Evaluation method for health status of big data of Internet of Things

Abstract

The invention belongs to the technical field of Internet of Things data analysis and equipment health management, and discloses an Internet of Things big data health state assessment method, which comprises the following steps: acquiring equipment operation data and timestamp data by using a sensing technology, and forming a feature data set through data preprocessing and feature extraction; inputting the feature data set into an anomaly detection model based on an auto-encoder, and accurately identifying abnormal data points; a health state evaluation model is constructed by using a long-short-term memory network, time dependence of data is fully considered, and accurate evaluation of the equipment health state is realized; the problem of inaccurate data anomaly detection and health assessment of the complex Internet of Things in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data analysis and device health management, and in particular to a method for evaluating the health status of Internet of Things big data. Background Art

[0002] With the rapid development of the Internet of Things (IoT) technology, a large number of heterogeneous devices have been widely deployed in various industries. These devices collect environmental and operational data, such as temperature, vibration, and pressure, through sensors, and transmit the data to a central server in real time over the network for processing. This large-scale data collection and transmission makes IoT big data high-dimensional, high-speed, and highly complex.

[0003] Existing anomaly detection methods mainly include statistical control chart methods and distance-based cluster analysis methods. However, these methods face many challenges when processing IoT big data. First, traditional statistical methods may fail in high-dimensional data and have difficulty capturing the nonlinear characteristics of the data; second, distance-based clustering methods have high computational complexity in high-dimensional space and are easily affected by noise and outliers; in addition, traditional model prediction methods usually assume that the distribution and characteristics of data are fixed, which makes it difficult to adapt to the dynamic changes in data distribution in the IoT environment; for health status assessment, traditional methods often rely on preset thresholds or rules and lack the ability to model long-term dependencies in time series data, resulting in insufficient accuracy and robustness of the assessment results.

[0004] Existing IoT data processing technologies are unable to effectively handle high-dimensional, nonlinear, and time-dependent data, making it difficult to detect anomalies and assess device health status in a timely and accurate manner. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is to solve the problem of inaccurate anomaly detection and health assessment of complex Internet of Things data in the prior art.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: using sensing technology to obtain the operating data and timestamp data of IoT devices;

[0009] Transmitting the operation data and the timestamp data to a central server via a network for data preprocessing and feature extraction to form a feature data set;

[0010] Input the feature data set into an anomaly detection model built based on the autoencoder to detect abnormal data points and output an anomaly detection result;

[0011] The anomaly detection results are used as input, and a health status assessment model is constructed using a long short-term memory network to output health status assessment results of IoT device operation data, where:

[0012] When the health status assessment result is ≥ the set threshold θ, it is judged to be in a healthy state;

[0013] When the health status evaluation result is less than the set threshold θ, it is determined to be an unhealthy state.

[0014] As a preferred embodiment of the method for evaluating the health status of IoT big data according to the present invention, the operating data at least includes temperature, vibration, pressure, power consumption, and load;

[0015] The timestamp data is expressed in UNIX timestamp format;

[0016] The collected data also includes the IoT device ID, which is represented in UUID format to identify each IoT device.

[0017] As a preferred solution of the method for evaluating the health status of IoT big data described in the present invention, the collected operating data, the timestamp data, and the IoT device ID are transmitted to a central server in real time through the MQTT protocol.

[0018] As a preferred solution of the method for evaluating the health status of IoT big data according to the present invention, the data preprocessing includes:

[0019] The collected data is denoised by wavelet transform;

[0020] The denoised data are filled using Lagrange cubic interpolation;

[0021] Use the Min-Max normalization method to map the padded data to the [0,1] interval to obtain standardized data;

[0022] Feature extraction is performed on the standardized data to form a feature data set.

[0023] As a preferred solution of the method for evaluating the health status of IoT big data according to the present invention, forming the feature data set includes:

[0024] Use feature extraction technology to extract the features of the preprocessed data and combine all features into a feature vector;

[0025] Generate a feature vector for each time window to form a feature data set matrix;

[0026] All features include statistical features, time domain features and frequency domain features;

[0027] The statistical features include at least mean, variance, skewness, and kurtosis; the time domain features include at least autocorrelation coefficient and volatility; and the frequency domain features include spectral density.

