System and method for comprehensively monitoring abnormal multi-mode working conditions of 20KV electric brake switch based on voiceprint recognition

Abstract

According to the 20KV electric brake switch multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition, all internal faults of a 20KV generator brake switch are monitored online in real time through the voiceprint recognition technology, and the industry blank of real-time online monitoring of the 20KV generator brake switch is filled up; a voiceprint collector, a temperature sensor, a vibration sensor, an SF6 gas monitoring device, a position node relay and the like are installed in a proper area, and multi-modal data are integrated into the same system, so that comprehensive analysis of the working condition of the 20KV generator brake switch is realized; and deep learning and fault judgment process logic design are carried out on each data parameter, so that the multi-mode working condition abnormity comprehensive diagnosis of the 20KV generator brake switch is realized, and the timeliness of working condition abnormity or fault judgment of the 20KV generator brake switch equipment is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of safe operation of large-scale hydro-generator systems, and specifically relates to a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring system and method based on voiceprint recognition. Background Art

[0002] The 20KV generator brake switch device is usually composed of two parts: SF6 generator circuit breaker and three-phase short-circuit wire (107). The 20KV generator circuit breaker is a horizontally arranged structure, and is operated by a three-phase mechanical linkage (102) and a hydraulic spring mechanism (109). Three sets of voltage transformers (103, 104, 105) are installed on the side of the moving contact (phase-separated closed busbar of the main circuit of the generator) of each set of brake switch devices for use by protection, speed regulation, excitation and other systems. The electrical connection diagram is shown in the figure below. Figure 1 shown.

[0003] After a large hydro-turbine generator set is shut down, the speed will slow down after 50% due to inertia. In order to reduce the impact of long-term low speed on the equipment, it is necessary to put in electrical braking to achieve rapid braking. When the speed of the generator set drops to 50%, the brake switch is put in. After the brake switch is put in, the control device of the electrical brake will increase the excitation of the generator, and the current flowing through the electrical brake device will quickly rise to about the rated current of the generator. At this time, the three phases of the stator winding are symmetrically short-circuited, and the rotor is excited so that a braking current equal to the current value of the maximum capacity operating condition flows through the stator winding, generating an electric braking torque to achieve rapid shutdown.

[0004] Common faults of 20KV generator brake switches include SF6 gas leakage pressure drop, loose or deformed three-way connecting rod, frequent oil pump startup, partial discharge, and poor contact and burning of voltage transformer. Although the 20KV generator brake switch is only put into operation during the shutdown process of the unit, its equipment body is actually directly electrically connected to the generator outlet closed bus and the main transformer. Once the three-way connecting rod is loose or deformed, partial discharge, poor contact and burning of the voltage transformer occur, resulting in the grounding of the primary equipment or abnormal misoperation of the protective device measurement value, it will directly cause the unit to shut down due to electrical accidents, seriously affecting the reliability of large-scale hydropower generator units during grid-connected operation, and thus affecting the stability of the power system.

[0005] The current power generation industry has no online monitoring of 20KV generator brake switches. Most large generator sets only upload the position status of the electrical brake switch to the monitoring system. The operating personnel only conduct regular inspections to check whether the equipment is normal, and perform related routine inspections during unit maintenance. There are no other monitoring methods.

[0006] Since the existing technology cannot realize real-time online monitoring of the 20KV generator brake switch, the operators can only conduct inspections based on their experience. Therefore, before the 20KV generator brake switch is put into operation during the unit shutdown process, it is impossible to accurately judge whether the 20KV generator brake switch is in normal working condition. The normal working condition can only be verified by the electrical quantities of the stator and rotor after the switch is put into operation. Once there is an abnormality before the switch is put into operation, the three-phase imbalance of the stator winding may occur for a long time, affecting the reliability of the generator; secondly, during the operation of the hydro-turbine generator set, if the voltage transformer fails due to local discharge, poor contact of the voltage transformer, etc., it may cause the unit to shut down directly. This type of fault is an internal fault and cannot be directly observed by the operators. Summary of the invention

[0007] In order to solve the current technical problems, the main purpose of the present invention is to provide a 20KV electrical brake switch multi-modal operating condition abnormal comprehensive monitoring system and method based on voiceprint recognition. This method uses voiceprint recognition technology to realize real-time online monitoring of all internal faults of the 20KV generator brake switch, filling the industry gap of real-time online monitoring of the 20KV generator brake switch; by installing bone conduction pickups, cameras with thermal infrared temperature measurement functions, vibration sensors, SF6 gas monitoring devices, position node relays, etc. in appropriate areas, and integrating multi-modal data into the same system, a comprehensive analysis of the 20KV generator brake switch operating condition is achieved; by deep learning of each data parameter and logical design of the fault judgment process, a comprehensive diagnosis of the multi-modal operating condition abnormality of the 20KV generator brake switch is achieved, which greatly improves the timeliness of the abnormal operating condition or fault judgment of the 20KV generator brake switch equipment.

[0008] In order to realize the above technical features, the purpose of the present invention is achieved as follows: a 20KV electrical brake switch multi-modal working condition abnormality comprehensive monitoring system based on voiceprint recognition, comprising: a parameter acquisition module, a network transmission module, a voiceprint processing module, a multi-modal analysis module, a fault judgment module and a result output module;

[0009] The parameter acquisition module is used to collect multi-modal raw data of the electrical brake switch from various sensors and devices;

[0010] The network transmission module is used to transmit the multimodal raw data collected by the parameter acquisition module to the voiceprint processing module and the multimodal analysis module through the network;

[0011] The voiceprint processing module is used to extract voice features from the multimodal raw data, compare them with the preset voiceprint model, and give a similarity or abnormality score;

[0012] The multimodal analysis module is used to comprehensively analyze data from different modalities, fuse multiple data, and use deep learning algorithms to perform data analysis and pattern recognition to obtain analysis results;

[0013] The fault judgment module uses preset rules or models to perform fault detection and diagnosis based on the analysis results of the multimodal analysis module to determine whether there is a fault in the system or equipment, as well as the type and severity of the fault;

[0014] The result output module is used to display the final analysis and judgment results in the form of charts, reports or alarms.

[0015] Preferably, the monitoring method is implemented using the monitoring system according to claim 1, comprising:

[0016] S1, acquisition of multi-modal raw data of electrical brake switch:

[0017] A voiceprint collector, a vibration sensor, a SF6 gas pressure gauge, a temperature sensor and a position switch are arranged and installed at appropriate positions of the electric brake switch to form a parameter acquisition module. The parameter acquisition module collects multi-modal raw data of different modes of the electric brake switch during operation, including voiceprint, vibration, gas leakage, temperature and position status data.

[0018] S2, transmission of multi-modal raw data of electrical brake switch:

[0019] The multimodal raw data collected in S1 is transmitted to the voiceprint processing module and the multimodal analysis module in real time through the network transmission module to realize remote data transmission;

[0020] S3, extraction, analysis and scoring of voiceprint features:

[0021] First, the voiceprint data of the multimodal raw data is preprocessed, and then the features of the voiceprint data are extracted to form sound features. The extracted sound features are compared with the preset voiceprint model through the voiceprint processing module, and a similarity or anomaly score is given. The voiceprint data is anomaly detected using anomaly detection technology to identify possible anomalies for subsequent device status or fault judgment;

[0022] S4, analysis and processing of multimodal raw data:

[0023] The result of the soundprint processing module in S3 is transmitted to the multimodal analysis module as part of the input data for the analysis of the multimodal analysis module, and the remaining multimodal raw data of the vibration, gas leakage, temperature and position status data obtained in S1 are comprehensively analyzed and processed, and the multimodal raw data are fused at the same time. The multimodal data are fused and analyzed using a deep learning algorithm to extract characteristic information that can reflect the working condition of the electrical brake switch; then the difference between the current data and the normal working condition data is compared to determine whether there is an abnormality; if there is an abnormality, proceed to the next step for fault detection and diagnosis; otherwise, continue to monitor and update the normal working condition database for subsequent comparison;

[0024] S5, fault judgment:

[0025] Based on the analysis results in S4, the fault judgment module uses preset rules or models to perform fault detection and diagnosis to determine whether the system or equipment has a fault, as well as the type and severity of the fault, so as to timely discover and handle potential abnormal situations to improve the stability and safety of the system; at the same time, based on historical data and current data, possible fault trends are predicted to provide maintenance personnel with early warning information and maintenance suggestion services, so as to understand the working condition of the electric brake switch and take corresponding maintenance measures to extend the service life and reduce maintenance costs;

[0026] S6, output of results:

[0027] The analysis and judgment results finally obtained in S4 are presented to the user or system administrator in an appropriate form with the help of the result output module.

[0028] Preferably, the specific steps of S1 are:

[0029] S1.1, select multiple voiceprint collector points at appropriate locations of the electrical brake switch, and arrange and install voiceprint collectors at the voiceprint collector points; select multiple vibration sensor points at appropriate locations of the electrical brake switch, and arrange and install vibration sensors at the vibration sensor points; arrange and install SF6 gas pressure gauges at the lower part of the three-phase short-circuit closed busbar casing of the electrical brake switch; arrange and install multiple temperature sensors at appropriate locations of the electrical brake switch; install position switches on the electrical brake switch;

[0030] S1.2, through various types of raw data during the operation of voiceprint collector, vibration sensor, SF6 gas pressure gauge, temperature sensor and position switch, different types of raw data together constitute multimodal raw data.

[0031] Preferably, there are 4 voiceprint collector points in S1.1, and there are 4 corresponding voiceprint collectors, which respectively correspond to the circuit breaker operating connecting rod end installed on the electrical brake switch, the ground knife operating connecting rod end, the circuit breaker oil pump motor housing, and the three-phase short-circuit closed busbar housing, and the voiceprint data is collected through the voiceprint collectors.

