LU505201B1 - Intelligent safety early warning method and system of electric power plant based monitoring data analysis - Google Patents
Intelligent safety early warning method and system of electric power plant based monitoring data analysis Download PDFInfo
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Abstract
The present invention discloses an intelligent safety early warning method and system of an electric power plant based on monitoring data analysis, including: determining a standard operation set of the electric power plant; classifying importance of electric power plant equipment; obtaining real-time operation monitoring data of the electric power plant; calculating an operation state index of each equipment; obtaining an image similarity of the equipment; performing a comprehensive calculation of an operation risk of the electric power plant; and sending different early warning signals to a working terminal based on the operation risk of the electric power plant. According to the present invention, the possible fault hidden trouble of the electric power plant can be effectively identified, and the maintenance and overhauling plan can be made for the electric power plant, realizing the pre-overhauling of the fault of the electric power plant.
Description
INTELLIGENT SAFETY EARLY WARNING METHOD AND 0505201
SYSTEM OF ELECTRIC POWER PLANT BASED MONITORING DATA ANALYSIS
The present invention relates to the field of electric power plant safety monitoring, and especially, to an intelligent safety early warning method and system for an electric power plant based on monitoring data analysis.
The centralized monitoring and early warning system of an electric power plant uses a computer communication technology, an intelligent measurement and a control technology, a big data technology and an automatic monitoring technology to realize centralized monitoring and management of a plurality of electric power plants with decentralized monitoring and management.
The problems of unreasonable resource allocation, an unmanned operation of some electric power plants and difficult management caused by remote geographical location are solved, so as to realize a real-time alarm of the electric power plant.
In the prior art, it is difficult to comprehensively analyze the fault risk operation monitoring of the electric power plant by combining an operation parameter of the electric power plant and a monitoring image of the electric power plant, and it is impossible to quickly and accurately identify the potential faults in the electric power plant, the overhauling can only be performed when the electric power plant fails. Moreover, it is difficult to guarantee the immediacy of the electric power plant fault maintenance and overhauling for the electric power plant in remote areas.
In order to solve the above problems, an intelligent safety early warning method and system for an electric power plant based on monitoring data analysis is provided; the technical solution solves the problem that it is difficult to ensure the immediacy of power plant fault maintenance and overhauling in the prior art in the above background technology.
In order to achieve the above object, the present invention adopts the following technical solutions: an intelligent early warning method for an electric power plant based on monitoring data analysis, including: determining a standard operation set of the electric power plant;
classifying importance of electric power plant equipment according to the importance oF 505201 interior equipment of the electric power plant; obtaining real-time operation monitoring data of the electric power plant, the operation monitoring data comprising a real-time monitoring image of the electric power plant and a real- time operation parameter of the electric power plant equipment; calculating an operation state index of each equipment; calculating an image similarity of each equipment; performing a comprehensive calculation of an operation risk of the electric power plant; and sending different early warning signals to a working terminal based on the operation risk of the electric power plant, and reminding staff to overhaul the electric power plant.
Preferably, the calculating an operation state index of each equipment specifically includes: a deviation value between the real-time operation parameter of the electric power plant equipment and a standard operation parameter of the equipment is calculated, it is determined whether the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of the equipment is greater than a first preset value, if so, then it is determined that the equipment is at a risk of first-class fault; if not, then it is determined whether the deviation value is greater than a second preset value, if so, it is determined that the equipment is at a risk of second-class fault; if not, it is determined whether the deviation value is greater than a third preset value, and if so, it is determined that the equipment 1s at a risk of third-class fault; and if not, it is determined that the equipment is operating normally; for the electric power plant equipment determined to be in normal operation, an operation trend of the electric power plant equipment is calculated according to the operation trend calculation method; it is determined whether the operation trend of the equipment is greater than a preset value of an trend index, if so, it is determined that the equipment is at a risk of the third-class fault, and if not, it is determined that the equipment is in a normal state of an operation prediction; and for the electric power plant equipment at the risk of the first-class fault, an additional operation state index is 1; for the electric power plant equipment at the risk of the second -class fault, the additional operation state index is 0.5; for the electric power plant equipment at the risk of the third-class class, the additional operation state index is 0.25; and for the electric power plant equipment in the normal state of the operation prediction, the additional operation state index is 0.209201
Preferably, the calculation method of the operation trend specifically includes: the average value of the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of equipment in each monitoring cycle is calculated respectively according to a set cycle duration, and recorded as a standard deviation value; a total number k of a trend calculation data is set: k historical monitoring cycles are numbered from small to large respectively according to the time from far to near, and timing sequence numbers are obtained; and based on the standard deviation values and the timing sequence numbers in the k historical monitoring cycles, the operation trend of the equipment is calculated according to the trend index calculation formula.
