Multi-channel panoramic monitoring transformer visual monitoring device

Abstract

The invention discloses a multi-channel panorama monitoring transformer visual monitoring device, which comprises a multi-channel monitoring unit, a data transmission unit, a monitoring host, a panorama monitoring unit, an alarm unit, a power supply unit and a communication interface, and is characterized in that the panorama monitoring unit is in wireless data connection with the monitoring host, receives data and then constructs an analysis model algorithm in sequence; the whole power system of the monitoring host is subjected to panoramic monitoring, whether the state is abnormal or not is synchronously judged, key parameters of multiple pieces of power equipment are monitored in real time, the operation safety of the power system is improved, the whole power system is comprehensively monitored, abnormal conditions are found in time, and the risk of fault occurrence is reduced. The problems that an existing power equipment monitoring device can only monitor single equipment, the monitoring function is single, and the requirement for comprehensively monitoring a power system cannot be met are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of station transformer monitoring, and in particular to a station transformer visualization monitoring device with multi-channel panoramic monitoring. Background Art

[0002] As society's demand for electricity grows, the operational safety of power equipment has become a critical issue. In order to ensure the stable operation of the power system, real-time monitoring of power equipment is required.

[0003] Existing power equipment monitoring devices can often only monitor a single device, and the monitoring function is relatively simple, which cannot meet the needs of comprehensive monitoring of the power system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above problems existing in the existing power equipment monitoring device, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is to solve the problem that the existing power equipment monitoring device can often only monitor a single device and has a relatively single monitoring function and cannot meet the demand for comprehensive monitoring of the power system.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a multi-channel panoramic monitoring station transformer visualization monitoring device, comprising the following components: a multi-channel monitoring unit, comprising a plurality of monitoring modules, each monitoring module is used to monitor the key parameters of an electric power equipment in real time; a data transmission unit, used to transmit the data collected by each of the monitoring modules to a monitoring host; a monitoring host, used to receive and process the data of each monitoring channel, and store and display each data; a panoramic monitoring unit, which is wirelessly connected to the monitoring host, and after receiving each data, sequentially constructs an analysis model algorithm, performs panoramic monitoring of the entire power system of the monitoring host, and simultaneously makes a judgment on whether the state is abnormal; an alarm unit, which is electrically connected to the panoramic monitoring unit, and when the panoramic monitoring unit makes a judgment on the abnormal state, the signal triggers the alarm unit to send an alarm signal; a power supply unit, which provides working power for the device; a communication interface, which is used to communicate data with an external system; wherein the multi-channel monitoring unit is used to monitor the corresponding key parameters in real time, specifically including: a voltage monitoring module, which is used to monitor the voltage of the electric power equipment Parameters; temperature monitoring module for monitoring the thermal state of power equipment; vibration monitoring module for monitoring the vibration of power equipment; sound monitoring module for monitoring the sound signal of power equipment; image monitoring module for real-time acquisition of circumferential image information of power equipment; wherein the monitoring host is embedded with a processing preprocessing unit for preprocessing the data of each parameter; wherein the data preprocessing specifically includes: cleaning, denoising and normalization; wherein the image monitoring module specifically includes a high-definition camera with three angles of forward, rear and downward vision; wherein the panoramic monitoring unit constructs an analysis model algorithm in sequence after receiving each data, and performs panoramic monitoring of the entire power system of the monitoring host specifically including: establishing an internal state monitoring model based on the acquired voltage parameters, thermal state and vibration conditions, and obtaining an internal state value; establishing an external state monitoring model based on the acquired sound signal and circumferential image information, and obtaining an external state value; the panoramic monitoring unit establishes a comprehensive state model based on the internal state value and the external state value, and outputs a comprehensive state value; wherein the internal state monitoring model is specifically:

[0008]

[0009] Wherein, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total voltage port within the rated time; α n is the voltage value of the nth reference point in the voltage output curve of the total voltage port within the rated time; n is the number of reference points; T max γ is the highest temperature that the temperature monitoring module can monitor within the rated time; maxis the maximum amplitude that the vibration monitoring module can monitor within the rated time; 1.204 and 1.39 are adjustment constants; wherein, the external state monitoring model is specifically:

