CN113657622A - Method, device, terminal and storage medium for fusing multidimensional state data of electrical equipment - Google Patents
Method, device, terminal and storage medium for fusing multidimensional state data of electrical equipment Download PDFInfo
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Abstract
The invention relates to the technical field of power equipment state diagnosis, in particular to a method, a device, a terminal and a storage medium for fusing multi-dimensional state data of power equipment. The trend factor is mainly composed of three parts, and the state quantity weight reflects the severity of the abnormal monitoring quantity data. The repetition factor reflects the persistence of the anomaly in the state monitoring volume data. The attenuation factor reflects the recoverability of the abnormal state monitoring data. In order to adaptively adjust and fuse the transformer monitoring data processed by the trend factors into corresponding transformer fault key variables, a self-encoder is adopted to fuse the multidimensional state data processed by the trend factors. The fused data can automatically extract features, the defects of traditional manual feature extraction are effectively reduced, the fused data is used for state evaluation, and the accuracy is high.
Description
Technical Field
The invention relates to the technical field of data processing of electrical equipment, in particular to a method, a device, a terminal and a storage medium for fusing multidimensional state data of electrical equipment.
Background
The power equipment is one of the most expensive, most important and most complex equipment in the power transmission and transformation system, and has various monitoring parameters reflecting the state of the power equipment, including electric quantity and non-electric quantity parameters such as partial discharge, dielectric loss, oil temperature, gas dissolved in oil and the like. Monitoring the multidimensional state parameters of the power equipment and diagnosing the state of the power equipment according to the multidimensional state parameters have important significance for reliable operation of the power transmission and transformation system.
However, at present, most of the state diagnoses of the power equipment pass through industry standards, but the method for judging the insulation fault of the power equipment generally has hysteresis, and the multiple state quantities are not beneficial for operation and maintenance personnel to judge, and often fail to be considered in actual operation, so that feature extraction needs to be performed on the state quantities related to the fault.
Most of the current state diagnosis is to directly judge the fault through the size relation between the monitoring quantity and the guide threshold value, the relevance of the monitoring quantity on a time sequence is ignored in the direct judgment, and most of the time, the abnormal stage of the power equipment is greatly related to the fault of the power equipment.
In summary, the multi-dimensional status information of the power equipment is redundant, the effective status monitoring information is difficult to extract, and the current situation of lack of fusion of the multi-dimensional status information hinders the technical progress in the field.
Based on this, it is desirable to obtain a method for performing fusion and state evaluation on multi-dimensional state data of an electric power device.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a terminal and a storage medium for fusing multi-dimensional state data of electric power equipment, which are used for solving the problem of insufficient relevance of the state data of the electric power equipment.
In a first aspect, an embodiment of the present invention provides a method for fusing multidimensional state data of electrical equipment, including:
acquiring a state monitoring quantity and a fault type database corresponding to the state monitoring quantity;
obtaining a trend factor according to the state monitoring quantity and the fault type database, wherein the trend factor is used for reflecting the severity of the monitoring quantity and the relevance in time;
calculating to obtain multidimensional state data according to the trend factor and the state monitoring quantity, wherein the multidimensional state data is used for representing the abnormal degree of the state monitoring quantity deviating from a normal value;
and inputting the multidimensional state data into a self-encoder to obtain multidimensional state fusion data.
In one possible implementation manner, after the multi-dimensional state data is input into the self-encoder and the multi-dimensional state fusion data is obtained, the method further includes:
and comparing the size of the multi-dimensional state fusion data with the fault type database to obtain the state of the power equipment.
In a possible implementation manner, the obtaining a trend factor according to the state monitoring amount and the fault type database includes:
acquiring state quantity weight, repetition factors and attenuation factors according to the state monitoring quantity and the fault type database;
obtaining the trend factor from the state quantity weight, the repetition factor, the decay factor, and a first formula, the first formula:
trend factor (weight of state quantity) repetition factor (1-attenuation factor).
In a possible implementation manner, the obtaining the status monitoring amount and the fault type database corresponding to the status monitoring amount includes:
acquiring monitoring data of the electrical equipment;
and grouping the power equipment monitoring data according to the power equipment fault types which can be caused by the power equipment monitoring data to obtain the state monitoring quantity and the fault type database corresponding to the state detection quantity.
