Equipment operation evaluation method based on multi-source data fusion

Abstract

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation evaluation, and in particular to an equipment operation evaluation method based on multi-source data fusion. Background Art

[0002] With the continuous development of industrial technology, various equipment are increasingly used in production and life. The normal operation of equipment is crucial to ensuring production efficiency, improving product quality and ensuring personnel safety. However, traditional equipment operation evaluation methods often rely on a single data source, such as equipment sensor data or operation logs, which are difficult to fully and accurately reflect the actual operation status of the equipment. On the one hand, relying solely on sensor data may not capture some complex situations in equipment operation. For example, the sensor may not detect potential failures caused by improper operation or environmental factors. Moreover, the data of a single sensor may contain errors or noise, affecting the accuracy of the evaluation. On the other hand, although the operation log can record the operation of the equipment, it has limited revelation of the internal operation status and potential failures of the equipment. At the same time, different types of equipment may require different evaluation methods, and traditional methods lack versatility and flexibility. In addition, with the intelligence and complexity of equipment, the amount of data generated during the operation of the equipment is increasing, and the data types are becoming more and more diverse. How to effectively integrate these multi-source data and extract valuable information to achieve accurate evaluation of the equipment operation status has become a problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present invention is implemented by the following technical solutions: a device operation evaluation method based on multi-source data fusion, comprising the following steps:

[0004] S1, data collection: collect multi-source data of the equipment in real-time operation status, where the multi-source data includes the equipment's sensor data, operation log, environmental data and maintenance history;

[0005] S2, data fusion: The multi-source data collected in step S1 are fused and processed, and the fusion process uses a deep learning model to integrate the multi-source data to generate a fused data set of the equipment operation status;

[0006] S3, data analysis and evaluation model construction: based on the fused fusion data set, a machine learning algorithm is used to build an evaluation model for the equipment operation status;

[0007] S4, generate evaluation results and alarms: output the evaluation status of the equipment operation status according to the evaluation model in S3. When the equipment operation status is lower than the preset threshold or a potential failure is predicted, an alarm signal and maintenance suggestions are automatically generated to assist in equipment maintenance and failure prevention.

[0008] Preferably, the data collection step S1 further comprises:

[0009] Sensor data acquisition: Sensors are installed on equipment components to collect operating parameters of equipment components. Sensors include temperature sensors, pressure sensors, and vibration sensors.

[0010] Operation data collection: record the operation data of the equipment operator, including operation time, operation nature and operation results;

[0011] Environmental data collection: record the environmental data of the equipment operating environment, including ambient temperature, humidity, electromagnetic interference, vibration and dust pollutants;

[0012] Maintenance data collection: record the maintenance history of the equipment, including maintenance time, maintenance type and maintenance effect.

[0013] Preferably, the data fusion step S2 further comprises:

[0014] Data preprocessing: De-noise, standardize and process missing values ​​of the collected multi-source data to ensure that the data quality meets the requirements of subsequent processing; multi-source data is composed of information from different data sources, and the data types of multi-source data include equipment temperature, pressure, vibration, power usage rate, and environmental temperature and humidity;

[0015] Data fusion: Apply convolutional neural networks to combine information from different data sources to generate a fused data set that reflects the overall status of the equipment.

[0016] Preferably, the data analysis and evaluation model building step S3 further includes:

[0017] Feature extraction: Apply big data analysis techniques, including machine learning algorithms and data mining, to extract key features from the fused data set;

[0018] Model training: Use the extracted key features to train and build an evaluation model for the equipment's operating status. The resulting evaluation model is used to evaluate and predict the equipment's operating status in real time. The evaluation model outputs an evaluation status that reflects the equipment's current operating status and potential faults. The evaluation status includes the equipment's health status, performance indicators, and fault predictions.

[0019] Model optimization: Adjust and optimize the evaluation model based on actual equipment operation data and maintenance feedback to improve the accuracy and adaptability of the model.