[0028] As a preferred solution of the method for evaluating the health status of IoT big data according to the present invention, the feature data set is input into an anomaly detection model constructed based on an autoencoder to perform abnormal data point detection, including:

[0029] Map the input feature vector F to a low-dimensional representation h;

[0030] Reconstruct the low-dimensional representation h as output

[0031] For each eigenvector F i Reconstruct and calculate the reconstruction error e i :

[0032]

[0033] According to the mean of the reconstruction error and standard deviation σ e Set the threshold ∈:

[0034]

[0035] Among them, F i is the i-th input feature vector, is the i-th reconstructed eigenvector, e i is the reconstruction error of the i-th data point, ∈ is the threshold;

[0036] When e i >∈, the i-th data point is determined to be an abnormal data point, and the abnormal detection result is marked as 1, that is, l i =1;

[0037] When e i When ≤∈, the i-th data point is judged to be a normal data point, and the abnormal detection result is marked as 0, that is, l i =0.

[0038] As a preferred solution of the method for evaluating the health status of IoT big data according to the present invention, the output abnormality detection results use sequence E as the input of the health status evaluation model:

[0039] E=[e1,e2,…,e M ]

[0040] Label=[l1,l2,…,l M ]

[0041] Among them, l i =1 indicates abnormality, l i =0 means normal, M is the number of eigenvectors.

[0042] As a preferred solution of the method for evaluating the health status of IoT big data described in the present invention, a health status evaluation model is constructed using a long short-term memory network. The input layer receives the anomaly detection result sequence E, and a hidden layer is composed of several LSTM units to capture the long-term dependency of the time series. The output layer outputs the health status evaluation value y, including:

[0043] y = σ(LSTM(E))

[0044] Where y is the health status assessment value, ranging from [0, 1], σ is the Sigmoid activation function, and LSTM(E) is the output of the LSTM network for the anomaly detection result sequence E.

[0045] As a preferred solution of the method for evaluating the health status of IoT big data according to the present invention, a threshold value θ=0.5 is set according to the value range of the health status evaluation value, where:

[0046] If y ≥ 0.5, it is judged to be in a healthy state;

[0047] If y<0.5, it is judged to be in an unhealthy state.

[0048] Beneficial effects of the present invention:

[0049] 1. Collect the key operating parameters of the equipment and the corresponding precise time information to ensure the integrity and timeliness of the data, provide a reliable basic data source for subsequent data analysis and processing, avoid analysis deviations caused by missing or inaccurate data, and improve system data reliability and monitoring accuracy;

[0050] 2. Perform preprocessing operations such as cleaning, denoising, and standardization on the collected raw data, and extract characteristic indicators that can represent the essence of the data. This not only improves data quality and reduces the impact of noise and redundant information, but also provides high-quality input data for subsequent model training and prediction, improving model accuracy and reducing computing resource consumption;

[0051] 3. Leveraging the unsupervised learning capabilities of autoencoders, the normal patterns of data are automatically learned, thereby accurately detecting abnormal data points that deviate from the normal patterns, thus improving the accuracy and robustness of anomaly detection.

[0052] 4. Use long short-term memory networks (LSTM) to model the long-term dependencies of time series data to improve equipment operation reliability and extend equipment service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] 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. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0054] Figure 1 Schematic diagram of the process of the method for evaluating the health status of IoT big data shown in the present invention;

[0055] Figure 2 Schematic diagram showing the accuracy comparison between the method of the present invention and the traditional method;

[0056] Figure 3 Schematic diagram showing a comparison of the sensitivity of the method of the present invention and the traditional method;

[0057] Figure 4 Schematic diagram showing the comparison of robustness of the method of the present invention and the traditional method under different noise levels;

[0058] Figure 5 Schematic diagram of an abnormality scoring curve shown in the present invention;

[0059] Figure 6 This is a schematic diagram of the health status assessment result curve shown in the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, 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. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0061] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making any creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] According to an embodiment of the present invention, Figure 1 The flowchart shown is a method for evaluating the health status of IoT big data, which specifically includes the following steps:

[0064] S1. Use sensor technology to obtain the operating data, timestamp data, and ID of the IoT device. Note that:

[0065] Operation data shall at least include temperature, vibration, pressure, power consumption, and load;

[0066] Timestamp data is expressed in UNIX timestamp format;

[0067] The IoT device ID is represented in UUID format and is used to identify each IoT device.

