CN113506001A - Intelligent management and control auxiliary decision method for lean safety risk of operation site - Google Patents
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Abstract
The invention discloses an intelligent management and control assistant decision method for lean safety risk of an operation site, relates to risk management and control, and solves the technical problems that the traditional management and control mode cannot cover the operation site owned by a person in a district and is difficult to realize omnibearing 24-hour supervision. The method comprises the following steps of constructing an operation safety risk identification model of the electric power operation site through the framework of an intelligent control big data system; constructing an operation risk evaluation index system frame according to operation risk factors of the electric power operation site; and calculating and analyzing the risk value of the operation risk factor, and constructing a safety risk evaluation model of the electric power operation site according to the risk value and the influence degree of the operation risk factor. The invention realizes the full-coverage real-time intelligent supervision and management of the operation site, provides reliable and timely auxiliary decision information for operation supervision personnel, effectively reduces the safety risk of the operation site, saves the manual maintenance cost and realizes the economic benefit.
Description
Technical Field
The invention relates to risk management and control, in particular to an intelligent management and control assistant decision method for safety risk lean of an operation site.
Background
With the increasing expansion of the scale of the power grid, the power operation activities become frequent, and the operation risk factors on the power operation site are more, so that the lean and modern management requirements of power supply enterprises in new situation are difficult to meet by the traditional manual site supervision and inspection and the management and control mode reviewed afterwards. Therefore, there is an urgent need for power enterprises to establish a visual and intelligent management and control platform for a power operation site to assist power operation supervisors in more efficient and intelligent cooperative supervision and management of the power operation site. The traditional management and control mode of the power operation mainly has the following defects: (1) the system can not cover all operation sites in the jurisdiction completely, and has the problems of blind areas, weakness in remote monitoring and the like. (2) For the power management department, the traditional management and control mode is difficult to realize the comprehensive 24-hour supervision of the power operation.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is not enough, provides an intelligent management and control auxiliary decision method for lean safety risk of an operation site, and solves the problems that the traditional management and control mode cannot cover the operation site owned by a district and is difficult to realize omnibearing 24-hour supervision.
The invention relates to an intelligent management and control assistant decision method for lean safety risk of an operation site, which comprises the following steps:
the method comprises the steps that firstly, an operation safety risk identification model of an electric power operation site is constructed through the framework of an intelligent control big data system and is used for identifying site risk elements of the current electric power operation site and learning information characteristics in the site risk elements;
constructing an operation risk assessment index system framework according to operation risk factors of the electric power operation site, setting risk parameters of the operation risk factors in the operation risk assessment index system framework, and formulating an index grading and scoring standard for evaluating the risk grade of the site risk factors;
analyzing risk parameters through an electric power operation site safety risk assessment model based on the combination of improved subjective and objective weights, determining the risk value of the operation risk factor, constructing the electric power operation site safety risk assessment model according to the risk value and the influence degree of the operation risk factor, evaluating the risk value of the site risk factor, and dynamically assessing and adjusting the site risk factor according to the risk value and the risk level.
The operation safety risk identification model specifically comprises,
the field data source layer is used for acquiring field data of a power operation field and sensing risk data existing in the field data;
the edge calculation layer is used for storing and digitally processing the risk data and uploading the processed risk data to a big data processing center;
the network interconnection layer is used for realizing the communication between the edge calculation layer and the big data processing center;
and the big data processing layer is used for extracting information characteristics of the risk data through an artificial intelligence algorithm and fusing the information characteristic values with sample data characteristics in a historical database.
The safety risk assessment model of the electric power operation site comprises the following steps,
wherein D is a risk value; w is a weight coefficient matrix; n is the total number of risk dimensions; l is the influence occurrence probability of the kth dimension factor; e is the frequency of exposure of the operator in the hazardous environment; c is the value of the magnitude of the risk consequence; m is the overall operation risk management level of the unit to which different operations belong; q is the quality level of the whole operator of the unit to which different operations belong.
Setting an operation risk evaluation sample according to operation risk factors of an electric power operation site, constructing a scoring matrix according to the operation risk evaluation sample and the total number of risk dimensions, and assigning values to the scoring matrix; and analyzing the scoring matrix by using a principal component analysis method to determine a weight coefficient matrix.
The job risk assessment sample is calculated by the following formula,
m＝r×s×h：
wherein m is an operation risk assessment sample; r is the number of risk events of the operation risk factor; s is the number of operation types of the operation risk factors; h is the number of subordinate units of the operation risk factor;
the scoring matrix is then constructed by the following formula,
X＝m×N：
wherein, X is a scoring matrix.