[0020] Preferably, the step S4 of generating the evaluation result and the alarm further comprises:

[0021] Status determination: The evaluation model outputs the evaluation status of the equipment's operating status to determine the equipment's operating status, which includes normal, warning or fault status;

[0022] Alarm generation: When the equipment operating status is lower than the preset threshold or a potential fault is predicted, the corresponding alarm signal and fault diagnosis information are generated, and the maintenance personnel are notified to handle it;

[0023] Maintenance recommendations: Generate maintenance recommendations based on fault diagnosis information and historical maintenance data generated under the current operating status of the equipment to help maintenance personnel effectively maintain and repair equipment.

[0024] Preferably, the generation process of the alarm signal is as follows:

[0025] According to the equipment's instruction manual, industry standards, and actual operating experience, clarify the equipment's normal operating parameter range and preset thresholds for key performance indicators; adjust preset thresholds based on the equipment's historical operating data and real-time monitoring data;

[0026] Through statistical analysis and machine learning algorithms, the fused data set is monitored and anomaly detected to obtain the equipment operation status parameters, which are compared with the preset thresholds. When it is found that the equipment operation status parameters exceed the preset threshold range, or abnormal trends and patterns appear, the alarm generation mechanism is triggered. Among them, the level of the alarm signal is determined according to the abnormal degree of the equipment operation status. The level of the alarm signal is divided into emergency alarm, important alarm and general alarm, so that maintenance personnel can take corresponding processing measures according to the alarm level. According to the alarm level, the corresponding alarm signal is generated. The alarm signal includes sound, light, text message, and email to notify the maintenance personnel in time. When the alarm signal is generated, the alarm signal also contains the equipment's identification information, alarm level, and abnormal parameters, so that maintenance personnel can quickly locate the problem equipment and understand the severity of the problem.

[0027] Preferably, the fault diagnosis information generation process is as follows:

[0028] When the equipment operation status shows an abnormal trend, signal processing technology and feature extraction algorithms are used to further analyze the collected equipment operation status data and extract fault characteristics; fault characteristics include the specific value of abnormal parameters, change trend, and time of occurrence; the data is analyzed and processed to extract feature information that can reflect the nature of the fault;

[0029] Based on the extracted fault features, a fault diagnosis algorithm is used to classify and locate the fault, determine the cause and impact range of the fault, and obtain the fault diagnosis results and fault analysis results; fault diagnosis information is generated based on the fault diagnosis results and fault analysis results; the fault diagnosis information includes the identification information of the faulty equipment, the fault type, the cause of the fault, the impact range, and the recommended treatment measures; and the fault diagnosis information is sent to maintenance personnel in the form of a report so that they can quickly understand the fault situation and take appropriate treatment measures.

[0030] Preferably, the data fusion processing further includes:

[0031] Advanced data matching: Use time synchronization and spatial alignment to jointly execute matching algorithms on data from different sources to ensure data consistency and integrity;

[0032] Multi-dimensional fusion: Utilize multi-source data fusion technology, including multi-modal deep learning and multi-view ensemble learning, to fuse multi-source data in multiple dimensions to improve the accuracy and robustness of the evaluation model;

[0033] Dynamic weight adjustment: According to the reliability and relevance of different data sources, the weights of multi-source data are adjusted during the fusion process to optimize the effect of data fusion.

[0034] Preferably, the process of multi-dimensional fusion of multi-source data is as follows:

[0035] First, the image data is processed based on the architecture of the convolutional neural network, the text data is processed based on the long short-term memory network, and a fully connected network is built for the sensor data. The convolutional neural network, the long short-term memory network and the fully connected network are combined to form a multimodal deep learning model, completing the construction of the multimodal deep learning model; wherein the multi-source data includes image data, sensor data and text data;

[0036] The image features are extracted through the convolutional neural network to obtain a high-dimensional image feature vector. The text features are extracted through the long short-term memory network to obtain a text feature vector. The sensor features are extracted using the fully connected network to obtain a sensor feature vector. Subsequently, different feature vectors are spliced ​​together to form a comprehensive feature vector.