Preferably, the trend index calculation formula is: ep - CEE)
Qf 7
YE, 1? i ) in the formula, Q is the operation trend of the equipment, and P, is the standard deviation value in the historical monitoring cycle numbered 1.
Preferably, the calculating the image similarity of each equipment specifically includes: a suspected dangerous area in the real-time monitoring image of the electric power plant is obtained; the suspected dangerous area is performed screen capture processing to obtain real-time image data of the suspected dangerous area; an operational standard image of the suspected dangerous areas is obtained; and through the calculation formula of a similarity fitting degree, the similarity between the real- time image data of the suspected dangerous area and the operation standard image of the suspected dangerous area is recorded as the equipment image similarity.
Preferably, the formula for calculating the similarity fitting degree is:
G z= [16s = ul s=1 in the formula, Z is the equipment image similarity, G is the total number of feature points bY 505201 the suspected dangerous area, 0; is a coordinate of the st feature point in the real-time image data of the suspected dangerous area, U | is the coordinate of the sin feature point in the operating standard image of the suspected dangerous area, and [I] is a modulo function.
Preferably, the performing a comprehensive calculation of an operation risk of the electric power plant specifically includes: an important weight p of the operation parameter and an important weight q of the equipment image are determined; and the important weight of the operation parameter and the important weight of the device image satisfy p + q = 1; according to the electric power plant equipment, the importance grade is classified and the importance index of the equipment is determined; and the operation risk of the electric power plant is calculated based on the comprehensive risk calculation formula.
Preferably, the comprehensive risk calculation formula is:
F=) alpxQ+axz] i= in the formula, F is the operation risk of the electric power plant, n is the total number of the electric power plant equipment, a; is the importance index of the im equipment, Q; is the operation state index of the im equipment, and Z; is the equipment image similarity of the im equipment.
Further, an intelligent safety early warning system for an electric power plant based on monitoring data analysis is provided for realizing the intelligent safety early warning method for an electric power plant based on monitoring data analysis, including: a storage module, the storage module being used for storing a standard operation parameter of the electric power plant equipment and an operation standard image of the electric power plant equipment; a parameter monitoring module, the parameter monitoring module being used for monitoring a real-time operation parameter of the electric power plant equipment in real time; an image monitoring module, the image monitoring module being used for collecting real- time monitoring images of the electric power plant in real time;
a parameter analysis module, the parameter being electrically connected to the parameter 505201 monitoring module, and used for calculating an operation state index of each equipment; an image analysis module, the image analysis module being electrically connected to the image analysis module, and used for calculating an image similarity of each equipment; and 5 a comprehensive risk calculation module, the comprehensive risk calculation module being electrically connected to the parameter analysis module and the image analysis module, and used for comprehensively calculating an operation risk of the electric power plant.
Compared with the prior art, the present invention has the following beneficial effects: the trend of an operation parameter of the power plant equipment is studied, hidden faults existing in the operation process of the electric power plant equipment are accurately identified, and the comprehensive operation risk assessment of the electric power plant is performed by combining the monitoring image data of the electric power plant. In this way, the possible fault hidden trouble of the electric power plant can be effectively identified, and the maintenance and overhauling plan can be made for the electric power plant, realizing the pre-overhauling of the fault of the electric power plant, and ensuring the efficient and stable operation of the electric power plant.
FIG. 1 1s a flow diagram of an intelligent safety early warning method for an electric power plant based on monitoring data analysis provided by the present invention;
FIG. 2 is a flow diagram of calculating an operation state index of equipment according to the present invention;
FIG. 3 is a flow diagram of a calculation method of an operation trend according to the present invention;
FIG. 4 is a flow diagram of calculating an image similarity of the equipment according to the present invention; and
FIG. 5 is a flow diagram of the calculation of an operation risk of the electric power plant according to the present invention.
