CN115860714A - Power equipment safe operation management system and method based on industrial Internet - Google Patents
Power equipment safe operation management system and method based on industrial Internet Download PDFInfo
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Abstract
The invention discloses a power equipment safe operation management system and method based on an industrial internet, and relates to the technical field of power equipment safe operation management. The system comprises a data acquisition module, a data processing module, an influence function construction and analysis module, a classification model construction and analysis module, a similarity judgment and analysis module and a feedback reminding module; the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the influence function construction analysis module; the output end of the influence function building and analyzing module is connected with the input end of the classification model building and analyzing module; the output end of the classification model construction analysis module is connected with the input end of the similarity judgment analysis module; the output end of the similarity judging and analyzing module is connected with the input end of the feedback reminding module.
Description
Technical Field
The invention relates to the technical field of power equipment safe operation management, in particular to a power equipment safe operation management system and method based on an industrial internet.
Background
The industrial internet is a novel infrastructure, an application mode and an industrial ecology deeply integrated by a new generation of information communication technology and industrial economy, and a brand new manufacturing and service system covering a whole industrial chain and a whole value chain is constructed by comprehensively connecting people, machines, objects, systems and the like, so that a realization approach is provided for the digitalization, networking and intelligent development of industry and even industry, and the industrial internet is an important basic stone of the fourth industrial revolution.
The power equipment mainly comprises power generation equipment and power supply equipment, wherein the power generation equipment mainly comprises a power station boiler, a steam turbine, a gas turbine, a water turbine, a generator, a transformer and the like, and the power supply equipment mainly comprises power transmission lines, mutual inductors, contactors and the like with various voltage grades. In the prior art, generally, experienced operation and maintenance personnel are mainly relied on to check each fault point which is possibly in a problem one by one, and finally the fault point which generates a fault is determined.
Disclosure of Invention
The present invention aims to provide a system and a method for managing the safe operation of power equipment based on the industrial internet, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme:
the safe operation management method of the power equipment based on the industrial Internet comprises the following steps:
step S1: acquiring historical data of power equipment by utilizing industrial internet big data, wherein the power equipment comprises an oil-immersed transformer, the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer, and performing spectrum analysis on the historical fault sound signals to obtain a historical fault sound spectrum characteristic set;
step S2: constructing a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer to generate a training sample data set, and calculating the Euclidean distance between any two training samples;
and step S3: acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer, constructing an environment influence function, and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
and step S4: setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing a current fault sound signal, when the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal does not exceed the threshold value, carrying out spectrum analysis on the current fault sound signal of the oil-immersed transformer to obtain a current fault sound spectrum characteristic set, taking the current fault sound signal of the oil-immersed transformer as a current test sample, taking the current fault sound spectrum characteristic set as a sample characteristic set of the current test sample, substituting the current fault sound spectrum characteristic set into a machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
step S5: setting Euclidean distance threshold values of a current test sample and a training sample, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold values, obtaining a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample to obtain a current operation fault point under a current fault sound signal, and feeding the current operation fault point back to operation and maintenance personnel;
step S6: when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager and stops feeding back the current operation fault point position under the current fault sound signal.
Further, in step S1,
acquiring historical fault sound signal set A = { x) of oil-immersed transformer W 1 (n)，x 2 (n)，...，x m (n) }; wherein x is 1 (n)，x 2 (n)，...，x m (n) respectively representing the 1 st, 2 nd, the. Historical operation fault point position set C = { y) of corresponding oil-immersed transformer W 1 ，y 2 ，...，y m }; wherein, y 1 ，y 2 ，...，y m Respectively representing the 1 st, 2 nd, the. Carrying out spectrum analysis on the historical fault sound signal to obtain a historical fault sound spectrum feature set
Wherein it is present>
Respectively representing the 1 st fault sound signal, the 2 nd fault sound signal and the a th fault sound signal;
performing spectral analysis on the historical fault sound signal includes:
performing spectral analysis on the historical fault sound signal by using discrete Fourier transform;
according to the formula:
wherein k =0,1,2, ·, N-1; x (K) represents a frequency spectrum of the ith historical fault sound signal after discrete Fourier transform; x is a radical of a fluorine atom i (n) represents the ith historical failure sound signal.
Further, in step S2, the constructing a machine learning classification model includes:
combining the historical fault sound signal set and the historical operation fault point position set of the oil-immersed transformer W to generate a training sample data set D = { (x) 1 (n)，y 1 )，(x 2 (n)，y 2 )，......，(x m (n)，y m ) }; wherein x is 1 (n)，x 2 (n)，...，x m (n) represents the 1 st, 2 nd, the. y is 1 ，y 2 ，...，y m Respectively representing the output results of the 1 st sample, the 2 nd sample, the.
Any of the training samples x i (n) including a sample feature set
According to a formula, calculating the Euclidean distance between any two training samples as follows:
wherein L is 2 (x i (n)，x r (n)) represents a training sample x i (n) and training sample x r (n) the euclidean distance between; l denotes a training sample x i (n) and training sample x r (n) serial number of sample feature.
In the technical scheme, discrete Fourier transform is the most basic method for signal analysis, and the Fourier transform is the core of Fourier analysis and is used for transforming the signal from a time domain to a frequency domain so as to research the frequency spectrum structure and the change rule of the signal; in the application, different historical failure sound frequency spectrum characteristics can be rapidly and clearly obtained by performing discrete Fourier transform on different historical failure sound signals, and a basis is provided for constructing a machine learning classification model.
