CN118095845A - Potential safety hazard management method and system, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure relates to the field of potential safety hazard treatment of power stations, and provides a potential safety hazard treatment method and system, electronic equipment and storage medium, wherein the method comprises the following steps: acquiring monitoring data in the power station in real time by using a sensor; when the numerical value of the monitoring data changes, determining the hidden danger type and the danger level corresponding to the numerical value change of the monitoring data by using a hidden danger analysis algorithm based on a pre-constructed hidden danger knowledge base, and generating a corresponding hidden danger treatment strategy; and generating a regulating instruction according to the hidden danger treatment strategy, and removing the potential safety hazard indicated by the numerical change of the monitoring data by using the regulating instruction. The method and the device cover the identification, analysis and treatment processes of the potential safety hazard by the power station by utilizing the emerging intelligent information technology, effectively solve the problems existing in the prior art that monitoring personnel manually find and treat the potential safety hazard, greatly eliminate the influence of the experience level of the personnel on the operation of the power station and the treatment of the potential safety hazard, and improve the identification accuracy and the treatment efficiency of the potential safety hazard of the power station.
Description
Technical Field
The disclosure relates to the technical field of potential safety hazard management of power stations, in particular to a potential safety hazard management method and system, electronic equipment and a storage medium.
Background
Hydropower is a very specific type of electricity. At present, in different seasons, the key points of hydropower stations in operation monitoring are different, and different power station regulation strategies are adopted. In the prior art, the potential safety hazard of the hydropower station is mainly discovered and reported by monitoring personnel, and then the discovered potential safety hazard is treated by manpower. However, the mode of manually finding and treating the potential safety hazard is not only low in efficiency, but also has the defects of large influence of subjective factors of monitored personnel, unattended period, blind areas of vision and the like.
Disclosure of Invention
The disclosure aims to at least solve one of the problems existing in the prior art, and provides a potential safety hazard management method and system, electronic equipment and storage medium.
In one aspect of the present disclosure, a method for treating a potential safety hazard is provided, the method comprising:
Acquiring monitoring data in the power station in real time by using a sensor;
When the numerical value of the monitoring data changes, determining the hidden danger type and the danger level corresponding to the numerical value change of the monitoring data by using a hidden danger analysis algorithm based on a pre-constructed hidden danger knowledge base, and generating a corresponding hidden danger treatment strategy;
and generating a regulation and control instruction according to the hidden danger treatment strategy, and removing the potential safety hazard indicated by the numerical change of the monitoring data by using the regulation and control instruction.
Optionally, the hidden danger knowledge base comprises key parameters, influence weights of the key parameters on technical indexes, analysis algorithm function expressions, analysis algorithm function value ranges and corresponding hidden danger types and danger levels thereof;
The hidden danger type and the danger level corresponding to the numerical change of the monitoring data are determined by using a hidden danger analysis algorithm based on a pre-constructed hidden danger knowledge base, and the hidden danger type and the danger level comprise:
Determining a target technical index of the monitoring data;
combing key parameters affecting the target technical index to obtain target key parameters;
Acquiring target influence weights of the target key parameters on the target technical indexes from the hidden danger knowledge base;
Determining a corresponding analysis algorithm function analytic expression based on the target influence weight and the analysis algorithm function expression, and determining a function value corresponding to the numerical variation of the target key parameter based on the analysis algorithm function analytic expression;
Inquiring the value range of the analysis algorithm function in which the function value is located from the hidden danger knowledge base, and taking the hidden danger type and the danger level corresponding to the inquired value range of the analysis algorithm function as the hidden danger type and the danger level corresponding to the numerical change of the monitoring data.
Optionally, after the determining the function value corresponding to the numerical change of the target key parameter, the potential safety hazard management method further includes:
and comparing the function value with the actual situation corresponding to the numerical variation of the target key parameter, and adjusting the analysis algorithm function analytic expression according to the error of the function value and the actual situation.
Optionally, the hidden danger knowledge base comprises key parameters, an abnormality troubleshooting scheme and an abnormality reason thereof;
The generating the corresponding hidden danger treatment strategy comprises the following steps:
Determining abnormal key parameters and abnormal probability thereof with numerical value change according to the numerical value change of the monitoring data;
Acquiring an abnormality investigation scheme corresponding to the abnormality probability of the abnormality key parameter from the hidden danger knowledge base, and determining a corresponding abnormality reason;
and determining the corresponding hidden danger treatment strategy based on the determined abnormal reason.
Optionally, after determining the corresponding hidden danger management policy based on the determined abnormality cause, the potential safety hazard management method further includes:
The abnormal key parameters are tidied, and initial abnormal probability is set for the abnormal key parameters;
and recording the numerical value change condition of the abnormal key parameter, comparing the abnormal probability corresponding to the abnormal key parameter with the initial abnormal probability, and adjusting the initial abnormal probability according to a comparison result.
Optionally, the hidden danger knowledge base comprises hidden danger management modes corresponding to hidden danger types and danger levels thereof;
The generating the corresponding hidden danger treatment strategy comprises the following steps:
inquiring hidden danger types corresponding to the change of the monitoring data and hidden danger management modes corresponding to the danger levels of the hidden danger types from the hidden danger knowledge base, and taking the inquired hidden danger management modes as the hidden danger management strategies.
Optionally, after the generating the corresponding hidden danger treatment policy, the potential safety hazard treatment method further includes:
And feeding back the numerical change of the monitoring data and the corresponding hidden danger treatment strategy to the hidden danger knowledge base so as to enable the hidden danger knowledge base to perform self-learning.
