A digital factory monitoring data mining method and system

Abstract

The present invention discloses a digital factory monitoring data mining method and system, which relates to the field of artificial intelligence technology, including real-time capture of multimodal data through adaptive mode, fusion mapping to a unified data space after edge computing preprocessing, and the use of deep learning models to identify patterns and detect anomalies, thereby improving monitoring accuracy and response speed. The preliminary analysis results are uploaded to the cloud to build an equipment health status model, predict operating trends, achieve predictive maintenance, and reduce unplanned downtime. Based on the prediction results, maintenance recommendations and production scheduling strategies are generated to optimize production processes, improve efficiency, and reduce costs. This method enhances data processing capabilities, promotes intelligent decision-making, and is of great value in improving the level of factory automation and intelligence, ensuring production continuity and efficiency, and enhancing overall operational efficiency and market competitiveness.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a digital factory monitoring data mining method and system. Background Art

[0002] In recent years, with the rapid advancement of the Internet of Things, big data, and artificial intelligence technologies, the concept of Industry 4.0 has been widely promoted and applied worldwide. Digital factories enable comprehensive monitoring and intelligent management of production processes. Traditional rule-based approaches are gradually being replaced by data-driven approaches, effectively improving equipment reliability and production efficiency. However, while existing technologies have improved equipment operation and maintenance management to a certain extent, they still face numerous challenges in the complex and changing industrial environment and the massive amount of heterogeneous data.

[0003] While most current industrial monitoring systems are capable of collecting equipment operational data, they still suffer from significant deficiencies in data processing and analysis. The lack of effective data fusion mechanisms reduces the accuracy and efficiency of data analysis. Existing anomaly detection technologies mostly rely on single feature extraction or preset thresholds, which impacts prediction accuracy. Traditional monitoring systems lack the ability to predict future trends when generating maintenance recommendations and production scheduling strategies, making them unable to respond promptly to potential equipment failures or performance degradation. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a digital factory monitoring data mining method and system to solve the problems of heterogeneity and real-time performance of multimodal data in digital factories, optimize the accuracy of equipment status monitoring and fault prediction, and improve the efficiency of production scheduling decisions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a digital factory monitoring data mining method, which includes using an adaptive mode to capture multimodal data of physical parameters and environmental variables of equipment operating status in real time and upload it to an edge computing device for preliminary processing;

[0008] Fuse the preliminarily processed multimodal data and map them into a unified data space;

[0009] Perform preliminary pattern recognition and anomaly detection on the fused multimodal data through machine learning models, output preliminary analysis results and upload them to the cloud platform;

[0010] Based on the preliminary analysis results, a health status model of the equipment is constructed to identify abnormal operation patterns of the equipment and predict future operation trends;

[0011] Based on the prediction results, maintenance recommendations and production scheduling strategies are generated.

[0012] As a preferred solution of the digital factory monitoring data mining method of the present invention, the method of using an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment operating status in real time includes the following steps:

[0013] Monitor temperature changes during equipment operation through temperature sensors to detect overheating and other temperature anomalies;

[0014] Vibration sensors capture the vibration frequency and intensity of equipment during operation and analyze the mechanical stability of the equipment;

[0015] Pressure sensors monitor the pressure of fluids and gases to maintain stable equipment operation;

[0016] The sound sensor records the sound of the device during operation and identifies abnormal noise through voiceprint analysis;

[0017] Image sensors capture images of the device's appearance and key parts for visual inspection and defect detection;

[0018] Design an adaptive data acquisition frequency algorithm to automatically adjust the sensor sampling frequency based on the current load and operating status of the equipment;

[0019] Upload the collected multimodal data to the edge computing device.

[0020] As a preferred solution of the digital factory monitoring data mining method described in the present invention, the preliminary processing includes data cleaning, denoising, filtering and data standardization processing on the collected data on the edge computing device.

[0021] As a preferred solution of the digital factory monitoring data mining method of the present invention, fusing the preliminarily processed multimodal data and mapping it into a unified data space includes the following steps:

[0022] Extract key features from the data of each modality based on the nature of the data and the analysis objectives;

[0023] Use data fusion algorithms to combine features from different modalities to form a high-dimensional feature vector;

[0024] Create a unified data framework, and all fused data are mapped to a unified data space using a mapping function or transformation matrix.

