CN112862241A - Hidden danger identification method, equipment and medium based on emergency safety production - Google Patents
Hidden danger identification method, equipment and medium based on emergency safety production Download PDFInfo
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- CN112862241A CN112862241A CN202011613793.6A CN202011613793A CN112862241A CN 112862241 A CN112862241 A CN 112862241A CN 202011613793 A CN202011613793 A CN 202011613793A CN 112862241 A CN112862241 A CN 112862241A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/063—Operations research, analysis or management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Abstract
The invention provides a hidden danger identification method based on emergency safety production, which comprises the steps of respectively obtaining accident data in an accident case library and hidden danger data in a hidden danger report library, wherein the accident data comprises an accident type and an accident risk factor; carrying out word vector conversion on the hidden danger data according to preset standard hidden danger vocabularies to obtain converted hidden danger data containing a plurality of hidden danger vocabularies; determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type; screening out a corresponding accident type and an accident weight from an accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity corresponding to the hidden danger vocabularies and the accident type; and pushing the accident similarity sequencing result to a supervision department management background in real time. The hidden danger identification method based on emergency safety production reduces the accident rate, improves the hidden danger identification efficiency and reduces the pressure of supervision personnel.
Description
Technical Field
The invention relates to the field of hidden danger identification, in particular to a hidden danger identification method, equipment and medium based on emergency safety production.
Background
Hidden dangers refer to unsafe conditions of workplaces, equipment and facilities, unsafe behaviors of people and defects in management. The hidden danger is a direct reason for causing safety accidents, and the major accident hidden danger refers to the accident hidden danger which can cause major personal casualties or major economic losses, so that the control and management of the major accident hidden danger are enhanced, and the method has important significance for preventing the extra-large safety accidents.
At present, along with continuous reinforcement of government supervision and continuous increase of supervision means, the collection channel of hidden dangers is wider and wider, the data volume is also larger and larger, the workload of hidden danger auditing and supervising flows is huge, the existing hidden danger auditing and supervising flows are operated by emergency supervision personnel, but the flow of artificial hidden danger identification can not meet the requirement of intensive supervision completely, the efficiency of the whole identification process is lower, and the identification result has errors.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a hidden danger identification method based on emergency safety production, which can solve the problems that the flow of artificial hidden danger identification can not meet the requirement of intensified supervision, the efficiency of the whole identification process is low, and the identification result has errors.
The second objective of the present invention is to provide an electronic device, which can solve the problems that the flow of identifying the hidden danger has not been able to satisfy the requirement of the reinforcement supervision, the efficiency of the whole identification process is low, and the identification result has errors.
The invention also aims to provide a computer readable storage medium, which can solve the problems that the flow of the artificial hidden danger identification can not meet the requirement of the strengthened supervision at all, the efficiency of the whole identification process is low, and the identification result has errors.
One of the purposes of the invention is realized by adopting the following technical scheme:
the hidden danger identification method based on emergency safety production comprises the following steps:
acquiring data, namely acquiring accident data in an accident case library and hidden danger data in a hidden danger report library respectively, wherein the accident data comprises accident types and accident risk factors, and each accident type corresponds to at least one accident risk factor;
performing word vector conversion, namely performing word vector conversion on the hidden danger data according to preset standard hidden danger vocabularies to obtain converted hidden danger data containing a plurality of hidden danger vocabularies;
generating an accident risk factor special question bank, determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight with the accident risk factor and the accident type to obtain the accident risk factor special question bank;
generating accident similarity, screening out a corresponding accident type and the accident weight from the accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity of the hidden danger vocabularies and the accident type;
sorting accident similarity, namely sorting all the hidden danger vocabularies corresponding to the accident similarity from high to low to obtain a sorting sequence number corresponding to each hidden danger vocabulary, and establishing association among the sorting sequence number, the hidden danger vocabularies and the accident similarity to obtain an accident similarity sorting result containing the sorting sequence number, the hidden danger vocabularies and the accident similarity;
and pushing the accident similarity sequencing result, and pushing the accident similarity sequencing result to a supervision department management background in real time.
