Substation Equipment Monitoring and Early Warning Method Based on Voiceprint Data

Abstract

The present application discloses a method for monitoring and warning of substation equipment based on voiceprint data, which relates to the technical field of data analysis. In this application, the working conditions of the target substation equipment monitored by the voiceprint sensor are compared with the actual working conditions, and then the position of the voiceprint sensor is optimized, so as to avoid the influence of the equipment environment noise of the target substation on the voiceprint sensor. Each optimized voiceprint sensor monitors each component group of the target substation equipment respectively, and any component in each component group is associated with at least two component groups. When a fault occurs in the target substation equipment, the faulty component group can be accurately obtained through the voiceprint sensors in each component group, and then the fault analysis is carried out on the component groups associated with the faulty component group, so as to accurately obtain each faulty component, which greatly improves the accuracy of fault monitoring and also provides convenience for the subsequent maintenance work of relevant staff.

Description

Technical Field

[0001] This application relates to the technical field of data analysis, and specifically to a method for monitoring and warning substation equipment based on voiceprint data. Background Art

[0002] Substation equipment is one of the most important equipment in the power system. Maintaining the stable and reliable operation of substation equipment is crucial for the development of social industry and economy. Therefore, this application proposes a method for monitoring and warning substation equipment based on voiceprint data.

[0003] The prior art, such as an invention patent application with publication number CN113380258A, discloses a method for identifying voiceprint for substation fault determination. It overcomes the problem in the prior art that it is difficult to determine equipment faults due to the increasing number of substations and in-station equipment. The method includes first establishing a system model to store the common noise types in the substation, storing the waveforms and voiceprint information of various noises in the system model, constructing multiple filters based on the known noises, and collecting the in-station noise information of the substation in real time. The real-time collected noise information is passed through the filters of the system model. If the filtered noise can be associated with a pre-stored noise waveform, it is determined that there is a substation fault; the present invention uses the method of voiceprint recognition to obtain a conclusion by filtering the sound and then comparing the waveforms of the graphics.

[0004] For the above solution, there are at least the following technical problems: 1. The current technology mainly stores the waveforms and voiceprint information of common noises in the system model, and determines substation faults by associating the in-station noise information collected in real time with the stored noises after processing. The current technology lacks a method for determining substation faults when new types of noises appear, and as the service life of the equipment in the substation increases, the aging of each equipment may cause changes in the noise waveforms, thus making it impossible to accurately determine substation faults.

[0005] 2. The current technology mainly determines faults for multiple substation equipment in the substation, lacking the determination of faults for specific components in specific substation equipment, and unable to accurately locate the fault location, making the subsequent detection and maintenance work of relevant personnel less convenient, and also making the fault determination method lack accuracy and perfection. Summary of the Invention

[0006] The purpose of this application is to provide a method for monitoring and warning substation equipment based on voiceprint data, which solves the problems existing in the background art.

[0007] To solve the above technical problems, this application adopts the following technical solutions: This application provides a method for monitoring and warning substation equipment based on voiceprint data. Step 1: Obtain the voiceprint data of the target substation equipment, analyze to obtain the working evaluation coefficient of the target substation equipment, and determine whether it is necessary to optimize the installation position of the voiceprint sensor.

[0008] Step 2: Use the optimized voiceprint sensor to monitor the operation of the target substation equipment, analyze whether the target substation equipment has a fault, and then obtain the faulty components.

[0009] Step 3: Display the obtained faulty components and fault information on the terminal device and give a warning prompt.

[0010] Preferably, the voiceprint data of the target substation equipment includes amplitude, fundamental frequency, and zero-crossing rate; the amplitude represents the maximum distance that the sound signal of the target substation equipment deviates from the equilibrium position; the fundamental frequency represents the speed at which the sound signal waveform repeats within one period; the zero-crossing rate represents the number of times the sound signal crosses the zero point within one period.

[0011] Preferably, the optimization of the position of the voiceprint sensor is as follows: A1. Obtain the sound frequencies of each component when the target substation equipment is operating, mark the specified component as the reference component, and mark a component adjacent to the specified component as the matching component. When the difference between the sound frequencies of the reference component and the matching component is greater than or equal to the threshold of the difference between the sound frequencies of the reference component and the matching component in the database, mark the reference component and the matching component as the first target component group; when the difference between the sound frequencies of the reference component and the matching component is less than the threshold of the difference between the sound frequencies of the reference component and the matching component in the database, re-mark the matching component until the first target component group is obtained.