In one possible implementation manner, the obtaining multidimensional state data by calculating according to the trend factor and the state monitoring amount includes:
and multiplying the state monitoring quantity by the trend factor to obtain the multidimensional state data.
In one possible implementation manner, the inputting the multidimensional state data into an auto-encoder to obtain multidimensional state fusion data includes:
self-encoder training: inputting the multi-dimensional state data into the self-encoder, and training the self-encoder, wherein the input and the preset output of the self-encoder are the multi-dimensional state data;
determining a calculated square loss function value from the self-encoder input, the self-encoder actual output, and a second formula, the second formula:
L＝(y-f(x))2
wherein L is a squared loss function value, y is the self-encoder input, f (x) is the self-encoder actual output;
if the square loss function value is in the received range, taking the output of the self-encoder hidden layer as the multi-dimensional state fusion data;
and if the square loss function value is not in the received range, skipping to the self-encoder training step.
In a second aspect, an embodiment of the present invention provides an electrical equipment multidimensional state data fusion apparatus, including: the data acquisition module is used for acquiring the state monitoring quantity and a fault type database corresponding to the state monitoring quantity;
the trend factor calculation module is used for obtaining a trend factor according to the state monitoring quantity and the fault type database, and the trend factor is used for reflecting the severity of the monitoring quantity and the relevance in time;
the multidimensional state data calculation module is used for calculating and obtaining multidimensional state data according to the trend factors and the state monitoring quantity, and the multidimensional state data are used for representing the abnormal degree of the state monitoring quantity deviating from a normal value; and the number of the first and second groups,
and the multi-dimensional state fusion data output module is used for inputting the multi-dimensional state data into the self-encoder to obtain the multi-dimensional state fusion data.
In one possible implementation manner, the method further includes: and the power equipment state evaluation module is used for comparing the size of the multi-dimensional state fusion data with the fault type database to obtain the state of the power equipment.
In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect when executing the computer program.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any one of the possible implementations of the first aspect.
Compared with the prior art, the implementation mode of the invention has the following beneficial effects:
the embodiment of the invention discloses a method for fusing multi-dimensional state data of electric power equipment, which is based on the combination of a trend factor and a self-encoder, can represent the abnormal degree of the electric power equipment deviating from a normal value, and simultaneously reflect the damage accumulation effect generated by abnormal state monitoring quantity, thereby reflecting the front and back dependency of the data on the time dimension, and adopts the self-encoder to fuse the multi-dimensional state data processed by the trend factor. The fused data can automatically extract features, the defects of traditional manual feature extraction are effectively reduced, the fused data is used for state evaluation, and the accuracy is high.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flowchart of a method for fusing multidimensional state data of electrical equipment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multidimensional state data calculation method provided by an embodiment of the invention;
FIG. 3 is a flow chart of the self-encoder fusing multi-dimensional state data according to an embodiment of the present invention;
FIG. 4 is a block diagram of a self-encoder provided by an embodiment of the present invention;
fig. 5 is a functional block diagram of a multidimensional state data fusion device for electrical equipment according to an embodiment of the present invention;
fig. 6 is a functional block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made with reference to the accompanying drawings.
The following is a detailed description of the embodiments of the present invention, which is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Fig. 1 is a flowchart of a method for fusing multidimensional state data of electrical equipment according to an embodiment of the present invention.
As shown in fig. 1, an implementation flow of the multidimensional state data fusion method for the electrical equipment according to the embodiment of the present invention may include steps 101 to 104.
In step 101, a status monitoring quantity and a fault type database corresponding to the status monitoring quantity are obtained.
In some embodiments, step 101 may comprise:
acquiring monitoring data of the electrical equipment;
and grouping the power equipment monitoring data according to the power equipment fault types which can be caused by the power equipment monitoring data to obtain the state monitoring quantity and the fault type database corresponding to the state detection quantity.
In step 102, a trend factor is obtained according to the state monitoring quantity and the fault type database, and the trend factor is used for reflecting the severity and the time relevance of the monitoring quantity.
In some embodiments, step 102 may comprise:
acquiring state quantity weight, repetition factors and attenuation factors according to the state monitoring quantity and the fault type database;
obtaining a trend factor from the state quantity weight, the repetition factor, the decay factor, and a first formula, the first formula:
trend factor (weight of state quantity) repetition factor (1-attenuation factor).
