# US 2026/0094601 A1 · Method, system and medium for recognizing panic behavior based on panic semantic analysis

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- Number: US 2026/0094601 A1
- Document ID: US20260094601A1
- Status: Published application
- Kind: A1
- Application number: 19/182,851
- Filed: 2025-04-18
- Published: 2026-04-02
- Earliest claimed: 2024-09-29
- Assignees: Tongji University
- Applicants: Tongji University
- Inventors: Rongyong Zhao; Bingyu Wei; Cuiling Li; Yuxin Cai; Chuanfeng Han; Hao Zhang; Yunlong Ma
- Web page: https://patentgrep.com/patent/US20260094601A1

## CPC

- G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning}
- G10L 25/63: for estimating an emotional state
- G10L 2015/088: {Word spotting}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A method for recognizing panic behavior based on panic semantic analysis includes: calling an AI interface to recognize audio streaming as semantic information in real-time, and performing, based on the semantic information and a basic concept, layered division to recognize key phrases; determining a specific layer and a position of each key phrase according to a position coordinate matrix; and obtaining a weight of each key phrase according to the specific layer and position using a description matrix, and performing consistency matching on a panic degree in a scene to determine whether the panic behavior occurs. Establishment steps of the panic semantic reasoning network model include: selecting a panic scene as a description object; defining a basic concept in the panic scene by using OWL; supplementing knowledge elements in the panic scene to improve the panic semantic model; and defining reasoning rules to establish the panic semantic reasoning network model.

## Claims

### Claim 1

A method for recognizing panic behavior based on panic semantic analysis, comprising:
calling an artificial intelligence (AI) interface to recognize audio streaming as semantic information in real-time, and performing, based on the semantic information and a basic concept, layered division to recognize key phrases in the semantic information;
determining a specific layer and a position of each of the key phrases in a panic semantic model according to a position coordinate matrix; and
obtaining a weight of each of the key phrases according to the specific layer and the position of each of the key phrases using a description matrix of a panic semantic reasoning network model, and performing consistency matching on a panic degree in a scene to determine whether the panic behavior occurs in the scene;
wherein establishment steps of the panic semantic reasoning network model comprise:
selecting a panic scene as a description object;
defining a basic concept in the panic scene by using a web ontology language;
supplementing knowledge elements in the panic scene to improve a panic semantic model in the panic scene; and
defining, based on the panic semantic model in the panic scene, reasoning rules to establish the panic semantic reasoning network model with a panic semantic analysis ability and a panic event reasoning ability.

### Claim 2 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein the semantic information comprises shouting, cries for help and conversation content in crowds.

### Claim 3 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein the panic scene comprises medical disturbance scenes, natural disaster scenes and crowded scenes.

### Claim 4 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein the knowledge elements in the panic scene comprise the key phrases, crowd statuses and occurrence conditions.

### Claim 5 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein the position coordinate matrix is expressed as follows:
A
=
{
a
(
q
+
1
)
×
col
r
}
=
{
i
,
j
,
…
,
z
,
r
i
,
j
,
…
,
z
,
r
+
1
⋮
i
,
j
,
…
,
z
,
r
+
q
}
=
{
A
0
A
1
⋮
A
q
}
;
where i=colr−1, colr represents a total length of each of the key phrases, Ai represents key phrases that have a same total length but are different in a last layer, q+1 represents a total number of keywords in a layer where each of the key phrase is located.

### Claim 6 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein the description matrix of the panic semantic reasoning network model is expressed as follows:
M
=
{
m
i
,
j
}
=
{
m
A
0
ω
A
1
m
A
1
ω
A
2
⋮
⋮
m
A
q
-
1
ω
A
q
m
A
q
ω
A
q
+
1
}
;
wherein mAq represents position information of a (q+1)th key phrase, WA, represents a weight of the (q+1)th key phrase, and
∑
1
q
⁢
ω
A
q
=
1.

### Claim 7 (depends on 6)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 6, wherein the description matrix comprises key phrase information and weight information.

### Claim 8 (depends on 1)

The method for recognizing panic behavior based on panic semantic analysis as claimed in claim 1, wherein, when the panic semantic reasoning network model matches with key phrases related to panic and a weighted structure exceeds a threshold, a matching result of the panic semantic reasoning network model is γ=1; otherwise, γ=0.

### Claim 9 (depends on 1)

A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is stored with a computer program, and the computer program is configured to, when is executed by a processor, implement the method for recognizing the panic behavior based on panic semantic analysis as claimed in claim 1.

### Claim 10

A system for recognizing panic behavior based on panic semantic analysis, comprising:
a key phrase recognition module, configured to call an AI interface to recognize audio streaming as semantic information in real-time, and perform, based on the semantic information and a basic concept, layered division to recognize key phrases in the semantic information;
a layered position module, configured to determine a specific layer and a position of each of the key phrases in a panic semantic model according to a position coordinate matrix; and
a panic behavior determination model, configured to obtain a weight of each of the key phrases according to the specific layer and the position of each of the key phrases using a description matrix of a panic semantic reasoning network model, and perform consistency matching on a panic degree in a scene to determine whether the panic behavior occurs in the scene;
wherein establishment steps of the panic semantic reasoning network model comprise:
selecting a panic scene as a description object;
defining a basic concept in the panic scene by using a web ontology language;
supplementing knowledge elements in the panic scene to improve a panic semantic model in the panic scene; and
defining, based on the panic semantic model in the panic scene, reasoning rules to establish the panic semantic reasoning network model with a panic semantic analysis ability and a panic event reasoning ability.