Data acquisition system processing method and data acquisition system

Abstract

The invention discloses a data acquisition system processing method and a data acquisition system, and relates to the field of data processing, and the system comprises the following components: a predictive maintenance module which selects a proper deep learning or machine learning algorithm, constructs a prediction model by using extracted feature information, and stores the prediction model; according to the method, a predictive maintenance module is used for analyzing and modeling equipment operation state data through deep learning and a mode recognition technology, historical data is used for verifying and evaluating a prediction model, the prediction accuracy and reliability of the model are checked, and the trained prediction model is used for predicting and analyzing the data collected in real time. Therefore, accurate prediction of equipment faults is realized, the limitation of dependence on regular inspection and empirical judgment in traditional equipment maintenance is broken through, potential fault risks of the equipment can be found in advance, decision support is provided for operation and maintenance personnel, maintenance and repair are carried out in time, and production interruption and loss are avoided.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data acquisition system processing method and a data acquisition system. Background Art

[0002] With the intelligence and complexity of industrial equipment, in the current industrial production and operation process, equipment failure has become an important factor restricting production efficiency, affecting product quality, and even causing production safety accidents. However, traditional equipment maintenance methods mainly rely on regular inspections and manual experience judgment. Although regular inspections can prevent the occurrence of equipment failures to a certain extent, due to the limitations of the inspection cycle, some sudden or progressive failures are often unable to be discovered and handled in time. In addition, the inspection process mainly relies on the experience and judgment of the operation and maintenance personnel. This highly subjective method is easily affected by human factors, resulting in misjudgment or missed judgment.

[0003] Therefore, with the rapid development of big data, cloud computing and artificial intelligence technologies, the field of equipment maintenance has ushered in new opportunities. By collecting real-time data during equipment operation and using deep learning and pattern recognition technology for analysis and processing, accurate prediction and early warning of equipment failures can be achieved. This data-driven predictive maintenance method can not only detect potential equipment failure risks in advance, but also provide operation and maintenance personnel with accurate fault location and repair suggestions, greatly improving the efficiency and accuracy of equipment maintenance.

[0004] However, there is currently no mature, efficient and widely applicable data acquisition system and processing method based on predictive maintenance and fault warning on the market. Therefore, it is of great practical application value to develop a data acquisition system and processing method that can collect equipment operation data in real time, use deep learning and pattern recognition technology for fault prediction and warning, and provide operation and maintenance personnel with accurate fault location and repair suggestions. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a data acquisition system processing method and a data acquisition system, which can collect operation data in real time through a data acquisition module, use a predictive maintenance module and a fault prediction module to perform fault prediction and early warning, and provide accurate fault location and repair suggestions for operation and maintenance personnel, thereby realizing intelligent and efficient equipment maintenance.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data acquisition system and a processing method, characterized in that the system comprises the following components:

[0007] Data acquisition module: According to the type and interface of the data source, configure the corresponding data acquisition equipment, set the parameters of the data acquisition equipment, and collect the equipment operation data from the data source in real time through the data acquisition equipment to ensure the accuracy and completeness of the data and avoid data loss or error;

[0008] Data processing module: cleans the collected raw data, removes duplicate, invalid or abnormal data, formats the data, unifies the data format to facilitate subsequent processing and analysis, and uses data analysis and machine learning algorithms to extract feature information that can reflect the operating status of the equipment from the processed data;

[0009] Predictive maintenance module: Select appropriate deep learning or machine learning algorithms, use the extracted feature information to build a prediction model, use historical data to verify and evaluate the prediction model, check the prediction accuracy and reliability of the model, use the trained prediction model to perform predictive analysis on the real-time collected data, and determine whether the equipment is likely to fail based on the prediction results, and predict the type of failure and the time of occurrence;

[0010] Fault warning module: Use the real-time data processed by the data processing module to monitor the operating status of the equipment in real time, compare it with the set normal operating status threshold, and determine whether the equipment has abnormalities or potential faults. Once an abnormality or potential fault is found in the equipment, the warning mechanism is immediately triggered. Combined with real-time data and historical data, the fault warning model is used to accurately locate the fault, and detailed fault location and repair suggestions are provided to the operation and maintenance personnel to help them quickly locate and solve the problem.

