Digital control system and method

Abstract

The invention relates to the technical field of digital control, in particular to a digital control system and method. The method comprises the following steps: acquiring an industrial equipment object and constructing a simulation model based on the industrial equipment object so as to simulate the running state of the equipment and acquire corresponding simulation data; analyzing the transient current surge phenomenon of the equipment according to the simulation data, and further analyzing the multi-dimensional interactive aging trend of the equipment, so as to estimate the attenuation condition of the dynamic precision of the equipment; estimating the equipment fault risk through the equipment dynamic precision attenuation data, and estimating the quality attenuation state of the industrial product according to the fault risk; the system transmits fault risk and product quality attenuation data to a digital control system, carries out equipment abnormity early warning through the system, and adjusts equipment parameters according to an early warning result. According to the invention, the industrial equipment is digitally controlled, so that the operation efficiency of the industrial equipment is higher.

Description

Technical Field

[0001] The present invention relates to the field of digital control technology, and particularly to a digital control system and method. Background Art

[0002] Digital control technology is widely used in industrial equipment. Traditional industrial equipment control methods mostly rely on fixed logic control, manual experience adjustment, and single-variable feedback systems, which are difficult to meet the requirements of modern high precision and high dynamic response. In a complex manufacturing environment, industrial equipment faces a series of problems during long-term operation, such as equipment load expansion, electrical short circuit, arc discharge, instantaneous high-voltage pulse, power grid harmonic pollution, and transient current surge. These problems will seriously affect the equipment stability, processing accuracy, and service life. How to perform real-time monitoring, fault prediction, and parameter optimization on the equipment operation state through an efficient and accurate digital control method. During the equipment operation process, phenomena such as load expansion, electrical short circuit, harmonic pollution, and transient current surge often couple with each other, and it is difficult for the existing control systems to achieve multi-factor collaborative analysis and intelligent optimization. Therefore, traditional digital control has problems of inaccurate analysis of industrial equipment fault risks and inaccurate analysis of equipment transient current surges. Summary of the Invention

[0003] Based on this, it is necessary to provide a digital control system and method to solve at least one of the above technical problems.

[0004] To achieve the above object, a digital control method includes the following steps:

[0005] Step S1: Obtain an industrial equipment object; construct an industrial equipment simulation model based on the industrial equipment object, and perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data;

[0006] Step S2: Analyze the phenomenon of equipment transient current surge according to the industrial equipment simulation data, and perform multi-dimensional interactive aging trend analysis of the equipment according to the phenomenon of equipment transient current surge. Estimate the dynamic accuracy decay of the equipment by combining the equipment multi-dimensional interactive aging trend and the phenomenon of equipment transient current surge to obtain equipment dynamic accuracy decay data;

[0007] Step S3: Estimate the industrial equipment fault risk based on the equipment dynamic accuracy decay data, and estimate the quality decay state of industrial products according to the industrial equipment fault risk to obtain industrial equipment product quality decay data;

[0008] Step S4: Transmit the industrial equipment fault risk and industrial equipment product quality decay data to the digital control system, perform industrial equipment anomaly warning according to the digital control system, and perform industrial equipment parameter adjustment according to the industrial equipment anomaly warning to generate industrial equipment adjustment parameters.

[0009] Through simulation and simulation analysis, the present invention can accurately evaluate the operating state of industrial equipment under different working conditions, identify potential problems in advance, and avoid production interruptions. By analyzing the phenomenon of sudden increase in transient current of the equipment and the multi-dimensional interactive aging trend, the unstable factors of the equipment can be revealed, early warning of equipment accuracy attenuation can be given in advance, the stability of equipment operation can be ensured, and the failure rate can be reduced. In addition, by combining dynamic accuracy attenuation data for fault risk estimation, the safety of the equipment can be effectively evaluated, potential hazards can be discovered in time, equipment failure can be prevented, and production interruptions can be avoided. The estimation of the product quality attenuation state helps to carry out quality control in advance and reduce the unqualified rate of products. By transmitting the fault risk and product quality attenuation data to the digital control system, the equipment can be monitored in real time, early warning can be given in time and the equipment parameters can be adjusted, the operating state of the equipment can be optimized, the equipment can be ensured to maintain the best performance under various working conditions, the equipment life can be extended, the production efficiency can be improved, the failure rate can be reduced, and the overall production capacity can be enhanced. Therefore, the present invention is an optimized treatment for a traditional digital control method, solving the problems existing in a traditional digital control method, such as inaccurate analysis of the fault risk of industrial equipment and inaccurate analysis of the sudden increase in transient current of the equipment, and improving the accuracy of the fault risk analysis of industrial equipment and the accuracy of the analysis of the sudden increase in transient current of the equipment.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Set the pressure measurement range of the pressure sensor to 0 - 100 bar, set the minimum pressure change to 1 mV / bar, and set the sampling frequency to 1000 Hz;

[0012] Step S12: Set the voltage measurement range of the voltage sensor to 0 - 250 V, set the minimum accuracy to ±1%, and set the sampling frequency to 2000 Hz;

[0013] Step S13: Obtain the industrial equipment object; and collect the operating state of the industrial equipment according to the pressure sensor and the voltage sensor for the industrial equipment object;

[0014] Step S14: Conduct functional analysis on the industrial equipment according to the industrial equipment object to obtain industrial equipment function data;

[0015] Step S15: Construct an industrial equipment dynamic simulation model based on the industrial equipment function data and the operating state of the industrial equipment;

[0016] Step S16: Conduct simulation on the industrial equipment according to the industrial equipment simulation model to obtain industrial equipment simulation data.

