A voiceprint visual imaging method applied to power transmission and transformation projects

Abstract

The application discloses a kind of acoustic fingerprint visualization imaging methods applied to power transmission and transformation engineering, it is related to acoustic fingerprint visualization imaging field, comprising the following specific steps: S1: data acquisition is carried out to power transmission and transformation engineering equipment;S2: the data collected are transmitted and summarized;S3: the data summarized are optimized and are handled, and feature extraction is carried out;S4: model is established according to the features extracted;S5: according to the model established, fault judgment and analysis are carried out to power transmission and transformation engineering equipment;S6: visualization imaging display is carried out;S7: maintenance is carried out to power transmission and transformation engineering equipment, and maintenance suggestion is issued;S8: and data repository is established.The application utilizes a kind of acoustic fingerprint visualization imaging methods applied to power transmission and transformation engineering, carries out data acquisition to electric power equipment, according to the data collected, visualization imaging display is carried out, the distribution state of sound source in space is shown in real time, quickly determines electric power equipment fault, accurately locates equipment defect.

Description

Technical Field

[0001] This invention relates to the field of voiceprint visualization imaging, and in particular to a voiceprint visualization imaging method applied to power transmission and transformation engineering. Background Technology

[0002] Power transmission and transformation projects are a series of engineering projects that transmit electrical energy from power plants to users. They mainly include two parts: transmission lines and substations. Substations are usually composed of transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors, and high-voltage switchgear.

[0003] With the continuous growth of electricity demand, the complexity and importance of power transmission and transformation equipment are also increasing. Traditional fault detection and maintenance methods mostly rely on periodic inspections and manual patrols, which have drawbacks such as long response times and low fault detection rates. With the application of acoustic fingerprint technology, a new solution has been provided for fault monitoring and analysis of power transmission and transformation equipment. Based on the analysis of sound wave signals generated during equipment operation, acoustic fingerprint technology can realize real-time monitoring of equipment status and fault diagnosis. By deploying sensors to collect sound wave signals and processing and analyzing them, characteristic information of the equipment can be extracted, thereby judging its operating status and potential faults.

[0004] Furthermore, how to present the monitored fault information to maintenance personnel in an intuitive way so that they can respond quickly is also a problem to be solved in current technology. Therefore, it is necessary to develop a method based on voiceprint visualization imaging that can effectively integrate data acquisition, signal processing, fault diagnosis and visualization, and provide strong support for the intelligent operation and maintenance of power transmission and transformation projects. Summary of the Invention

[0005] The purpose of this invention is to provide a voiceprint visualization imaging method for power transmission and transformation engineering, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a voiceprint visualization imaging method applied to power transmission and transformation engineering, comprising the following specific steps:

[0007] S1: Deploy sensors on the monitored power transmission and transformation equipment and collect data from the power transmission and transformation equipment;

[0008] S2: Transmit and summarize the data collected above;

[0009] S3: Optimize the aggregated data, preprocess the collected acoustic signals, and extract features;

[0010] S4: Establish a model based on the features extracted above, and update the model in real time based on the data collected in real time from the power transmission and transformation equipment.

[0011] S5: Based on the established model, perform fault diagnosis and analysis on power transmission and transformation equipment;

[0012] S6: Based on the above steps, perform multi-directional, multi-page real-time visualization and imaging display of power transmission and transformation engineering equipment, and establish a visualization and imaging display operating system;

[0013] S7: Based on the information displayed in the visualization imaging of the power transmission and transformation project, maintain the equipment of the power transmission and transformation project and issue maintenance recommendations;

[0014] S8: Establish a data storage library to store the data for the visualization of power transmission and transformation projects.

[0015] Preferably, the power transmission and transformation equipment monitored in S1 includes, but is not limited to, transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors, and high-voltage switchgear, and the data collection of the power transmission and transformation equipment includes, but is not limited to, equipment information data, geographical area information data, time information data, voiceprint information data, and acoustic imaging information, and one or more of these.

