.

4. The objectification method and system for railway police's zero-human, fully AI-based alarm reception and incident handling as described in claim 1, characterized in that, Step S3 further includes at least one of the following: the fact rule constraint system is constructed based on the national public security crime classification standard and historical crime data of the jurisdiction, and includes three types of hard constraint rules: crime type determination rules, element integrity rules, and risk level matching rules. The large model inference results must meet all rule requirements; the confidence accumulation inference mechanism is as follows: each crime element corresponds to a confidence score. When the confidence scores of all core elements reach the preset threshold, the final judgment result is automatically generated. If the threshold is not reached, the supplementary information collection process is automatically triggered, and no subjective judgment result is output. The entire process of incident determination masks non-objective attributes such as the identity, occupation, and region of the person reporting the incident, and makes judgments based solely on the objective elements of the incident itself. Incidents with high risk and high urgency are automatically cross-validated twice, and the consistency of the judgment results is ensured through independent reasoning and comparison of two major models.

5. The objectification method and system for railway public security's zero-human, fully AI-based alarm reception and incident handling according to claim 1, characterized in that, Step S4 further includes at least one of the following: the real-time situation of the digital twin of the police force in the jurisdiction, which synchronizes the location, status, equipment configuration, and handling authority information of all on-duty police forces in real time, as well as the real-time status of all monitoring, checkpoints, and security forces in the jurisdiction. The optimization objectives of the multi-objective reinforcement learning algorithm include: the shortest emergency response time, the best utilization rate of police resources, the highest matching degree between the ability of emergency responders and the type of emergency, and the lowest risk of congestion along the emergency response route. Once the dispatch plan is generated, it automatically pushes a complete package of police incident elements, on-site handling instructions, and the optimal navigation route to the police officers on duty, without the need for manual relay. When a sudden incident causes a conflict of police resources, it automatically triggers a dynamic adjustment of the global dispatch plan to ensure that all incidents have corresponding police forces to handle them.

6. The objectification method and system for railway public security's zero-human, fully AI-based alarm receiving and incident handling according to claim 1, characterized in that, Step S5 further includes at least one of the following: the end-to-end inference chain evidence storage includes a complete log of the input data, rule basis, intermediate calculation results, and final output results of each step of the large model inference, supporting backtracking by node; it supports one-click auditing of the entire process of a single police incident, automatically generates a police incident handling compliance audit report, and marks all possible violation risk points; the retention period of all evidence storage data complies with the regulations for public security file management, and is automatically destroyed in accordance with regulations upon expiration, with full traceability; It supports statistical analysis of the entire chain of data for similar police incidents, automatically optimizes the incident judgment rules and scheduling algorithms, and realizes closed-loop iteration of the system.

7. The objectification method and system for railway police's zero-human, fully AI-based alarm reception and incident handling according to claim 1, characterized in that, It also includes a multi-agent collaborative processing mechanism, which includes at least one of the following: an alarm-receiving agent, responsible for the entire process of collecting alarm information, extracting elements, and storing and locking evidence, without human intervention; an alarm-handling agent, responsible for objectively judging the alarm, generating handling guidelines, and providing real-time assistance in the handling process; a dispatching agent, responsible for global police force situation awareness, generating dispatching plans, and dynamically adjusting and optimizing them; and a security defense agent, responsible for adversarial sample detection in the large model inference process, compliance verification of output results, and desensitization of sensitive information. The auditing intelligence agent is responsible for data storage, compliance auditing, and early warning of abnormal behavior throughout the entire process.

8. The objective method and system for railway police's zero-human, fully AI-based alarm reception and incident handling according to claim 1, characterized in that, It also includes a multi-layered defense mechanism to protect the objectivity of police reports. This multi-layered defense mechanism includes at least one of the following: input layer defense, which performs adversarial sample detection on all input police report information to eliminate maliciously constructed misleading inputs; reasoning layer defense, which uses hard constraints based on factual rules to force the large model's reasoning process to comply with public security business norms and prohibits the output of subjective assumptions; output layer defense, which verifies the fairness and compliance of all output judgment results and scheduling plans to ensure that there is no discriminatory or illegal content; and evidence storage layer defense, which uses blockchain technology to lock all data in an immutable manner to ensure that the data throughout the entire process is traceable and verifiable.

9. An objective method and system for railway police to receive and handle alarms entirely through AI with zero human intervention, characterized in that: include: The multimodal perception module is used to simultaneously collect multi-dimensional alarm information from the alarm user's voice, text, location, and environment, and to complete multi-channel cross-verification. The system includes: a lossless recording module for police incident elements, used for structured extraction of core elements of police incidents and hash chain storage and locking of raw data; an objective judgment module for police incidents, based on knowledge graphs in the public security field and a large model confidence inference mechanism, to complete purely objective hierarchical judgment of police incidents; an intelligent police force dispatch module, based on the digital twin potential of police forces in the jurisdiction, to generate a globally optimal dispatch scheme through multi-objective reinforcement learning algorithms; and a full-link audit and traceability module, used for evidence storage of the entire police incident inference chain and compliance audit backtracking.

10. The objectified method and system for railway police's zero-human, fully AI-based alarm reception and incident handling according to claim 9, characterized in that, It also includes at least one of the following: a multi-agent collaboration engine, used to schedule five types of intelligent agents—alarm receivers, alarm handlers, dispatchers, security agents, and auditors—to collaborate in completing the entire alarm receiving and handling process; an objective security protection engine, used to implement four layers of objective defense protection: input layer, inference layer, output layer, and evidence storage layer; and a system iteration optimization module, used to automatically optimize judgment rules and scheduling algorithms based on historical alarm data to achieve closed-loop improvement of system capabilities. The public security system integration module is used to seamlessly integrate with the existing 110 emergency call system, police command system, GIS map system, and police communication terminal without replacing the existing system architecture.