Further, in step S3, a current fault sound signal and a current operating environment of the oil-immersed transformer W are obtained;
the current operation environment of the oil-immersed transformer E comprises the current fault of the oil-immersed transformer WTime T of barrier sound signal w And the industrial noise intensity Q of the position of the oil-immersed transformer W w ；
The time T for acquiring the current fault sound signal of the oil-immersed transformer W w ∈[0，24]；
The industrial noise intensity Q of the position of the oil-immersed transformer W w ∈[Q min ，Q max ](ii) a Wherein Q is min The minimum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented; q max The maximum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented;
for the time T for acquiring the current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w Carrying out normalization processing to obtain a normalization value T of the time for acquiring the current fault sound signal of the oil-immersed transformer W v And the normalized value Q of the industrial noise intensity of the position of the oil-immersed transformer W v ；
Setting and acquiring an influence coefficient of time of a current fault sound signal of the oil-immersed transformer W, and recording the influence coefficient as alpha;
setting an influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W, and marking as beta;
constructing an environment influence function:
p v ＝α*T v +β*Q v
wherein p is v The probability of the influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal is represented; alpha represents the influence coefficient of the time for acquiring the current fault sound signal of the oil-immersed transformer W; t is v Representing a normalized value of time for acquiring a current fault sound signal of the oil-immersed transformer W; beta represents the influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W; q v And expressing the normalized value of the industrial noise intensity of the position of the oil-immersed transformer W.
In the technical scheme, different operating environments of the oil-immersed transformer can be caused by different application places of the oil-immersed transformer, and in the application, different influences of the different operating environments on the acquired fault sound signal of the oil-immersed transformer are considered, for example, the influence of the operating environment on the fault sound signal of the oil-immersed transformer acquired in the daytime of a working day is greater than that at night of the working day; the fault sound signal of the oil-immersed transformer acquired in the factory building with high industrial noise intensity is influenced by the operation environment more than that of the fault sound signal of the oil-immersed transformer acquired in the open room with low industrial noise intensity, so that the larger the influence of the operation environment on the acquired fault sound signal of the oil-immersed transformer is, the larger the judgment error of the system on the fault point is, and an influence function model is constructed to preprocess the operation environment and can be used for optimizing the precision of the system.
Further, in steps S4-S6,
setting a probability threshold value of influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal, and recording as p 0 ；
When the probability that the current operating environment of the oil-immersed transformer W influences the current fault sound signal does not exceed a threshold value, performing spectrum analysis on the current fault sound signal of the oil-immersed transformer W to obtain a current fault sound spectrum characteristic set;
taking the current fault sound signal of the oil-immersed transformer W as a current test sample, and recording as x u (n) taking the current fault sound frequency spectrum characteristic set as the sample characteristic set of the current test sample
Calculating the current test sample x u (n) and training sample x i (n) Euclidean distance between:
wherein L is 2 (x i (n)，x u (n)) represents the current test sample x u (n) and training sample x i (n) the euclidean distance between;
representing a training sample x i The l sample characteristic of (n); />
Representing the current test sample x u The l sample characteristic of (n);
setting a current test sample x u (n) and training sample x i (n) Euclidean distance threshold between (n) and is recorded as
When the temperature is higher than the set temperature
Then, the sample x is judged and tested currently u (n) the most similar training sample x i (n)；
Obtaining training samples x i Sample output result y of (n) i (ii) a Outputting the sample to result y i Output result y as a sample of the current test sample u Obtaining the current operation fault point position under the current fault sound signal;
when the probability that the current operation environment of the oil-immersed transformer W influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager, and stops feeding back the current operation fault point under the current fault sound signal.
In the above technical solution, the machine learning classification model specifically refers to a KNN learning algorithm, that is, a K nearest neighbor learning algorithm, and its working principle is to input a new sample (test sample) whose category is unknown, compare each feature of the new sample with a feature corresponding to a sample in a training sample set, and calculate an euclidean distance between the test sample and each sample in the training sample set; sorting the obtained Euclidean distances from small to large, wherein the smaller the Euclidean distance is, the more similar the Euclidean distance is; the K nearest neighbor learning algorithm has the advantages of simple and understandable model, high precision, insensitivity to abnormal values, no need of training and no need of parameter estimation. In the application, a historical fault sound signal of the oil-immersed transformer W is used as a training sample of a machine learning classification model, a current fault sound signal of the oil-immersed transformer E is used as a current test sample, and the Euclidean distance between the historical fault sound signal and the current fault sound signal is calculated, so that the training sample most similar to the current test sample is obtained, and the current operation fault point position under the current fault sound signal can be obtained because different training samples correspond to different sample output results.
The system comprises a data acquisition module, a data processing module, an influence function construction and analysis module, a classification model construction and analysis module, a similarity judgment and analysis module and a feedback reminding module;
the data acquisition module is used for acquiring historical data of power equipment by using industrial internet big data, and acquiring a current fault sound signal and a current operation environment of an oil-immersed transformer, wherein the power equipment comprises the oil-immersed transformer, and the historical data comprises a historical fault sound signal and a historical operation fault point position of the oil-immersed transformer; the data processing module is used for carrying out spectrum analysis on a historical fault sound signal and a current fault sound signal of the oil-immersed transformer to obtain a historical fault sound spectrum characteristic set and a current fault sound spectrum characteristic set; the influence function building and analyzing module is used for building an environment influence function, calculating the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal based on the current operation environment of the oil-immersed transformer, setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing the current fault sound signal, and judging whether the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value; the classification model building and analyzing module is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point set of the oil-immersed transformer to generate a training sample data set, calculating the Euclidean distance between any two training samples, taking a current fault sound signal of the oil-immersed transformer as a current test sample, substituting the current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample into the machine learning classification model, and calculating the Euclidean distance between the current test sample and the training sample; the similarity judging and analyzing module is used for setting Euclidean distance threshold values of the current test sample and the training samples, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold values, obtaining sample output results of the training samples, taking the sample output results as sample output results of the current test sample to obtain current operation fault point positions under current fault sound signals, and feeding the current operation fault point positions back to operation and maintenance personnel; the feedback reminding module is used for sending early warning reminding to a manager by the system when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, and stopping feeding back the current operation fault point position under the current fault sound signal;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the influence function construction analysis module; the output end of the influence function building and analyzing module is connected with the input end of the classification model building and analyzing module; the output end of the classification model construction analysis module is connected with the input end of the similarity judgment analysis module; and the output end of the similarity judging and analyzing module is connected with the input end of the feedback reminding module.
