CN116090830B - Operation risk monitoring method - Google Patents
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Abstract
The invention provides a job risk monitoring method, which comprises the steps of firstly compiling a risk database storing job risk data content, after acquiring job planning data from the risk database, distributing job risk monitoring tasks to different terminals according to the job planning data, enabling a user to acquire job process data corresponding to the job risk monitoring tasks through the terminals, analyzing the job process data to acquire the job risk data, generating a corresponding job risk supervision table according to the job risk data, and sending the job risk supervision table to project participants, so that safety supervision staff does not need to manually check field monitoring videos, the risk existing in the job process can be found in time while labor input is reduced, and the project participants can rectify and improve field problems according to the job risk supervision table, thereby avoiding safety accidents.
Description
Technical Field
The invention relates to the technical field of operation process monitoring, in particular to an operation risk monitoring method.
Background
In the operation process of a power station, safety supervision is an important means for guaranteeing site standard operation. In the prior power grid operation, the risk of an operation site occurs in real time, and the existing management and control means belong to post analysis and have a lag in timeliness. Meanwhile, the monitoring of the operation site needs to check the video manually, and the investment of the full-concentration is difficult to keep for a long time, so that key risks in the operation process are easy to miss.
Disclosure of Invention
Accordingly, it is an object of the present invention to provide a method for monitoring risk of operation, which overcomes or at least partially solves the above-mentioned problems of the prior art.
In order to achieve the above object, the present invention provides a method for monitoring operation risk, comprising the steps of:
s101, compiling a risk database covering the whole construction process of a pumped storage power station, preparing management and control measures according to existing risks, generating operation risk data content corresponding to the risks and the measures one by one, wherein the risk database provides basic data for operation planning, construction scheme compiling, safety technology mating and operation risk supervision checking;
s102, acquiring operation risk data content through a terminal when a construction scheme is compiled, and carrying out risk identification work according to the operation content;
s103, acquiring operation risk data content through a terminal when compiling construction operation tickets, acquiring the risk data according to the operation content, and carrying out safety technical mating on operators;
s104, acquiring operation plan data from a risk database, and distributing operation risk monitoring tasks to different terminals according to the operation plan data;
s105, acquiring operation process data corresponding to an operation risk monitoring task through a terminal;
s106, analyzing the operation process to obtain operation risk data, wherein the operation risk data are divided into civil engineering and electromechanical engineering, and the operation risk data comprise hazard names, risk descriptions, accident consequences and risk types and corresponding consequences measures;
s107, generating a corresponding job risk supervision table according to the job risk data and sending the job risk supervision table to the project participants;
s108, the project participants rectify the field operation problem according to the operation risk supervision table.
Further, the operation risk data further comprises unit engineering information, subsection engineering information and working procedures.
Further, acquiring the operation process data corresponding to the operation risk monitoring task through the terminal comprises acquiring text operation process data recorded in the format text by a risk supervisor and video operation process data exported to the terminal by the video monitoring equipment.
Further, when the work process data is text work process data, the parsing of the work process data in S106 specifically includes the following steps:
s201, extracting a plurality of title keywords in the text operation process data through a preset keyword extraction algorithm;
s202, identifying the title keywords to determine the types of the recorded contents in the detailed content columns associated with the title keywords, wherein the determined types are called recorded content types;
s203, judging whether the literal operation process data corresponds to an operation risk monitoring task according to the recorded content type which is recorded in a detailed content column of the project, the operation surface, the operation task and the working procedure to which the literal operation process data belongs;
s204, generating a hazard name and a risk description according to the recorded content types as the recorded content in the detailed content column of the inspection item, the inspection result and the inspection condition description, and determining the corresponding accident types and risk types.
Further, when the job process data is video-type job process data, the parsing of the job process data in S106 specifically includes the following steps:
s301, processing video operation process data into multi-frame images, and extracting key frame images in the multi-frame images;
s302, identifying scenes in the key frame images, and judging whether the operation process data corresponds to an operation risk monitoring task according to an identification result;
s303, carrying out hazard identification on people and articles in the key frame image, generating hazard names and risk descriptions according to hazard identification results, and determining corresponding accident types and risk types.