[0068] In an optional embodiment, the frequency of collecting the operating data is once per second to ensure the real-time and continuity of the data.

[0069] For example, a digital temperature sensor, such as the DS18B20, can be used to collect the internal and external temperatures of the device. A three-axis accelerometer, such as the ADXL345, can be used to collect the vibration signals of the device during operation. A piezoresistive pressure sensor, such as the MPX5700, can be used to collect the internal pressure of the device. An energy metering chip, such as the ADE7755, can be used to monitor the current and voltage of the device and calculate the power consumption. The workload of the device can be obtained through indicators such as CPU utilization and task queue length. The system clock can be used to record the time of data collection in the UNIX timestamp format, accurate to milliseconds.

[0070] Exemplarily, each data record includes: device ID (string type, UUID format), timestamp (integer type, UNIX timestamp), temperature (floating point number, unit ℃), vibration (floating point number, unit g), pressure (floating point number, unit Pa), power consumption (floating point number, unit W), and load (floating point number, unit %).

[0071] For example, consider a motor device on an industrial production line that requires real-time monitoring of its operating status and health assessment. Sensors installed on the motor collect data over a period of time, including:

[0072] Sampling frequency: data is collected once per second, that is, the sampling frequency is 1Hz;

[0073] Collection time: 5 minutes (300 seconds), a total of 300 sets of data were collected;

[0074] Device ID: UUID format, such as 550e8400-e29b-41d4-a716-446655440000;

[0075] Each data record contains the following fields:

[0076] Device ID: 550e8400-e29b-41d4-a716-446655440000;

[0077] Timestamp: expressed as a UNIX timestamp, accurate to milliseconds, such as 1633072800000 (corresponding to 2021-10-01 12:00:00.000);

[0078] Temperature: such as 75.2°C;

[0079] Vibration: such as 0.05g;

[0080] Pressure: such as 101325Pa;

[0081] Power consumption: such as 500W;

[0082] Load: such as 85%.

[0083] S2. Transmit the operation data and timestamp data to the central server via the network for data preprocessing and feature extraction to form a feature data set.

[0084] The collected operating data, timestamp data, and IoT device ID are transmitted to the central server in real time via the MQTT protocol. QoS level 1 of the MQTT protocol is used to ensure that messages are delivered at least once. Data is transmitted through an encrypted channel (such as TLS / SSL) to ensure data security.

[0085] The collected data is denoised by wavelet transform; where:

[0086] Perform db4 wavelet decomposition on the data sequence of each sensor and estimate the noise standard deviation:

[0087]

[0088] Where d1 is the first-layer detail coefficient obtained after wavelet decomposition, which represents the high-frequency part of the signal, and 0.6745 is a constant used to convert the median absolute deviation to the standard deviation;

[0089] Perform soft thresholding on the detail coefficients:

[0090]

[0091] Where λ is the threshold used for threshold processing, σ is the estimated noise standard deviation,

[0092] N is the data length of the signal, that is, the number of data points, and 2ln N is used to adjust the threshold size to make it related to the data length;

[0093] Wavelet reconstruction: get the denoised signal;

[0094] Detect missing points in the data;

[0095] Select three known data points before and after the missing point and fill them with the cubic Lagrange interpolation method. The interpolation polynomial is:

[0096]

[0097] Among them, y i is the value of the known data point, t i is the corresponding time point, f(t) is the interpolation function;

[0098] Use the Min-Max normalization method to map the padded data to the [0,1] interval. The formula is:

[0099]

[0100] Among them, x is the original data, x min and x max are the minimum and maximum values of the feature respectively, and x′ is the normalized data;

[0101] Use feature extraction technology to extract the features of the pre-processed data. All features include statistical features, time domain features and frequency domain features.