Further, the data acquisition module comprises a historical data acquisition unit and a current data acquisition unit;
the historical data acquisition unit is used for acquiring historical data of power equipment by using industrial internet big data, the power equipment comprises an oil-immersed transformer, and the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer;
the current data acquisition unit is used for acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer;
the current operation environment comprises time T for acquiring current fault sound signals of the oil-immersed transformer w And the industrial noise intensity Q of the position of the oil-immersed transformer w ；
The output end of the historical data acquisition unit is connected with the input end of the current data acquisition unit; the output end of the current data acquisition unit is connected with the input end of the data processing module;
the data processing module comprises a spectrum analysis unit and a spectrum characteristic acquisition unit;
the frequency spectrum analysis unit is used for carrying out frequency spectrum analysis on a historical fault sound signal and a current fault sound signal of the oil-immersed transformer;
the frequency spectrum characteristic acquisition unit is used for acquiring a historical fault sound frequency spectrum characteristic set and a current fault sound frequency spectrum characteristic set;
the output end of the spectrum analysis unit is connected with the input end of the spectrum characteristic acquisition unit; and the output end of the frequency spectrum characteristic acquisition unit is connected with the input end of the influence function construction analysis module.
Further, the influence function constructing and analyzing module comprises an influence function constructing unit and an analyzing and judging unit;
the influence function construction unit is used for constructing an environment influence function and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
the analysis and judgment unit is used for setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing the current fault sound signal and judging whether the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value;
the output end of the influence function constructing unit is connected with the input end of the analysis and judgment unit; the output end of the analysis and judgment unit is connected with the input end of the classification model construction and analysis module.
Further, the classification model building and analyzing module comprises a machine learning classification model building unit and a machine learning classification model analyzing unit;
the machine learning classification model building unit is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer, generating a training sample data set, and calculating the Euclidean distance between any two training samples;
the machine learning classification model analysis unit is used for taking a current fault sound signal of the oil-immersed transformer as a current test sample, taking a current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample, substituting the sample characteristic set into the machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
the output end of the machine learning classification model building unit is connected with the input end of the machine learning classification model analyzing unit; the output end of the machine learning classification model analysis unit is connected with the input end of the similarity judgment analysis module;
further, the similarity judging and analyzing module comprises a threshold setting unit and a judging and outputting unit;
the threshold setting unit is used for setting a Euclidean distance threshold between a current test sample and a training sample;
the judgment output unit is used for judging a training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed a threshold value, acquiring a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample, obtaining a current operation fault point position under a current fault sound signal, and feeding the current operation fault point position back to operation and maintenance personnel;
the output end of the threshold setting unit is connected with the input end of the judgment output unit; and the output end of the judgment output unit is connected with the input end of the feedback reminding module.
Compared with the prior art, the invention has the following beneficial effects: the method can perform spectrum analysis on the historical fault sound signal of the oil-immersed transformer to obtain a historical fault sound spectrum characteristic set, and calculates the Euclidean distance between the current test sample and the training sample by using a machine learning classification model to quickly determine the historical fault sound signal which is most similar to the current test sample, namely the current fault sound signal, so as to determine the historical operation fault point location; the method can be used for preprocessing the operation environment of the oil-immersed transformer, rapidly judging the influence probability of the operation environment on the current fault signal, improving the precision of analyzing and obtaining the current fault point position by a system, further simplifying the complex step that operation and maintenance personnel need to carry out one-by-one troubleshooting on each fault point position which possibly has problems, saving the time for the operation and maintenance personnel to maintain the power equipment, improving the maintenance efficiency of the operation and maintenance personnel, and reducing the influence on the life of residents caused by power failure.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of an industrial internet-based power equipment safe operation management system of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution:
the safe operation management method of the power equipment based on the industrial Internet comprises the following steps:
step S1: acquiring historical data of power equipment by utilizing industrial internet big data, wherein the power equipment comprises an oil-immersed transformer, the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer, and performing spectrum analysis on the historical fault sound signals to obtain a historical fault sound spectrum characteristic set;
step S2: constructing a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer to generate a training sample data set, and calculating the Euclidean distance between any two training samples;
and step S3: acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer, constructing an environment influence function, and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
and step S4: setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing a current fault sound signal, when the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal does not exceed the threshold value, carrying out spectrum analysis on the current fault sound signal of the oil-immersed transformer to obtain a current fault sound spectrum characteristic set, taking the current fault sound signal of the oil-immersed transformer as a current test sample, taking the current fault sound spectrum characteristic set as a sample characteristic set of the current test sample, substituting the current fault sound spectrum characteristic set into a machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
step S5: setting Euclidean distance threshold values of a current test sample and a training sample, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold value, acquiring a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample to obtain a current operation fault point position under a current fault sound signal, and feeding the current operation fault point position back to operation and maintenance personnel;
step S6: when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager and stops feeding back the current operation fault point position under the current fault sound signal.
Further, in step S1,
acquiring historical fault sound signal set A = { x ] of oil-immersed transformer W 1 (n)，x 2 (n)，...，x m (n) }; wherein x is 1 (n)，x 2 (n)，...，x m (n) respectively representing the 1 st, 2 nd, the... Ang., and the m th historical fault sound signals of the oil-immersed transformer E; historical operation fault point position set C = { y) of corresponding oil-immersed transformer W 1 ，y 2 ，...，y m }; wherein, y 1 ，y 2 ，...，y m Respectively representing the 1 st, 2 nd, the. Carrying out spectrum analysis on the historical fault sound signal to obtain a historical fault sound spectrum feature set
Wherein it is present>
Respectively representing the 1 st fault sound signal, the 2 nd fault sound signal, the a nd fault sound signal and the ith fault sound signal;
performing spectral analysis on the historical fault sound signal includes:
performing spectral analysis on the historical fault sound signal by using discrete Fourier transform;
according to the formula:
wherein k =0,1,2, ·, N-1; x (K) represents a frequency spectrum of the ith historical fault sound signal after discrete Fourier transform; x is the number of i (n) represents the ith historical failure sound signal.