[0011] Furthermore, the specific steps of the data acquisition module are:

[0012] (1) Sensor configuration: Determine the type and quantity of sensors that need to collect data, as well as the location of the collection points, based on the characteristics and maintenance requirements of the equipment. Install the sensors at the designated collection points and perform necessary configurations to ensure that the sensors can accurately and real-time collect the equipment's operating data.

[0013] (2) Data transmission: The data collected by the sensor is transmitted to the data acquisition module via wired or wireless means to ensure the real-time and integrity of the data.

[0014] Furthermore, the data acquisition module specifically comprises the following steps:

[0015] (1) Data conversion: Clean the collected raw data, remove duplicate, invalid or abnormal data, format the data, unify the data format, and convert the cleaned data as needed to ensure data consistency and comparability, providing a basis for subsequent feature extraction and modeling;

[0016] (2) Feature extraction: Using data analysis and machine learning algorithms, feature information that can reflect the operating status of the equipment is extracted from the processed data.

[0017] Furthermore, the predictive maintenance module includes the following specific steps:

[0018] (1) Model building: Select an appropriate machine learning algorithm, use the extracted feature information to build a prediction model, and train the model so that it can accurately predict the possible fault types and occurrence time of the equipment;

[0019] (2) Model optimization: Use historical data to verify and evaluate the prediction model, check the prediction accuracy and reliability of the model, and optimize and adjust the model based on the evaluation results to improve the prediction performance;

[0020] (3) Fault prediction: Use the trained prediction model to perform predictive analysis on the data collected in real time. Based on the prediction results, determine whether the equipment is likely to fail, and predict the type of failure and time of occurrence.

[0021] Furthermore, the fault warning module has the following specific steps:

[0022] (1) Real-time monitoring: Use the real-time data processed by the data processing module to monitor the operating status of the equipment in real time, compare it with the set normal operating status threshold, and determine whether the equipment has abnormalities or potential failures;

[0023] (2) Fault warning: When an abnormality or potential fault is found in the equipment, the warning mechanism is triggered immediately, and warning information is sent to the operation and maintenance personnel through the system interface, SMS, and email to remind them to pay attention and handle it;

[0024] (3) Fault location and repair suggestions: Combine real-time data with historical data and use the fault warning model to accurately locate the fault, and provide detailed fault location and repair suggestions to operation and maintenance personnel to help them quickly locate and solve the problem.

[0025] Furthermore, the data processing module extracts features from the data by using a convolutional neural network (CNN). The CNN model is usually composed of multiple convolutional layers, pooling layers, and fully connected layers. For the input data (X), the convolutional layer performs convolution operations through the convolution kernel (W) to extract local features. The formula for the convolution operation is: S(i,j)=(X*W)(i,j)=∑ mX(i+m, j+n)·W(m, n), where S(i, j) represents the value of the feature map at position (i, j) after convolution, X(i+m, j+n) represents the value of the input data at position (i+m, j+n), and W(m, n) represents the weight of the convolution kernel at position (m, n). The activation function is used to perform nonlinear transformation on the feature map after convolution to enhance the expression ability of the model. For the pooling layer, it is used to reduce the dimension of the feature map, reduce the amount of calculation, and extract more robust features. The formula is: Among them, P(i, j) represents the value of the feature map at position ((i, j)) after pooling, R ij Represents the area of ​​the input feature map covered by the pooling window at position (i, j), S(p) represents the value of position (p) in the input feature map. After being processed by multiple convolutional layers and pooling layers, the feature map is flattened into a one-dimensional vector and input into the fully connected layer for classification or regression. The calculation formula of the fully connected layer is: Y=σ(Z)=σ(W·X+b), where Y represents the output of the fully connected layer, W represents the weight matrix, X represents the input vector (i.e., the flattened feature map), b represents the bias term, and σ represents the activation function. By using the CNN algorithm to operate on the collected data, accurate prediction and early warning of equipment failures are achieved, providing effective technical support for predictive maintenance and fault early warning.

