Super-efficient explosion-proof motor predictive maintenance system based on digital twinning

Abstract

The invention relates to the field of industrial production, and discloses a predictive maintenance system for an ultra-efficient explosion-proof motor based on digital twinning, which comprises the following steps: when key operation parameters of the motor deviate from a normal working condition interval, judging whether the key operation parameters reach a digital twinning model activation threshold value or not; based on historical operation data, sensor real-time data and environmental factors, a digital twin model is called to construct virtual mapping of motor operation, a current state evolution trajectory is generated, and whether the operation trend has a fault or not is predicted; the state track change mode is matched with a historical fault library, and whether a potential fault path exists or not is evaluated; according to the fault risk level and the predicted occurrence time, in combination with the current production plan and the equipment health tolerance, whether an advanced intervention measure needs to be taken is judged; and generating an intervention scheme according to state regulation and control parameters fed back by the digital twinning model in combination with specific working conditions. The method has the advantage that the operation safety and the maintenance efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial production, and in particular to a digital twin-based ultra-efficient explosion-proof motor predictive maintenance system. Background Art

[0002] In industrial production, explosion-proof motors, as critical power equipment, are widely used in flammable and explosive environments such as chemical, coal mining, and petroleum industries. Their operating status is directly related to the safety and stability of production systems. Currently, many explosion-proof motor maintenance systems still rely on fixed threshold-based alarm mechanisms. These mechanisms set warning thresholds for parameters such as temperature, current, and vibration, triggering an alarm if the actual monitored data exceeds the set range. While this approach has improved the detection efficiency of motor anomalies to a certain extent, it still has significant shortcomings. Threshold strategies are often based on experience or universal standards, failing to fully consider the individual characteristics of motors in different operating environments and conditions, resulting in frequent false alarms and missed alarms. This approach is essentially a "post-event" mechanism, issuing alarms only after the equipment status has significantly deviated from the normal range, lacking the ability to identify and intervene in the early stages of a fault. Therefore, it is necessary to design an ultra-efficient digital twin-based predictive maintenance system for explosion-proof motors that can proactively detect potential faults and precisely intervene. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides an ultra-efficient explosion-proof motor predictive maintenance system based on digital twins, which has the advantages of improving operational safety and maintenance efficiency, and solves the problems in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose of improving operational safety and maintenance efficiency, the present invention provides the following technical solution: an ultra-efficient explosion-proof motor predictive maintenance system based on digital twins, comprising:

[0005] Operation status identification module: When the key operating parameters of the motor deviate from the normal operating range, it determines whether the digital twin model activation threshold has been reached. If so, it enters the state modeling module;

[0006] State modeling module: Based on historical operating data, real-time sensor data, and environmental factors, the digital twin model is used to construct a virtual map of the motor's operation, generate the current state evolution trajectory, and predict whether there are any faults in the operating trend. If so, the module enters the fault risk assessment module.

[0007] Fault risk assessment module: This module matches the state trajectory change pattern with the historical fault database to assess whether there is a potential fault path. If so, it enters the intervention demand analysis module.

[0008] Intervention Needs Analysis Module: This module determines whether early intervention measures are needed based on the fault risk level and expected time of occurrence, combined with the current production plan and equipment health tolerance. If necessary, it then enters the intervention strategy generation module.

[0009] Intervention strategy generation module: Generates intervention plans based on the state control parameters fed back by the digital twin model and combined with specific working conditions.

[0010] Preferably, the process of determining whether the digital twin model activation threshold is reached is:

[0011] Set key operating parameters and calculate the deviation of current parameters from the normal operating range;

[0012] If the deviation is greater than 0, it means that the motor operating parameters have exceeded the warning value, and it is evaluated whether it is close to the activation threshold.

[0013] Preferably, evaluating whether the activation threshold is approached comprises:

[0014] Calculate the difference between the current parameter and the activation threshold to obtain the activation proximity value;

[0015] If the activation proximity value is less than the activation proximity threshold, it is necessary to predict the state of the current parameter;

[0016] If the activation proximity value is greater than or equal to the activation proximity threshold, there is no need to perform state prediction on the current parameter.