Further, in step S2, the constructing a machine learning classification model includes:
combining the historical fault sound signal set and the historical operation fault point location set of the oil-immersed transformer W to generate a training sample data set D = { (x) 1 (n)，y 1 )，(x 2 (n)，y 2 )，......，(x m (n)，y m ) }; wherein x is 1 (n)，x 2 (n)，...，x m (n) represents the 1 st, 2 nd, the. y is 1 ，y 2 ，...，y m Respectively representing the output results of the 1 st sample, the 2 nd sample, the.
Any of the training samples x i (n) includes a sample feature set
According to a formula, calculating the Euclidean distance between any two training samples as follows:
wherein L is 2 (x i (n)，x r (n)) represents a training sample x i (n) and training sample x r (n) the euclidean distance between; l denotes a training sample x i (n) and training sample x r (n) serial number of sample feature.
Further, in step S3, a current fault sound signal and a current operating environment of the oil-immersed transformer W are obtained;
the current operation environment of the oil-immersed transformer W comprises the time T for acquiring the current fault sound signal of the oil-immersed transformer E w And the industrial noise intensity Q of the position of the oil-immersed transformer W w ；
The time T for acquiring the current fault sound signal of the oil-immersed transformer W w ∈[0，24]；
Industrial noise intensity Q of position of oil-immersed transformer W w ∈[Q min ，Q max ](ii) a Wherein Q is min The minimum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented; q max The maximum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented;
current fault to the oil-immersed transformer WTime T of barrier sound signal w And the industrial noise intensity Q of the position of the oil-immersed transformer W w Carrying out normalization processing to obtain a normalization value T of the time for acquiring the current fault sound signal of the oil-immersed transformer W v And the normalized value Q of the industrial noise intensity of the position of the oil-immersed transformer E v ；
Setting and acquiring an influence coefficient of time of a current fault sound signal of the oil-immersed transformer W, and recording the influence coefficient as alpha;
setting an influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W, and marking as beta;
constructing an environment influence function:
p v ＝α*T v +β*Q v
wherein p is v Representing the probability of the influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal; alpha represents the influence coefficient of the time for acquiring the current fault sound signal of the oil-immersed transformer W; t is a unit of v The normalization value of the time for acquiring the current fault sound signal of the oil-immersed transformer W is represented; beta represents the influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W; q v And expressing the normalized value of the industrial noise intensity of the position of the oil-immersed transformer W.
Further, in steps S4-S6,
setting a probability threshold value of influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal, and recording as p 0 ；
When the probability that the current operating environment of the oil-immersed transformer W influences the current fault sound signal does not exceed a threshold value, performing spectrum analysis on the current fault sound signal of the oil-immersed transformer W to obtain a current fault sound spectrum characteristic set;
taking the current fault sound signal of the oil-immersed transformer W as a current test sample, and recording as x u (n) taking the current fault sound frequency spectrum characteristic set as the sample characteristic set of the current test sample
Calculating the current test sample x u (n) and training sample x i (n) Euclidean distance between:
wherein L is 2 (x i (n)，x u (n)) represents the current test sample x u (n) and training sample x i (n) the euclidean distance between;
representing a training sample x i The l sample characteristic of (n); />
Representing the current test sample x u The l sample characteristic of (n); />
Setting a current test sample x u (n) and training sample x i (n) Euclidean distance threshold between (n) and is recorded as
When in use
Then, the sample x is judged and tested currently u (n) the most similar training sample x i (n)；
Obtaining training samples x i (n) sample output result y i (ii) a Outputting the sample to result y i Output result y as a sample of the current test sample u Obtaining the current operation fault point position under the current fault sound signal;
when the probability that the current operation environment of the oil-immersed transformer W influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager, and stops feeding back the current operation fault point under the current fault sound signal.
The system comprises a data acquisition module, a data processing module, an influence function construction and analysis module, a classification model construction and analysis module, a similarity judgment and analysis module and a feedback reminding module;
the data acquisition module is used for acquiring historical data of power equipment by using industrial internet big data, and acquiring a current fault sound signal and a current operation environment of an oil-immersed transformer, wherein the power equipment comprises the oil-immersed transformer, and the historical data comprises a historical fault sound signal and a historical operation fault point position of the oil-immersed transformer; the data processing module is used for carrying out frequency spectrum analysis on a historical fault sound signal and a current fault sound signal of the oil-immersed transformer to obtain a historical fault sound frequency spectrum characteristic set and a current fault sound frequency spectrum characteristic set; the influence function building and analyzing module is used for building an environment influence function, calculating the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal based on the current operation environment of the oil-immersed transformer, setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing the current fault sound signal, and judging whether the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value; the classification model building and analyzing module is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point set of the oil-immersed transformer to generate a training sample data set, calculating the Euclidean distance between any two training samples, taking a current fault sound signal of the oil-immersed transformer as a current test sample, substituting the current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample into the machine learning classification model, and calculating the Euclidean distance between the current test sample and the training sample; the similarity judging and analyzing module is used for setting Euclidean distance threshold values of the current test sample and the training samples, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold values, obtaining sample output results of the training samples, taking the sample output results as sample output results of the current test sample to obtain current operation fault point positions under current fault sound signals, and feeding the current operation fault point positions back to operation and maintenance personnel; the feedback reminding module is used for sending early warning reminding to a manager when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, and stopping feeding back the current operation fault point position under the current fault sound signal;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the influence function construction analysis module; the output end of the influence function building and analyzing module is connected with the input end of the classification model building and analyzing module; the output end of the classification model construction analysis module is connected with the input end of the similarity judgment analysis module; the output end of the similarity judging and analyzing module is connected with the input end of the feedback reminding module.
