Voiceprint visual imaging method applied to power transmission and transformation project

Abstract

The invention discloses a voiceprint visual imaging method applied to a power transmission and transformation project, and relates to the field of voiceprint visual imaging, and the method comprises the following specific steps: S1, carrying out the data collection of power transmission and transformation project equipment; s2, transmitting and summarizing the acquired data; s3, performing optimization processing on the summarized data, and performing feature extraction; s4, establishing a model according to the extracted features; s5, performing fault judgment and analysis on the power transmission and transformation engineering equipment according to the established model; s6, carrying out visual imaging display; s7, maintaining the power transmission and transformation project equipment, and giving a maintenance suggestion; and S8, establishing a data storage library. According to the voiceprint visual imaging method applied to the power transmission and transformation project, data acquisition is carried out on power equipment, visual imaging display is carried out according to the acquired data, the distribution state of a sound source in space is displayed in real time, power equipment faults are rapidly determined, and equipment defects are accurately positioned.

Description

Technical Field

[0001] The present invention relates to the field of voiceprint visualization imaging, and in particular to a voiceprint visualization imaging method applied to power transmission and transformation projects. Background Art

[0002] Power transmission and transformation projects are a series of engineering projects that transmit electric 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] As electricity demand continues to grow, the complexity and importance of power transmission and transformation equipment are also increasing. Traditional fault detection and maintenance methods rely heavily on regular inspections and manual patrols, which have drawbacks such as long response times and low fault detection rates. The application of voiceprint technology has provided a new solution for fault monitoring and analysis of power transmission and transformation equipment. Voiceprint technology analyzes the acoustic signals generated during equipment operation, enabling real-time monitoring of equipment status and fault diagnosis. By deploying sensors to collect acoustic signals, and processing and analyzing them, it can extract characteristic information about the equipment, thereby determining its operating status and potential faults.

[0004] In addition, how to display the fault information obtained from monitoring to the operation and maintenance personnel in an intuitive way so that they can respond quickly is also a problem to be solved in current technology. It is necessary to develop a method based on voiceprint visualization imaging that can effectively integrate various links of data acquisition, signal processing, fault judgment and visualization display, which will provide strong support for the intelligent operation and maintenance of power transmission and transformation projects. Summary of the Invention

[0005] The purpose of the present invention is to provide a voiceprint visualization imaging method applied to power transmission and transformation projects to solve the problems raised in the above background technology.

[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 projects, comprising the following specific steps: S1: Deploy sensors on the monitored power transmission and transformation equipment and collect data from the equipment; S2: Transmit and summarize the above collected data; S3: Optimize the aggregated data, pre-process the collected acoustic wave signals, and perform feature extraction; S4: Building a model based on the extracted features and updating the model in real time based on the data collected in real time by the power transmission and transformation engineering equipment; S5: Conduct fault diagnosis and analysis on power transmission and transformation equipment based on the established model; S6: Based on the above steps, a multi-directional and multi-page real-time visualization imaging display of power transmission and transformation engineering equipment is performed, and a visualization imaging display operating system is established; S7: Maintain power transmission and transformation project equipment based on the information displayed by the visualization of the power transmission and transformation project, and issue maintenance recommendations; S8: A data storage library is established to store the data for visualization of power transmission and transformation projects.

[0007] Preferably, the power transmission and transformation engineering equipment monitored in S1 includes but is not limited to one or more transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors and high-voltage switch cabinets, and the data collected by the power transmission and transformation engineering equipment includes but is not limited to one or more equipment information data, geographic area information data, time information data, voiceprint information data and acoustic imaging information.