Illustratively, in order to better fuse the monitoring quantities of multiple states, the data preprocessing is carried out on the monitoring quantities of the multiple states by utilizing the correlation among the monitoring quantities of the multiple states and generating an action relation with the preset power equipment fault. One way of preprocessing that can be implemented is to determine the weight of the condition monitoring quantity according to the magnitude of the value of the condition monitoring quantity and the frequency of occurrence.
Therefore, in order to reflect the severity and the temporal relevance of the state monitoring quantity, a trend factor concept is proposed based on the corresponding relationship between the state monitoring quantity and the fault type.
The trend factor mainly comprises three parts, namely state quantity weight, repetition factor and attenuation factor, and the trend factor and the state quantity weight, the repetition factor and the attenuation factor satisfy the following relations:
trend factor (state quantity weight) repeat factor (1-attenuation factor)
The state quantity weight is used for reflecting the severity of the abnormal state monitoring quantity data and representing the severity of the detected state monitoring quantity. On the basis of exceeding the fault limit value, different state quantity weights are given in different state value intervals through section division, and the more deviation from normal values is, the larger the state quantity weight is, and the more serious the abnormal condition of the state monitoring quantity data is.
The state quantity weight will be described by taking the discharge quantity of the partial discharge as an example. When the local discharge amount is less than the 50pC limit value, the state quantity weight is set to 1; when the partial discharge amount is between 50pC and 100pC, the state quantity weight is set to 1.5, and so on. After the partial discharge amount exceeds the 50pC limit value, the larger the partial discharge amount is, the higher the weight setting value is.
The repetition factor is used for reflecting the abnormal continuity of the state monitoring quantity data, and shows the proportion of the state monitoring quantity deviating from a normal value in a short-time detection period with fixed time length. The more the deviation times of the state monitoring quantity from the normal value are, the longer the deviation time is, and the corresponding repetition factor is larger. Therefore, the repetition factor may also reflect the severity of the abnormal state monitoring quantity from the side. The repetition factor is determined according to the difference value between the state monitoring quantity and the limit value, and the larger the difference value is, the larger the value of the repetition factor is.
The repetition factor will be described by taking the number of partial discharges as an example. When the partial discharge frequency in one hour is less than a set limit value, the repetition factor is 1; when the number of partial discharges in one hour is higher than the limit value, the corresponding repetition factor value (greater than 1) is set according to the number of partial discharges. As in one embodiment, the repetition factor is 1 when the number of partial discharges in one hour is less than 3, the repetition factor is 2 when the number of partial discharges in one hour reaches 3, the repetition factor is 3 when the number of partial discharges in one hour reaches 4, and so on.
The attenuation factor is used for reflecting the abnormal recoverability of the state monitoring quantity data, and is represented by the frequency of the occurrence of the event that the state monitoring quantity deviates from a normal value in a long detection period, namely the time interval of two adjacent events. The shorter the interval between two events, the smaller the decay factor and the corresponding (1-decay factor) is. Also, the attenuation factor may reflect the severity of the abnormal state monitoring quantity from the side.
And determining the numerical values of the state quantity weight, the repetition factor and the attenuation factor in the trend factor through expert presetting or a genetic algorithm, and calculating the value of the trend factor to calculate the value of the trend factor.
In step 103, multidimensional state data is obtained through calculation according to the trend factor and the state monitoring quantity, and the multidimensional state data is used for representing the abnormal degree of the state monitoring quantity deviating from a normal value.
In some embodiments, step 103 may comprise:
and multiplying the state monitoring quantity by the trend factor to obtain the multidimensional state data.
Illustratively, as shown in fig. 2, the state monitoring amount is multiplied by a trend factor, and multidimensional state data processed by the trend factor is calculated.
After the trend factor processing, the multidimensional state data can represent the abnormal degree of the state monitoring quantity deviating from a normal value, and simultaneously reflect the damage accumulation effect generated by the abnormal state monitoring quantity, so that the front and back dependency of the data on the time dimension is reflected.
After the monitoring quantity data of the power equipment is processed by the trend factor, the multiple monitoring quantity data need to be fused to obtain the state key variable values capable of expressing different fault types of the power equipment.
Some fault types can only be reflected by some fixed monitored quantities and are insensitive to changes in other monitored quantities. Meanwhile, for the same fault type, the influence degrees caused by different monitoring amount changes are different.