[0016] Preferably, the sensor deployment in S1 includes the following steps:

[0017] S1.1: Sensor selection: The sensor is a non-contact acoustic fingerprint sensor, a contact acoustic fingerprint sensor, and an acoustic imaging component.

[0018] S1.2: Install the sensor according to the equipment being tested;

[0019] S1.21: The installation of transformer detection sensors uses non-contact acoustic sensors to cover three sides of the transformer. The non-contact acoustic sensors are located between two-fifths and three-fifths of the transformer body height, and are one to two meters away from the transformer body.

[0020] S1.22: Installation of GIS equipment detection sensors: The GIS equipment's acoustic signature is collected by deploying contact acoustic signature sensors. Contact acoustic signature sensors are deployed around the internal switches, knife switches, and switch contacts of the GIS equipment. The contact acoustic signature sensors are directly fixed by adhesive bonding. The cables are run through the bottom cable tray to install the contact acoustic signature sensors.

[0021] S1.23: Installation of high voltage circuit breaker detection sensor: A non-contact acoustic fingerprint sensor is used to place one on the front of the circuit breaker, located at the top of the high voltage reactor tripod at the circuit breaker crossbeam, and centered about one meter away from the circuit breaker mechanism box.

[0022] S1.24: The installation of the high-voltage reactor detection sensor adopts a combination of acoustic sensor and acoustic imaging component. The acoustic imaging component is deployed using a floor bracket, covering the high-voltage reactor according to the imaging angle, with an installation distance of no more than 20 meters. The acoustic sensor is installed nearby, with a horizontal installation distance of one to two meters.

[0023] S1.25: Installation of detection sensors for high-voltage switchgear: One set of non-contact sensor equipment is installed in a single cabinet. The sensor is installed at the bottom of the switchgear cabinet. Power outage is required for implementation. Shielded cables are used for communication and power supply.

[0024] Preferably, the data transmission in S2 is through intranet transmission, and the access switch receives the data collected in S1. The data preprocessing in S3 is to perform noise reduction, smoothing and segmentation. Feature extraction is to use Fourier transform, wavelet transform and other methods to extract the frequency domain and time-frequency domain features of the sound wave signal and analyze the spectrum and amplitude changes of the sound wave.

[0025] Preferably, the model establishment in S4 includes the following steps:

[0026] S4.1: Select an appropriate model. Choose a suitable machine learning model based on the characteristics of the data and the application requirements. The learning model includes, but is not limited to, one or more of the following: support vector machine, decision tree, random forest, and neural network.

[0027] S4.2: Model training, using the training dataset to train the selected model to learn the relationship between features and classification labels;

[0028] S4.3: Model validation, using cross-validation to evaluate the model's performance to ensure its generalization ability;

[0029] S4.4: Model evaluation, including performance evaluation metrics and error analysis. Performance evaluation metrics include accuracy, recall, and F1-score. The confusion matrix is ​​used to evaluate the classification performance of the model. Error analysis analyzes the model's misclassification, identifies its shortcomings, and makes improvements accordingly.

[0030] Preferably, the fault judgment and analysis in S5 is to classify the real-time collected voiceprint signals according to the model established in S4, determine whether the equipment has a fault, identify the specific fault type according to the classification results output by the model, analyze the cause and severity of the fault, and issue an alarm when the power equipment has a fault.

[0031] Preferably, the visualization and imaging display in S6 includes the following steps:

[0032] S6.1: Generate images based on the model established in S4. According to the requirements of the model, input the corresponding prompts, including but not limited to text descriptions and image features, and input the prompts into the trained model. The model generates the corresponding images based on the input information and performs post-processing on the generated images.