[0037] Dividing the preprocessed multi-source data into different views; dividing the image data into a first view, the sensor data into a second view, and the text data into a third view;

[0038] An independent learning model is constructed for each view. For image views, a convolutional neural network is used for classification to obtain an image view model. For sensor views, a support vector machine is used for regression analysis to obtain a sensor view model. For text views, a long short-term memory network is used for classification to obtain a text view model.

[0039] The image view model, sensor view model and text view model are fused through the weighted averaging method. Different weights are assigned according to the importance of each view model through the weighted averaging method, and the prediction results of the model are weighted averaged to obtain a multi-dimensional fusion result.

[0040] Preferably, the data analysis and evaluation model building process further includes:

[0041] Model calibration: By comparing the prediction results of the evaluation model with the actual equipment operation data, the model is calibrated to reduce deviations and improve the accuracy of the prediction;

[0042] By collecting actual equipment operation data, obtaining the prediction results of the evaluation model, making the actual equipment operation data correspond to the prediction results of the evaluation model in time, clarifying the equipment operation status parameters to be compared, making the actual equipment operation data and the prediction results of the evaluation model contain the same parameters, and the meaning and unit of the parameters are consistent; for each parameter to be compared, the root mean square error is used to calculate the error between the prediction results of the evaluation model and the actual equipment operation data, and a chart is drawn through a visualization tool to show the difference and trend between the prediction results and the actual data, and then the error is statistically analyzed to understand the distribution and statistical characteristics of the error, and the results of the comparative analysis are obtained. According to the results of the comparative analysis, the gradient descent method in the optimization algorithm is used to adjust the parameters of the data analysis and evaluation model, and the data analysis and evaluation model is calibrated. After the model is calibrated, the model is run again and compared with the actual equipment operation data to verify the calibration effect; if the error decreases, it means that the calibration is successful; if the error does not decrease, it means that the calibration is unsuccessful, and the calibration should be continued;

[0043] The specific steps and characteristics of the equipment operation evaluation method based on multi-source data fusion are clearly defined, ensuring the comprehensiveness and efficiency of the method, which is applicable to various complex industrial environments to improve equipment reliability and maintenance efficiency.

[0044] The present invention provides an equipment operation evaluation method based on multi-source data fusion, which has the following beneficial effects:

[0045] 1. This equipment operation evaluation method based on multi-source data fusion can fully understand the equipment's operating status from multiple angles by collecting multi-source information such as equipment sensor data, operation logs, environmental data, and maintenance history. This helps to discover potential problems and risks of the equipment and improve the accuracy and comprehensiveness of equipment status monitoring. It uses deep learning models and multi-source data fusion technology to integrate multi-source data to generate a fused data set. This fusion can make full use of the advantages of different data sources, overcome the limitations of a single data source, and more comprehensively reflect the overall status of the equipment.

[0046] 2. The equipment operation evaluation method based on multi-source data fusion can provide accurate equipment status monitoring and fault prediction, and can also continuously optimize its performance and accuracy according to the actual equipment operation data and environmental conditions of the equipment, thereby realizing efficient and intelligent equipment management and maintenance strategies; the specific steps and characteristics of the equipment operation evaluation method based on multi-source data fusion are clearly defined, ensuring the comprehensiveness and efficiency of the method, and it is suitable for various complex industrial environments to improve the reliability and maintenance efficiency of equipment.