In another aspect of the disclosure, a potential safety hazard management system is provided, the system comprising a potential self-perception subsystem, a potential self-analysis subsystem and a potential intelligent management subsystem;
The hidden danger self-perception subsystem is used for acquiring monitoring data in the power station in real time by utilizing a sensor, and when the monitoring data has numerical value change, the numerical value change of the monitoring data and a hidden danger knowledge base constructed in advance are sent to the hidden danger self-analysis subsystem;
The hidden danger self-analysis subsystem is used for determining hidden danger types and danger levels corresponding to the numerical variation of the monitoring data by utilizing a hidden danger analysis algorithm based on the hidden danger knowledge base, and generating a corresponding hidden danger treatment strategy;
The hidden danger intelligent treatment subsystem is used for generating a regulation and control instruction according to the hidden danger treatment strategy, and relieving the potential safety hazard indicated by the numerical change of the monitoring data by utilizing the regulation and control instruction.
Optionally, the hidden danger knowledge base comprises key parameters, influence weights of the key parameters on technical indexes, analysis algorithm function expressions, analysis algorithm function value ranges and corresponding hidden danger types and danger levels thereof;
the hidden danger self-analysis subsystem is used for determining hidden danger types and danger levels corresponding to the numerical variation of the monitoring data by using a hidden danger analysis algorithm based on the hidden danger knowledge base, and comprises the following steps:
The hidden danger self-analysis subsystem is used for:
Determining a target technical index of the monitoring data;
combing key parameters affecting the target technical index to obtain target key parameters;
Acquiring target influence weights of the target key parameters on the target technical indexes from the hidden danger knowledge base;
Determining a corresponding analysis algorithm function analytic expression based on the target influence weight and the analysis algorithm function expression, and determining a function value corresponding to the numerical variation of the target key parameter based on the analysis algorithm function analytic expression;
Inquiring the value range of the analysis algorithm function in which the function value is located from the hidden danger knowledge base, and taking the hidden danger type and the danger level corresponding to the inquired value range of the analysis algorithm function as the hidden danger type and the danger level corresponding to the numerical change of the monitoring data.
Optionally, the hidden danger self-analysis subsystem is further configured to:
and comparing the function value with the actual situation corresponding to the numerical variation of the target key parameter, and adjusting the analysis algorithm function analytic expression according to the error of the function value and the actual situation.
Optionally, the hidden danger knowledge base comprises key parameters, an abnormality troubleshooting scheme and an abnormality reason thereof;
the hidden danger self-analysis subsystem is used for generating a corresponding hidden danger treatment strategy and comprises the following steps:
The hidden danger self-analysis subsystem is used for:
Determining abnormal key parameters and abnormal probability thereof with numerical value change according to the numerical value change of the monitoring data;
Acquiring an abnormality investigation scheme corresponding to the abnormality probability of the abnormality key parameter from the hidden danger knowledge base, and determining a corresponding abnormality reason;
and determining the corresponding hidden danger treatment strategy based on the determined abnormal reason.
Optionally, the hidden danger self-analysis subsystem is further configured to:
The abnormal key parameters are tidied, and initial abnormal probability is set for the abnormal key parameters;
and recording the numerical value change condition of the abnormal key parameter, comparing the abnormal probability corresponding to the abnormal key parameter with the initial abnormal probability, and adjusting the initial abnormal probability according to a comparison result.
Optionally, the hidden danger knowledge base comprises hidden danger management modes corresponding to hidden danger types and danger levels thereof;
the hidden danger self-analysis subsystem is used for generating a corresponding hidden danger treatment strategy and comprises the following steps:
The hidden danger self-analysis subsystem is used for:
inquiring hidden danger types corresponding to the change of the monitoring data and hidden danger management modes corresponding to the danger levels of the hidden danger types from the hidden danger knowledge base, and taking the inquired hidden danger management modes as the hidden danger management strategies.
Optionally, the hidden danger self-analysis subsystem is further configured to:
And feeding back the numerical change of the monitoring data and the corresponding hidden danger treatment strategy to the hidden danger knowledge base so as to enable the hidden danger knowledge base to perform self-learning.
In another aspect of the present disclosure, there is provided an electronic device including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of managing safety hazards described above.
In another aspect of the disclosure, a computer readable storage medium is provided, in which a computer program is stored, which when executed by a processor, implements the aforementioned method of managing a safety hazard.
Compared with the prior art, the intelligent information technology covers the identification, analysis and treatment processes of the potential safety hazard of the power station, effectively solves the problems existing in the prior art due to the fact that monitoring personnel manually find and treat the potential safety hazard, can replace the on-site monitoring personnel of the power station to the greatest extent, greatly eliminates the influence of the experience level of the personnel on the operation of the power station and the treatment of the potential safety hazard, reduces the workload of the on-site monitoring personnel, improves the identification accuracy and the treatment efficiency of the potential safety hazard of the power station, and helps the on-site monitoring personnel to rapidly and high-quality complete the identification and treatment work of the potential safety hazard.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures do not depict a proportional limitation unless expressly stated otherwise.
FIG. 1 is a flow chart of a method for managing potential safety hazards according to an embodiment of the disclosure;
FIG. 2 is a flow chart of a method for managing potential safety hazards according to another embodiment of the disclosure;
FIG. 3 is a schematic diagram of a potential safety hazard abatement system according to another embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a safety hazard abatement system according to another embodiment of the present disclosure;
Fig. 5 is a schematic structural diagram of an electronic device according to another embodiment of the present disclosure.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present disclosure, numerous technical details have been set forth in order to provide a better understanding of the present disclosure. The technical solutions claimed in the present disclosure can be implemented without these technical details and with various changes and modifications based on the following embodiments. The following divisions of the various embodiments are for convenience of description, and should not be construed as limiting the specific implementations of the disclosure, and the various embodiments may be mutually combined and referred to without contradiction.