In step 104, the multi-dimensional state data is input into a self-encoder to obtain multi-dimensional state fusion data.
In some embodiments, step 104 may include:
self-encoder training: inputting the multi-dimensional state data into the self-encoder, and training the self-encoder, wherein the input and the preset output of the self-encoder are the multi-dimensional state data;
determining a calculated square loss function value from the self-encoder input, the self-encoder actual output, and a second formula, the second formula:
L＝(y-f(x))2
wherein L is a squared loss function value, y is the self-encoder input, f (x) is the self-encoder actual output;
if the square loss function value is in the received range, taking the output of the self-encoder hidden layer as the multi-dimensional state fusion data;
and if the square loss function value is not in the received range, skipping to the self-encoder training step.
Illustratively, the conventional data preprocessing method needs to establish a corresponding full-connection network coefficient matrix through guiding rules and engineering practical conditions. And establishing a relation between each monitored quantity and the fault type of the power equipment through a connection network, and representing the influence of the monitored quantity on the fault through the coefficient. The method can only roughly describe the relation between the monitoring quantity and the fault type, but cannot accurately quantitatively reflect the influence relation, and meanwhile, cannot perform self-adaptive adjustment according to different monitoring quantities.
In order to adaptively adjust and fuse the multidimensional state monitoring data into a power equipment fault key variable, a self-encoder is adopted to fuse the data.
The self-encoder is one of artificial intelligence networks, and the main effect is to carry out the compression and the processing of data, and the self-encoder has two parts: respectively, an encoder responsible for data compression or special encoding, and a decoder that restores the encoded data to the original input, which may also be referred to as compression and decompression. According to the limitation that the input of the self-encoder is equal to the output, the network carries out self-training through input data, and the weight is continuously modified and forgotten, so that a network model which is most suitable for the function is constructed.
As shown in fig. 3, the operation steps of fusing the multidimensional state data by using the self-encoder are as follows:
(1) the encoder and the decoder shown in fig. 4 are constructed by using a plurality of layers of fully-connected neural networks, wherein X is input data, H is hidden layer output data, X' is preset output data, weight matrixes and offset vectors of the encoder and the decoder are initialized by using random numbers, and the number of neurons in a middle hidden layer is set to be 1.
(2) And inputting the multidimensional state data into a self-encoder, setting the input to be the same as the preset output, and training an encoder and a decoder of the self-encoder.
(3) Comparing the actual decoder output with the preset output, namely input data (the preset output is the same as the input, namely the requirement), calculating a square loss function value L of the characterization error:
L＝(y-f(x))2
wherein: l is the square loss, y is the predetermined output value, and f (x) is the actual output value. Judging whether the error is within a preset acceptable range, if so, outputting the hidden layer as self-encoder output to represent the fused key variable reflecting the state of the power equipment; if not, continuing to train the self-encoder, and repeating the step (2).
In some embodiments, after step 104, the method for fusing multidimensional state data of an electrical device may further include step 105 of evaluating a state of the electrical device.
In step 105, comparing the multidimensional state fusion data with the fault type database in size to obtain the state of the electrical equipment.
Illustratively, a trained self-encoder is used for power device state evaluation. The evaluation steps are as follows:
(1) the monitoring data of the power equipment obtained by detection is processed through a trend factor, the abnormal degree of the state monitoring quantity deviating from a normal value is represented, and meanwhile, the damage accumulation effect generated by the abnormal state monitoring quantity is reflected, so that the front and back dependency of the data on the time dimension is reflected.
(2) And fusing the multi-dimensional state data processed by the trend factors by using a trained self-encoder, extracting the hidden layer output as the self-encoder output, and evaluating the state of the power equipment according to the size comparison between the hidden layer output data and the data used in the training.
The following is a detailed description of the embodiments of the present invention, which is implemented on the premise of the technical solution of the present invention, and the detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the following examples.
(1) In this embodiment, the multidimensional monitoring data of partial discharge of a transformer with a voltage level of 500kV is collated to form two state monitoring quantities, namely, an average amplitude of partial discharge and a discharge time interval of the transformer. And determining parameters during calculation of the trend factors according to historical training results.