[0032] Preferably, there are two vibration sensor points in S1.1, and there are two corresponding vibration sensors, which are respectively installed at the ground base of the operating mechanism bracket of the electric brake switch, and vibration data is collected by the vibration sensors.

[0033] Preferably, there is one SF6 gas pressure gauge in S1.1;

[0034] There are three temperature sensors in S1.1, which are respectively installed at the lower shell of the three-phase voltage transformer PT;

[0035] There is one position switch in S1.1, which is installed at the front end of the circuit breaker operating mechanism, and the position data is collected through the position switch.

[0036] Preferably, the network transmission module in S2 specifically includes: a data output unit, a local control unit, a network transmission device, a server device and a system platform;

[0037] The data output unit is responsible for physically connecting the parameter sensor to the network after collecting the signal, and sending the data to the external device or system through the transmission interface;

[0038] The local control unit is used to collect the status and data of the field equipment, send control instructions, exchange data with the host computer or other equipment, and control and monitor the field equipment in real time;

[0039] Network transmission equipment, composed of routers and switch network equipment, is responsible for data routing and forwarding, ensuring that data can be accurately and efficiently transmitted to the target location;

[0040] Server equipment, consisting of high-performance computer systems, large-capacity storage devices, and network connection devices, provides data storage, processing, and analysis services, and supports a variety of network services;

[0041] The system platform provides a user interaction interface and is responsible for data integration, analysis and display, and sends processed requests or instructions to the corresponding network device or server for processing.

[0042] Preferably, the specific steps of performing voiceprint feature extraction, analysis and scoring based on the voiceprint processing module in S3 are:

[0043] S3.1, voiceprint MFCC extraction:

[0044] S3.1.1, Pre-emphasis:

[0045] Pre-emphasis is to enhance the high-frequency components in the audio signal through a high-pass filter, and balance the spectrum so that in the subsequent spectrum analysis, the low-frequency and high-frequency components can be processed with the same signal-to-noise ratio;

[0046] Set a pre-emphasis coefficient, which is a number less than 1;

[0047] To perform a differential operation on the audio signal, use the formula:

[0048] s′(n)=s(n)-α×s(n-1);

[0049] Where: s(n) represents the audio signal sampling value at the current moment, s(n-1) represents the audio signal sampling value at the previous moment, α is a pre-emphasis coefficient, and s'(n) is the sampling value at the current moment after pre-emphasis processing;

[0050] S3.1.2, Framing:

[0051] Framing is to cut the continuous audio signal into shorter segments for short-term analysis. Each frame contains N sampling points, where N is 256 or 512. There is a certain overlap between frames to ensure the continuity of the signal. The framing steps are as follows:

[0052]

[0053] In the formula, F represents the total number of frames obtained after the framing operation in audio processing; L represents the total length of the audio signal, which is the sampling frequency multiplied by time; N is the length of each frame, that is, the number of sampling points contained in each frame; M is the frame shift, which represents the number of sampling points in the overlapping part between two consecutive frames, and is also the number of sampling points that a new frame moves relative to the previous frame; It represents the smallest integer not less than x, that is, rounded up;

[0054] S3.1.3, Windowing:

[0055] By adding windows, the continuity of the signal between frames is increased and the spectrum leakage is reduced. The Hamming window is used and is defined as follows:

[0056] The mathematical expression of the Hamming window is:

[0057]

[0058] Where n is an integer from 0 to N-1, and N is the size of the window;

[0059] According to the mathematical expression of the Hamming window, the value w(n) of the Hamming window is calculated for the sampling point n in each frame. This calculation can be repeated for each frame.

[0060] S3.1.4, Windowing Operation:

[0061] Multiply the signal S(n) of each frame by the corresponding Hamming window value w(n). Mathematically, this can be expressed as:

[0062] S w (n) = S (n) × w (n);

[0063] Among them, S w (n) is the windowed signal;

[0064] S3.1.5, Mel filtering:

[0065] The energy spectrum is passed through a set of Mel-scale triangular filter banks, and a filter bank with M filters is defined. The filter used is a triangular filter; the logarithmic energy of each filter bank output is calculated; the MFCC coefficients are obtained by discrete cosine transform; the dynamic differential parameters are extracted; the differential parameters can be calculated using the following formula:

[0066]

[0067] Where L is half the size of the differential window, that is, the total window size is 2L±1, C t is the MFCC coefficient vector of the tth frame, ΔC t is the first-order difference coefficient vector of the t-th frame;

[0068] The second-order difference is to apply the difference operation again to the first-order difference coefficient to capture faster changes in the audio signal. The formula is as follows:

[0069]

[0070] In the formula, Δ 2 C t is the second-order differential coefficient vector of the tth frame, ΔC t is the obtained first-order difference coefficient vector;

[0071] The N-dimensional MFCC parameters are composed of (N / 3 MFCC coefficients + N / 3 first-order difference parameters + N / 3 second-order difference parameters) + frame energy;

[0072] S3.2, calculate UBM:

[0073] First, audio data irrelevant to the target is collected to train a UBM. Then, the target audio data is used to adjust the parameters of the UBM through an adaptive algorithm to obtain the target model parameters.

[0074] S3.3, calculate i-vector:

[0075] i-vector defines a low-dimensional vector R×1,w~N(0,I) to represent an audio segment;

[0076]

[0077] Where M is the ideal feature super vector corresponding to a device being modeled; As the UBM mean supervector, assuming that the UBM contains C Gaussian mixture components g, then is the mean vector m of all mixture components c ,c=1,...,C combination, The dimension of the transformation matrix T is usually CF×R, where C is the number of Gaussian components in the Gaussian mixture model, F is the dimension of the acoustic feature, and R is the dimension of the i-vector; w is the required i-vector;

[0078] For each mixture component c, there is also a parameter mixture weight w c , and covariance matrix Σc; split the T matrix according to Gaussian components, then for each component c, a submatrix V can be obtained C , whose dimension is F×R; this submatrix V C In fact, it represents the linear transformation from the feature space of the Gaussian component to the i-vector space. Mathematically, the T matrix is ​​expressed as:

[0079]

[0080] Among them, each V C It is an F×R matrix, corresponding to a Gaussian component in GMM:

[0081] μ c =m c +V c w;

[0082] In the formula, μ c represents the mean vector corresponding to the cth Gaussian component after a given i-vector w; m c is the original mean vector of the cth Gaussian component in the UBM;

[0083] Define a piece of audio feature data X, whose feature dimension is F and time sequence is T, that is, X = X1,...,XT, the subset of X belonging to the cth Gaussian component is Xc, and a certain frame in the subset but

[0084]

[0085] This gives the following formula:

[0086]

[0087] The calculation formula of i-vector is:

[0088] w=(I+T T ∑ -1 N(u)T) -1 T T ∑ -1 F(u);

[0089] Where w is the required i-vector; I is the identity matrix; T is the transformation matrix, which maps the i-vector space to the feature space; N(u) is a diagonal matrix of dimension CF×CF, and the block matrices on its diagonal are the counts of each Gaussian component; F(u) is a supervector of first-order Baum-Welch statistics, which is a supervector of CF×1, consisting of all first-order BW statistics F~c;

[0090] S3.4, Linear Probability Discriminant Analysis:

[0091] Linear probability discriminant analysis is based on i-vector features and provides channel compensation for it. Assume that the training data audio consists of audio from I devices, where each device has J different audio segments. The i-vector of the j-th audio segment of the i-th device is denoted by D ij , then PLDA defines:

[0092] D ij =μ+Fh i +Gω ij +ε ij ;

[0093] In the formula, the device information part is μ+Fh i , which is only related to device i and describes the difference between devices; the noise part is Gω ij +ε ij , describing the differences between devices; μ represents the mean of all training data; F is regarded as the identity space, which contains information that can be used to represent various devices; h i It is regarded as the identity of a specific device; G is the error space, which contains information used to represent different audio changes of the same device; ω ij represents the position in G space; ε ij is the final residual noise term, used to represent something that has not yet been explained; this term is zero-mean Gaussian with variance Σ;

[0094] Assume that each latent variable conforms to the following distribution:

[0095]

[0096] Assuming that the model M describes the relationship between the identity factor h and the input feature i-vector, the test process is to determine ivert x p Whether to register ivivor x i Share the same device identity h;

[0097] hypothesis M0 represents x i and x p From different identity latent variables, hypothesis M1 represents x i and x p From the same identity latent variable, the likelihood ratio calculates the score value:

[0098]

[0099] The numerator P(x1,xp / M1) represents the assumption that M1: x i and x p From the same device, the speech sample x is observed i and x p The denominator P(x1,xp / M0) represents the joint probability of i and x p Speech samples x are observed from different devices. i and x p By calculating the score, we can measure the similarity between the two audios. The higher the value, the higher the score, and the greater the possibility that the two audios belong to the same device; otherwise, the smaller the possibility.

[0100] Preferably, the analysis and processing of the multimodal raw data in S4 specifically includes:

[0101] S4.1, Data selection:

[0102] The hardware partitions used are to exclude the impact of equipment abnormalities in other areas on the fault judgment of this area. The partitions are as follows: circuit breaker area, motherboard sealing area, and knife switch area. Each input parameter: voiceprint score value, vibration sensor peak-to-peak value, position switch signal, and temperature are standardized so that the value range is between 0 and 1.

[0103] For each parameter p i , whose standardized value n i Calculated by the following formula:

[0104]

[0105] Among them, p min and p max They are the parameters p i possible minimum and maximum values;

[0106] S4.2, set basic scoring value and weight:

[0107] Set a basic score for each parameter, and assign different weights to them according to the importance of each parameter. The sum of the weights should be 1.

