Transformer oil temperature data processing method, system, terminal and storage medium

Abstract

The present invention relates to the field of data processing technology, and specifically provides a transformer oil temperature data processing method, system, terminal and storage medium, including: collecting transformer oil temperature, and caching the transformer oil temperature as time series data according to the collection time; presetting a first threshold and a second threshold based on historical fault data, the first threshold is less than the second threshold; if the transformer oil temperature reaches the second threshold, a temperature alarm prompt message is generated; if the transformer oil temperature reaches the first threshold and does not reach the second threshold, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormal identification on the sample data. The computing resources occupied by data processing are greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a transformer oil temperature data processing method, system, terminal and storage medium. Background Art

[0002] As the scale of power grids continues to expand and the power load continues to increase, power users have higher and higher requirements for the operation level of power grids and the reliability of power supply. Transformer temperature is an important indicator for judging whether the transformer is operating normally, but the temperature measurement and control circuit of the main transformer equipment may also fail, and the temperature reading may be wrong, resulting in inaccurate control of the actual operating status of the transformer by the substation staff and dispatchers. If the fault cannot be discovered and eliminated in time, it will bury hidden dangers for the safe operation of the main transformer equipment and seriously affect the reliability of the equipment power supply.

[0003] Existing transformer temperature detection methods, such as transformer temperature detection through Internet of Things technology, include several real-time acquisition units, which are electrically connected to a communication module. By setting several real-time acquisition units, the operation data of the distribution network line transformer is collected in real time, and the collected data sets are classified and input into the cloud server in real time, and a temperature model is established. The temperature model can be used for accurate judgment of unknown data through training and iterative update. By establishing a thermal model of the transformer, the working state of the transformer can be effectively monitored in real time to determine whether the transformer operation is abnormal due to the environment and load temperature, or whether the transformer temperature rises sharply, the current changes abnormally, and the equipment vibrates abnormally due to equipment short circuit and failure in the distribution network, and early warning and alarm processing are carried out for abnormal operation of the transformer in real time, and timely response and situation processing are carried out.

[0004] The above method does not differentiate the data, and the temperature model processes all reported data, which undoubtedly increases the computational burden of the remote monitoring center. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a transformer oil temperature data processing method, system, terminal and storage medium to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a transformer oil temperature data processing method, comprising:

[0007] Collect transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time;

[0008] Based on the historical fault data, a first threshold and a second threshold are preset, and the first threshold is smaller than the second threshold;

[0009] If the transformer oil temperature reaches a second threshold, a temperature alarm prompt message is generated;

[0010] If the transformer oil temperature reaches the first threshold value but does not reach the second threshold value, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormality identification on the sample data.

[0011] In an optional implementation, the transformer oil temperature is collected and the transformer oil temperature is cached as time series data according to the collection time, including:

[0012] The transformer oil temperature is detected by a temperature detection device, wherein the temperature detection device includes a platinum resistor and a temperature transmitter;

[0013] The oil temperature reported by the temperature detection device every 1 minute is updated to the time series data.

[0014] In an optional implementation, based on historical fault data, a first threshold and a second threshold are pre-set, and the first threshold is less than the second threshold, including:

[0015] Acquire historical fault data, wherein the historical fault data includes transformer fault type and fault occurrence time;

[0016] Extracting pre-fault data of a specified time length before the fault occurrence time and the oil temperature at the time of the fault corresponding to the fault occurrence time from the historical time series data;

[0017] Filter out the maximum value from all pre-fault data, and set the maximum oil temperature in the pre-fault data as the first threshold;

[0018] A minimum value is selected from all fault-time oil temperatures, and the minimum fault-time oil temperature is set as the second threshold value.