[0026] Furthermore, the predictive maintenance module predicts equipment failure by adopting a multi-layer perceptron (MLP) model, and inputs the equipment state feature vector x extracted by the data analysis module into the multi-layer perceptron (MLP) model. In the MLP model, the output layer adopts a softmax activation function, and its formula is: F(x)=softmax(W T ·φ(x)+b), where W is the weight matrix, φ(x) is the mapping of the hidden layer activation function to the feature vector x (usually a nonlinear transformation), and b is the bias term. The weight matrix W and the bias term b are the parameters that the model needs to learn during the training process. The historical fault data is used as the training sample. Through the back propagation algorithm, the weights and bias terms of the model are continuously adjusted to make the predicted output of the model as close as possible to the actual fault type.

[0027] Furthermore, the fault warning module uses a support vector machine (SVM) algorithm for fault warning and classification. By classifying the equipment status features, it is determined whether the equipment is in an abnormal state and the warning mechanism is triggered in advance. The feature vector for fault warning is extracted through the data processing module, and the SVM model is constructed using historical fault data. The classification decision function f(x) is obtained through training and learning. The decision function of SVM is expressed as: Among them, α iis the Lagrange multiplier, y i is the sample label, K(x,x i ) is a kernel function used to calculate the similarity between samples. In the early warning process, the real-time collected device status features are input into the SVM model. By calculating the output value of the decision function, it is determined whether the device is in an abnormal state. If the output value exceeds the preset threshold, the early warning mechanism is triggered and the corresponding early warning information is output.

[0028] Furthermore, the system supports remote monitoring and management functions. Operation and maintenance personnel can view the equipment operating status, fault warning information and fault handling progress in real time by remotely accessing the system interface, thereby realizing remote operation and management.

[0029] Furthermore, the specific steps of the method are:

[0030] (1) System deployment and configuration: deploy the data acquisition system near the equipment to be monitored or connect it to the equipment data source through the network, configure the data acquisition module, set the frequency, data type and transmission method of data acquisition, configure the data processing module, set the rules for data cleaning, conversion and storage, configure the predictive maintenance module and fault warning module, and set the model parameters and warning thresholds;

[0031] (2) Data collection and preprocessing: The data collection module collects equipment operation data from the data source in real time. The data processing module cleans the collected data, removes noise and outliers, and converts the cleaned data into a format suitable for analysis and modeling. The cleaned and converted data is stored in the system for subsequent use;

[0032] (3) Feature extraction: The data processing module uses pre-set algorithms and rules to extract feature information of the equipment operating status from the stored data;

[0033] (4) Fault prediction: The predictive maintenance module receives the extracted feature information and uses deep learning and pattern recognition technology to analyze and model it. By training the model, it predicts the type and time of possible faults that may occur in the equipment. The model can be dynamically adjusted based on real-time data to adapt to changes in the equipment's operating status.

[0034] (5) Fault warning and location: The fault warning module monitors the equipment operating status in real time and shares data with the predictive maintenance module. Once an abnormality or potential fault in the equipment operating status is found, the warning mechanism is immediately triggered. The fault warning model constructed by combining real-time data and historical data provides accurate fault location and repair suggestions for operation and maintenance personnel.

[0035] (6) Operation and maintenance personnel response: The operation and maintenance personnel receive fault warning information and repair suggestions, and perform fault troubleshooting and repair according to the prompts. The operation and maintenance personnel can adjust the system configuration and parameters according to the actual situation to improve the accuracy and efficiency of the warning;

[0036] (7) System optimization and upgrade: Regularly evaluate system performance, optimize and improve the system based on the evaluation results, and upgrade system algorithms and models according to changes in equipment type and operating environment to better adapt to actual needs.

[0037] Compared with the prior art, the data acquisition system processing method and the data acquisition system have the following beneficial effects:

[0038] 1. The present invention uses deep learning and pattern recognition technology to analyze and model the data of equipment operation status through a predictive maintenance module, thereby realizing accurate prediction of equipment failures. It breaks through the limitations of traditional equipment maintenance that relies on regular inspections and empirical judgments, and can detect potential equipment failure risks in advance, provide decision support for operation and maintenance personnel, and perform maintenance and repairs in a timely manner to avoid production interruptions and losses.

