Building electrical safety risk intelligent diagnosis method and system based on big data

Abstract

The invention relates to the technical field of building electrical safety, and discloses a building electrical safety risk intelligent diagnosis method and system based on big data, and the method comprises the steps: collecting the operation condition data of a building electrical system in real time; cleaning the collected operation condition data to remove noise, and performing normalization processing on the denoised operation condition data to obtain preprocessed data; feature extraction is carried out on the preprocessed data, time domain features and frequency domain features are extracted, energy distribution under different scales is obtained through wavelet transform, electrical signal abnormity is captured, and multi-dimensional electrical feature data are obtained; inputting the multi-dimensional electrical characteristic data into an intelligent diagnosis model, diagnosing the electrical safety state of the building through the intelligent diagnosis model, and outputting a safety diagnosis result; determining a building electrical safety risk level according to the safety diagnosis result, and sending corresponding early warning information based on the safety risk level; according to the invention, the electrical potential safety hazard of the building is accurately identified in real time, and the electricity safety of the building is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of building electrical safety, and particularly relates to an intelligent diagnosis method and system for building electrical safety risks based on big data. Background Art

[0002] In modern buildings, the electrical system is complex and huge, covering multiple subsystems such as lighting, power, sockets, and fire protection electricity. Traditional electrical safety inspections mostly rely on manual regular inspections, relying on experience and simple instrument measurements. This method has low efficiency and strong subjectivity, and it is difficult to capture potential risks in real time and comprehensively. For example, problems such as wire aging, overload, short circuit, and grounding faults may instantly cause electrical fires or equipment damage, and hidden dangers are difficult to detect in time during the manual inspection interval. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems, and an intelligent diagnosis method and system for building electrical safety risks based on big data are designed.

[0004] In the first aspect of the present invention, an intelligent diagnosis method for building electrical safety risks based on big data is provided. The intelligent diagnosis method for building electrical safety risks based on big data includes the following steps: Real-time collect the original parameter data of the building electrical system through a variety of sensors, connect with the building automation system to obtain the operation status information, connect the power quality monitor to collect the power quality index data, and integrate the original parameter data, operation status information, and power quality index data to obtain the operation condition data of the building electrical system; Clean the collected operation condition data to remove noise, and perform normalization processing on the denoised operation condition data to obtain the preprocessed data; Extract features from the preprocessed data, extract time-domain features and frequency-domain features, obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; Input the multi-dimensional electrical feature data into the intelligent diagnosis model, diagnose the building electrical safety status through the intelligent diagnosis model, and output the safety diagnosis result; Determine the building electrical safety risk level according to the safety diagnosis result, and send corresponding warning information based on the safety risk level.

[0005] Optionally, in the first implementation manner of the first aspect of the present invention, the cleaning of the collected operation condition data to remove noise and the normalization processing of the denoised operation condition data to obtain the preprocessed data include: Obtain the collected operating condition data, traverse the operating condition data to check whether there are missing values in each data record, and use the linear interpolation method to fill the detected missing values. For the position of each missing value, estimate the missing value through linear calculation based on the valid data points adjacent before and after it; Use a filtering algorithm based on statistical characteristics to detect outliers in the filled data, calculate the mean and standard deviation of each data type, calculate the absolute value of the difference between each data point and the mean. If the absolute value of the difference of a certain data point is greater than the threshold, the corresponding data point is determined as an outlier, where the threshold is three times the standard deviation; Use smoothing processing to correct the detected outliers, and use a median filter to remove noise from the data after missing value and outlier processing to obtain denoised data; Use the Min - Max normalization method to normalize the denoised data to obtain pre - processed data.

[0006] Optionally, in the second implementation manner of the first aspect of the present invention, perform feature extraction on the pre - processed data, extract time - domain features and frequency - domain features, obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi - dimensional electrical feature data, including: Obtain the pre - processed data, arrange the pre - processed data in chronological order to obtain a data sequence; Calculate the mean, standard deviation and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values, and calculate the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain time - domain features; Apply the fast Fourier transform to convert the time - domain features of the data sequence into frequency - domain features. For the frequency - domain sequence, calculate the frequency positions of each harmonic according to the fundamental frequency, extract the amplitudes at the corresponding frequency positions, and count the proportion of the amplitudes of each harmonic to the fundamental amplitude to obtain the harmonic frequency spectrum distribution; Determine the low - frequency range and extract the frequency - domain components within this range, and analyze the changes in the amplitude and phase of the low - frequency components over time, where the low - frequency range is below 100Hz; Obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi - dimensional electrical feature data.

[0007] Optionally, in the third implementation manner of the first aspect of the present invention, the obtaining the energy distribution at different scales through wavelet transform, capturing electrical signal anomalies, and obtaining multi - dimensional electrical feature data includes: Perform N - layer wavelet decomposition on the data sequence using the Haar wavelet to decompose the signal into an approximation component and a detail component; Calculate the energy of the approximate component and the detail component of each layer, and integrate the extracted time-domain features, frequency-domain features, and the energy distribution at different scales obtained by wavelet transform to form a multi-dimensional feature vector, which is used as the final multi-dimensional electrical feature data.

