Intelligent analysis method and device for detection current and voltage data of power supply instrument

Abstract

The embodiment of the invention provides an intelligent analysis method and device for current and voltage data detected by a power supply instrument. The method comprises the following steps: acquiring electrical data from a power system by using high-precision power supply instrument equipment; the server receives the electrical data transmitted by the power supply instrument equipment in a wireless communication mode; the server determines whether the electrical data is missing or not; and if not, inputting the electrical data into a pre-trained intelligent analysis model, and outputting a change curve of current and voltage, numerical values of various characteristic indexes and early warning information. According to the invention, reading and recording are not needed, and all the information is transmitted to the server by the device in a wireless communication mode; besides, through efficient integration with a railway management system, data islands are broken, data sharing and comprehensive analysis are realized, dependence on manual operation is reduced, human error risks are reduced, and efficiency of a detection process is improved. And the power system can be continuously monitored and early warning can be given out before a potential problem occurs.

Description

Technical Field

[0001] The present application relates to the technical field of power supply meter detection, and in particular to a method and device for intelligently analyzing current and voltage data detected by a power supply meter. Background Art

[0002] As the railway industry continues to advance, the increase in the number of railway lines has brought about an increase in the complexity of the power system. Although traditional power supply meters can detect basic data such as current and voltage, they usually lack the ability to conduct in-depth analysis of these data, which limits the possibility of timely detection and handling of potential power problems.

[0003] In addition, traditional instrument detection has not yet been effectively integrated with other railway management systems, resulting in data islands, making it impossible to share and comprehensively analyze data. Moreover, most of the data are in paper form, which requires a lot of data entry work and high labor costs. This further leads to the fact that power anomalies can only be repaired manually after they occur, and predictions cannot be made in advance, which brings great inconvenience.

[0004] Therefore, how to solve the above problems is an issue that needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for intelligently analyzing current and voltage data detected by a power supply meter, aiming to improve the above-mentioned problems.

[0006] In a first aspect, the present application provides a method for intelligently analyzing current and voltage data detected by a power supply meter, the method comprising:

[0007] Use high-precision power supply instrumentation equipment to collect electrical data from the power system, the electrical data including voltage data and current data;

[0008] The server receives the electrical data transmitted by the power supply meter device via wireless communication;

[0009] The server determines whether the electrical property data is missing;

[0010] If not, the electrical data is input into a pre-trained intelligent analysis model to output a current and voltage change curve, values ​​of various characteristic indicators, and early warning information.

[0011] In a possible embodiment, the training process of the pre-trained intelligent analysis model includes:

[0012] By applying the 3σ criterion, real-time outlier detection and elimination are performed on each collected electrical data to obtain preliminary electrical data;

[0013] The preliminary electrical data are smoothed using a moving average filtering algorithm with a window size of 5 to obtain intermediate electrical data to reduce random fluctuations and highlight the trend of the data;

[0014] Performing normalization processing on the intermediate electrical property data to obtain target electrical property data;

[0015] Feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed.

[0016] In a possible embodiment, the step of performing real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data includes:

[0017] The electrical data collected within a predetermined time period are averaged by applying the 3σ criterion to obtain the standard deviation;

[0018] Abnormal electrical property data exceeding ±3 times of the standard deviation in the electrical property data are eliminated to obtain preliminary electrical property data.

[0019] In a possible embodiment, performing normalization processing on the intermediate electrical property data to obtain target electrical property data includes:

[0020] Obtaining the maximum and minimum values ​​of the intermediate electrical property data within a preset time period;

[0021] Each of the intermediate electrical property data within the preset time period is standardized according to the maximum value and the minimum value to obtain target electrical property data.

[0022] In a possible embodiment, the target electrical property data satisfies:

[0023] d=(L i -L min )(L max -L i );

[0024] Where d is the target electrical data, L i is the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

[0025] In a second aspect, the present application provides a device for intelligently analyzing current and voltage data detected by a power supply meter, the device comprising: a high-precision power supply meter device and a server; wherein:

[0026] The high-precision power supply meter equipment is scheduled to collect electrical data from the power system, and the electrical data includes voltage data and current data;

[0027] The server is used to receive the electrical data transmitted by the power supply meter device via wireless communication;

[0028] The server is also used to determine whether the electrical data is missing; if not, the electrical data is input into a pre-trained intelligent analysis model to output a current and voltage change curve, values ​​of various characteristic indicators, and warning information.