One embodiment of the present disclosure relates to a method for treating potential safety hazards, the flow of which is shown in fig. 1, comprising:
And step S110, acquiring monitoring data in the power station in real time by using the sensor.
Specifically, various monitoring data in the power station such as data of sound, light, gas, electricity, water, wind, temperature, humidity and the like can be obtained in real time by corresponding sensors such as sound, light, gas, electricity, water, wind, temperature, humidity and the like. Of course, step S110 may also use a positioning sensor, an image sensor, such as a camera, to obtain monitoring data of relevant positions, field pictures, etc. in the power station in real time. The present embodiment is not limited to the specific type of sensor and monitoring data.
Step S120, when the numerical value of the monitoring data changes, based on a pre-constructed hidden danger knowledge base, determining hidden danger types and danger levels corresponding to the numerical value changes of the monitoring data by using a hidden danger analysis algorithm, and generating a corresponding hidden danger treatment strategy.
Specifically, in general, when a potential safety hazard occurs, one or more corresponding monitoring data also generates an abnormality, and corresponding numerical changes occur, so that a mapping relationship is formed between the potential safety hazard and the corresponding numerical changes of the monitoring data. For example, when the power transmission line leaks, the monitored data such as line voltage, voltage drop, current, sulfur hexafluoride and the like in the power station all have numerical changes, so that a mapping relationship is formed between the numerical changes of the monitored data such as line voltage, voltage drop, current, sulfur hexafluoride and the like and the potential safety hazard of the power transmission line leakage. Therefore, based on the mapping relationship between the potential safety hazard and the corresponding numerical variation of the monitoring data, the mathematical idea can be utilized, the corresponding mapping relationship is established in advance according to the type of the monitoring data and the numerical variation condition thereof obtained by the sensors in the power station and the corresponding potential hazard type and the dangerous grade thereof, the potential hazard knowledge base is constructed, the potential hazard knowledge base is used as the working basis of the potential safety hazard treatment method, when the sensors in the power station acquire the actual numerical variation of the monitoring data in real time, the step S120 can determine the potential hazard type and the dangerous grade corresponding to the actual numerical variation of the monitoring data by utilizing the potential hazard analysis algorithm based on the potential hazard knowledge base, and then the corresponding potential hazard treatment strategy is generated.
And step S130, generating a regulation and control instruction according to the hidden danger treatment strategy, and removing the potential safety hazard indicated by the numerical change of the monitoring data by using the regulation and control instruction.
Specifically, step S130 is mainly to complete execution of the potential safety hazard management strategy, and update the current state of the potential safety hazard after the potential safety hazard management is completed. Step S130 may employ two management methods: one is an automatic treatment mode, namely, a control instruction is utilized to drive a corresponding machine to automatically remove potential safety hazards indicated by numerical change of monitoring data; the other is a manual treatment mode, namely, a regulation instruction is utilized to early warn related personnel to manually remove potential safety hazards indicated by numerical change of the monitoring data.
For the automatic control mode, step S130 can generate corresponding electric signals through the control instruction, and the electric signals are used for directly controlling the on-off, restarting, power-off, ventilation, fire-extinguishing and the like of the related equipment. For example, when the fire protection facility is normally in the off state and the potential safety hazard is a fire risk, step S130 generates a corresponding electrical signal by using the control command, and the fire protection facility is started by the electrical signal, so that the fire protection facility is in the on state, thereby completing the treatment of the fire risk.
For the manual treatment mode, step S130 can generate a corresponding early warning signal through a regulation instruction, and send the early warning signal to related personnel through broadcasting, short message pushing, positioning work board vibration and other modes, so that the related personnel can timely treat potential safety hazards indicated by numerical change of monitoring data after receiving the early warning signal.
Compared with the prior art, the potential safety hazard treatment method provided by the embodiment of the disclosure utilizes the sensor to automatically sense the monitoring data in the power station, automatically discovers related potential safety hazards through the change of the monitoring data, automatically analyzes the change of the monitoring data related to the potential safety hazards, determines the potential hazard type and the danger level of the potential safety hazards, automatically generates the corresponding potential hazard treatment strategy, and intelligently processes the potential safety hazards by utilizing the potential hazard treatment strategy, thereby utilizing the emerging intelligent information technology to cover the identification, analysis and treatment flow of the potential safety hazards by the power station, effectively solving the problems existing in the prior art that the potential safety hazards are discovered and treated manually by monitoring personnel, replacing the on-site monitoring personnel of the power station to the greatest extent, greatly eliminating the influence of the experience level of personnel on the operation and the treatment of the potential safety hazards of the power station, reducing the workload of the on-site monitoring personnel, improving the identification accuracy and the treatment efficiency of the potential safety hazards of the power station, and helping the on-site monitoring personnel to rapidly and high-quality finish the identification and treatment work of the potential safety hazards.
The hidden danger knowledge base comprises key parameters, influence weights of the key parameters on technical indexes, analysis algorithm function expressions, analysis algorithm function value ranges and corresponding hidden danger types and danger levels.