[0008] Optionally, in the fourth implementation manner of the first aspect of the present invention, the multi-dimensional electrical feature data is input into an intelligent diagnosis model, and the intelligent diagnosis model diagnoses the building electrical safety status and outputs a safety diagnosis result, including: Construct an intelligent diagnosis model based on a DNN network and an LSTM network, and input the multi-dimensional electrical feature data into the intelligent diagnosis model; The multi-dimensional electrical feature data is input into the input layer of the DNN network and is calculated layer by layer in the hidden layer of the DNN network. For each neuron in each layer, the output of the previous layer is used as the input, weighted summation is performed, and a bias is added. A non-linear factor is introduced through the ReLU activation function to obtain the output of this layer. After calculation through multiple hidden layers, the output of the output layer of the DNN network is obtained; The output of the output layer of the DNN network is used as the input of the LSTM network. At each time step, the LSTM network calculates according to the current input, the hidden state at the previous moment, and the cell state to capture the time series correlation of electrical parameters. After processing all time steps, the final output of the LSTM network is obtained; Calculate the probabilities of the building electrical in different safety states through the Softmax function for the final output of the LSTM network. According to the calculated probabilities, select the safety state with the highest probability as the current building electrical safety state, and organize the determined safety state and the corresponding probability value to output the safety diagnosis result.

[0009] Optionally, in the fifth implementation manner of the first aspect of the present invention, the construction of the intelligent diagnosis model based on the DNN network and the LSTM network includes: Obtain the multi-modal data of historical multi-source sensors, use the dynamic feature selection algorithm to screen the features of the collected multi-modal data, evaluate the importance of each feature, obtain the features highly relevant to the building electrical safety diagnosis, and remove redundant features, where the multi-modal data is the operating status of the building electrical system under different working conditions in the past; Adopt the attention mechanism to dynamically adjust the weights of the data obtained after feature screening, and use the adaptive noise reduction method combining wavelet transform and generative adversarial network for noise reduction processing to obtain a training set; Input the training set into the DNN network for training, and introduce the self-attention mechanism in the DNN network to pay attention to the mutual relationship between different features; Use the output of the DNN network as the input of the LSTM network. The LSTM network introduces Peephole connections and Zoneout regularization. The Peephole connections add additional connections to the forget gate, input gate, and output gate, enabling them to receive information about the cell state. Adopt a differentiable gating mechanism to construct a dynamic feature fusion layer, fuse the features output by the DNN network and the LSTM network, and dynamically allocate weights according to the importance of different features in the current diagnostic task. Construct a four-dimensional optimization objective function, use the MOSHO algorithm for hyperparameter optimization to obtain the optimal solution, and thus construct an intelligent diagnostic model.

[0010] Optionally, in the sixth implementation manner of the first aspect of the present invention, the constructing a four-dimensional optimization objective function, using the MOSHO algorithm for hyperparameter optimization to obtain the optimal solution, and thus constructing an intelligent diagnostic model includes: Determine that the four indicators of the four-dimensional optimization objective function are diagnostic accuracy, inference speed, memory occupancy, and model stability, and assign weights to the four indicators. Randomly initialize a certain number of particles in the hyperparameter space of the model to be optimized. Each particle represents a set of hyperparameter combinations, and assign an initial velocity and position to each particle. According to the initial hyperparameter combinations and the corresponding objective function values, perform initial training on the Bayesian model. Apply the hyperparameter combinations represented by each particle in the current particle swarm to the intelligent diagnostic model for training and evaluation, and calculate the corresponding four-dimensional optimization objective function values. For each particle, compare the objective function value at the current position of each particle with the objective function value at the historical optimal position. If the objective function value at the current position is better, then update the historical optimal position of the particle. Compare the objective function values at the historical optimal positions of all particles, and select the position with the optimal objective function value as the global optimal position. Introduce quantum behavior simulation to improve the update rule of the particle swarm. Use the trained Bayesian model to predict hyperparameter combinations that can obtain better objective function values in the hyperparameter space, and add the predicted hyperparameter combinations as new particles to the particle swarm. Add the hyperparameter combinations of the newly added particles and the corresponding objective function values to the training data of the Bayesian model, and retrain the Bayesian model. Check whether the current iteration number reaches the upper limit. If the termination condition is met, stop the iteration; otherwise, return to continue iterative optimization. After the iteration ends, the hyperparameter combination represented by the global optimal position is the optimal solution of the four-dimensional optimization objective function, and use the optimal hyperparameter combination to construct an intelligent diagnostic model.