[0025] As a preferred solution of the digital factory monitoring data mining method of the present invention, the following steps are included: performing preliminary pattern recognition and anomaly detection on the fused multimodal data through a machine learning model, outputting preliminary analysis results and uploading them to the cloud platform:

[0026] Adopt the convolutional neural network and long short-term memory network combination model in deep learning;

[0027] Convolutional neural networks are used to extract spatial features of data from different modalities, and long short-term memory networks are used to capture dependencies in time series.

[0028] The multimodal data mapped into the unified data space after fusion is input into the model for preliminary pattern recognition and anomaly detection. The expression is:

[0029] ;

[0030] Where t is the current time, It's at the time The abnormal detection value of , N is the total number of modes, It is The importance weight of the modal data, The fusion function The calculated modality at a point in time The fusion value of The first modality at a point in time The predicted fusion value of and They are The mean and standard deviation of the modal data, is a balance factor between statistical anomaly detection and prediction bias, is a non-negative exponent used to adjust the sensitivity of anomaly detection to outliers, is a non-negative exponent used to adjust the sensitivity of anomaly detection to prediction bias;

[0031] The preliminary analysis results and processed data are compressed and packaged, and the data packets are encrypted using the SSL protocol and uploaded to the cloud platform for storage.

[0032] As a preferred embodiment of the digital factory monitoring data mining method of the present invention, the following steps are included: constructing a health status model of the equipment based on the preliminary analysis results, identifying abnormal operation modes of the equipment, and predicting future operation trends:

[0033] The cloud platform decrypts the preliminary analysis results and the processed encrypted data packets;

[0034] The hybrid expert network in deep learning is combined with the long short-term memory network to build the health status model of the equipment. The real-time health status and future trend prediction of the equipment are integrated to design the health status prediction function. The expression is:

[0035] ;

[0036] in, It's at the time The predicted value of the equipment health status, Z is the normalization constant, and They are The center position and width parameters of the abnormal detection value of the modal data, By long short-term memory network at time point The predicted The health status value of the modal data, It is Sensitivity of trend prediction for modal data, It is The threshold value for trend prediction of modal data, is the long short-term memory network at time point Predicted future trend value of the device.

[0037] As a preferred solution of the digital factory monitoring data mining method of the present invention, generating maintenance suggestions and production scheduling strategies based on the prediction results includes the following steps:

[0038] In-depth analysis of the health status model's prediction results to identify trends in equipment health status;

[0039] Develop a preventive maintenance schedule based on the forecast results;

[0040] Assess the impact of failures of various equipment and components on the overall production process and determine maintenance priorities;

[0041] Organize maintenance recommendations into reports based on maintenance priorities;

[0042] Submit maintenance recommendation reports to the maintenance team for execution, and set up a tracking mechanism to monitor maintenance progress and effectiveness;

[0043] Combine real-time monitoring data with predictive models to regularly assess the health status of all equipment;

[0044] Predict possible future maintenance needs based on health status assessment;

[0045] Replan production scheduling based on maintenance demand forecasts;

[0046] Introduce flexible production strategies to cope with equipment failures and maintenance needs.

[0047] In a second aspect, the present invention provides a digital factory monitoring data mining system, comprising:

[0048] Multimodal data acquisition and edge computing preprocessing module: This module uses an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment's operating status in real time and uploads it to the edge computing device for preliminary processing.

[0049] Multimodal data fusion module: fuses the multimodal data after preliminary processing and maps it into a unified data space;

[0050] Pattern recognition and anomaly detection module: This module uses machine learning models to perform preliminary pattern recognition and anomaly detection on the fused multimodal data, outputs preliminary analysis results, and uploads them to the cloud platform.

[0051] Cloud data analysis and prediction module: Based on preliminary analysis results, it builds a health status model for the equipment, identifies abnormal operation patterns of the equipment, and predicts future operation trends;

[0052] Decision-making and optimization module: Generates maintenance recommendations and production scheduling strategies based on prediction results.

[0053] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the digital factory monitoring data mining method described in the first aspect of the present invention is implemented.

[0054] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the digital factory monitoring data mining method as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of this invention include: Real-time capture of multimodal device data through adaptive models, pre-processing with edge computing, and fusion mapping into a unified data space. Deep learning models are used to identify patterns and detect anomalies, outputting analysis results to the cloud. Based on this data, a device health status model is constructed to predict operational trends. This method comprehensively perceives device status, improving data quality and analysis efficiency. It also enhances prediction accuracy through model-based quantification of anomaly detection. The health status model combines deep learning with expert networks to enable proactive maintenance and production scheduling optimization, reducing unplanned downtime and improving production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 This is a flow chart of the digital factory monitoring data mining method in Example 1. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] 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.