[0017] Preferably, the process of generating the current state evolution trajectory is:

[0018] According to the current key operating parameters of the motor, a multi-dimensional timing curve of the motor state is constructed with the X axis as time and the Y axis as various operating parameters;

[0019] Based on historical operating data, the state evolution trajectory curve of the motor under standard conditions is constructed as a reference curve;

[0020] Select several sampling points with the same time points on the motor's current state timing curve and the reference state trajectory curve;

[0021] Obtaining operating parameter values ​​of the motor's current state curve at different sampling points and integrating them into a first state data group;

[0022] Obtaining the values ​​of the operating parameters of the reference state trajectory curve at the same sampling point and integrating them into a second state data set;

[0023] The cosine similarity between the first state data set and the second state data set is calculated to obtain a trajectory similarity value.

[0024] Preferably, set the curve similarity threshold:

[0025] If the trajectory similarity value is less than the curve similarity threshold, it means that the curve type is linear;

[0026] If the trajectory similarity value is greater than or equal to the curve similarity threshold, it means that the curve type is nonlinear.

[0027] Preferably, the process of predicting whether there is a fault in the operating trend includes:

[0028] If the curve type is linear, the least squares method is used to fit the change trend curve of the key operating parameters of the motor to obtain the function equation of the fitting line;

[0029] The critical fault threshold of the operating parameter is used as the input of the function equation to predict the time point when the operating parameter reaches the fault threshold, that is, the critical time point.

[0030] Preferably, the critical time point is compared with the end time point of the current maintenance cycle:

[0031] If the critical time point is less than the maintenance cycle end time point, it means that the motor operation status is unsafe in the current cycle;

[0032] If the critical time point is greater than or equal to the maintenance cycle end time point, it means that the motor operation status is safe in the current cycle.

[0033] Preferably, the process of predicting whether there is a fault in the operating trend further includes:

[0034] If the curve type is nonlinear, perform growth analysis on the first state data group to determine the fastest growth rate of the key operating parameters. The first state data group is the historical change data of the key parameters in the time dimension. By performing a differential operation on the first state data, the change rate of the parameters at each moment is calculated, and the maximum growth rate is determined from it.

[0035] Calculate the difference between the current value of the key parameter and the fault threshold to obtain the parameter approach value. Divide the parameter approach value by the fastest growth rate to obtain the duration for the parameter to reach the fault threshold, i.e., the critical duration. The critical duration plus the current time point is the critical time point.

[0036] Preferably, the process of determining whether early intervention measures are needed is:

[0037] Obtain current production plan information and the equipment health tolerance of the motor;

[0038] Combine the equipment health status data with the production plan data to construct the equipment health data group and the production task data group;

[0039] The Pearson correlation coefficient was used to calculate the correlation between the health data set and the production plan data set.

[0040] Preferably, the process of generating an intervention plan is:

[0041] Collect operating condition information related to equipment operation, search and match the intervention strategy library, screen out intervention measures that are suitable for the current scenario, and form an intervention plan based on the selected strategy, combined with resource scheduling and production tasks.

[0042] Compared with the existing technology, the present invention provides an ultra-efficient explosion-proof motor predictive maintenance system based on digital twins, which has the following beneficial effects:

[0043] Through the collaborative work of multiple modules, this invention achieves intelligent predictive maintenance throughout the entire process, from anomaly identification to intervention decisions. The digital twin model is activated at the first sign of anomalies in key motor parameters, pre-establishing a virtual map of the operating state and predicting its evolutionary trend. This effectively identifies potential fault risks and enables precise fault risk assessment through trajectory pattern matching. By combining production rhythm with equipment tolerance, a scientific decision is made on whether to intervene, and the optimal intervention plan is dynamically generated based on operating conditions. This not only improves the accuracy and response efficiency of fault prediction, but also significantly reduces unplanned downtime and maintenance costs, ensuring the safety and continuity of motor operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the structure of the present invention;

[0045] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] Example 1

[0048] See also Figure 1 As shown, the ultra-efficient explosion-proof motor predictive maintenance system based on digital twins according to an embodiment of the present invention includes:

[0049] Operation status identification module: When the key operating parameters of the motor deviate from the normal operating range, it determines whether the digital twin model activation threshold is reached. If so, it enters the state modeling module.