Further, the step S106 specifically includes the following steps:
s401, inquiring a historical operation risk monitoring database according to the first operation risk data, and inquiring historical operation risk data of the same accident type and risk type;
s402, further inquiring the number of the historical operation risk data with corresponding accidents in the historical operation risk data inquired in the step S401, calculating the percentage of the historical operation risk data with the same accident type and risk type, and generating risk occurrence probability data;
s403, generating possible risk consequences according to the historical operation risk data inquired in the step S401;
s404, quantitatively scoring the risk occurrence possibility and risk consequences possibly caused, and generating corresponding risk level analysis information according to the quantitatively scoring result; the risk level analysis information comprises a likelihood score, a result score and a risk value;
s405, judging corresponding risk levels according to the risk values, and adding risk occurrence possibility, risk consequences possibly caused, risk level analysis information and risk levels to the operation risk data.
Further, the step S107 specifically includes the following steps:
s501, inquiring a general measure database according to the risk level in the operation risk data, and acquiring general risk control measures, supervision unit risk control measures and owner risk control measures required to be adopted by a construction unit in a project participant;
s502, inquiring a special measure database according to the operation risk data to acquire special risk control measures required to be adopted by a construction unit;
s503, generating a corresponding operation risk supervision table according to the operation risk monitoring task information, the operation process data, the general risk control measures, the supervision unit risk control measures, the owner risk control measures and the special risk control measures.
Further, after determining the risk level of the job risk monitoring task in step S106, the important monitoring task is determined according to the risk level of each job risk monitoring task, and stricter risk management and control measures are adopted for the important monitoring task.
Further, the key monitoring task is determined according to the risk level of each job risk monitoring task, and the method specifically comprises the following steps:
s601, calculating different values of two random job risk monitoring tasks according to the risk levels of all job risk monitoring tasks;
s602, calculating abnormal factors of each operation risk monitoring task through an LOF (Local Outlier Factor, local abnormal factor detection) algorithm by taking the different values as distances corresponding to two operation risk monitoring tasks;
s603, respectively carrying out difference calculation on the abnormal factors of each operation risk monitoring task and a standard threshold value, and sequencing according to the sequence from the big to the small according to the difference value corresponding to each operation risk monitoring task;
s604, sequentially extracting the first N operation risk monitoring tasks as key monitoring tasks, wherein N is more than or equal to 1.
Compared with the prior art, the invention has the beneficial effects that:
according to the job risk monitoring method provided by the invention, after the job planning data is acquired from the risk database, the job risk monitoring task is distributed to different terminals according to the job planning data, the user acquires the job process data corresponding to the job risk monitoring task through the terminals, analyzes the job process data to acquire the job risk data, generates the corresponding job risk monitoring table according to the job risk data and sends the job risk monitoring table to the project participants, so that safety supervision staff does not need to manually check on-site monitoring videos, the risk existing in the job process can be found in time while the manpower input is reduced, and the project participants rectify and improve on-site problems according to the job risk monitoring table, thereby avoiding safety accidents.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only preferred embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic overall flow chart of an operation risk monitoring method according to an embodiment of the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the illustrated embodiments are provided for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Referring to fig. 1, the present embodiment provides a job risk monitoring method, including the following steps:
s101, compiling a risk database covering the whole construction process of the pumped storage power station, formulating management and control measures against existing risks, generating operation risk data content corresponding to risks and measures one by one, and providing basic data for operation planning, construction scheme compiling, safety technology mating and operation risk supervision checking.
S102, acquiring operation risk data content through a terminal when a construction scheme is compiled, and carrying out risk identification work according to the operation content.
And S103, acquiring operation risk data content through a terminal when compiling construction operation tickets, acquiring the risk data according to the operation content, and carrying out safety technical mating on operators.
S104, acquiring operation plan data from the risk database, and distributing operation risk monitoring tasks to different terminals according to the operation plan data. In this embodiment, the job plan data includes a job time, a job site, a job content, a project participant, a security measure, and the like. The terminal is used by safety supervision personnel and can be a tablet computer or other mobile intelligent terminals.
S105, acquiring operation process data corresponding to the operation risk monitoring task through the terminal.
S106, analyzing the operation process to obtain operation risk data, wherein the operation risk data are divided into civil engineering and electromechanical engineering, and the operation risk data comprise hazard names, risk descriptions, accident consequences and risk types and corresponding consequences measures. Wherein the hazard names are filled in according to hazard types and hazard factors, for example, the hazard types can comprise physical hazard, chemical hazard, biological hazard, man-machine-efficiency hazard and the like, and for the physical hazard, the hazard factors can comprise: noise; vibrating; equipment and facilities easy to collide; defective devices, facilities or components; uneven ground; high temperature; low temperature; a sharp object; a sharp knife; tool with unqualified quality; steep mountain roads; electromagnetic radiation, and the like. The accident types can be classified according to the classification of casualties of enterprises (GB 6441). Risk types may include personal risk, grid risk, network security, equipment risk, environmental and occupational health risk, and the like.