[0033] S6.2: Implement multi-page imaging design, display multiple pages on the screen, use a single page to display images of power equipment in the same area, the images include model diagrams, bar charts, pie charts and line charts, and display multiple images on a single page, with the model diagram overlaid on the bar charts, pie charts and line charts;

[0034] S6.3: Set the encoding for the imaging page, and the encoded digital information corresponds to the power equipment data information of the same region on a single page;

[0035] S5.6: When a power equipment malfunctions, the corresponding page expands to cover the screen, and the malfunctioning power equipment is displayed in red on the model diagram;

[0036] S5.6: Page retrieval and display: By inputting the code of the image page, the retrieved display page is directly displayed, and clicking on the image on the page directly displays one of the corresponding model diagram, bar chart, pie chart, and line chart. The model diagram, bar chart, pie chart, and line chart can be enlarged.

[0037] Preferably, the image post-processing in S6.1 includes interpolation reconstruction, smoothing and detail restoration of the image, and calculation of unknown points using a bicubic interpolation formula, as follows:

[0038] ,

[0039] in These are the coefficients of a cubic polynomial. It is the value of a known point.

[0040] Preferably, the visualization imaging display operating system in S6 includes:

[0041] The data management module is used to manage and control the data, and to store the corresponding data.

[0042] The image generation module is used to generate model diagrams, bar charts, pie charts, and line graphs corresponding to power equipment, and to render the graphics.

[0043] The page design module is used to design the pages for power equipment, enabling multi-page and multi-overlap settings for the displayed pages;

[0044] The operation module is used by personnel to operate the display page, and to control the corresponding display page when the corresponding power equipment fails.

[0045] The image optimization module is used to optimize image details and improve image resolution.

[0046] Preferably, the maintenance suggestion issued in S7 is based on the power equipment fault information displayed by the visual imaging, generating power equipment fault maintenance instructions, formulating preventive maintenance plans and predictive maintenance based on fault modes and historical data, proposing state-based maintenance suggestions based on data analysis and fault prediction models, carrying out maintenance in a timely manner, clarifying the specific steps of fault repair and the required tools and materials, setting priorities for maintenance tasks according to the severity of the fault and the degree of impact on the system, and conducting power equipment assessment after power equipment maintenance to check whether the power equipment has returned to normal operation.

[0047] The technical effects and advantages of this invention are as follows:

[0048] This invention utilizes a voiceprint visualization imaging method applied to power transmission and transformation engineering. By collecting data from power equipment and visualizing the collected data, it displays the spatial distribution of sound sources in real time, quickly identifies power equipment faults, accurately locates equipment defects, improves inspection efficiency and maintenance speed, enhances the ability of voiceprint monitoring technology to solve key problems, and accelerates the achievement of the three-stage goals of "being able to hear", "being able to understand", and "being able to hear". Attached Figure Description

[0049] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] This invention provides, for example Figure 1 The acoustic signature visualization imaging method shown here, applied to power transmission and transformation engineering, includes the following specific steps:

[0052] S1: Deploy sensors on the monitored power transmission and transformation equipment, collect data from the power transmission and transformation equipment, and obtain real-time data of the monitored power transmission and transformation equipment;

[0053] S2: Transmit and summarize the collected data to facilitate subsequent power equipment fault diagnosis and visual imaging display based on the collected data;

[0054] S3: Optimize the aggregated data, preprocess the collected acoustic signals, and extract features;

[0055] S4: Based on the features extracted above, a model is established, and the model is updated in real time according to the data collected in real time from the power transmission and transformation equipment, so as to ensure that the model is consistent with the real-time update of the power equipment data.

[0056] S5: Based on the established model, fault judgment and analysis are performed on the power transmission and transformation equipment to determine whether the power equipment has malfunctioned.

[0057] S6: Based on the above steps, perform multi-directional, multi-page real-time visualization imaging display of power transmission and transformation engineering equipment, and establish a visualization imaging display operating system, thereby eliminating the need for personnel to listen to sounds on-site, reducing manual labor intensity, and realizing real-time online monitoring;

[0058] S7: Based on the information displayed in the visualization imaging of the power transmission and transformation project, maintain the equipment of the power transmission and transformation project and issue maintenance recommendations;

[0059] S8: Establish a data storage library to store the data for the visualization of power transmission and transformation projects.