[0102] Among them, the statistical features include at least mean, variance, skewness, and kurtosis; the time domain features include at least autocorrelation coefficient and volatility; and the frequency domain features include spectral density;

[0103] Combine all features into a feature vector;

[0104] Generate a feature vector for each time window to form a feature dataset matrix.

[0105] As an example, a time window of 5 minutes contains 300 data points (assuming sampling once per second);

[0106] Compute features within each time window:

[0107] Mean:

[0108]

[0109] Where μ is the mean value of the data, x i is the total number of data points in the 300 time window for the i-th data point (assuming sampling once per second, 5 minutes totaling 300 seconds);

[0110] variance:

[0111]

[0112] Among them, σ 2 is the variance of the data, indicating the degree of dispersion of the data;

[0113] Skewness:

[0114]

[0115] Among them, γ is the skewness of the data, which measures the asymmetry of the data distribution, σ is the standard deviation of the data, and σ 3 is the cube of the standard deviation, used to standardize skewness;

[0116] Kurtosis:

[0117]

[0118] Among them, β is the kurtosis of the data, which measures the sharpness of the data distribution, and σ 4 is the fourth power of the standard deviation, used to normalize kurtosis;

[0119] Autocorrelation coefficient (with lag order k=1):

[0120]

[0121] Among them, r1 is the autocorrelation coefficient with a lag order of 1, which measures the correlation between the sequence itself after one time step, and x i+1 is the i+1th data point;

[0122] Volatility:

[0123]

[0124] Among them, V is volatility, which measures the relative change of data, x i-1 is the i-1th data point;

[0125] For the data sequence x i Perform the fast Fourier transform algorithm to convert the time domain signal into the frequency domain signal and obtain the spectrum X(f) and power spectrum density:

[0126]

[0127] Where X(f) is the spectrum of the signal, which represents the complex value at frequency f and contains amplitude and phase information. N is the total number of data points, which is used to normalize the power. P(f) is the power spectral density at frequency f, which represents the power distribution of the signal at that frequency.

[0128] Combine the above extracted features into a feature vector F:

[0129] F=[μ,σ 2 ,γ,β,r k ,V,P(f)]

[0130] Generate a feature vector for each time window to form the feature dataset matrix D:

[0131]

[0132] Among them, M is the number of feature samples.

[0133] For example, assume that there is some noise and missing values in the collected data and preprocessing is required. Take the temperature data as an example, and perform denoising and missing value filling according to the above preprocessing process to obtain a temperature value of 75.5°C. Then, normalize the temperature value:

[0134]

[0135] The temperature value is converted to the [0,1] interval for easy subsequent processing;

[0136] In each 5-minute time window, feature extraction is performed on the preprocessed temperature data;

[0137] Taking 5 minutes as a unit, each time window contains 300 data points. Calculate the features according to the above feature extraction steps and combine all features into a feature vector F:

[0138] F=[μ,σ 2 ,γ,β,r1,V,main frequency,amplitude]

[0139] For example:

[0140] F=[75.3,0.06,0.1,-0.5,0.95,0.001,0.01,0.5].

[0141] S3: Input the feature dataset into the anomaly detection model built based on the autoencoder to detect abnormal data points and output the anomaly detection results.

[0142] The network structure of the anomaly detection model is:

[0143] Input layer: feature vector dimension size;

[0144] Hidden layer 1: 64 neurons, activation function is ReLU;

[0145] Hidden layer 2: 32 neurons, activation function is ReLU;

[0146] Decoding layer: Symmetrically decodes the output of hidden layer 2 back to the original dimension;

[0147] Encoder: maps the input feature vector F to a low-dimensional representation h;

[0148] h=σ(W e Fb e )

[0149] Among them, W e is the encoder weight matrix, b e is the bias vector, σ() is the activation function (such as ReLU);

[0150] Decoder: reconstructs the low-dimensional representation h into output

[0151]

[0152] Among them, W d is the decoder weight matrix, b d is the bias vector;

[0153] Loss function: Mean square error (MSE):

[0154]