[0029] In a possible embodiment, the training process of the pre-trained intelligent analysis model includes:

[0030] By applying the 3σ criterion, real-time outlier detection and elimination are performed on each collected electrical data to obtain preliminary electrical data;

[0031] The preliminary electrical data are smoothed using a moving average filtering algorithm with a window size of 5 to obtain intermediate electrical data to reduce random fluctuations and highlight the trend of the data;

[0032] Performing normalization processing on the intermediate electrical property data to obtain target electrical property data;

[0033] Feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed.

[0034] In a possible embodiment, the step of performing real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data includes:

[0035] The electrical data collected within a predetermined time period are averaged by applying the 3σ criterion to obtain the standard deviation;

[0036] Abnormal electrical property data exceeding ±3 times of the standard deviation in the electrical property data are eliminated to obtain preliminary electrical property data.

[0037] In a possible embodiment, performing normalization processing on the intermediate electrical property data to obtain target electrical property data includes:

[0038] Obtaining the maximum and minimum values ​​of the intermediate electrical property data within a preset time period;

[0039] Each of the intermediate electrical property data within the preset time period is standardized according to the maximum value and the minimum value to obtain target electrical property data.

[0040] In a possible embodiment, the target electrical property data satisfies:

[0041] d=(L i -L min )(L max -L i );

[0042] Where d is the target electrical data, L i is the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

[0043] The above-mentioned application provides a method and device for intelligent analysis of current and voltage data detected by a power supply meter. The application uses high-precision power supply meter equipment to collect electrical data from the power system, and the electrical data includes voltage data and current data; the server receives the electrical data transmitted by the power supply meter equipment through wireless communication; the server determines whether the electrical data is missing; if not, the electrical data is input into the pre-trained intelligent analysis model, and the change curve of current and voltage, the values ​​of various characteristic indicators and early warning information are output. As a result, the electrical data does not need to be read or entered, and all are transmitted to the server by the device through wireless communication, so that the staff only needs to view it through a tablet or computer to prevent omission or loss; in addition, through efficient integration with the railway management system, the data island is broken, data sharing and comprehensive analysis are realized, and at the same time, its automated intelligent analysis function reduces the dependence on manual operation, thereby reducing the risk of human error and improving the efficiency of the detection process; and, using the pre-trained intelligent analysis model, a deep analysis of current and voltage data is realized, providing insights beyond traditional instruments, and at the same time, it can continuously monitor the power system and issue early warnings before potential problems occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of the structure of a server provided in the first embodiment of the present application;

[0046] Figure 2 A flowchart of a method for intelligently analyzing current and voltage data detected by a power supply meter provided in the second embodiment of the present application;

[0047] Figure 3 for Figure 2 Another flow chart of a method for intelligently analyzing current and voltage data detected by a power supply meter is shown;

[0048] Figure 4 for Figure 2A schematic diagram of the training process of the intelligent analysis model in the intelligent analysis method for current and voltage data detected by a power supply meter;

[0049] Figure 5 A schematic diagram of the functional modules of a device for intelligently analyzing current and voltage data detected by a power supply meter provided in the third embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0051] First embodiment:

[0052] Figure 1 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, which can be used in the present application Figure 1 The schematic diagram shown is used to describe an example server 100 for implementing the method and device for intelligent analysis of current and voltage data detected by a power supply meter according to an embodiment of the present application.

[0053] like Figure 1 The server 100 includes one or more processors 102, one or more storage devices 104, an input device 106, and an output device 108. These components are interconnected via a bus system and / or other forms of connection mechanisms (not shown). It should be noted that Figure 1 The components and structure of the server 100 shown are only exemplary and not restrictive. The electronic device may have Figure 1 Some of the components shown may also have Figure 1 Other components and structures are not shown.

[0054] The processor 102 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the server 100 to perform desired functions.

[0055] It should be understood that the processor 102 in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0056] The storage device 104 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media.

[0057] It should be understood that the storage device 104 in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0058] Among them, one or more computer program instructions can be stored on the computer-readable storage medium, and the processor 102 can execute the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present application described below and / or other desired functions. Various applications and various data, such as various data used and / or generated by the application, can also be stored in the computer-readable storage medium.

[0059] The input device 106 may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.