[0012] A2. Update the matching component in the first target component group as the reference component, and repeat the above steps until the second target component group is obtained.

[0013] A3. Update the matching component in the second component group as the reference component, and repeat the above steps until the third target component group is obtained.

[0014] A4. Repeat the above steps until all components of the target substation equipment are grouped.

[0015] A5. Install a voiceprint sensor at the midpoint position of each target component group to monitor the voiceprint data of each target component group.

[0016] Preferably, the process of obtaining the sound frequencies of each component when the target substation equipment is operating is as follows: Place the target substation equipment in a test chamber, set the test chamber environment to be the same as the working environment of the target substation equipment. After all equipment is normally connected, start each component of the target substation equipment respectively, repeatedly collect the sounds of each component when the target substation equipment is operating, preprocess the collected sounds, analyze the sound spectra of each component through a spectrogram, and store them in the database. After the storage is completed, end the test.

[0017] Preferably, the process of obtaining the faulty components is as follows: The process of obtaining the faulty components is as follows: S1. Obtain the component groups that match the reference components and the matching components in the target fault group, and denote them as the first test component group and the second test component group respectively.

[0018] S2. Compare the waveform diagram of the voiceprint signal of the first component test group with the energy distribution waveform diagram of the corresponding voiceprint signal in the database. If the energy distributions of the two are Figure 1 the same, it means that the first component test group has no fault, and it is determined that the faulty device is the matching component in the target fault group; if the energy distribution waveform diagrams of the two are inconsistent, it is determined that the first component test group has a fault.

[0019] S3. Compare the energy distribution waveform diagram of the voiceprint signal of the second component test group with the energy distribution waveform diagram of the corresponding voiceprint signal in the database. If the energy distributions of the two are Figure 1 the same, it is determined that the second component test group has no fault, and it is determined that the fault is the reference component in the target fault group; if the energy distribution waveform diagrams of the two are inconsistent, it is determined that the second component test group has a fault.

[0020] S4. If it is determined that the target fault group, the first component test group, and the second component test group all have faults, then each component in the target fault group, the first component test group, and the second component test group is detected respectively to obtain the faulty components.

[0021] Preferably, the warning prompt content includes the faulty device and the target part group where the faulty device is located; the warning prompt forms include voice prompt and information prompt.

[0022] The beneficial effects of this application are as follows: 1. The substation equipment monitoring and warning method based on voiceprint data provided by this application compares the working conditions of the target substation equipment monitored by the voiceprint sensor with the actual working conditions, and then optimizes the position of the voiceprint sensor, thereby avoiding the influence of the equipment environmental noise of the target substation on the voiceprint sensor. Each optimized voiceprint sensor monitors each component group of the target substation equipment respectively. Any component in each component group is associated with at least two component groups. When a fault occurs in the target substation equipment, the faulty component group can be accurately obtained through the voiceprint sensors in each component group, and then the fault analysis is carried out on the component groups associated with the faulty component group, so as to accurately obtain each faulty component, which greatly improves the accuracy of fault monitoring and also provides convenience for the subsequent maintenance work of relevant staff.

[0023] 2. This application obtains the voiceprint data of the target substation equipment, compares the working condition of the target substation equipment analyzed from the voiceprint data with the actual working condition of the target substation equipment, and then optimizes the installation position of the voiceprint sensor, greatly improving the accuracy and integrity of the working condition of the target substation equipment monitored by the voiceprint sensor, and also greatly reducing the influence of environmental noise on the collected voiceprint data.

[0024] 3. This application monitors the operation of the target substation equipment through the optimized voiceprint sensor. The various components of the target substation equipment are divided in the form of component groups. Any component group has components that overlap with other component groups. By comparing adjacent faulty component groups, the faulty components can be directly analyzed, thus avoiding the errors caused by the cumbersome calculation process, greatly improving the accuracy of fault monitoring and fault location, and making the work of the voiceprint sensor in the field of target substation equipment detection more reasonable and perfect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the implementation steps of the method of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0028] Refer to Figure 1 As shown, the present application provides a monitoring and early warning method for substation equipment based on voiceprint data, including the following steps: Step 1: Obtain the voiceprint data of the target substation equipment, analyze to obtain the working evaluation coefficient of the target substation equipment, and determine whether it is necessary to optimize the installation position of the voiceprint sensor.