[0017] This digital control method of the present invention ensures high-precision acquisition of pressure and voltage data by precisely setting the measurement range and sampling frequency of the sensors, making the device status monitoring more real-time and accurate. The configuration of the pressure sensor and voltage sensor optimizes the detection of the operating status of industrial devices, provides accurate feedback data, and supports the comprehensive analysis of device functions. Through the functional analysis of industrial device objects, detailed operating data of the devices can be obtained, laying a foundation for the subsequent construction of dynamic simulation models. Combining the device operating status and functional data, the constructed dynamic simulation model can truly reflect the operating laws and potential problems of the devices, and then conduct device simulation. The obtained simulation data helps predict the performance of the devices under different working conditions, effectively evaluate the performance and reliability of the devices, and identify potential failure risks in advance. This data-driven intelligent control method not only improves the operating efficiency and stability of the devices, but also enhances the scientific nature of fault prediction and maintenance decision-making, avoids production interruptions caused by sudden failures, and optimizes the full-life cycle management of industrial devices.

[0018] Preferably, step S15 includes the following steps:

[0019] Step S151: Count the device operation cycle of the operating status of the industrial device;

[0020] Step S152: Calculate the operating current flow of the industrial device according to the device operation cycle;

[0021] Step S153: Calculate the power consumption of the industrial device according to the operating current flow of the industrial device;

[0022] Step S154: Use the industrial device function data to collect the device dynamic response characteristics;

[0023] Step S155: Evaluate the initial operating status of the device based on the device dynamic response characteristics and the industrial device function data, and record the initial operating status data of the device;

[0024] Step S156: Construct a dynamic simulation model of the industrial device using the initial operating status data of the device and the power consumption of the industrial device.

[0025] By precisely counting the equipment operation cycle, calculating the current flow and power consumption, the present invention effectively quantifies the energy usage and workload of industrial equipment, providing a data basis for subsequent performance evaluation. By collecting the dynamic response characteristics of the equipment, it is possible to deeply analyze the working behavior and response mechanism of the equipment, ensuring that the simulation model can truly reflect the dynamic characteristics of the equipment. Combining the functional data and dynamic response characteristics of the equipment, the initial state of equipment operation is evaluated, providing accurate initial conditions for equipment simulation and ensuring the accuracy and reliability of subsequent simulations. The dynamic simulation model constructed based on these data not only accurately predicts the performance of the equipment under different operating conditions, but also provides a scientific basis for equipment performance optimization, supports continuous monitoring of equipment operation status and fault warning. Overall, the entire process improves the intelligent level of industrial equipment management, makes equipment maintenance more efficient, extends the service life of equipment, reduces operating costs, and at the same time ensures production stability and safety.

[0026] Preferably, the analysis of the equipment transient current surge phenomenon in step S2 includes:

[0027] Statistical industrial equipment simulation data of industrial equipment with a rated power exceeding 80% of the simulation operation duration;

[0028] When the industrial equipment simulation operation duration exceeds 1000h, detect industrial equipment load expansion;

[0029] When the industrial equipment load expands to 20%, predict the probability of industrial equipment short circuit;

[0030] Based on the probability of industrial equipment short circuit, determine whether the current exceeds 20A, and estimate the arc discharge phenomenon when the current exceeds 20A to obtain arc discharge phenomenon estimation data;

[0031] According to the arc discharge phenomenon estimation data and the probability of industrial equipment short circuit, detect the instantaneous high voltage pulse of the equipment;

[0032] Based on the instantaneous high voltage pulse of the equipment, determine whether the voltage exceeds 280V, and analyze the harmonic pollution of the equipment power grid when the voltage exceeds 280V to obtain equipment power grid harmonic pollution data

[0033] According to the equipment power grid harmonic pollution data and the instantaneous high voltage pulse of the equipment, analyze the equipment transient current surge phenomenon.

[0034] The present invention provides clear operating conditions for further evaluating the performance of industrial equipment by counting the operating duration of industrial equipment simulation data exceeding 80% of the rated power, which helps to timely identify potential overload risks. By detecting the equipment load expansion exceeding 1000 hours, the long-term operating state of the equipment can be accurately monitored, the load change trend can be identified, and excessive wear or damage of the equipment can be prevented. According to the degree of equipment load expansion, the short-circuit probability is predicted, the risk of electrical faults can be effectively identified in advance, and equipment downtime caused by electrical problems can be reduced. By analyzing the arc discharge phenomenon and the short-circuit probability when the current exceeds 20A, the stability of the electrical system can be deeply evaluated, thus avoiding potential equipment damage. Further, by analyzing and detecting power grid harmonic pollution through high-voltage pulses, abnormal phenomena in the power system can be revealed, the quality of the equipment power supply can be effectively monitored, and the impact of power supply quality problems on the equipment can be reduced. Combining this information, the transient current surge phenomenon of the equipment is accurately analyzed, providing a scientific basis for the prevention of equipment failures, ensuring the long-term stable operation of the equipment, avoiding the occurrence of sudden failures, and improving the reliability and safety of the equipment.

[0035] Preferably, the multi-dimensional interactive aging trend analysis of the equipment described in step S2 includes the following steps:

[0036] Detect the cable insulation deterioration condition of the transient current surge phenomenon of the equipment;

[0037] Calculate the insulation breakdown probability of the industrial equipment according to the cable insulation deterioration condition of the transient current surge phenomenon of the equipment;

[0038] Detect the current over-limit conduction condition according to the insulation breakdown probability of the industrial equipment and the transient current surge phenomenon of the equipment;

[0039] Calculate the local heat accumulation of the equipment for the current over-limit conduction condition;

[0040] Calculate the growth trend of thermal stress for the local heat accumulation of the equipment;

[0041] Estimate the local thermal expansion of the equipment for the local heat accumulation of the equipment;

[0042] Detect the associated jamming fault of equipment parts based on the local thermal expansion of the equipment;

[0043] Analyze the deformation trend of equipment parts for the associated jamming fault of equipment parts;

[0044] Conduct multi-dimensional interactive aging trend analysis of the equipment according to the growth trend of thermal stress and the deformation trend of equipment parts to obtain the multi-dimensional interactive aging trend of the equipment.

