Workshop visual monitoring platform and method based on digital twinning

Abstract

The invention discloses a workshop visual monitoring platform and method based on digital twinning, and belongs to the technical field of industrial manufacturing visualization, the workshop visual monitoring platform comprises a data acquisition module, and the data acquisition module comprises an acquisition unit, an environment monitoring unit and a video monitoring unit; the acquisition unit is used for acquiring operation parameters of production equipment; the environment monitoring unit is used for monitoring environment parameters of the workshop; the environment parameters comprise humidity, temperature and air quality; the video monitoring unit is used for monitoring the production process of the production equipment; and the data processing module responds to the data acquisition module and is used for processing the operation parameters and the environment parameters. By setting the risk threshold value and automatically updating the operation state of the production equipment, automatic decision support can be provided for the production process, the need of manual intervention is reduced, the automation degree and decision efficiency of the production process are improved, and production management of enterprises is more intelligent and efficient.

Description

Technical Field

[0001] The present invention relates to the field of industrial manufacturing visualization technology, and in particular to a workshop visualization monitoring platform and method based on digital twins. Background Art

[0002] In modern industrial production, workshop monitoring and management are crucial for improving production efficiency, ensuring product quality, and ensuring production safety. Traditional monitoring methods, which rely primarily on manual inspections, decentralized sensor data collection, and simple video surveillance systems, have many limitations.

[0003] Regarding this research, application document CN202411053988.8 provides an intelligent workshop control system and method based on digital twin visualization technology. This technical solution includes a workshop data acquisition module, a workshop data analysis module, a workshop data service module, and a workshop data control module. The workshop data acquisition module is responsible for collecting data, the data analysis module calculates the workshop protection unit based on the workshop encryption index Qiy, the virtual workshop data homomorphic encryption index Rrz, the privacy protection index Bzf, and the access control coefficient Wrq. The data workshop data service module increases or decreases the data encryption tools at the connections between all modules based on the workshop encryption index Qiy, the virtual workshop data homomorphic encryption index Rrz, and the privacy protection index Bzf. This technical solution ensures that the virtual workshop maintains data security and privacy while operating efficiently.

[0004] Another application document, CN202410817883.9, provides a method and system for visually monitoring a manufacturing workshop based on digital twins. This technical solution involves acquiring the motion scenes of production equipment in the workshop and constructing a digital twin model based on historical motion equipment entities. Various equipment sensors are set up in the entity-constructed digital twin model; process data of equipment operation is collected, processed, and stored as multi-source heterogeneous data; equipment operation status information obtained by equipment sensors during the production process is monitored, and the operation status information data is filtered and analyzed to predict the equipment operation route. This technical solution updates the entity-constructed digital twin model based on the monitoring information; establishes a visual monitoring platform to provide users with real-time monitoring information and achieve overall control of the production process.

[0005] However, these technical solutions still have shortcomings. In reality, as production activities progress on a shop floor, factors such as equipment wear, process adjustments, and environmental changes can cause the state of physical entities to change. However, digital twin models often experience a certain lag in updating, failing to reflect the latest state of the physical entity in real time. For example, after long-term operation, equipment may wear out and its performance parameters may change, but the model may not be able to update these changes in a timely manner, resulting in monitoring results that do not match actual conditions. Summary of the Invention

[0006] In view of the above problems existing in the existing technical field of industrial manufacturing visualization, the present invention is proposed.

[0007] Therefore, one of the purposes of the present invention is to provide a workshop visualization monitoring platform and method based on digital twins, which can provide automated decision support for the production process by setting risk thresholds and automatically updating the operating status of production equipment, reducing the need for manual intervention, improving the degree of automation and decision-making efficiency of the production process, and making the company's production management more intelligent and efficient.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In one aspect, the present invention provides a digital twin-based workshop visualization monitoring platform, comprising:

[0010] Data acquisition module, which includes an acquisition unit, an environmental monitoring unit and a video monitoring unit;

[0011] The acquisition unit is used to collect the operating parameters of the production equipment;

[0012] The environmental monitoring unit is used to monitor the environmental parameters of the workshop; environmental parameters include humidity, temperature and air quality;

[0013] The video monitoring unit is used to monitor the production process of the production equipment;

[0014] The data processing module responds to the data acquisition module and is used to process the operating parameters and environmental parameters. The data fusion module is used to fuse the processed operating parameters and environmental parameters, and build a digital twin model to analyze the impact of changes in environmental parameters on operating parameters.