Further, the generating of the accident risk factor topic database specifically includes: and processing the accident data by utilizing a preset word segmentation model and adopting a natural language processing algorithm, outputting an accident weight of each accident risk factor in the accident type by the preset word segmentation model according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight, the accident risk factors and the accident type to obtain an accident risk factor special question bank.
Further, when a plurality of accident types all contain the same accident risk factor, the highest value of the occupation proportion of the accident risk factor in the corresponding accident type is used as the accident weight of the accident risk factor.
Further, the hidden danger reporting library includes enterprise names corresponding to the hidden danger data, and an enterprise portrait corresponding to the enterprise names is generated according to the accident similarity and the accident type corresponding to the hidden danger vocabulary in each hidden danger data.
And further, preprocessing is further included before the word vector conversion, and the hidden danger data is preprocessed by utilizing a preset deep learning model according to preset hidden danger features, a preset incidence relation and a natural language processing algorithm to obtain hidden danger data containing a plurality of hidden danger features.
Further, the word vector conversion specifically includes: and performing word vector conversion on the hidden danger features in the hidden danger data according to preset standard hidden danger words to obtain converted hidden danger data containing the hidden danger words.
Further, the preset deep learning model is a BilSTM-CRF model.
The second purpose of the invention is realized by adopting the following technical scheme:
an electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising a hazard identification method for emergency safety production based application.
The third purpose of the invention is realized by adopting the following technical scheme:
a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor for performing the emergency safety production based risk identification method of the present application.
Compared with the prior art, the invention has the beneficial effects that: the invention discloses a hidden danger identification method based on emergency safety production, which comprises the following steps: respectively acquiring accident data in an accident case library and hidden danger data in a hidden danger reporting library, wherein the accident data comprises accident types and accident risk factors, and each accident type corresponds to at least one accident risk factor; carrying out word vector conversion on the hidden danger data according to preset standard hidden danger vocabularies to obtain converted hidden danger data containing a plurality of hidden danger vocabularies; determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight with the accident risk factors and the accident type to obtain an accident risk factor special question bank; screening out a corresponding accident type and an accident weight from an accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity corresponding to the hidden danger vocabularies and the accident type; sequencing accident similarity corresponding to all the hidden danger vocabularies from high to low to obtain a sequencing serial number corresponding to each hidden danger vocabulary, and establishing association among the sequencing serial number, the hidden danger vocabularies and the accident similarity to obtain an accident similarity sequencing result containing the sequencing serial number, the hidden danger vocabularies and the accident similarity; and pushing the accident similarity sequencing result to a supervision department management background in real time. The hidden danger data are associated with the accident, the accident similarity corresponding to the hidden danger data is automatically identified, and the hidden danger data are actively pushed to a supervision department, so that the accident occurrence rate is reduced, the hidden danger identification efficiency is improved, and the pressure of supervision personnel is reduced.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic flow chart of the method for identifying hidden dangers based on emergency safety production according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
As shown in fig. 1, the present embodiment provides a hidden danger identification method based on emergency safety production, including the following steps:
and acquiring data, namely acquiring accident data in an accident case library and hidden danger data in a hidden danger report library respectively, wherein the accident data comprises accident types and accident risk factors, and each accident type corresponds to at least one accident risk factor. Each accident type may contain a single accident risk factor or may contain multiple accident risk factors.
And preprocessing, namely preprocessing the hidden danger data by utilizing a preset deep learning model according to preset hidden danger characteristics, a preset incidence relation and a natural language processing algorithm to obtain hidden danger data containing a plurality of hidden danger characteristics. The preset hidden danger characteristics in the embodiment comprise concealment, danger, burstiness, causality, continuity, repeatability, accident, timeliness, particularity, seasonality and the like. The preset deep learning model adopted in the embodiment is a BilSTM-CRF model. The preset association relationship in this embodiment is: a plurality of risk factors form a hazard source, the hazard source becomes a hidden danger when exceeding a set safety state and behavior, the hidden danger becomes an accident when being out of control internally, and the risk factors can be directly excited by external factors to become the hidden danger.