[0019] In an optional implementation, if the transformer oil temperature reaches the first threshold value and does not reach the second threshold value, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormality identification on the sample data, including:

[0020] Setting the length of the sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data;

[0021] Scaling the sample data to [-1,1] to obtain a standard sequence;

[0022] By encoding the oil temperature value in the standard sequence as angular cosine, encoding the timestamp corresponding to the oil temperature value as radius, and converting the standard sequence into a polar coordinate system, a two-dimensional feature matrix of the standard sequence is obtained;

[0023] A neural network model is used to identify abnormalities in the two-dimensional feature matrix and predict the fault type.

[0024] In a second aspect, the present invention provides a transformer oil temperature data processing system, comprising:

[0025] The data acquisition module is used to collect the transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time;

[0026] A threshold setting module, used to pre-set a first threshold and a second threshold based on historical fault data, wherein the first threshold is smaller than the second threshold;

[0027] An alarm generation module, configured to generate a temperature alarm prompt message if the transformer oil temperature reaches a second threshold;

[0028] The abnormality identification module is used to extract sample data of a fixed time length from the time series data using a sliding time window if the transformer oil temperature reaches a first threshold value and does not reach a second threshold value, and to perform abnormality identification on the sample data using a neural network model.

[0029] In an optional embodiment, the data acquisition module includes:

[0030] A data acquisition unit, used to detect the oil temperature of the transformer through a temperature detection device, wherein the temperature detection device includes a platinum resistance and a temperature transmitter;

[0031] The data cache unit is used to update the oil temperature reported by the temperature detection device every 1 minute into time series data.

[0032] In an optional implementation, the threshold setting module includes:

[0033] A data acquisition unit, used to acquire historical fault data, wherein the historical fault data includes the transformer fault type and the time when the fault occurred;

[0034] A data analysis unit, used to extract pre-fault data of a specified time length before the fault occurrence time and the oil temperature at the time of the fault corresponding to the fault occurrence time from the historical time series data;

[0035] A first setting unit, used for screening out a maximum value from all pre-fault data, and setting the maximum oil temperature in the pre-fault data as a first threshold;

[0036] The second setting unit is used to select a minimum value from all the oil temperatures at fault and set the minimum oil temperature at fault as a second threshold value.

[0037] In an optional embodiment, the abnormality identification module includes:

[0038] A sample extraction unit, used to set the length of the sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data;

[0039] A data scaling unit, used for scaling the sample data to [-1, 1] to obtain a standard sequence;

[0040] A sequence conversion unit, used for converting the standard sequence into a polar coordinate system by encoding the oil temperature value in the standard sequence as an angular cosine and encoding the timestamp corresponding to the oil temperature value as a radius, so as to obtain a two-dimensional feature matrix of the standard sequence;

[0041] The abnormality identification unit is used to use a neural network model to perform abnormality identification on the two-dimensional feature matrix and predict the fault type.

[0042] In a third aspect, a terminal is provided, including:

[0043] processor, memory, wherein:

[0044] The memory is used to store computer programs.

[0045] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0046] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0047] The beneficial effect of the present invention is that the transformer oil temperature data processing method, system, terminal and storage medium provided by the present invention distinguish the transformer status by setting a two-level threshold value, do not process the data in an absolutely healthy state, and only perform abnormal identification on the data suspected of causing a fault, thereby greatly reducing the computing resources occupied by data processing.

[0048] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0051] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0052] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0055] The transformer oil temperature data processing method provided in the embodiment of the present invention is executed by a computer device, and accordingly, the transformer oil temperature data processing system runs in the computer device.

[0056] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a transformer oil temperature data processing system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0057] like Figure 1 As shown, the method includes:

[0058] Step 110, collecting the transformer oil temperature, and caching the transformer oil temperature as time series data according to the collection time;

[0059] Step 120, presetting a first threshold and a second threshold based on historical fault data, wherein the first threshold is smaller than the second threshold;

[0060] Step 130, if the transformer oil temperature reaches a second threshold, generating a temperature alarm prompt message;

[0061] Step 140: If the transformer oil temperature reaches the first threshold value but does not reach the second threshold value, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormality identification on the sample data.