[0047] 3. The equipment operation evaluation method based on multi-source data fusion builds an evaluation model based on the fused data set using a machine learning algorithm. It can evaluate and predict the operating status of the equipment in real time. When the equipment operating status is lower than the preset threshold or a potential fault is predicted, an alarm signal and fault diagnosis information are automatically generated to notify maintenance personnel for processing. This helps to detect equipment failures in a timely manner, reduce equipment downtime, and improve equipment reliability and availability. At the same time, it provides maintenance personnel with detailed fault information, which helps them to quickly and accurately locate and solve faults and improve maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flowchart of a device operation evaluation method based on multi-source data fusion. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0050] like Figure 1 As shown, the present invention provides a technical solution: a device operation evaluation method based on multi-source data fusion, comprising the following steps:

[0051] First, data collection: Collect multi-source data of the equipment in real-time operation, including sensor data, operation logs, environmental data and maintenance history of the equipment. By collecting multi-source data of the equipment, we can fully understand the operation status of the equipment and provide a rich data foundation for accurate evaluation.

[0052] Among them, it includes sensor data collection: installing sensors on equipment components to collect operating parameters of equipment components. Sensors include temperature sensors, pressure sensors and vibration sensors, which help to detect abnormal conditions of equipment components in a timely manner; operation data collection: recording the operation data of equipment operators. The operation data includes operation time, operation nature and operation results, which helps to analyze the impact of operations on equipment operation; environmental data collection: recording environmental data of the equipment operating environment. Environmental data includes ambient temperature, humidity, electromagnetic interference, vibration and dust pollutants, which helps to evaluate the impact of environmental factors on equipment; maintenance data collection: recording the maintenance history of the equipment. The maintenance history includes maintenance time, maintenance type and maintenance effect, which can provide a reference for equipment maintenance and management.

[0053] Then, data preprocessing is performed: the collected multi-source data is denoised, standardized, and missing value processed to ensure that the data quality meets the requirements of subsequent processing; the multi-source data is composed of information from different data sources, and the data types of the multi-source data include the temperature, pressure, vibration, power usage rate of the equipment, and the temperature and humidity of the environment; data fusion: a convolutional neural network is applied to combine information from different data sources to generate a fused data set to reflect the overall status of the equipment;

[0054] Then data fusion: the multi-source data collected in step S1 are fused, and the fusion process uses a deep learning model to integrate the multi-source data to generate a fused data set of the equipment operation status; it can integrate data from different sources to more comprehensively reflect the overall status of the equipment.

[0055] Then the data analysis and evaluation model is constructed: based on the fused data set, a machine learning algorithm is used to construct an evaluation model for the equipment operation status;

[0056] The data analysis and evaluation model construction step S3 further includes: feature extraction: applying big data analysis technology, including machine learning algorithms and data mining, to extract features from the fused data set after fusion, extract key features, and provide more valuable information for equipment operation status evaluation; model training: using the extracted key features to train and construct an evaluation model for the equipment operation status, and the obtained evaluation model is used to evaluate and predict the equipment's operating status in real time. The evaluation model outputs the evaluation status, and the generated evaluation status is used to reflect the equipment's current operating status and potential faults. The evaluation status includes the equipment's health status, performance indicators, and fault prediction; providing decision support for equipment maintenance and management; model optimization: adjusting and optimizing the evaluation model based on actual equipment operation data and maintenance feedback to improve the model's accuracy and adaptability.

[0057] Finally, the evaluation results and alarms are generated: the evaluation status of the equipment operating status is output according to the evaluation model in S3. When the equipment operating status is lower than the preset threshold or a potential failure is predicted, an alarm signal and maintenance suggestions are automatically generated to assist in equipment maintenance and fault prevention, which helps to detect equipment problems in a timely manner and take measures to improve the reliability and stability of the equipment.

[0058] The step S4 of generating the evaluation result and the alarm further comprises:

[0059] Status determination: The evaluation model outputs the evaluation status of the equipment's operating status to determine the equipment's operating status, which includes normal, warning or fault status. The status determination accurately determines the equipment's operating status and provides a basis for equipment maintenance and management.