[0010] η=S max 1.476 ·L 3.58 ·P 0.81

[0011] Wherein, η is the external state value; S max is the maximum sound signal that the sound monitoring module can monitor; L is the number of foreign objects monitored by the image monitoring module at the current judgment time; P is the size of the foreign objects monitored by the image monitoring module at the current judgment time; 1.476, 3.58 and 0.81 are all adjustment constants; wherein the comprehensive state model is specifically:

[0012] μ=δ 1.71 ·η 1.09

[0013] Among them, μ is the comprehensive state value; δ is the internal state value; η is the external state value; 1.71 and 1.09 are adjustment constants.

[0014] As a preferred solution of the multi-channel panoramic monitoring platform-variable visualization monitoring device described in the present invention, the panoramic monitoring unit synchronously makes a judgment on whether the state is abnormal by comparing the comprehensive state value with the set abnormal threshold. When the comprehensive state value is higher than the abnormal threshold, it is determined that the state is abnormal, and the signal triggers the alarm unit to send an alarm signal.

[0015] As a preferred solution of the multi-channel panoramic monitoring platform variable visualization monitoring device described in the present invention, the abnormal threshold is specifically 3.92 or 3.921 or 3.922.

[0016] As a preferred solution of the multi-channel panoramic monitoring station transformer visualization monitoring device of the present invention, when the acquired voltage parameters satisfy the following model:

[0017]

[0018] Then, the internal state monitoring model is simplified as follows:

[0019] δ = α1·3.77 or 3.771

[0020] Then, the comprehensive state model is simplified as follows:

[0021] μ=δ·η 1.09

[0022] Wherein, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total voltage port within the rated time; α n is the voltage value of the nth reference point in the voltage total port voltage output curve within the rated time; n is the number of reference points; μ is the comprehensive state value; δ is the internal state value; η is the external state value; and 1.09 is the adjustment constant.

[0023] As a preferred solution of the multi-channel panoramic monitoring platform variable visualization monitoring device described in the present invention, the abnormal threshold is specifically 3.80.

[0024] The present invention provides a multi-channel panoramic monitoring platform transformer visualization monitoring device, which has the following beneficial effects:

[0025] 1. The multi-channel monitoring unit can monitor the key parameters of multiple power equipment in real time at the same time, improving the operational safety of the power system;

[0026] 2. The panoramic monitoring unit can comprehensively monitor the entire power system, detect abnormal conditions in time, and reduce the risk of failure;

[0027] 3. The alarm unit can send out an alarm signal in time when abnormal conditions are detected, so that the operation and maintenance personnel can handle them in time;

[0028] 4. The communication interface can communicate data with external systems, realize remote monitoring and data analysis, and improve the convenience and intelligence of power equipment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0030] Figure 1 This is a component structure diagram of the platform-based visualization monitoring device for multi-channel panoramic monitoring provided by the present invention.

[0031] Figure 2 A flowchart of a method in which the panoramic monitoring unit provided by the present invention sequentially constructs analysis model algorithms after receiving various data to perform panoramic monitoring of the entire power system of the monitoring host.

[0032] Figure 3 This is an example diagram of the image recognition algorithm provided by the present invention.

[0033] Figure 4 Schematic diagram of channel-by-channel convolution provided by the present invention.

[0034] Figure 5 Schematic diagram of point-by-point convolution provided by the present invention.

[0035] Figure 6 A schematic diagram of the modification of the overall network structure provided by the present invention.

[0036] Figure 7 This is a schematic diagram of the model pruning provided by the present invention.

[0037] Figure 8 This is a schematic diagram of model reconstruction provided by the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] Existing power equipment monitoring devices can often only monitor a single device, and the monitoring function is relatively simple, which cannot meet the needs of comprehensive monitoring of the power system.