[0011] In the second aspect of the present invention, an intelligent diagnosis system for building electrical safety risks based on big data is provided. The intelligent diagnosis system for building electrical safety risks based on big data includes a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent diagnosis module, and an early warning module. Among them, the data acquisition module is used to collect the original parameter data of the building electrical system in real time through a variety of sensors, connect with the building automation system to obtain the operation status information, connect the power quality monitor to collect the power quality index data, and integrate the original parameter data, operation status information, and power quality index data to obtain the operation condition data of the building electrical system; The data preprocessing module is used to clean the collected operation condition data to remove noise, and perform normalization processing on the denoised operation condition data to obtain the preprocessed data; The feature extraction module is used to extract features from the preprocessed data, extract time-domain features and frequency-domain features, obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; The intelligent diagnosis module is used to input the multi-dimensional electrical feature data into the intelligent diagnosis model, diagnose the building electrical safety status through the intelligent diagnosis model, and output the safety diagnosis result; The early warning module is used to determine the building electrical safety risk level according to the safety diagnosis result, and send corresponding early warning information based on the safety risk level.

[0012] Optionally, in the first implementation manner of the second aspect of the present invention, the data preprocessing module includes a filling sub-module, a calculation sub-module, a correction sub-module, and a normalization processing sub-module. Among them, the filling sub-module is used to obtain the collected operation condition data, traverse the operation condition data to check whether there are missing values in each data record, and use linear interpolation to fill the detected missing values. For each position where a missing value is located, estimate the missing value through linear calculation according to the valid data points adjacent before and after it; The calculation sub-module is used to detect outliers in the filled data based on a filtering algorithm with statistical characteristics, calculate the mean and standard deviation of each data type, calculate the absolute value of the difference between each data point and the mean, and if the absolute value of the difference of a certain data point is greater than the threshold, determine the corresponding data point as an outlier, where the threshold is three times the standard deviation; The correction sub-module is used to correct the detected outliers by smoothing processing, and use a median filter to remove noise from the data after missing value and outlier processing to obtain the denoised data; The normalization processing sub-module is used to perform normalization processing on the denoised data using the Min-Max normalization method to obtain the preprocessed data.

[0013] Optionally, in the second implementation manner of the second aspect of the present invention, the feature extraction module includes an arrangement sub-module, a traversal sub-module, a conversion sub-module, an analysis sub-module, and a capture sub-module. Among them, the arrangement sub-module is used to obtain the preprocessed data, arrange the preprocessed data in chronological order to obtain a data sequence; The traversal sub-module is used to calculate the mean value, standard deviation, and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value, and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values, and calculate the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain time-domain features; The conversion sub-module is used to apply the fast Fourier transform to convert the time-domain features of the data sequence into frequency-domain features. For the frequency-domain sequence, calculate the frequency positions of each harmonic according to the fundamental frequency, extract the amplitudes at the corresponding frequency positions, and count the ratio of the amplitudes of each harmonic to the fundamental amplitude to obtain the harmonic spectrum distribution; The analysis sub-module is used to determine the low-frequency range, extract the frequency-domain components in this range, and analyze the changes in the amplitude and phase of the low-frequency components over time, where the low-frequency range is below 100 Hz; The capture sub-module is used to obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data.

[0014] In the technical solution provided by the present invention, the original parameter data of the building electrical system is collected in real time through a variety of sensors, and is docked with the building automation system to obtain the operation status information. The power quality monitor is connected to collect the power quality index data. The original parameter data, operation status information, and power quality index data are integrated to obtain the operation condition data of the building electrical system; the collected operation condition data is cleaned to remove noise, and the denoised operation condition data is normalized to obtain the preprocessed data; the preprocessed data is subjected to feature extraction to extract time-domain features and frequency-domain features, obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; the multi-dimensional electrical feature data is input into the intelligent diagnosis model, the building electrical safety status is diagnosed through the intelligent diagnosis model, and the safety diagnosis result is output; according to the safety diagnosis result, the building electrical safety risk level is determined, and the corresponding warning information is sent based on the safety risk level; the present invention accurately and real-time identifies the building electrical safety hazards and ensures the safety of building electricity use. Description of the Drawings

[0015] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0016] Figure 1 Schematic diagram of the first embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiments of the present invention; Figure 2 Schematic diagram of the second embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiments of the present invention; Figure 3 Schematic diagram of the third embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiments of the present invention; Figure 4 Schematic diagram of the structure of the intelligent diagnosis system for building electrical safety risks based on big data provided by the embodiments of the present invention. Detailed implementation manners