[0015] The LSTM network fusion processing module is used to perform fusion processing on the analyzed correlation effects; the LSTM network fusion processing module includes a differentiation unit, an analysis unit, a calculation unit, and an update unit;

[0016] The differentiation unit is used to differentiate the analyzed associated impacts into mild impact, moderate impact and severe impact in a step-by-step manner, and to analyze the characteristic changes of environmental parameters in different degrees of impact;

[0017] The analysis unit is based on the slight impact and is used to analyze the parameters that cause the slight impact according to the environmental parameters and mark the parameters as first environmental parameters; and generate a parameter group according to the first environmental parameters, the parameter group including 10 to 15 parameters that appear the most frequently; the calculation unit responds to the parameter group and is used to calculate the regular changes of each parameter in the parameter group;

[0018] The updating unit is used to update the operating status of the production equipment based on the parameter group. When the environmental parameters collected in the future period are the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state. As a preferred embodiment of the present invention, the data processing module cleans, filters, and converts the operating and environmental parameters, and removes noise data and outliers.

[0019] It also includes data analysis and feature extraction of operating parameters and environmental parameters. The data analysis includes calculating the average operating time of production equipment and calculating the mean time between equipment failures based on the average operating time, as well as the mean and variance of the workshop's environmental parameters.

[0020] Feature extraction includes dimensionality reduction and feature extraction of the operating parameters and environmental parameters of the production equipment using a machine learning algorithm. As a preferred embodiment of the present invention, the processed operating parameters and environmental parameters are fused in the data fusion module, including fusion using a physical model fusion method, in which the operating parameters and environmental parameters of the production equipment are fused based on the physical principles of the production equipment and the physical environment of the workshop.

[0021] It also includes fusion in the form of data-driven model fusion, establishing a data-driven model for generating the operating parameters and environmental parameters of the equipment through machine learning algorithms to fuse the operating parameters and environmental parameters. The machine learning algorithms include support vector machines and neural networks.

[0022] As a preferred solution of the present invention, in the calculation unit, the regular changes of each parameter in the parameter group are calculated according to the following formula:

[0023] Among them, θ represents the index of the air quality in the workshop;

[0024] Where C i represents the concentration of the i-th pollutant, C max，i and C min，i Represent the highest and lowest concentrations of the i-th pollutant, I min，i and I max，i Represent the lowest and highest air quality indexes of the i-th pollutant respectively.

[0025] As a preferred solution of the present invention, the regular changes of the parameters in the calculation parameter group are further calculated according to the following formula:

[0026] Among them, η represents the diffusion rate of pollutants over time;

[0027] Where C represents the concentration of pollutants, t represents time, D represents the diffusion coefficient of pollutants, u represents wind speed, and S represents the source term of pollutants; represents the Laplace operator.

[0028] As a preferred solution of the present invention, the environmental parameters causing moderate impact are predicted based on the calculated regular changes, and the prediction method includes:

[0029] The maximum parameter and the median parameter are obtained from the parameter group, the regular characteristics of the median parameter changing toward the maximum parameter are analyzed, and based on the regular characteristics, the difference of adjacent parameters between the median parameter and the maximum parameter is obtained, the average difference is calculated based on the difference, and the risk threshold is preset with the average difference. When the average difference of the environmental parameters collected in the future period exceeds the risk threshold, the operating state of the production equipment is updated to a moderately affected fault state, and the average difference is marked as a reference value. As a preferred embodiment of the present invention, if the operating state of the production equipment is updated to a slightly affected fault state, the change of the operating parameter is collected based on the parameter corresponding to the slightly affected fault state, and the abnormal change of the production equipment is obtained based on the change of the operating parameter and the video of the production process of the monitored production equipment. If the change of the operating parameter is less than the difference of the adjacent parameters obtained between the median parameter and the maximum parameter, the update of the operating state of the production equipment to the slightly affected fault state is canceled.