In step S120, based on the pre-constructed hidden danger knowledge base, determining the hidden danger type and the danger level corresponding to the numerical variation of the monitored data by using the hidden danger analysis algorithm includes: determining a target technical index of the monitoring data; combing key parameters affecting target technical indexes to obtain target key parameters; acquiring target influence weights of target key parameters on target technical indexes from a hidden danger knowledge base; determining a corresponding analysis algorithm function analytic expression based on the target influence weight and the analysis algorithm function expression, and determining a function value corresponding to the numerical variation of the target key parameter based on the analysis algorithm function analytic expression; inquiring the value range of the analysis algorithm function in which the function value is located from the hidden danger knowledge base, and taking the hidden danger type and the danger level corresponding to the inquired value range of the analysis algorithm function as the hidden danger type and the danger level corresponding to the numerical change of the monitoring data.
Specifically, the target technical index refers to the technical index of the monitoring data obtained in step S110, for example, the power transmission line index may be represented by N. The target key parameters, i.e. key parameters affecting the target technical index, such as voltage, current, voltage drop, sulfur hexafluoride concentration, etc., can be denoted by a. The target influence weight is the influence weight of the target key parameter on the target technical index N, and represents the weight of the influence of the target key parameter on the target technical index N.
The analysis algorithm functional expression f (N) can reflect different abnormal target technical indexes N, f (N) can be determined by a plurality of monitoring values X1, X2, … … and Xn which are changed in the monitoring data, and the core of the analysis algorithm is to process the monitoring values. The mathematical formula followed by the analysis algorithm is a complex of multiple functions, namely f (N) = [ A1 (X1), A2 (X2), … …, an (Xn) ], wherein A1, A2, … …, an respectively represent the algorithm function analytic formula of the correlation between the change of the corresponding monitoring values X1, X2, … …, xn and the abnormal target technical index, and the result set is stored in the hidden danger knowledge base as the analysis algorithm. When the monitored data acquired by the sensor in real time changes in value, step S120 may directly call the related data before and after the monitored data changes in value and the hidden danger knowledge base, take the monitored data including the changed monitored values X1, X2, … … and Xn as independent variables X, analyze formula f (N) by using the analysis algorithm function corresponding to the hidden danger analysis algorithm, calculate to obtain the corresponding function value, then analyze according to the function value, and judge the hidden danger type corresponding to the monitored data with the changed value and the danger level thereof by using the hidden danger knowledge base.
Taking an index of a power transmission line as an example, when the hydropower plant normally operates, for a power transmission line with a unit length, target key parameter output voltage U, current I, line voltage drop DeltaU and sulfur hexafluoride concentration C all have corresponding standard values, which can be respectively expressed as U0, I0, deltaU 0 and C0. Based on this, the information stored in the hidden danger knowledge base can be represented as the following table 1.
Table 1 list of key parameters
When the electric transmission line is in electric leakage, the target key parameters of the electric transmission line, namely the voltage U, the current I, the voltage drop delta U and the sulfur hexafluoride concentration C, are all changed in numerical value. The 4 target key parameters are discussed in two groups: the relation between the voltage U, the current I and the voltage drop delta U and the electric transmission line leakage belongs to a necessary insufficient logic relation, namely the electric transmission line leakage cannot be judged according to the change of the voltage U or the current I or the voltage drop delta U, but the electric transmission line leakage can cause the change of the voltage U, the current I and the voltage drop delta U; the relation between the change of the sulfur hexafluoride concentration C and the electric leakage of the electric transmission line is close to the logic relation of the charging condition, and the sulfur hexafluoride concentration C is a representation of strong correlation, and the monitoring of the sulfur hexafluoride concentration C is also a means basis specially used for electric leakage monitoring in the electric power industry. Therefore, by combining the actual working experience of the power system, the analysis algorithm corresponding to the power transmission line index can be designed into a weighted algorithm combining Boolean operation and probability, and the probability of occurrence of the potential leakage hazard of the power transmission line is reflected by whether the 4 target key parameters change or not. When each key parameter reaches a critical value, a specific characterization is presented as a leakage phenomenon. The specific numerical value change of the target key parameters is converted into a representation mode whether the critical value is reached or not by using Boolean operation, wherein 0 is used for indicating that the specific numerical value change of the target key parameters does not reach the critical value, and 1 is used for indicating that the specific numerical value change of the target key parameters reaches the critical value, so that the 4 target key parameters are respectively indicated as A1-A4, and for the potential leakage hazard of the power transmission line, the self-variable definition domain corresponding to the analysis algorithm function analytic formula f (N) only has 0 and 1 numerical values.
And respectively giving corresponding weights according to the correlation between the 4 target key parameters and the potential leakage hazards of the power transmission line by combining a weighted probability algorithm, and converting the field experience of the staff into the influence of the specific gravity of each target key parameter on an analysis algorithm function. Initial weights corresponding to the 4 target key parameters are shown in table 1. Then, the analysis algorithm function expression f (N) = [ A1 (X1), A2 (X2), … …, an (Xn) ] may be specifically rewritten as the analysis algorithm function analysis expression:
f (N) = [0.2×1+0.2×2+0.2×3+0.4×4], (X1, X2, X3, x4=0 or 1)
And (3) leading the values of X1 to X4 corresponding to the numerical changes of the 4 target key parameters into the analysis algorithm function analysis mode to obtain a function value f (N) with a value range within a [0,1] interval.