The job risk data also includes unit engineering information, sub engineering information, and processes. The unit engineering information comprises a plurality of different types, such as first operation risk data of civil engineering, the unit engineering information comprises factory building system engineering, environmental protection engineering, construction auxiliary engineering and the like, each type of unit engineering information comprises a plurality of types of sub engineering information, and the sub engineering information under construction auxiliary engineering comprises temporary construction supporting holes, temporary wind tunnels, construction temporary roads and the like. Each piece of sub-engineering information comprises a plurality of pieces of sub-engineering information, for example, the sub-engineering information under temporary construction support holes comprises slope support, pavement engineering, drainage engineering and the like. Each item of engineering information comprises a plurality of procedures, such as procedures of slope support including measuring paying-off, measuring lofting, reinforcing mesh installation, anchor rod installation, concrete spraying, dangerous stone cleaning and prying, drilling and the like.
And S107, generating a corresponding job risk supervision table according to the job risk data and sending the job risk supervision table to the project participants.
S108, the project participants rectify the field operation problem according to the operation risk supervision table.
In this embodiment, acquiring, by the terminal, operation process data corresponding to the operation risk monitoring task includes acquiring text operation process data recorded in a format text by a field supervisor and video operation process data exported to the terminal by a video monitoring device. In the working process, the on-site supervisor faithfully records the on-site working process in a text form through a terminal, and in order to improve the efficiency and facilitate the processing of the data in the subsequent steps, the literal working process data are recorded in a format text, the format text is a text with a preset filling format, and the on-site supervisor only needs to fill corresponding contents according to the news and the title prompts of each detailed content column required to be filled in the format text. If the video monitoring equipment is arranged on the operation site, the video operation process data are preferentially acquired.
When the operation process data is text operation process data, the analyzing the operation process data in S106 specifically includes the following steps:
s201, extracting a plurality of title keywords in the text operation process data through a preset keyword extraction algorithm. The keyword extraction algorithm may be an existing keyword extraction algorithm, such as TF-IDF, textRank, etc., which is not specifically limited in this embodiment.
S202, identifying the title keywords to determine the types of the recorded contents in the detailed content columns associated with the title keywords, wherein the determined types are called recorded content types. The format text comprises a plurality of detailed content columns which are required to be filled by the on-site supervisor, each detailed content column has a corresponding title, and the title is used for prompting the on-site supervisor which content is required to be filled in the column.
S203, judging whether the literal operation process data corresponds to an operation risk monitoring task according to the recorded content type which is recorded in a detailed content column of the project, the operation surface, the operation task and the working procedure to which the literal operation process data belongs;
s204, generating a hazard name and a risk description according to the recorded content types as the recorded content in the detailed content column of the inspection item, the inspection result and the inspection condition description, and determining the corresponding accident types and risk types.
When the operation process data is video type operation process data, the analyzing the operation process data in S106 specifically includes the following steps:
s301, processing the video operation process data into multi-frame images, and extracting key frame images in the multi-frame images. The key frame image may be extracted by randomly extracting a certain number of key frames from the multi-frame image, or extracting key frames from the multi-frame image according to a preset time interval, or manually designating the extracted key frames.
S302, identifying scenes in the key frame images, judging whether the operation process data corresponds to an operation risk monitoring task according to the identification result, specifically judging whether the scenes in the key frame images are identical to the operation places in the operation plan data according to the identification result, and judging whether the operation process data corresponds to the operation risk monitoring task.
S303, carrying out hazard identification on people and articles in the key frame image, generating hazard names and risk descriptions according to hazard identification results, and determining corresponding accident types and risk types.
In this embodiment, the identification of the scene, the person and the object in the key frame image may be implemented by using an image identification algorithm, and a person skilled in the art may select different algorithms according to the actual situation, which is not specifically limited in the present invention.
The step S106 specifically further includes the following steps:
s401, querying a historical operation risk monitoring database according to the operation risk data, and querying historical operation risk data of the same accident type and risk type. The historical operation risk monitoring database stores operation risk data collected in the past.
S402, further inquiring the number of the historical operation risk data with the same type of accidents in the historical operation risk data inquired in the step S401, calculating the percentage of the historical operation risk data with the same accident type and risk type, and generating risk occurrence probability data.
S403, generating possible risk consequences according to the historical operation risk data queried in the step S401. The historical operation risk database also stores safety accidents and consequences caused by the historical operation risk data, and accident consequences in the historical operation risk data of the same accident type are used as possible risk consequences.