[0039] 2. The present invention constructs a fault warning model by combining real-time data and historical data through a fault warning module, and monitors the equipment operation status in real time, so that it can promptly detect equipment abnormalities or potential faults, and immediately trigger the warning mechanism to remind operation and maintenance personnel to handle them. At the same time, through the fault warning model, it can also provide operation and maintenance personnel with accurate fault location and repair suggestions, thereby improving the efficiency and accuracy of fault handling.

[0040] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is the data acquisition system processing method and the data acquisition system process operation diagram. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1: Equipment failure prediction based on deep learning.

[0045] In this embodiment, by using a data acquisition system based on deep learning and a processing method thereof, fault prediction is performed on equipment of a certain production line, and convolutional neural network (CNN) and multi-layer perceptron (MLP) algorithms are used to process and analyze equipment operation data, thereby achieving accurate equipment fault prediction. First, the data acquisition module is used to collect equipment operation data from sensors and control systems on the production line in real time, and the collected raw data is input into the data processing module for cleaning and formatting. In the data cleaning stage, the mean filling and median filling methods are used to process missing values, and the sliding window or threshold method is used to remove outliers. In the data formatting stage, we unify the data of different sensors into the same format and scale to facilitate subsequent processing and analysis. In the feature extraction stage, the convolutional neural network (CNN) algorithm is used. The convolution layer in the CNN model performs convolution operation on the input data through the convolution kernel to extract local features. The formula of the convolution operation is as follows:

[0046]

[0047] Among them, (S(i, j)) represents the value of the feature map after convolution at position ((i, j)), (I(i, j)) represents the value of the input data at position ((i, j)), and (K(m, n)) represents the value of the convolution kernel at position ((m, n)). Through the convolution operation, we can extract the local features in the input data, and extract higher-level features through the superposition of multiple convolutional layers. After the feature extraction is completed, we input the extracted feature vector into the multi-layer perceptron (MLP) model for fault prediction. The forward propagation formula of the MLP model is as follows:

[0048] z (l) =W (l) ·a (l-1) +b (l)

[0049] a (l) =σ(z (l) )

[0050] Among them, z (l) represents the input of the (l)th layer, W (l) and b (l)denote the weight and bias term of the (l)th layer, respectively, and a (l-1) represents the output of the (l-1)th layer (i.e., the input of the (l)th layer), σ represents the activation function. Through the nonlinear transformation of the multilayer perceptron, the feature vector can be mapped to the probability distribution of the fault type. In order to train the MLP model, the back propagation algorithm is used. By calculating the gradient of the loss function to the weight and bias terms, and using the gradient descent algorithm to update the weight and bias terms, the formula of the loss function is as follows:

[0051]

[0052] Among them, y i represents the true label of the sample, p i Represents the probability of model prediction. By minimizing the loss function, the model's predicted output can be made as close to the true label as possible. Once the MLP model is trained, we can deploy it to the predictive maintenance module to predict faults based on the real-time collected equipment operation data. When the equipment status characteristics are input into the model, the model will evaluate the equipment status based on the learned knowledge and output the prediction results. If the prediction results show that the equipment may fail, the predictive maintenance module will immediately trigger the early warning mechanism to remind the operation and maintenance personnel to handle it.

[0053] In summary, through this embodiment, the deep learning algorithm is successfully used to process and analyze the equipment operation data, and accurate equipment failure prediction is achieved, which helps operation and maintenance personnel to promptly discover potential failures, take corresponding treatment measures, and ensure the stable operation of the production line.

[0054] Example 2: Fault warning and classification based on support vector machine

[0055] This embodiment introduces a fault warning and classification method based on support vector machine (SVM), which is applied to fault warning and classification of equipment. The method realizes the warning and classification of equipment faults by collecting the operation data of the equipment in real time and classifying the equipment status by using the SVM algorithm. First, the operation data of the equipment is collected in real time by the data collection module, and the collected raw data is cleaned and formatted by the data processing module. Data cleaning includes removing duplicate data, filling missing values, and processing abnormal values. Data formatting is to convert the cleaned data into the format required by the SVM algorithm and extract the feature vector for fault warning. When constructing the SVM model, we use historical fault data as training samples. The training sample set is set to D={(x1, y1), (x2, y2), ..., (x m ,y m )}, where x i is the device state feature vector, y iis the corresponding fault category label. The basic form of the SVM model is a classification decision function, and its formula is:

[0056] f(x)=sign(ω·x+b)

[0057] Where ω is the normal vector and b is the intercept. In the case of nonlinear separability, by introducing the kernel function φ(x), the original space is mapped to a high-dimensional feature space so that the samples are linearly separable in the space. The training process of SVM is to solve the following optimization problem:

[0058]

[0059] sty i (ω·φ(x i )+b)≥1-ξ i ,ξ i ≥0,i=1,2,...,m

[0060] Among them, C is the penalty coefficient, which is used to control the degree of penalty for misclassified samples; ξi is a slack variable, which is used to deal with nonlinear separable situations. By solving the above optimization problem, the parameters ω and b of the SVM model can be obtained, and then the classification decision function f(x) can be determined. In the early warning process, the real-time collected device status features are input into the trained SVM model, and the output value of the decision function is calculated to determine whether the device is in an abnormal state. Suppose the real-time collected device status features are x, then it is input into the SVM model, and the value of f(x) is calculated. If the value of f(x) exceeds the preset threshold, it is judged that the device is in an abnormal state, triggering the early warning mechanism, and outputting the corresponding early warning information so that the operation and maintenance personnel can handle it in time.

[0061] In summary, this embodiment realizes fault warning and classification of equipment through a fault warning and classification method based on support vector machine, collects equipment operation data in real time, and uses SVM algorithm to classify equipment status, thereby improving the work efficiency and accuracy of operation and maintenance personnel.

[0062] Embodiment 3: Data acquisition system processing method

[0063] The specific steps of the data acquisition system processing method are:

[0064] (1) System deployment and configuration: deploy the data acquisition system near the equipment to be monitored or connect it to the equipment data source through the network, configure the data acquisition module, set the frequency, data type and transmission method of data acquisition, configure the data processing module, set the rules for data cleaning, conversion and storage, configure the predictive maintenance module and fault warning module, and set the model parameters and warning thresholds;

[0065] (2) Data collection and preprocessing: The data collection module collects equipment operation data from the data source in real time. The data processing module cleans the collected data, removes noise and outliers, and converts the cleaned data into a format suitable for analysis and modeling. The cleaned and converted data is stored in the system for subsequent use;

[0066] (3) Feature extraction: The data processing module uses pre-set algorithms and rules to extract feature information of the equipment operating status from the stored data;

[0067] (4) Fault prediction: The predictive maintenance module receives the extracted feature information and uses deep learning and pattern recognition technology to analyze and model it. By training the model, it predicts the type and time of possible faults that may occur in the equipment. The model can be dynamically adjusted based on real-time data to adapt to changes in the equipment's operating status.

[0068] (5) Fault warning and location: The fault warning module monitors the equipment operating status in real time and shares data with the predictive maintenance module. Once an abnormality or potential fault in the equipment operating status is found, the warning mechanism is immediately triggered. The fault warning model constructed by combining real-time data and historical data provides accurate fault location and repair suggestions for operation and maintenance personnel.

[0069] (6) Operation and maintenance personnel response: The operation and maintenance personnel receive fault warning information and repair suggestions, and perform fault troubleshooting and repair according to the prompts. The operation and maintenance personnel can adjust the system configuration and parameters according to the actual situation to improve the accuracy and efficiency of the warning;

[0070] (7) System optimization and upgrade: Regularly evaluate system performance, optimize and improve the system based on the evaluation results, and upgrade system algorithms and models according to changes in equipment type and operating environment to better adapt to actual needs.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A data acquisition system processing method and a data acquisition system, characterized in that: The system consists of the following components: Data acquisition module: According to the type and interface of the data source, configure the corresponding data acquisition equipment, set the parameters of the data acquisition equipment, and collect the equipment operation data from the data source in real time through the data acquisition equipment to ensure the accuracy and completeness of the data and avoid data loss or error; Data processing module: cleans the collected raw data, removes duplicate, invalid or abnormal data, formats the data, unifies the data format to facilitate subsequent processing and analysis, and uses data analysis and machine learning algorithms to extract feature information that can reflect the operating status of the equipment from the processed data; Predictive maintenance module: Select appropriate deep learning or machine learning algorithms, use the extracted feature information to build a prediction model, use historical data to verify and evaluate the prediction model, check the prediction accuracy and reliability of the model, use the trained prediction model to perform predictive analysis on the real-time collected data, and determine whether the equipment is likely to fail based on the prediction results, and predict the type of failure and the time of occurrence; Fault warning module: Use the real-time data processed by the data processing module to monitor the operating status of the equipment in real time, compare it with the set normal operating status threshold, and determine whether the equipment has abnormalities or potential faults. Once an abnormality or potential fault is found in the equipment, the warning mechanism is immediately triggered. Combined with real-time data and historical data, the fault warning model is used to accurately locate the fault, and detailed fault location and repair suggestions are provided to the operation and maintenance personnel to help them quickly locate and solve the problem.

2. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The specific steps of the data acquisition module are as follows: (1) Sensor configuration: Determine the type and quantity of sensors that need to collect data, as well as the location of the collection points, based on the characteristics and maintenance requirements of the equipment. Install the sensors at the designated collection points and perform necessary configurations to ensure that the sensors can accurately and real-time collect the equipment's operating data. (2) Data transmission: The data collected by the sensor is transmitted to the data acquisition module via wired or wireless means to ensure the real-time and integrity of the data.

3. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The specific steps of the data acquisition module are as follows: (1) Data conversion: Clean the collected raw data, remove duplicate, invalid or abnormal data, format the data, unify the data format, and convert the cleaned data as needed to ensure data consistency and comparability, providing a basis for subsequent feature extraction and modeling; (2) Feature extraction: Using data analysis and machine learning algorithms, feature information that can reflect the operating status of the equipment is extracted from the processed data.

4. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The specific steps of the predictive maintenance module are as follows: (1) Model building: Select an appropriate machine learning algorithm, use the extracted feature information to build a prediction model, and train the model so that it can accurately predict the possible fault types and occurrence time of the equipment; (2) Model optimization: Use historical data to verify and evaluate the prediction model, check the prediction accuracy and reliability of the model, and optimize and adjust the model based on the evaluation results to improve the prediction performance; (3) Fault prediction: Use the trained prediction model to perform predictive analysis on the data collected in real time. Based on the prediction results, determine whether the equipment is likely to fail, and predict the type of failure and time of occurrence.

5. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The specific steps of the fault warning module are as follows: (1) Real-time monitoring: Use the real-time data processed by the data processing module to monitor the operating status of the equipment in real time, compare it with the set normal operating status threshold, and determine whether the equipment has abnormalities or potential failures; (2) Fault warning: When an abnormality or potential fault is found in the equipment, the warning mechanism is triggered immediately, and warning information is sent to the operation and maintenance personnel through the system interface, SMS, and email to remind them to pay attention and handle it; (3) Fault location and repair suggestions: Combine real-time data with historical data and use the fault warning model to accurately locate the fault, and provide detailed fault location and repair suggestions to operation and maintenance personnel to help them quickly locate and solve the problem.

6. The data acquisition system processing method and the data acquisition system according to claim 3, characterized in that: The data processing module extracts features from data by using a convolutional neural network (CNN). The CNN model is usually composed of multiple convolutional layers, pooling layers, and fully connected layers. For input data (X), the convolutional layer performs convolution operations through convolution kernels (W) to extract local features. The formula for convolution operations is: S(i,j)=(X*W)(i,j)=∑ m X(i+m,j+n)·W(m,n), where S(i,j) represents the value of the feature map at position (i,j) after convolution, X(i+m,j+n) represents the value of the input data at position (i+m,j+n), and W(m,n) represents the weight of the convolution kernel at position (m,n). The activation function is used to perform nonlinear transformation on the feature map after convolution to enhance the expression ability of the model. For the pooling layer, it is used to reduce the dimension of the feature map, reduce the amount of calculation, and extract more robust features. The formula is: Among them, P(i, j) represents the value of the feature map at position ((i, j)) after pooling, R ij Represents the area of ​​the input feature map covered by the pooling window at position (i, j), S(p) represents the value of position (p) in the input feature map. After being processed by multiple convolutional layers and pooling layers, the feature map is flattened into a one-dimensional vector and input into the fully connected layer for classification or regression. The calculation formula of the fully connected layer is: Y=σ(Z)=σ(W·X+b), where Y represents the output of the fully connected layer, W represents the weight matrix, X represents the input vector (i.e., the flattened feature map), b represents the bias term, and σ represents the activation function. By using the CNN algorithm to operate on the collected data, accurate prediction and early warning of equipment failures are achieved, providing effective technical support for predictive maintenance and fault early warning.