[0017] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific processes of the embodiments of the present invention are described below. Please refer to Figure 1 Schematic diagram of the first embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiments of the present invention. The method specifically includes the following steps: Step 101: Real-time collect the original parameter data of the building electrical system through a variety of sensors, dock with the building automation system to obtain the operation status information, connect the power quality monitor to collect the power quality index data, and integrate the original parameter data, operation status information and power quality index data to obtain the operation condition data of the building electrical system; In this embodiment, the current transformer focuses on capturing the magnitude of the current in the line and continuously monitors the real-time value of the current, such as the normal current during stable operation and the current fluctuations caused by sudden load changes; the voltage transformer obtains the voltage condition and accurately measures the voltage value at each point; the temperature sensor is attached to the surface of the electrical equipment or key line parts to sense the temperature changes of the equipment and the line in real time, because excessive temperature is often the prelude to electrical faults. Problems such as line overload and poor contact will cause the temperature to rise sharply; the leakage sensor closely monitors the leakage current; the building automation system (BAS) controls the operating status of many aspects of the electrical equipment. The opening and closing status of the circuit breaker directly reflects the on / off situation of the circuit. If the circuit breaker opens and closes frequently, it may indicate problems such as short circuit and overload in the downstream line; frequent operation of the contactor may imply that the equipment it controls is operating unstably or the control logic is disordered; equipment that runs continuously for a long time is more likely to have problems such as fatigue wear and overheating; the power quality monitor comprehensively detects the power quality, and the harmonic content is one of its key detection indicators. With the use of a large number of non-linear loads (such as electronic equipment, frequency conversion devices, etc.) in modern buildings, the harmonic problem has become increasingly prominent. Excessive harmonics will interfere with the normal operation of other electrical equipment and even damage the equipment; the monitoring of the three-phase unbalance degree is also crucial. Three-phase unbalance may cause excessive load on a certain phase, leading to consequences such as line heating and equipment damage; the power factor reflects the energy efficiency of the power system. A low power factor not only wastes electric energy but may also overload the power supply equipment and affect the power supply stability.

[0019] Step 102: Clean the collected operating condition data to remove noise, and perform normalization processing on the denoised operating condition data to obtain preprocessed data. In this embodiment, the collected operating condition data is obtained, and the operating condition data is traversed to check whether there are missing values in each data record. The linear interpolation method is used to fill the detected missing values. For the position of each missing value, based on the adjacent valid data points before and after it, the missing value is estimated through linear calculation; the outlier of the filled data is detected by a filtering algorithm based on statistical characteristics, the mean and standard deviation of each data type are calculated, and the absolute value of the difference between each data point and the mean is calculated. If the absolute value of the difference of a certain data point is greater than the threshold, the corresponding data point is determined as an outlier, where the threshold is three times the standard deviation; the detected outliers are corrected by smoothing processing, and the median filter is used to remove the noise from the data after missing value and outlier processing to obtain denoised data; the Min-Max normalization method is used to perform normalization processing on the denoised data to obtain preprocessed data.

[0020] Step 103: Extract features from the preprocessed data, including time-domain features and frequency-domain features. Obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; Step 104: Input the multi-dimensional electrical feature data into the intelligent diagnosis model, diagnose the electrical safety status of the building through the intelligent diagnosis model, and output the safety diagnosis result; Step 105: Determine the electrical safety risk level of the building according to the safety diagnosis result, and send corresponding warning information based on the safety risk level.

[0021] In the embodiment of the present invention, through the intelligent diagnosis model, whether it is a small leakage caused by wire aging, a gradual change in electrical parameters caused by long-term overload, or complex and hidden faults such as harmonic interference caused by non-linear loads, can be accurately identified, greatly shortening the maintenance time and reducing power outage losses; Implement hierarchical management for building electrical facilities. For low-risk areas, continuous routine monitoring is maintained to avoid waste of resources caused by over-maintenance; When in the medium-risk area, start the detailed diagnosis process, specifically check potential hidden dangers, and reasonably allocate human and material resources for precise maintenance; When it is determined as a high-risk area, immediately take emergency measures such as power outage, and organize a professional team for emergency repair. This risk-based dynamic operation and maintenance strategy optimizes the allocation of operation and maintenance resources, ensures that under the premise of ensuring electrical safety, the operation and maintenance efficiency is maximized and the operation and maintenance cost is reduced; Effectively prevent safety accidents such as electrical fires, equipment damage, and power supply interruptions caused by various factors such as line faults, equipment aging, and power quality problems, providing a solid guarantee for the life and property safety of the people in the building; Through real-time monitoring and intelligent diagnosis, ensure that key electrical systems such as lighting, air conditioning, elevators, and fire protection are always in good operating condition, avoid the failure of these systems due to electrical faults, and thus ensure the normal use function of the building, improving the experience and satisfaction of building users. Whether it is a commercial building, a residential building or a public facility building, it can play a stable role in its professional field.

[0022] Please refer to Figure 2 , the schematic diagram of the second embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiment of the present invention. The method includes: Step 201: Obtain the preprocessed data, arrange the preprocessed data in chronological order to obtain a data sequence; Step 202: Calculate the mean, standard deviation, and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value, and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values, and calculate the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain time-domain features; Step 203: Apply the fast Fourier transform to convert the time-domain features of the data sequence into frequency-domain features. For the frequency-domain sequence, calculate the frequency positions of each harmonic according to the fundamental frequency, extract the amplitudes at the corresponding frequency positions, and statistically calculate the ratio of the amplitude of each harmonic to the amplitude of the fundamental wave to obtain the harmonic frequency spectrum distribution. Step 204: Determine the low-frequency range, extract the frequency-domain components within this range, and analyze the variation of the amplitude and phase of the low-frequency components over time. In this embodiment, the low-frequency range is below 100 Hz.