[0030] As a preferred solution of the present invention, if the change in the operating parameter is less than the difference between adjacent parameters obtained between the median parameter and the maximum parameter, a monitoring period is preset for the change in the operating parameter. The monitoring period is preset based on the time taken for the median parameter to change to the maximum parameter, and the duration of the monitoring period is at least one third of the time. During the monitoring period, if the average difference corresponding to the change in the operating parameter is half of the reference value, the operating status of the production equipment is updated to a slightly affected fault state.

[0031] On the other hand, the present invention provides a method for applying the above-mentioned digital twin-based workshop visualization monitoring platform, comprising the following steps:

[0032] Collect the operating parameters of production equipment and the environmental parameters of the workshop, and monitor the production process of production equipment;

[0033] Processing of operating parameters and environmental parameters, including cleaning, filtering and format conversion of operating parameters and environmental parameters, as well as removal of noise data and outliers;

[0034] The processed operating parameters and environmental parameters are integrated and a digital twin model is constructed to analyze the impact of changes in environmental parameters on operating parameters.

[0035] The analyzed associated impacts are integrated, including classifying the analyzed associated impacts into mild impact, moderate impact and severe impact in a step-by-step manner, and analyzing the characteristic changes of environmental parameters in different degrees of impact;

[0036] Analyze the parameters that cause slight impact based on the environmental parameters and mark the parameters as the first environmental parameters; generate a parameter group based on the first environmental parameters, the parameter group includes 10 to 15 parameters that appear the most frequently, and calculate the regular changes of each parameter in the parameter group;

[0037] The operating status of the production equipment is updated according to the parameter group. When the environmental parameters collected in the future period are the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state.

[0038] Beneficial effects:

[0039] 1. The LSTM network fusion processing module integrates the analyzed correlated impacts and uses a differentiation unit to differentiate the impact levels in a step-by-step manner. This processing method can automatically learn and identify complex patterns and regularities in the data, thereby more accurately updating the operating status of production equipment. For example, for mild impacts, the analysis unit and calculation unit determine key environmental parameters and their changing patterns, and use this information to update the operating status of production equipment, thereby identifying possible faults in advance.

[0040] 2. The update unit updates the operating status of production equipment based on the parameter group and can dynamically adjust the operating status of production equipment based on environmental parameters collected in the future. This dynamic update mechanism enables the monitoring platform to reflect the actual operating status of production equipment in real time, promptly identify potential failure risks, and adjust the operating strategy of production equipment based on actual conditions, thereby reducing the impact of production equipment failures on production.

[0041] 3. When the operating status of production equipment is updated to a slightly affected fault state, abnormal changes in the production equipment can be obtained based on changes in operating parameters and videos of the production process, and a judgment can be made on whether to remove the fault state. In addition, a preset monitoring period is set to continuously monitor changes in operating parameters. This mechanism not only avoids misjudgments caused by short-term fluctuations in environmental parameters, but also can respond in a timely manner when there are substantial changes in the operating status of production equipment, further improving the accuracy and reliability of the prediction and processing of operating status. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 labor.

[0043] in:

[0044] Figure 1 Schematic diagram of the modular structure of a digital twin-based workshop visualization monitoring platform according to an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of a method flow in an embodiment of the present invention;

[0046] Numbers in the figure: 110-data acquisition module; 1101-acquisition unit; 1102-environmental monitoring unit; 1103-video monitoring unit; 120-data processing module; 130-data fusion module; 140-LSTM network fusion processing module; 1401-differentiation unit; 1402-analysis unit; 1403-calculation unit; 1404-update unit. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0048] Since the update of digital twin models in existing technologies has a certain lag, it is unable to reflect the latest status of physical entities in real time, resulting in monitoring results that are inconsistent with the actual situation.