[0029] In a specific example, the voiceprint data of the target power transformation equipment includes amplitude, fundamental frequency, and zero-crossing rate; the amplitude represents the maximum distance that the sound signal of the target power transformation equipment deviates from the equilibrium position; the fundamental frequency represents the speed at which the sound signal waveform repeats within one period; the zero-crossing rate represents the number of times the sound signal crosses the zero point within one period.

[0030] It should be noted that the voiceprint data of the target power transformation equipment is any parameter related to the target power transformation equipment collected by the voiceprint sensor when the target power transformation equipment is operating stably, which can be amplitude, fundamental frequency, bandwidth, or the highest cut-off frequency, so it should not be construed as a limitation to this application.

[0031] In a specific example, the working evaluation coefficient of the target power transformation equipment is obtained through the following specific analysis process: Denote the amplitude, fundamental frequency, and zero-crossing rate of the voiceprint of the target power transformation equipment collected by the voiceprint sensor within any one period as F, K, and L respectively. According to the calculation formula: Obtain the working evaluation coefficient α of the target power transformation equipment, where F′, K ′ and L′ respectively represent the amplitude standard value, fundamental frequency standard value, and zero-crossing rate standard value of the target power transformation equipment in the database, and ζ1, ζ2, and ζ3 respectively represent the weight factors corresponding to the amplitude, fundamental frequency, and zero-crossing rate of the target power transformation equipment in the database.

[0032] It should be noted that the amplitude standard value, fundamental frequency standard value, and zero-crossing rate standard value of the target power transformation equipment in the database respectively represent the actual amplitude, fundamental frequency, and zero-crossing rate when the target power transformation equipment is operating stably.

[0033] It should be noted that 0 < ζ1 < 1, 0 < ζ2 < 1, 0 < ζ2 < 1, and ζ1 + ζ2 + ζ3 = 1.

[0034] It should be noted that the weight factors corresponding to the amplitude, fundamental frequency, and zero-crossing rate in the voiceprint data of the target power transformation equipment are obtained through factor analysis. First, perform information condensation on the spatial path attenuation of the amplitude value, fundamental frequency, and zero-crossing rate in the voiceprint data of the target power transformation equipment, and then obtain the variance explained rate after rotation. Divide the cumulative variance explained rate to obtain the weight.

[0035] It should be noted that factor analysis is a well-known technique, which is a multivariate statistical analysis method that starts from the study of the internal correlation and dependence relationship of variables and reduces a number of variables with intricate relationships to a few comprehensive factors; information condensation is expressed as calculating the median; the variance explained rate is the amount of information extracted by the factor, and the variance explained rate = eigenvalue / total number of analysis items; the variance explained rate after rotation is the variance explained rate of the factor after maximum variance rotation.

[0036] In a specific example, the judgment on whether it is necessary to optimize the installation position of the voiceprint sensor is as follows: Compare the working evaluation coefficient of the target substation equipment with the standard value of the working evaluation coefficient of the target substation equipment in the database. When the ratio of the working evaluation coefficient of the target substation equipment to the standard value of the working evaluation coefficient of the target working equipment in the database is within the range of [0.8, 1.0], it is judged that the monitoring result of the voiceprint sensor is accurate and there is no need to optimize the position of the voiceprint sensor; otherwise, it is judged that the monitoring result of the voiceprint sensor is inaccurate and the position of the voiceprint sensor needs to be optimized.

[0037] It should be noted that the standard value of the working evaluation coefficient of the target substation equipment in the database represents the actual working evaluation coefficient when the target substation equipment operates stably.

[0038] In a specific example, the optimization of the position of the voiceprint sensor is as follows:

[0039] A1. Obtain the sound frequencies of each component when the target substation equipment is working. Denote the specified component as the reference component, and denote a certain component adjacent to the specified component as the matching component. When the difference between the sound frequencies of the reference component and the matching component is greater than or equal to the threshold value of the difference between the sound frequencies of the reference component and the matching component in the database, mark the reference component and the matching component as the first target component group; when the difference between the sound frequencies of the reference component and the matching component is less than the threshold value of the difference between the sound frequencies of the reference component and the matching component in the database, re-mark the matching component until the first target component group is obtained.

[0040] A2. Update the matching component in the first target component group as the reference component, and repeat the above steps until the second target component group is obtained.