[0045] The present invention provides real-time monitoring for the electrical safety of equipment by detecting the cable insulation deterioration condition in the phenomenon of transient current surge in the equipment, and timely identifies potential cable aging problems. Combining the cable insulation deterioration situation, calculates the probability of insulation breakdown of industrial equipment, and effectively warns of serious faults occurring in the equipment electrical system. Further analyzing the influence of transient current surge in the equipment on the current over-limit conduction condition helps to identify the conduction path of current overload and prevent equipment damage caused by overcurrent. By calculating the local heat accumulation caused by current over-limit, it is possible to deeply understand the thermal management problem of the equipment during high-load operation and prevent equipment failures caused by overheating. Calculating the growth trend of thermal stress further reveals the structural problems generated during the long-term operation of the equipment and helps to optimize the heat dissipation system of the equipment. Analyzing the part jamming fault based on the local thermal expansion of the equipment can effectively prevent system shutdown or damage caused by component failure.

[0046] Preferably, the estimation of equipment dynamic accuracy decay described in step S2 includes:

[0047] Estimate the dynamic electromagnetic radiation interference generated by the transient current surge phenomenon in the equipment;

[0048] Detect the electromagnetic radiation reverse interference of the dynamic electromagnetic radiation interference;

[0049] Estimate the sensor reading error of the electromagnetic radiation reverse interference;

[0050] Detect the growth of the associated void ratio of equipment parts according to the multi-dimensional interaction aging trend of the equipment;

[0051] Evaluate the equipment part loosening trend of the growth of the associated void ratio of equipment parts;

[0052] Perform equipment operation vibration detection according to the equipment part loosening trend and the growth of the associated void ratio of equipment parts to obtain equipment operation vibration data;

[0053] Calculate the equipment part resonance probability of the equipment operation vibration data and the equipment part loosening trend;

[0054] Measure the equipment operation stability decay condition according to the equipment part resonance probability;

[0055] Based on the equipment operation stability decay condition and the sensor reading error, perform equipment dynamic accuracy decay estimation to obtain equipment dynamic accuracy decay data.

[0056] Through a series of precise analysis steps, the present invention effectively identifies and predicts the risk of precision decline that occurs during the use of the device. By estimating the electromagnetic radiation interference generated by the transient current surge phenomenon of the device, it is possible to detect in advance the impact of electromagnetic interference on the normal operation of the device and avoid irreversible damage to the device's precision caused by the interference. Detecting the electromagnetic radiation reverse interference and its error impact on the sensor readings helps to improve the accuracy of the sensor and prevent the device performance from being misaligned due to external interference. Combining the multi-dimensional interaction aging trend of the device, timely detecting the increase in the porosity of the device parts, thereby predicting the trend of component loosening, preventing the device operation from being unstable due to loosening, and avoiding device failures. Based on the component loosening and the increase in porosity, collecting and analyzing the device operation vibration data, evaluating the impact of vibration on the device, and judging in advance whether the device has a resonance phenomenon, so as to avoid more serious mechanical failures caused by resonance.

[0057] Preferably, step S3 includes the following steps:

[0058] Step S31: Calculate the device error accumulation based on the device dynamic precision decay data, thereby obtaining the device error accumulation data;

[0059] Step S32: Detect the device motion trajectory deviation based on the device error accumulation data and the device dynamic precision decay data, thereby obtaining the device motion trajectory deviation;

[0060] Step S33: Estimate the industrial device failure risk according to the device motion trajectory deviation and the device error accumulation data, obtaining the industrial device failure risk;

[0061] Step S34: Estimate the industrial product quality decay state according to the industrial device failure risk data and the device motion trajectory deviation, obtaining the industrial device product quality decay data.

[0062] By calculating the error accumulation based on the device dynamic precision decay data, the present invention can accurately grasp the error development trend of the device during long-term use and timely identify potential performance degradation factors. Combining the error accumulation data with the dynamic precision decay data to detect the device motion trajectory deviation can effectively judge the deviation between the actual operation trajectory of the device and the preset trajectory, reveal in advance the mechanical deviation or motion incoordination problems that occur, and reduce the damage or precision decline caused by the trajectory deviation. On this basis, combining the device error accumulation and the motion trajectory deviation information to evaluate the industrial device failure risk can accurately predict the risk degree of device failures and formulate a more reasonable maintenance plan. Combining the failure risk data and the motion trajectory deviation, estimating the industrial product quality decay state can effectively guide product quality control, avoid quality problems caused by device failures, and ensure the consistency and reliability of products during the production process.

[0063] Preferably, step S33 includes the following steps:

[0064] Step S331: Estimate the data of increased equipment wear based on the equipment error accumulation data and the deviation of the equipment motion trajectory;

[0065] Step S332: Calculate the data of increased surface roughness of the equipment operation based on the data of increased equipment wear;

[0066] Step S333: Conduct surface defect detection on the equipment operation surface according to the data of increased surface roughness of the equipment operation to obtain the surface defect data of the equipment operation;

[0067] Step S334: Calculate the equipment collision probability of the equipment motion trajectory deviation;

[0068] Step S335: Estimate the industrial equipment failure risk based on the equipment collision probability and the surface defect data of the equipment operation.

[0069] By calculating the data of increased surface roughness of the equipment operation, the present invention can accurately analyze the surface wear condition of the equipment, provide data support for subsequent equipment maintenance and repair, and avoid the reduction of equipment performance or failure caused by excessive surface roughness. Combining the roughness growth data for surface defect detection helps to timely identify the surface defects of the equipment, thereby reducing the equipment damage or failure caused by surface defects. Further calculating the collision probability of the equipment motion trajectory deviation can clarify the collision risk caused by the trajectory deviation during the equipment operation, providing an early warning for preventing accidental failures. By combining the collision probability and the surface defect data, the risk of equipment failure can be accurately estimated, providing data support for enterprises to formulate more scientific maintenance and replacement cycles.