[0155] Where L is the loss function of the autoencoder model, the reconstruction error is measured using the mean square error (MSE), M is the total number of training samples, and F is the i is the i-th input feature vector, containing the extracted features, is the i-th reconstructed feature vector, that is, the reconstruction result output by the autoencoder;

[0156] The Adam optimizer was used, the learning rate was set to 0.001, the number of training iterations was 100, and the batch size was 32;

[0157] Reconstruction error calculation: For each eigenvector F i Reconstruct and calculate the reconstruction error e i :

[0158]

[0159] According to the mean of the reconstruction error and standard deviation σ e Set the threshold ∈:

[0160]

[0161] Among them, F i is the i-th input feature vector, is the i-th reconstructed eigenvector, e i is the reconstruction error of the i-th data point, ∈ is the threshold;

[0162] When e i >∈, the i-th data point is determined to be an abnormal data point, and the abnormal detection result is marked as 1, that is, l i =1;

[0163] When e i When ≤∈, the i-th data point is judged to be a normal data point, and the abnormal detection result is marked as 0, that is, l i =0;

[0164] Output anomaly detection results in sequence E:

[0165] E=[e1,e2,…,e M ]

[0166] Label=[l1,l2,…,l M ]

[0167] Among them, l i =1 indicates abnormality, l i =0 means normal, M is the number of eigenvectors.

[0168] As an example, suppose the reconstruction error e is calculated i =0.02, mean is 0.01, standard deviation σ e is 0.005, due to e i =0.02, which is equal to the threshold ∈=0.02 and is determined to be a normal data point.

[0169] S4. Use the anomaly detection results as input and use the long short-term memory network to build a health status assessment model, and output the health status assessment results of the IoT device operation data, where:

[0170] When the health status assessment result is ≥ the set threshold θ, it is judged to be in a healthy state;

[0171] When the health status evaluation result is less than the set threshold θ, it is determined to be an unhealthy state.

[0172] In an optional embodiment, a long short-term memory network is used to construct a health status assessment model, and the model structure is:

[0173] The input layer receives the anomaly detection result sequence E, such as 20 time steps, with one input value per step;

[0174] The hidden layer consists of several LSTM units to capture the long-term dependencies of the time series, such as 50 hidden units;

[0175] Fully connected layer: 1 output node, activation function is Sigmoid;

[0176] The health status evaluation value y is output through the output layer, including:

[0177] y = σ(LSTM(E))

[0178] Where y is the health status assessment value, ranging from [0, 1], σ() is the Sigmoid activation function, and LSTM(E) is the output of the LSTM network for the anomaly detection result sequence E;

[0179] Model parameters: The number of hidden units in the LSTM layer is 50, the activation function uses the Sigmoid function, the number of training iterations is 50, and the learning rate is 0.001;

[0180] According to the range of health status assessment values, the threshold θ is set to 0.5, where:

[0181] If y ≥ 0.5, it is judged to be in a healthy state;

[0182] If y<0.5, it is judged to be in an unhealthy state;

[0183] When the system is judged to be in an unhealthy state, the alarm mechanism is triggered and an alarm message is sent to relevant personnel via SMS, email or system notification. The alarm message includes:

[0184] Device ID;

[0185] Exception time (current timestamp);

[0186] Abnormality type (such as abnormal temperature, abnormal vibration).

[0187] As an example, the input sequence is the anomaly detection result sequence of the past 20 time windows, such as:

[0188] [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

[0189] Among them, 1 means that an anomaly is detected in the 4th time window, and the corresponding health status label y i =1 (assuming the device is still in a healthy state);

[0190] The LSTM layer processes the input sequence and captures temporal dependencies;

[0191] Model output:

[0192] y = σ(LSTM(E))

[0193] The calculated LSTM(E)=0.2, then:

[0194]

[0195] Health status assessment:

[0196] Threshold θ = 0.5

[0197] Since y = 0.55 > θ, it is determined to be in a healthy state;

[0198] Although there was one anomaly in the past sequence (the fourth time window), the overall number of anomalies was small, and the model believed that the device was still in a healthy state. Since it was determined to be in a healthy state, the system did not trigger an alarm;

[0199] If multiple consecutive anomalies are detected in the future, causing the health status assessment value y to drop below 0.5, the system will trigger an alarm.