[0041] A3. Update the matching component in the second component group as the reference component, and repeat the above steps until the third target component group is obtained.

[0042] A4. Repeat the above steps until all components of the target substation equipment are grouped.

[0043] A5. Install the voiceprint sensor at the midpoint position of each target component group to monitor the voiceprint data of each target component group.

[0044] It should be noted that the greater the difference in sound frequencies between components, the smaller the mutual frequency influence.

[0045] It should be noted that the threshold value of the difference in sound frequencies between the reference component parts and the matching component parts in the database is the minimum value of the sound frequency difference when any two adjacent component parts are not affected by each other's sound frequencies.

[0046] It should be noted that the number of component parts in each target component part group can be set by relevant staff themselves, which can be two, three, or four. Therefore, it should not be construed as a limitation to this application.

[0047] In a specific example, the process of obtaining the sound frequencies of each component part when the target power transformation equipment is working is as follows: Place the target power transformation equipment in a test chamber, set the test chamber environment to be the same as the working environment of the target power transformation equipment. After all the equipment is normally connected, start each component part of the target power transformation equipment respectively, repeatedly collect the sounds of each component part when the target power transformation equipment is working, preprocess the collected sounds, obtain the sound spectra of each component part through spectrogram analysis, and store them in the database. After the storage is completed, end the test.

[0048] It should be noted that a spectrum analyzer is used to collect the sound spectra of each component part when the target power transformation equipment is working during the test process.

[0049] It should be noted that the preprocessing includes pre-emphasis, framing, and windowing operations on the sounds of each component part to obtain a clearer sound spectrum image.

[0050] The same environment includes the same temperature, humidity, atmospheric pressure, and environmental noise.

[0051] Step 2: Use the optimized voiceprint sensor to monitor the operation of the target power transformation equipment, analyze whether the target power transformation equipment has a fault, and then obtain the faulty component parts.

[0052] In a specific example, the process of using the optimized voiceprint sensor to monitor the operation of the target power transformation equipment is as follows: Denote the voiceprint signals of the corresponding target part groups collected by each voiceprint sensor in the target power transformation equipment as X j (n), where n represents the sample index in the original signal sequence, n = 0, 1, 2......N - 1, and j represents the number of each voiceprint sensor, j = 1, 2, 3......g. According to the function transformation formula: Obtain the energy distribution of the voiceprint signals of the corresponding target part groups collected by each voiceprint sensor at different frequencies, and then separate the high-frequency signals and low-frequency signals in the voiceprint signals. Here, π is the circumference ratio, k represents the index in the transformed frequency domain sequence, k = 0, 1, 2......N - 1, and N represents the signal length within one period.

[0053] In a specific example, the analysis of whether the target substation equipment fails is performed as follows: the energy distribution waveforms of the soundprint signals monitored by the soundprint sensors in each target parts group at different frequencies are compared with the standard energy distribution waveforms of the target parts group in the database. When the energy distribution waveforms of the soundprint signals monitored by the soundprint sensors in a target parts group at different frequencies are inconsistent with the standard energy distribution waveforms of the target parts group in the database, it is determined that the components in the target parts group are faulty and recorded as a target fault group. Otherwise, it is determined that the components in the target parts group are not faulty and recorded as a target normal group.

[0054] It should be noted that the standard energy distribution waveform diagram of each target part group in the database is represented by an energy distribution waveform diagram of the voiceprint signal monitored by the voiceprint sensor in each target part group when the target substation equipment is working stably.

[0055] It should be noted that the inconsistency of the energy distribution waveform diagram is indicated by the inconsistency of the shape, period or amplitude of the waveform.

[0056] In a specific example, the specific acquisition process of acquiring the faulty components is as follows: S1, acquiring a component group that matches the reference component and the matching component in the target fault group, and recording them as a first test component group and a second test component group, respectively.

[0057] S2. Compare the waveform of the voiceprint signal of the first component test group with the energy distribution waveform of the voiceprint signal of the corresponding group in the database. If the energy distribution waveforms of the two Figure 1 If the energy distribution waveforms of the two are inconsistent, then the first component test group is not faulty and the faulty device is judged to be a matching component in the target fault group; if the energy distribution waveforms of the two are inconsistent, then it is judged that the first component test group is faulty.