[0049] Based on this, the present invention proposes a digital twin-based workshop visualization monitoring platform and method. By setting risk thresholds and automatically updating the operating status of production equipment, it can provide automated decision support for the production process, reducing the need for manual intervention, improving the degree of automation and decision-making efficiency of the production process, and making the company's production management more intelligent and efficient. The following examples and accompanying drawings further illustrate this solution.

[0050] Reference Figures 1 to 2 , is an embodiment of the present invention, which provides a digital twin-based workshop visualization monitoring platform, including:

[0051] Data acquisition module 110, the data acquisition module 110 includes an acquisition unit 1101, an environment monitoring unit 1102 and a video monitoring unit 1103;

[0052] The collection unit 1101 is used to collect the operating parameters of the production equipment;

[0053] In this embodiment, various types of sensors, such as temperature sensors, pressure sensors, vibration sensors, current sensors, etc., are installed on designated production equipment to collect parameter data during the operation of the production equipment;

[0054] The environmental monitoring unit 1102 is used to monitor the environmental parameters of the workshop; the environmental parameters include humidity, temperature and air quality;

[0055] In this embodiment, environmental monitoring equipment deployed in the workshop, such as temperature and humidity sensors, air quality sensors, etc., is used to monitor the environmental conditions of the workshop to ensure that the production environment meets the process requirements;

[0056] The video monitoring unit 1103 is used to monitor the production process of the production equipment;

[0057] In this embodiment, high-definition cameras are installed at key locations of designated production equipment to achieve real-time video monitoring of the production process; this is used for auxiliary monitoring and post-event traceability analysis;

[0058] The data processing module 120 responds to the data acquisition module and is used to process the operating parameters and environmental parameters. The data fusion module 130 is used to fuse the processed operating parameters and environmental parameters, and build a digital twin model to analyze the impact of changes in environmental parameters on the operating parameters.

[0059] The LSTM network fusion processing module 140 is used to perform fusion processing on the analyzed correlation influences; the LSTM network fusion processing module 140 includes a distinguishing unit 1401, an analyzing unit 1402, a calculating unit 1403 and an updating unit 1404;

[0060] The differentiation unit 1401 is used to differentiate the analyzed associated impacts into mild impact, moderate impact and severe impact in a step-by-step manner, and analyze the characteristic changes of environmental parameters in different degrees of impact;

[0061] The analyzing unit 1402 is configured to analyze parameters causing a slight impact based on the environmental parameters and mark the parameters as first environmental parameters; and generate a parameter group based on the first environmental parameters, the parameter group including 10 to 15 parameters with the highest number of occurrences; the calculating unit 1403 is configured to calculate regular changes of each parameter in the parameter group in response to the parameter group;

[0062] The updating unit 1404 is used to update the operating status of the production equipment according to the parameter group. When the environmental parameters collected in the future period are the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state.

[0063] To further illustrate, by collecting the operating parameters of production equipment and the environmental parameters of the workshop in real time and combining them with video monitoring, the monitoring platform can reflect the actual situation of the workshop in real time and provide timely and accurate information for production management;

[0064] The use of digital twin models and LSTM networks enables in-depth analysis and processing of data, automatically identifying the impact of environmental parameter changes on the operation of production equipment and updating the operating status of production equipment accordingly, thus improving the level of intelligent monitoring.

[0065] In the data processing module, the operating parameters and environmental parameters are cleaned, filtered, and formatted, and noise data and outliers are removed;

[0066] It also includes data analysis and feature extraction of operating parameters and environmental parameters. The data analysis includes calculating the average operating time of production equipment and calculating the mean time between equipment failures based on the average operating time, as well as the mean and variance of the workshop's environmental parameters.