[0060] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0061] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a digital factory monitoring data mining method, comprising the following steps:

[0062] S1. Using an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment's operating status in real time includes the following steps:

[0063] Monitor temperature changes during equipment operation through temperature sensors to detect overheating or other temperature anomalies;

[0064] Vibration sensors capture the vibration frequency and intensity of equipment during operation and are used to analyze the mechanical stability of the equipment;

[0065] Pressure sensors monitor the pressure of fluids and gases to ensure stable system operation;

[0066] The sound sensor records the sound of the device during operation and identifies abnormal noise through voiceprint analysis;

[0067] Image sensors capture images of the device's appearance and key parts for visual inspection and defect detection;

[0068] Design an adaptive data acquisition frequency algorithm to automatically adjust the sensor sampling frequency based on the current load and operating status of the equipment;

[0069] Upload the collected multimodal data to the edge computing device.

[0070] Furthermore, the expression of the adaptive data acquisition frequency algorithm is:

[0071] ;

[0072] in, For the current moment, For the moment The frequency of data collection, For the device at all times The load level, The running status of the device (0 means stop, 1 means start, 2 means high load), and are the adjustment factor and the base load level, Controls the steepness of the load change's effect on the acquisition frequency. Determining the load threshold at which the acquisition frequency begins to increase significantly;

[0073] When the device is not started, =0, at this time =0, data collection is suspended; when the device is started but has not reached high load, =1, acquisition frequency Will depend on load level Automatically adjusts as near , the collection frequency gradually increases; when the device is in a high load state, =2, at this time no matter What is the specific value of will automatically be raised to their maximum value to ensure that all critical data is captured.

[0074] S2. Preliminary processing includes data cleaning, denoising, filtering, and data standardization of the collected data on the edge computing device.

[0075] Furthermore, statistical methods (such as Z-score, IQR) or machine learning algorithms (such as IsolationForest, Local Outlier Factor) are used to identify and remove outliers caused by sensor failures; missing values are filled through neighboring point interpolation (such as linear interpolation), model-based prediction (such as KNN Imputation), mean / median filling, etc.; low-pass filters are used to remove high-frequency noise and retain the basic trend of the signal, while high-pass filters are used to eliminate slowly changing background signals and highlight the rapidly changing parts of the signal; data is smoothed by calculating the average value within a sliding window of the data sequence; data is converted to a standard normal distribution with a mean of 0 and a standard deviation of 1, which is suitable for cases where the data has no obvious boundaries or follows a normal distribution; data is scaled to between 0 and 1, which is suitable for cases where the data has clear boundaries.

[0076] S3. Fusing the preliminarily processed multimodal data and mapping it into a unified data space includes the following steps:

[0077] Extract key features from the data of each modality based on the nature of the data and the analysis objectives;

[0078] Use data fusion algorithms (such as weighted fusion, decision-level fusion, feature-level fusion, etc.) to combine features from different modalities to form a high-dimensional feature vector;

[0079] Create a unified data framework, and all fused data are mapped to a unified data space using a mapping function or transformation matrix.

[0080] Furthermore, a multivariate data fusion function F is constructed to map multimodal data into a unified space, which is expressed as:

[0081] ;

[0082] Where T represents the temperature data feature vector, V represents the vibration data feature vector, P represents the pressure data feature vector, S represents the sound data feature vector, I represents the image data feature vector, i is the modal data index, and N is the total number of modes. is the eigenvector of the i-th mode, is the weight coefficient corresponding to the i-th modal data, indicating the importance of the modal data in the overall fusion. is the data fusion function of the i-th modal data.

[0083] Furthermore, for each data type, a specific fusion function is designed, expressed as:

[0084] ;

[0085] in, is the eigenvector of the i-th modal data at the j-th time point, and are the mean and standard deviation of the temperature data, respectively, used to normalize the data.

[0086] S4. Performing preliminary pattern recognition and anomaly detection on the fused multimodal data using a machine learning model, outputting preliminary analysis results, and uploading them to the cloud platform includes the following steps:

[0087] Adopt the convolutional neural network and long short-term memory network combination model in deep learning;

[0088] Convolutional neural networks are used to extract spatial features of data from different modalities, and long short-term memory networks are used to capture dependencies in time series.