[0040] Therefore, please refer to Figure 1 The present invention provides a multi-channel panoramic monitoring platform variable visualization monitoring device, including the following components:

[0041] A multi-channel monitoring unit, including multiple monitoring modules, each monitoring module is used to monitor a key parameter of an electric power device in real time;

[0042] A data transmission unit, used to transmit the data collected by each monitoring module to the monitoring host;

[0043] The monitoring host is used to receive and process the data of each monitoring channel, and store and display the data;

[0044] The panoramic monitoring unit is connected to the monitoring host via wireless data. After receiving the data, it builds the analysis model algorithm in sequence, performs panoramic monitoring of the entire power system of the monitoring host, and simultaneously makes a judgment on whether the status is abnormal;

[0045] The alarm unit is electrically connected to the panoramic monitoring unit. When the panoramic monitoring unit determines that the state is abnormal, the signal triggers the alarm unit to send out an alarm signal;

[0046] A power supply unit, which provides working power for the device;

[0047] Communication interface, used for data communication with external systems.

[0048] Specifically, the multi-channel monitoring unit is used to monitor the corresponding key parameters in real time, including:

[0049] Voltage monitoring module, used to monitor voltage parameters of power equipment;

[0050] Temperature monitoring module, used to monitor the thermal status of power equipment;

[0051] Vibration monitoring module, used to monitor the vibration of power equipment;

[0052] Sound monitoring module, used to monitor the sound signals of power equipment;

[0053] The image monitoring module is used to collect circumferential image information of power equipment in real time.

[0054] It should be noted that the voltage monitoring module, temperature monitoring module, vibration monitoring module, sound monitoring module and image monitoring module involved in the present invention all use existing commonly used basic electrical components for data collection, and no unnecessary elaboration is made here.

[0055] Furthermore, a preprocessing unit is embedded in the monitoring host to perform data preprocessing on each parameter data;

[0056] Among them, data preprocessing specifically includes: cleaning, denoising and normalization.

[0057] The data preprocessing steps are the application of existing conventional data processing steps, which are briefly described below:

[0058] 1. Data cleaning:

[0059] Identify and handle missing values: There may be missing data points in a dataset, which may be caused by equipment failure, data transmission errors, or other reasons. Methods for handling missing values ​​include filling missing values ​​(for example, using the mean, median, or mode), deleting records with missing values, or using interpolation methods (such as linear interpolation or spline interpolation) to estimate missing values.

[0060] Identify and handle outliers: Outliers may be caused by measurement errors, equipment failures, or abnormal operations. Methods for handling outliers include identifying outliers using box plots (IQR) or other statistical methods and considering removing or replacing these values.

[0061] Data transformation: For non-numeric data, data transformation, such as encoding or normalization, may be required to facilitate subsequent analysis and modeling.

[0062] 2. Data denoising:

[0063] Time series smoothing: Use techniques such as moving average, low-pass filter, wavelet filter, etc. to smooth time series data to reduce the impact of noise.

[0064] Frequency domain filtering: The time series data is converted to the frequency domain through Fourier transform, and then the frequency domain filter is used to remove the unnecessary frequency components, and then it is returned to the time domain through inverse transform.

[0065] Kalman Filter: The Kalman filter is an efficient recursive filter that is able to estimate the state of a linear dynamic system in the presence of noise.

[0066] 3. Data normalization:

[0067] Linear normalization: Scale the data to a fixed range, such as [0,1] or [-1,1].

[0068] Logarithmic Normalization: For log-transformed data, log normalization can reduce the proportional differences in the data.

[0069] Standardization: Standardization is the process of scaling the data to a normal distribution with a mean of 0 and a standard deviation of 1.

[0070] It should be noted that the image monitoring module specifically includes high-definition cameras for three angles of view: forward, rear and downward, to achieve panoramic monitoring.

[0071] It should be additionally explained that the image monitoring module uses an intelligent recognition algorithm to obtain the number and size of foreign objects at the current judgment moment based on the high-definition camera.