Assuming that the value range of the analysis algorithm function stored in the hidden danger knowledge base and the corresponding hidden danger type and risk level thereof are shown in the following table 2, it can be known from the look-up table 2 that when the function value f (N) is in the range of [0,0.6 ], the corresponding hidden danger type and risk level thereof are free from leakage risks. When the function value f (N) is in the range of [0.6,0.8 ], the corresponding hidden danger type and risk level thereof are 40% of the occurrence probability of the electric leakage phenomenon. When the function value f (N) is in the range of [0.8,1 ], the corresponding hidden danger type and risk level thereof are 80% of the occurrence probability of the electric leakage phenomenon. When the function value f (N) is 1, the corresponding hidden danger type and risk level thereof are 100% of the occurrence probability of the electric leakage phenomenon.
Table 2 analysis algorithm function value range and corresponding hidden danger type and danger level thereof
| Value range | Type of hidden trouble and its risk level |
| [0，0.6) | No risk of electric leakage |
| [0.6，0.8) | The leakage probability is 40% |
| [0.8，1) | The leakage probability is 80% |
| 1 | The leakage probability is 100% |
According to the method, the hidden danger type and the danger level corresponding to the numerical variation of the monitoring data are determined by utilizing the hidden danger analysis algorithm through the function value corresponding to the analysis algorithm function analysis based on the hidden danger knowledge base, so that the identification accuracy of the potential safety hazards of the power station is further improved.
Illustratively, after determining the function value corresponding to the numerical variation of the target key parameter, the potential safety hazard management method further includes: and comparing the function value with the actual situation corresponding to the numerical variation of the target key parameter, and adjusting the analysis algorithm function according to the error magnitude of the function value and the actual situation.
Specifically, according to the embodiment, the composition of the analysis algorithm function analytic expression can be adjusted according to the error between the function value corresponding to the numerical change of the target key parameter and the actual situation, so that the hidden danger data can be self-learned, and the intelligence and the accuracy of the analysis algorithm function analytic expression are improved.
It should be noted that, the generation modes of the hidden danger treatment strategy mainly include an imitation generation mode and an innovation generation mode. The impersonation generation mode is that after the hidden danger knowledge base is established, when similar problems of the stored problems in the hidden danger knowledge base are encountered, the existing key parameters, hidden danger types, risk grades and solutions in the hidden danger knowledge base can be directly called to analyze the problems, and comprehensive description is given, which is equivalent to 'answer reading'. The innovative generation mode is to automatically judge the reason for abnormality of the monitoring data through the existing key parameters in the hidden danger knowledge base, the abnormality investigation scheme and the abnormality reason thereof when the hidden danger knowledge base has no corresponding hidden danger explanation, and provide a corresponding investigation scheme to automatically or guide on-site staff to manually remove related hidden dangers.
The hidden danger knowledge base includes hidden danger management modes corresponding to hidden danger types and danger levels thereof. In this case, when the hidden danger treatment strategy is generated by adopting the simulated generation mode, in step S120, a corresponding hidden danger treatment strategy is generated, including: inquiring hidden danger types corresponding to the change of the monitoring data and hidden danger treatment modes corresponding to the danger levels from a hidden danger knowledge base, and taking the inquired hidden danger treatment modes as hidden danger treatment strategies.
Specifically, according to the hidden danger type and the danger level corresponding to the numerical change of the monitoring data determined in step S120, in combination with related execution standards such as industry specifications, working regulations of the hydropower station, etc., for each risk level, a corresponding hidden danger treatment mode can be set, and corresponding manpower, equipment resources, etc. are mobilized to carry out hidden danger treatment.
Still taking the transmission line index as an example, the related information stored in the hidden danger knowledge base may be represented as the following table 3.
TABLE 3 hidden danger type and hidden danger management mode corresponding to danger level
Based on the above table 3, when the hidden danger type and the risk level thereof are no leakage risk, the corresponding hidden danger treatment strategy is normal operation. When the hidden danger type and the risk level thereof are 40% of the occurrence probability of the electric leakage phenomenon, the corresponding hidden danger treatment strategy is to inform operators on duty to conduct hidden danger investigation. When the hidden danger type and the risk level thereof are 80% of the occurrence probability of the electric leakage phenomenon, the corresponding hidden danger treatment strategy is to switch the standby line and organize emergency. When the hidden danger type and the risk level thereof are 100% of the occurrence probability of the electric leakage phenomenon, the electric leakage phenomenon is indicated, the corresponding hidden danger treatment strategy is to stop production, people are evacuated, and emergency rescue is organized.
According to the hidden danger management method and device, the hidden danger type and the hidden danger management mode corresponding to the dangerous level are determined by inquiring the hidden danger knowledge base, so that corresponding hidden danger management strategies are generated, and the generation efficiency of the hidden danger management strategies can be effectively improved.
The hidden danger knowledge base comprises key parameters, an abnormality troubleshooting scheme and an abnormality reason. In this case, when the hidden danger treatment strategy is generated by adopting the innovative generation mode, in step S120, a corresponding hidden danger treatment strategy is generated, including: determining abnormal key parameters and abnormal probability thereof with numerical variation according to the numerical variation of the monitoring data; acquiring an abnormality investigation scheme corresponding to the abnormality probability of the abnormality key parameter from a hidden danger knowledge base, and determining a corresponding abnormality reason; and determining a corresponding hidden danger treatment strategy based on the determined abnormal reason.
Specifically, the hidden danger knowledge base contains all key parameters. When the monitoring data change, the key parameters in the hidden danger knowledge base can be checked one by one, the abnormal key parameters with numerical value change are listed, then the abnormal key parameters are compared with the previous abnormal checking schemes stored in the hidden danger knowledge base, the matched abnormal checking schemes are found out to determine the corresponding abnormal reasons, and the corresponding hidden danger treatment strategies are determined based on the determined abnormal reasons. On this basis, each specific site anomaly can generate such hidden trouble shooting strategies.