[0060] Furthermore, the power transmission and transformation equipment monitored in S1 includes, but is not limited to, transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors, and high-voltage switchgear, and the data collected from the power transmission and transformation equipment includes, but is not limited to, equipment information data, geographic area information data, time information data, voiceprint information data, and acoustic imaging information, and one or more of these.

[0061] In particular, sensor deployment in S1 includes the following steps:

[0062] S1.1: Sensor selection. The sensors include non-contact acoustic fingerprint sensors, contact acoustic fingerprint sensors, and acoustic imaging components. The acoustic imaging components consist of acoustic sensors, microphones, signal processing modules, data processing modules, data storage and management systems, and visualization display modules. Acoustic sensor arrays and microphone arrays are arranged in the detection area to capture sound wave signals emitted by the device or environment. The arrangement and number of sensor arrays affect the resolution and accuracy of the imaging. The captured sound wave signals are often weak, so the signal is first amplified by a front-end amplifier. Then, an analog-to-digital converter (ADC) is used to convert the analog signal into a digital signal for subsequent processing. By applying various signal processing algorithms, the acquired sound signal is denoised, smoothed, and features extracted. These processing steps help improve signal quality and extract key features, and then an image is generated.

[0063] S1.2: Install the sensor according to the equipment being tested;

[0064] S1.21: The installation of transformer detection sensors uses non-contact acoustic sensors to cover three sides of the transformer. The non-contact acoustic sensors are located between two-fifths and three-fifths of the transformer body height, and are one to two meters away from the transformer body.

[0065] S1.22: Installation of GIS equipment detection sensors: The GIS equipment's acoustic signature is collected by deploying contact acoustic signature sensors. Contact acoustic signature sensors are deployed around the internal switches, knife switches, and switch contacts of the GIS equipment. The contact acoustic signature sensors are directly fixed by adhesive bonding. The cables are run through the bottom cable tray to install the contact acoustic signature sensors.

[0066] S1.23: Installation of high voltage circuit breaker detection sensor: A non-contact acoustic fingerprint sensor is used to place one on the front of the circuit breaker, located at the top of the high voltage reactor tripod at the circuit breaker crossbeam, and centered about one meter away from the circuit breaker mechanism box.

[0067] S1.24: The installation of the high-voltage reactor detection sensor adopts a combination of acoustic sensor and acoustic imaging component. The acoustic imaging component is deployed using a floor bracket, covering the high-voltage reactor according to the imaging angle, with an installation distance of no more than 20 meters. The acoustic sensor is installed nearby, with a horizontal installation distance of one to two meters.

[0068] S1.25: Installation of high-voltage switchgear detection sensors: One set of non-contact sensor equipment is installed in a single cabinet. The sensor is installed at the bottom of the switchgear cabinet and requires a power outage. Shielded cables are used for communication and power supply to prevent signal interference between the primary and secondary cables. All data is ultimately aggregated to the local edge aggregation unit.

[0069] Preferably, data transmission in S2 is performed via intranet transmission, and the access switch receives the data collected in S1. Data preprocessing in S3 involves denoising, smoothing, and segmentation. Feature extraction uses methods such as Fourier transform and wavelet transform to extract the frequency domain and time-frequency domain features of the sound wave signal, and analyzes the spectrum and amplitude changes of the sound wave.

[0070] Furthermore, the model building in S4 includes the following steps:

[0071] S4.1: Select an appropriate model. Choose a suitable machine learning model based on the characteristics of the data and the application requirements. The learning model includes, but is not limited to, one or more of the following: support vector machine, decision tree, random forest, and neural network.