[0060] Second embodiment:

[0061] Reference Figure 2 The flowchart of a method for intelligently analyzing current and voltage data detected by a power supply meter is shown, and the method specifically comprises the following steps:

[0062] Step S201, using high-precision power supply meter equipment to collect electrical data from the power system.

[0063] The electrical data includes voltage data and current data.

[0064] For example, Figure 3 As shown in the figure, the high-precision power supply meter equipment collects current and voltage data from the power system and transmits the current and voltage data to the server through the Bluetooth module.

[0065] Step S202: the server receives the electrical data transmitted by the power supply meter device via wireless communication.

[0066] Wherein, the server is a railway management system server.

[0067] Optionally, the wireless communication method is Bluetooth communication.

[0068] Step S203: the server determines whether the electrical data is missing.

[0069] Step S204: if not, input the electrical data into a pre-trained intelligent analysis model, and output a current and voltage variation curve, values ​​of various characteristic indicators, and warning information.

[0070] For example, please continue to refer to Figure 3 As shown in the figure, the server processes missing values ​​of the electrical data collected by the power supply meter equipment. If there is a large amount of missing data, the records containing missing data are deleted. Otherwise, the data is filled in through the preset model. Delete some abnormal values ​​and values ​​that suddenly increase or decrease in a short period of time. Through the formula The data is standardized, and then the original features are extracted and new features are created. The features of the current and voltage data, including the fundamental frequency and harmonic content, are extracted using methods such as Fourier transform. The cleaned data is grouped into different intervals (bins), and the number or frequency of data points in each interval is calculated. Each interval corresponds to a column in the histogram, and the width of the interval is called the "bin width". The horizontal axis of the histogram represents the numerical range of voltage or current, and the vertical axis represents the frequency or number of data points in each numerical range; the difference between each interval of the histogram is calculated, and the difference = (maximum value - minimum value) / 4. The minimum and maximum values ​​of each interval are obtained. The minimum value of the first interval = the minimum value, and the maximum value = the minimum value + the difference. The minimum value of the second interval = the minimum value + the difference, and the maximum value = the minimum value + (difference * 2). The minimum value of the third interval = the minimum value + (difference * 2), and the maximum value = the minimum value + (difference * 3). The minimum value of the fourth interval = minimum value + (difference * 3), and the maximum value = maximum value; using machine learning algorithms, for unbalanced data sets in classification problems, undersampling, oversampling or synthesizing minority category data can be used to balance the sample size of different categories. Through intelligent analysis technology, abnormal data can be accurately and quickly detected, characteristic data can be analyzed, and abnormal patterns in the power system can be identified; users are allowed to set parameters and adjust analysis strategies, including anomaly detection, fault prediction and optimization suggestions; data curves and analysis results are output in the form of reports, and a user interface is provided.

[0071] Optionally, the training process of the pre-trained intelligent analysis model includes: performing real-time outlier detection and elimination on the electrical data collected each time by applying the 3σ criterion to obtain preliminary electrical data; using a moving average filtering algorithm with a window size of 5 to smooth the preliminary electrical data to obtain intermediate electrical data to reduce random fluctuations and highlight data trends; performing normalization processing on the intermediate electrical data to obtain target electrical data; performing feature extraction on the target electrical data and constructing an intelligent analysis model.

[0072] In a possible embodiment, the method performs real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data, including: averaging the electrical data collected within a predetermined time period by applying the 3σ criterion to obtain a standard deviation; and eliminating abnormal electrical data in the electrical data that exceeds ±3 times of the standard deviation to obtain preliminary electrical data.

[0073] Optionally, the predetermined time period may be in units of days, such as one day.

[0074] For example, if a data point with a value of 500A appears in the current data of a certain day, and the average value of the current data on that day is 100A and the standard deviation is 5A, then the data point is identified as an outlier and is removed because its value exceeds the range of ±3 times the standard deviation of the mean.

[0075] In a possible embodiment, normalization processing is performed on the intermediate electrical data to obtain target electrical data, including: obtaining the maximum value and the minimum value of the intermediate electrical data within a preset time period; and normalizing each intermediate electrical data within the preset time period according to the maximum value and the minimum value to obtain the target electrical data.

[0076] Optionally, the preset time period may be in units of hours, such as 1 hour.

[0077] In a possible embodiment, the target electrical property data satisfies:

[0078] d=(L i -L min )(L max -L i );

[0079] Where d is the target electrical data, L i is the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

[0080] For example, if the minimum value of the current data in a certain hour is 100A and the maximum value is 300A, then a data point with an original current value of 220A will be converted to (220-100) / (300-100) = 0.6 through standardization. This process ensures that data of different dimensions and magnitudes can be compared and analyzed on the same scale.