[0058] S3, compare the energy distribution waveform of the voiceprint signal of the second component test group with the energy distribution waveform of the voiceprint signal of the corresponding group in the database. If the energy distribution waveforms of the two Figure 1 If the energy distribution waveforms of the two are inconsistent, it is determined that the second component test group has not failed, and the fault is determined to be the reference component in the target fault group; if the energy distribution waveforms of the two are inconsistent, it is determined that the second component test group has failed.

[0059] S4. If it is determined that the target fault group, the first component test group and the second component test group are all faulty, each component in the target fault group, the first component test group and the second component test group is tested respectively to obtain the faulty component.

[0060] It should be noted that the database includes standard values ​​of amplitude, base frequency and zero-crossing rate of target substation equipment; it also includes weight factors corresponding to the amplitude, base frequency and zero-crossing rate of target substation equipment; it also includes standard values ​​of working evaluation coefficients of target substation equipment; it also includes threshold values ​​of the difference in sound frequency between reference components and matching components; it also includes energy distribution waveforms of each voiceprint signal monitored by the voiceprint sensor in each target parts group at different frequencies.

[0061] Step 3: Display the acquired faulty parts and fault information on the terminal device and issue an early warning prompt.

[0062] In a specific example, the early warning prompt content includes the faulty device and the target parts group where the faulty device is located; the early warning prompt form includes voice prompt and information prompt.

[0063] The substation equipment monitoring and early warning method based on voiceprint data provided by the present application compares the working condition of the target substation equipment monitored by the voiceprint sensor with the actual working condition, and then optimizes the position of the voiceprint sensor, thereby avoiding the influence of the equipment environment noise of the target substation on the voiceprint sensor. The optimized voiceprint sensors respectively monitor the component groups of the target substation equipment, and any component in each component group is associated with at least two component groups. When the target substation equipment fails, the faulty component group can be accurately obtained through the voiceprint sensors in each component group, and then the fault analysis is performed on the component groups associated with the faulty component group, and then each faulty component is accurately obtained, which greatly improves the accuracy of fault monitoring and provides convenience for the subsequent maintenance work of relevant staff.

[0064] The above contents are merely examples and explanations of the concept of the present application. The technicians in this technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in the present application, they should all fall within the protection scope of the present application.

Claims

1. A monitoring and early warning method for substation equipment based on voiceprint data, characterized in that, Including: Step 1: Obtain the voiceprint data of the target substation equipment, analyze to obtain the working evaluation coefficient of the target substation equipment, and determine whether it is necessary to optimize the installation position of the voiceprint sensor; The optimization of the voiceprint sensor position is as follows: A1. Obtain the sound frequencies of each component during the operation of the target substation equipment. Denote the specified component as the reference component and a certain component adjacent to the specified component as the matching component. When the difference between the sound frequencies of the reference component and the matching component is greater than or equal to the threshold of the difference between the sound frequencies of the reference component and the matching component in the database, mark the reference component and the matching component as the first target component group; When the difference between the sound frequencies of the reference component and the matching component is less than the threshold of the difference between the sound frequencies of the reference component and the matching component in the database, re-mark the matching component until the first target component group is obtained; A2. Update the matching component in the first target component group as the reference component, and repeat the above steps until the second target component group is obtained; A3. Update the matching component in the second component group as the reference component, and repeat the above steps until the third target component group is obtained; A4. Repeat the above steps until all components of the target substation equipment are grouped; A5. Install the voiceprint sensor at the midpoint position of each target component group to monitor the voiceprint data of each target component group; Step 2: Use the optimized voiceprint sensor to monitor the operation of the target substation equipment, analyze whether the target substation equipment has a fault, and then obtain the faulty components; Step 3: Display the obtained faulty components and fault information on the terminal device and give a warning prompt.

2. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 1, characterized in that The voiceprint data of the target substation equipment includes amplitude, fundamental frequency, and zero-crossing rate; the amplitude represents the maximum distance of the sound signal deviating from the equilibrium position; the fundamental frequency represents the speed at which the sound signal waveform repeats within one period; the zero-crossing rate represents the number of times the sound signal crosses the zero point within one period.

3. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 2, characterized in that The specific analysis process for obtaining the working evaluation coefficient of the target substation equipment is as follows: The amplitude, fundamental frequency, and zero-crossing rate of the voiceprint of the target substation equipment collected by the voiceprint sensor within any one period are respectively denoted as , and , according to the calculation formula: Obtain the working evaluation coefficient of the target power transformation equipment , where , and respectively represent the amplitude standard value, fundamental frequency standard value, and zero-crossing rate standard value of the target power transformation equipment in the database, , and respectively represent the weight factor corresponding to the amplitude, the weight factor corresponding to the fundamental frequency, and the weight factor corresponding to the zero-crossing rate of the target power transformation equipment in the database.

4. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 3, characterized in that The specific judgment process for determining whether it is necessary to optimize the installation position of the voiceprint sensor is as follows: Compare the working evaluation coefficient of the target power transformation equipment with the standard value of the working evaluation coefficient of the target power transformation equipment in the database. When the ratio of the working evaluation coefficient of the target power transformation equipment to the standard value of the working evaluation coefficient of the target working equipment in the database is within the interval it is determined that the monitoring result of the voiceprint sensor is accurate and there is no need to optimize the position of the voiceprint sensor; otherwise, it is determined that the monitoring result of the voiceprint sensor is inaccurate and the position of the voiceprint sensor needs to be optimized.

5. The monitoring and early warning method for substation equipment based on voiceprint data according to claim 4, wherein, The specific process for obtaining the sound frequencies of each component during the operation of the target substation equipment is as follows: Place the target substation equipment in the test chamber, set the test chamber environment to be the same as the working environment of the target substation equipment. After all equipment is normally connected, start each component of the target substation equipment respectively, repeatedly collect the sounds of each component during the operation of the target substation equipment, preprocess the collected sounds, analyze the sound spectra of each component through the spectrogram, and store them in the database. After the storage is completed, end the test.

6. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 5, wherein The specific monitoring process for using the optimized voiceprint sensor to monitor the operation of the target substation equipment is as follows: The voiceprint signals of the corresponding target parts group collected by each voiceprint sensor in the target substation equipment are recorded as ,in represents the sample index in the original signal sequence, , Indicates the number of each voiceprint sensor, , according to the function transformation formula: Obtain the energy distribution of the voiceprint signals corresponding to the target part groups collected by each voiceprint sensor at different frequencies, and further realize the separation of high-frequency signals and low-frequency signals in the voiceprint signals, where represents the index in the transformed frequency-domain sequence, , represents the signal length within one period.

7. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 6, characterized in that The specific analysis process for analyzing whether the target substation equipment has a fault is as follows: Compare the energy distribution waveform diagrams of the voiceprint signals monitored by the voiceprint sensors in each target part group at different frequencies with the standard energy distribution waveform diagrams of the corresponding target part groups in the database. When the energy distribution waveform diagrams of the voiceprint signals monitored by the voiceprint sensors in a certain target part group are inconsistent with the standard energy distribution waveform diagrams of this target part group in the database, it is determined that the components in this target part group are faulty, and it is recorded as the target fault group. Otherwise, it is determined that the components in this target part group are not faulty, and it is recorded as the target normal group.

8. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 7, characterized in that, The specific process of obtaining the faulty components is as follows: S1. Obtain the part groups that match the reference components and the matching components in the target fault group, and record them as the first test part group and the second test part group respectively; S2. Compare the voiceprint signal waveform diagram of the first part test group with the energy distribution waveform diagram of the corresponding voiceprint signal in the database. If the energy distribution waveform diagrams of the two are consistent, it means that the first part test group is not faulty, and it is determined that the faulty device is the matching component in the target fault group; if the energy distribution waveform diagrams of the two are inconsistent, it is determined that the first part test group is faulty; S3. Compare the energy distribution waveform diagram of the voiceprint signal of the second part test group with the energy distribution waveform diagram of the corresponding voiceprint signal in the database. If the energy distribution waveform diagrams of the two are consistent, it is determined that the second part test group is not faulty, and it is determined that the fault is the reference component in the target fault group; If the energy distribution waveform diagrams of the two are inconsistent, it is determined that the second part test group is faulty; S4. If it is determined that the target fault group, the first part test group, and the second part test group are all faulty, then detect each component in the target fault group, the first part test group, and the second part test group respectively to obtain the faulty components.

9. The method for monitoring and warning of power transformation equipment based on voiceprint data according to claim 8, characterized in that, The early warning prompt content includes the faulty device and the target part group where the faulty device is located; The early warning prompt forms include voice prompts and information prompts.

Images