[0008] Preferably, the sensor deployment in S1 includes the following steps: S1.1: Selection of sensors, including non-contact voiceprint sensors, contact voiceprint sensors, and acoustic imaging components; S1.2: Install the sensor according to the equipment being tested; S1.21: Install transformer detection sensors. Use non-contact voiceprint sensors to cover three positions of the transformer. The non-contact voiceprint sensors are located between two-fifths and three-fifths of the transformer body height. The non-contact voiceprint sensors are between one and two meters away from the transformer body. S1.22: Installation of GIS equipment detection sensors. GIS equipment voiceprint collection is achieved by deploying contact voiceprint sensors. These sensors are deployed around the internal switches, knife switches, and switch contacts of the GIS equipment. The contact voiceprint sensors are directly fixed and installed by direct gluing. The cables are routed through the bottom cable tray to install the contact voiceprint sensors. S1.23: Installation of high-voltage circuit breaker detection sensors: A non-contact voiceprint sensor is placed on the front of the circuit breaker, located at the top of the high-voltage reactor tripod, on the circuit breaker beam, and centered about one meter away from the circuit breaker mechanism box. S1.24: Installation of high-voltage reactor detection sensors shall be carried out using a combination of acoustic sensors and acoustic imaging components. The acoustic imaging components shall be deployed using floor-standing brackets, covering the high-voltage reactor according to the imaging angle. The installation distance shall not exceed 20 meters. The voiceprint sensor shall be installed nearby, at a horizontal distance of one to two meters. S1.25: Installation of high-voltage switchgear detection sensors: A set of non-contact sensor equipment is arranged in a single cabinet, and installed at the bottom of the switchgear box. Implementation requires power outage, and shielded cables are used for communication and power supply.

[0009] Preferably, the data transmission in S2 is 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 to extract the frequency domain and time-frequency domain characteristics of the sound wave signal using methods such as Fourier transform and wavelet transform, and analyze the spectrum and amplitude changes of the sound wave.

[0010] Preferably, the establishment of the model in S4 comprises the following steps: S4.1: Select an appropriate model. Choose an appropriate machine learning model based on the data characteristics and application requirements. Learning models may include but are not limited to support vector machines, decision trees, random forests, and neural networks. S4.2: Model training, use the training dataset to train the selected model to learn the relationship between features and classification labels; S4.3: Model validation, using cross-validation to evaluate the performance of the model to ensure the generalization ability of the model; S4.4: Model evaluation, including performance evaluation indicators and error analysis. Performance evaluation indicators include accuracy, recall rate, and F1-score indicators. The confusion matrix is ​​used 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.

[0011] Preferably, the fault judgment and analysis in S5 is to classify the voiceprint signals collected in real time according to the model established in S4, judge whether there is a fault in the equipment, and 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 a fault occurs in the power equipment.

[0012] Preferably, the visual imaging display in S6 includes the following steps: S6.1: Generate images based on the model established in S4. According to the requirements of the model, input corresponding prompt information, including but not limited to text descriptions and one or more image features, and input the prompt information into the trained model. The model generates corresponding images based on the input information, and the generated images are post-processed. S6.2: Design a multi-page image display. Display the images of power equipment in the same area on a single page. The images include model diagrams, bar graphs, pie charts, and line graphs. Multiple images are displayed on a single page, with the model diagram overlaid on the bar graphs, pie charts, and line graphs. S6.3: Encode the image page, and the encoded digital information corresponds to the data information of the power equipment in the same area on a single page; S5.6: When a power device fails, the corresponding page will be expanded to cover the screen, and the failed power device will be displayed in red on the model diagram; S5.6: Retrieval and display of the page. By inputting the code of the imaging page, the retrieved display page is directly displayed, and by clicking on the image of the page, one of the corresponding model diagrams, bar graphs, pie charts and linear graphs is directly displayed, and the model diagrams, bar graphs, pie charts and linear graphs can be enlarged and displayed.

[0013] Preferably, the image post-processing in S6.1 includes interpolation reconstruction, smoothing and detail restoration of the image, and calculating unknown points using a bicubic interpolation formula, the bicubic interpolation formula is as follows: , in are the coefficients of the cubic polynomial, is the value of the known point.

[0014] Preferably, the visual imaging display operating system in S6 includes: Data management module, used to manage and control data and store corresponding data; Image generation module, used to generate model diagrams, bar charts, pie charts and linear charts corresponding to power equipment, and render the graphics; Page design module, used to design the power equipment page, so that the display page can be set to multiple pages and multiple overlaps; The operation module is used for personnel to operate the display page and adjust the corresponding display page when the corresponding power equipment fails; Image optimization module is used to optimize image details and improve image pixels.