[0070] Preferably, step S4 includes the following steps:

[0071] Step S41: Conduct equipment performance decline evaluation on the industrial equipment failure risk and the industrial equipment product quality attenuation data to obtain the equipment performance decline data;

[0072] Step S42: Transmit the equipment performance decline data and the industrial equipment failure risk to the digital control system, and conduct industrial equipment operation defect evaluation according to the digital control system to obtain the industrial equipment operation defect data;

[0073] Step S43: Conduct industrial equipment anomaly early warning on the industrial equipment defect data according to the digital control system, and adjust the industrial equipment parameters according to the industrial equipment anomaly early warning, thereby generating the industrial equipment adjustment parameters.

[0074] By evaluating the decline in equipment performance through industrial equipment failure risks and product quality degradation data, the present invention can promptly identify the degradation trend of equipment performance, thereby taking measures in advance to avoid large-scale failures or shutdowns during the production process. This evaluation provides a basis for the rational arrangement of equipment maintenance cycles, improving production continuity and equipment utilization efficiency. After transmitting the equipment performance decline data and failure risks to the digital control system, further evaluation of industrial equipment operation defects can accurately assess the current operating condition of the equipment, promptly detect potential defects, reduce the probability of equipment failures, and enhance the safety and stability of the equipment.

[0075] The present invention also provides a digital control system for implementing the digital control method described above. The digital control system includes:

[0076] An industrial equipment simulation module for obtaining industrial equipment objects; constructing an industrial equipment simulation model based on the industrial equipment objects and performing industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data;

[0077] A dynamic precision decay prediction module for analyzing the phenomenon of sudden surges in equipment transient current based on the industrial equipment simulation data, and performing multi-dimensional interaction aging trend analysis of the equipment according to the phenomenon of sudden surges in equipment transient current. Predicting the dynamic precision decay of the equipment by combining the multi-dimensional interaction aging trend of the equipment and the phenomenon of sudden surges in equipment transient current to obtain equipment dynamic precision decay data;

[0078] A product quality decay state prediction module for estimating the industrial equipment failure risk based on the equipment dynamic precision decay data, and predicting the industrial product quality decay state according to the industrial equipment failure risk to obtain industrial equipment product quality decay data;

[0079] An industrial equipment parameter adjustment module for transmitting the industrial equipment failure risk and industrial equipment product quality decay data to the digital control system, giving an early warning of industrial equipment anomalies according to the digital control system, and adjusting the industrial equipment parameters according to the early warning of industrial equipment anomalies to generate industrial equipment adjustment parameters.

[0080] The present invention lies in that through simulation and simulation analysis, the operating state of industrial equipment under different working conditions can be accurately evaluated, potential problems can be identified in advance, and production interruptions can be avoided. Through the analysis of the phenomenon of sudden surges in transient current of the equipment and the multi-dimensional interactive aging trend, the unstable factors of the equipment can be revealed, early warning of the attenuation of equipment accuracy can be given, the stability of equipment operation can be ensured, and the failure rate can be reduced. In addition, by combining dynamic accuracy attenuation data for fault risk estimation, the safety of the equipment can be effectively evaluated, potential hazards can be discovered in time, equipment failure can be prevented, and production interruptions can be avoided. The prediction of the attenuation state of product quality helps to carry out quality control in advance and reduce the unqualified rate of products. By transmitting fault risk and product quality attenuation data to the digital control system, the equipment can be monitored in real time, early warnings can be given in time and equipment parameters can be adjusted, the operating state of the equipment can be optimized, the equipment can be ensured to maintain the best performance under various working conditions, the service life of the equipment can be extended, the production efficiency can be improved, the failure rate can be reduced, and the overall production capacity can be enhanced. Therefore, the present invention is an optimization of a traditional digital control method, solving the problems existing in a traditional digital control method, namely inaccurate analysis of the fault risk of industrial equipment and inaccurate analysis of sudden surges in transient current of the equipment, and improving the accuracy of the analysis of the fault risk of industrial equipment and the accuracy of the analysis of sudden surges in transient current of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of the step flow of a digital control method;

[0082] Figure 2 is Figure 1 a detailed implementation step flow diagram of step S³ in

[0083] Figure 3 is Figure 2 a detailed implementation step flow diagram of step S33 in

[0084] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0086] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0087] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0088] To achieve the above object, please refer to Figures 1 to 3 , a digital control method, comprising the following steps:

[0089] Step S1: Obtain an industrial equipment object; construct an industrial equipment simulation model based on the industrial equipment object, and perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data;

[0090] Step S2: Analyze the phenomenon of sudden increase in transient current of the equipment according to the industrial equipment simulation data, and perform multi-dimensional interaction aging trend analysis of the equipment according to the phenomenon of sudden increase in transient current of the equipment. Estimate the dynamic accuracy decay of the equipment by combining the multi-dimensional interaction aging trend of the equipment and the phenomenon of sudden increase in transient current of the equipment to obtain equipment dynamic accuracy decay data;

[0091] Step S3: Estimate the failure risk of the industrial equipment based on the equipment dynamic accuracy decay data, and estimate the attenuation state of the industrial product quality according to the failure risk of the industrial equipment to obtain industrial equipment product quality decay data;

[0092] Step S4: Transmit the industrial equipment failure risk and the industrial equipment product quality decay data to the digital control system, perform abnormal warning of the industrial equipment according to the digital control system, and adjust the parameters of the industrial equipment according to the abnormal warning of the industrial equipment to generate industrial equipment adjustment parameters.