[0089] The multimodal data mapped into the unified data space after fusion is input into the model for preliminary pattern recognition and anomaly detection. The expression is:

[0090] ;

[0091] Where t is the current time, It's at the time The abnormal detection value of , N is the total number of modes, It is The importance weight of the modal data, The fusion function The calculated modality at a point in time The fusion value of The first modality at a point in time The predicted fusion value of and They are The mean and standard deviation of the modal data, is a balance factor between statistical anomaly detection and prediction bias, is a non-negative exponent used to adjust the sensitivity of anomaly detection to outliers, is a non-negative exponent used to adjust the sensitivity of anomaly detection to prediction bias;

[0092] The preliminary analysis results and processed data are compressed and packaged, and the data packets are encrypted using the SSL protocol and uploaded to the cloud platform for storage.

[0093] Furthermore, the anomaly detection function The value range of is [0, +∞], and the threshold value of anomaly detection is defined as ,when < When , it means the equipment is operating normally without any abnormality; when ≥ , it indicates that the equipment is operating abnormally and needs further observation and maintenance measures if necessary.

[0094] S5. Based on the preliminary analysis results, construct a health status model for the equipment, identify abnormal operation patterns of the equipment, and predict future operation trends. This includes the following steps:

[0095] The cloud platform decrypts the preliminary analysis results and the processed encrypted data packets;

[0096] The hybrid expert network in deep learning is combined with the long short-term memory network to build the health status model of the equipment. The real-time health status and future trend prediction of the equipment are integrated to design the health status prediction function. The expression is:

[0097] ;

[0098] in, It's at the time The predicted value of the equipment health status, Z is the normalization constant, and They are The center position and width parameters of the abnormal detection value of the modal data, By long short-term memory network at time point The predicted The health status value of the modal data, It is Sensitivity of trend prediction for modal data, It is The threshold value for trend prediction of modal data, is the long short-term memory network at time point Predicted future trend value of the device.

[0099] Furthermore, the health status prediction function The value range of is generally designed to be 0 to 1. When it is close to 0, it means that the health of the device is poor and there may be an impending failure. When it is close to 1, it means that the equipment is in good health, running stably, and the risk of failure is low.

[0100] S6. Generating maintenance recommendations and production scheduling strategies based on the prediction results includes the following steps:

[0101] In-depth analysis of the prediction results of the health status model to identify the changing trends of the equipment health status; based on the prediction results, formulate a schedule for preventive maintenance, including but not limited to regular inspections, component lubrication, cleaning and replacement of parts that are about to fail; evaluate the impact of the failure of each equipment and component on the overall production process and determine the maintenance priority; based on the maintenance priority, organize the maintenance recommendations into a report, including recommended maintenance activities, estimated maintenance time, required resources and spare parts list, and possible maintenance team assignments; submit the maintenance recommendation report to the maintenance team for execution, and set up a tracking mechanism to monitor the maintenance progress and effectiveness; combine real-time monitoring data and predictive models to regularly evaluate the health status of all equipment; based on the health status assessment, predict possible future maintenance needs, including expected maintenance time windows and possible downtime; based on the maintenance demand forecast, re-plan production scheduling, which may include rescheduling tasks on the production line, adjusting work shifts or purchasing necessary raw materials in advance; introduce flexible production strategies, such as activating spare production lines, arranging overtime or outsourcing some production tasks to deal with equipment failures and maintenance needs.

[0102] This embodiment also provides a digital factory monitoring data mining system, including:

[0103] Multimodal data acquisition and edge computing preprocessing module: This module uses an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment's operating status in real time and uploads it to the edge computing device for preliminary processing.

[0104] Multimodal data fusion module: fuses the multimodal data after preliminary processing and maps it into a unified data space;

[0105] Pattern recognition and anomaly detection module: This module uses machine learning models to perform preliminary pattern recognition and anomaly detection on the fused multimodal data, outputs preliminary analysis results, and uploads them to the cloud platform.

[0106] Cloud data analysis and prediction module: Based on preliminary analysis results, it builds a health status model for the equipment, identifies abnormal operation patterns of the equipment, and predicts future operation trends;

[0107] Decision-making and optimization module: Generates maintenance recommendations and production scheduling strategies based on prediction results.

[0108] This embodiment also provides a computer device suitable for the digital factory monitoring data mining method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the digital factory monitoring data mining method proposed in the above embodiment.

[0109] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0110] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the digital factory monitoring data mining method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0111] In summary, this invention captures multimodal device data in real time through adaptive models, preprocesses it through edge computing, and then integrates and maps it into a unified data space. Deep learning models are used to identify patterns and detect anomalies, outputting analysis results to the cloud. Based on this, a device health status model is constructed to predict operational trends. This method comprehensively perceives device status, improves data quality and analysis efficiency, and enhances prediction accuracy through model-based anomaly detection. The health status model combines deep learning with expert networks to achieve proactive maintenance and production scheduling optimization, reducing unplanned downtime and improving production efficiency and product quality.