[0067] In this embodiment, these statistics can be used to understand the basic conditions of equipment operation and workshop environment, providing basic data for subsequent monitoring and optimization;

[0068] Feature extraction includes dimensionality reduction and feature extraction of the operating parameters and environmental parameters of the generated equipment through machine learning algorithms. In this embodiment, feature extraction based on machine learning uses machine learning algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimensionality and extract features of the operating parameters and environmental parameters of the generated equipment. For example, principal component analysis (PCA) is used to extract the main components of the equipment operating parameters and environmental parameters, reduce the data dimension, and retain the main information of the data, thereby improving the efficiency and accuracy of subsequent training and analysis of the digital twin model.

[0069] To further illustrate, this embodiment performs data analysis and feature extraction, and uses machine learning algorithms to reduce the dimension and extract features of the data, thereby mining useful information hidden in the data, further enhancing the value of the data, and providing stronger support for subsequent production equipment failure prediction and production process optimization.

[0070] In the data fusion module, the processed operating parameters and environmental parameters are fused, including fusion through physical model fusion. Based on the physical principles of production equipment and the physical environment of the workshop, the operating parameters and environmental parameters of the generated equipment are fused.

[0071] In this embodiment, for example, for motor equipment, the motor's operating parameters such as current, voltage, and power are integrated with environmental parameters such as the workshop's ambient temperature and ventilation conditions based on the motor's thermodynamic model. The actual operating temperature of the motor is calculated using the thermodynamic model, and possible overheating faults of the motor are predicted in advance.

[0072] It also includes fusion in the form of data-driven model fusion, establishing a data-driven model for generating operating parameters and environmental parameters of the equipment through machine learning algorithms to fuse the operating parameters and environmental parameters. The machine learning algorithms include support vector machines and neural networks.

[0073] In this embodiment, for example, a neural network model is trained using a data-driven model. Vibration data of the production equipment and noise data of the workshop are used as inputs in the neural network model, and the fault status of the production equipment is used as output. The operating parameters and environmental parameters are integrated through the neural network model to improve the accuracy of fault diagnosis.

[0074] To further illustrate, this embodiment adopts a combination of physical model fusion and data-driven model fusion, which not only takes into account the physical principles of production equipment and the physical environment of the workshop, but also utilizes the powerful data processing capabilities of machine learning algorithms. It can more comprehensively and accurately integrate operating parameters and environmental parameters, giving full play to the advantages of both fusion methods and improving the effect of data fusion.

[0075] Physical model fusion can better adapt to changes in the physical characteristics of equipment and the environment, while data-driven model fusion can automatically adjust model parameters based on changes in actual data, making the entire fusion process more adaptable and flexible, and better able to cope with various complex situations in the workshop production process;

[0076] In the calculation unit, the regular changes of each parameter in the calculation parameter group are calculated according to the following formula: Among them, θ represents the index of the air quality of the workshop; i represents the concentration of the i-th pollutant, C max，i and C min，i Represent the highest and lowest concentrations of the i-th pollutant, I min，i and I max，i The lowest and highest air quality indices of the i-th pollutant respectively;

[0077] In this embodiment, the formula can be used to quantitatively evaluate the air quality of the workshop, and the air quality index can be used to intuitively reflect the quality of the air. This helps monitoring personnel quickly understand the air quality status of the workshop and provides a reference for environmental control and equipment maintenance during the production process.

[0078] The formula also takes into account the concentrations of different pollutants and their corresponding maximum and minimum concentration ranges, as well as the corresponding air quality index range. It can conduct a comprehensive assessment of multiple pollutants in the workshop, is highly targeted, and can more accurately reflect the actual impact of workshop air quality on production equipment and production processes.

[0079] The regular changes of the parameters in the calculation parameter group are also calculated according to the following formula:

[0080] Among them, η represents the diffusion rate of pollutants over time;

[0081] Where C represents the concentration of pollutants, t represents time, D represents the diffusion coefficient of pollutants, u represents wind speed, and S represents the source term of pollutants; represents the Laplace operator;

[0082] In this embodiment, in real life, the air quality of a workshop affects the operation of production equipment. For example, impurities such as particulate matter and dust in the air can clog the equipment's filters and ventilation systems, reducing ventilation efficiency and affecting normal operation. For example, in a food processing workshop, dust in the air can clog ventilation ducts, causing increased temperature and humidity within the workshop, thus affecting the efficiency of production equipment.