[0093] In the embodiment of the present invention, as shown in reference to Figure 1 , in this example, the digital control method includes the following steps:

[0094] Step S1: Obtain an industrial equipment object; construct an industrial equipment simulation model based on the industrial equipment object, and perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data;

[0095] In the embodiment of the present invention, in the industrial equipment operating environment, a specific industrial equipment is selected, including machining equipment, automated production equipment or high-precision measurement equipment, and its equipment parameter information is obtained. Using data acquisition equipment, such as high-precision pressure sensors (range 0-100 bar, minimum resolution 1 mV / bar, sampling frequency 1000 Hz) and voltage sensors (range 0-250 V, minimum resolution ±1%, sampling frequency 2000 Hz), the pressure, temperature, voltage, current, etc. of the equipment during operation are recorded in real time and stored in the industrial database. Using the stored data, the data cleaning method is used to remove outliers, and the spectral characteristics in the data are extracted by Fourier transform to analyze the vibration and electrical signal change trends of the equipment under different working conditions. The finite element analysis method is used to calculate the stress distribution of the key structures of the equipment, and the dynamic simulation technology is used to simulate the mechanical, thermal, and electrical characteristics changes of the equipment under different working conditions. According to the simulation calculation results, an equipment simulation data set is established, and a dynamic system simulation environment is constructed in MATLAB Simulink, and the simulation is run and the equipment simulation data is output, including the force conditions of key components, the current change trend, power loss, etc.

[0096] Step S2: Analyze the phenomenon of transient current surge of the equipment according to the industrial equipment simulation data, and perform multi-dimensional interaction aging trend analysis of the equipment according to the phenomenon of transient current surge of the equipment. Estimate the dynamic accuracy decay of the equipment by combining the multi-dimensional interaction aging trend and the phenomenon of transient current surge of the equipment to obtain equipment dynamic accuracy decay data;

[0097] In the embodiments of the present invention, based on the device simulation data, a current signal is extracted, and the short-time Fourier transform (STFT) is used to calculate the time-frequency characteristics of the transient current. The running time with the rated power exceeding 80% is statistically analyzed, and the amplitude and duration of each transient current surge are recorded. When the duration of the current surge exceeds 5 ms and the amplitude reaches more than 1.2 times the peak current, it is determined as a transient current surge event, and the time point and associated parameters of the event are recorded. According to the cable insulation state evaluation data, the current over-limit conduction situation is calculated, and the finite element thermal analysis method is combined to analyze the local heat accumulation phenomenon of the device caused by the current over-limit conduction. The local temperature change of the device is measured, and the growth trend of the thermal stress is calculated to analyze the thermal expansion of the device when the temperature rise exceeds 40°C. The tiny displacement of the device parts caused by thermal expansion (within an error of 0.01 mm) is measured, and the jamming risk of the parts due to thermal expansion is analyzed. The running vibration data of the device is statistically analyzed, and the vibration frequency of the device parts is measured based on an acceleration sensor (sampling frequency 10 kHz, measurement range ±50 g) to calculate the resonance probability of the device. When the vibration frequency is close to the natural frequency of the device, the loosening trend of the parts is further analyzed to obtain the device dynamic accuracy attenuation data.

[0098] Step S3: Based on the device dynamic accuracy attenuation data, the industrial equipment failure risk is estimated, and the industrial product quality attenuation state is predicted according to the industrial equipment failure risk to obtain the industrial equipment product quality attenuation data;

[0099] In the embodiments of the present invention, based on the dynamic precision attenuation data of the device, the error accumulation rate is calculated, the time points when the error accumulation exceeds 0.01 mm are recorded, and the error growth trend is calculated. Combining the error accumulation data, machine vision technology is used to analyze the deviation of the device movement trajectory. A laser displacement sensor (measurement accuracy of 0.001 mm) is used to detect the processing trajectory of the device, and the deviation between the processing path and the theoretical trajectory is calculated. When the deviation exceeds 0.05 mm, the device operation trajectory deviation data is recorded, and its impact on the product accuracy is evaluated. Combining the movement trajectory deviation data, the wear condition of the key components of the device is evaluated. A surface roughness measuring instrument (measurement range of 0.1 nm - 10 μm, accuracy of 0.01 μm) is used to detect the microscopic structure of the device part surface, and the roughness change rate is calculated. When the growth of the Ra value of the roughness exceeds 10%, the surface defect information of the device part is recorded. Based on the surface defect condition of the device part, the collision probability of the device during long-term operation is calculated, and the stress condition of the key components of the device is analyzed by combining finite element collision simulation. When the impact force on the device component exceeds 200 N, the potential structural damage risk of the device is evaluated, and the probability of device failure occurring within the next 500 hours is calculated. Combining the device failure risk assessment data, the quality attenuation state of industrial products is calculated. Based on parameters such as error accumulation, movement trajectory deviation, and surface roughness growth, a statistical regression model is used to calculate the product quality attenuation trend and verify it based on the actual product detection data. When the product size deviation exceeds 0.02 mm of the standard value, the product quality attenuation data is recorded.

[0100] Step S4: Transmit the industrial equipment failure risk and industrial equipment product quality attenuation data to the digital control system, perform industrial equipment anomaly warning according to the digital control system, and adjust the industrial equipment parameters according to the industrial equipment anomaly warning to generate industrial equipment adjustment parameters.