[0072] S4.2: Model training. The selected model is trained using the training dataset to learn the relationship between features and classification labels. Model training includes forward propagation, loss calculation, backpropagation, and parameter update. Forward propagation involves inputting training data and calculating the model's output. Loss calculation uses a loss function to calculate the difference between the model's output and the true label. Backpropagation calculates the gradient of the loss with respect to the model parameters using the backpropagation algorithm. Parameter update updates the model parameters based on the calculated gradient and the selected optimization algorithm.

[0073] S4.3: Model validation, using cross-validation to evaluate the model's performance to ensure its generalization ability;

[0074] S4.4: Model evaluation, including performance evaluation metrics and error analysis. Performance evaluation metrics include accuracy, recall, and F1-score. The confusion matrix is ​​used to evaluate the classification performance of the model. Error analysis analyzes the model's misclassification, identifies its shortcomings, and makes improvements accordingly.

[0075] Furthermore, the fault judgment and analysis in S5 is based on the model established in S4 to classify the real-time collected voiceprint signals, determine whether the equipment has a fault, identify the specific fault type based on the classification results output by the model, analyze the cause and severity of the fault, and issue an alarm when the power equipment has a fault, so that the personnel can easily discover the power equipment fault under the reminder of the alarm.

[0076] In particular, the visualization of images in S6 includes the following steps:

[0077] S6.1: Generate images based on the model established in S4. According to the requirements of the model, input the corresponding prompts, including but not limited to text descriptions and image features, and input the prompts into the trained model. The model generates the corresponding images based on the input information and performs post-processing on the generated images.

[0078] S6.2: Implement multi-page imaging design, display multiple pages on the screen, use a single page to display images of power equipment in the same area, the images include model diagrams, bar charts, pie charts and line charts, and display multiple images on a single page, with the model diagram overlaid on the bar charts, pie charts and line charts;

[0079] S6.3: Set the encoding for the imaging page. The encoded digital information corresponds to the power equipment data information of the same region on the single page, so that the power equipment image of the corresponding region can be retrieved directly according to the encoding.

[0080] S5.6: When a power equipment malfunctions, the corresponding page expands to cover the screen, and the malfunctioning power equipment is displayed in red on the model diagram. Furthermore, based on the cause of the malfunction, the corresponding location of the power equipment is also displayed in red.

[0081] S5.6: Page retrieval and display: By inputting the code of the image page, the retrieved display page is directly displayed, and clicking on the image on the page directly displays one of the corresponding model diagram, bar chart, pie chart, and line chart. The model diagram, bar chart, pie chart, and line chart can be enlarged.

[0082] Furthermore, image post-processing in S6.1 includes interpolation reconstruction, smoothing and detail restoration of the image, and calculation of unknown points using a bicubic interpolation formula, as follows:

[0083] ,

[0084] in These are the coefficients of a cubic polynomial. Given the values ​​of points, interpolation reconstruction can be used to fill in new pixel values ​​when scaling an image. In image processing, interpolation can be used to fill in blank areas caused by noise or missing data, thereby improving the quality of visual imaging and reducing the occurrence of data loss and image damage.

[0085] Specifically, the S6 visualization imaging display operating system includes a data management module, an image generation module, a page design module, an operation module, and an image optimization module. The data management module is used to manage and control the data and store the corresponding data. The image generation module is used to generate corresponding model diagrams, bar charts, pie charts, and line graphs for power equipment and to render the graphics. The page design module is used to design the power equipment pages, enabling multi-page and multi-overlap settings for the display pages. The operation module is used for personnel to operate the display pages and to adjust the corresponding display pages when the corresponding power equipment malfunctions. The image optimization module is used to optimize image details and improve image resolution.