The following description is used to disclose the present invention and enable those skilled in the art to realize the present invention. The preferred examples in the following description are only examples, and other obvious variations can be thought to those skilled in the art. 10505201
As shown in FIG .1, an intelligent early warning method for an electric power plant based on monitoring data analysis, including: a standard operation set of the electric power plant is determined based on a historical management operation management data of an enterprise, the standard operation set includes a standard operation parameter of the electric power plant equipment and an operation standard imagine of the electric power plant equipment; the importance grade of the electric power plant equipment is classified according to the importance of an interior equipment of the electric power plant; and the importance grade includes a general grade, an attention-needed grade, an important grade and a key grade; an operation monitoring data of the electric power plant is obtained in a real-time, and the operation monitoring data includes a real-time monitoring image of the electric power plant and a real-time operation parameter of equipment of the electric power plant; based on the standard operation parameter of the electric power plant equipment and the real - time operation parameter of the electric power plant equipment, the operation state index of each equipment is calculated, the similarity between the standard image of the electric power plant equipment operation and the real-time monitoring image of the electric power plant is calculated and the image similarity of equipment is obtained; the operation risk of the electric power plant is comprehensively calculated by combining the operation state index of the equipment, the image similarity of the equipment and the importance grade of the equipment; and different early warning signals are sent to the working terminal based on the operation risk of the electric power plant to remind staff to overhaul the electric power plant.
The trend of an operation parameter of the electric power plant equipment is studied, hidden faults existing in the operation process of the electric power plant equipment are accurately identified, and the comprehensive operation risk assessment of the electric power plant is performed by combining the monitoring image data of the electric power plant. In this way, the possible fault hidden trouble of the electric power plant can be effectively identified.
As shown in FIG. 2, based on the standard operation parameter of the equipment and the real-
time operation parameter of the electric power plant equipment, the calculating an operation state 505201 index of each equipment specifically includes: a deviation value between the real-time operation parameter of the electric power plant equipment and a standard operation parameter of the equipment is calculated; it is determined whether the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of the equipment is greater than a first preset value, if so, then it is determined that the equipment is at a risk of first-class fault; if not, then it is determined whether the deviation value is greater than a second preset value, if so, it is determined that the equipment is at a risk of second-class fault; if not, it is determined whether the deviation value is greater than a third preset value, and if so, it is determined that the equipment is at a risk of third-class fault; and if not, it is determined that the equipment is operating normally; for the electric power plant equipment determined to be in normal operation, an operation trend of the electric power plant equipment is calculated according to the operation trend calculation method; it is determined whether the operation trend of the equipment is greater than a preset value of an trend index, if so, it is determined that the equipment is at a risk of the third-class fault, and if not, it is determined that the equipment is in a normal state of an operation prediction; and for the electric power plant equipment at the risk of the first-class fault, an additional operation state index is 1; for the electric power plant equipment at the risk of the second -class fault, the additional operation state index is 0.5; for the electric power plant equipment at the risk of the third-class class, the additional operation state index is 0.25; and for the electric power plant equipment in the normal state of the operation prediction, the additional operation state index is 0.
As shown in FIG. 3, the calculation method of the operation trend specifically includes: the average value of the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of equipment in each monitoring cycle is calculated respectively according to a set cycle duration, and recorded as a standard deviation value; a total number k of a trend calculation data is set; k historical monitoring cycles are numbered from small to large respectively according to the time from far to near, and timing sequence numbers are obtained; and based on the standard deviation values and the timing sequence numbers in the k historical 22220] monitoring cycles, the operation trend of the equipment is calculated according to the trend index calculation formula.
The trend index calculation formula is: gi ep CDE) == yk, 12 — (Zi, in the formula, Q is the operation trend of the equipment, and P, is the standard deviation value in the historical monitoring cycle numbered 1.
In the solution, based on the deviation value of the calculation operation parameter, it is determined whether the equipment is in normal operation; and combined with the variation trend of the deviation value of the operation parameter, it is determined whether there is hidden fault in the equipment; and if an upward trend of operation parameter is too fast, then it means that the equipment is in a state of deteriorating operation, at this time, even though the operation parameters of the equipment are within the normal fluctuation range, which is still determined that the equipment is at the risk of three-class fault.
As shown in FIG. 4, the similarity between the operation standard image of the electric power plant equipment and the real-time monitoring image of the electric power plant is calculated, and the obtaining the image similarity of the equipment specifically includes: a suspected dangerous area in the real-time monitoring image of the electric power plant is obtained; the suspected dangerous area is performed screen capture processing to obtain real-time image data of the suspected dangerous area; an operational standard image of the suspected dangerous areas is obtained; and through the calculation formula of a similarity fitting degree, the similarity between the real- time image data of the suspected dangerous area and the operation standard image of the suspected dangerous area is recorded as the equipment image similarity.