[0062] To facilitate understanding of the present invention, the transformer oil temperature data processing method provided by the present invention is further described below based on the principle of the transformer oil temperature data processing method of the present invention and in combination with the process of processing the transformer oil temperature data in the embodiment.

[0063] Specifically, the transformer oil temperature data processing method includes:

[0064] S1. Collect the transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time.

[0065] The transformer oil temperature is detected by a temperature detection device, wherein the temperature detection device includes a platinum resistor and a temperature transmitter; the oil temperature reported by the temperature detection device every 1 minute is updated to the time series data.

[0066] Platinum resistance, referred to as platinum thermal resistance, its resistance value will change with the change of temperature. Platinum resistance is used to detect transformer oil temperature.

[0067] S2. Preset a first threshold and a second threshold based on historical fault data, wherein the first threshold is smaller than the second threshold.

[0068] Obtain historical fault data, which includes the transformer fault type and the time when the fault occurred; extract pre-fault data of a specified time length before the fault occurrence time and the fault oil temperature corresponding to the fault occurrence time from the historical time series data; filter out the maximum value from all pre-fault data, and set the maximum oil temperature in the pre-fault data as the first threshold; filter out the minimum value from all fault oil temperatures, and set the minimum fault oil temperature as the second threshold.

[0069] For example, the oil temperature data sequence of a certain fault is [T1, T2, T3, ..., T k ], where T k is the oil temperature collected for the kth time, assuming that T k is the oil temperature at the time of the fault, and the time interval between two adjacent oil temperature collections is 1 minute. Based on experience, the time length is specified as 15 minutes, so the pre-fault data intercepted from the oil temperature data sequence is T k-15 ,T k-14 ,…,T k-1 .

[0070] The pre-fault data intercepted from all historical time series data are summarized, and the maximum value is selected as the first threshold. The first threshold is the normal oil temperature before the fault occurs, but it is higher than other normal oil temperatures, so it can be used as a risk signal.

[0071] The historical fault data and the corresponding pre-fault data are synchronously saved to the data set for training the neural network model.

[0072] S3. Compare the latest collected oil temperature value with the first threshold and the second threshold. If the oil temperature value is lower than the first threshold, it is only cached in the time series data.

[0073] S4. If the transformer oil temperature reaches a second threshold, a temperature alarm prompt message is generated.

[0074] If the latest collected oil temperature value reaches the second threshold, a temperature alarm prompt message is generated so that the operation and maintenance personnel can perform fault maintenance on the transformer in time.

[0075] S5. If the transformer oil temperature reaches the first threshold value but does not reach the second threshold value, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormality identification on the sample data.

[0076] S501. Set the length of a sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data.

[0077] The time series acquisition granularity is 1 minute, and the acquisition duration is 30 minutes, so the length is 30.

[0078] S502, scaling the sample data to [-1, 1] to obtain a standard sequence; encoding the oil temperature value in the standard sequence as angular cosine, encoding the timestamp corresponding to the oil temperature value as radius, converting the standard sequence to a polar coordinate system, and obtaining a two-dimensional feature matrix of the standard sequence.

[0079] The formulas for encoding the oil temperature values ​​in the standard sequence as angular cosine and encoding the timestamps corresponding to the oil temperature values ​​as radius include:

[0080] ;

[0081] in, is the scaled oil temperature, N is the constant factor of the span of the regularized polar coordinate system, t i is the corresponding timestamp.

[0082] The converted feature matrix retains the temperature and timing features.

[0083] S503: Use a neural network model to perform abnormality recognition on the two-dimensional feature matrix and predict the fault type.

[0084] The neural network model adopts a convolutional neural network (CNN) model. Since the training set of the neural network model includes the fault type, the neural network model convolves the two-dimensional feature matrix and finally outputs the fault type.