[0050] In the operating state identification module, when the key operating parameters of the motor deviate from the normal operating range, the process of determining whether the digital twin model activation threshold is reached is as follows:

[0051] Set key operating parameters, including real-time parameter values ​​P real , working condition warning value P alert and the digital twin model activation threshold P max , calculate the deviation degree of the current parameter relative to the normal operating range, the formula is:

[0052] ΔP alert =P real -P alert

[0053] It should be noted that the working condition warning value P alert <Digital twin model activation threshold P max , working condition warning value P alert It is set by those skilled in the art based on the motor structure, usage scenario, historical data, etc., in order to prevent the real-time parameter value P aaal Exceeding the digital twin model activation threshold P max The set warning value;

[0054] If ΔP aleat >0, indicating that the motor operating parameters have exceeded the warning value and need to be further evaluated to see if they are close to the activation threshold;

[0055] Calculate the difference between the current parameter and the activation threshold to obtain the activation proximity value ΔP margin :

[0056] ΔP aaaaaa =P max -P real

[0057] If the activation proximity value is less than the activation proximity threshold P thresh , then it is necessary to predict the state of the current parameters;

[0058] If the activation proximity value is greater than or equal to the activation proximity threshold P thresh , then there is no need to predict the state of the current parameters;

[0059] It should be noted that the real-time motor key parameter values ​​P are obtained through sensors such as current, voltage, temperature, vibration, and speed. real , determine the acceptable limit of the parameter, i.e. the activation threshold P, through experiments or empirical data max , and then calculate the activation proximity value ΔP margin , represents the safety margin between the current state and the activation threshold, and the set proximity threshold P thresh It is a predefined tolerance range used to determine whether the digital twin model needs to be called for subsequent predictive analysis;

[0060] It is understandable that the role of determining whether the activation threshold of the digital twin model has been reached is:

[0061] Function 1: By setting the activation threshold, the digital twin model is triggered only when the motor operating state is close to danger or abnormal, avoiding excessive response to slight fluctuations or changes within the normal range.

[0062] Function 2: Digital twin models have high computational costs and large data requirements. On-demand activation is achieved through a threshold judgment mechanism to avoid unnecessary long-term operation of the model.

[0063] It is understandable that by setting the activation threshold, accurate identification and graded response to abnormal motor conditions can be achieved, and digital twin modeling analysis can be started only when the potential fault risk is high, thereby avoiding ineffective consumption of system resources, improving the response efficiency and practical value of predictive maintenance, and effectively reducing the false alarm rate and missed alarm rate, making fault warning more timely and accurate, and enhancing the system's intelligent and refined operation and maintenance capabilities.

[0064] State modeling module: Based on historical operating data, real-time sensor data, and environmental factors, the digital twin model is called to build a virtual map of the motor operation, generate the current state evolution trajectory, and predict whether there is a fault in the operating trend. If so, it enters the fault risk assessment module.

[0065] The process of calling the digital twin model in the state modeling module to build a virtual map of the motor operation and generate the current state evolution trajectory is as follows:

[0066] According to the current key operating parameters of the motor, a multi-dimensional timing curve of the motor state is constructed with the X axis as time and the Y axis as various operating parameters;

[0067] Based on historical operating data, the state evolution trajectory curve of the motor under standard conditions is constructed as a reference curve;

[0068] Select several sampling points with the same time points on the motor's current state timing curve and the reference state trajectory curve;

[0069] Obtaining operating parameter values ​​of the motor's current state curve at different sampling points and integrating them into a first state data group;

[0070] Obtaining the values ​​of the operating parameters of the reference state trajectory curve at the same sampling point and integrating them into a second state data set;