[0072] In addition, in addition to the common intelligent recognition algorithms on the existing market, the present invention also provides a set of optimized recognition algorithms;

[0073] The present invention mainly uses image recognition algorithms based on deep learning to identify them. These include image classification algorithms, image segmentation algorithms, target detection algorithms, moving target recognition algorithms, etc. based on deep learning. Figure 3 .

[0074] In order to enable deep learning models to perform inference in edge devices, the research team studied domestic and foreign literature and combined it with existing technologies to confirm the deployment and implementation path of the algorithm, namely: adopt a lightweight model structure and use a pruning algorithm to prune the model. On this basis, the model is quantized to further reduce the model parameters.

[0075] The operation of identifying hidden dangers in the present invention is mainly carried out around deep learning algorithms. The following is a detailed explanation of the image algorithm based on deep learning and the theory of lightweight deployment of deep learning. The image recognition algorithm based on deep learning essentially uses a deep learning neural network to extract the features of existing pictures, learn the correlation and distinction between different targets, and convert them into parameters and store them in the constructed artificial neural network. When faced with new data, the learned parameters are used to identify new pictures.

[0076] Deep learning is based on artificial neural networks, which usually contain multiple hidden layers and a large number of neurons. Through learning and training, the network's weights and bias parameters are continuously optimized to achieve more accurate prediction and classification tasks. With the continuous development of neural networks, special neural network structures such as convolutional neural networks (CNN) and recurrent neural networks (RNN) have emerged. In this technology project, most defect detection uses convolutional neural networks to process images. The earliest convolutional neural networks are mainly composed of convolutional layers, activation layers, pooling layers, and fully connected layers.

[0077] Due to the performance limitation of the monitoring device in the present invention, the station change monitoring unit based on intelligent recognition is preferably equipped with a lightweight deep learning network.

[0078] First, in terms of model construction, the present invention mainly adopts two solutions to reduce the amount of calculation and parameters of the model. The first is to replace the convolution structure commonly used in deep learning convolutional neural network models with deep separable convolution; second, after weighing the reasoning speed and accuracy, a model with smaller depth and width is adopted. This greatly reduces the number of parameters and calculations of the model and reduces the performance consumption of the monitoring equipment chip. Regarding the first point, deep separable convolution is a variant of the convolution layer, one of the basic units of neural networks. Unlike the original convolution layer, deep separable convolution is mainly divided into two processes, namely channel-by-channel convolution and point-by-point convolution.

[0079] In channel-by-channel convolution, one convolution kernel is responsible for one channel, and one channel is convolved by only one convolution kernel. The number of channels of the feature map generated by this process is exactly the same as the number of channels of the input. For example, for a 5×5 pixel, three-channel color input image, the channel-by-channel convolution first undergoes the first convolution operation. The number of convolution kernels used is the same as the number of channels in the previous layer (channels and convolution kernels correspond one to one). After calculation, a three-channel image is calculated to generate three feature maps. The number of feature maps after the channel-by-channel convolution is completed is the same as the number of channels in the input layer, and the feature map cannot be expanded. Moreover, this operation performs convolution operations on each channel of the input layer independently, and does not effectively utilize the feature information of different channels at the same spatial position. Therefore, point-by-point convolution is required to combine these feature maps to generate new feature maps. Please refer to Figure 4 .

[0080] Point-by-point convolution is very similar to conventional convolution. Its convolution kernel size is 1×1×M, where M is the number of channels in the previous layer. Therefore, the convolution operation here will perform a weighted combination of the feature map of the previous step in the depth direction to generate a new feature map. There are several output feature maps for several convolution kernels. Point-by-point convolution can change the dimension of the channel, so that the features obtained by each convolution kernel can be interactively fused. Please refer to Figure 5 .

[0081] For the second point, the present invention makes comprehensive adjustments to the network depth, network width, and input image resolution of the model with reference to the EfficientNet algorithm to obtain the optimal network parameters for specific needs, so that the network has the dual advantages of network size and recognition accuracy. Please refer to Figure 6 .