S404, quantitatively scoring the risk occurrence possibility and risk consequences possibly caused, and generating corresponding risk level analysis information according to the quantitatively scoring result; the risk level analysis information includes a likelihood score, a outcome score, and a risk value. In this embodiment, the risk occurrence probability is scored according to the size of the risk occurrence probability, and the higher the risk occurrence probability is, the higher the probability score is; the more serious the corresponding consequences, the higher the outcome score. The risk value may take the product of the likelihood score and the outcome score.
S405, judging corresponding risk levels according to the risk values. The higher the risk value, the higher the risk level. Adding risk occurrence probability, possibly caused risk consequences, risk level analysis information and risk level to the job risk data
The step S107 specifically includes the following steps:
s501, inquiring a general measure database according to the risk level in the operation risk data, and acquiring general risk control measures, supervision unit risk control measures and owner risk control measures required to be adopted by a construction unit in a project participant;
s502, inquiring a special measure database according to the operation risk data to acquire special risk control measures required to be adopted by a construction unit;
s503, generating a corresponding operation risk supervision table according to the operation risk monitoring task information, the operation process data, the general risk control measures, the supervision unit risk control measures, the owner risk control measures and the special risk control measures.
As a preferred example, after determining the risk level of the job risk monitoring task in step S106, the important monitoring task is also determined according to the risk level of each job risk monitoring task, and stricter risk management measures are taken for the important monitoring task. The key monitoring task is determined according to the risk level of each job risk monitoring task, and the method specifically comprises the following steps:
s601, calculating different values of two random job risk monitoring tasks according to the risk levels of the job risk monitoring tasks. Illustratively, the dissimilarity value is calculated by the following formula:
wherein D (X, Y) represents a different value of the job risk monitoring task X, Y, X r Representing the risk value, Y, of a job risk monitoring task X r The risk value representing the job risk monitoring task Y, exp represents an exponential function based on e.
S602, calculating abnormal factors of each job risk monitoring task through an LOF (Local Outlier Factor, local abnormal factor detection) algorithm by taking the different values as the reachable distances of the corresponding two job risk monitoring tasks.
S603, respectively carrying out difference calculation on the abnormal factors of the operation risk monitoring tasks and the standard threshold value, and sequencing the abnormal factors according to the difference values corresponding to the operation risk monitoring tasks from high to low. The value of the standard threshold may be set according to actual requirements, which is not specifically limited in this embodiment.
S604, sequentially extracting the first N operation risk monitoring tasks as key monitoring tasks, wherein N is more than or equal to 1. The value of N can be set according to actual requirements, which is not particularly limited in this embodiment.
According to the embodiment, after the risk levels of different operation risk monitoring tasks are obtained, different values of different European operation risk monitoring tasks are calculated, abnormal factors of each operation risk monitoring task are further calculated by means of a local abnormal factor detection algorithm, and the operation risk monitoring task needing to be monitored in a key mode is determined based on the abnormal factors, so that stricter risk control measures are adopted for the operation risk monitoring tasks with risk hidden danger different from other tasks, the safety of an operation process is further guaranteed, and safety accidents are prevented.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method of job risk monitoring, the method comprising the steps of:
s101, compiling a risk database covering the whole construction process of a pumped storage power station, preparing management and control measures according to existing risks, generating operation risk data content corresponding to the risks and the measures one by one, wherein the risk database provides basic data for operation planning, construction scheme compiling, safety technology mating and operation risk supervision checking;
s102, acquiring operation risk data content through a terminal when a construction scheme is compiled, and carrying out risk identification work according to the operation content;
s103, acquiring operation risk data content through a terminal when compiling construction operation tickets, acquiring the risk data according to the operation content, and carrying out safety technical mating on operators;
s104, acquiring operation plan data from a risk database, and distributing operation risk monitoring tasks to different terminals according to the operation plan data;
s105, acquiring operation process data corresponding to an operation risk monitoring task through a terminal;
s106, analyzing the operation process to obtain operation risk data, wherein the operation risk data are divided into civil engineering and electromechanical engineering, and the operation risk data comprise hazard names, risk descriptions, accident consequences and risk types and corresponding consequences measures;
s107, generating a corresponding job risk supervision table according to the job risk data and sending the job risk supervision table to the project participants;
s108, the project participants rectify and change the field operation problems according to the operation risk supervision table;
the step S106 specifically further includes the following steps:
s401, inquiring a historical operation risk monitoring database according to operation risk data, and inquiring historical operation risk data of the same accident type and risk type;