[0085] In some embodiments, the transformer oil temperature data processing system may include a plurality of functional modules composed of computer program segments. The computer programs of the various program segments in the transformer oil temperature data processing system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function of transformer oil temperature data processing.

[0086] In this embodiment, the transformer oil temperature data processing system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: a data acquisition module 210, a threshold setting module 220, an alarm generation module 230 and an abnormality identification module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0087] The data acquisition module is used to collect the transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time;

[0088] A threshold setting module, used to pre-set a first threshold and a second threshold based on historical fault data, wherein the first threshold is smaller than the second threshold;

[0089] An alarm generation module, configured to generate a temperature alarm prompt message if the transformer oil temperature reaches a second threshold;

[0090] The abnormality identification module is used to extract sample data of a fixed time length from the time series data using a sliding time window if the transformer oil temperature reaches a first threshold value and does not reach a second threshold value, and to perform abnormality identification on the sample data using a neural network model.

[0091] Optionally, as an embodiment of the present invention, the data acquisition module includes:

[0092] A data acquisition unit, used to detect the oil temperature of the transformer through a temperature detection device, wherein the temperature detection device includes a platinum resistance and a temperature transmitter;

[0093] The data cache unit is used to update the oil temperature reported by the temperature detection device every 1 minute into time series data.

[0094] Optionally, as an embodiment of the present invention, the threshold setting module includes:

[0095] A data acquisition unit, used to acquire historical fault data, wherein the historical fault data includes the transformer fault type and the time when the fault occurred;

[0096] A data analysis unit, used to extract pre-fault data of a specified time length before the fault occurrence time and the oil temperature at the time of the fault corresponding to the fault occurrence time from the historical time series data;

[0097] A first setting unit, used for screening out a maximum value from all pre-fault data, and setting the maximum oil temperature in the pre-fault data as a first threshold;

[0098] The second setting unit is used to select a minimum value from all the oil temperatures at fault and set the minimum oil temperature at fault as a second threshold value.

[0099] Optionally, as an embodiment of the present invention, the abnormality identification module includes:

[0100] A sample extraction unit, used to set the length of the sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data;

[0101] A data scaling unit, used for scaling the sample data to [-1, 1] to obtain a standard sequence;

[0102] A sequence conversion unit, used for converting the standard sequence into a polar coordinate system by encoding the oil temperature value in the standard sequence as an angular cosine and encoding the timestamp corresponding to the oil temperature value as a radius, so as to obtain a two-dimensional feature matrix of the standard sequence;

[0103] The abnormality identification unit is used to use a neural network model to perform abnormality identification on the two-dimensional feature matrix and predict the fault type.

[0104] Figure 3 The present invention provides a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the transformer oil temperature data processing method provided in an embodiment of the present invention.

[0105] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0106] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0107] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0108] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0109] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0110] Therefore, the present invention distinguishes the transformer status by setting a two-level threshold value. The data in an absolutely healthy state is not processed, and only abnormalities are identified for the data suspected of causing faults, which greatly reduces the computing resources occupied by data processing. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0111] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0112] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0113] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0114] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0116] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person skilled in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and they shall be within the scope of protection of the present invention.

Claims

1. A transformer oil temperature data processing method, characterized in that: include: Collect transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time; Based on the historical fault data, a first threshold and a second threshold are preset, and the first threshold is smaller than the second threshold; If the transformer oil temperature reaches a second threshold, a temperature alarm prompt message is generated; If the transformer oil temperature reaches the first threshold value and does not reach the second threshold value, extracting sample data of a fixed time length from the time series data using a sliding time window, and performing abnormality identification on the sample data using a neural network model; Based on the historical fault data, a first threshold and a second threshold are preset, and the first threshold is smaller than the second threshold, including: Acquire historical fault data, wherein the historical fault data includes transformer fault type and fault occurrence time; Extracting pre-fault data of a specified time length before the fault occurrence time and the oil temperature at the time of the fault corresponding to the fault occurrence time from the historical time series data; Filter out the maximum value from all pre-fault data, and set the maximum oil temperature in the pre-fault data as the first threshold; A minimum value is selected from all fault-time oil temperatures, and the minimum fault-time oil temperature is set as the second threshold value.