7. The data acquisition system processing method and the data acquisition system according to claim 4, characterized in that: The predictive maintenance module predicts equipment failure by using a multi-layer perceptron (MLP) model. The equipment state feature vector x extracted by the data analysis module is input into the multi-layer perceptron (MLP) model. In the MLP model, the output layer uses a softmax activation function, and its formula is: F(x) = softmax(W T ·φ(x)+b), where W is the weight matrix, φ(x) is the mapping of the hidden layer activation function to the feature vector x (usually a nonlinear transformation), and b is the bias term. The weight matrix W and the bias term b are the parameters that the model needs to learn during the training process. The historical fault data is used as the training sample. Through the back propagation algorithm, the weights and bias terms of the model are continuously adjusted to make the predicted output of the model as close as possible to the actual fault type.

8. The data acquisition system processing method and the data acquisition system according to claim 5, characterized in that: The fault warning module uses the support vector machine (SVM) algorithm for fault warning and classification. By classifying the equipment status features, it is determined whether the equipment is in an abnormal state and the warning mechanism is triggered in advance. The feature vector for fault warning is extracted through the data processing module, and the SVM model is constructed using historical fault data. The classification decision function f(x) is obtained through training and learning. The decision function of SVM is expressed as: Among them, α i is the Lagrange multiplier, y i is the sample label, K(x,x i ) is a kernel function used to calculate the similarity between samples. In the early warning process, the real-time collected device status features are input into the SVM model. By calculating the output value of the decision function, it is determined whether the device is in an abnormal state. If the output value exceeds the preset threshold, the early warning mechanism is triggered and the corresponding early warning information is output.

9. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The system supports remote monitoring and management functions. Operation and maintenance personnel can remotely access the system interface to view the equipment operating status, fault warning information, and fault handling progress in real time, thereby realizing remote operation and management.

10. The data acquisition system processing method and the data acquisition system according to claim 1, characterized in that: The specific steps of the method are: (1) System deployment and configuration: deploy the data acquisition system near the equipment to be monitored or connect it to the equipment data source through the network, configure the data acquisition module, set the frequency, data type and transmission method of data acquisition, configure the data processing module, set the rules for data cleaning, conversion and storage, configure the predictive maintenance module and fault warning module, and set the model parameters and warning thresholds; (2) Data collection and preprocessing: The data collection module collects equipment operation data from the data source in real time. The data processing module cleans the collected data, removes noise and outliers, and converts the cleaned data into a format suitable for analysis and modeling. The cleaned and converted data is stored in the system for subsequent use; (3) Feature extraction: The data processing module uses pre-set algorithms and rules to extract feature information of the equipment operating status from the stored data; (4) Fault prediction: The predictive maintenance module receives the extracted feature information and uses deep learning and pattern recognition technology to analyze and model it. By training the model, it predicts the type and time of possible faults that may occur in the equipment. The model can be dynamically adjusted based on real-time data to adapt to changes in the equipment's operating status. (5) Fault warning and location: The fault warning module monitors the equipment operating status in real time and shares data with the predictive maintenance module. Once an abnormality or potential fault in the equipment operating status is found, the warning mechanism is immediately triggered. The fault warning model constructed by combining real-time data and historical data provides accurate fault location and repair suggestions for operation and maintenance personnel. (6) Operation and maintenance personnel response: Operation and maintenance personnel receive fault warning information and repair suggestions, and perform fault troubleshooting and repair according to the prompts. Operation and maintenance personnel can adjust system configuration and parameters according to actual conditions to improve the accuracy and efficiency of warnings; (7) System optimization and upgrade: Regularly evaluate system performance, optimize and improve the system based on the evaluation results, and upgrade system algorithms and models according to changes in equipment type and operating environment to better adapt to actual needs.

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