[0060] Alarm generation: When the equipment operating status is lower than the preset threshold or a potential fault is predicted, the corresponding alarm signal and fault diagnosis information are generated, and the maintenance personnel are notified to handle it; through the alarm generation mechanism, when the equipment operating status is lower than the preset threshold or a potential fault is predicted, the alarm signal and fault diagnosis information are issued in time, and the maintenance personnel are notified to handle it, thereby improving the reliability and safety of the equipment;

[0061] Maintenance suggestions: Based on the fault diagnosis information and historical maintenance data generated under the current operating status of the equipment, maintenance suggestions are generated to help maintenance personnel effectively maintain and repair equipment, thereby improving maintenance efficiency.

[0062] The alarm signal generation process is as follows:

[0063] According to the equipment's instruction manual, industry standards, and actual operating experience, clarify the equipment's normal operating parameter range and preset thresholds for key performance indicators; adjust preset thresholds based on the equipment's historical operating data and real-time monitoring data;

[0064] The fused data set is monitored and anomaly detected through statistical analysis and machine learning algorithms to obtain equipment operation status parameters, which are compared with preset thresholds. When it is found that the equipment operation status parameters exceed the preset threshold range, or abnormal trends and patterns appear, an alarm generation mechanism is triggered. The level of the alarm signal is determined according to the abnormal degree of the equipment operation status.

[0065] The alarm signal levels are divided into emergency alarms, important alarms and general alarms, so that maintenance personnel can take appropriate measures according to the alarm level; according to the alarm level, the corresponding alarm signal is generated; the alarm signal includes sound, light, text message, and email, so as to notify the maintenance personnel in time; when the alarm signal is generated, the alarm signal also contains the equipment's identification information, alarm level, and abnormal parameters, so that maintenance personnel can quickly locate the problem equipment and understand the severity of the problem.

[0066] It should be further explained that, in the specific implementation process, clarifying the normal operating parameter range of the equipment and the preset thresholds of key performance indicators, and adjusting them according to historical operating data and real-time monitoring data, can improve the accuracy and timeliness of the alarm signal; by monitoring and detecting anomalies in the fused data set, abnormal conditions of the equipment operating status parameters can be discovered in time, triggering the alarm generation mechanism; determining the alarm signal level according to the degree of abnormality in the equipment operating status, and generating an alarm signal containing equipment identification information, alarm level and abnormal parameters, which helps maintenance personnel quickly locate problem equipment and understand the severity of the problem.

[0067] The process of generating fault diagnosis information is as follows: when the equipment operation status shows an abnormal trend, the signal processing technology and feature extraction algorithm are used to further analyze the collected equipment operation status data and extract the fault characteristics; the fault characteristics include the specific value, change trend and occurrence time of the abnormal parameters; the data is analyzed and processed to extract the characteristic information that can reflect the nature of the fault;

[0068] According to the extracted fault features, a fault diagnosis algorithm is used to classify and locate the fault, determine the cause and impact range of the fault, and obtain the fault diagnosis results and fault analysis results; fault diagnosis information is generated based on the fault diagnosis results and fault analysis results; the fault diagnosis information includes the identification information of the faulty equipment, the fault type, the fault cause, the impact range, and the recommended treatment measures; and the fault diagnosis information is sent to maintenance personnel in the form of a report so that they can quickly understand the fault situation and take corresponding treatment measures; through the above steps, when the equipment operating status is lower than the preset threshold or a potential fault is predicted, an alarm signal and fault diagnosis information can be generated in time to notify the maintenance personnel to handle it and ensure the normal operation of the equipment.

[0069] The data fusion process further includes:

[0070] Advanced data matching: Using time synchronization and spatial alignment to jointly perform matching algorithms on data from different sources can improve the accuracy and availability of multi-source data and ensure data consistency and integrity;

[0071] Multi-dimensional fusion: Utilize multi-source data fusion technology, including multi-modal deep learning and multi-view integrated learning, to fuse multi-source data in multiple dimensions to improve the accuracy and robustness of the evaluation model and more comprehensively reflect the overall status of the equipment;

[0072] Dynamic weight adjustment: According to the reliability and relevance of different data sources, the weights of multi-source data are adjusted during the fusion process to optimize the effect of data fusion and improve the accuracy and reliability of the fused data.