[0108] S4.3, calculate the rate of change:

[0109] For each parameter, calculate the rate of change between its current value and the initial value or the last measured value, the rate of change r i Calculated by the following formula:

[0110]

[0111] Among them, p i,current is the current value of the parameter, p i,previous is the previous value of the parameter;

[0112] S4.4, determine the threshold and adjust the score value:

[0113] If the change rate of any parameter exceeds 20%, the score of the parameter will be reduced to 0 points; if the change rate of more than half of the parameters exceeds 10%, the score of these parameters will be halved; the score of each parameter will be adjusted according to the change rate. i :

[0114]

[0115] Among them, base_score i is the base score of parameter i, I is an indicator function: it returns 1 if the condition is true, otherwise it returns 0, and n is the total number of parameters;

[0116] S4.5, calculate the final score:

[0117] According to the current score value of each parameter and its weight, calculate the weighted average as the final score value;

[0118] Output score value:

[0119]

[0120] Among them, w i is the weight of parameter i and satisfies Output the final score, which should be between 0 and 10.

[0121] Preferably, the fault judgment in S5 specifically includes: internal fault judgment of the oil pump motor, SF6 gas leakage fault judgment, operating mechanism connecting rod fault judgment and PT poor contact discharge fault judgment.

[0122] Preferably, the oil pump motor internal fault judgment process is:

[0123] S5.1.1: The circuit breaker zone score value drops to 6, triggering the fault judgment process;

[0124] S5.1.2: The vibration sensor parameters and voiceprint scoring values ​​are judged simultaneously. If the 1# / 2# vibration sensor and 3# voiceprint sensor are out of limit, proceed to the next step;

[0125] S5.1.3: Compare the current fault voiceprint data with the standards in the fault library. If there is a matching fault and event, directly output the corresponding fault event alarm; if it cannot be matched with the oil pump pressure model, calculate the change rate of the first five pressure times. If the change rate is greater than 0, it means that the inside of the oil pump motor is slowly deteriorating, and it is necessary to open the cover to check and determine the fault, and mark the specific fault into the model library;

[0126] S5.1.4: If the matching model is the oil pump pressure model, the timer will be 2 minutes, and the number of pressures plus 1 and the duration will be recorded, and then go to the next step;

[0127] S5.1.5: When the oil pump is pressurized for less than 2 minutes, the output is the oil pump motor pressurization event;

[0128] S5.1.6: When the oil pump pressurization exceeds 2 minutes, the oil pump motor pressurization timeout alarm is output and the next step is entered;

[0129] S5.1.7: To prevent the stator winding from being damaged due to the opening lock or three-phase inconsistency during the operation of the 20KV electrical brake switch with a defect, it is necessary to lock the switch operation and notify the equipment maintenance personnel by phone to handle it on site.

[0130] Preferably, the SF6 gas leakage fault judgment process is:

[0131] S5.2.1: The score of the mother area drops to 6, triggering the fault judgment process;

[0132] S5.2.2: The vibration sensor parameters, voiceprint score and SF6 pressure are judged simultaneously. If the 1# / 2# vibration sensors are normal but the 4# voiceprint sensor and SF6 gas pressure gauge are out of limit, proceed to the next step;

[0133] S5.2.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0134] S5.2.4: When the match is SF6 gas leakage, an SF6 gas leakage alarm is issued and the next step is entered;

[0135] S5.2.5: In the event of confirmed SF6 gas leakage, the system will initiate the corresponding emergency plan and lock the switch operation, which includes shutting down related equipment, ventilation, and evacuating personnel to ensure safety.

[0136] Preferably, the operating mechanism connecting rod fault judgment process is:

[0137] S5.3.1: The circuit breaker zone or switch zone score value drops to 6, triggering the fault judgment process;

[0138] S5.3.2: The circuit breaker position switch determines the position change, checks whether the 1# / 2# vibration sensor is abnormal, checks whether the voiceprint score value of the 1# voiceprint sensor and the voiceprint score value of the 2# voiceprint sensor are abnormal, if abnormal, proceed to the next step;

[0139] S5.3.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0140] S5.3.4: Handle the circuit breaker connecting rod failure. If the fault is matched to the circuit breaker connecting rod failure, the system will immediately lock the circuit breaker operation to ensure safety. At the same time, the system will determine whether the unit is in the process of shutdown. If so, the system will start the emergency shutdown process and skip the 20KV electrical brake switch input step to prevent the fault from expanding or causing more serious consequences;

[0141] S5.3.5: Handle knife switch linkage failure. If the fault is determined to be a knife switch linkage failure, the system will immediately lock the knife switch operation to prevent the fault from worsening. At the same time, the system will check whether the unit is in the electrical brake engaged state. If so, the system will issue an electrical brake exit command and disconnect the electrical brake circuit breaker to ensure the stability and safety of the system.

[0142] Preferably, the PT poor contact discharge fault judgment process is:

[0143] S5.4.1: The scoring value of the circuit breaker area or the switch area is normal, but the scoring value of the motherboard area drops to 6, triggering the fault judgment process;

[0144] S5.4.2: Determine the status of the sensor parameters in the sealing mother area. The 4# sound print sensor is abnormal, the SF6 gas pressure gauge is normal, the 1# / 2# vibration sensor is normal, and the temperature sensor is abnormally high. If the above conditions are met, proceed to the next step;

[0145] S5.4.3: If abnormal, locate the fault point and identify the phase according to the abnormal condition of the temperature sensor, that is, determine which phase has a problem;

[0146] S5.4.4: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0147] S5.4.5: After completing the comparison between the fault library and the model library, the system will determine whether the current fault matches the PT poor contact discharge fault. If it matches, the system will output the PT poor contact discharge fault alarm and proceed to the next step;

[0148] S5.4.6: Determine the operating status of the unit and handle it; confirm the current status of the unit. If the unit is in shutdown state, the system will set the unit to the prohibition of startup mode to prevent the unit from being started by mistake before the fault is handled, and immediately arrange personnel to check and repair;

[0149] S5.4.7: Adjust the shutdown priority of the unit; if the unit is currently in the on state, the system will increase the shutdown priority of the unit to the highest; once the branch plant needs to shut down, this unit will be given priority for shutdown, and during this period, attention and monitoring of the unit must be strengthened to ensure safety.

[0150] The present invention has the following beneficial effects:

[0151] 1. The present invention realizes multi-modal online real-time monitoring of 20KV electrical brake switch equipment area by installing various types of acquisition sensors, filling the gap in the industry.

[0152] 2. The bone conduction microphone of the present invention collects audio data of 50Hz-4KHz, and can collect almost all abnormal conditions in the 20KV electrical brake switch equipment area. It is also assisted by temperature, vibration, gas monitoring and other data. Therefore, this solution can identify all faults in the 20KV electrical brake switch equipment area of ​​the equipment, greatly improving the accuracy of abnormal monitoring of the working condition of the 20KV electrical brake switch equipment.

[0153] 3. The present invention uses voiceprint recognition technology and comprehensive judgment and analysis of multimodal data such as temperature, vibration, image, and gas monitoring to greatly improve the accuracy of abnormal operating condition monitoring of 20KV electrical brake switch equipment, save human resources, and reduce the time required for fault identification, thereby ensuring safe and stable operation of the unit and avoiding accidents during startup and shutdown. BRIEF DESCRIPTION OF THE DRAWINGS

[0154] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0155] Figure 1This is the electrical connection diagram of the generator brake switch device of the present invention.

[0156] Figure 2 It is a system structure diagram of the present invention.

[0157] Figure 3 This is the hardware diagram of the parameter acquisition module of the present invention.

[0158] Figure 4 This is a structural diagram of the network transmission module of the present invention.

[0159] Figure 5 This is the MFCC extraction flow chart of the present invention.

[0160] Figure 6 The present invention intercepts 100ms of original sound data as continuous sound samples.

[0161] Figure 7 This is a waveform diagram of 100ms audio data after pre-emphasis processing according to the present invention.

[0162] Figure 8 The waveform after framing of the present invention takes the fifth frame as an example.

[0163] Fig. 9 This is a waveform comparison diagram before and after windowing of the present invention.

[0164] Fig.10 It is the 100ms logarithmic energy of the present invention.

[0165] Fig.11 It is the 13th-order MFCC value of the present invention.

[0166] Fig.12 FIG. 4 is a flow chart of calculating UBM of the present invention.

[0167] Fig.13 The audio energy diagram of the 15s speech sample of the present invention compared with the model.

[0168] Fig.14 This is the analysis and processing flow of the multimodal raw data of the present invention.

[0169] Fig.15 It is the peak-to-peak value signal of the vibration sensor of the present invention.

[0170] Fig.16 It is the switch position signal of the present invention.

[0171] Fig.17 It is the temperature signal of the present invention.

[0172] Fig.18 This is the internal fault judgment process of the oil pump motor of the present invention.

[0173] Fig.19 This is the SF6 gas leakage fault judgment process of the present invention.

[0174] Fig. 20 This is the operating mechanism connecting rod fault judgment process of the present invention.

[0175] Fig.21 This is the PT poor contact discharge fault judgment process of the present invention. DETAILED DESCRIPTION

[0176] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0177] Embodiment 1:

[0178] See also Figure 2 A 20KV electrical brake switch multi-modal working condition abnormality comprehensive monitoring system based on voiceprint recognition, comprising: a parameter acquisition module 1, a network transmission module 2, a voiceprint processing module 3, a multi-modal analysis module 4, a fault judgment module 5 and a result output module; the parameter acquisition module 1 is used to collect multi-modal raw data of the electrical brake switch from various sensors and devices; the network transmission module 2 is used to transmit the multi-modal raw data collected by the parameter acquisition module 1 to the voiceprint processing module 3 and the multi-modal analysis module 4 through the network; the voiceprint processing module 3 is used to extract voiceprints from the multi-modal raw data The multimodal analysis module 4 is used to comprehensively analyze data from different modes, integrate multiple data, and use deep learning algorithms to perform data analysis and pattern recognition to obtain analysis results; the fault judgment module 5 is based on the analysis results of the multimodal analysis module 4 and uses preset rules or models to perform fault detection and diagnosis to determine whether there is a fault in the system or equipment, as well as the type and severity of the fault; the result output module is used to display the final analysis and judgment results in the form of charts, reports or alarms.