Further, the data acquisition module comprises a historical data acquisition unit and a current data acquisition unit;
the historical data acquisition unit is used for acquiring historical data of power equipment by using industrial internet big data, the power equipment comprises an oil-immersed transformer, and the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer;
the current data acquisition unit is used for acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer;
the current operation environment comprises time T for acquiring current fault sound signals of the oil-immersed transformer w And the industrial noise intensity Q of the position of the oil-immersed transformer w ；
The output end of the historical data acquisition unit is connected with the input end of the current data acquisition unit; the output end of the current data acquisition unit is connected with the input end of the data processing module;
the data processing module comprises a spectrum analysis unit and a spectrum characteristic acquisition unit;
the frequency spectrum analysis unit is used for carrying out frequency spectrum analysis on the historical fault sound signal and the current fault sound signal of the oil-immersed transformer;
the frequency spectrum characteristic acquisition unit is used for acquiring a historical fault sound frequency spectrum characteristic set and a current fault sound frequency spectrum characteristic set;
the output end of the spectrum analysis unit is connected with the input end of the spectrum characteristic acquisition unit; and the output end of the frequency spectrum characteristic acquisition unit is connected with the input end of the influence function construction analysis module.
Further, the influence function constructing and analyzing module comprises an influence function constructing unit and an analyzing and judging unit;
the influence function construction unit is used for constructing an environment influence function and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
the analysis and judgment unit is used for setting a probability threshold value of the current operating environment of the oil-immersed transformer influencing the current fault sound signal and judging whether the probability of the current operating environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value;
the output end of the influence function construction unit is connected with the input end of the analysis and judgment unit; the output end of the analysis and judgment unit is connected with the input end of the classification model construction and analysis module.
Further, the classification model building and analyzing module comprises a machine learning classification model building unit and a machine learning classification model analyzing unit;
the machine learning classification model building unit is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer, generating a training sample data set, and calculating the Euclidean distance between any two training samples;
the machine learning classification model analysis unit is used for taking a current fault sound signal of the oil-immersed transformer as a current test sample, taking a current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample, substituting the sample characteristic set into the machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
the output end of the machine learning classification model building unit is connected with the input end of the machine learning classification model analyzing unit; the output end of the machine learning classification model analysis unit is connected with the input end of the similarity judgment and analysis module;
further, the similarity judging and analyzing module comprises a threshold setting unit and a judging and outputting unit;
the threshold setting unit is used for setting a Euclidean distance threshold between a current test sample and a training sample;
the judgment output unit is used for judging a training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed a threshold value, acquiring a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample, obtaining a current operation fault point position under a current fault sound signal, and feeding the current operation fault point position back to operation and maintenance personnel;
the output end of the threshold setting unit is connected with the input end of the judgment output unit; and the output end of the judgment output unit is connected with the input end of the feedback reminding module.
Example 1:
when the oil-immersed transformer is in normal operation, uniform 'hum' is generated, which is caused by vibration of an iron core due to change of alternating magnetic flux when alternating current passes through a winding of the transformer, but if the transformer generates the following abnormal sounds, the transformer is in failure: (1) Strong and uneven noise occurs and the amplitude is increased, because the core penetrating screw of the iron core is not firmly fixed, the iron core is loosened, and the silicon steel sheets generate vibration; (2) The 'squeak' discharge sound is generated in the transformer, and is caused by the fact that the winding or the lead-out wire carries out flashover discharge on the outer shell or the grounding wire of the iron core breaks, so that the iron core induces the outer shell (ground) to generate high voltage and discharge; (3) The 'jingle' sound is generated in the transformer, and the sound is generated due to the fact that individual parts on the transformer are not firmly fixed; (4) An array of camp sounds are generated in the transformer, and are caused by vibration of the end parts of certain silicon steel sheets away from the lamination under the condition of light load or empty load; (5) The transformer has a breakdown place inside, so that a 'squeak' or 'humming' sound can be produced, and the sound of the pilot strand becomes coarse and thin suddenly;
acquiring historical fault sound signal set A = { x ] of oil-immersed transformer W 1 (n)，x 2 (n)，...，x 5 (n) }; wherein x is 1 (n)，x 2 (n)，...，x 5 (n) respectively represents the 1 st, 2 nd, 5 th historical fault sound signals of the oil-immersed transformer W; historical operation fault point position set C = { y) of corresponding oil-immersed transformer W 1 ，y 2 ，...，y 5 }; wherein, y 1 ，y 2 ，...，y 5 Respectively representing the 1 st, 2 nd, 9 th and 5 th historical operation fault point positions of the oil-immersed transformer W; carrying out spectrum analysis on the historical fault sound signal to obtain a historical fault sound spectrum feature set
Wherein it is present>
Respectively representing 1 st fault sound frequency spectrum characteristic values, 2 nd fault sound frequency spectrum characteristic values and 4 th fault sound frequency spectrum characteristic values of the ith historical fault sound signal;
therefore, the 1 st historical failure sound signal x 1 (n) historical failure sound spectral feature set
2 nd historical fault sound signal x 2 (n) historical failure sound spectral feature set
3 rd historical fault sound signal x 3 (n) historical failure sound spectral feature set
4 th historical fault sound signal x 4 (n) historical failure sound spectral feature set
The 5 th historical failure sound signal x 5 (n) historical failure sound spectral feature set
Combining the historical fault sound signal set and the historical operation fault point location set of the oil-immersed transformer W to generate a training sample data set D = { (x) 1 (n)，y 1 )，(x 2 (n)，y 2 )，......，(x 5 (n)，y 5 ) }; wherein x is 1 (n)，x 2 (n)，...，x 5 (n) represents the 1 st, 2 nd, 5 th training samples of the machine learning classification model, respectively; y is 1 ，y 2 ，...，y 5 Respectively representing the output results of the 1 st sample, the 2 nd sample, the. Any of the training samples x i (n) includes a sample feature set
Acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer W;
the current operation environment of the oil-immersed transformer W comprises time T for acquiring a current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w ；
The time T for acquiring the current fault sound signal of the oil-immersed transformer W w ∈[0，24]；
Industrial noise intensity Q of position of oil-immersed transformer W w ∈[20，100]；
Acquiring time T of current fault sound signal of oil-immersed transformer W w ＝10；
Obtaining industrial noise intensity Q of position of oil-immersed transformer W w ＝70；
For the time T for acquiring the current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w Carrying out normalization processing to obtain a normalization value T of the time for acquiring the current fault sound signal of the oil-immersed transformer W v =0.5 and the normalized value Q of the industrial noise intensity at the position of the oil-immersed transformer W v ＝0.6；
Setting an influence coefficient alpha =0.3 for acquiring the time of the current fault sound signal of the oil-immersed transformer W;
setting an influence coefficient beta =0.7 of the industrial noise intensity of the position of the oil-immersed transformer W;
the probability of the influence of the current operating environment of the oil-immersed transformer W on the current fault sound signal is as follows:
p v ＝α*T v +β*Q v ＝0.3*0.5+0.6*0.7＝0.57
wherein p is v Representing the probability of the influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal; alpha represents the influence coefficient of the time for acquiring the current fault sound signal of the oil-immersed transformer W; t is v Representing a normalized value of time for acquiring a current fault sound signal of the oil-immersed transformer W; beta represents the influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W; q v And expressing the normalized value of the industrial noise intensity of the position of the oil-immersed transformer W.
Setting a probability threshold value p of influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal 0 ＝0.6；
Because of p v ＜p 0 Therefore, the current fault sound signal of the oil-immersed transformer W is subjected to spectrum analysis to obtain a current fault sound spectrum characteristic set;
taking the current fault sound signal of the oil-immersed transformer W as a current test sample, and recording as x u (n) taking the current fault sound frequency spectrum characteristic set as the sample characteristic set of the current test sample
Calculating the current test sample x u (n) and training sample x i (n) Euclidean distance between:
can obtain L 2 (x 1 (n)，x u (n))＝2.87；L 2 (x 2 (n)，x u (n))＝0.7；L 2 (x 3 (n)，x u (n))＝5.12；L 2 (x 4 (n)，x u (n))＝6.18；L 2 (x 5 (n)，x u (n))＝3.04；
Setting a current test sample x u (n) and training sample x i (n) Euclidean distance threshold between
Because L is 2 (x 2 (n)，x u (n)) =0.7 < 1, and judging the current test sample x u (n) the most similar training sample x 2 (n)；
Obtaining training samples x 2 (n) sample output result y 2 (ii) a Outputting the sample to a result y 2 Output result y as a sample of the current test sample u That is, the current operation fault point position under the current fault sound signal is y 2 。
Example 2:
acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer W;
the current operation environment of the oil-immersed transformer W comprises time T for acquiring a current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w ；
The time T for acquiring the current fault sound signal of the oil-immersed transformer W w ∈[0，24]；
Industrial noise intensity Q of position of oil-immersed transformer W w ∈[20，100]；
Acquiring time T of current fault sound signal of oil-immersed transformer W w ＝11；
Obtaining industrial noise intensity Q of position of oil-immersed transformer W w ＝80；
For the time T for acquiring the current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w Carrying out normalization processing to obtain a normalization value T of the time for acquiring the current fault sound signal of the oil-immersed transformer W v =0.6 and normalized value Q of industrial noise intensity at the position of oil-immersed transformer W v ＝0.7；
Setting an influence coefficient alpha =0.3 for acquiring the time of the current fault sound signal of the oil-immersed transformer W;
setting an influence coefficient beta =0.7 of the industrial noise intensity of the position of the oil-immersed transformer W;
probability of influence of the current operating environment of the oil-immersed transformer W on the current fault sound signal:
p v ＝α*T v +β*Q v ＝0.3*0.6+0.7*0.7＝0.67
wherein p is v Representing the probability of the influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal; alpha represents an influence coefficient of time for acquiring a current fault sound signal of the oil-immersed transformer W; t is v Representing a normalized value of time for acquiring a current fault sound signal of the oil-immersed transformer W; beta represents the influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W；Q v Expressing the normalized value of the industrial noise intensity of the position of the oil-immersed transformer W;
setting probability threshold value p of influence of current operation environment of oil-immersed transformer W on current fault sound signal 0 ＝0.6；
Because of p v ＞p 0 Therefore, the system sends out early warning reminding to the management personnel and stops feeding back the current operation fault point under the current fault sound signal.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that various changes, modifications and substitutions can be made without departing from the spirit and scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The power equipment safe operation management method based on the industrial Internet is characterized by comprising the following steps:
step S1: acquiring historical data of power equipment by utilizing industrial internet big data, wherein the power equipment comprises an oil-immersed transformer, the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer, and performing spectrum analysis on the historical fault sound signals to obtain a historical fault sound spectrum characteristic set;
step S2: constructing a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer to generate a training sample data set, and calculating the Euclidean distance between any two training samples;
and step S3: acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer, constructing an environment influence function, and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
and step S4: setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing a current fault sound signal, when the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal does not exceed the threshold value, carrying out spectrum analysis on the current fault sound signal of the oil-immersed transformer to obtain a current fault sound spectrum characteristic set, taking the current fault sound signal of the oil-immersed transformer as a current test sample, taking the current fault sound spectrum characteristic set as a sample characteristic set of the current test sample, substituting the current fault sound spectrum characteristic set into a machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
step S5: setting Euclidean distance threshold values of a current test sample and a training sample, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold values, obtaining a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample to obtain a current operation fault point under a current fault sound signal, and feeding the current operation fault point back to operation and maintenance personnel;
step S6: when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager and stops feeding back the current operation fault point position under the current fault sound signal.