[0023] Step 205: Obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data.

[0024] In this embodiment, the data sequence is subjected to N-layer wavelet decomposition using the Haar wavelet, and the signal is decomposed into an approximation component and a detail component; calculate the energy of the approximation component and the detail component at each layer, and integrate the extracted time-domain features, frequency-domain features, and the energy distribution at different scales obtained by wavelet transform to form a multi-dimensional feature vector as the final multi-dimensional electrical feature data.

[0025] Please refer to Figure 3 , the schematic diagram of the third embodiment of the intelligent diagnosis method for building electrical safety risks based on big data provided by the embodiment of the present invention. The method includes: Step 301: Build an intelligent diagnosis model based on the DNN network and the LSTM network, and input the multi-dimensional electrical feature data into the intelligent diagnosis model. In this embodiment, obtain the multi-modal data of historical multi-source sensors, use the dynamic feature selection algorithm to screen the features of the collected multi-modal data, evaluate the importance of each feature, obtain the features highly relevant to building electrical safety diagnosis, and remove redundant features, where the multi-modal data is the operating states of the building electrical system under different working conditions in the past; use the attention mechanism to dynamically adjust the weights of the data obtained after feature screening, and perform noise reduction processing using an adaptive noise reduction method combining wavelet transform and generative adversarial network to obtain a training set; input the training set into the DNN network for training, introduce the self-attention mechanism in the DNN network to focus on the mutual relationship between different features; use the output of the DNN network as the input of the LSTM network, and introduce the Peephole connection and Zoneout regularization in the LSTM network, where the Peephole connection adds additional connections to the forget gate, input gate, and output gate and can receive the information of the cell state; use the differentiable gating mechanism to construct a dynamic feature fusion layer to fuse the features output by the DNN network and the LSTM network, and dynamically allocate weights according to the importance of different features in the current diagnosis task; construct a four-dimensional optimization objective function, and use the MOSHO algorithm for hyperparameter optimization to obtain the optimal solution to build the intelligent diagnosis model.

[0026] In this embodiment, the four indicators for determining the four-dimensional optimization objective function are diagnostic accuracy, inference speed, memory occupancy, and model stability, and weights are assigned to the four indicators; a certain number of particles are randomly initialized in the hyperparameter space of the model to be optimized, each particle represents a set of hyperparameter combinations, and an initial velocity and position are assigned to each particle; according to the initial hyperparameter combinations and the corresponding objective function values, the Bayesian model is initialized for training, and the hyperparameter combinations represented by each particle in the current particle swarm are applied to the intelligent diagnosis model for training and evaluation, and the corresponding four-dimensional optimization objective function values are calculated; for each particle, compare the objective function value at the current position of each particle with the objective function value at the historical optimal position, if the objective function value at the current position is better, then update the historical optimal position of the particle; compare the objective function values at the historical optimal positions of all particles, and select the position with the optimal objective function value as the global optimal position; introduce quantum behavior simulation to improve the update rule of the particle swarm, use the trained Bayesian model to predict the hyperparameter combination with a better objective function value in the hyperparameter space, and add the predicted hyperparameter combination as a new particle to the particle swarm; add the hyperparameter combination and the corresponding objective function value of the newly added particle to the training data of the Bayesian model, and retrain the Bayesian model; check whether the current iteration number reaches the upper limit, if the termination condition is met, stop the iteration; otherwise, return to continue the iterative optimization; after the iteration ends, the hyperparameter combination represented by the global optimal position is the optimal solution of the four-dimensional optimization objective function, and an intelligent diagnosis model is constructed using the optimal hyperparameter combination.

[0027] Step 302: The multi-dimensional electrical feature data is input into the input layer of the DNN network and is calculated layer by layer in the hidden layer of the DNN network. For each neuron in each layer, the output of the previous layer is used as the input, weighted summation is performed, and a bias is added. A non-linear factor is introduced through the ReLU activation function to obtain the output of this layer. After calculation through multiple hidden layers, the output of the output layer of the DNN network is obtained. Step 303: The output of the output layer of the DNN network is used as the input of the LSTM network. At each time step, the LSTM network calculates based on the current input, the hidden state and cell state of the previous moment to capture the time series correlation of the electrical parameters. After processing all time steps, the final output of the LSTM network is obtained. Step 304: The final output of the LSTM network is used to calculate the probabilities of the building electrical in different safety states through the Softmax function. According to the calculated probabilities, the safety state with the highest probability is selected as the current safety state of the building electrical. The determined safety state and the corresponding probability values are sorted out to output the safety diagnosis result.