The specific calculation steps of the weight coefficient values in the weight coefficient matrix are,
first, a correlation coefficient matrix is calculated by the following formula,
of these, cov (x)i，xj) Represents a vector (x)i，xj) The covariance of (a); var (x)i) Represents the variable xiThe sample variance of (2); i and j are the number of risk dimensions;
secondly, solving all characteristic values through a characteristic function between the correlation coefficient matrix and the unit matrix, and arranging the characteristic values; simultaneously, acquiring a feature vector of the image according to the feature value;
wherein the characteristic function is | λ E-R | ═ 0;
in the formula, lambda is a characteristic value; e is an identity matrix;
thirdly, calculating the accumulated contribution rate of the principal component, wherein the calculation formula is as follows,
fourthly, calculating coefficients of indexes in the scoring matrix in different component linear combinations through the following formula,
wherein e isijFor the ith principal component to the variable xjThe number of loads of (d);
fifthly, calculating the contribution rate of the principal component according to the calculation formula,
sixthly, determining an index coefficient through the following formula,
seventhly, obtaining a weight coefficient value through normalizing the index coefficient, wherein the calculation formula is as follows,
advantageous effects
The invention has the advantages that: the operation safety risk identification model of the electric power operation site is constructed through the framework of the intelligent control big data system, and meanwhile, the site risk elements of the electric power operation site are analyzed through the operation risk assessment index system framework and the safety risk assessment model to determine corresponding risk control measures, so that control decisions of the electric power operation site are realized, and the safety risk of the operation site is effectively reduced. Moreover, the manual maintenance cost is saved, and the economic benefit is realized.
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FIG. 1 is a schematic diagram of a working process of a management and control platform according to the present invention;
FIG. 2 is a schematic diagram of an operational safety risk identification model architecture according to the present invention;
FIG. 3 is a schematic diagram of a job risk assessment indicator system of the present invention;
FIG. 4 is a table of the severity levels of the risk outcomes of the present invention.
Detailed Description
The invention is further described below with reference to examples, but not to be construed as being limited thereto, and any number of modifications which can be made by anyone within the scope of the claims are also within the scope of the claims.
Referring to fig. 1, the intelligent management and control decision-making method for lean safety risk of an operation site of the invention comprises the following steps:
step one, constructing an operation safety risk identification model of an electric power operation field through the framework of an intelligent control big data system. The operation safety risk identification model is mainly used for identifying site risk elements of the current electric power operation site and learning information characteristics in the site risk elements.
As shown in fig. 2, the architecture of the intelligent management and control big data system includes a field data source layer, an edge computing layer, a network interconnection layer, and a big data processing layer. Therefore, the constructed work safety risk identification model specifically comprises,
and the field data source layer is used for acquiring field data of the electric power operation field and sensing risk data existing in the field data. The data source of the electric power operation site mainly comprises an intelligent camera, a high-precision sensor for gas monitoring, temperature and humidity monitoring and the like, an intelligent terminal, and structured data such as audio and video real-time transmission of the operation site, a work ticket and the like. The data can be used for sensing unsafe behaviors of the operation site, background factors of the unsafe behaviors, accident handling and other behavior data. Moreover, according to the requirements of the network security of the power enterprise, the sensing sensor mostly adopts a non-invasive element.
And the edge calculation layer is used for storing and digitally processing the risk data and uploading the processed risk data to the big data processing center. The edge computing layer is formed by network edge nodes, is widely distributed between the terminal equipment and the computing center, and can be the intelligent terminal equipment. The layer completes transmission, storage, calculation and control of terminal information, and can calculate operation site data which can be processed in a digital mode, such as moving object detection, moving object tracking, gas detection, temperature and humidity monitoring and the like, through the edge layer, and then the result is transmitted to the big data processing layer.
And the network interconnection layer is used for realizing the communication between the edge computing layer and the big data processing center. The layer consists of two parts, a local area communication network and a remote communication network. The local area communication network is responsible for communication between the sensing layer node and the edge computing node and comprises communication modes such as wireless Bluetooth, microwave transmission, field bus and the like. The remote communication network is responsible for communication between the edge computing node and the big data processing center, and comprises technologies such as Ethernet, wireless network, satellite communication and power line carrier communication.