And performing word vector conversion, namely performing word vector conversion on the hidden danger data by adopting a natural language processing algorithm according to preset standard hidden danger words to obtain converted hidden danger data containing a plurality of hidden danger words. The method specifically comprises the following steps: and performing word vector conversion on the hidden danger features in the hidden danger data into hidden danger words by adopting a natural language processing algorithm according to preset standard hidden danger words to obtain converted hidden danger data containing the hidden danger words.
Generating an accident risk factor subject database, determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight with the accident risk factor and the accident type to obtain the accident risk factor subject database. The method specifically comprises the following steps: and processing the accident data by utilizing a preset word segmentation model and adopting a natural language processing algorithm, outputting an accident weight of each accident risk factor in the accident type by the preset word segmentation model according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight, the accident risk factors and the accident type to obtain an accident risk factor special question bank. And when the accident types contain the same accident risk factor, taking the highest value of the occupation proportion of the accident risk factor in the corresponding accident type as the accident weight of the accident risk factor. When one accident risk factor only appears in one accident type, the occupation proportion in the accident type is the accident weight.
And generating accident similarity, screening out a corresponding accident type and the accident weight from the accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity of the hidden danger vocabularies and the accident type.
And (2) accident similarity sequencing, namely sequencing all the hidden danger vocabularies corresponding to the accident similarity from high to low to obtain a sequencing serial number corresponding to each hidden danger vocabulary, and associating the sequencing serial numbers, the hidden danger vocabularies and the accident similarity to obtain an accident similarity sequencing result containing the sequencing serial numbers, the hidden danger vocabularies and the accident similarity. In this embodiment, the hidden danger reporting library includes an enterprise name corresponding to the hidden danger data, and an enterprise portrait corresponding to the enterprise name is generated according to the accident similarity and the accident type corresponding to the hidden danger vocabulary in each of the hidden danger data.
And pushing the accident similarity sequencing result, and pushing the accident similarity sequencing result to a supervision department management background in real time.
The present embodiment also provides an electronic device, including: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising a hazard identification method for emergency safety production based application.
The embodiment also provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to execute the method for identifying a hidden danger based on emergency safety production of the present application.
The invention discloses a hidden danger identification method based on emergency safety production, which comprises the following steps: respectively acquiring accident data in an accident case library and hidden danger data in a hidden danger reporting library, wherein the accident data comprises accident types and accident risk factors, and each accident type corresponds to at least one accident risk factor; carrying out word vector conversion on the hidden danger data according to preset standard hidden danger vocabularies to obtain converted hidden danger data containing a plurality of hidden danger vocabularies; determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight with the accident risk factors and the accident type to obtain an accident risk factor special question bank; screening out a corresponding accident type and an accident weight from an accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity corresponding to the hidden danger vocabularies and the accident type; sequencing accident similarity corresponding to all the hidden danger vocabularies from high to low to obtain a sequencing serial number corresponding to each hidden danger vocabulary, and establishing association among the sequencing serial number, the hidden danger vocabularies and the accident similarity to obtain an accident similarity sequencing result containing the sequencing serial number, the hidden danger vocabularies and the accident similarity; and pushing the accident similarity sequencing result to a supervision department management background in real time. The hidden danger data are associated with the accident, the accident similarity corresponding to the hidden danger data is automatically identified, and the hidden danger data are actively pushed to a supervision department, so that the accident occurrence rate is reduced, the hidden danger identification efficiency is improved, and the pressure of supervision personnel is reduced.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (9)
1. The hidden danger identification method based on emergency safety production is characterized by comprising the following steps: the method comprises the following steps:
acquiring data, namely acquiring accident data in an accident case library and hidden danger data in a hidden danger report library respectively, wherein the accident data comprises accident types and accident risk factors, and each accident type corresponds to at least one accident risk factor;
performing word vector conversion, namely performing word vector conversion on the hidden danger data according to preset standard hidden danger vocabularies to obtain converted hidden danger data containing a plurality of hidden danger vocabularies;
generating an accident risk factor special question bank, determining the accident weight of each accident risk factor in the accident type according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight with the accident risk factor and the accident type to obtain the accident risk factor special question bank;
generating accident similarity, screening out a corresponding accident type and the accident weight from the accident risk factor special subject library according to the hidden danger vocabularies in the converted hidden danger data, and taking the accident weight as the accident similarity of the hidden danger vocabularies and the accident type;
sorting accident similarity, namely sorting all the hidden danger vocabularies corresponding to the accident similarity from high to low to obtain a sorting sequence number corresponding to each hidden danger vocabulary, and establishing association among the sorting sequence number, the hidden danger vocabularies and the accident similarity to obtain an accident similarity sorting result containing the sorting sequence number, the hidden danger vocabularies and the accident similarity;
and pushing the accident similarity sequencing result, and pushing the accident similarity sequencing result to a supervision department management background in real time.