[0083] Pollutants in the air may affect the stability of equipment, causing vibration, noise and other problems during operation. For example, in a textile workshop, fiber particles in the air may adhere to the transmission components of the equipment, causing unstable operation, vibration and noise.

[0084] Poor air quality can increase equipment failure rates, leading to increased equipment downtime and impacting production efficiency. For example, in a machining workshop, dust in the air can enter the equipment's electrical control system, causing short circuits in electrical components, poor contact, and other issues, increasing equipment failure rates.

[0085] Therefore, it is of practical significance to calculate the regular changes in the air quality of the workshop;

[0086] Furthermore, this formula can dynamically calculate the diffusion rate of pollutants over time based on factors such as pollutant concentration, diffusion coefficient, wind speed, and source term. This provides a theoretical basis for predicting the diffusion trend of pollutants within a workshop, enabling the monitoring platform to foresee the potential impact of pollutants on equipment and production processes in advance, thereby taking appropriate preventive measures. Compared with simple static analysis methods, this formula can more accurately predict the diffusion trend and impact range of pollutants.

[0087] Based on the calculated regular changes, the environmental parameters that will cause moderate impact are predicted. The prediction methods include:

[0088] The maximum parameter and the median parameter are obtained from the parameter group, and the regular characteristics of the median parameter changing toward the maximum parameter are analyzed. Based on the regular characteristics, the difference of adjacent parameters between the median parameter and the maximum parameter is obtained, and the average difference is calculated based on the difference. The risk threshold is preset based on the average difference. When the average difference of the environmental parameters collected in the future time period exceeds the risk threshold, the operating status of the production equipment is updated to a fault state with a moderate impact, and the average difference is marked as a reference value. In this embodiment, the method is based on an in-depth analysis of the parameter change pattern, and by calculating the difference and the average difference of adjacent parameters and using this as the basis for setting the risk threshold, it has certain scientificity and accuracy, and can more reliably predict the degree of impact of environmental parameter changes on equipment operation.

[0089] If the operating status of the production equipment is updated to a slightly affected fault state, the changes in the operating parameters are collected based on the parameters corresponding to the slightly affected fault state, and the abnormal changes in the production equipment are obtained based on the changes in the operating parameters and the video of the production process of the monitored production equipment. If the change in the operating parameters is less than the difference between the adjacent parameters obtained between the median parameter and the maximum parameter, the update of the operating status of the production equipment to the slightly affected fault state is canceled. In this embodiment, this mechanism enables the monitoring platform to flexibly adjust the operating status of the equipment according to actual conditions, avoids misjudgment caused by short-term fluctuations in environmental parameters, and improves the accuracy and reliability of the equipment operating status update.

[0090] By combining the changes in operating parameters with the production process video, a comprehensive judgment can be made to gain a more comprehensive understanding of the actual operating status of the equipment, providing a more sufficient basis for updating the equipment's operating status, and enabling the monitoring platform to more dynamically reflect the equipment's actual operating conditions.

[0091] If the change in the operating parameter is less than the difference between adjacent parameters obtained between the median parameter and the maximum parameter, a monitoring period is preset for the change in the operating parameter. The monitoring period is preset based on the time taken for the median parameter to change to the maximum parameter, and the duration of the monitoring period is at least one-third of the time. During the monitoring period, if the average difference corresponding to the change in the operating parameter is half of the reference value, the operating status of the production equipment is updated to a slightly affected fault state.

[0092] In this embodiment, by setting reasonable monitoring periods and reference values, the update of the equipment operating status is made more reliable, frequent status updates due to short-term parameter fluctuations are avoided, the possibility of misjudgment is reduced, and the stability and reliability of the monitoring system are improved.

[0093] Based on the above, this application can provide automated decision support for the production process by setting risk thresholds and automatically updating the operating status of production equipment, reducing the need for manual intervention, improving the degree of automation and decision-making efficiency of the production process, and making the company's production management more intelligent and efficient.