[0071] Calculating the cosine similarity between the first state data group and the second state data group to obtain a trajectory similarity value;

[0072] For example, it is assumed that the first state data set (parameters on the current state curve of the motor) is: A = {A1, A2, ..., A n}, the second state data set (parameters on the reference state trajectory curve) is: B = {B1, B2, ..., B n};

[0073] Calculate the cosine similarity between the first state data group and the second state data group to obtain the trajectory similarity value. The specific calculation formula is:

[0074]

[0075] Set the curve similarity threshold D thresh :

[0076] If the trajectory similarity value is less than the threshold D thresh , indicating that the curve type is linear;

[0077] If the trajectory similarity value is greater than or equal to the threshold D thresh , indicating that the curve type is nonlinear.

[0078] The state modeling module combines the curve type analysis results to predict the subsequent motor operation state and the process of predicting whether there is a fault in the operation trend includes:

[0079] If the curve type is linear, the least squares method is used to fit the trend curve of the key operating parameters of the motor to obtain the function equation of the fitted line, y = k*t + b, where y represents the value of the key operating parameter, t represents time, k represents the slope of the fitted line, and b represents the intercept;

[0080] Taking the critical fault threshold of the operating parameter as the input of the function equation, the time point when the operating parameter reaches the fault threshold, i.e. the critical time point, is predicted;

[0081] Compare the critical time point with the end time point of the current maintenance period:

[0082] If the critical time point is less than the maintenance cycle end time point, it means that the motor operation status is unsafe in the current cycle;

[0083] If the critical time point is greater than or equal to the maintenance cycle end time point, it means that the motor operation status is safe in the current cycle.

[0084] The state modeling module combines the curve type analysis results to predict the subsequent motor operation state, and the process of predicting whether there is a fault in the operation trend also includes:

[0085] If the curve type is nonlinear, perform a growth analysis on the first state data set to determine the fastest growth rate of the key operating parameters. The first state data set is the historical change data of the key parameters over time. By performing a differential operation on this data, the speed of change of the parameters at each moment is calculated, and the maximum growth rate is determined from this.

[0086] Calculate the difference between the current value of the key parameter and the fault threshold to obtain the parameter approach value. Divide the parameter approach value by the fastest growth rate to obtain the duration for the parameter to reach the fault threshold, that is, the critical duration. Add the critical duration to the current time point to obtain the critical time point.

[0087] If the critical time point is less than the maintenance cycle end time point, it means that the motor will fail during this cycle;

[0088] If the critical time point is greater than or equal to the maintenance cycle end time point, it means that the motor will not fail during this cycle.

[0089] Fault risk assessment module: By matching the state trajectory change pattern with the historical fault library, it is evaluated whether there is a potential fault path. If so, it enters the intervention demand analysis module.

[0090] The technical solution of this embodiment is: by setting the normal operating range of key operating parameters, the sensor operating data of the motor is collected in real time, and the historical operating data and current environmental factors are combined and input into the trained digital twin model to construct a virtual mapping of the current operating state of the motor. If the key operating parameters deviate from the normal operating range, the process of judging whether the activation threshold of the twin model has been reached is triggered, and the evolution trajectory of the current state is further generated. By performing trend modeling and type analysis on the evolution trajectory, it is determined whether there is a trend of evolving towards a fault state. If the trend analysis results show a potential fault risk, the fault risk assessment module is entered to provide data support and decision-making basis for subsequent predictive maintenance and regulation. It not only improves the visualization and trend prediction capabilities of the motor's operating status, but also effectively reduces the probability of sudden failures, and improves the safety, stability and operation and maintenance efficiency of equipment operation.

[0091] Example 2

[0092] like Figure 1 As shown in the figure, the ultra-efficient explosion-proof motor predictive maintenance system based on digital twins also includes the following modules:

[0093] Intervention demand analysis module: Based on the fault risk level and expected occurrence time, combined with the current production plan and equipment health tolerance, it is determined whether early intervention measures are needed. If necessary, the intervention strategy generation module is entered.