The formula for calculating the similarity fitting degree is:
G z= [16s = ul s=1 in the formula, Z is the equipment image similarity, G is the total number of feature points bY 505201 the suspected dangerous area, Og is a coordinate of the st feature point in the real-time image data of the suspected dangerous area, 1 _ is the coordinate of the st feature point in the operating standard image of the suspected dangerous area, and [I] is a modulo function.
The image similarity is determined by calculating the vector distance of feature points in the image, the small similarity of the equipment image means that it is closer to the operation standard image, the lower of an abnormal image value of the equipment, on the contrary, the higher of the abnormal image value of the equipment.
As shown in FIG. 5, the operation risk of the electric power plant is comprehensively calculated by combining the operation state index of the equipment, the image similarity of the equipment and the importance of the equipment: an important weight p of the operation parameter and an important weight qof the equipment image are determined; and the important weight of the operation parameter and the important weight of the equipment image satisfy p+q = 1; according to the electric power plant equipment, the importance is classified and the importance index of the equipment is determined; the importance indexes of general, attention- needed, important and key are 0.25, 0.5, 0.75 and 1, respectively. the operation risk of the electric power plant is calculated based on the comprehensive risk calculation formula.
The comprehensive risk calculation formula is: n
F= >, lp X Qi + q x Zi] in the formula, F is the operation risk of the electric power plant, n is the total number of the electric power plant equipment, à; is the importance index of the im equipment, Q; is the operation state index of the im equipment, and Z; is the equipment image similarity of the im equipment.
It can be understood that the operation parameter of the electric power plant can convey greater fault information, therefore, in some examples, the importance weight of the operation parameter 1s 0.75, and the importance weight of the equipment image 1s 0.25.
Further, based on the same invention concept, the solution further provides an intelligent safety early warning system of an electric power plant based on monitoring data analysis, including. 505201 a storage module, the storage module being used for storing a standard operation parameter of the electric power plant equipment and an operation standard image of the electric power plant equipment; a parameter monitoring module, the parameter monitoring module being used for monitoring a real-time operation parameter of the electric power plant equipment in real time; an image monitoring module, the image monitoring module being used for collecting real- time monitoring images of the electric power plant in real time; a parameter analysis module, the parameter being electrically connected to the parameter monitoring module, and used for calculating an operation state index of each equipment; an image analysis module, the image analysis module being electrically connected to the image analysis module, and used for calculating an image similarity of each equipment; and a comprehensive risk calculation module, the comprehensive risk calculation module being electrically connected to the parameter analysis module and the image analysis module, and used for comprehensively calculating an operation risk of the electric power plant.
In conclusion, the present invention has the advantages that the possible fault hidden trouble of the electric power plant can be effectively identified, and the maintenance and overhauling plan can be made for the electric power plant, realizing the pre-overhauling of the fault of the electric power plant, and ensuring the efficient and stable operation of the electric power plant.
The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art is to be understood that the present invention is not limited by the above examples. The above examples and specification describes only a principle of the present invention. Without deviating from the spirit and scope of the present invention, which will also have various changes and improvements. These changes and improvements fall within the scope of the claimed present invention. The scope of protection required by the present invention is defined by the attached claims and their equivalents.
Claims (9)
1. An intelligent safety early warning method for an electric power plant based on monitoring data analysis, comprising: determining a standard operation set of the electric power plant; classifying importance of electric power plant equipment according to the importance of interior equipment of the electric power plant; obtaining real-time operation monitoring data of the electric power plant, the operation monitoring data comprising a real-time monitoring image of the electric power plant and a real- time operation parameter of the electric power plant equipment; calculating an operation state index of each equipment, calculating an image similarity of each equipment; performing a comprehensive calculation of an operation risk of the electric power plant; and sending different early warning signals to a working terminal based on the operation risk of the electric power plant, and reminding staff to overhaul the electric power plant.
2. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 1, wherein the calculating an operation state index of each equipment specifically comprises: a deviation value between the real-time operation parameter of the electric power plant equipment and a standard operation parameter of the equipment is calculated; it is determined whether the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of the equipment is greater than a first preset value, if so, then it is determined that the equipment is at a risk of first-class fault; if not, then it is determined whether the deviation value is greater than a second preset value, if so, itis determined that the equipment is at a risk of second-class fault; if not, it is determined whether the deviation value is greater than a third preset value, and if so, it is determined that the equipment is at a risk of third-class fault; and if not, it is determined that the equipment is operating normally; for the electric power plant equipment determined to be in normal operation, an operation trend of the electric power plant equipment is calculated according to the operation trend calculation method;
it is determined whether the operation trend of the equipment is greater than a preset value oF 505201 an trend index, if so, it is determined that the equipment is at a risk of the third-class fault, and if not, it is determined that the equipment is in a normal state of an operation prediction; and for the electric power plant equipment at the risk of the first-class fault, an additional operation state index is 1; for the electric power plant equipment at the risk of the second -class fault, the additional operation state index is 0.5; for the electric power plant equipment at the risk of the third-class class, the additional operation state index is 0.25; and for the electric power plant equipment in the normal state of the operation prediction, the additional operation state index is 0.
3. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 2, wherein the calculation method of the operation trend specifically comprises: the average value of the deviation value between the real-time operation parameter of the electric power plant equipment and the standard operation parameter of equipment in each monitoring cycle is calculated respectively according to a set cycle duration, and recorded as a standard deviation value; a total number k of a trend calculation data is set; k historical monitoring cycles are numbered from small to large respectively according to the time from far to near, and timing sequence numbers are obtained; and based on the standard deviation values and the timing sequence numbers in the k historical monitoring cycles, the operation trend of the equipment is calculated according to the trend index calculation formula.
4. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 3, wherein the trend index calculation formula is: pi, tx n - Ea) P) sia) in the formula, Q is the operation trend of the equipment, and P, is the standard deviation value in the historical monitoring cycle numbered 1.
5. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 1, wherein the calculating the image similarity of each equipment specifically comprises: 0505201 a suspected dangerous area in the real-time monitoring image of the electric power plant 1s obtained; the suspected dangerous area is performed screen capture processing to obtain real-time image data of the suspected dangerous area; an operational standard image of the suspected dangerous areas 1s obtained; and through the calculation formula of a similarity fitting degree, the similarity between the real- time image data of the suspected dangerous area and the operation standard image of the suspected dangerous area 1s recorded as the equipment image similarity.
6. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 5, wherein the formula for calculating the similarity fitting degree is: G z= [16s = ul s=1 in the formula, Z is the equipment image similarity, G is the total number of feature points in the suspected dangerous area, Og is a coordinate of the sm feature point in the real-time image data of the suspected dangerous area, H _ is the coordinate of the sw feature point in the operating standard image of the suspected dangerous area, and [I] is a modulo function.
7. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 1, wherein the performing a comprehensive calculation of an operation risk of the electric power plant specifically comprises: an important weight pof the operation parameter and an important weight q of the equipment image are determined; and the important weight of the operation parameter and the important weight of the device image satisfy p+q = 1; according to the electric power plant equipment, the importance grade is classified and the importance index of the equipment is determined; and the operation risk of the electric power plant is calculated based on the comprehensive risk calculation formula.
8. The intelligent safety early warning method for an electric power plant based on monitoring data analysis according to claim 7, wherein the comprehensive risk calculation formula is: 7505201 n F= >, lp X Qi + q x Zi] in the formula, F is the operation risk of the electric power plant, nis the total number of the electric power plant equipment, a; is the importance index of the im equipment, Q; is the operation state index of the im equipment, and Z; is the equipment image similarity of the im equipment.
9. An intelligent safety early warning system for an electric power plant based on monitoring data analysis for realizing the intelligent safety early warning method for an electric power plant based on monitoring data analysis according to any one of the claims 1-8, comprising: a storage module, the storage module being used for storing a standard operation parameter of the electric power plant equipment and an operation standard image of the electric power plant equipment; a parameter monitoring module, the parameter monitoring module being used for monitoring a real-time operation parameter of the electric power plant equipment in real time; an image monitoring module, the image monitoring module being used for collecting real- time monitoring images of the electric power plant in real time; a parameter analysis module, the parameter being electrically connected to the parameter monitoring module, and used for calculating an operation state index of each equipment; an image analysis module, the image analysis module being electrically connected to the image analysis module, and used for calculating an image similarity of each equipment; and a comprehensive risk calculation module, the comprehensive risk calculation module being electrically connected to the parameter analysis module and the image analysis module, and used for comprehensively calculating an operation risk of the electric power plant.
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| LU505201A LU505201B1 (en) | 2023-09-28 | 2023-09-28 | Intelligent safety early warning method and system of electric power plant based monitoring data analysis |
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| LU505201A LU505201B1 (en) | 2023-09-28 | 2023-09-28 | Intelligent safety early warning method and system of electric power plant based monitoring data analysis |
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2023
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Effective date: 20240328 |