And the big data processing layer is used for extracting information characteristics of the risk data through an artificial intelligence algorithm and fusing the information characteristic values with sample data characteristics in the historical database. Specifically, the big data processing layer is a comprehensive platform for realizing multi-source heterogeneous data fusion, data storage exchange and data analysis. The method is characterized in that redis is used as a cache layer of data, an Hbase database is used for carrying out persistent storage on the data, and information characteristic values of management data such as behavior data, operation environment information, technical schemes, work tickets and the like of an operation site are extracted through artificial intelligent algorithms such as machine learning, deep learning and the like, so that identification on site risk elements is realized. Meanwhile, the method also integrates the sample data characteristics in the historical database, and achieves the purpose of further perfecting the operation safety risk identification model by learning the information characteristics in the site risk elements.
And step two, constructing an operation risk assessment index system framework according to operation risk factors of the electric power operation site. In this embodiment, an operation risk situation assessment index system framework is mainly constructed according to personnel, equipment, an operation method, an environment and 15 operation risk factors in the 5 risk dimensions.
Specifically, as shown in fig. 3, the human factors include 3 sub-factors of average age, skill proficiency of work, psychological and physiological states. The equipment factors include 3 sub-factors of equipment type, equipment quality defect and construction machinery inspection defect. The operation method comprises 3 sub-factors of equipment state, personal protective equipment configuration and tool configuration. The environmental factors include 3 sub-factors of working weather, working area and working time. The management factors comprise 3 sub-factors of a safety training system, a mechanical material management system and a sudden emergency system.
After the construction of the operation risk assessment index system framework is finished, setting risk parameters of operation risk factors in the operation risk assessment index system framework, and making index grading and scoring standards for evaluating the risk grade of site risk factors.
When the site risk elements are evaluated, the risk grade of the site risk elements can be evaluated according to the established index grading and scoring standard and the risk consequence severity grade comparison table. The risk outcome severity level comparison table is shown in fig. 4.
And thirdly, analyzing the risk parameters through an electric power operation site safety risk assessment model based on the combination of the improved subjective and objective weights, determining the risk value of the operation risk factor, and constructing the electric power operation site safety risk assessment model according to the risk value and the influence degree of the operation risk factor. The safety risk evaluation model is used for evaluating the risk value of the site risk element and dynamically evaluating and adjusting the site risk element according to the risk value and the risk grade so as to match with the corresponding risk control measure, thereby realizing the decision of the operation supervisor on the risk control measure.
Specifically, the safety risk assessment model of the electric power operation site is as follows,
wherein D is a risk value; w is a weight coefficient matrix; n is the total number of risk dimensions, and the value in this embodiment is 5; l is the influence occurrence probability of the kth dimension factor; e is the frequency of exposure of the operator in the hazardous environment; c is the value of the magnitude of the risk consequence; m is the overall operation risk management level of the unit to which different operations belong; q is the quality level of the whole operator of the unit to which different operations belong. Wherein the set risk parameters include L, E, C, M, Q.
The LEC method is one of the most commonly used methods in the field of job risk assessment. The risk occurrence probability is calculated and the risk consequence is graded and assigned according to the occurrence probability and the influence degree of each factor and by combining historical data and enterprise standards, and the specific calculation formula is as follows:
D＝L×E×C。
wherein L represents the possibility of occurrence of an accident; e represents how frequently the worker is exposed to the hazardous environment; c represents the possible consequences of an accident.
However, the risk assessment considered by the LEC method is based on a single enterprise, and fails to consider that different subordinate units in the same enterprise have different risk management levels, and the overall safety awareness of the operators and the operation specification compliance capability often vary greatly. Particularly for large-scale national enterprises such as power grid enterprises, the whole system has a rough processing mode of the same operation risk assessment method. Therefore, the job risk management levels of different sub-companies and the overall quality of the operators must be incorporated into the job risk assessment method to meet the requirement of differential evaluation.
Based on the above two considerations, the present invention provides an improved LEC method on the conventional LEC method:
from the view of the value method, the values of the LEC method mainly depend on the preset values evaluated and preset for the corresponding power operation site. On the other hand, the conventional LEC method divides the risk assessment into L, E, C in 3 aspects, and considers these 3 three faces as equally important, which is a way to simplify the problem. In the actual management and control work of the risks of the electric power operation site, the contributions of the risk influencing sub-factors to the final risks are considered to be different, and particularly after the risk management level and the quality level of the operators are added, the difference is more obvious. Based on the two reasons, in order to objectively evaluate the risk level of the electric power operation site and reflect the weight difference between different risk sub-factors, the invention introduces a risk weight coefficient on the basis of the traditional LEC method, and finally obtains the safety risk evaluation model of the electric power operation site.