[0094] This embodiment combines the above-mentioned digital twin-based workshop visualization monitoring platform and also proposes a method applied to the platform as follows:

[0095] S10: Collect the operating parameters of the production equipment and the environmental parameters of the workshop, and monitor the production process of the production equipment;

[0096] S20: Processing the operating parameters and environmental parameters, including cleaning, filtering, and format conversion of the operating parameters and environmental parameters, and removing noise data and outliers;

[0097] S30: Fusing the processed operating parameters and environmental parameters, and building a digital twin model to analyze the impact of changes in environmental parameters on operating parameters;

[0098] S40: performing integration processing on the analyzed associated impacts, including classifying the analyzed associated impacts into light impact, moderate impact and heavy impact in a step-by-step manner, and analyzing the characteristic changes of environmental parameters in different degrees of impact;

[0099] S50: Analyzing parameters that cause minor impacts based on environmental parameters, marking the parameters as first environmental parameters; generating a parameter group based on the first environmental parameters, the parameter group including 10 to 15 parameters that appear the most frequently, and calculating regular changes of the parameters in the parameter group;

[0100] S60: updating the operating status of the production equipment according to the parameter group. When the environmental parameter collected in the future period is the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state.

[0101] In summary, the present invention can provide automated decision support for the production process by setting risk thresholds and automatically updating the operating status of production equipment, reducing the need for manual intervention, improving the degree of automation and decision-making efficiency of the production process, and making the company's production management more intelligent and efficient.

[0102] 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. The digital twin-based workshop visualization monitoring platform is characterized by: include: A data acquisition module, comprising an acquisition unit, an environment monitoring unit, and a video monitoring unit; The acquisition unit is used to collect operating parameters of the production equipment; The environmental monitoring unit is used to monitor the environmental parameters of the workshop; the environmental parameters include humidity, temperature and air quality; The video monitoring unit is used to monitor the production process of the production equipment; a data processing module, the data processing module responding to the data acquisition module and configured to process the operating parameters and environmental parameters; A data fusion module is used to fuse the processed operating parameters and environmental parameters, build a digital twin model, and analyze the impact of changes in environmental parameters on operating parameters; An LSTM network fusion processing module is used to perform fusion processing on the analyzed correlation influences; the LSTM network fusion processing module includes a differentiation unit, an analysis unit, a calculation unit, and an update unit; The differentiation unit is used to differentiate the analyzed associated impacts into mild impact, moderate impact and severe impact in a step-by-step manner, and analyze characteristic changes of the environmental parameters in the impacts of different degrees; The analyzing unit is configured to analyze a parameter causing the slight impact according to the environmental parameter based on the slight impact, and mark the parameter as a first environmental parameter; and generating a parameter group according to the first environmental parameter, wherein the parameter group includes 10 to 15 parameters that appear the most times; The calculation unit responds to the parameter group and is used to calculate the regular changes of each parameter in the parameter group; The updating unit is used to update the operating status of the production equipment according to the parameter group. When the environmental parameters collected in the future period are the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state.

2. The digital twin-based workshop visualization monitoring platform according to claim 1, characterized in that: In the data processing module, the operating parameters and environmental parameters are cleaned, filtered, and formatted, and noise data and outliers are removed; The method further includes performing data analysis and feature extraction on the operating parameters and environmental parameters; wherein the data analysis includes calculating the average operating time of the production equipment, and calculating the mean time between equipment failures based on the average operating time, and also includes the mean and variance of the environmental parameters of the workshop; Feature extraction involves dimensionality reduction and feature extraction of the operating parameters and environmental parameters of the generated equipment through machine learning algorithms.

3. The digital twin-based workshop visualization monitoring platform according to claim 1 is characterized in that: In the data fusion module, the processed operating parameters and environmental parameters are fused, including fusion in the form of physical model fusion, in which the operating parameters and environmental parameters of the generated equipment are fused according to the physical principles of the production equipment and the physical environment of the workshop; It also includes fusion in the form of data-driven model fusion, establishing a data-driven model for generating operating parameters and environmental parameters of the equipment through a machine learning algorithm to fuse the operating parameters and environmental parameters, and the machine learning algorithm includes a support vector machine and a neural network.