[0081] As an example of smoothing, for example, when processing a set of continuous current data [119A, 120A, 122A, 119A, 121A], the algorithm will average each data point and its adjacent data points to obtain the smoothed data [120A, 120A, 120A, 120A, 120A], thereby providing a more stable and continuous data view.

[0082] It should be understood that the calculation results here are only examples, and the actual data smoothing process will be performed strictly according to the formula of the moving average filtering algorithm. No specific limitation is made here.

[0083] Optionally, feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed, including:

[0084] 1) Perform Fourier transform on each current and voltage data to obtain frequency domain features, and identify the main frequency components and harmonic content by analyzing these frequency domain data. For example, if significant 3rd and 5th harmonic components at a fundamental frequency of 50Hz are detected in the current data at a certain moment, this may indicate the presence of nonlinear loads in the power system, thus providing important clues for further system analysis and fault diagnosis;

[0085] 2) We collected historical current and voltage data over a period of time and divided them into training set, validation set, and test set in a ratio of 80%, 10%, and 10%. After preprocessing and feature extraction, the data was input into the model for training;

[0086] 3) Combining the model analysis results, feature extraction information, and the topological structure and equipment parameters of the power system, the function of preliminary diagnosis and precise location of possible faults is realized;

[0087] 4) Develop a visual interface to display the current and voltage change curves, the values ​​of various characteristic indicators and early warning information in real time, which enhances the readability and comprehensibility of the data and makes monitoring and fault diagnosis more efficient.

[0088] In summary, after the device collects data, the present invention does not need to read or enter data. All data is transmitted to the server by the device through the Bluetooth module, and personnel can view it through a tablet or computer to prevent omissions or losses. Reduce costs: The present invention breaks the data island through efficient integration with the railway management system, realizes data sharing and comprehensive analysis, and at the same time, its automated intelligent analysis function reduces dependence on manual operation, thereby reducing the risk of human error and improving the efficiency of the detection process. Analysis and prediction: The present invention integrates intelligent analysis algorithms and advanced sensor technology to achieve in-depth analysis of current and voltage data, providing insights beyond traditional instruments. At the same time, it can continuously monitor the power system and issue early warnings before potential problems occur.

[0089] Third embodiment:

[0090] See also Figure 5 The device for intelligently analyzing current and voltage data detected by a power supply meter shown in the figure comprises: a high-precision power supply meter device 400 and a server 500; wherein,

[0091] The high-precision power supply meter device 400 is scheduled to collect electrical data from the power system, and the electrical data includes voltage data and current data;

[0092] The server 500 is used to receive the electrical data transmitted by the power supply meter device 400 via wireless communication;

[0093] The server 500 is also used to determine whether the electrical data is missing; if not, the electrical data is input into a pre-trained intelligent analysis model to output a current and voltage change curve, values ​​of various characteristic indicators, and warning information.

[0094] In a possible embodiment, the training process of the pre-trained intelligent analysis model includes:

[0095] By applying the 3σ criterion, real-time outlier detection and elimination are performed on each collected electrical data to obtain preliminary electrical data;

[0096] The preliminary electrical data are smoothed using a moving average filtering algorithm with a window size of 5 to obtain intermediate electrical data to reduce random fluctuations and highlight the trend of the data;

[0097] Performing normalization processing on the intermediate electrical property data to obtain target electrical property data;

[0098] Feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed.

[0099] In a possible embodiment, the step of performing real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data includes:

[0100] The electrical data collected within a predetermined time period are averaged by applying the 3σ criterion to obtain the standard deviation;

[0101] Abnormal electrical property data exceeding ±3 times of the standard deviation in the electrical property data are eliminated to obtain preliminary electrical property data.

[0102] In a possible embodiment, performing normalization processing on the intermediate electrical property data to obtain target electrical property data includes:

[0103] Obtaining the maximum and minimum values ​​of the intermediate electrical property data within a preset time period;

[0104] Each of the intermediate electrical property data within the preset time period is standardized according to the maximum value and the minimum value to obtain target electrical property data.

