Fire-fighting emergency plan deduction verification method and system based on case reasoning

By constructing a fire case database and a generalized stochastic Petri net model, the problems of resource constraint differences and emergency plan optimization in existing technologies have been solved, enabling effective simulation and resource scheduling optimization of fire emergency plans, and improving the scientific nature and feasibility of the plans.

CN122366816APending Publication Date: 2026-07-10RONSK TECH (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202610277703.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-09
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies struggle to integrate multi-dimensional feature retrieval, adapt to varying resource constraints, and cannot perform simulations, verifications, or optimizations of emergency plans. This results in resource conflicts or infeasible scheduling issues in the actual implementation of the generated fire emergency plans.

Method used

A historical fire case library containing structured environmental attributes and fire evolution characteristics is constructed. The feature attribute weights are calculated using rough set conditional entropy and cloud model inverse generator to generate initial plans for resource allocation and action sequence. Concurrent conflict detection and time sequence logic deduction are performed through a generalized stochastic Petri net model to iteratively optimize resource scheduling parameters.

Benefits of technology

It enables the objective weighting and qualitative-quantitative conversion of fire emergency plans, identifies potential resource conflicts and temporal logic loopholes, improves the scientific rigor and accuracy of the plans, and ensures their feasibility and optimization effectiveness.

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Abstract

The invention provides a case reasoning-based fire-fighting emergency plan deduction verification method and system, and the method comprises the steps: building a structured historical fire case library, calculating the mixed similarity of a current scene and historical cases based on a rough set and a cloud model, and screening out similar source cases; adaptive migration is carried out on the disposal strategy of the source case through the constraint satisfaction model, and an initial to-be-verified plan is generated; establishing a generalized stochastic Petri network model for representing disaster-affected body state evolution and rescue resource interaction, mapping an initial to-be-verified plan into a transition rate and a place initial identifier in the model, performing concurrent conflict detection and sequential logic deduction, and evaluating a transient performance index; and if the target rescue success index does not reach the preset threshold value, carrying out iterative correction on resource scheduling parameters of the plan according to a sensitivity analysis result of the key transition, repeatedly executing the deduction step until a termination condition is met, and outputting a target fire-fighting emergency plan.
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