[0028] Please refer to Figure 4, Structural schematic diagram of the intelligent diagnosis system for building electrical safety risks based on big data provided by the embodiments of the present invention. The system includes a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent diagnosis module, and a warning module. Among them, the data acquisition module is used to collect the original parameter data of the building electrical system in real time through a variety of sensors, connect with the building automation system to obtain the operation status information, connect to the power quality monitor to collect the power quality index data, and integrate the original parameter data, operation status information, and power quality index data to obtain the operation condition data of the building electrical system; The data preprocessing module is used to clean the collected operation condition data to remove noise, and perform normalization processing on the denoised operation condition data to obtain the preprocessed data; The feature extraction module is used to extract features from the preprocessed data, extract time-domain features and frequency-domain features, obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; The intelligent diagnosis module is used to input the multi-dimensional electrical feature data into the intelligent diagnosis model, diagnose the building electrical safety status through the intelligent diagnosis model, and output the safety diagnosis result; The warning module is used to determine the building electrical safety risk level according to the safety diagnosis result, and send corresponding warning information based on the safety risk level.

[0029] In this embodiment, the data preprocessing module includes a filling sub-module, a calculation sub-module, a correction sub-module, and a normalization processing sub-module. Among them, the filling sub-module is used to obtain the collected operation condition data, traverse the operation condition data to check whether there are missing values in each data record, and use the linear interpolation method to fill the detected missing values. For each position where a missing value is located, estimate the missing value through linear calculation based on the valid data points adjacent to its front and back; The calculation sub-module is used to detect outliers in the filled data based on a filtering algorithm with statistical characteristics, calculate the mean and standard deviation of each data type, calculate the absolute value of the difference between each data point and the mean, and if the absolute value of the difference of a certain data point is greater than the threshold, determine the corresponding data point as an outlier, where the threshold is three times the standard deviation; The correction sub-module is used to correct the detected outliers by smoothing processing, and use a median filter to remove noise from the data after missing value and outlier processing to obtain the denoised data; The normalization processing sub-module is used to perform normalization processing on the denoised data using the Min-Max normalization method to obtain the preprocessed data.

[0030] In this embodiment, the feature extraction module includes an arrangement sub-module, a traversal sub-module, a conversion sub-module, an analysis sub-module, and a capture sub-module. Among them, the arrangement sub-module is used to obtain the preprocessed data, arrange the preprocessed data in chronological order to obtain a data sequence; The traversal sub-module is used to calculate the mean, standard deviation, and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value, and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values, and calculate the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain the time-domain features; The conversion sub-module is used to apply the fast Fourier transform to convert the time-domain features of the data sequence into frequency-domain features. For the frequency-domain sequence, calculate the frequency positions of each harmonic according to the fundamental frequency, extract the amplitudes at the corresponding frequency positions, and count the proportion of the amplitudes of each harmonic to the fundamental amplitude to obtain the harmonic frequency spectrum distribution; The analysis sub-module is used to determine the low-frequency range, extract the frequency-domain components within this range, and analyze the changes in the amplitude and phase of the low-frequency components over time, where the low-frequency range is below 100 Hz; The capture sub-module is used to obtain the energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data.

[0031] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent diagnosis method for building electrical safety risks based on big data, characterized in that: The intelligent diagnosis method for building electrical safety risks based on big data includes the following steps: The original parameter data of the building electrical system is collected in real time through a variety of sensors, and connected with the building automation system to obtain the operating status information, connect with the power quality monitor to collect the power quality index data, and integrate the original parameter data, operating status information and power quality index data to obtain the operating condition data of the building electrical system; Cleaning the collected operating condition data to remove noise, and normalizing the denoised operating condition data to obtain preprocessed data; Perform feature extraction on the preprocessed data, extract time domain features and frequency domain features, obtain energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; Inputting the multi-dimensional electrical characteristic data into an intelligent diagnosis model, diagnosing the electrical safety status of the building through the intelligent diagnosis model, and outputting a safety diagnosis result; The building electrical safety risk level is determined according to the safety diagnosis result, and corresponding warning information is sent based on the safety risk level.

2. The intelligent diagnosis method for building electrical safety risks based on big data according to claim 1 is characterized in that: The collected operating condition data is cleaned to remove noise, and the denoised operating condition data is normalized to obtain pre-processed data, including: Acquire the collected operating condition data, traverse the operating condition data to check whether there are missing values ​​in each data record, fill the detected missing values ​​using linear interpolation, and estimate the missing value by linear calculation based on the valid data points before and after the missing value at the location of each missing value; The filtering algorithm based on statistical characteristics detects outliers in the filled data, calculates the mean and standard deviation of each data type, and calculates the absolute value of the difference between each data point and the mean. If the absolute value of the difference of a data point is greater than a threshold, the corresponding data point is determined to be an outlier, where the threshold is three times the standard deviation. Smoothing is used to correct the detected outliers, and a median filter is used to remove noise from the data after missing values ​​and outliers are processed to obtain denoised data; The Min-Max normalization method is used to normalize the denoised data to obtain the preprocessed data.