[0101] In the embodiments of the present invention, industrial equipment failure risk data and industrial equipment product quality attenuation data are uploaded to a digital control system, and edge computing technology is used to perform real-time analysis on the data, and abnormal historical data of the equipment is stored in a database. A time series analysis method is used to detect abnormal change trends of equipment parameters, and equipment failure prediction is performed based on a machine learning model (such as an LSTM neural network). When the equipment failure risk probability exceeds 50%, an abnormal warning is triggered, and warning information is recorded, including equipment number, failure type, failure occurrence probability, etc. Combining the abnormal warning data, the operating parameters of the industrial equipment are adjusted. For example, in a CNC machining equipment, if it is detected that the tool movement trajectory deviation exceeds 0.05 mm, the control parameters of the servo motor are adjusted to reduce the feed speed by 10% to improve the machining accuracy. In an electrical equipment, if a current transient surge event is detected, the current load is automatically reduced to prevent equipment short-circuit failure. According to the equipment adjustment parameters, an adjustment instruction is sent to the PLC (programmable logic controller) to modify the equipment operating parameters, and the operating state of the adjusted equipment is re-measured to ensure that the equipment can operate stably after the parameter adjustment. After adjusting the parameters, continue to collect the equipment operating data, and repeat steps S1 - S4 to ensure that the system can continuously optimize the equipment operating state.

[0102] Preferably, step S1 includes the following steps:

[0103] Step S11: Set the pressure measurement range of the pressure sensor to 0 - 100 bar, set the minimum pressure change to 1 mV / bar, and set the sampling frequency to 1000 Hz;

[0104] Step S12: Set the voltage measurement range of the voltage sensor to 0 - 250 V, set the minimum accuracy to ±1%, and set the sampling frequency to 2000 Hz;

[0105] Step S13: Obtain the industrial equipment object; and collect the operating state of the industrial equipment object according to the pressure sensor and the voltage sensor;

[0106] Step S14: Perform industrial equipment function analysis according to the industrial equipment object to obtain industrial equipment function data;

[0107] Step S15: Construct an industrial equipment dynamic simulation model based on the industrial equipment function data and the industrial equipment operating state;

[0108] Step S16: Perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data.

[0109] In the embodiments of the present invention, in the operating environment of industrial equipment, a high-precision pressure sensor is selected to ensure that its range meets the working requirements of the industrial equipment. A pressure sensor with a range of 0-100 bar is adopted to ensure that its minimum pressure change detection accuracy is 1 mV / bar, and the sampling frequency is set to 1000 Hz to ensure high-precision acquisition of pressure data. A high-precision signal acquisition module is used to monitor the pressure signal in real time, and an anti-interference filter (such as a Butterworth low-pass filter with a cut-off frequency of 500 Hz) is used to reduce signal noise. The pressure sensor is installed at the key pressure detection points of the industrial equipment, such as the inlet and outlet of the hydraulic system, the front and back of the valve of the high-pressure gas pipeline, or the pressure interface of the spindle cooling system of the machine tool. The parameters of the sensor are configured through an industrial controller (such as a PLC or an embedded controller), the output data of the sensor is read using the Modbus communication protocol, and is transmitted to the data acquisition system through the RS485 or CAN bus interface. At the data acquisition terminal, data buffering technology (such as a FIFO queue) is used to store the acquired pressure data. When selecting a voltage sensor, ensure that its measurement range covers the rated operating voltage of the equipment, adopt a measurement range of 0-250 V, and set the minimum accuracy to ±1%. To improve the voltage measurement accuracy, a Δ-Σ analog-to-digital converter (ADC) is used for high-precision signal acquisition, and a sampling frequency of 2000 Hz is adopted. The voltage sensor is installed at the power input terminal of the industrial equipment, the power control module, or key power-consuming components (such as servo motors, solenoid valves, or high-frequency transformers), and shielded cables are used to reduce electromagnetic interference (EMI). Opto-isolation technology (such as the high-voltage optocoupler TLP250) is adopted to prevent the influence of power supply noise on the data acquisition system. A high-precision operational amplifier (such as INA333) is used for signal conditioning, and the processed voltage signal is transmitted to the data acquisition terminal. During the data storage process, a second-order IIR low-pass filter (cut-off frequency 1000 Hz) is used to smooth the acquired data to reduce the influence of high-frequency noise. The data is transmitted to the central control system in real time through the Modbus-TCP / IP protocol and stored in the database. In the industrial environment, target industrial equipment is selected, including numerically controlled machine tools, automated production equipment, or electrical control equipment, and based on the installed pressure sensors and voltage sensors, data acquisition of the operating state of the equipment is carried out. During the data acquisition process, the acquisition period is set to 1 ms, and a synchronous clock (such as the IEEE 1588 Precision Time Protocol) is used to ensure the time alignment of the data of multiple sensors. For pressure data, a pressure compensation algorithm is used to calculate the influence of temperature change on pressure measurement, and the measurement error is corrected based on historical data. For voltage data, FFT (Fast Fourier Transform) is used to analyze the frequency components of the voltage signal to detect existing electrical anomalies, such as voltage harmonics or power supply noise interference.The collected pressure and voltage data are stored in an industrial database (such as SQL Server or InfluxDB), and the storage space is reduced by a data compression algorithm (such as Zlib). To ensure data integrity, the CRC (Cyclic Redundancy Check) method is used to verify the correctness of data transmission, and an alarm signal is triggered when abnormal data is detected. Based on the collected operating status data of industrial equipment, the functional characteristics of the equipment are analyzed, and the deviation between the actual operating status and the theoretical value is calculated according to the rated operating parameters of the equipment. For example, in a hydraulic system, the liquid flow rate is calculated using the pressure sensor data (based on Bernoulli's equation), and the pump efficiency is calculated in combination with the motor voltage data. For a numerically controlled machine tool, a voltage sensor is used to measure the input voltage of the servo motor, and the power consumption of the motor is calculated. Combining the pressure data of the machine tool spindle, the force on the cutting tool during the cutting process is analyzed, and the spindle deformation is calculated based on the machine tool stiffness model. For automated production equipment, the operating status of the pneumatic actuator is analyzed by combining pressure and voltage data, and the energy consumption of the equipment when performing tasks is calculated. During the data analysis process, the principal component analysis (PCA) method is used to extract key influencing factors, and a multivariable regression model is used to calculate the equipment operating characteristics under different working conditions. Based on the functional data and operating status data of industrial equipment, a dynamic simulation model of industrial equipment is constructed. During the modeling process, the finite element analysis method is used to calculate the force on the key components of the equipment, and the model parameters are corrected based on the equipment operating data. For hydraulic equipment, a hydraulic fluid simulation model is established based on the Navier-Stokes equation, and the liquid flow state is simulated in combination with the pressure data. For electrical equipment, a circuit simulation model is constructed based on Kirchhoff's circuit laws, and the power supply load change is simulated in combination with the voltage data. For machine tool equipment, a machine tool motion model is established based on the rigid body dynamics equation, and the tool path change is simulated in combination with the motor input voltage data. During the model parameter adjustment process, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters, and the dynamic simulation model of industrial equipment is obtained through model verification using historical operating data. Based on the established dynamic simulation model of industrial equipment, equipment simulation analysis is carried out. A simulation software (such as MATLAB Simulink or ANSYS Fluent) is used to run the simulation, and the initial operating status of the equipment is input based on the actual collected data. During the simulation process, the simulation time step is set to 1 ms, and the operation of the equipment under different working conditions is simulated. For example, in the numerical control machine tool simulation, the force change of the spindle at different cutting depths is simulated, and the tool wear trend is calculated. In the hydraulic system simulation, the influence of liquid pressure fluctuation on the system stability is simulated, and the equipment response time during pressure shock is calculated. In the electrical equipment simulation, the influence of power supply voltage fluctuation on the motor speed is analyzed, and the energy consumption change of the equipment under different load conditions is calculated. After the simulation is completed, the simulation data, including parameters such as the pressure, temperature, voltage, current, and power consumption of the equipment, are recorded and compared with the actual operating data for analysis.Based on the error analysis results, adjust the simulation model parameters to ensure the accuracy of the model and obtain the simulation data of the industrial equipment.