2. The potential hazard identification method based on emergency safety production as claimed in claim 1, characterized in that: the special topic library for generating the accident risk factors specifically comprises the following steps: and processing the accident data by utilizing a preset word segmentation model and adopting a natural language processing algorithm, outputting an accident weight of each accident risk factor in the accident type by the preset word segmentation model according to the occupation proportion of each accident risk factor in the accident type, and associating and storing the accident weight, the accident risk factors and the accident type to obtain an accident risk factor special question bank.
3. The potential hazard identification method based on emergency safety production as claimed in claim 2, characterized in that: and when the accident types contain the same accident risk factor, taking the highest value of the occupation proportion of the accident risk factor in the corresponding accident type as the accident weight of the accident risk factor.
4. The potential hazard identification method based on emergency safety production as claimed in claim 1, characterized in that: the hidden danger reporting library comprises enterprise names corresponding to the hidden danger data, and enterprise images corresponding to the enterprise names are generated according to the accident similarity and the accident type corresponding to the hidden danger vocabularies in each hidden danger data.
5. The potential hazard identification method based on emergency safety production as claimed in claim 1, characterized in that: and preprocessing is further included before the word vector conversion, and the hidden danger data is preprocessed by utilizing a preset deep learning model according to preset hidden danger features, a preset incidence relation and a natural language processing algorithm to obtain hidden danger data containing a plurality of hidden danger features.
6. The potential hazard identification method based on emergency safety production as claimed in claim 5, characterized in that: the word vector conversion specifically comprises: and performing word vector conversion on the hidden danger features in the hidden danger data according to preset standard hidden danger words to obtain converted hidden danger data containing the hidden danger words.
7. The potential hazard identification method based on emergency safety production as claimed in claim 5, characterized in that: the preset deep learning model is a BilSTM-CRF model.
8. An electronic device, characterized by comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the method of any one of claims 1-7.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor for performing the method according to any of claims 1-7.
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| CN202011613793.6A CN112862241A (en) | 2020-12-30 | 2020-12-30 | Hidden danger identification method, equipment and medium based on emergency safety production |
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| CN202011613793.6A CN112862241A (en) | 2020-12-30 | 2020-12-30 | Hidden danger identification method, equipment and medium based on emergency safety production |
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| CN110347805A (en) * | 2019-07-22 | 2019-10-18 | 中海油安全技术服务有限公司 | Petroleum industry security risk key element extracting method, device, server and storage medium |
| CN111667192A (en) * | 2020-06-12 | 2020-09-15 | 北京卓越讯通科技有限公司 | Safety production risk assessment method based on NLP big data |
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2020
- 2020-12-30 CN CN202011613793.6A patent/CN112862241A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110077028A1 (en) * | 2009-09-29 | 2011-03-31 | Wilkes Iii Samuel M | System and Method for Integrating Smartphone Technology Into a Safety Management Platform to Improve Driver Safety |
| CN109240258A (en) * | 2018-07-09 | 2019-01-18 | 上海万行信息科技有限公司 | Vehicle failure intelligent auxiliary diagnosis method and system based on term vector |
| CN110347805A (en) * | 2019-07-22 | 2019-10-18 | 中海油安全技术服务有限公司 | Petroleum industry security risk key element extracting method, device, server and storage medium |
| CN111667192A (en) * | 2020-06-12 | 2020-09-15 | 北京卓越讯通科技有限公司 | Safety production risk assessment method based on NLP big data |
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