[0015] Preferably, the maintenance recommendation issued in S7 is based on the power equipment fault information displayed by visual imaging, generates power equipment fault maintenance instructions, formulates preventive maintenance plans based on fault modes and historical data, and formulates predictive maintenance. Based on data analysis and fault prediction models, it puts forward state-based maintenance suggestions, performs maintenance in a timely manner, and clarifies the specific steps and required tools and materials for fault repair. It also sets priorities for maintenance tasks based on the severity of the fault and the degree of impact on the system, and after the power equipment maintenance, conducts power equipment evaluation to check whether the power equipment has resumed normal operation.

[0016] Technical effects and advantages of the present invention: The present invention utilizes a voiceprint visualization imaging method applied to power transmission and transformation projects. By collecting data from power equipment and performing visualization imaging based on the collected data, the spatial distribution of sound sources can be displayed in real time, power equipment faults can be quickly determined, equipment defects can be accurately located, inspection efficiency and maintenance speed can be improved, the ability of voiceprint monitoring technology to solve key problems can be enhanced, and the three-stage goals of "being able to hear", "being able to understand", and "being able to hear" can be achieved at an accelerated pace. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0019] The present invention provides Figure 1 The voiceprint visualization imaging method shown in the figure is applied to power transmission and transformation projects, and includes the following specific steps: S1: Deploy sensors on the monitored power transmission and transformation engineering equipment and collect data from the power transmission and transformation engineering equipment to obtain real-time data of the monitored power transmission and transformation engineering equipment; S2: Transmit and summarize the above collected data to facilitate subsequent power equipment fault diagnosis and visual imaging display based on the collected data; S3: Optimize the aggregated data, pre-process the collected acoustic wave signals, and perform feature extraction; S4: Building a model based on the features extracted above, and updating the model in real time based on the data collected in real time by the power transmission and transformation engineering equipment, thereby ensuring that the model is updated in real time with the power equipment data; S5: Based on the established model, fault diagnosis and analysis of power transmission and transformation equipment is performed to determine whether the power equipment has failed; S6: Based on the above steps, a multi-directional and multi-page real-time visualization of power transmission and transformation engineering equipment is performed, and a visualization imaging display operating system is established, thereby eliminating the need for on-site personnel to listen to sounds, reducing manual labor intensity, and achieving real-time online monitoring; S7: Maintain power transmission and transformation project equipment based on the information displayed by the visualization of the power transmission and transformation project, and issue maintenance recommendations; S8: A data storage library is established to store the data for visualization of power transmission and transformation projects.

[0020] Furthermore, the power transmission and transformation engineering equipment monitored in S1 includes but is not limited to one or more transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors and high-voltage switch cabinets, and the data collection of the power transmission and transformation engineering equipment includes but is not limited to one or more equipment information data, geographic area information data, time information data, voiceprint information data and acoustic imaging information.

[0021] In particular, the sensor deployment in S1 includes the following steps: S1.1: Selection of sensors. The sensors include non-contact voiceprint sensors, contact voiceprint sensors, and acoustic imaging components. The acoustic imaging component consists of an acoustic sensor, a microphone, a signal processing module, a data processing module, a data storage and management system, and a visual display module. An acoustic sensor array and a microphone array are arranged in the detection area to capture the acoustic wave signals emitted by the device or environment. The arrangement and number of sensor arrays will affect the resolution and accuracy of imaging. The captured acoustic wave signals are often weak, so they are first amplified by a front-end amplifier. Subsequently, an analog-to-digital converter (ADC) is used to convert the analog signal into a digital signal in preparation for subsequent processing. By applying various signal processing algorithms, the collected sound signals are denoised, smoothed, and feature extracted. These processing steps can help improve signal quality and extract key features, and then generate an image. S1.2: Install the sensor according to the equipment being tested; S1.21: Install transformer detection sensors. Use non-contact voiceprint sensors to cover three positions of the transformer. The non-contact voiceprint sensors are located between two-fifths and three-fifths of the transformer body height. The non-contact voiceprint sensors are between one and two meters away from the transformer body. S1.22: Installation of GIS equipment detection sensors. GIS equipment voiceprint collection is achieved by deploying contact voiceprint sensors. These sensors are deployed around the internal switches, knife switches, and switch contacts of the GIS equipment. The contact voiceprint sensors are directly fixed and installed by direct gluing. The cables are routed through the bottom cable tray to install the contact voiceprint sensors. S1.23: Installation of high-voltage circuit breaker detection sensors: A non-contact voiceprint sensor is placed on the front of the circuit breaker, located at the top of the high-voltage reactor tripod, on the circuit breaker beam, and centered about one meter away from the circuit breaker mechanism box. S1.24: Installation of high-voltage reactor detection sensors shall be carried out using a combination of acoustic sensors and acoustic imaging components. The acoustic imaging components shall be deployed using floor-standing brackets, covering the high-voltage reactor according to the imaging angle. The installation distance shall not exceed 20 meters. The voiceprint sensor shall be installed nearby, at a horizontal distance of one to two meters. S1.25: Installation of high-voltage switchgear detection sensors: A set of non-contact sensor equipment is arranged in a single cabinet and installed at the bottom of the switchgear box. Implementation requires power outage. Shielded cables are used for communication and power supply to ensure that the primary cable does not interfere with the secondary cable signal. All data is finally aggregated to the local edge aggregation unit.