[0179] Embodiment 2:

[0180] See also Figure 2 The monitoring method is implemented by the monitoring system according to claim 1, comprising:

[0181] S1, acquisition of multi-modal raw data of electrical brake switch:

[0182] A voiceprint collector, a vibration sensor, an SF6 gas pressure gauge, a temperature sensor and a position switch are respectively arranged and installed at appropriate positions of the electric brake switch to form a parameter acquisition module 1; the parameter acquisition module 1 collects multi-modal raw data of different modes of the electric brake switch during operation, specifically including: voiceprint, vibration, gas leakage, temperature and position status data; these sensors can collect the working condition data of the electric brake switch in real time and transmit it to the data processing center;

[0183] S2, transmission of multi-modal raw data of electrical brake switch:

[0184] The multimodal raw data collected in S1 is transmitted to the voiceprint processing module 3 and the multimodal analysis module 4 in real time through the network transmission module 2 to realize remote data transmission; specifically, signal cables, optical fibers or wireless network transmission can be used;

[0185] S3, extraction, analysis and scoring of voiceprint features:

[0186] First, the voiceprint data of the multimodal raw data is preprocessed, such as noise reduction, filtering and other operations, to improve the quality of the sound data; then, the features of the voiceprint data are extracted to form sound features, and the extracted sound features are compared with the preset voiceprint model through the voiceprint processing module 3, and a similarity or abnormality score is given, and the voiceprint data is detected for abnormalities using anomaly detection technology to identify possible abnormal situations for subsequent device status or fault judgment;

[0187] S4, analysis and processing of multimodal raw data:

[0188] The result of the voiceprint processing module 3 in S3 is transmitted to the multimodal analysis module 4 as part of the input data for the analysis of the multimodal analysis module 4, and the remaining multimodal raw data of the vibration, gas leakage, temperature and position status data obtained in S1 are comprehensively analyzed and processed, and the multimodal raw data are fused at the same time. The multimodal data are fused and analyzed using a deep learning algorithm to extract characteristic information that can reflect the working condition of the electrical brake switch; then the difference between the current data and the normal working condition data is compared to determine whether there is an abnormality; if there is an abnormality, the next step is to perform fault detection and diagnosis; otherwise, the normal working condition database is continued to be monitored and updated for subsequent comparison;

[0189] S5, fault judgment:

[0190] Based on the analysis results in S4, the fault judgment module 5 uses preset rules or models to perform fault detection and diagnosis to determine whether there is a fault in the system or equipment, as well as the type and severity of the fault, so as to timely discover and handle potential abnormal situations to improve the stability and safety of the system; at the same time, based on historical data and current data, possible fault trends are predicted to provide maintenance personnel with early warning information and maintenance suggestion services, so as to understand the working condition of the electric brake switch and take corresponding maintenance measures to extend the service life and reduce maintenance costs;

[0191] S6, output of results:

[0192] The analysis and judgment results finally obtained in S4 are presented to the user or system administrator in an appropriate form with the help of the result output module.

[0193] Embodiment 3:

[0194] The specific steps of S1 are:

[0195] S1.1, select multiple voiceprint collector points at appropriate locations of the electrical brake switch, and arrange and install voiceprint collectors at the voiceprint collector points; select multiple vibration sensor points at appropriate locations of the electrical brake switch, and arrange and install vibration sensors at the vibration sensor points; arrange and install SF6 gas pressure gauges at the lower part of the three-phase short-circuit closed busbar casing of the electrical brake switch; arrange and install multiple temperature sensors at appropriate locations of the electrical brake switch; install position switches on the electrical brake switch;

[0196] S1.2, through various types of raw data during the operation of voiceprint collector, vibration sensor, SF6 gas pressure gauge, temperature sensor and position switch, different types of raw data together constitute multimodal raw data.

[0197] Preferably, there are 4 voiceprint collector points in S1.1, and there are also 4 corresponding voiceprint collectors 113-116, which respectively correspond to the circuit breaker operating connecting rod end, the ground knife operating connecting rod end, the circuit breaker oil pump motor housing, and the three-phase short-circuit closed busbar housing installed on the electrical brake switch, and the voiceprint data is collected through the voiceprint collector.

[0198] Preferably, there are two vibration sensor points in S1.1, and there are two corresponding vibration sensors 118-119, which are respectively installed at the ground base of the operating mechanism bracket of the electric brake switch, and vibration data is collected through the vibration sensors.

[0199] Preferably, there is one SF6 gas pressure gauge 117 in S1.1;

[0200] There are three temperature sensors 120-122 in S1.1, which are respectively installed at the lower shell of the three-phase voltage transformer PT;

[0201] There is one position switch 123 in S1.1, which is installed at the front end of the circuit breaker operating mechanism, and the position data is collected through the position switch.

[0202] Furthermore, in this embodiment:

[0203] Voiceprint collector:

[0204] Model: ZKHY-IVOD-001 industrial diagnostic pickup;

[0205] Points: 113-116, 4 points in total;

[0206] Installation location: circuit breaker operating rod end, ground knife operating rod end, circuit breaker oil pump motor housing, three-phase short-circuit enclosed busbar housing;

[0207] Vibration Sensor:

[0208] Type: DSP vibration sensor;

[0209] Points: 118-119, 2 points in total;

[0210] Installation location: operating mechanism bracket ground base;

[0211] SF6 gas pressure gauge:

[0212] Model: GDM-100;

[0213] Points: 117, 1 point in total;

[0214] Installation location: the lower part of the three-phase short-circuit enclosed busbar housing;

[0215] Temperature Sensor:

[0216] Model: PT100;

[0217] Points: 120-122, 3 points in total;

[0218] Installation location: below the casing of the three-phase voltage transformer PT;

[0219] Position switch:

[0220] Type: LX series limit switch;

[0221] Point: 123, a total of 1 point;

[0222] Installation location: front end of circuit breaker operating mechanism;

[0223] This comprehensive monitoring system can monitor the working status of 20KV electrical brake switches in an all-round way through multi-modal monitoring methods such as soundprint, vibration, gas leakage, temperature, and position status, and promptly detect and handle abnormal situations, thereby ensuring the stable and safe operation of the power system. Each sensor and device is carefully selected and configured to meet different monitoring needs and provide comprehensive data support.

[0224] Embodiment 4:

[0225] See also Figure 4 The network transmission module 2 in S2 specifically includes: data output units 206-210, a local control unit 205, a network transmission device 204, a server device 201 and a system platform 202-203;

[0226] The data output units 206-210 are responsible for physically connecting the parameter sensors to the network after collecting signals, and sending the data to the external device or system through the transmission interface;

[0227] The local control unit 205 is used to collect the status and data of the field equipment, send control instructions, exchange data with the host computer or other equipment, and control and monitor the field equipment in real time;

[0228] The network transmission equipment 204, which is composed of routers and switch network equipment, is responsible for data routing and forwarding, ensuring that data can be accurately and efficiently transmitted to the target location;

[0229] Server device 201, composed of a high-performance computer system, a large-capacity storage device, and a network connection device, provides data storage, processing, and analysis services, and supports a variety of network services;

[0230] The system platform 202-203 provides a user interaction interface, is responsible for data integration, analysis and display, and sends the processed requests or instructions to the corresponding network device or server for processing.

[0231] Embodiment 4:

[0232] The voiceprint recognition algorithm engine performs weighted dimensionality reduction optimization based on MFCC feature vectors, and applies vector quantization algorithm to identify mechanical noise signals outside the cable line, including voiceprint MFCC extraction, UBM calculation, i-vector calculation, and linear probability discriminant analysis.

[0233] The specific steps of performing voiceprint feature extraction, analysis and scoring based on the voiceprint processing module 3 in S3 are as follows:

[0234] S3.1, voiceprint MFCC extraction:

[0235] The MFCC extraction process includes preprocessing, fast Fourier transform, Mei filter bank, logarithmic operation, discrete cosine transform, dynamic feature extraction and other steps. Figure 5 shown.

[0236] Get continuous audio: intercept 100ms of original sound data as continuous sound samples, such as Figure 6 shown.

[0237] S3.1.1, Pre-emphasis:

[0238] Pre-emphasis is to enhance the high frequency components in the audio signal through a high-pass filter. The purpose of this step is to balance the spectrum so that in the subsequent spectrum analysis, the low-frequency and high-frequency components can be processed with the same signal-to-noise ratio.

[0239] Set a pre-emphasis coefficient, which is a number less than 1;

[0240] To perform a differential operation on the audio signal, use the formula:

[0241] s′(n)=s(n)-α×s(n-1);

[0242] Where: s(n) represents the audio signal sampling value at the current moment, s(n-1) represents the audio signal sampling value at the previous moment, α is a pre-emphasis coefficient, which is 0.95, and s'(n) is the sampling value at the current moment after pre-emphasis processing;

[0243] The waveform of 100ms audio data after pre-emphasis processing is as follows Figure 7 shown.

[0244] S3.1.2, Framing:

[0245] Framing is to cut the continuous audio signal into shorter segments for short-term analysis. Each frame contains N sampling points, where N is 256 or 512. There is a certain overlap between frames to ensure the continuity of the signal. The framing steps are as follows:

[0246]

[0247] In the formula, F represents the total number of frames obtained after the framing operation in audio processing; L represents the total length of the audio signal, which is usually expressed as the number of sampling points, which is the sampling frequency multiplied by time: 16000*0.1=1600; N is the length of each frame, that is, the number of sampling points contained in each frame, which is 256; M is the frame shift, which represents the number of sampling points in the overlapping part between two consecutive frames, and is also the number of sampling points that a new frame moves relative to the previous frame, which is 128; x represents the smallest integer not less than x, that is, rounded up;

[0248] Take the 5th frame as an example of the waveform after framing. Figure 8 shown.