In the embodiment, an operation risk evaluation sample is set according to operation risk factors of an electric power operation site, a scoring matrix is constructed according to the operation risk evaluation sample and the total number of risk dimensions, and the scoring matrix is assigned; and analyzing the scoring matrix by using a principal component analysis method to determine a weight coefficient matrix. The advantage of using principal component analysis method to carry out weight assignment is that it is based on the variance information of data itself, completely reflects the objective information of data, and well supplements the set risk parameters.
Specifically, the job risk assessment sample is calculated by the following formula,
m＝r×s×h；
wherein m is an operation risk assessment sample; r is the number of risk events of the operation risk factor; s is the number of operation types of the operation risk factors; h is the number of subordinate units of the operation risk factor.
Then a scoring matrix is constructed through the following formula,
X＝m×N；
wherein, X is a scoring matrix.
Order to
The electric power operation site safety risk assessment model can be written as:
D＝W×L×E×C×M×Q。
by assigning the score matrix as a product between L, E, C, M, Q, the power job site safety risk assessment model can be simplified to,
d ═ W × X. I.e. the final risk value may be expressed as,
the specific calculation steps of the weight coefficient values in the weight coefficient matrix are,
first, a correlation coefficient matrix is calculated by the following formula,
of these, cov (x)i，xj) Represents the vector (x) in the scoring matrixi，xj) The covariance of (a); var (x)i) Representing variable x in a scoring matrixiThe sample variance of (2).
And secondly, solving all characteristic values through a characteristic function between the correlation coefficient matrix and the unit matrix, and arranging the characteristic values. When arranging, in order of magnitude of the characteristic values, e.g. λ1≥λ2…≥λpIs more than or equal to 0. Simultaneously, the characteristic vector of the image is obtained according to the characteristic value, and the characteristic vector is recorded as ei(i＝1，2，…p)，p＝N。
Wherein the characteristic function is | λ E-R | ═ 0.
In the formula, lambda is a characteristic value; e is an identity matrix.
Thirdly, calculating the accumulated contribution rate of the principal component, wherein the calculation formula is as follows,
fourthly, calculating coefficients of indexes in the scoring matrix in different component linear combinations through the following formula,
wherein e isijFor the ith principal component to the variable xjThe number of loads of (c).
Fifthly, calculating the contribution rate of the principal component according to the calculation formula,
sixthly, determining an index coefficient through the following formula,
seventhly, obtaining a weight coefficient value through normalizing the index coefficient, wherein the calculation formula is as follows,
in order to realize intelligent assessment of the safety risk of the electric power operation site and provide reliable auxiliary decision information, based on the intelligent management and control auxiliary decision method for the safety risk of the operation site, the embodiment provides an auxiliary decision system for the safety risk of the electric power operation site, which mainly comprises an electric power operation site safety risk assessment module, a risk management and control module in maintenance operation and a statistical analysis module after maintenance operation.
When the system works, firstly, the risk evaluation is carried out on the operation of the electric power operation site through the safety risk evaluation module of the electric power operation site. The method comprises the steps of calibrating site risk elements such as an operation range, a travelling route and an environmental risk to obtain a risk level and a risk value of each power operation site risk element and specific existing hazard factors, dynamically evaluating and adjusting an operation risk state by using a safety risk evaluation model of a power operation site, and sending early warning signals to site operators and operation supervisors.
And the risk management and control module in the maintenance operation automatically matches the specific hazard factors, the specific risk distribution, the operation risk early warning and the management and control measures of the site risk elements according to the risk level and the risk value of the risk elements existing in the operation to determine the corresponding risk management and control measures, so that the decision of the operation supervisory personnel on the management and control measures is assisted. And selecting a specific operation by an operation supervisor, and automatically popping up the functions of risk management and control auxiliary measures and the like. The decision-making workload of the electric power operation field supervisor is greatly simplified, and the decision-making accuracy and timeliness are improved.
After maintenance operation, the statistical analysis module summarizes the conditions of whether risk factors exist in the operation process, whether chapter violations exist, whether field cleaning is finished, whether safety measures are recovered, whether legacy tools exist on the operation field and the like, and the identification and self-learning capacity of the operation safety risk identification model in the system is improved.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various changes and modifications without departing from the structure of the invention, which will not affect the effect of the invention and the practicability of the patent.