[0022] Preferably, the data transmission in S2 is through the intranet, and the access switch receives the data collected in S1. The data preprocessing in S3 is to perform denoising, smoothing and segmentation processing. The feature extraction is to use Fourier transform, wavelet transform and other methods to extract the frequency domain and time-frequency domain characteristics of the sound wave signal, and analyze the spectrum and amplitude changes of the sound wave.

[0023] Furthermore, the establishment of the model in S4 includes the following steps: S4.1: Select an appropriate model. Choose an appropriate machine learning model based on the data characteristics and application requirements. Learning models may include but are not limited to support vector machines, decision trees, random forests, and neural networks. S4.2: Model training: Use the training dataset to train the selected model to learn the relationship between features and classification labels. Model training includes forward propagation, loss calculation, backpropagation, and parameter update. Forward propagation is to input training data and calculate the output of the model. Loss calculation is to use the loss function to calculate the difference between the model output and the true label. Backpropagation is to calculate the gradient of the loss with respect to the model parameters through the backpropagation algorithm. Parameter update is to update the model parameters based on the calculated gradient and the selected optimization algorithm. S4.3: Model validation, using cross-validation to evaluate the performance of the model to ensure the generalization ability of the model; S4.4: Model evaluation, including performance evaluation indicators and error analysis. Performance evaluation indicators include accuracy, recall rate, and F1-score indicators. The confusion matrix is ​​used 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.

[0024] Furthermore, the fault judgment and analysis in S5 is to classify the voiceprint signals collected in real time according to the model established in S4, to determine whether there is a fault in the equipment, and to identify the specific fault type according to the classification results output by the model, and to analyze the cause and severity of the fault, and to issue an alarm when the power equipment fails, so that personnel can easily discover the power equipment fault under the reminder of the alarm.

[0025] In particular, the visualization in S6 includes the following steps: S6.1: Generate images based on the model established in S4. According to the requirements of the model, input corresponding prompt information, including but not limited to text descriptions and one or more image features, and input the prompt information into the trained model. The model generates corresponding images based on the input information, and the generated images are post-processed. S6.2: Design a multi-page image display. Display the images of power equipment in the same area on a single page. The images include model diagrams, bar graphs, pie charts, and line graphs. Multiple images are displayed on a single page, with the model diagram overlaid on the bar graphs, pie charts, and line graphs. S6.3: Encode the image page, with the coded digital information corresponding to the data information of the power equipment in the same region on a single page, so that the image of the power equipment in the corresponding region can be directly retrieved according to the code; S5.6: When a fault occurs in the power equipment, the corresponding page is expanded to cover the screen, and the faulty power equipment is displayed in red on the model diagram. In addition, the corresponding position of the power equipment is displayed in red according to the cause of the fault; S5.6: Retrieval and display of the page. By inputting the code of the imaging page, the retrieved display page is directly displayed, and by clicking on the image of the page, one of the corresponding model diagrams, bar graphs, pie charts and linear graphs is directly displayed, and the model diagrams, bar graphs, pie charts and linear graphs can be enlarged and displayed.

[0026] Furthermore, the image in S6.1 is post-processed, including interpolation reconstruction, smoothing and detail restoration of the image, and calculation of unknown points using the bicubic interpolation formula. The bicubic interpolation formula is as follows: , in are the coefficients of the cubic polynomial, It is the value of a known point. When scaling an image, interpolation reconstruction can be used to fill in new pixel values. In image processing, interpolation can be used to fill in blank areas caused by noise or lost data, thereby improving the quality of visual imaging and reducing the occurrence of data loss and image damage.