[0249] S3.1.3, Windowing:

[0250] By adding windows, the continuity of the signal between frames is increased and the spectrum leakage is reduced. The Hamming window is used and is defined as follows:

[0251] The mathematical expression of the Hamming window is:

[0252]

[0253] Where n is an integer from 0 to N-1, and N is the size of the window;

[0254] According to the mathematical expression of the Hamming window, the value w(n) of the Hamming window is calculated for the sampling point n in each frame. This calculation can be repeated for each frame.

[0255] S3.1.4, Windowing Operation:

[0256] Multiply the signal S(n) of each frame by the corresponding Hamming window value w(n). Mathematically, this can be expressed as:

[0257] S w (n) = S (n) × w (n);

[0258] Among them, S w (n) is the windowed signal;

[0259] The waveform comparison before and after windowing is as follows Fig. 9 shown.

[0260] S3.1.5, Mel filtering:

[0261] The energy spectrum is passed through a set of Mel-scale triangular filter banks, and a filter bank with M filters is defined, where the filter used is a triangular filter; the logarithmic energy of each filter bank output is calculated; the MFCC coefficients are obtained by discrete cosine transform; the logarithmic energy; the dynamic classification parameter extraction;

[0262] The energy spectrum is passed through a set of Mel-scale triangular filter banks to define a filter bank with M filters (the number of filters is close to the number of critical bands). The filters used are triangular filters, and M is 24.

[0263] Calculate the logarithmic energy of each filter group output: Logarithmic operation includes taking absolute value and log operation. Taking absolute value is to use only amplitude value and ignore the influence of phase, because phase information is not very useful in voiceprint recognition. After FFT transformation, convolution becomes multiplication, and after taking logarithm, multiplication becomes addition, converting the convolution signal into additive signal. 100ms logarithmic energy is as follows Fig.10 shown.

[0264] Obtain MFCC coefficients through discrete cosine transform (DCT): In the previous step, the fundamental tone information and the channel information became additive, and another DCT can separate them, which is called the "cepstrum domain". Therefore, the low-frequency part of the cepstrum domain depicts the channel information, and the high-frequency part depicts the fundamental tone information. Substitute the above logarithmic energy into the discrete cosine transform and calculate the L-order Mel-scale Cepstrum parameter. The L-order refers to the MFCC coefficient order, which is 13. Here M is the number of triangular filters.

[0265] It turns out that the filter bank coefficients calculated in the previous step are highly correlated, which can be problematic in some machine learning algorithms. Therefore, a discrete cosine transform (DCT) is applied to decorrelate the filter bank coefficients and produce a compressed representation of the filter bank. The resulting cepstral coefficients 2-13 are retained and the rest are discarded; num_ceps = 12.

[0266] Logarithmic energy: The volume (i.e. energy) of a frame is also an important feature of audio and is very easy to calculate. Therefore, adding the logarithmic energy of a frame (definition: the sum of the squares of the signals in a frame, then taking the logarithmic value with base 10, and multiplying it by 10) makes the basic audio features of each frame have one more dimension, including a logarithmic energy and the remaining cepstrum parameters.

[0267] Extraction of dynamic identification parameters (including first-order difference and second-order difference): The standard MFCC parameters only reflect the static characteristics of audio parameters. The dynamic characteristics of audio can be described by the differential spectrum of these static characteristics. Experiments have shown that combining dynamic and static characteristics can effectively improve the recognition performance of the system. Extraction of dynamic differential parameters; The calculation of differential parameters can use the following formula:

[0268]

[0269] Where L is half the size of the differential window (i.e., the total window size is 2L±1), and C t is the MFCC coefficient vector of the tth frame, ΔC t is the first-order difference coefficient vector of the t-th frame;

[0270] The second-order difference is to apply the difference operation again to the first-order difference coefficient to capture faster changes in the audio signal. The formula is as follows:

[0271]

[0272] In the formula, Δ 2 C t is the second-order differential coefficient vector of the tth frame, ΔC t is the obtained first-order difference coefficient vector.

[0273] From the above steps, we can see that MFCC is actually composed of: N-dimensional MFCC parameters (N / 3 MFCC coefficients + N / 3 first-order difference parameters + N / 3 second-order difference parameters) + frame energy (this item can be replaced according to needs).

[0274] The 13th-order MFCC value is as follows Fig.11 shown.

[0275] S3.2, calculate UBM:

[0276] First, audio data irrelevant to the target is collected to train a UBM. Then, the target audio data is used to adjust the parameters of the UBM through an adaptive algorithm to obtain the target model parameters.

[0277] S3.3, calculate i-vector:

[0278] i-vector defines a low-dimensional vector R×1,w~N(0,I) to represent an audio segment;

[0279]

[0280] Where M is the ideal feature super vector corresponding to a device being modeled; As the UBM mean supervector, assuming that the UBM contains C Gaussian mixture components g, then is the mean vector m of all mixture components c ,c=1,...,C combination, The dimension of the transformation matrix T is usually CF×R, where C is the number of Gaussian components in the Gaussian mixture model, F is the dimension of the acoustic feature, and R is the dimension of the i-vector; w is the required i-vector;

[0281] For each mixture component c, there is also a parameter mixture weight w c , and covariance matrix Σc; split the T matrix according to Gaussian components, then for each component c, a submatrix V can be obtained C , whose dimension is F×R; this submatrix V C In fact, it represents the linear transformation from the feature space of the Gaussian component to the i-vector space. Mathematically, the T matrix is ​​expressed as:

[0282]

[0283] Among them, each V C It is an F×R matrix, corresponding to a Gaussian component in GMM:

[0284] μ c =mc +V c w;

[0285] In the formula, μ c represents the mean vector corresponding to the cth Gaussian component after a given i-vector w; m c is the original mean vector of the cth Gaussian component in the UBM;

[0286] Define a piece of audio feature data X, whose feature dimension is F and time sequence is T, that is, X = X1,...,XT, the subset of X belonging to the cth Gaussian component is Xc, and a certain frame in the subset but

[0287]

[0288] This gives the following formula:

[0289]

[0290] The calculation formula of i-vector is:

[0291] w=(I+T T ∑ -1 N(u)T) -1 T T ∑ -1 F(u);

[0292] Where w is the required i-vector; I is the identity matrix; T is the transformation matrix, which maps the i-vector space to the feature space; N(u) is a diagonal matrix of dimension CF×CF, and the block matrices on its diagonal are the counts of each Gaussian component; F(u) is a supervector of first-order Baum-Welch statistics, which is a supervector of CF×1, consisting of all first-order BW statistics F~c;

[0293] S3.4, Linear Probability Discriminant Analysis:

[0294] Linear probability discriminant analysis is based on i-vector features and provides channel compensation for it. Assume that the training data audio consists of audio from I devices, where each device has J different audio segments. The i-vector of the j-th audio segment of the i-th device is denoted by D ij , then PLDA defines:

[0295] D ij =μ+F i +Gω ij +ε ij ;

[0296] In the formula, the device information part is μ+Fi , which is only related to device i and describes the difference between devices; the noise part is Gω ij +ε ij , describing the differences between devices; μ represents the mean of all training data; F is regarded as the identity space, which contains information that can be used to represent various devices; i It is regarded as the identity of a specific device; G is the error space, which contains information used to represent different audio changes of the same device; ω ij represents the position in G space; ε ij is the final residual noise term, used to represent something that has not yet been explained; this term is zero-mean Gaussian with variance Σ;

[0297] Assume that each latent variable conforms to the following distribution:

[0298]

[0299] Training set D = {D ij}, the model parameters are θ = {μ, F, G, Σ}, and the maximum likelihood estimation MLE criterion is used for optimization, that is:

[0300] arg max{P(θ / D)}∝argmax{P(D / θ)};

[0301] ① Initialize parameter θ;

[0302] ②E-step: estimate latent variables;

[0303] ③M-step: Update the parameter θ based on the estimated latent variable;

[0304] ④Repeat ② and ③ N times.

[0305] The likelihood ratio strategy is used for device identification.

[0306] Assuming that the model M describes the relationship between the identity factor h and the input feature i-vector, the test process is to determine ivert x p Whether to register ivivor x i Share the same device identity h;

[0307] hypothesis M0 represents x i and x p From different identity latent variables, hypothesis M1 represents x i and x p From the same identity latent variable, the likelihood ratio calculates the score value:

[0308]

[0309] The numerator P(x1,xp / M1) represents the assumption that M1: x i and x p From the same device, the speech sample x is observed i and x p The denominator P(x1,xp / M0) represents the joint probability of i and x p Speech samples x are observed from different devices. i and x p By calculating the score, we can measure the similarity between the two audios. The higher the value, the higher the score, and the greater the possibility that the two audios belong to the same device; otherwise, the smaller the possibility.

[0310] like Fig.13 As shown in the figure, it is an audio energy graph comparing a 15s speech sample with the model. The final score of this audio segment is 90.

[0311] Embodiment 5:

[0312] See also Fig.14 Preferably, the analysis and processing of the multimodal raw data in S4 specifically includes:

[0313] S4.1, Data selection:

[0314] The hardware partitioning used in the 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is to exclude the impact of equipment abnormalities in other areas on the fault judgment of this area. The partitioning is as follows:

[0315] Circuit breaker area: voiceprint collectors 113, 115, position switch 123, vibration sensors 118, 119;

[0316] Sealing mother area: voiceprint collector 116, SF6 gas pressure gauge 117, temperature measurement RTD 120, 121, 122

[0317] Knife gate area: voiceprint collector 114, vibration sensors 118, 119

[0318] Each input parameter (voiceprint score, vibration sensor peak-to-peak value, position switch signal, temperature) is normalized so that its value range is between 0 and 1. Linear transformation or other normalization methods can be used.