3. The intelligent diagnosis method for building electrical safety risks based on big data according to claim 1 is characterized in that: The pre-processed data is subjected to feature extraction, time domain features and frequency domain features are extracted, energy distribution at different scales is obtained through wavelet transform, electrical signal anomalies are captured, and multi-dimensional electrical feature data is obtained, including: Acquire the preprocessed data, arrange the preprocessed data in chronological order, and obtain a data sequence; Calculate the mean, standard deviation and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values ​​and the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain the time domain characteristics; Fast Fourier transform is applied to convert the time domain features of the data sequence into frequency domain features. For the frequency domain sequence, the frequency position of each harmonic is determined according to the fundamental frequency, and the amplitude of the corresponding frequency position is extracted. The proportion of each harmonic amplitude to the fundamental amplitude is counted to obtain the harmonic spectrum distribution. Determine the low-frequency range, extract the frequency domain components within the range, and analyze the changes in the amplitude and phase of the low-frequency components over time, wherein the low-frequency range is less than 100 Hz; The energy distribution at different scales is obtained through wavelet transform, the electrical signal anomalies are captured, and multi-dimensional electrical characteristic data are obtained.

4. The intelligent diagnosis method for building electrical safety risks based on big data as claimed in claim 3 is characterized in that: The energy distribution at different scales is obtained by wavelet transform, electrical signal anomalies are captured, and multi-dimensional electrical characteristic data are obtained, including: Performing N-layer wavelet decomposition on the data sequence using Haar wavelet to decompose the signal into approximate components and detail components; The energy of the approximate components and detail components of each layer is calculated, and the extracted time domain features, frequency domain features and energy distribution at different scales obtained by wavelet transform are integrated to form a multidimensional feature vector as the final multidimensional electrical feature data.

5. The intelligent diagnosis method for building electrical safety risks based on big data according to claim 1 is characterized in that: The multi-dimensional electrical characteristic data is input into the intelligent diagnosis model, the building electrical safety status is diagnosed by the intelligent diagnosis model, and the safety diagnosis results are output, including: Building an intelligent diagnosis model based on a DNN network and an LSTM network, and inputting the multi-dimensional electrical characteristic data into the intelligent diagnosis model; The multi-dimensional electrical characteristic data is input to the input layer of the DNN network, and is calculated layer by layer in the hidden layer of the DNN network. For each layer of neurons, the output of the previous layer is used as input, weighted summation is performed, and a bias is added. The nonlinear factor is introduced through the ReLU activation function to obtain the output of the layer. After calculations in multiple hidden layers, the output of the DNN network output layer is obtained; The output of the DNN network output layer is used as the input of the LSTM network. At each time step, the LSTM network performs calculations based on the current input, the hidden state at the previous moment, and the cell state to capture the time series correlation of the electrical parameters. After processing all time steps, the final output of the LSTM network is obtained. The final output of the LSTM network is used through the Softmax function to calculate the probability that the building electrical system is in different safety states. Based on the calculated probability, the safety state with the highest probability is selected as the current safety state of the building electrical system. The determined safety states and corresponding probability values ​​are sorted out to output the safety diagnosis results.

6. The intelligent diagnosis method for building electrical safety risks based on big data according to claim 5 is characterized in that: The intelligent diagnosis model is constructed based on the DNN network and the LSTM network, including: Obtain multimodal data from historical multi-source sensors, use dynamic feature selection algorithms to screen the collected multimodal data, evaluate the importance of each feature, obtain features that are highly relevant to building electrical safety diagnosis, and remove redundant features. The multimodal data is the operating status of the building electrical system under different working conditions in the past; The attention mechanism is used to dynamically adjust the weight of the data obtained after feature screening, and the adaptive denoising method combining wavelet transform and adversarial generative network is used to perform denoising to obtain the training set; The training set is input into the DNN network for training, and a self-attention mechanism is introduced into the DNN network to focus on the relationship between different features; The output of the DNN network is used as the input of the LSTM network. The LSTM network introduces Peephole connection and Zoneout regularization. The Peephole connection adds additional connections to the forget gate, input gate, and output gate to receive information about the cell state. A dynamic feature fusion layer is constructed using a differentiable gating mechanism to fuse the features output by the DNN network and the LSTM network, and dynamically assign weights based on the importance of different features in the current diagnostic task. A four-dimensional optimization objective function was constructed, and the MOSHO algorithm was used to optimize the hyperparameters to obtain the optimal solution to build an intelligent diagnosis model.