Taking sulfur hexafluoride concentration anomaly in the monitoring data as an example, the anomaly investigation scheme and anomaly cause of sulfur hexafluoride concentration anomaly stored in the hidden danger knowledge base can be shown as the following table 4.
TABLE 4 investigation scheme for sulfur hexafluoride concentration anomalies
Therefore, on the basis of table 4, when the probability of abnormal sulfur hexafluoride concentration is 60%, the corresponding investigation scheme is: checking the voltage drop (automatic completion) of the line A to be 10%, and preferentially checking suspected abnormal causes; the corresponding abnormality is: and the line A is leaked. When the probability of abnormal sulfur hexafluoride concentration is 30%, the corresponding investigation scheme is as follows: checking the air pressure and the flow (automatic completion) of the air pipe in the area B, wherein the deviation from the normal value is 2%, and the air pressure and the flow are in a critical range; the corresponding abnormality is: zone B gas leaks. When the probability of abnormal sulfur hexafluoride concentration is 5%, the corresponding investigation scheme is as follows: the meter was used for 1 year. No problem exists until the last routine inspection date is x days; the corresponding abnormality is: the monitoring instrument is damaged.
Illustratively, after determining the corresponding hidden danger management strategy based on the determined abnormality cause, the potential safety hazard management method further includes: sorting the abnormal key parameters, and setting initial abnormal probability for the abnormal key parameters; recording the numerical variation condition of the abnormal key parameters, comparing the abnormal probability corresponding to the abnormal key parameters with the initial abnormal probability, and adjusting the initial abnormal probability according to the comparison result.
Specifically, when the initial anomaly probability is adjusted according to the comparison result, if the anomaly probability corresponding to the anomaly key parameter does not coincide with the initial anomaly probability, the initial anomaly probability can be properly reduced; if the abnormal probability corresponding to the abnormal key parameter is identical with the initial abnormal probability, the initial abnormal probability can be kept unchanged or increased appropriately, so that the scientificity and the accuracy of hidden danger identification analysis according to the numerical variation condition of the key parameter and the timeliness and the effectiveness of corresponding hidden danger treatment strategy generation are continuously improved.
Illustratively, after the corresponding hidden danger management policy is generated in step S120, the potential safety hazard management method further includes: and feeding back the numerical change of the monitoring data and the corresponding hidden danger treatment strategy to a hidden danger knowledge base, so that the hidden danger knowledge base carries out self-learning.
Specifically, the self-sensing, self-analysis and intelligent treatment process of each hidden danger is a sample data, which can be used for learning a hidden danger knowledge base and a corresponding analysis algorithm, and the scientificity and accuracy of hidden danger identification and analysis and the timeliness and effectiveness of hidden danger treatment strategy generation are continuously improved.
After the hidden danger knowledge base carries out self-learning based on typical images including hidden danger or violations in monitoring data, if an actual monitoring image obtained by monitoring the site situation of a power station in real time by an image sensor such as a camera is similar to a certain typical image, the same violations or hidden danger may appear.
The potential safety hazard management method can also determine the specific position of the person near the potential safety hazard indicated by the numerical change of the monitoring data by using a position-based electronic fence recognition algorithm and the like. The electronic fence recognition algorithm is to connect with a positioning base point on site, encircle a region as a fence, write a positioning label by personnel, and when the label is displayed in the fence, represent personnel to move in the region, otherwise represent out-of-range.
It should be further noted that, the hidden danger knowledge base in the above embodiment may be implemented by an expert system, a knowledge graph, or the like, which is not limited by the embodiment of the present disclosure.
In order to enable a person skilled in the art to better understand the above embodiments, a specific example will be described below.
Referring to fig. 2, a potential safety hazard treatment method based on self-sensing and self-learning includes the following steps:
And acquiring monitoring data in the power station in real time by using the sensor.
When the numerical value of the monitoring data changes, the hidden danger type and the danger level corresponding to the numerical value change of the monitoring data are determined by using a hidden danger analysis algorithm based on a hidden danger knowledge base constructed in advance, and a corresponding hidden danger treatment strategy is generated.
Generating a regulating command according to a hidden danger treatment strategy, removing potential safety hazards indicated by the numerical change of the monitoring data by using the regulating command, realizing intelligent treatment of the potential safety hazards, and generating corresponding hidden danger treatment records according to the numerical change of the monitoring data, the corresponding hidden danger types, the corresponding danger levels and the hidden danger treatment strategy, so that a hidden danger knowledge base and an analysis algorithm perform self-learning based on the hidden danger treatment records.
Another embodiment of the present disclosure relates to a safety hazard remediation system, as shown in fig. 3, comprising a hazard self-awareness subsystem 310, a hazard self-analysis subsystem 320, and a hazard intelligent remediation subsystem 330.
The hidden danger self-perception subsystem 310 is used for acquiring monitoring data in the power station in real time by using a sensor, and when the monitoring data has numerical value change, the numerical value change of the monitoring data and a hidden danger knowledge base constructed in advance are sent to the hidden danger self-analysis subsystem.
The hidden danger self-analysis subsystem 320 is configured to determine, based on the hidden danger knowledge base, a hidden danger type and a danger level corresponding to the numerical change of the monitored data by using a hidden danger analysis algorithm, and generate a corresponding hidden danger treatment policy.
The hidden danger intelligent control subsystem 330 is configured to generate a control instruction according to a hidden danger control strategy, and remove the potential safety hazard indicated by the numerical change of the monitoring data by using the control instruction.