[0110] Preferably, step S15 includes the following steps:

[0111] Step S151: Statistically analyze the equipment operation cycle of the industrial equipment operation status;

[0112] Step S152: Calculate the operation current flow of the industrial equipment according to the equipment operation cycle;

[0113] Step S153: Calculate the power consumption of the industrial equipment according to the operation current flow of the industrial equipment;

[0114] Step S154: Use the industrial equipment function data acquisition equipment dynamic response characteristics;

[0115] Step S155: Evaluate the initial state of the equipment operation based on the equipment dynamic response characteristics and the industrial equipment function data, and record the initial state data of the equipment operation;

[0116] Step S156: Construct an industrial equipment dynamic simulation model using the initial state data of the equipment operation and the power consumption of the industrial equipment.

[0117] In the embodiments of the present invention, the start, operation, and stop times of the device are recorded through an industrial control system (such as a PLC or SCADA system) or a data acquisition module, and the duration of a single operation cycle is calculated based on the time series data. For machine tool equipment, the spindle encoder signal is used to detect the start and stop times of the spindle, and the machining time of a single part is recorded based on the machining instructions to determine the operation cycle. For the hydraulic system, the pressure fluctuation cycle is detected based on the pressure sensor data, and the operation duration of the hydraulic system is calculated in combination with the switch signal of the solenoid valve. Through these methods, the time for each complete operation of the device is obtained, and then the operation cycle of the device is calculated. During the automatic machining process of a numerically controlled machine tool, each completed cutting task is a cycle, and the length of the cycle is directly related to the complexity of the machining task. By statistically analyzing the cycles of a large number of machining tasks, the stability of the operation cycle of the device is calculated. According to the operation cycle data of the device, a current sensor is used to collect the working current of the industrial device in real time. For example, a Hall current sensor is used to accurately measure the change of the current. The operating current flow of the device is calculated by collecting data and performing numerical integration. The current data of the device is recorded in real time and the power consumption of the device is calculated according to the working cycle. For example, in a machine tool, the change of the current flow is directly related to the speed, load, and cutting force of the spindle motor. By collecting these data, the change law of the current flow under different working loads is analyzed. For hydraulic equipment, the current flow is closely related to the working state of the hydraulic pump, and the load condition of the hydraulic system is inferred from the change of the current. After the calculation of the current flow is completed, the calculation of the power consumption of the device continues. A power analyzer is used to measure the working voltage and current data of the device. The voltage and current data will help determine the power consumption of the device. For equipment driven by an electric motor, the power consumption is directly related to the current flow and voltage level, and the current and voltage are measured by monitoring the power consumption of the device in real time. For example, for a numerically controlled machine tool, the current and voltage of the electric motor are measured in real time under different speed conditions. The power consumption of the electric motor changes with the change of the load, and the power consumption under different working loads is analyzed to judge the energy efficiency performance of the device under different working conditions. For the hydraulic system, by measuring the power consumption of the hydraulic pump, the efficiency of the hydraulic system is calculated, especially the change trend of the power when the pressure load changes. To accurately evaluate the performance of the device, based on the functional data of the device, the dynamic response characteristics of the device are collected. For machine tool equipment, the spindle vibration characteristics are an important indicator for evaluating its operation stability and machining accuracy. A vibration sensor, such as an accelerometer, is used to measure the vibration response of the spindle. By analyzing these vibration data, the dynamic characteristics of the device are understood, including frequency response, vibration amplitude, and waveform change, etc. These characteristic data will reflect the response of the device under different loads and working environments. For the hydraulic system, a pressure sensor is used to collect the dynamic pressure change of the hydraulic system.The dynamic response characteristics of the hydraulic system are obtained by analyzing the frequency and amplitude of pressure fluctuations and the pressure fluctuations during the rapid change process. These characteristics help to determine whether there are potential fault risks in the system, especially during hydraulic shock. For motor equipment, the dynamic response of the current waveform is also an important characteristic. The working current of the motor is monitored through a current sensor, especially the current waveform during startup, acceleration, deceleration, etc., to identify whether there are abnormal conditions in the motor, such as overload or unstable operation. Using the dynamic response characteristics and functional data of the equipment, evaluate the initial state of the equipment during the startup phase. For example, the startup phase of a machine tool spindle is accompanied by high vibrations, especially when the load is large, which will generate a greater startup impact. At this time, the state of the equipment during the startup process is evaluated through the vibration data collected by the accelerometer. For the hydraulic system, analyze the initial pressure change during the startup of the hydraulic system by real-time monitoring the data of the pressure sensor. The evaluation of the initial state of the hydraulic system helps to determine whether there is an abnormal startup response in the system, especially in scenarios of extreme load or rapid response. The startup process of the motor will generate a large current impact, and this dynamic response can be recorded by a current sensor. Through these recorded data, evaluate the state of the motor during the startup process to determine whether there are abnormal phenomena, such as too high or too low current, etc. After collecting the initial state data and power consumption data of the equipment, enter the construction stage of the industrial equipment dynamic simulation model. According to the operating characteristics and dynamic response characteristics of the equipment, select a suitable simulation tool for modeling. For machine tool equipment, rigid body dynamics modeling is adopted, combined with data such as the rotational speed, load, and power consumption of the equipment, to construct a dynamic response model. For the hydraulic system, based on the principles of fluid mechanics, a fluid simulation model is used to predict the performance of the hydraulic system under different working conditions. The simulation of motor equipment is based on the electromagnetic field model, combined with the changes in current and voltage to predict the performance of the motor under different loads and working conditions. Through these simulation models, accurately simulate the performance of the equipment.