Claims (6)
1. An intelligent management and control assistant decision method for lean safety risk of an operation site is characterized by comprising the following steps:
the method comprises the steps that firstly, an operation safety risk identification model of an electric power operation site is constructed through the framework of an intelligent control big data system and is used for identifying site risk elements of the current electric power operation site and learning information characteristics in the site risk elements;
constructing an operation risk assessment index system framework according to operation risk factors of the electric power operation site, setting risk parameters of the operation risk factors in the operation risk assessment index system framework, and formulating an index grading and scoring standard for evaluating the risk grade of the site risk factors;
analyzing risk parameters through an electric power operation site safety risk assessment model based on the combination of improved subjective and objective weights, determining the risk value of the operation risk factor, constructing the electric power operation site safety risk assessment model according to the risk value and the influence degree of the operation risk factor, evaluating the risk value of the site risk factor, and dynamically assessing and adjusting the site risk factor according to the risk value and the risk level.
2. The intelligent management and control decision-making method for safety risk refinement of job site as claimed in claim 1, wherein the job safety risk identification model specifically comprises,
the field data source layer is used for acquiring field data of a power operation field and sensing risk data existing in the field data;
the edge calculation layer is used for storing and digitally processing the risk data and uploading the processed risk data to a big data processing center;
the network interconnection layer is used for realizing the communication between the edge calculation layer and the big data processing center;
and the big data processing layer is used for extracting information characteristics of the risk data through an artificial intelligence algorithm and fusing the information characteristic values with sample data characteristics in a historical database.
3. The intelligent management and control decision-making method for site safety risk refinement of claim 1, wherein the electric power site safety risk assessment model is,
wherein D is a risk value; w is a weight coefficient matrix; n is the total number of risk dimensions; l is the influence occurrence probability of the kth dimension factor; e is the frequency of exposure of the operator in the hazardous environment; c is the value of the magnitude of the risk consequence; m is the overall operation risk management level of the unit to which different operations belong; q is the quality level of the whole operator of the unit to which different operations belong.
4. The intelligent management and control decision-making method for security risk refinement of operation sites as claimed in claim 3, wherein operation risk assessment samples are set according to operation risk factors of the electric power operation site, a scoring matrix is constructed according to the operation risk assessment samples and the total number of risk dimensions, and the scoring matrix is assigned; and analyzing the scoring matrix by using a principal component analysis method to determine a weight coefficient matrix.
5. The intelligent management and control decision-making method for safety risk refinement of job site as claimed in claim 4, wherein the job risk assessment sample is calculated by the following formula,
m＝r×s×h；
wherein m is an operation risk assessment sample; r is the number of risk events of the operation risk factor; s is the number of operation types of the operation risk factors; h is the number of subordinate units of the operation risk factor;
the scoring matrix is then constructed by the following formula,
X＝m×N；
wherein, X is a scoring matrix.
6. The method as claimed in claim 5, wherein the weighting coefficients in the weighting coefficient matrix are calculated by steps of,
first, a correlation coefficient matrix is calculated by the following formula,
of these, cov (x)i，xj) Represents a vector (x)i,xj) The covariance of (a); var (x)i) Represents the variable xiThe sample variance of (2); i and j are the number of risk dimensions;
secondly, solving all characteristic values through a characteristic function between the correlation coefficient matrix and the unit matrix, and arranging the characteristic values; simultaneously, acquiring a feature vector of the image according to the feature value;
wherein the characteristic function is | λ E-R | ═ 0;
in the formula, lambda is a characteristic value; e is an identity matrix;
thirdly, calculating the accumulated contribution rate of the principal component, wherein the calculation formula is as follows,
wherein p ═ N;
fourthly, calculating coefficients of indexes in the scoring matrix in different component linear combinations through the following formula,
wherein e isijFor the ith principal component to the variable xjThe number of loads of (d);
fifthly, calculating the contribution rate of the principal component according to the calculation formula,
sixthly, determining an index coefficient through the following formula,
seventhly, obtaining a weight coefficient value through normalizing the index coefficient, wherein the calculation formula is as follows,
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| CN115966073A (en) * | 2022-12-30 | 2023-04-14 | 江苏中烟工业有限责任公司 | Risk early warning method and device, electronic equipment and storage medium |
| CN116667203A (en) * | 2023-05-30 | 2023-08-29 | 国网湖北省电力有限公司超高压公司 | Electric power basic operation safety protection method and system based on gas detector |
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