The hidden danger knowledge base comprises key parameters, influence weights of the key parameters on technical indexes, analysis algorithm function expressions, analysis algorithm function value ranges and corresponding hidden danger types and danger levels.
The hidden danger self-analysis subsystem 320 is configured to determine, based on a hidden danger knowledge base, a hidden danger type and a danger level corresponding to a numerical change of the monitored data by using a hidden danger analysis algorithm, where the hidden danger type and the danger level include:
The hidden danger self-analysis subsystem 320 is configured to: determining a target technical index of the monitoring data; combing key parameters affecting target technical indexes to obtain target key parameters; acquiring target influence weights of target key parameters on target technical indexes from a hidden danger knowledge base; determining a corresponding analysis algorithm function analytic expression based on the target influence weight and the analysis algorithm function expression, and determining a function value corresponding to the numerical variation of the target key parameter based on the analysis algorithm function analytic expression; inquiring the value range of the analysis algorithm function in which the function value is located from the hidden danger knowledge base, and taking the hidden danger type and the danger level corresponding to the inquired value range of the analysis algorithm function as the hidden danger type and the danger level corresponding to the numerical change of the monitoring data.
Illustratively, the hidden danger self-analysis subsystem 320 is also configured to: and comparing the function value with the actual situation corresponding to the numerical variation of the target key parameter, and adjusting the analysis algorithm function according to the error magnitude of the function value and the actual situation.
The hidden danger knowledge base includes hidden danger management modes corresponding to hidden danger types and danger levels thereof.
The hidden danger self-analysis subsystem 320 is configured to generate a corresponding hidden danger management policy, including:
The hidden danger self-analysis subsystem 320 is configured to: inquiring hidden danger types corresponding to the change of the monitoring data and hidden danger treatment modes corresponding to the danger levels from a hidden danger knowledge base, and taking the inquired hidden danger treatment modes as hidden danger treatment strategies.
The hidden danger knowledge base comprises key parameters, an abnormality troubleshooting scheme and an abnormality reason.
The hidden danger self-analysis subsystem 320 is configured to generate a corresponding hidden danger management policy, including:
the hidden danger self-analysis subsystem 320 is configured to: determining abnormal key parameters and abnormal probability thereof with numerical variation according to the numerical variation of the monitoring data; acquiring an abnormality investigation scheme corresponding to the abnormality probability of the abnormality key parameter from a hidden danger knowledge base, and determining a corresponding abnormality reason; and determining a corresponding hidden danger treatment strategy based on the determined abnormal reason.
Illustratively, the hidden danger self-analysis subsystem 320 is also configured to: sorting the abnormal key parameters, and setting initial abnormal probability for the abnormal key parameters; recording the numerical variation condition of the abnormal key parameters, comparing the abnormal probability corresponding to the abnormal key parameters with the initial abnormal probability, and adjusting the initial abnormal probability according to the comparison result.
Illustratively, the hidden danger self-analysis subsystem 320 is also configured to: and feeding back the numerical change of the monitoring data and the corresponding hidden danger treatment strategy to a hidden danger knowledge base, so that the hidden danger knowledge base carries out self-learning.
The specific implementation method of the potential safety hazard management system provided by the embodiment of the present disclosure may be referred to the potential safety hazard management method provided by the embodiment of the present disclosure, and will not be described herein again.
Compared with the prior art, the potential safety hazard management system provided by the embodiment of the disclosure covers the identification, analysis and treatment processes of the potential safety hazard by the power station by utilizing the emerging intelligent information technology, effectively solves the problems existing in the prior art that monitoring personnel manually find and manage the potential safety hazard, can replace the on-site monitoring personnel of the power station to the greatest extent, greatly eliminates the influence of the experience level of the personnel on the transportation of the power station and the treatment of the potential safety hazard, reduces the workload of the on-site monitoring personnel, improves the identification accuracy and the treatment efficiency of the potential safety hazard of the power station, and helps the on-site monitoring personnel to quickly and high-quality complete the identification and the treatment work of the potential safety hazard.
After the hidden danger treatment strategy is generated, the sensor required for acquiring the related monitoring data and the hidden danger self-analysis subsystem can be combined and packaged to form a set of small-size and light on-site acquisition analysis box. The main functions of the field collection analysis box can include, but are not limited to: 1. the integrator of various sensors comprises related sensors such as voltage, current, gas, temperature, humidity and the like which are necessary for a hydropower plant, so that multi-sense combination is formed; 2. the system can collect the monitoring data acquired by the integrator, analyze the monitoring data through an algorithm stored in the integrator, and convert the monitoring data into qualitative analysis results, which can include, but are not limited to, overall situation evaluation scoring, fault hidden danger description, risk level evaluation, suggested processing methods and the like.
FIG. 4 shows a specific example of a safety hazard remediation system. As shown in fig. 4, the potential safety hazard management system includes a potential hazard self-sensing subsystem (not shown), a potential hazard self-analysis subsystem (not shown), and a potential hazard intelligent management subsystem 3. The hidden danger self-perception subsystem comprises a specific sensor 1-1 and a hidden danger knowledge base. The hidden danger self-analysis subsystem comprises an in-plant server 2-1 and an analysis algorithm built in the in-plant server, wherein the analysis algorithm can comprise various algorithms based on artificial intelligence technology, and the analysis algorithm can adopt a function expression f (N).
Another embodiment of the present disclosure relates to an electronic device, as shown in fig. 5, comprising:
At least one processor 501; and
A memory 502 communicatively coupled to the at least one processor 501; wherein,
The memory 502 stores instructions executable by the at least one processor 501, the instructions being executable by the at least one processor 501 to enable the at least one processor 501 to perform the method of managing safety hazards as described in the above embodiments.