2. The industrial internet-based power equipment safe operation management method according to claim 1, characterized in that: in the step S1, the first step is performed,
acquiring historical fault sound signal set A = { x) of oil-immersed transformer W 1 (n),x 2 (n),...,x m (n) }; wherein x is 1 (n),x 2 (n),...,x m (n) respectively representing the 1 st, 2 nd, the. Historical operation fault point position set C = { y) of corresponding oil-immersed transformer W 1 ,y 2 ,...,y m }; wherein, y 1 ,y 2 ,...,y m Respectively representing the 1 st, 2 nd, the. Carrying out spectrum analysis on the historical fault sound signal to obtain a historical fault sound spectrum feature set
Wherein it is present>
Respectively representing the 1 st fault sound signal, the 2 nd fault sound signal, the a nd fault sound signal and the ith fault sound signal;
performing spectral analysis on the historical fault sound signal includes:
performing spectral analysis on the historical fault sound signal by using discrete Fourier transform;
according to the formula:
wherein k =0,1,2,. Cndot., N-1; x (K) represents a frequency spectrum of the ith historical fault sound signal after discrete Fourier transform; x is the number of i (n) represents the ith historical failure sound signal.
3. The industrial internet-based power equipment safe operation management method according to claim 2, characterized in that: in step S2, the constructing a machine learning classification model includes:
combining the historical fault sound signal set and the historical operation fault point location set of the oil-immersed transformer W to generate a training sample data set D = { (x) 1 (n),y 1 ),(x 2 (n),y 2 ),......,(x m (n),y m ) }; wherein x is 1 (n),x 2 (n),...,x m (n) represents the 1 st, 2 nd, the. y is 1 ,y 2 ,...,y m Respectively representing the output results of the 1 st sample, the 2 nd sample, the.
Any of the training samples x i (n) includes a sample feature set
According to a formula, calculating the Euclidean distance between any two training samples as follows:
wherein L is 2 (x i (n),x r (n)) represents a training sample x i (n) and training sample x r (n) the euclidean distance between; l represents a training sample x i (n) and training sample x r (n) serial number of sample feature.
4. The industrial internet-based power equipment safe operation management method according to claim 3, characterized in that: in step S3, acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer W;
the current operation environment of the oil-immersed transformer W comprises time T for acquiring a current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w ；
The time T for acquiring the current fault sound signal of the oil-immersed transformer W w ∈[0,24]；
Industrial noise intensity Q of position of oil-immersed transformer W w ∈[Q min ,Q max ](ii) a Wherein Q is min The minimum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented; q max The maximum value of the industrial noise intensity of the position where the oil-immersed transformer W is located is represented;
for the time T for acquiring the current fault sound signal of the oil-immersed transformer W w And the industrial noise intensity Q of the position of the oil-immersed transformer W w Carrying out normalization processing to obtain a normalization value T of the time for acquiring the current fault sound signal of the oil-immersed transformer W v And the normalized value Q of the industrial noise intensity of the position of the oil-immersed transformer W v ；
Setting and acquiring an influence coefficient of time of a current fault sound signal of the oil-immersed transformer W, and recording the influence coefficient as alpha;
setting an influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W, and marking as beta;
constructing an environment influence function:
p v ＝α*T v +β*Q v
wherein p is v Representing the probability of the influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal; alpha represents the influence coefficient of the time for acquiring the current fault sound signal of the oil-immersed transformer W; t is v Representing a normalized value of time for acquiring a current fault sound signal of the oil-immersed transformer W; beta represents the influence coefficient of the industrial noise intensity of the position of the oil-immersed transformer W; q v And the normalized value represents the industrial noise intensity of the position of the oil-immersed transformer W.
5. The industrial internet-based power equipment safe operation management method according to claim 4, characterized in that: in the steps S4-S6,
setting a probability threshold value of influence of the current operation environment of the oil-immersed transformer W on the current fault sound signal, and recording as p 0 ；
When the probability that the current operating environment of the oil-immersed transformer W influences the current fault sound signal does not exceed a threshold value, performing spectrum analysis on the current fault sound signal of the oil-immersed transformer W to obtain a current fault sound spectrum characteristic set;
taking the current fault sound signal of the oil-immersed transformer W as a current test sample, and recording as x u (n) taking the current fault sound frequency spectrum characteristic set as the sample characteristic set of the current test sample
Calculating the current test sample x u (n) and training sample x i (n) Euclidean distance between:
wherein L is 2 (x i (n),x u (n)) represents the current test sample x u (n) and training sample x i (n) the euclidean distance between;
representing a training sample x i The l sample characteristic of (n); />
Representing the current test sample x u The l sample characteristic of (n);
setting a current test sample x u (n) and training sample x i Euclidean distance thresholds between (n), noted
When in use
Then, the sample x is judged and tested currently u (n) the most similar training sample x i (n)；
Obtaining training samples x i (n) ofSample output result y i (ii) a Outputting the sample to result y i Output result y as a sample of the current test sample u Obtaining the current operation fault point position under the current fault sound signal;
when the probability that the current operation environment of the oil-immersed transformer W influences the current fault sound signal exceeds a threshold value, the system sends out early warning reminding to a manager, and stops feeding back the current operation fault point under the current fault sound signal.