4. The digital twin-based workshop visualization monitoring platform according to claim 1, characterized in that: In the calculation unit, the regular changes of each parameter in the parameter group are calculated according to the following formula: Among them, θ represents the index of the air quality in the workshop; Where C i represents the concentration of the i-th pollutant, C max，i and C min，i Represent the highest and lowest concentrations of the i-th pollutant, I min，i and I max，i Represent the lowest and highest air quality indexes of the i-th pollutant respectively.

5. The digital twin-based workshop visualization monitoring platform according to claim 4 is characterized in that: The regular changes of the parameters in the calculation parameter group are also calculated according to the following formula: Among them, η represents the diffusion rate of pollutants over time; Where C represents the concentration of pollutants, t represents time, D represents the diffusion coefficient of pollutants, u represents wind speed, and S represents the source term of pollutants; represents the Laplace operator.

6. The digital twin-based workshop visualization monitoring platform according to any one of claims 4 to 5, characterized in that: The environmental parameters causing the moderate impact are predicted based on the calculated regular changes, and the prediction methods include: A maximum parameter and a median parameter are obtained from the parameter group, regular characteristics of the median parameter changing toward the maximum parameter are analyzed, and based on the regular characteristics, differences between adjacent parameters between the median parameter and the maximum parameter are obtained, an average difference is calculated based on the differences, and a risk threshold is preset with the average difference. When the average difference of the environmental parameters collected in a future time period exceeds the risk threshold, the operating status of the production equipment is updated to a moderately affected fault state, and the average difference is marked as a reference value.

7. The digital twin-based workshop visualization monitoring platform according to claim 6, characterized in that: If the operating status of the production equipment is updated to a slightly affected fault state, the changes in the operating parameters are collected based on the parameters corresponding to the slightly affected fault state, and the abnormal changes in the production equipment are obtained based on the changes in the operating parameters and the video of the production process of the monitored production equipment. If the change in the operating parameters is less than the difference between the adjacent parameters obtained between the median parameter and the maximum parameter, the update of the operating status of the production equipment to the slightly affected fault state is canceled.

8. The digital twin-based workshop visualization monitoring platform according to claim 7 is characterized in that: If the change in the operating parameter is less than the difference between adjacent parameters obtained between the median parameter and the maximum parameter, a monitoring period is preset for the change in the operating parameter. The monitoring period is preset based on the time taken for the median parameter to change to the maximum parameter, and the duration of the monitoring period is at least one third of the time taken. During the monitoring period, if the average difference corresponding to the change in the operating parameter is half of the reference value, the operating status of the production equipment is updated to a slightly affected fault state.

9. The method applied to the digital twin-based workshop visualization monitoring platform according to claim 1, characterized in that: The following steps are involved: Collect the operating parameters of production equipment and the environmental parameters of the workshop, and monitor the production process of production equipment; Processing the operating parameters and environmental parameters, including cleaning, filtering, and format conversion of the operating parameters and environmental parameters, and removing noise data and outliers; The processed operating parameters and environmental parameters are integrated, and a digital twin model is constructed to analyze the impact of changes in environmental parameters on the operating parameters; The analyzed associated impacts are integrated, including classifying the analyzed associated impacts into mild impact, moderate impact, and severe impact in a step-by-step manner, and analyzing the characteristic changes of environmental parameters described in the different degrees of impact; Analyzing the parameters causing the slight impact based on the environmental parameters, marking the parameters as first environmental parameters; generating a parameter group based on the first environmental parameters, the parameter group including 10 to 15 parameters with the most occurrences, and calculating regular changes of each parameter in the parameter group; The operating status of the production equipment is updated according to the parameter group. When the environmental parameters collected in the future period are the same as any parameter in the parameter group, the operating status of the production equipment is updated to a slightly affected fault state.

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