[0082] Secondly, prune the organized model to trim redundant or unimportant network structures and parameters. The pruned model can accelerate reasoning, compress storage space and prevent overfitting to a certain extent. Please refer to Figure 7 .

[0083] Again, the present invention further reconstructs the model structure, including merging conv+batchnorm+relu into CBR layer, merging the same tensors in the same layer, and eliminating the concat layer. Please refer to Figure 8 .

[0084] Finally, this scientific and technological project quantizes the processed model. The original model parameters are fp32, and the present invention project quantizes them to int8, which greatly improves the inference speed of the model on the monitoring equipment chip while slightly reducing the accuracy.

[0085] For edge devices, it is known that the storage and memory of the devices themselves are not that large, and model quantization can bring less storage overhead and bandwidth requirements, faster computing speed, lower energy consumption and occupied area. When deployed at the edge, the model can infer images in real time with extremely low energy consumption, thus meeting customer needs.

[0086] Further, participation Figure 2 After receiving the data, the panoramic monitoring unit builds the analysis model algorithm in turn, and performs panoramic monitoring of the entire power system of the monitoring host, including:

[0087] An internal state monitoring model is established based on the obtained voltage parameters, thermal state and vibration conditions to obtain the internal state value;

[0088] An external state monitoring model is established based on the acquired sound signals and circumferential image information to obtain the external state value;

[0089] The panoramic monitoring unit establishes a comprehensive state model according to the internal state value and the external state value, and outputs the comprehensive state value.

[0090] Furthermore, the internal state monitoring model is specifically:

[0091]

[0092] Among them, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total port voltage within the rated time, V; α n is the voltage value of the nth reference point in the voltage output curve of the total voltage port within the rated time, V; n is the number of reference points; T max is the highest temperature that the temperature monitoring module can monitor within the rated time, °C; γ max is the maximum amplitude that the vibration monitoring module can monitor within the rated time, mm; 1.204 and 1.39 are both adjustment constants.

[0093] It should be noted that the rated time can be selected as 12h, which does not affect the operation of the overall model. A reference point is selected at equal intervals.

[0094] Furthermore, the external state monitoring model is specifically:

[0095] η=S max 1.476 ·L 3.58 ·P 0.81

[0096] Where η is the external state value; S max is the maximum sound signal that the sound monitoring module can monitor, db; L is the number of foreign objects monitored by the image monitoring module at the current judgment time, pieces; P is the size of the foreign object monitored by the image monitoring module at the current judgment time, m 3 ; 1.476, 3.58 and 0.81 are adjustment constants.

[0097] Furthermore, the comprehensive state model is specifically:

[0098] μ=δ 1.71 ·η 1.09

[0099] Among them, μ is the comprehensive state value; δ is the internal state value; η is the external state value; 1.71 and 1.09 are adjustment constants.

[0100] Specifically, the panoramic monitoring unit synchronously determines whether the state is abnormal by comparing the comprehensive state value with the set abnormal threshold. When the comprehensive state value is higher than the abnormal threshold, it is determined that the state is abnormal, and the signal triggers the alarm unit to send an alarm signal.

[0101] Specifically, the abnormal threshold is 3.92 or 3.921 or 3.922.

[0102] In addition, in order to simplify the plan, save computing power and cost, when the voltage parameters obtained meet the following model:

[0103]

[0104] Then, the internal state monitoring model is simplified as follows:

[0105] δ = α1·3.77 or 3.771

[0106] Then, the comprehensive state model is simplified as:

[0107] μ=δ·η 1.09

[0108] Among them, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total port within the rated time; α n is the voltage value of the nth reference point in the voltage total port voltage output curve within the rated time; n is the number of reference points; μ is the comprehensive state value; δ is the internal state value; η is the external state value; 1.09 is the adjustment constant.

[0109] Furthermore, the abnormal threshold is specifically 3.80.

[0110] In order to verify the technical effect of the present invention, the following experimental process is first performed:

[0111] Purpose of the test:

[0112] Verify the effectiveness of the multi-channel panoramic monitoring transformer visualization monitoring device in real-time monitoring of key parameters of power equipment, comprehensive monitoring of power system status and timely alarm.