6. An industrial internet-based power equipment safe operation management system to which the industrial internet-based power equipment safe operation management method of any one of claims 1 to 5 is applied, characterized in that: the system comprises a data acquisition module, a data processing module, an influence function construction and analysis module, a classification model construction and analysis module, a similarity judgment and analysis module and a feedback reminding module;
the data acquisition module is used for acquiring historical data of power equipment by using industrial internet big data, and acquiring a current fault sound signal and a current operation environment of an oil-immersed transformer, wherein the power equipment comprises the oil-immersed transformer, and the historical data comprises a historical fault sound signal and a historical operation fault point position of the oil-immersed transformer; the data processing module is used for carrying out spectrum analysis on a historical fault sound signal and a current fault sound signal of the oil-immersed transformer to obtain a historical fault sound spectrum characteristic set and a current fault sound spectrum characteristic set; the influence function constructing and analyzing module is used for constructing an environment influence function, calculating the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal based on the current operation environment of the oil-immersed transformer, setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing the current fault sound signal, and judging whether the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value; the classification model building and analyzing module is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point set of the oil-immersed transformer to generate a training sample data set, calculating the Euclidean distance between any two training samples, taking a current fault sound signal of the oil-immersed transformer as a current test sample, substituting the current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample into the machine learning classification model, and calculating the Euclidean distance between the current test sample and the training sample; the similarity judging and analyzing module is used for setting Euclidean distance threshold values of the current test sample and the training samples, judging the training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed the threshold values, obtaining sample output results of the training samples, taking the sample output results as sample output results of the current test sample to obtain current operation fault point positions under current fault sound signals, and feeding the current operation fault point positions back to operation and maintenance personnel; the feedback reminding module is used for sending early warning reminding to a manager by the system when the probability that the current operation environment of the oil-immersed transformer influences the current fault sound signal exceeds a threshold value, and stopping feeding back the current operation fault point position under the current fault sound signal;
the output end of the data acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the influence function construction analysis module; the output end of the influence function building and analyzing module is connected with the input end of the classification model building and analyzing module; the output end of the classification model construction analysis module is connected with the input end of the similarity judgment analysis module; the output end of the similarity judging and analyzing module is connected with the input end of the feedback reminding module.
7. The industrial internet-based power equipment safe operation management system according to claim 6, characterized in that: the data acquisition module comprises a historical data acquisition unit and a current data acquisition unit;
the historical data acquisition unit is used for acquiring historical data of power equipment by using industrial internet big data, the power equipment comprises an oil-immersed transformer, and the historical data comprises historical fault sound signals and historical operation fault point positions of the oil-immersed transformer;
the current data acquisition unit is used for acquiring a current fault sound signal and a current operation environment of the oil-immersed transformer;
the current operation environment comprises time T for acquiring current fault sound signals of the oil-immersed transformer w And the industrial noise intensity Q of the position of the oil-immersed transformer w ；
The output end of the historical data acquisition unit is connected with the input end of the current data acquisition unit; the output end of the current data acquisition unit is connected with the input end of the data processing module;
the data processing module comprises a spectrum analysis unit and a spectrum characteristic acquisition unit;
the frequency spectrum analysis unit is used for carrying out frequency spectrum analysis on a historical fault sound signal and a current fault sound signal of the oil-immersed transformer;
the frequency spectrum characteristic acquisition unit is used for acquiring a historical fault sound frequency spectrum characteristic set and a current fault sound frequency spectrum characteristic set;
the output end of the spectrum analysis unit is connected with the input end of the spectrum characteristic acquisition unit; and the output end of the spectrum characteristic acquisition unit is connected with the input end of the influence function construction analysis module.
8. The industrial internet-based power equipment safe operation management system according to claim 6, characterized in that: the influence function construction analysis module comprises an influence function construction unit and an analysis judgment unit;
the influence function construction unit is used for constructing an environment influence function and calculating the probability of influence of the current operation environment of the oil-immersed transformer on the current fault sound signal based on the current operation environment of the oil-immersed transformer;
the analysis and judgment unit is used for setting a probability threshold value of the current operation environment of the oil-immersed transformer influencing the current fault sound signal and judging whether the probability of the current operation environment of the oil-immersed transformer influencing the current fault sound signal exceeds the threshold value;
the output end of the influence function construction unit is connected with the input end of the analysis and judgment unit; the output end of the analysis and judgment unit is connected with the input end of the classification model construction and analysis module.
9. The industrial internet-based power equipment safe operation management system according to claim 6, characterized in that: the classification model building and analyzing module comprises a machine learning classification model building unit and a machine learning classification model analyzing unit;
the machine learning classification model building unit is used for building a machine learning classification model, combining a historical fault sound signal set and a historical operation fault point location set of the oil-immersed transformer, generating a training sample data set, and calculating the Euclidean distance between any two training samples;
the machine learning classification model analysis unit is used for taking a current fault sound signal of the oil-immersed transformer as a current test sample, taking a current fault sound frequency spectrum characteristic set as a sample characteristic set of the current test sample, substituting the sample characteristic set into the machine learning classification model, and calculating the Euclidean distance between the current test sample and a training sample;
the output end of the machine learning classification model building unit is connected with the input end of the machine learning classification model analyzing unit; and the output end of the machine learning classification model analysis unit is connected with the input end of the similarity judgment analysis module.
10. The industrial internet-based power equipment safe operation management system according to claim 6, characterized in that: the similarity judging and analyzing module comprises a threshold setting unit and a judging and outputting unit;
the threshold setting unit is used for setting a Euclidean distance threshold between a current test sample and a training sample;
the judgment output unit is used for judging a training sample most similar to the current test sample when the Euclidean distance between the current test sample and the training sample does not exceed a threshold value, acquiring a sample output result of the training sample, taking the sample output result as the sample output result of the current test sample, obtaining a current operation fault point position under a current fault sound signal, and feeding the current operation fault point position back to operation and maintenance personnel;
the output end of the threshold setting unit is connected with the input end of the judgment output unit; and the output end of the judgment output unit is connected with the input end of the feedback reminding module.
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