s402, further inquiring the number of the historical operation risk data with corresponding accidents in the historical operation risk data inquired in the step S401, calculating the percentage of the historical operation risk data with the same accident type and risk type, and generating risk occurrence probability data;
s403, generating possible risk consequences according to the historical operation risk data inquired in the step S401;
s404, quantitatively scoring the risk occurrence possibility and risk consequences possibly caused, and generating corresponding risk level analysis information according to the quantitatively scoring result; the risk level analysis information comprises a likelihood score, a result score and a risk value;
s405, judging corresponding risk levels according to the risk values, and adding risk occurrence possibility, risk consequences possibly caused, risk level analysis information and risk levels into the operation risk data;
after determining the risk level of the job risk monitoring task in step S106, determining a key monitoring task according to the risk level of each job risk monitoring task, and taking stricter risk management and control measures on the key monitoring task;
the key monitoring task is determined according to the risk level of each job risk monitoring task, and the method specifically comprises the following steps:
s601, calculating different values of two random job risk monitoring tasks according to the risk levels of all job risk monitoring tasks;
s602, calculating abnormal factors of each operation risk monitoring task through a local abnormal factor detection algorithm by taking the different values as the reachable distances of the corresponding two operation risk monitoring tasks;
s603, respectively carrying out difference calculation on the abnormal factors of each operation risk monitoring task and a standard threshold value, and sequencing according to the sequence from the big to the small according to the difference value corresponding to each operation risk monitoring task;
s604, sequentially extracting the first N operation risk monitoring tasks as key monitoring tasks, wherein N is more than or equal to 1.
2. The method of claim 1, wherein the job risk data further comprises unit engineering information, sub engineering information, and procedures.
3. The method according to claim 1, wherein acquiring, by the terminal, the job process data corresponding to the job risk monitoring task includes acquiring text type job process data entered into the terminal by a field supervisor and video type job process data exported to the terminal by a video monitoring device, the text type job process data being recorded in a format text.
4. A method for monitoring risk of operation according to claim 3, wherein when the operation process data is text operation process data, the parsing of the operation process data in S106 specifically comprises the steps of:
s201, extracting a plurality of title keywords in the text operation process data through a preset keyword extraction algorithm;
s202, identifying the title keywords to determine the types of the recorded contents in the detailed content columns associated with the title keywords, wherein the determined types are called recorded content types;
s203, judging whether the literal operation process data corresponds to an operation risk monitoring task according to the recorded content type which is recorded in a detailed content column of the project, the operation surface, the operation task and the working procedure to which the literal operation process data belongs;
s204, generating a hazard name and a risk description according to the recorded content types as the recorded content in the detailed content column of the inspection item, the inspection result and the inspection condition description, and determining the corresponding accident types and risk types.
5. A method for monitoring risk of operation according to claim 3, wherein when the operation process data is video type operation process data, the parsing of the operation process data in S106 specifically comprises the steps of:
s301, processing video operation process data into multi-frame images, and extracting key frame images in the multi-frame images;
s302, identifying scenes in the key frame images, and judging whether the operation process data corresponds to an operation risk monitoring task according to an identification result;
s303, carrying out hazard identification on people and articles in the key frame image, generating hazard names and risk descriptions according to hazard identification results, and determining corresponding accident types and risk types.
6. The job risk monitoring method according to claim 1, wherein the step S107 specifically comprises the steps of:
s501, inquiring a general measure database according to the risk level in the operation risk data, and acquiring general risk control measures, supervision unit risk control measures and owner risk control measures required to be adopted by a construction unit in a project participant;
s502, inquiring a special measure database according to the operation risk data to acquire special risk control measures required to be adopted by a construction unit;
s503, generating a corresponding operation risk supervision table according to the operation risk monitoring task information, the operation process data, the general risk control measures, the supervision unit risk control measures, the owner risk control measures and the special risk control measures.
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| US20120011077A1 (en) * | 2010-07-12 | 2012-01-12 | Bhagat Bhavesh C | Cloud Computing Governance, Cyber Security, Risk, and Compliance Business Rules System and Method |
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| CN110009221A (en) * | 2019-03-29 | 2019-07-12 | 江苏核电有限公司 | A kind of power station operating risk method for safety monitoring |
| CN112541712A (en) * | 2020-12-29 | 2021-03-23 | 华能洋浦热电有限公司 | Electric power construction site safety risk management and control method, device and platform |
| CN112686570A (en) * | 2021-01-11 | 2021-04-20 | 国家电投集团内蒙古能源有限公司 | Intelligent security risk management and control system |
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