[0113] Test equipment and materials:

[0114] 1.Multi-channel monitoring unit.

[0115] 2. Panoramic monitoring and analysis unit.

[0116] 3. Alarm unit.

[0117] 4. Power equipment (such as transformers, etc.).

[0118] 5.Embedded data acquisition and analysis system.

[0119] 6. Recording equipment (such as cameras, data loggers, etc.).

[0120] Test steps

[0121] 1. Multi-channel monitoring unit test

[0122] a. Data collection

[0123] Start the monitoring unit and monitor the following parameters in real time:

[0124] Voltage parameters

[0125] Thermal state (such as temperature distribution)

[0126] Vibration

[0127] Sound signal

[0128] Circumferential image information

[0129] Data were collected continuously, and each parameter was recorded every 1 hour.

[0130] b. Part of the data record form is shown in Table 1 below:

[0131] Table 1: Partial data record form

[0132]

[0133] 2. Panoramic monitoring unit test

[0134] a. Data processing

[0135] An internal state monitoring model is established using the monitored voltage parameters, thermal state, and vibration conditions.

[0136] An external state monitoring model is established using sound signals and circumferential image information.

[0137] Combine the internal state model and the external state model to establish a comprehensive state model.

[0138] b. Status Assessment

[0139] Output comprehensive status value to evaluate the power system status.

[0140] Record abnormal status and corresponding time points.

[0141] 3. Alarm unit test

[0142] a. Anomaly Detection

[0143] When the monitoring unit detects an abnormal state, the alarm unit is triggered.

[0144] Record the alarm time and alarm type.

[0145] b. Alarm record form, see Table 2 below:

[0146] Table 2: Alarm Record

[0147] Time (h) Alarm Type Processing Status 1 Abnormal vibration Unprocessed 4 Abnormal temperature Unprocessed ... ... ... 10 Abnormal sound Processed

[0148] 4. Result analysis and verification

[0149] a. Data Analysis

[0150] Analyze the collected data of the multi-channel monitoring unit and the panoramic monitoring unit to verify the accuracy and effectiveness of the model.

[0151] Analyze the response time and processing status of the alarm unit and evaluate the timeliness of the alarm.

[0152] b. Result verification form, as shown in Table 3 below:

[0153] Table 3: Result verification form

[0154]

[0155]

[0156] in conclusion

[0157] Through the above test verification process, the following conclusions can be drawn:

[0158] The multi-channel monitoring unit can monitor the key parameters of power equipment in real time and has high data acquisition accuracy and real-time performance.

[0159] The panoramic monitoring unit can effectively monitor the power system comprehensively and detect abnormal situations in a timely manner.

[0160] The alarm unit can send out alarm signals in time when abnormal situations are detected, which helps operation and maintenance personnel to respond and handle them quickly.

[0161] The present invention provides a multi-channel panoramic monitoring platform transformer visualization monitoring device, which has the following beneficial effects:

[0162] 1. The multi-channel monitoring unit can monitor the key parameters of multiple power equipment in real time at the same time, improving the operational safety of the power system;

[0163] 2. The panoramic monitoring unit can comprehensively monitor the entire power system, detect abnormal conditions in time, and reduce the risk of failure;

[0164] 3. The alarm unit can send out an alarm signal in time when abnormal conditions are detected, so that the operation and maintenance personnel can handle them in time;

[0165] 4. The communication interface can communicate data with external systems, realize remote monitoring and data analysis, and improve the convenience and intelligence of power equipment monitoring.