[0073] The process of multi-dimensional fusion of multi-source data is as follows:

[0074] First, the image data is processed based on the architecture of the convolutional neural network, the text data is processed based on the long short-term memory network, and a fully connected network is built for the sensor data. The convolutional neural network, the long short-term memory network and the fully connected network are combined to form a multimodal deep learning model, completing the construction of the multimodal deep learning model; wherein the multi-source data includes image data, sensor data and text data;

[0075] The image features are extracted through the convolutional neural network to obtain a high-dimensional image feature vector. The text features are extracted through the long short-term memory network to obtain a text feature vector. The sensor features are extracted using the fully connected network to obtain a sensor feature vector. Subsequently, different feature vectors are spliced ​​together to form a comprehensive feature vector.

[0076] Dividing the preprocessed multi-source data into different views; dividing the image data into a first view, the sensor data into a second view, and the text data into a third view;

[0077] An independent learning model is constructed for each view. For image views, a convolutional neural network is used for classification to obtain an image view model. For sensor views, a support vector machine is used for regression analysis to obtain a sensor view model. For text views, a long short-term memory network is used for classification to obtain a text view model.

[0078] The image view model, sensor view model and text view model are fused through the weighted averaging method. Different weights are assigned according to the importance of each view model through the weighted averaging method, and the prediction results of the model are weighted averaged to obtain the multi-dimensional fusion result. Through the above multi-dimensional fusion method, the multi-source data in the sample storage system of the laboratory department can be effectively fused, and the information of different data sources can be fully utilized to improve the accuracy and reliability of sample analysis and storage management.

[0079] The data analysis and evaluation model building process further includes: Model calibration: by comparing the evaluation model prediction results with the actual equipment operation data, the model is calibrated to reduce the deviation and improve the accuracy of the prediction;

[0080] By collecting actual equipment operation data, obtaining the prediction results of the evaluation model, the actual equipment operation data and the prediction results of the evaluation model are matched in time, and the equipment operation status parameters to be compared are clarified, so that the actual equipment operation data and the prediction results of the evaluation model contain the same parameters, and the meaning and unit of the parameters are consistent; for each parameter to be compared, the root mean square error is used to calculate the error between the prediction results of the evaluation model and the actual equipment operation data, and a chart is drawn through a visualization tool to show the difference and trend between the prediction results and the actual data, and then the error is statistically analyzed to understand the distribution and statistical characteristics of the error, and the results of the comparative analysis are obtained. Based on the results of the comparative analysis, The gradient descent method in the optimization algorithm is used to adjust the parameters of the data analysis and evaluation model, and the data analysis and evaluation model is calibrated. After the model is calibrated, the model is run again and compared with the actual equipment operation data to verify the calibration effect; if the error decreases, it means that the calibration is successful; if the error does not decrease, it means that the calibration is unsuccessful, and the calibration is continued; a device operation evaluation method that integrates multi-source data fusion technology, advanced data analysis methods and real-time evaluation capabilities has been established; this method can not only provide accurate equipment status monitoring and fault prediction, but also continuously optimize its performance and accuracy according to the actual equipment operation data and environmental conditions of the equipment, thereby realizing efficient and intelligent equipment management and maintenance strategies.

[0081] The evaluation model is also adjusted and optimized based on actual equipment operation data and maintenance feedback. This enables the evaluation model to continuously adapt to the actual operation of the equipment, improve the accuracy and reliability of the model, and further optimize the maintenance management of the equipment.

[0082] The specific steps and characteristics of the equipment operation evaluation method based on multi-source data fusion are clearly defined, ensuring the comprehensiveness and efficiency of the method, which is applicable to various complex industrial environments to improve equipment reliability and maintenance efficiency.