[0094] The process of determining whether to implement early intervention measures in the intervention demand analysis module by combining the current production plan and equipment health tolerance is as follows:

[0095] Obtain current production plan information and the motor's equipment health tolerance, including the tolerance and remaining healthy life of the equipment under different health conditions. Equipment health tolerance refers to the ability of the motor to continue operating without failure risk or within an acceptable risk range in its current health state. It reflects the equipment's remaining operating capacity and risk tolerance in the face of potential operating loads or failure trends.

[0096] Set a health score range: for example, 0-100, where 80-100 indicates good health; 50-79 indicates tolerable but requires attention; 0-49 indicates poor health. The health tolerance is defined as the distance from the current score to the lower tolerance limit: TL = current health score - tolerance threshold score.

[0097] Set a maximum tolerable fault probability threshold. If the current predicted fault probability approaches or exceeds the threshold, it is considered to be out of tolerance: TL = tolerance threshold probability - current fault prediction probability;

[0098] If the predicted remaining life of the equipment is less than the required operating time + the buffer period, it is judged that there is no tolerance: TL = predicted remaining life - required operating time * safety factor.

[0099] Combine equipment health status data with production plan data to construct equipment health data groups and production task data groups. These two data groups need to be paired at the same time point or under the same working conditions to ensure data matching;

[0100] The Pearson correlation coefficient is used to calculate the correlation between the health data group and the production plan data group. The Pearson correlation coefficient is used to measure the matching degree between the equipment health status and the production plan. The formula is:

[0101]

[0102] Where, X i , Y i Represent the observation values ​​of the health status data group and the production plan data group respectively; Represent the means of the health status data group and the production plan data group respectively.

[0103] Calculate the gap between the equipment's health status and the tolerance threshold. If the equipment's health status approaches or exceeds the upper limit of the health tolerance, and the production plan has high requirements for the equipment load, it means that the equipment may not be able to operate stably and there is a risk of failure. If the Pearson correlation coefficient shows a strong correlation between the health status and the production task, and the equipment's health status is about to exceed the tolerance threshold, the system will determine whether early intervention measures are needed based on the urgency of the production plan and the priority of the task. When the system determines that early intervention is necessary, it will initiate corresponding intervention measures, such as adjusting the load of the production task, shortening the equipment's operating time, performing local maintenance, or activating backup equipment, to ensure that the equipment can continue to operate and reduce the risk of failure.

[0104] Intervention strategy generation module: Generates intervention plans based on the state control parameters fed back by the digital twin model and combined with specific working conditions.

[0105] The process of generating an intervention plan in the intervention strategy generation module based on specific working conditions is as follows:

[0106] After modeling and predicting the motor's operating status, the digital twin model outputs key control parameters, such as current operating load, safety margin, vibration level, temperature rise trend, and cooling efficiency. These parameters are used to characterize the current system's operating health and potential risks.

[0107] Synchronously collect information related to equipment operation, including: current production rhythm and task load; equipment maintenance history and current maintenance status; environmental conditions; and available resources.

[0108] Compare the control parameters recommended by the digital twin model with the current operating conditions to see if there are any conflicts or potential risk points, and determine whether intervention trigger conditions are met, such as: insufficient safety margin; key parameters approaching or exceeding warning thresholds; operating condition changes leading to parameter instability trends;

[0109] Search and match the applicable intervention strategy library to select the intervention measures that best suit the current scenario, including: load reduction operation; partial shutdown inspection; advance lubrication maintenance; activation of spare modules or deployment of production line resources; etc.

[0110] Based on the selected strategy, combined with resource scheduling and production tasks, a complete intervention plan is formed that includes intervention content, time schedule, resource requirements, and execution steps; an impact assessment is conducted on the generated intervention plan to analyze its impact on production continuity, operation and maintenance costs, and equipment life, and if necessary, it is submitted for manual review for confirmation or adjustment; after the final plan is confirmed, it is automatically issued to relevant personnel and system modules for execution, such as reserving downtime, pushing maintenance work orders, and allocating required spare parts.