(2) And (3) calculating the weight of the state quantity: when the average amplitude of the partial discharge is smaller than the limit value of 50pC, the state quantity weight is set to be 1, and when the average amplitude of the partial discharge is between 50pC and 100pC, the state quantity weight is set to be 1.5; the state quantity weight is set to 1 when the discharge time interval average is greater than 1 minute, and is set to 1.5 when the discharge time interval average is less than 1 minute.
(3) And (3) calculating a repetition factor: when the number of times that the average amplitude of partial discharge is more than 100pC in one hour is less than 10, the repetition factor is 1, and when the average amplitude of partial discharge is more than 10, the repetition factor value is 1.5; the repetition factor is 1 when the number of times of the discharge time interval average value less than 1 minute within one hour is less than 10 times, and the repetition factor value is 1.5 when it is more than 10 times.
(4) And (3) attenuation factor calculation: when the occurrence time of the partial discharge average amplitude value larger than 100pC in one day is longer than 2 hours, the attenuation factor is 0.8, and when the occurrence time is shorter than 2 hours, the attenuation factor value is 0.9; when the average value of the discharge time intervals in one day is less than 1 minute, the decay factor is 0.8 when it is longer than 2 hours, and the value of the decay factor is 0.9 when it is shorter than 2 hours.
(5) And calculating data values of the partial discharge amplitude and the time interval after passing through the attenuation factor, and inputting the data values into a self-encoder.
(6) The encoder and decoder of the self-encoder are two layers of fully-connected neural networks respectively, the number of neurons in a hidden layer is 1, the input and output are set as data values of discharge amplitude values of a training part and time intervals after passing through attenuation factors, and the self-encoder is trained to enable a loss function value to be less than 10-4。
(7) And outputting data of the hidden layer, wherein the data is the fused discharge characteristic state quantity of the transformer, the data output is 0.8, and the transformer has discharge faults when the output is more than 0.7 by comparing the database, so that the transformer has the discharge faults.
The implementation mode of the multidimensional state data fusion method of the power equipment is based on the combination of the trend factor and the self-encoder, can represent the abnormal degree of the power equipment deviating from a normal value, and simultaneously reflects the damage accumulation effect generated by abnormal state monitoring quantity, so that the front and back dependency of the data on the time dimension is reflected, and the self-encoder is adopted to fuse the multidimensional state data processed by the trend factor. The fused data can automatically extract features, the defects of traditional manual feature extraction are effectively reduced, the fused data is used for state evaluation, and the accuracy is high.
It should be understood that the sequence numbers of the steps in the above embodiments do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are apparatus embodiments of the invention, and for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 5 shows a functional block diagram of a multidimensional state data fusion device for electrical equipment according to an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, and detailed descriptions are as follows:
a power equipment multidimensional state data fusion device comprises: a data acquisition module 51, a trend factor calculation module 52, a multidimensional state data calculation module 53 and a multidimensional state fusion data output module 54.
And a data obtaining module 51, configured to obtain the status monitoring amount and a fault type database corresponding to the status monitoring amount.
And a trend factor calculation module 52, configured to obtain a trend factor according to the state monitoring amount and the fault type database, where the trend factor is used to reflect the severity of the monitoring amount and the relevance in time.
And a multidimensional state data calculating module 53, configured to calculate and obtain multidimensional state data according to the trend factor and the state monitoring amount, where the multidimensional state data is used to represent an abnormal degree of the state monitoring amount deviating from a normal value.
And a multidimensional state fusion data output module 54, configured to input the multidimensional state data into the self-encoder to obtain multidimensional state fusion data.
In some embodiments, further comprising: and the power equipment state evaluation module 55 is configured to obtain the state of the power equipment according to the size comparison between the multidimensional state fusion data and the fault type database.
Fig. 6 is a functional block diagram of a terminal according to an embodiment of the present invention. As shown in fig. 6, the terminal 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60. The processor 60 executes the computer program 62 to implement the above-mentioned method for fusing multidimensional state data of electrical equipment and the steps of the method for fusing multidimensional state data of electrical equipment, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, implements the functions of the modules/units in the device embodiments described above, such as the modules/units 51 to 55 shown in fig. 5.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 62 in the terminal 6. For example, the computer program 62 may be divided into the modules/units 51 to 55 shown in fig. 5.