[0200] Preferably, this embodiment is experimentally compared with a traditional fixed threshold determination method to verify the effectiveness of the health status assessment method for IoT big data provided by the embodiment of the present invention, compare its performance differences with the traditional fixed threshold determination method, and analyze the advantages of the method of the present invention in terms of accuracy, sensitivity, and robustness by comparing the anomaly detection and health status assessment results; wherein:

[0201] Six key IoT devices on an industrial production line were selected, numbered Device_1, Device_2, Device_3, Device_4, Device_5, and Device_6. These devices include both normally operating devices and devices with potential failures, and can reflect data characteristics under different operating conditions.

[0202] The collected parameters include temperature data, vibration data, pressure data, current data, and load data. The sampling frequency is once per second, and the collection duration is 7 consecutive days, totaling 604,800 sets of data (6 devices, 86,400 sets of data per device).

[0203] The data is transmitted to the central server in real time via industrial Ethernet. During the experiment, Device_4, Device_5, and Device_6 set abnormal data within a specific time period, as follows:

[0204] Device_4: On the third day, the temperature sensor malfunctioned, causing the temperature data to rise abnormally.

[0205] Device_5: On the fifth day, mechanical parts wear out, causing vibration data to increase;

[0206] Device_6: On the 6th day, the pressure was unstable, resulting in frequent fluctuations in the pressure data;

[0207] The method of the present invention is applied to perform anomaly detection and health status assessment: the collected raw data is subjected to denoising (wavelet transform), missing value filling (linear interpolation), and standardization processing, and hourly statistical features (mean, standard deviation), time domain features (autocorrelation coefficient), and frequency domain features (main frequency, amplitude) are calculated. The feature data set is input into an anomaly detection model based on an autoencoder, and an anomaly score is output. The anomaly score is input into a health status assessment model based on LSTM, and a health status assessment result is output.

[0208] The traditional fixed threshold judgment method is used for anomaly detection and health status assessment: the temperature threshold is set to 80°C, the vibration threshold is set to 0.1g, and the pressure threshold is set to 1MPa. The threshold is directly compared with each sensor data. If the threshold is exceeded, it is judged as an anomaly and the device is in an unhealthy state.

[0209] Reference Figure 5 It can be seen intuitively that the anomaly score of the proposed method remained at a low level of 0.02 throughout the entire time period, and the curve was stable, indicating that no anomalies were detected in normal devices. The anomaly score of the traditional method also remained at a level of 0.03, but it could not reflect the actual abnormal situation of the device. The anomaly score of Device_4 showed obvious peaks on the third and fourth days, reaching 0.20 and 0.18 respectively, which were significantly higher than the normal level, indicating that the device had an abnormality on these two days.

[0210] Reference Figure 6 It can be seen intuitively that the health assessment result of the proposed method always remains at a high level of 0.98, and the curve is stable, indicating that the device is in a healthy state. The health assessment result of the traditional method remains at 0.95, but cannot reflect abnormal changes in the device. The health assessment result of Device_4 drops sharply on the third and fourth days, falling to 0.40 and 0.45 respectively, which is lower than the set threshold of 0.5, indicating that the device is in an unhealthy state on these two days and requires timely maintenance.

[0211] Reference Figure 2 As can be seen from the figure, the accuracy of the method of the present invention is 1.0, and the accuracy of the traditional method is 0.67. The accuracy of the method of the present invention is obviously better than that of the traditional method. Figure 3 The sensitivity of the method of the present invention is 1.0, while the sensitivity of the traditional method is 0.33. The method of the present invention has higher sensitivity in detecting abnormal devices and can effectively reduce missed reports;

[0212] Reference Figure 4 As can be seen from the curve, with the increase of noise level, the accuracy of the method of the present invention slowly decreases from 1.0 to 0.88, showing strong robustness. The accuracy of the traditional method decreases rapidly from 0.67 to 0.40, and is greatly affected by noise. Therefore, the method of the present invention can still maintain high accuracy in a noisy environment.