7. The intelligent diagnosis method for building electrical safety risks based on big data according to claim 6 is characterized in that: The four-dimensional optimization objective function is constructed, and the MOSHO algorithm is used to optimize the hyperparameters to obtain the optimal solution to construct the intelligent diagnosis model, including: Determine the four indicators of the four-dimensional optimization objective function as diagnostic accuracy, reasoning speed, memory usage, and model stability, and assign weights to the four indicators; A certain number of particles are randomly initialized in the hyperparameter space of the model to be optimized. Each particle represents a set of hyperparameter combinations, and an initial velocity and position are assigned to each particle. According to the initial hyperparameter combination and the corresponding objective function value, the Bayesian model is initialized and trained, and the hyperparameter combination represented by each particle in the current particle swarm is applied to the intelligent diagnosis model for training and evaluation, and the corresponding four-dimensional optimization objective function value is calculated; For each particle, compare the objective function value of the current position of each particle with the objective function value of the historical optimal position. If the objective function value of the current position is better, update the historical optimal position of the particle. Compare the objective function values ​​of the historical optimal positions of all particles, and select the position with the best objective function value as the global optimal position; The quantum behavior simulation is introduced to improve the update rules of the particle swarm. The trained Bayesian model is used to predict the hyperparameter combination that obtains a better objective function value in the hyperparameter space, and the predicted hyperparameter combination is added to the particle swarm as a new particle. Add the hyperparameter combination of the newly added particles and the corresponding objective function value to the training data of the Bayesian model, and retrain the Bayesian model; Check whether the current number of iterations has reached the upper limit. If the termination condition is met, stop the iteration; otherwise, return to continue iterative optimization; After the iteration, the hyperparameter combination represented by the global optimal position is the optimal solution of the four-dimensional optimization objective function, and the intelligent diagnosis model is constructed using the optimal hyperparameter combination.

8. The intelligent diagnosis system for building electrical safety risks based on big data is characterized by: The big data-based intelligent diagnosis system for building electrical safety risks includes a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent diagnosis module and an early warning module, wherein the data acquisition module is used to collect the original parameter data of the building electrical system in real time through a variety of sensors, and connect with the building automation system to obtain the operating status information, connect with the power quality monitor to collect the power quality index data, and integrate the original parameter data, the operating status information and the power quality index data to obtain the operating condition data of the building electrical system; The data preprocessing module is used to clean the collected operating condition data to remove noise, and normalize the denoised operating condition data to obtain preprocessed data; The feature extraction module is used to extract features from the preprocessed data, extract time domain features and frequency domain features, obtain energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical feature data; An intelligent diagnosis module, used to input the multi-dimensional electrical characteristic data into an intelligent diagnosis model, diagnose the electrical safety status of the building through the intelligent diagnosis model, and output a safety diagnosis result; The early warning module is used to determine the building electrical safety risk level according to the safety diagnosis result, and send corresponding early warning information based on the safety risk level.

9. The intelligent diagnostic system for building electrical safety risks based on big data according to claim 8, characterized in that: The data preprocessing module includes a filling submodule, a calculation submodule, a correction submodule and a normalization processing submodule, wherein the filling submodule is used to obtain the collected operating condition data, traverse the operating condition data to check whether there are missing values ​​in each data record, use linear interpolation to fill the detected missing values, and estimate the missing value by linear calculation according to the valid data points adjacent to each missing value at the location of each missing value; The calculation submodule is used to detect outliers in the filled data based on the filtering algorithm of statistical characteristics, calculate the mean and standard deviation of each data type, and calculate the absolute value of the difference between each data point and the mean. If the absolute value of the difference of a data point is greater than a threshold, the corresponding data point is determined to be an outlier, where the threshold is three times the standard deviation; The correction submodule is used to correct the detected outliers by smoothing, and to remove noise from the data after the missing values ​​and outliers are processed by using a median filter to obtain denoised data; The normalization processing submodule is used to normalize the denoised data using the Min-Max normalization method to obtain preprocessed data.

10. The intelligent diagnosis system for building electrical safety risks based on big data according to claim 8, characterized in that: The feature extraction module includes an arrangement submodule, a traversal submodule, a conversion submodule, an analysis submodule and a capture submodule, wherein the arrangement submodule is used to obtain preprocessed data, arrange the preprocessed data in chronological order, and obtain a data sequence; A traversal submodule is used to calculate the mean, standard deviation and effective value of the data sequence, traverse the data sequence to find the maximum value, minimum value and peak value, obtain the peak factor based on the ratio of the peak value to the effective value, calculate the difference between adjacent voltage values, and calculate the standard deviation of the difference to obtain the voltage fluctuation standard deviation, and integrate to obtain the time domain characteristics; The conversion submodule is used to convert the time domain features of the data sequence into frequency domain features by applying fast Fourier transform, and for the frequency domain sequence, the frequency position of each harmonic is determined according to the fundamental frequency, and the amplitude of the corresponding frequency position is extracted, and the proportion of each harmonic amplitude to the fundamental amplitude is counted to obtain the harmonic spectrum distribution; An analysis submodule is used to determine the low-frequency range, extract the frequency domain components within the range, and analyze the changes in the amplitude and phase of the low-frequency components over time, wherein the low-frequency range is less than 100 Hz; The capture submodule is used to obtain energy distribution at different scales through wavelet transform, capture electrical signal anomalies, and obtain multi-dimensional electrical characteristic data.

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