[0112] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of the digital factory monitoring data mining method is provided.

[0113] First, the experimental object was a high-speed stamping machine, a key equipment on an automated production line in a manufacturing factory. The high-speed stamping machine was equipped with temperature sensors, vibration sensors, pressure sensors, sound sensors, and image sensors.

[0114] The device is connected to an edge computing device with a built-in adaptive data acquisition frequency algorithm. The algorithm parameters are set as follows: α = 0.5, β = 80% of the maximum load. Data preprocessing and analysis software is installed on the edge computing device, performing data cleaning, denoising, standardization, and subsequent data fusion and pattern recognition. Data analysis results are uploaded to a secure cloud server for long-term storage and further deep learning analysis.

[0115] Before the equipment is started, all sensors are in standby mode and the data collection frequency is 0. When the equipment is started and enters normal operation, the adaptive data collection frequency algorithm automatically adjusts the sensor sampling frequency according to the equipment load level. Every 10 minutes, the edge computing device performs data preprocessing, including outlier identification, missing value filling, filtering, and smoothing. The processed data is fused and mapped to a unified data space, and then a deep learning model is used for preliminary pattern recognition and anomaly detection. The anomaly detection results and preliminary analysis are uploaded to the cloud for further health status prediction and trend analysis. Finally, maintenance recommendations and production scheduling strategies are generated based on the prediction results, as shown in Table 1:

[0116] Table 1 Equipment health status monitoring system performance evaluation table

[0117]

[0118] Analyzing the data in the table above clearly demonstrates the effectiveness of the adaptive data collection frequency algorithm and its significant improvement in the performance of the equipment health monitoring system. As the equipment load level increases, the collection frequency automatically increases. This ensures that more critical data is captured when the equipment is under high load, thereby improving the accuracy of anomaly detection. For example, at a 90% load level, the collection frequency of Press C reached 8Hz, and the anomaly detection score also increased to 0.20, indicating a potential health risk for the equipment at this stage.

[0119] The health prediction value reflects the overall health of the equipment, with values closer to 1 indicating better equipment health. As can be seen from the data, the health prediction values for presses A and B are 0.95 and 0.90, respectively, indicating stable operation and low failure risk at low load levels. However, as the load increases, as seen for presses D and E, the health prediction values drop to 0.60 and 0.55, respectively, suggesting that preventive maintenance may be required.

[0120] Compared to fixed-frequency data collection, adaptive data collection not only improves data quality and analysis efficiency but also significantly reduces unnecessary data storage and transmission costs. Furthermore, combined with deep learning models for anomaly detection and health status prediction, it can promptly identify potential equipment issues and proactively perform maintenance, effectively avoiding unplanned downtime and improving production efficiency and product quality.

[0121] In summary, the equipment health status monitoring system that adopts adaptive data acquisition frequency algorithm and multimodal data fusion has shown significant advantages in improving monitoring intelligence and optimizing maintenance decisions, and plays an important role in promoting the automation and intelligence of industrial production.

[0122] 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 digital factory monitoring data mining method, characterized by: include, Adopting an adaptive mode, it captures multimodal data of physical parameters and environmental variables of the equipment's operating status in real time and uploads it to the edge computing device for preliminary processing; Fuse the preliminarily processed multimodal data and map them into a unified data space; Perform preliminary pattern recognition and anomaly detection on the fused multimodal data through machine learning models, output preliminary analysis results and upload them to the cloud platform; Based on the preliminary analysis results, a health status model of the equipment is constructed to identify abnormal operation patterns of the equipment and predict future operation trends; Generate maintenance recommendations and production scheduling strategies based on prediction results; Based on the preliminary analysis results, building a health model for the equipment, identifying abnormal patterns in equipment operation, and predicting future operating trends involves the following steps: The cloud platform decrypts the preliminary analysis results and the processed encrypted data packets; The hybrid expert network in deep learning is combined with the long short-term memory network to build a health status model for the equipment. The real-time health status and future trend prediction of the equipment are integrated to design a health status prediction function. The expression is: ； in, It's at the time The predicted value of the equipment health status, Z is the normalization constant, and They are The center position and width parameters of the abnormal detection value of the modal data, By long short-term memory network at time point The predicted The health status value of the modal data, It is Sensitivity of trend prediction for modal data, It is The threshold value for trend prediction of modal data, is the long short-term memory network at time point Predicted future trend value of the device.