[0105] In a possible embodiment, the target electrical property data satisfies:

[0106] d=(L i -L min )(L max -L i );

[0107] Where d is the target electrical data, L iis the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

[0108] Furthermore, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processing device, the steps of any one of the methods for intelligently analyzing current and voltage data detected by a power supply meter provided in the above-mentioned embodiment 2 are executed.

[0109] The embodiments of the present application provide a computer program product of a method and device for intelligently analyzing current and voltage data detected by a power supply meter, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.

[0110] It should be noted that the above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0111] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0112] In this application, "at least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0113] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units 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 devices or units, which can be electrical, mechanical or other forms.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0119] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

Claims

1. A method for intelligent analysis of current and voltage data detected by a power supply meter, characterized in that: The method comprises: Use high-precision power supply instrumentation equipment to collect electrical data from the power system, the electrical data including voltage data and current data; The server receives the electrical data transmitted by the power supply instrument equipment via wireless communication, and the server is a railway management system server; The server determines whether the electrical property data is missing; If not, the electrical data is input into a pre-trained intelligent analysis model to output a current and voltage change curve, values ​​of various characteristic indicators, and early warning information.

2. The method according to claim 1, characterized in that The training process of the pre-trained intelligent analysis model includes: By applying the 3σ criterion, real-time outlier detection and elimination are performed on each collected electrical data to obtain preliminary electrical data; The preliminary electrical data are smoothed using a moving average filtering algorithm with a window size of 5 to obtain intermediate electrical data to reduce random fluctuations and highlight the trend of the data; Performing normalization processing on the intermediate electrical property data to obtain target electrical property data; Feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed.

3. The method according to claim 2, characterized in that The method of performing real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data includes: The electrical data collected within a predetermined time period are averaged by applying the 3σ criterion to obtain the standard deviation; Abnormal electrical property data exceeding ±3 times of the standard deviation in the electrical property data are eliminated to obtain preliminary electrical property data.

4. The method according to claim 2, characterized in that: Normalizing the intermediate electrical property data to obtain target electrical property data includes: Obtaining the maximum and minimum values ​​of the intermediate electrical property data within a preset time period; Each of the intermediate electrical property data within the preset time period is standardized according to the maximum value and the minimum value to obtain target electrical property data.

5. The method according to claim 4, characterized in that The target electrical data meets the following requirements: d=(L i -L min )(L max -L i ); Where d is the target electrical data, L i is the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

6. An intelligent analysis device for current and voltage data detected by a power supply meter, characterized in that: The device comprises: a high-precision power supply instrument device and a server, wherein the server is a railway management system server; wherein, The high-precision power supply meter equipment is scheduled to collect electrical data from the power system, and the electrical data includes voltage data and current data; The server is used to receive the electrical data transmitted by the power supply meter device via wireless communication; The server is also used to determine whether the electrical data is missing; if not, the electrical data is input into a pre-trained intelligent analysis model to output a current and voltage change curve, values ​​of various characteristic indicators, and warning information.

7. The device according to claim 6, characterized in that The training process of the pre-trained intelligent analysis model includes: By applying the 3σ criterion, real-time outlier detection and elimination are performed on each collected electrical data to obtain preliminary electrical data; The preliminary electrical data are smoothed using a moving average filtering algorithm with a window size of 5 to obtain intermediate electrical data to reduce random fluctuations and highlight the trend of the data; Performing normalization processing on the intermediate electrical property data to obtain target electrical property data; Feature extraction is performed on the target electrical property data, and an intelligent analysis model is constructed.

8. The device according to claim 7, characterized in that The method of performing real-time outlier detection and elimination on each collected electrical data by applying the 3σ criterion to obtain preliminary electrical data includes: The electrical data collected within a predetermined time period are averaged by applying the 3σ criterion to obtain the standard deviation; Abnormal electrical property data exceeding ±3 times of the standard deviation in the electrical property data are eliminated to obtain preliminary electrical property data.

9. The device according to claim 7, characterized in that Normalizing the intermediate electrical property data to obtain target electrical property data includes: Obtaining the maximum and minimum values ​​of the intermediate electrical property data within a preset time period; Each of the intermediate electrical property data within the preset time period is standardized according to the maximum value and the minimum value to obtain target electrical property data.

10. The device according to claim 6, characterized in that The target electrical data meets the following requirements: d=(L i -L min )(L max -L i ); Where d is the target electrical data, L i is the ith intermediate electrical property data within the preset time period; min is the minimum value, L max is the maximum value.

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