[0086] Furthermore, the maintenance recommendations issued in S7 are generated based on the power equipment fault information displayed by the visual imaging. Preventive maintenance plans are formulated according to fault modes and historical data, and predictive maintenance is also developed. Based on data analysis and fault prediction models, state-based maintenance recommendations are proposed, and maintenance is carried out in a timely manner. Specific steps for fault repair and the required tools and materials are clearly defined, thereby improving the efficiency of power equipment fault maintenance. Furthermore, maintenance tasks are prioritized according to the severity of the fault and its impact on the system, thus prioritizing the maintenance of severely faulty power equipment. After maintenance, a power equipment assessment is conducted to check whether the power equipment has returned to normal operation, ensuring the safe operation of the power equipment.

[0087] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A voiceprint visualized imaging method applied to power transmission and transformation projects, characterized in that, Comprise the following specific steps: S1: sensor deployment is carried out to the monitored power transmission and transformation engineering equipment, and data collection is carried out to the power transmission and transformation engineering equipment; S2: the data collected above are transmitted and summarized; S3: the summarized data are optimized, the collected acoustic wave signals are preprocessed, and feature extraction is carried out; S4: model establishment is carried out according to the extracted features, and the model is updated in real time according to the data collected in real time by the power transmission and transformation engineering equipment; S5: the established model is used to judge and analyze the faults of the power transmission and transformation engineering equipment; S6: according to the above steps, multi-azimuth and multi-page real-time power transmission and transformation engineering equipment visualization imaging display is carried out, and a visualization imaging display operating system is established, wherein the visualization imaging display in S6 comprises the following steps: S6.1: image generation is carried out according to the model established in S4, according to the requirements of the model, input corresponding prompt information, including but not limited to one or more of text description and image features, and input the prompt information into the trained model, and the model generates corresponding images according to the input information, and post-processing is carried out on the generated images; S6.2: imaging multi-page design, multi-page display and screen, using single-page display of the same region power equipment image, the image includes model diagram, column chart, pie chart and linear chart, and single-page multi-image display, and the model diagram is overlaid on the column chart, pie chart and linear chart; S6.3: coding setting is carried out on the imaging page, and the coding digital information corresponds to the power equipment data information of the same region in the single page; S6.4: when the power equipment fails, the corresponding page expands to cover the screen, and the power equipment that fails is displayed in red in the model diagram; S6.5: page retrieval display, by inputting the coding of the imaging page, the displayed page is directly displayed, and by clicking the image of the page, one of the corresponding model diagram, column chart, pie chart and linear chart is directly displayed, and the model diagram, column chart, pie chart and linear chart can be enlarged; S7: according to the information of the power transmission and transformation engineering visualization imaging display, the power transmission and transformation engineering equipment is maintained, and maintenance suggestions are given; S8: and a data storage library is established, and the data of the power transmission and transformation engineering visualization display are stored.

2. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 1, characterized in that, The monitored power transmission and transformation engineering equipment in S1 includes but is not limited to one or more of transformer, GIS equipment, high-voltage circuit breaker, high-voltage reactor and high-voltage switch cabinet, and the data collection of the power transmission and transformation engineering equipment includes but is not limited to one or more of equipment information data, geographical area information data, time information data, voiceprint information data and acoustic imaging information.

3. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 2, characterized in that, The sensor deployment in S1 comprises the following steps: S1.1: selection of sensor, the sensor is a non-contact voiceprint sensor, a contact voiceprint sensor and a sound imaging component; S1.2: according to the detected equipment, the installation of the sensor; S1.21: Installation of transformer detection sensor, non-contact acoustic fingerprint sensor is used to cover three directions of transformer, non-contact acoustic fingerprint sensor is located between two-fifths and three-fifths of the height of transformer body, and the distance between non-contact acoustic fingerprint sensor and transformer body is one to two meters; S1.22: Installation of GIS device detection sensor, contact acoustic fingerprint sensor is deployed to realize acoustic fingerprint collection of GIS device, and contact acoustic fingerprint sensor is deployed around internal switches, disconnectors and switch contacts of GIS device, and contact acoustic fingerprint sensor is fixed and installed by direct sticking, and cable bridge is installed at the bottom of cable to install contact acoustic fingerprint sensor; S1.23: Installation of high-voltage circuit breaker detection sensor, non-contact acoustic fingerprint sensor is arranged on the front of the circuit breaker, and is arranged at the top of the high-voltage reactor triangular support and the beam of the circuit breaker, and is arranged in the middle one meter away from the mechanism box of the circuit breaker; S1.24: Installation of high-voltage reactor detection sensor, acoustic sensor and acoustic imaging assembly are combined to deploy, acoustic imaging assembly is deployed by floor stand, imaging angle is covered according to high-voltage reactor, installation distance is not more than twenty meters, acoustic fingerprint sensor is installed nearby, and horizontal installation distance is one to two meters; S1.25: Installation of high-voltage switch cabinet detection sensor, a set of non-contact sensor equipment is arranged in a single set of cabinet, and is installed at the bottom of the switch cabinet box, power supply is stopped, shielded cable communication and power supply are used.

4. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 3, characterized in that, The data transmission in S2 is transmitted through the intranet, and the data collected in S1 is received by the access switch, the data preprocessing in S3 is denoising, smoothing and segmentation processing, the feature extraction is the frequency domain and time-frequency domain features of the acoustic signal extracted by Fourier transform, wavelet transform and other methods, and the frequency spectrum and amplitude change of the acoustic wave are analyzed.

5. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 1, characterized in that, The model establishment in S4 includes the following steps: S4.1: Selecting a suitable model, selecting a suitable machine learning model according to the characteristics of the data and application requirements, and learning the model including but not limited to support vector machine, decision tree, random forest and neural network more than one kind; S4.2: Model training, using training data set to train the selected model to learn the relationship between features and classification labels; S4.3: Model verification, using cross-validation method to evaluate the performance of the model to ensure the generalization ability of the model; S4.4: Model evaluation, including performance evaluation index and error analysis, performance evaluation index including accuracy, recall rate, F1-score index, using confusion matrix to evaluate the classification performance of the model, error analysis is to analyze the misclassification of the model, find the shortcomings of the model, and improve it.

6. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 5, characterized in that, The fault judgment and analysis in S5 is to classify the real-time collected acoustic fingerprint signal according to the model established in S4, judge whether the device has a fault, identify the specific fault type according to the classification result output by the model, analyze the cause and severity of the fault, and issue an alarm when the power equipment fails.

7. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 6, characterized in that, The image in S6.1 is post-processed, including interpolation reconstruction, smoothing and detail recovery of the graph, and calculation of unknown points by using a bicubic interpolation formula as follows: ； wherein is a cubic polynomial coefficient, is the value of the known point.

8. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 7, characterized in that, The visualization imaging display operation system in S6 comprises: a data management module for managing and regulating data and storing corresponding data; an image generation module for generating a model graph, a column graph, a pie chart and a linear graph corresponding to the power equipment, and colorizing the graph; a page design module for designing the design of the power equipment page, enabling the display page to be set in multiple pages and multiple overlaps; an operation module for personnel to operate the display page, and to regulate the corresponding display page when the corresponding power equipment fails; an image optimization module for optimizing image details to improve image pixels.

9. The voiceprint visualized imaging method applied to power transmission and transformation engineering according to claim 1, characterized in that, In S7, the maintenance suggestion is generated according to the power equipment failure information displayed by the visualization imaging, a power equipment failure maintenance instruction is generated, a preventive maintenance plan is formulated according to the failure mode and historical data, a predictive maintenance is formulated, a state-based maintenance suggestion is proposed based on data analysis and failure prediction model, timely maintenance is performed, and the specific steps, required tools and materials of fault repair are clear, the priority of the maintenance task is set according to the severity of the fault and the degree of influence on the system, and after the maintenance of the power equipment, the power equipment is evaluated to check whether the power equipment returns to normal operation.

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