[0083] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A device operation evaluation method based on multi-source data fusion, characterized in that: The following steps are involved: S1, data collection: collect multi-source data of the equipment in real-time operation status, where the multi-source data includes the equipment's sensor data, operation log, environmental data and maintenance history; S2, data fusion: The multi-source data collected in step S1 are fused, and the fusion process uses a deep learning model to integrate the multi-source data to generate a fused data set of the equipment operation status; S3, data analysis and evaluation model construction: Based on the fused fusion data set, a machine learning algorithm is used to build an evaluation model for the equipment operation status; S4, generate evaluation results and alarms: output the evaluation status of the equipment operation status according to the evaluation model in S3. When the equipment operation status is lower than the preset threshold or a potential failure is predicted, an alarm signal and maintenance suggestions are automatically generated.

2. The equipment operation evaluation method based on multi-source data fusion according to claim 1 is characterized in that: The data collection step S1 further comprises: Sensor data acquisition: Sensors are installed on equipment components to collect operating parameters of equipment components. Sensors include temperature sensors, pressure sensors, and vibration sensors. Operation data collection: record the operation data of the equipment operator, including operation time, operation nature and operation results; Environmental data collection: record the environmental data of the equipment operating environment, including ambient temperature, humidity, electromagnetic interference, vibration and dust pollutants; Maintenance data collection: record the maintenance history of the equipment, including maintenance time, maintenance type and maintenance effect.

3. The equipment operation evaluation method based on multi-source data fusion according to claim 2 is characterized in that: The data fusion step S2 further comprises: Data preprocessing: denoising, standardizing and processing missing values ​​of the collected multi-source data; the multi-source data consists of information from different data sources, and the data types of the multi-source data include the temperature, pressure, vibration, power usage rate of the equipment, and the temperature and humidity of the environment; Data fusion: Apply convolutional neural networks to combine information from different data sources to generate a fused data set that reflects the overall status of the equipment.

4. The equipment operation evaluation method based on multi-source data fusion according to claim 3 is characterized in that: The data analysis and evaluation model building step S3 further includes: Feature extraction: Apply big data analysis techniques, including machine learning algorithms and data mining, to extract key features from the fused data set; Model training: Use the extracted key features to train and build an evaluation model for the equipment's operating status. The resulting evaluation model is used to evaluate and predict the equipment's operating status in real time. The evaluation model outputs an evaluation status that reflects the equipment's current operating status and potential faults. The evaluation status includes the equipment's health status, performance indicators, and fault predictions. Model optimization: Adjust and optimize the evaluation model based on actual equipment operation data and maintenance feedback.

5. The equipment operation evaluation method based on multi-source data fusion according to claim 4 is characterized in that: The step S4 of generating evaluation results and alarming further comprises: Status determination: The evaluation model outputs the evaluation status of the equipment's operating status to determine the equipment's operating status, which includes normal, warning or fault status; Alarm generation: When the equipment operating status is lower than the preset threshold or a potential fault is predicted, the corresponding alarm signal and fault diagnosis information are generated, and the maintenance personnel are notified to handle it; Maintenance recommendations: Generate maintenance recommendations based on fault diagnosis information and historical maintenance data generated under the current operating status of the equipment.

6. The equipment operation evaluation method based on multi-source data fusion according to claim 5 is characterized in that: The generation process of the alarm signal is as follows: According to the equipment's instruction manual, industry standards, and actual operating experience, clarify the equipment's normal operating parameter range and preset thresholds for key performance indicators; adjust preset thresholds based on the equipment's historical operating data and real-time monitoring data; The fused dataset The fused data set is monitored and anomaly detected through statistical analysis and machine learning algorithms to obtain equipment operation status parameters, which are then compared with preset thresholds. When it is found that the equipment operating status parameters exceed the preset threshold range, or abnormal trends and patterns appear, the alarm generation mechanism is triggered; the level of the alarm signal is determined according to the abnormality of the equipment operating status; the level of the alarm signal is divided into emergency alarm, important alarm and general alarm; according to the alarm level, the corresponding alarm signal is generated; the alarm signal includes sound, light, text message, and email; when the alarm signal is generated, the alarm signal also includes the equipment's identification information, alarm level, and abnormal parameters.