2. The digital factory monitoring data mining method according to claim 1, wherein: The method of using an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment operating status in real time includes the following steps: Monitor temperature changes during equipment operation through temperature sensors to detect overheating and other temperature anomalies; Vibration sensors capture the vibration frequency and intensity of equipment during operation and analyze the mechanical stability of the equipment; Pressure sensors monitor the pressure of fluids and gases to maintain stable equipment operation; The sound sensor records the sound of the device during operation and identifies abnormal noise through voiceprint analysis; Image sensors capture images of the device's appearance and key parts for visual inspection and defect detection; Design an adaptive data acquisition frequency algorithm to automatically adjust the sensor sampling frequency based on the current load and operating status of the equipment; Upload the collected multimodal data to the edge computing device.

3. The digital factory monitoring data mining method according to claim 1, wherein: The preliminary processing includes data cleaning, denoising, filtering and data standardization of the collected data on the edge computing device.

4. The digital factory monitoring data mining method according to claim 3, wherein: The fusion of the preliminarily processed multimodal data and mapping it into a unified data space includes the following steps: Extract key features from the data of each modality based on the nature of the data and the analysis objectives; Use data fusion algorithms to combine features from different modalities to form a high-dimensional feature vector; Create a unified data framework, and all fused data are mapped to a unified data space using a mapping function or transformation matrix.

5. The digital factory monitoring data mining method according to claim 4, characterized in that: The following steps are involved in performing preliminary pattern recognition and anomaly detection on the fused multimodal data using a machine learning model, outputting preliminary analysis results, and uploading them to the cloud platform: Adopt the convolutional neural network and long short-term memory network combination model in deep learning; Convolutional neural networks are used to extract spatial features of data from different modalities, and long short-term memory networks are used to capture dependencies in time series. The multimodal data mapped into the unified data space after fusion is input into the model for preliminary pattern recognition and anomaly detection. The expression is: ； Where t is the current time, It's at the time The abnormal detection value of , N is the total number of modes, It is The importance weight of the modal data, The fusion function The calculated modality at a point in time The fusion value of The first modality at a point in time The predicted fusion value of and They are The mean and standard deviation of the modal data, is a balance factor between statistical anomaly detection and prediction bias, is a non-negative exponent used to adjust the sensitivity of anomaly detection to outliers, is a non-negative exponent used to adjust the sensitivity of anomaly detection to prediction bias; The preliminary analysis results and processed data are compressed and packaged, and the data packets are encrypted using the SSL protocol and uploaded to the cloud platform for storage.

6. The digital factory monitoring data mining method according to claim 5, characterized in that: Generating maintenance recommendations and production scheduling strategies based on the prediction results includes the following steps: In-depth analysis of the health status model's prediction results to identify trends in equipment health status; Develop a preventive maintenance schedule based on the forecast results; Assess the impact of failures of various equipment and components on the overall production process and determine maintenance priorities; Organize maintenance recommendations into reports based on maintenance priorities; Submit maintenance recommendation reports to the maintenance team for execution, and set up a tracking mechanism to monitor maintenance progress and effectiveness; Combine real-time monitoring data with predictive models to regularly assess the health status of all equipment; Predict possible future maintenance needs based on health status assessment; Replan production scheduling based on maintenance demand forecasts; Introduce flexible production strategies to cope with equipment failures and maintenance needs.

7. A digital factory monitoring data mining system, based on the digital factory monitoring data mining method according to any one of claims 1 to 6, characterized in that: include, Multimodal data acquisition and edge computing preprocessing module: This module uses an adaptive mode to capture multimodal data of physical parameters and environmental variables of the equipment's operating status in real time and uploads it to the edge computing device for preliminary processing. Multimodal data fusion module: fuses the multimodal data after preliminary processing and maps it into a unified data space; Pattern recognition and anomaly detection module: This module uses machine learning models to perform preliminary pattern recognition and anomaly detection on the fused multimodal data, outputs preliminary analysis results, and uploads them to the cloud platform. Cloud data analysis and prediction module: Based on preliminary analysis results, it builds a health status model for the equipment, identifies abnormal operation patterns of the equipment, and predicts future operation trends; Decision-making and optimization module: Generates maintenance recommendations and production scheduling strategies based on prediction results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the digital factory monitoring data mining method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital factory monitoring data mining method according to any one of claims 1 to 6 are implemented.

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