[0319] The peak-to-peak signal of the vibration sensor is Fig.15 As shown, the switch position signal is Fig.16 As shown, the temperature signal is Fig.17 shown.

[0320] For each parameter p i , whose standardized value ni Calculated by the following formula:

[0321]

[0322] Among them, p min and p max They are the parameters p i possible minimum and maximum values;

[0323] S4.2, set the basic scoring value and weight:

[0324] Set a basic score for each parameter. For example, set them all to 10 points to represent the healthiest state. According to the importance of each parameter, assign different weights to them. The sum of the weights should be 1.

[0325] S4.3, calculate the rate of change:

[0326] For each parameter, calculate the rate of change between its current value and the initial value or the last measured value, the rate of change r i Calculated by the following formula:

[0327]

[0328] Among them, p i,current is the current value of the parameter, p i,previous is the previous value of the parameter;

[0329] S4.4, determine the threshold and adjust the score value:

[0330] If the change rate of any parameter exceeds 20%, the score of the parameter will be reduced to 0 points; if the change rate of more than half of the parameters exceeds 10%, the score of these parameters will be halved; the score of each parameter will be adjusted according to the change rate. i :

[0331]

[0332] Among them, base_score i is the base score of parameter i, I is an indicator function: it returns 1 if the condition is true, otherwise it returns 0, and n is the total number of parameters;

[0333] S4.5, calculate the final score:

[0334] According to the current score value of each parameter and its weight, calculate the weighted average as the final score value;

[0335] Output score value:

[0336]

[0337] Among them, w i is the weight of parameter i and satisfies Output the final score, which should be between 0 and 10.

[0338] Embodiment 6:

[0339] The fault judgment in S5 specifically includes: internal fault judgment of the oil pump motor, SF6 gas leakage fault judgment, operating mechanism connecting rod fault judgment and PT poor contact discharge fault judgment.

[0340] See also Fig.18 Preferably, the oil pump motor internal fault judgment process is:

[0341] S5.1.1: The circuit breaker zone score value drops to 6, triggering the fault judgment process;

[0342] S5.1.2: The vibration sensor parameters and voiceprint scoring values ​​are judged simultaneously. If the 1# / 2# vibration sensor 118 / 119 and the 3# voiceprint sensor 115 are out of limit, proceed to the next step;

[0343] S5.1.3: Compare the current fault voiceprint data with the standards in the fault library. If there is a matching fault and event, directly output the corresponding fault event alarm; if it cannot be matched with the oil pump pressure model, calculate the change rate of the first five pressure times. If the change rate is greater than 0, it means that the inside of the oil pump motor is slowly deteriorating, and it is necessary to open the cover to check and determine the fault, and mark the specific fault into the model library;

[0344] S5.1.4: If the matching model is the oil pump pressure model, the timer will be 2 minutes, and the number of pressures plus 1 and the duration will be recorded, and then go to the next step;

[0345] S5.1.5: When the oil pump is pressurized for less than 2 minutes, the output is the oil pump motor pressurization event;

[0346] S5.1.6: When the oil pump pressurization exceeds 2 minutes, the oil pump motor pressurization timeout alarm is output and the next step is entered;

[0347] S5.1.7: To prevent the stator winding from being damaged due to the opening lock or three-phase inconsistency during the operation of the 20KV electrical brake switch with a defect, it is necessary to lock the switch operation and notify the equipment maintenance personnel by phone to handle it on site.

[0348] See also Fig.19 Preferably, the SF6 gas leakage fault judgment process is:

[0349] S5.2.1: The score of the mother area drops to 6, triggering the fault judgment process;

[0350] S5.2.2: The vibration sensor parameters, voiceprint score value and SF6 pressure are judged simultaneously. If the 1# / 2# vibration sensor 118 / 119 is normal but the 4# voiceprint sensor 116 and the SF6 gas pressure gauge 117 are out of limit, proceed to the next step;

[0351] S5.2.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0352] S5.2.4: When the match is SF6 gas leakage, an SF6 gas leakage alarm is issued and the next step is entered;

[0353] S5.2.5: In the event of confirmed SF6 gas leakage, the system will initiate the corresponding emergency plan and lock the switch operation, which includes shutting down related equipment, ventilation, and evacuating personnel to ensure safety.

[0354] See also Fig. 20 Preferably, the operating mechanism connecting rod fault judgment process is:

[0355] S5.3.1: The circuit breaker zone or switch zone score value drops to 6, triggering the fault judgment process;

[0356] S5.3.2: The circuit breaker position switch 123 determines the position change, checks whether the 1# / 2# vibration sensor 118 / 119 is abnormal, checks whether the voiceprint score value of the 1# voiceprint sensor 113 and the voiceprint score value of the 2# voiceprint sensor 11 are abnormal, if abnormal, proceed to the next step;

[0357] S5.3.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0358] S5.3.4: Handle the circuit breaker connecting rod failure. If the fault is matched to the circuit breaker connecting rod failure, the system will immediately lock the circuit breaker operation to ensure safety. At the same time, the system will determine whether the unit is in the process of shutdown. If so, the system will start the emergency shutdown process and skip the 20KV electrical brake switch input step to prevent the fault from expanding or causing more serious consequences;

[0359] S5.3.5: Handle knife switch linkage failure. If the fault is determined to be a knife switch linkage failure, the system will immediately lock the knife switch operation to prevent the fault from worsening. At the same time, the system will check whether the unit is in the electrical brake engaged state. If so, the system will issue an electrical brake exit command and disconnect the electrical brake circuit breaker to ensure the stability and safety of the system.

[0360] See also Fig.21Preferably, the PT poor contact discharge fault judgment process is:

[0361] S5.4.1: The scoring value of the circuit breaker area or the switch area is normal, but the scoring value of the motherboard area drops to 6, triggering the fault judgment process;

[0362] S5.4.2: Determine the status of the sensor parameters in the sealing mother area. The 4# soundprint sensor 116 is abnormal, the SF6 gas pressure gauge 117 is normal, the 1# / 2# vibration sensors 118 / 119 are normal, and the temperature sensors 120-122 are abnormally high. If the above conditions are met, proceed to the next step;

[0363] S5.4.3: If abnormal, locate the fault point and determine the phase difference according to the abnormal conditions of the temperature sensors 120-122, that is, determine which phase has a problem;

[0364] S5.4.4: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library.

[0365] S5.4.5: After completing the comparison between the fault library and the model library, the system will determine whether the current fault matches the PT poor contact discharge fault. If it matches, the system will output the PT poor contact discharge fault alarm and proceed to the next step;

[0366] S5.4.6: Determine the operating status of the unit and handle it; confirm the current status of the unit. If the unit is in shutdown state, the system will set the unit to the prohibition of startup mode to prevent the unit from being started by mistake before the fault is handled, and immediately arrange personnel to check and repair;

[0367] S5.4.7: Adjust the shutdown priority of the unit; if the unit is currently in the on state, the system will increase the shutdown priority of the unit to the highest; once the branch plant needs to shut down, this unit will be given priority for shutdown, and during this period, attention and monitoring of the unit must be strengthened to ensure safety.

[0368] In summary, the main innovative solutions of the present invention are:

[0369] 1. A comprehensive monitoring process for multi-modal abnormal working conditions of a 20KV electrical brake switch based on voiceprint recognition, which covers real-time monitoring and abnormal identification of multiple working condition parameters in a 20KV electrical brake switch.

[0370] 2. A method for arranging monitoring equipment in a 20KV electrical brake switch based on voiceprint recognition, including reasonably arranging specific points of monitoring equipment such as voiceprint sensors and vibration sensors in the equipment area to achieve comprehensive and effective equipment status monitoring.

[0371] 3. A 20KV electrical brake switch signal parameter network transmission system based on voiceprint recognition, which is used to transmit signal parameters collected by various sensors in the equipment area in real time to achieve remote monitoring and data analysis.

[0372] 4. A method for extracting voiceprint feature values ​​of a 20KV electrical brake switch based on voiceprint recognition, including specific processes and algorithms for extracting key voiceprint feature values ​​from signals collected by a voiceprint sensor.

[0373] 5. A 20KV electrical brake switch voiceprint probability linear discriminant analysis model algorithm based on voiceprint recognition, which uses probability linear discriminant analysis to perform discriminant analysis on the extracted voiceprint feature values ​​and gives corresponding scoring values.

[0374] 6. A multi-modal scoring model algorithm for a 20KV electrical brake switch based on voiceprint recognition. The algorithm uses hardware partitioning to avoid interference and is used to identify the working conditions of equipment in different areas of a 20KV electrical brake switch.

[0375] 7. A 20KV electrical brake switch oil pump motor internal fault judgment logic system based on voiceprint recognition, which judges the logic of the oil pump motor internal fault based on multimodal data, combined with the signal of voiceprint and vibration sensor;

[0376] 8. A 20KV electrical brake switch SF6 gas leakage fault judgment logic system based on voiceprint recognition, which judges the logic of SF6 gas leakage fault based on multimodal data, combined with voiceprint and SF6 gas leakage meter signals;

[0377] 9. A 20KV electrical brake switch operating mechanism connecting rod abnormal fault judgment logic system based on voiceprint recognition, based on multimodal data, combined with voiceprint, vibration sensor, position node signals, to judge the logic of operating mechanism connecting rod abnormal fault;

[0378] 10. A 20KV electrical brake switch PT poor contact discharge fault judgment logic system based on voiceprint recognition, based on multimodal data, combined with the signals of voiceprint, vibration sensor, SF6 gas leakage meter, and temperature sensor, to judge the logic of PT poor contact discharge fault.