[0166] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-channel panoramic monitoring platform variable visualization monitoring device, characterized in that it includes the following components: A multi-channel monitoring unit, including multiple monitoring modules, each monitoring module is used to monitor a key parameter of an electric power device in real time; A data transmission unit, used to transmit the data collected by each monitoring module to a monitoring host; The monitoring host is used to receive and process the data of each monitoring channel, and store and display the data; The panoramic monitoring unit is connected to the monitoring host by wireless data, and after receiving each data, it sequentially constructs the analysis model algorithm, performs panoramic monitoring of the entire power system of the monitoring host, and simultaneously makes a judgment on whether the state is abnormal; An alarm unit is electrically connected to the panoramic monitoring unit. When the panoramic monitoring unit determines that the state is abnormal, the signal triggers the alarm unit to send an alarm signal. A power supply unit, which provides working power for the device; Communication interface, used for data communication with external systems; The multi-channel monitoring unit is used to monitor the corresponding key parameters in real time, including: Voltage monitoring module, used to monitor voltage parameters of power equipment; Temperature monitoring module, used to monitor the thermal status of power equipment; Vibration monitoring module, used to monitor the vibration of power equipment; Sound monitoring module, used to monitor the sound signals of power equipment; Image monitoring module, used to collect circumferential image information of power equipment in real time; Wherein, a processing preprocessing unit is embedded in the monitoring host, which is used to perform data preprocessing on each parameter data; Among them, data preprocessing specifically includes: cleaning, denoising and normalization; The image monitoring module specifically includes high-definition cameras for front, rear and downward viewing; The panoramic monitoring unit sequentially constructs an analysis model algorithm after receiving each data, and performs panoramic monitoring of the entire power system of the monitoring host, specifically including: An internal state monitoring model is established based on the obtained voltage parameters, thermal state and vibration conditions to obtain the internal state value; An external state monitoring model is established based on the acquired sound signals and circumferential image information to obtain the external state value; The panoramic monitoring unit establishes a comprehensive state model according to the internal state value and the external state value, and outputs a comprehensive state value; Wherein, the internal state monitoring model is specifically: Wherein, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total voltage port within the rated time; α n is the voltage value of the nth reference point in the voltage output curve of the total voltage port within the rated time; n is the number of reference points; T max γ is the highest temperature that the temperature monitoring module can monitor within the rated time; max is the maximum amplitude that the vibration monitoring module can monitor within the rated time; 1.204 and 1.39 are adjustment constants; Wherein, the external state monitoring model is specifically: η=S max 1.476 ·L 3.58 ·P 0.81 Wherein, η is the external state value; S max is the maximum sound signal that the sound monitoring module can monitor; L is the number of foreign objects monitored by the image monitoring module at the current judgment moment; P is the size of the foreign objects monitored by the image monitoring module at the current judgment moment; 1.476, 3.58 and 0.81 are adjustment constants; in, The comprehensive state model is specifically: μ=δ 1.71 ·or 1.09 Among them, μ is the comprehensive state value; δ is the internal state value; η is the external state value; 1.71 and 1.09 are adjustment constants.

2. The multi-channel panoramic monitoring device according to claim 1 is characterized in that: The panoramic monitoring unit synchronously determines whether the state is abnormal by comparing the comprehensive state value with the set abnormal threshold. When the comprehensive state value is higher than the abnormal threshold, it is determined that the state is abnormal, and the signal triggers the alarm unit to send an alarm signal.

3. The multi-channel panoramic monitoring and visualization monitoring device according to claim 2 is characterized in that: The abnormal threshold is specifically 3.92 or 3.921 or 3.

922.

4. The multi-channel panoramic monitoring and visualization monitoring device according to claim 1 is characterized in that: When the obtained voltage parameters meet the following model: Then, the internal state monitoring model is simplified as follows: δ = α1·3.77 or 3.771 Then, the comprehensive state model is simplified as follows: μ=δ·η 1.09 Wherein, δ is the internal state value; α1 is the voltage value of the first reference point in the voltage output curve of the total voltage port within the rated time; α n is the voltage value of the nth reference point in the voltage total port voltage output curve within the rated time; n is the number of reference points; μ is the comprehensive state value; δ is the internal state value; η is the external state value; and 1.09 is the adjustment constant.

5. The multi-channel panoramic monitoring and visualization monitoring device according to claim 4 is characterized in that: The abnormal threshold is specifically 3.80.

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