7. The equipment operation evaluation method based on multi-source data fusion according to claim 6 is characterized by: The fault diagnosis information generation process is as follows: When the equipment operation status shows an abnormal trend, signal processing technology and feature extraction algorithms are used to further analyze the collected equipment operation status data and extract fault characteristics; According to the extracted fault features, the fault diagnosis algorithm is used to classify and locate the fault, determine the cause and impact range of the fault, and obtain the fault diagnosis results and fault analysis results; Generate fault diagnosis information based on the fault diagnosis results and fault analysis results; the fault diagnosis information includes the identification information of the faulty equipment, the fault type, the fault cause, the impact scope, and the recommended treatment measures; And send the fault diagnosis information to maintenance personnel in the form of a report.

8. The equipment operation evaluation method based on multi-source data fusion according to claim 7 is characterized by: The data fusion processing further includes: Advanced data matching: Use time synchronization and spatial alignment to jointly perform matching algorithms on data from different sources; Multi-dimensional fusion: Utilize multi-source data fusion technology, including multi-modal deep learning and multi-view integrated learning, to fuse multi-source data in multiple dimensions; Dynamic weight adjustment: The weights of multi-source data are adjusted during the fusion process based on the reliability and relevance of different data sources.

9. The equipment operation evaluation method based on multi-source data fusion according to claim 8 is characterized in that: The process of multi-dimensional fusion of multi-source data is as follows: First, the image data is processed based on the architecture of the convolutional neural network, the text data is processed based on the long short-term memory network, and a fully connected network is built for the sensor data. The convolutional neural network, the long short-term memory network and the fully connected network are combined to form a multimodal deep learning model, completing the construction of the multimodal deep learning model; wherein the multi-source data includes image data, sensor data and text data; The image features are extracted through the convolutional neural network to obtain a high-dimensional image feature vector. The text features are extracted through the long short-term memory network to obtain a text feature vector. The sensor features are extracted using the fully connected network to obtain a sensor feature vector. Subsequently, different feature vectors are spliced ​​together to form a comprehensive feature vector. Dividing the preprocessed multi-source data into different views; dividing the image data into a first view, the sensor data into a second view, and the text data into a third view; An independent learning model is constructed for each view. For image views, a convolutional neural network is used for classification to obtain an image view model. For sensor views, a support vector machine is used for regression analysis to obtain a sensor view model. For text views, a long short-term memory network is used for classification to obtain a text view model. The image view model, sensor view model and text view model are fused through the weighted averaging method. Different weights are assigned according to the importance of each view model through the weighted averaging method, and the prediction results of the model are weighted averaged to obtain a multi-dimensional fusion result.

10. The equipment operation evaluation method based on multi-source data fusion according to claim 9 is characterized in that: The data analysis and evaluation model building process further includes: Model calibration: by comparing the model prediction results with the actual equipment operation data; By collecting actual equipment operation data, obtaining the prediction results of the evaluation model, making the actual equipment operation data correspond to the prediction results of the evaluation model in time, clarifying the equipment operation status parameters to be compared, making the actual equipment operation data and the prediction results of the evaluation model contain the same parameters, and the meaning and unit of the parameters are consistent; for each parameter to be compared, the root mean square error is used to calculate the error between the prediction results of the evaluation model and the actual equipment operation data, and a chart is drawn through a visualization tool to show the difference and trend between the prediction results and the actual data, and then the error is statistically analyzed to understand the distribution and statistical characteristics of the error, and the results of the comparative analysis are obtained. According to the results of the comparative analysis, the gradient descent method in the optimization algorithm is used to adjust the parameters of the data analysis and evaluation model, and the data analysis and evaluation model is calibrated. After the model is calibrated, the model is run again and compared with the actual equipment operation data to verify the calibration effect; if the error decreases, it means that the calibration is successful; if the error does not decrease, it means that the calibration is unsuccessful, and the calibration should be continued.

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