Claims

1. A 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring system based on voiceprint recognition, characterized in that: include: Parameter collection module (1), network transmission module (2), voiceprint processing module (3), multimodal analysis module (4), fault judgment module (5) and result output module; The parameter acquisition module (1) is used to collect multi-modal raw data of the electrical brake switch from various sensors and devices; The network transmission module (2) is used to transmit the multimodal raw data collected by the parameter acquisition module (1) to the voiceprint processing module (3) and the multimodal analysis module (4) through the network; The voiceprint processing module (3) is used to extract voice features from the multimodal raw data, compare them with the preset voiceprint model, and give a similarity or abnormality score; The multimodal analysis module (4) is used to comprehensively analyze data from different modalities, fuse multiple data, and use deep learning algorithms to perform data analysis and pattern recognition to obtain analysis results; The fault judgment module (5) performs fault detection and diagnosis based on the analysis result of the multimodal analysis module (4) using preset rules or models to judge whether there is a fault in the system or equipment, as well as the type and severity of the fault; The result output module is used to display the final analysis and judgment results in the form of charts, reports or alarms.

2. A 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition, characterized in that: The monitoring method is implemented by using the monitoring system according to claim 1, comprising: S1, acquisition of multi-modal raw data of electrical brake switch: A soundprint collector, a vibration sensor, an SF6 gas pressure gauge, a temperature sensor and a position switch are respectively arranged and installed at appropriate positions of the electric brake switch to form a parameter collection module (1); the parameter collection module (1) collects multi-modal raw data of different modes of the electric brake switch during operation, specifically including: soundprint, vibration, gas leakage, temperature and position status data; S2, transmission of multi-modal raw data of electrical brake switch: The multimodal raw data collected in S1 is transmitted in real time to the voiceprint processing module (3) and the multimodal analysis module (4) through the network transmission module (2) to realize remote data transmission; S3, extraction, analysis and scoring of voiceprint features: First, the voiceprint data of the multimodal raw data is preprocessed, and then the features of the voiceprint data are extracted to form sound features. The extracted sound features are compared with the preset voiceprint model through the voiceprint processing module (3), and a similarity or abnormality score is given. The voiceprint data is detected for abnormalities using anomaly detection technology to identify possible abnormal situations for subsequent device status or fault judgment; S4, analysis and processing of multimodal raw data: The result of the soundprint processing module (3) in S3 is transmitted to the multimodal analysis module (4) as part of the input data for the analysis of the multimodal analysis module (4), and the remaining multimodal raw data of the vibration, gas leakage, temperature and position status data obtained in S1 are comprehensively analyzed and processed, and the multimodal raw data are fused at the same time. The multimodal data are fused and analyzed using a deep learning algorithm to extract characteristic information that can reflect the working condition of the electric brake switch; then the difference between the current data and the normal working condition data is compared to determine whether there is an abnormality; if there is an abnormality, the next step is to perform fault detection and diagnosis; otherwise, the normal working condition database is continuously monitored and updated for subsequent comparison; S5, fault judgment: Based on the analysis results in S4, a fault judgment module (5) is used to perform fault detection and diagnosis using preset rules or models to determine whether there is a fault in the system or equipment, as well as the type and severity of the fault, so as to timely discover and handle potential abnormal conditions and thus improve the stability and safety of the system; at the same time, possible fault trends are predicted based on historical data and current data to provide maintenance personnel with early warning information and maintenance suggestion services, so as to understand the working condition of the electric brake switch and take corresponding maintenance measures to extend the service life and reduce maintenance costs; S6, output of results: The analysis and judgment results finally obtained in S4 are presented to the user or system administrator in an appropriate form with the help of the result output module.

3. According to claim 2, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The specific steps of S1 are: S1.1, select multiple voiceprint collector points at appropriate locations of the electrical brake switch, and arrange and install voiceprint collectors at the voiceprint collector points; select multiple vibration sensor points at appropriate locations of the electrical brake switch, and arrange and install vibration sensors at the vibration sensor points; arrange and install SF6 gas pressure gauges at the lower part of the three-phase short-circuit closed busbar casing of the electrical brake switch; arrange and install multiple temperature sensors at appropriate locations of the electrical brake switch; install position switches on the electrical brake switch; S1.2, through various types of raw data during the operation of voiceprint collector, vibration sensor, SF6 gas pressure gauge, temperature sensor and position switch, different types of raw data together constitute multimodal raw data.

4. According to claim 3, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: There are 4 voiceprint collector points in S1.1, and there are also 4 corresponding voiceprint collectors (113-116), which correspond to the circuit breaker operating connecting rod end, the ground knife operating connecting rod end, the circuit breaker oil pump motor housing, and the three-phase short-circuit closed busbar housing installed on the electrical brake switch, and the voiceprint data is collected through the voiceprint collector.

5. According to claim 3, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: There are two vibration sensor points in S1.1, and there are two corresponding vibration sensors (118-119), which are respectively installed at the ground base of the operating mechanism bracket of the electric brake switch, and vibration data is collected through the vibration sensors.

6. According to claim 3, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: There is one SF6 gas pressure gauge (117) in S1.1; There are three temperature sensors (120-122) in S1.1, which are respectively installed at the lower shell of the three-phase voltage transformer PT; There is one position switch (123) in S1.1, which is installed at the front end of the circuit breaker operating mechanism, and position data is collected through the position switch.

7. According to claim 3, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The network transmission module (2) in S2 specifically includes: a data output unit (206-210), a local control unit (205), a network transmission device (204), a server device (201) and a system platform (202-203); The data output unit (206-210) is responsible for physically connecting the parameter sensor to the network after collecting the signal, and sending the data to the external device or system through the transmission interface; The local control unit (205) is used to collect the status and data of the on-site equipment, send control instructions, exchange data with the host computer or other equipment, and control and monitor the on-site equipment in real time; The network transmission equipment (204), which is composed of routers and switch network equipment, is responsible for routing and forwarding data to ensure that the data can be accurately and efficiently transmitted to the target location; The server device (201) is composed of a high-performance computer system, a large-capacity storage device, and a network connection device, providing data storage, processing and analysis services, and supporting a variety of network services; The system platform (202-203) provides a user interaction interface, is responsible for data integration, analysis and display, and sends the processed requests or instructions to the corresponding network device or server for processing.

8. According to claim 7, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The specific steps of performing voiceprint feature extraction, analysis and scoring based on the voiceprint processing module (3) in S3 are: S3.1, voiceprint MFCC extraction: S3.1.1, Pre-emphasis: Pre-emphasis is to enhance the high-frequency components in the audio signal through a high-pass filter, and balance the spectrum so that in the subsequent spectrum analysis, the low-frequency and high-frequency components can be processed with the same signal-to-noise ratio; Set a pre-emphasis coefficient, which is a number less than 1; To perform a differential operation on the audio signal, use the formula: s′(n)=s(n)-α×s(n-1); Where: s(n) represents the audio signal sampling value at the current moment, s(n-1) represents the audio signal sampling value at the previous moment, α is a pre-emphasis coefficient, and s'(n) is the sampling value at the current moment after pre-emphasis processing; S3.1.2, Framing: Framing is to cut the continuous audio signal into shorter segments for short-term analysis. Each frame contains N sampling points, where N is 256 or 512. There is a certain overlap between frames to ensure the continuity of the signal. The framing steps are as follows: In the formula, F represents the total number of frames obtained after the framing operation in audio processing; L represents the total length of the audio signal, which is the sampling frequency multiplied by time; N is the length of each frame, that is, the number of sampling points contained in each frame; M is the frame shift, which represents the number of sampling points in the overlapping part between two consecutive frames, and is also the number of sampling points that a new frame moves relative to the previous frame; It represents the smallest integer not less than x, that is, rounded up; S3.1.3, Windowing: By adding windows, the continuity of the signal between frames is increased and the spectrum leakage is reduced. The Hamming window is used and is defined as follows: The mathematical expression of the Hamming window is: Where n is an integer from 0 to N-1, and N is the size of the window; According to the mathematical expression of the Hamming window, the value w(n) of the Hamming window is calculated for each sampling point n in each frame. This calculation can be repeated for each frame. S3.1.4, Windowing Operation: Multiply the signal S(n) of each frame by the corresponding Hamming window value w(n). Mathematically, this can be expressed as: S w (n)=S(n)×w(n); Among them, S w (n) is the windowed signal; S3.1.5, Mel filtering: The energy spectrum is passed through a set of Mel-scale triangular filter banks, and a filter bank with M filters is defined. The filter used is a triangular filter; the logarithmic energy of each filter bank output is calculated; the MFCC coefficients are obtained by discrete cosine transform; the dynamic differential parameters are extracted; the differential parameters can be calculated using the following formula: Where L is half the size of the differential window, that is, the total window size is 2L±1, C t is the MFCC coefficient vector of the tth frame, ΔC t is the first-order difference coefficient vector of the t-th frame; The second-order difference is to apply the difference operation again to the first-order difference coefficient to capture faster changes in the audio signal. The formula is as follows: In the formula, Δ 2 C t is the second-order differential coefficient vector of the tth frame, ΔC t is the obtained first-order difference coefficient vector; The N-dimensional MFCC parameters are composed of (N / 3 MFCC coefficients + N / 3 first-order difference parameters + N / 3 second-order difference parameters) + frame energy; S3.2, calculate UBM: First, audio data irrelevant to the target is collected to train a UBM. Then, the target audio data is used to adjust the parameters of the UBM through an adaptive algorithm to obtain the target model parameters. S3.3, calculate i-vector: i-vector defines a low-dimensional vector R×1,w~N(0,I) to represent an audio segment; M = m + T × w; Where M is the ideal feature supervector corresponding to a device being modeled; m is the UBM mean supervector. Assuming that the UBM contains C Gaussian mixture components g, then m is the mean vector m of all mixture components. c ,c=1,...,C, the dimension of m is C*F; the dimension of the transformation matrix T is usually CF×R, where C is the number of Gaussian components in the Gaussian mixture model, F is the dimension of the acoustic feature, and R is the dimension of the i-vector; w is the required i-vector; For each mixture component c, there is also a parameter mixture weight w c , and covariance matrix Σc; split the T matrix according to Gaussian components, then for each component c, a submatrix V can be obtained C , whose dimension is F×R; this submatrix V C In fact, it represents the linear transformation from the feature space of the Gaussian component to the i-vector space. Mathematically, the T matrix is ​​expressed as: Among them, each V C It is an F×R matrix, corresponding to a Gaussian component in GMM: μ c =m c +V c w; In the formula, μ c represents the mean vector corresponding to the cth Gaussian component after a given i-vector w; m c is the original mean vector of the cth Gaussian component in the UBM; Define a piece of audio feature data X, whose feature dimension is F and time sequence is T, that is, X = X1,...,XT, the subset of X belonging to the cth Gaussian component is Xc, and a certain frame in the subset but This gives the following formula: The calculation formula of i-vector is: w=(I+T T ∑ -1 N(u)T) -1 T T ∑ -1 F(u); Where w is the required i-vector; I is the identity matrix; T is the transformation matrix, which maps the i-vector space to the feature space; N(u) is a diagonal matrix of dimension CF×CF, and the block matrices on its diagonal are the counts of each Gaussian component; F(u) is a supervector of first-order Baum-Welch statistics, which is a supervector of CF×1, consisting of all first-order BW statistics F~c; S3.4, Linear Probability Discriminant Analysis: Linear probability discriminant analysis is based on i-vector features and provides channel compensation for it. Assume that the training data audio consists of audio from I devices, where each device has J different audio segments. The i-vector of the j-th audio segment of the i-th device is denoted by D ij , then PLDA defines: D ij =μ+Fh i +Gω ij +e ij ; In the formula, the device information part is μ+Fh i , which is only related to device i and describes the difference between devices; the noise part is Gω ij +ε ij , describing the differences between devices; μ represents the mean of all training data; F is regarded as the identity space, which contains information that can be used to represent various devices; h i It is regarded as the identity of a specific device; G is the error space, which contains information used to represent different audio changes of the same device; ω ij represents the position in G space; ε ij is the final residual noise term, used to represent something that has not yet been explained; this term is zero-mean Gaussian with variance Σ; Assume that each latent variable conforms to the following distribution: h i ~N(0,I) ω ij ~N(0,I); e ij ~N(0,∑) Assuming that the model M describes the relationship between the identity factor h and the input feature i-vector, the test process is to determine ivectorx p Whether to register ivivor x i Share the same device identity h; hypothesis M0 represents x i and x p From different identity latent variables, hypothesis M1 represents x i and x p From the same identity latent variable, the likelihood ratio calculates the score value: The numerator P(x1,xp / M1) represents the assumption that M1: x i and x p From the same device, the speech sample x is observed i and x p The denominator P(x1,xp / M0) represents the joint probability of i and x p Speech samples x are observed from different devices. i and x p By calculating the score, we can measure the similarity between the two audios. The higher the value, the higher the score, and the greater the possibility that the two audios belong to the same device; otherwise, the smaller the possibility.