The terminal 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal 6 may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 5 is only an example of a terminal 6 and does not constitute a limitation of the terminal 6, and that it may comprise more or less components than those shown, or some components may be combined, or different components, for example the terminal may further comprise input output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the terminal 6, such as a hard disk or a memory of the terminal 6. The memory 61 may also be an external storage device of the terminal 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal 6. The memory 61 is used for storing the computer program and other programs and data required by the terminal. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment is focused on, and for parts that are not described or illustrated in detail in a certain embodiment, reference may be made to the description of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the above embodiments may be implemented by instructing related hardware through a computer program, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the implementation of the method and the apparatus for fusing multidimensional state data of electrical equipment may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for fusing multidimensional state data of electrical equipment is characterized by comprising the following steps:
acquiring a state monitoring quantity and a fault type database corresponding to the state monitoring quantity;
obtaining a trend factor according to the state monitoring quantity and the fault type database, wherein the trend factor is used for reflecting the severity of the monitoring quantity and the relevance in time;
calculating to obtain multidimensional state data according to the trend factor and the state monitoring quantity, wherein the multidimensional state data is used for representing the abnormal degree of the state monitoring quantity deviating from a normal value;
and inputting the multidimensional state data into a self-encoder to obtain multidimensional state fusion data.
2. The method for fusing multidimensional state data of power equipment according to claim 1, wherein after the multidimensional state data is input from an encoder and the multidimensional state fused data is obtained, the method further comprises:
and comparing the size of the multidimensional state fusion data with the fault type database to obtain the state of the power equipment.
3. The method for fusing the multidimensional state data of the power equipment according to claim 1, wherein the obtaining trend factors according to the state monitoring amount and the fault type database comprises:
acquiring state quantity weight, repetition factors and attenuation factors according to the state monitoring quantity and the fault type database;
obtaining the trend factor from the state quantity weight, the repetition factor, the decay factor, and a first formula, the first formula:
trend factor (weight of state quantity) repetition factor (1-attenuation factor).
4. The method for fusing the multidimensional state data of the power equipment according to claim 1, wherein the acquiring of the state monitoring quantity and the fault type database corresponding to the state monitoring quantity comprises:
acquiring monitoring data of the electrical equipment;
and grouping the power equipment monitoring data according to the power equipment fault types which can be caused by the power equipment monitoring data to obtain the state monitoring quantity and the fault type database corresponding to the state detection quantity.
5. The method for fusing the multidimensional state data of the power equipment according to claim 1, wherein the calculating and obtaining the multidimensional state data according to the trend factor and the state monitoring amount comprises:
and multiplying the state monitoring quantity by the trend factor to obtain the multidimensional state data.
6. The method for fusing multidimensional state data of power equipment according to claim 1, wherein the inputting the multidimensional state data into a self-encoder to obtain multidimensional state fusion data comprises:
self-encoder training: inputting the multi-dimensional state data into the self-encoder, and training the self-encoder, wherein the input and the preset output of the self-encoder are the multi-dimensional state data;
determining a calculated square loss function value from the self-encoder input, the self-encoder actual output, and a second formula, the second formula:
L＝(y-f(x))2
wherein L is a squared loss function value, y is the self-encoder input, f (x) is the self-encoder actual output;
if the square loss function value is in a preset range, taking the output of the self-encoder hidden layer as the multi-dimensional state fusion data;
and if the square loss function value is not in a preset range, skipping to the self-encoder training step.
7. A multi-dimensional state data fusion device for electrical equipment is characterized by comprising:
the data acquisition module is used for acquiring the state monitoring quantity and a fault type database corresponding to the state monitoring quantity;
the trend factor calculation module is used for obtaining a trend factor according to the state monitoring quantity and the fault type database, and the trend factor is used for reflecting the severity of the monitoring quantity and the relevance in time;
the multidimensional state data calculation module is used for calculating and obtaining multidimensional state data according to the trend factors and the state monitoring quantity, and the multidimensional state data are used for representing the abnormal degree of the state monitoring quantity deviating from a normal value; and the number of the first and second groups,
and the multi-dimensional state fusion data output module is used for inputting the multi-dimensional state data into the self-encoder to obtain the multi-dimensional state fusion data.
8. The multi-dimensional state data fusion device for electrical equipment according to claim 7, further comprising:
and the power equipment state evaluation module is used for comparing the size of the multi-dimensional state fusion data with the fault type database to obtain the state of the power equipment.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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