2. The method according to claim 1, characterized in that Collect the transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time, including: The transformer oil temperature is detected by a temperature detection device, wherein the temperature detection device includes a platinum resistor and a temperature transmitter; The oil temperature reported by the temperature detection device every 1 minute is updated to the time series data.

3. The method according to claim 1, characterized in that If the transformer oil temperature reaches the first threshold value and does not reach the second threshold value, a sliding time window is used to extract sample data of a fixed time length from the time series data, and a neural network model is used to perform abnormal identification on the sample data, including: Setting the length of the sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data; Scaling the sample data to [-1,1] to obtain a standard sequence; By encoding the oil temperature value in the standard sequence as angular cosine, encoding the timestamp corresponding to the oil temperature value as radius, and converting the standard sequence into a polar coordinate system, a two-dimensional feature matrix of the standard sequence is obtained; A neural network model is used to identify abnormalities in the two-dimensional feature matrix and predict the fault type.

4. A transformer oil temperature data processing system, characterized in that: include: The data acquisition module is used to collect the transformer oil temperature and cache the transformer oil temperature as time series data according to the collection time; A threshold setting module, used to pre-set a first threshold and a second threshold based on historical fault data, wherein the first threshold is smaller than the second threshold; An alarm generation module, configured to generate a temperature alarm prompt message if the transformer oil temperature reaches a second threshold; An abnormality identification module, used for extracting sample data of a fixed time length from the time series data using a sliding time window if the transformer oil temperature reaches a first threshold value and does not reach a second threshold value, and performing abnormality identification on the sample data using a neural network model; The threshold setting module comprises: A data acquisition unit, used to acquire historical fault data, wherein the historical fault data includes the transformer fault type and the time when the fault occurred; A data analysis unit, used to extract pre-fault data of a specified time length before the fault occurrence time and the oil temperature at the time of the fault corresponding to the fault occurrence time from the historical time series data; A first setting unit, used for screening out a maximum value from all pre-fault data, and setting the maximum oil temperature in the pre-fault data as a first threshold; The second setting unit is used to select a minimum value from all the oil temperatures at fault and set the minimum oil temperature at fault as a second threshold value.

5. The system according to claim 4, characterized in that The data acquisition module comprises: A data acquisition unit, used to detect the oil temperature of the transformer through a temperature detection device, wherein the temperature detection device includes a platinum resistance and a temperature transmitter; The data cache unit is used to update the oil temperature reported by the temperature detection device every 1 minute into time series data.

6. The system according to claim 4, characterized in that The abnormality identification module comprises: A sample extraction unit, used to set the length of the sliding time window so that the sliding time window extracts sample data of a fixed time length from the time series data; A data scaling unit, used for scaling the sample data to [-1, 1] to obtain a standard sequence; A sequence conversion unit, used for converting the standard sequence into a polar coordinate system by encoding the oil temperature value in the standard sequence as an angular cosine and encoding the timestamp corresponding to the oil temperature value as a radius, so as to obtain a two-dimensional feature matrix of the standard sequence; The abnormality identification unit is used to use a neural network model to perform abnormality identification on the two-dimensional feature matrix and predict the fault type.

7. A terminal, characterized in that: include: A memory, used for storing a transformer oil temperature data processing program; A processor is used to implement the steps of the transformer oil temperature data processing method according to any one of claims 1 to 3 when executing the transformer oil temperature data processing program.

8. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a transformer oil temperature data processing program, and when the transformer oil temperature data processing program is executed by the processor, the steps of the transformer oil temperature data processing method according to any one of claims 1 to 3 are implemented.

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