9. According to claim 8, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The analysis and processing of the multimodal raw data in S4 specifically includes: S4.1, Data selection: The hardware partitions used are to exclude the impact of equipment abnormalities in other areas on the fault judgment of this area. The partitions are as follows: circuit breaker area, motherboard sealing area, and knife switch area. Each input parameter: voiceprint score value, vibration sensor peak-to-peak value, position switch signal, and temperature are standardized so that the value range is between 0 and 1. For each parameter p i , whose standardized value n i Calculated by the following formula: Among them, p min and p max They are the parameters p i possible minimum and maximum values; S4.2, set the basic scoring value and weight: Set a basic score for each parameter, and assign different weights to them according to the importance of each parameter. The sum of the weights should be 1. S4.3, calculate the rate of change: For each parameter, calculate the rate of change between its current value and the initial value or the last measured value, the rate of change r i Calculated by the following formula: Among them, p i,current is the current value of the parameter, p i,previous is the previous value of the parameter; S4.4, determine the threshold and adjust the score value: If the change rate of any parameter exceeds 20%, the score of the parameter will be reduced to 0 points; if the change rate of more than half of the parameters exceeds 10%, the score of these parameters will be halved; the score of each parameter will be adjusted according to the change rate. i : Among them, base_score i is the base score of parameter i, I is an indicator function: it returns 1 if the condition is true, otherwise it returns 0, and n is the total number of parameters; S4.5, calculate the final score: According to the current score value of each parameter and its weight, calculate the weighted average as the final score value; Output score value: Among them, w i is the weight of parameter i and satisfies Output the final score, which should be between 0 and 10.

10. According to claim 9, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The fault judgment in S5 specifically includes: internal fault judgment of the oil pump motor, SF6 gas leakage fault judgment, operating mechanism connecting rod fault judgment and PT poor contact discharge fault judgment.

11. According to claim 10, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The internal fault judgment process of the oil pump motor is as follows: S5.1.1: The circuit breaker zone score value drops to 6, triggering the fault judgment process; S5.1.2: The vibration sensor parameters and voiceprint scoring values ​​are judged simultaneously. If the 1# / 2# vibration sensor (118 / 119) and the 3# voiceprint sensor (115) are out of limit, proceed to the next step; S5.1.3: Compare the current fault voiceprint data with the standards in the fault library. If there is a matching fault and event, directly output the corresponding fault event alarm; if it cannot be matched with the oil pump pressure model, calculate the change rate of the first five pressure times. If the change rate is greater than 0, it means that the inside of the oil pump motor is slowly deteriorating, and it is necessary to open the cover to check and determine the fault, and mark the specific fault into the model library; S5.1.4: If the matching model is the oil pump pressure model, the timer will be 2 minutes, and the number of pressures plus 1 and the duration will be recorded, and then go to the next step; S5.1.5: When the oil pump is pressurized for less than 2 minutes, the output is the oil pump motor pressurization event; S5.1.6: When the oil pump pressurization exceeds 2 minutes, the oil pump motor pressurization timeout alarm is output and the next step is entered; S5.1.7: To prevent the stator winding from being damaged due to the opening lock or three-phase inconsistency during the operation of the 20KV electrical brake switch with a defect, it is necessary to lock the switch operation and notify the equipment maintenance personnel by phone to handle it on site.

12. According to claim 10, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The SF6 gas leakage fault judgment process is as follows: S5.2.1: The score of the mother area drops to 6, triggering the fault judgment process; S5.2.2: The vibration sensor parameters, voiceprint score value and SF6 pressure are judged simultaneously. If the 1# / 2# vibration sensor (118 / 119) is normal but the 4# voiceprint sensor (116) and the SF6 gas pressure gauge (117) are out of limit, proceed to the next step; S5.2.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library. S5.2.4: When the match is SF6 gas leakage, an SF6 gas leakage alarm is issued and the next step is entered; S5.2.5: In the event of confirmed SF6 gas leakage, the system will initiate the corresponding emergency plan and lock the switch operation, which includes shutting down related equipment, ventilation, and evacuating personnel to ensure safety.

13. According to claim 10, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The operating mechanism connecting rod fault judgment process is as follows: S5.3.1: The circuit breaker zone or switch zone score value drops to 6, triggering the fault judgment process; S5.3.2: The circuit breaker position switch (123) determines the position change, checks whether the 1# / 2# vibration sensor (118 / 119) is abnormal, checks whether the voiceprint score value of the 1# voiceprint sensor (113) and the voiceprint score value of the 2# voiceprint sensor (11) are abnormal, and if abnormal, proceeds to the next step; S5.3.3: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library. S5.3.4: Handle the circuit breaker connecting rod failure. If the fault is matched to the circuit breaker connecting rod failure, the system will immediately lock the circuit breaker operation to ensure safety. At the same time, the system will determine whether the unit is in the process of shutdown. If so, the system will start the emergency shutdown process and skip the 20KV electrical brake switch input step to prevent the fault from expanding or causing more serious consequences; S5.3.5: Handle knife switch linkage failure. If the fault is determined to be a knife switch linkage failure, the system will immediately lock the knife switch operation to prevent the fault from worsening. At the same time, the system will check whether the unit is in the electrical brake engaged state. If so, the system will issue an electrical brake exit command and disconnect the electrical brake circuit breaker to ensure the stability and safety of the system.

14. According to claim 10, a 20KV electrical brake switch multi-modal operating condition abnormality comprehensive monitoring method based on voiceprint recognition is characterized in that: The PT poor contact discharge fault judgment process is as follows: S5.4.1: The scoring value of the circuit breaker area or the switch area is normal, but the scoring value of the motherboard area drops to 6, triggering the fault judgment process; S5.4.2: Determine the status of the sensor parameters in the sealing mother area. The 4# soundprint sensor (116) is abnormal, the SF6 gas pressure gauge (117) is normal, the 1# / 2# vibration sensors (118 / 119) are normal, and the temperature sensors (120-122) are abnormally high. If the above conditions are met, proceed to the next step; S5.4.3: If abnormal, locate the fault point and determine the phase difference according to the abnormal condition of the temperature sensor (120-122), that is, determine which phase has a problem; S5.4.4: Compare the current fault voiceprint data with the standard in the fault library. If they match, proceed to the next step. If they do not match, an on-site inspection is required to confirm the fault and enter it into the model library. S5.4.5: After completing the comparison between the fault library and the model library, the system will determine whether the current fault matches the PT poor contact discharge fault. If it matches, the system will output the PT poor contact discharge fault alarm and proceed to the next step; S5.4.6: Determine the operating status of the unit and handle it; confirm the current status of the unit. If the unit is in shutdown state, the system will set the unit to the prohibition of startup mode to prevent the unit from being started by mistake before the fault is handled, and immediately arrange personnel to check and repair; S5.4.7: Adjust the shutdown priority of the unit; if the unit is currently in the on state, the system will increase the shutdown priority of the unit to the highest; Once the branch plant needs to shut down, this unit will be given priority for shutdown, and during this period, increased attention and monitoring of the unit will be required to ensure safety.

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