[0027] Specifically, the visual imaging display operating system in S6 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 regulate data and store corresponding data. The image generation module is used to generate model diagrams, bar charts, pie charts and linear charts corresponding to the power equipment, and render the graphics. The page design module is used to design the power equipment page so that the display page can be set to multiple pages and multiple overlaps. The operation module is used for personnel to operate the display page, and when the corresponding power equipment fails, the corresponding display page is adjusted. The image optimization module is used to optimize the image details and improve the image pixels.

[0028] Furthermore, the maintenance recommendations issued in S7 are based on the power equipment fault information displayed by visual imaging, generating power equipment fault maintenance instructions, formulating preventive maintenance plans and predictive maintenance based on fault modes and historical data, and proposing state-based maintenance recommendations based on data analysis and fault prediction models. Maintenance is carried out in a timely manner, and the specific steps of fault repair and the required tools and materials are clarified, thereby improving the efficiency of power equipment fault maintenance. According to the severity of the fault and the degree of impact on the system, maintenance tasks are prioritized, so as to give priority to the maintenance of power equipment with serious faults. After the power equipment is maintained, the power equipment is evaluated to check whether the power equipment has resumed normal operation and ensure the safe operation of the power equipment.

[0029] Finally, it should be noted that the above 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 aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A voiceprint visualization imaging method applied to power transmission and transformation engineering, characterized in that: The specific steps include: S1: Deploy sensors on the monitored power transmission and transformation equipment and collect data from the equipment; S2: Transmit and summarize the above collected data; S3: Optimize the aggregated data, pre-process the collected acoustic wave signals, and perform feature extraction; S4: Building a model based on the extracted features and updating the model in real time based on the data collected in real time by the power transmission and transformation engineering equipment; S5: Conduct fault diagnosis and analysis on power transmission and transformation equipment based on the established model; S6: Based on the above steps, a multi-directional and multi-page real-time visualization imaging display of power transmission and transformation engineering equipment is performed, and a visualization imaging display operating system is established; S7: Maintain power transmission and transformation project equipment based on the information displayed by the visualization of the power transmission and transformation project, and issue maintenance recommendations; S8: A data storage library is established to store the data for visualization of power transmission and transformation projects.

2. The voiceprint visualization imaging method for power transmission and transformation engineering according to claim 1 is characterized in that: The power transmission and transformation engineering equipment monitored in S1 includes but is not limited to one or more types of transformers, GIS equipment, high-voltage circuit breakers, high-voltage reactors and high-voltage switch cabinets, and the data collected by the power transmission and transformation engineering equipment includes but is not limited to one or more types of equipment information data, geographic area information data, time information data, voiceprint information data and acoustic imaging information.

3. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 2 is characterized in that: The sensor deployment in S1 includes the following steps: S1.1: Selection of sensors, including non-contact voiceprint sensors, contact voiceprint sensors, and acoustic imaging components; S1.2: Install the sensor according to the equipment being tested; S1.21: Install transformer detection sensors. Use non-contact voiceprint sensors to cover three positions of the transformer. The non-contact voiceprint sensors are located between two-fifths and three-fifths of the transformer body height. The non-contact voiceprint sensors are between one and two meters away from the transformer body. S1.22: Installation of GIS equipment detection sensors. GIS equipment voiceprint collection is achieved by deploying contact voiceprint sensors. These sensors are deployed around the internal switches, knife switches, and switch contacts of the GIS equipment. The contact voiceprint sensors are directly fixed and installed by direct gluing. The cables are routed through the bottom cable tray to install the contact voiceprint sensors. S1.23: Installation of high-voltage circuit breaker detection sensors: A non-contact voiceprint sensor is placed on the front of the circuit breaker, located at the top of the high-voltage reactor tripod, on the circuit breaker beam, and centered about one meter away from the circuit breaker mechanism box. S1.24: Installation of high-voltage reactor detection sensors shall be carried out using a combination of acoustic sensors and acoustic imaging components. The acoustic imaging components shall be deployed using floor-standing brackets, covering the high-voltage reactor according to the imaging angle. The installation distance shall not exceed 20 meters. The voiceprint sensor shall be installed nearby, at a horizontal distance of one to two meters. S1.25: Installation of high-voltage switchgear detection sensors: A set of non-contact sensor equipment is arranged in a single cabinet, and installed at the bottom of the switchgear box. Implementation requires power outage, and shielded cables are used for communication and power supply.

4. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 3 is characterized in that: The data transmission in S2 is carried out through the intranet, and the data collected in S1 is received by the access switch. The data preprocessing in S3 is to perform denoising, smoothing and segmentation processing. The feature extraction is to extract the frequency domain and time-frequency domain features of the sound wave signal by using methods such as Fourier transform and wavelet transform, and analyze the spectrum and amplitude changes of the sound wave.

5. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 1 is characterized in that: The establishment of the model in S4 includes the following steps: S4.1: Select an appropriate model. Choose an appropriate machine learning model based on the data characteristics and application requirements. Learning models may include but are not limited to support vector machines, decision trees, random forests, and neural networks. S4.2: Model training, use the training dataset to train the selected model to learn the relationship between features and classification labels; S4.3: Model validation, using cross-validation to evaluate the performance of the model to ensure the generalization ability of the model; S4.4: Model evaluation, including performance evaluation indicators and error analysis. Performance evaluation indicators include accuracy, recall rate, and F1-score indicators. The confusion matrix is ​​used 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 visualization imaging method applied to power transmission and transformation engineering according to claim 5 is characterized in that: The fault judgment and analysis in S5 is to classify the voiceprint signals collected in real time according to the model established in S4, judge whether there is a fault in the equipment, and 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 a fault occurs in the power equipment.

7. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 6 is characterized in that: The visualization imaging display in S6 includes the following steps: S6.1: Generate images based on the model established in S4. According to the requirements of the model, input corresponding prompt information, including but not limited to text descriptions and one or more image features, and input the prompt information into the trained model. The model generates corresponding images based on the input information, and the generated images are post-processed. S6.2: Design a multi-page image display. Display the images of power equipment in the same area on a single page. The images include model diagrams, bar graphs, pie charts, and line graphs. Multiple images are displayed on a single page, with the model diagram overlaid on the bar graphs, pie charts, and line graphs. S6.3: Encode the image page, and the encoded digital information corresponds to the data information of the power equipment in the same area on a single page; S5.6: When a power device fails, the corresponding page will be expanded to cover the screen, and the failed power device will be displayed in red on the model diagram; S5.6: Retrieval and display of the page. By inputting the code of the imaging page, the retrieved display page is directly displayed, and by clicking on the image of the page, one of the corresponding model diagrams, bar graphs, pie charts and linear graphs is directly displayed, and the model diagrams, bar graphs, pie charts and linear graphs can be enlarged and displayed.

8. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 7 is characterized in that: The image post-processing in S6.1 includes interpolation reconstruction, smoothing and detail restoration of the image, and calculation of unknown points using the bicubic interpolation formula. The bicubic interpolation formula is as follows: ， in are the coefficients of the cubic polynomial, is the value of the known point.

9. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 7 is characterized in that: The visual imaging display operating system in S6 includes: Data management module, used to manage and control data and store corresponding data; Image generation module, used to generate model diagrams, bar charts, pie charts and linear charts corresponding to power equipment, and render the graphics; Page design module, used to design the power equipment page, so that the display page can be set to multiple pages and multiple overlaps; The operation module is used for personnel to operate the display page and adjust the corresponding display page when the corresponding power equipment fails; Image optimization module is used to optimize image details and improve image pixels.

10. The voiceprint visualization imaging method applied to power transmission and transformation engineering according to claim 1, characterized in that: The maintenance recommendations issued in S7 are based on the power equipment fault information displayed by visual imaging, generating power equipment fault maintenance instructions, formulating preventive maintenance plans and predictive maintenance based on fault modes and historical data, proposing condition-based maintenance recommendations based on data analysis and fault prediction models, carrying out maintenance in a timely manner, and clarifying the specific steps and required tools and materials for fault repair, setting priorities for maintenance tasks based on the severity of the fault and the degree of impact on the system, and conducting power equipment evaluation after the power equipment maintenance to check whether the power equipment has resumed normal operation.

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