[0111] Example 3

[0112] See also Figure 2 As shown in the figure, the predictive maintenance method for ultra-efficient explosion-proof motors based on digital twins includes the following steps:

[0113] S1: When the key operating parameters of the motor deviate from the normal operating range, determine whether the digital twin model activation threshold is reached. If so, enter the state modeling module.

[0114] When the key operating parameters of the motor deviate from the normal operating range, the process of determining whether the activation threshold of the digital twin model is reached is as follows:

[0115] Set key operating parameters, including real-time parameter values ​​P real , working condition warning value P alert and the digital twin model activation threshold P max , calculate the deviation degree of the current parameter relative to the normal operating range, the formula is:

[0116] ΔP alert =P real -P alert

[0117] If ΔP alert >0, indicating that the motor operating parameters have exceeded the warning value and need to be further evaluated to see if they are close to the activation threshold;

[0118] Calculate the difference between the current parameter and the activation threshold, that is, the activation proximity value ΔP margin :

[0119] ΔP margin =P max -P real

[0120] If the activation proximity value is less than the activation proximity threshold P thresh , then it is necessary to predict the state of the current parameters;

[0121] If the activation proximity value is greater than or equal to the activation proximity threshold P thresh , then there is no need to predict the state of the current parameters.

[0122] S2: Based on historical operating data, real-time sensor data, and environmental factors, the digital twin model is called to build a virtual map of the motor operation and generate the current state evolution trajectory to predict whether there is a fault in the operating trend. If so, the fault risk assessment module is entered.

[0123] The process of calling the digital twin model to build a virtual map of the motor operation and generate the current state evolution trajectory is as follows:

[0124] According to the current key operating parameters of the motor, a multi-dimensional timing curve of the motor state is constructed with the X axis as time and the Y axis as various operating parameters;

[0125] Based on historical operating data, the state evolution trajectory curve of the motor under standard conditions is constructed as a reference curve;

[0126] Select several sampling points with the same time points on the motor's current state timing curve and the reference state trajectory curve;

[0127] Obtaining operating parameter values ​​of the motor's current state curve at different sampling points and integrating them into a first state data group;

[0128] Obtaining the values ​​of the operating parameters of the reference state trajectory curve at the same sampling point and integrating them into a second state data set;

[0129] The cosine similarity between the first state data set and the second state data set is calculated to obtain a trajectory similarity value.

[0130] S3: By matching the state trajectory change pattern with the historical fault library, evaluate whether there is a potential fault path. If there is an obvious trend, enter the intervention demand analysis module.

[0131] S4: Based on the fault risk level and expected time of occurrence, combined with the current production plan and equipment health tolerance, determine whether early intervention measures are needed. If necessary, enter the intervention strategy generation module.

[0132] The process of determining whether to implement early intervention measures by combining the current production plan with the equipment health tolerance is as follows:

[0133] Obtain current production plan information and the health tolerance of the motor, including the tolerance and remaining health life of the equipment in different health states;

[0134] The Pearson correlation coefficient is used to calculate the correlation between the health data group and the production plan data group. The Pearson correlation coefficient is used to measure the matching degree between the equipment health status and the production plan. The formula is:

[0135]

[0136] Where, X i , Y i Represent the observation values ​​of the health status data group and the production plan data group respectively; Represent the means of the health status data group and the production plan data group respectively.

[0137] S5: Generate an intervention plan based on the state control parameters fed back by the digital twin model and the specific working conditions.

[0138] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0139] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twins is characterized by: include: Operation status identification module: When the key operating parameters of the motor deviate from the normal operating range, it determines whether the digital twin model activation threshold has been reached. If so, it enters the state modeling module; State modeling module: Based on historical operating data, real-time sensor data, and environmental factors, the digital twin model is used to construct a virtual map of the motor's operation, generate the current state evolution trajectory, and predict whether there are any faults in the operating trend. If so, the module enters the fault risk assessment module. Fault risk assessment module: This module matches the state trajectory change pattern with the historical fault database to assess whether there is a potential fault path. If so, it enters the intervention demand analysis module. Intervention Needs Analysis Module: This module determines whether early intervention measures are needed based on the fault risk level and expected time of occurrence, combined with the current production plan and equipment health tolerance. If necessary, it then enters the intervention strategy generation module. Intervention strategy generation module: Generates intervention plans based on the state control parameters fed back by the digital twin model and combined with specific working conditions.

2. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 1 is characterized in that: The process of determining whether the activation threshold of the digital twin model has been reached is as follows: Set key operating parameters and calculate the deviation of current parameters from the normal operating range; If the deviation is greater than 0, it means that the motor operating parameters have exceeded the warning value, and it is evaluated whether it is close to the activation threshold.

3. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 1 is characterized in that: Assessing whether an activation threshold is approaching includes: Calculate the difference between the current parameter and the activation threshold to obtain the activation proximity value; If the activation proximity value is less than the activation proximity threshold, it is necessary to predict the state of the current parameter; If the activation proximity value is greater than or equal to the activation proximity threshold, there is no need to perform state prediction on the current parameter.

4. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 1 is characterized in that: The process of generating the current state evolution trajectory is: According to the current key operating parameters of the motor, a multi-dimensional timing curve of the motor state is constructed with the X axis as time and the Y axis as various operating parameters; Based on historical operating data, the state evolution trajectory curve of the motor under standard conditions is constructed as a reference curve; Select several sampling points with the same time points on the motor's current state timing curve and the reference state trajectory curve; Obtaining operating parameter values ​​of the motor's current state curve at different sampling points and integrating them into a first state data group; Obtaining the values ​​of the operating parameters of the reference state trajectory curve at the same sampling point and integrating them into a second state data set; The cosine similarity between the first state data set and the second state data set is calculated to obtain a trajectory similarity value.

5. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 4 is characterized in that: Set the curve similarity threshold: If the trajectory similarity value is less than the curve similarity threshold, it means that the curve type is linear; If the trajectory similarity value is greater than or equal to the curve similarity threshold, it means that the curve type is nonlinear.

6. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 5 is characterized in that: The process of predicting whether an operating trend indicates a fault includes: If the curve type is linear, the least squares method is used to fit the change trend curve of the key operating parameters of the motor to obtain the function equation of the fitting line; The critical fault threshold of the operating parameter is used as the input of the function equation to predict the time point when the operating parameter reaches the fault threshold, that is, the critical time point.

7. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 6 is characterized in that: Compare the critical time point with the end time point of the current maintenance period: If the critical time point is less than the maintenance cycle end time point, it means that the motor operation status is unsafe in the current cycle; If the critical time point is greater than or equal to the maintenance cycle end time point, it means that the motor operation status is safe in the current cycle.

8. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 7 is characterized in that: The process of predicting whether an operating trend is faulty also includes: If the curve type is nonlinear, perform growth analysis on the first state data group to determine the fastest growth rate of the key operating parameters. The first state data group is the historical change data of the key parameters in the time dimension. By performing a differential operation on the first state data, the change rate of the parameters at each moment is calculated, and the maximum growth rate is determined from it. Calculate the difference between the current value of the key parameter and the fault threshold to obtain the parameter approach value. Divide the parameter approach value by the fastest growth rate to obtain the time it takes for the parameter to reach the fault threshold, which is the critical time. The critical time plus the current time point is the critical time point.

9. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 1 is characterized in that: The process of determining whether early intervention measures are needed is as follows: Obtain current production plan information and the equipment health tolerance of the motor; Combine the equipment health status data with the production plan data to construct the equipment health data group and the production task data group; The Pearson correlation coefficient was used to calculate the correlation between the health data set and the production plan data set.

10. The ultra-efficient explosion-proof motor predictive maintenance system based on digital twin according to claim 1 is characterized in that: The process of generating intervention plans is: Collect operating condition information related to equipment operation, search and match the intervention strategy library, screen out intervention measures that are suitable for the current scenario, and form an intervention plan based on the selected strategy, combined with resource scheduling and production tasks.

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