Where the memory and the processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors and the memory together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory may be used to store data used by the processor in performing operations.
Another embodiment of the present disclosure relates to a computer readable storage medium storing a computer program which, when executed by a processor, implements the method for managing potential safety hazards described in the above embodiments.
That is, it will be understood by those skilled in the art that all or part of the steps of the method described in the above embodiments may be implemented by a program stored in a storage medium, including several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the method described in the various embodiments of the disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for carrying out the present disclosure, and that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure.
Claims (10)
1. The potential safety hazard management method is characterized by comprising the following steps of:
Acquiring monitoring data in the power station in real time by using a sensor;
When the numerical value of the monitoring data changes, determining the hidden danger type and the danger level corresponding to the numerical value change of the monitoring data by using a hidden danger analysis algorithm based on a pre-constructed hidden danger knowledge base, and generating a corresponding hidden danger treatment strategy;
and generating a regulation and control instruction according to the hidden danger treatment strategy, and removing the potential safety hazard indicated by the numerical change of the monitoring data by using the regulation and control instruction.
2. The potential safety hazard management method according to claim 1, wherein the potential safety hazard knowledge base comprises key parameters, influence weights of the key parameters on technical indexes, analysis algorithm function expressions, analysis algorithm function value ranges and corresponding potential safety hazard types and risk levels thereof;
The hidden danger type and the danger level corresponding to the numerical change of the monitoring data are determined by using a hidden danger analysis algorithm based on a pre-constructed hidden danger knowledge base, and the hidden danger type and the danger level comprise:
Determining a target technical index of the monitoring data;
combing key parameters affecting the target technical index to obtain target key parameters;
Acquiring target influence weights of the target key parameters on the target technical indexes from the hidden danger knowledge base;
Determining a corresponding analysis algorithm function analytic expression based on the target influence weight and the analysis algorithm function expression, and determining a function value corresponding to the numerical variation of the target key parameter based on the analysis algorithm function analytic expression;
Inquiring the value range of the analysis algorithm function in which the function value is located from the hidden danger knowledge base, and taking the hidden danger type and the danger level corresponding to the inquired value range of the analysis algorithm function as the hidden danger type and the danger level corresponding to the numerical change of the monitoring data.
3. The potential safety hazard management method according to claim 2, further comprising, after the determining the function value corresponding to the numerical change of the target key parameter:
and comparing the function value with the actual situation corresponding to the numerical variation of the target key parameter, and adjusting the analysis algorithm function analytic expression according to the error of the function value and the actual situation.
4. The potential safety hazard management method according to claim 1, wherein the potential safety hazard knowledge base comprises key parameters, an abnormality troubleshooting scheme and an abnormality cause thereof;
The generating the corresponding hidden danger treatment strategy comprises the following steps:
Determining abnormal key parameters and abnormal probability thereof with numerical value change according to the numerical value change of the monitoring data;
Acquiring an abnormality investigation scheme corresponding to the abnormality probability of the abnormality key parameter from the hidden danger knowledge base, and determining a corresponding abnormality reason;
and determining the corresponding hidden danger treatment strategy based on the determined abnormal reason.
5. The method of claim 4, further comprising, after the determining the corresponding potential hazard remediation policy based on the determined abnormality cause:
The abnormal key parameters are tidied, and initial abnormal probability is set for the abnormal key parameters;
and recording the numerical value change condition of the abnormal key parameter, comparing the abnormal probability corresponding to the abnormal key parameter with the initial abnormal probability, and adjusting the initial abnormal probability according to a comparison result.
6. The potential safety hazard management method according to claim 1, wherein the potential safety hazard knowledge base comprises potential safety hazard management modes corresponding to potential safety hazard types and hazard levels thereof;
The generating the corresponding hidden danger treatment strategy comprises the following steps:
inquiring hidden danger types corresponding to the change of the monitoring data and hidden danger management modes corresponding to the danger levels of the hidden danger types from the hidden danger knowledge base, and taking the inquired hidden danger management modes as the hidden danger management strategies.
7. The potential safety hazard remediation method of any one of claims 1 to 6, wherein after the generating the corresponding potential safety hazard remediation strategy, the potential safety hazard remediation method further comprises:
And feeding back the numerical change of the monitoring data and the corresponding hidden danger treatment strategy to the hidden danger knowledge base so as to enable the hidden danger knowledge base to perform self-learning.
8. The potential safety hazard management system is characterized by comprising a potential safety hazard self-sensing subsystem, a potential safety hazard self-analysis subsystem and a potential safety hazard intelligent management subsystem;
The hidden danger self-perception subsystem is used for acquiring monitoring data in the power station in real time by utilizing a sensor, and when the monitoring data has numerical value change, the numerical value change of the monitoring data and a hidden danger knowledge base constructed in advance are sent to the hidden danger self-analysis subsystem;
The hidden danger self-analysis subsystem is used for determining hidden danger types and danger levels corresponding to the numerical variation of the monitoring data by utilizing a hidden danger analysis algorithm based on the hidden danger knowledge base, and generating a corresponding hidden danger treatment strategy;
The hidden danger intelligent treatment subsystem is used for generating a regulation and control instruction according to the hidden danger treatment strategy, and relieving the potential safety hazard indicated by the numerical change of the monitoring data by utilizing the regulation and control instruction.
9. An electronic device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the safety hazard management method of any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the potential safety hazard management method according to any one of claims 1 to 7.
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