[0213] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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 method for evaluating the health status of Internet of Things big data, characterized in that: include: Use sensor technology to obtain operating data and timestamp data of IoT devices; Transmitting the operation data and the timestamp data to a central server via a network for data preprocessing and feature extraction to form a feature data set; Input the feature data set into an anomaly detection model built based on the autoencoder to detect abnormal data points and output an anomaly detection result; The anomaly detection results are used as input, and a health status assessment model is constructed using a long short-term memory network to output health status assessment results of IoT device operation data, where: When the health status assessment result is ≥ the set threshold θ, it is judged to be in a healthy state; When the health status evaluation result is less than the set threshold θ, it is determined to be an unhealthy state.

2. The method for evaluating the health status of Internet of Things big data according to claim 1, characterized in that: The operating data at least includes temperature, vibration, pressure, power consumption, and load; The timestamp data is expressed in UNIX timestamp format; The collected data also includes the IoT device ID, which is represented in UUID format to identify each IoT device.

3. The method for evaluating the health status of Internet of Things big data according to claim 2, characterized in that: The collected operation data, timestamp data, and IoT device ID are transmitted to the central server in real time via the MQTT protocol.

4. The method for evaluating the health status of Internet of Things big data according to claim 3, characterized in that: The data preprocessing includes: The collected data is denoised by wavelet transform; The denoised data are filled using Lagrange cubic interpolation; Use the Min-Max normalization method to map the padded data to the [0,1] interval to obtain standardized data; Feature extraction is performed on the standardized data to form a feature data set.

5. The method for evaluating the health status of Internet of Things big data according to claim 1 or 4, characterized in that: Forming the feature data set includes: Use feature extraction technology to extract the features of the preprocessed data and combine all features into a feature vector; Generate a feature vector for each time window to form a feature data set matrix; All features include statistical features, time domain features and frequency domain features; The statistical features include at least mean, variance, skewness, and kurtosis; the time domain features include at least autocorrelation coefficient and volatility; and the frequency domain features include spectral density.

6. The method for evaluating the health status of Internet of Things big data according to claim 5, characterized in that: The feature dataset is input into an anomaly detection model built based on an autoencoder to detect abnormal data points, including: Map the input feature vector F to a low-dimensional representation h; Reconstruct the low-dimensional representation h as output For each eigenvector F i Reconstruct and calculate the reconstruction error e i : According to the mean of the reconstruction error and standard deviation σ e Set the threshold ∈: Among them, F i is the i-th input feature vector, is the i-th reconstructed eigenvector, e i is the reconstruction error of the i-th data point, ∈ is the threshold; When e i >∈, the i-th data point is determined to be an abnormal data point, and the abnormal detection result is marked as 1, that is, l i =1; When e i When ≤∈, the i-th data point is judged to be a normal data point, and the abnormal detection result is marked as 0, that is, l i =0.

7. The method for evaluating the health status of Internet of Things big data according to claim 6, characterized in that: The output anomaly detection results use sequence E as the input of the health status assessment model: E=[e1,e2,…,e M ] <h2 style=";text-align:left;direction:ltr">Label=[l1,l2,…,l<h2 style=";text-align:left;direction:ltr"> M <h2 style=";text-align:left;direction:ltr"> ] Among them, l i =1 indicates abnormality, l i =0 means normal, M is the number of eigenvectors.

8. The method for evaluating the health status of Internet of Things big data according to claim 7, characterized in that: The health status assessment model is constructed using a long short-term memory network. The input layer receives the anomaly detection result sequence E. The hidden layer is composed of several LSTM units to capture the long-term dependencies of the time series. The output layer outputs the health status assessment value y, which includes: y = σ(LSTM(E)) Where y is the health status assessment value, ranging from [0, 1], σ is the Sigmoid activation function, and LSTM(E) is the output of the LSTM network for the anomaly detection result sequence E.

9. The method for evaluating the health status of Internet of Things big data according to claim 8, characterized in that: According to the range of the health status assessment value, the threshold θ is set to 0.5, where: If y ≥ 0.5, it is judged to be in a healthy state; If y<0.5, it is judged to be in an unhealthy state.

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