[0118] Preferably, the analysis of the transient current surge phenomenon of the equipment described in step S2 includes:

[0119] Statistically analyze the simulated operation duration of industrial equipment with a rated power exceeding 80% in the industrial equipment simulation data;

[0120] Detect the load expansion of industrial equipment when the simulated operation duration of industrial equipment exceeds 1000h;

[0121] When the load of industrial equipment expands to 20%, predict the short-circuit probability of industrial equipment;

[0122] Based on the short-circuit probability of industrial equipment, determine whether the current exceeds 20A, and estimate the arc discharge phenomenon when the current exceeds 20A to obtain the estimated data of the arc discharge phenomenon;

[0123] Estimate the instantaneous high-voltage pulse of the industrial equipment based on the arc discharge phenomenon prediction data and the short-circuit probability detection of the industrial equipment;

[0124] Judge whether the voltage exceeds 280V based on the instantaneous high-voltage pulse of the equipment, and analyze the grid harmonic pollution of the equipment with the voltage exceeding 280V to obtain the grid harmonic pollution data of the equipment

[0125] Analyze the phenomenon of instantaneous current surge of the equipment according to the grid harmonic pollution data of the equipment and the instantaneous high-voltage pulse of the equipment.

[0126] In the embodiments of the present invention, the rated power value and the operating time information of the equipment are extracted from the simulation data of industrial equipment. For each industrial equipment, an 80% power threshold is set according to its rated power. For example, if the rated power of the equipment is 100 kW, the set power threshold is 80 kW. Through data analysis tools (such as the data analysis libraries of MATLAB and Python), the simulation data of industrial equipment is processed to calculate the time period during which the power value of each equipment exceeds this threshold during operation. The power data of the equipment is extracted and compared with the threshold, and the duration of all periods when the power exceeds 80% is recorded. For each equipment, the cumulative duration during which the power exceeds this power threshold during its simulated operation is counted to generate corresponding statistical data. Suppose a numerically controlled machine tool has a duration of 10 hours during which the power exceeds 80% due to changes in material hardness during processing. Based on these data, it is determined whether the equipment is in a high-load working state for a long time, and the load expansion detection of the equipment running for a long time is carried out according to the simulated operation duration of the equipment. Specifically, if the operation duration of the equipment exceeds 1000 hours, it is detected whether there is a load expansion phenomenon in the equipment. Load expansion means that after the equipment runs for a long time, the load increases under a certain working state, resulting in the equipment consuming more power under the same working conditions. By analyzing the power and load changes per hour in the simulation data of the equipment, signs of load expansion are detected. For a numerically controlled machine tool, as the processing tasks accumulate, tool wear will cause an increase in the processing load, which in turn leads to an increase in power demand. By analyzing its power consumption pattern and load fluctuation when the simulated operation duration of the equipment exceeds 1000 hours, it is determined whether there is a load expansion phenomenon. For example, if after running for 1000 hours, the power consumption of the machine tool increases by more than 15% and the load value continues to increase, it is determined that the equipment has a load expansion phenomenon. When it is detected that the equipment load expands to 20%, based on the power consumption increase situation and load change trend of the equipment, a fault prediction model is used to predict the short-circuit probability of industrial equipment. Data such as current and voltage of the equipment during the load expansion stage are collected, and the short-circuit probability of the equipment is predicted through an algorithm model (such as Monte Carlo simulation or regression analysis). For example, during the actual operation of a numerically controlled machine tool, when the load expands to 20%, the increase in the current of the equipment causes an increase in the circuit burden, which in turn increases the probability of a short circuit. By monitoring the current change and power consumption of the equipment, the risk of the equipment short-circuiting under this load condition is estimated using an algorithm. If the load of a machine tool expands to 20% and the current exceeds 10% of the designed current value, the probability of a short circuit will increase significantly with the increase in the current. On the basis of predicting the short-circuit probability, it is further analyzed whether an arc discharge phenomenon will be caused when the current exceeds 20 A during the short circuit of the equipment. Arc discharge is caused by the intense internal current flow in the equipment under a high-current impact, and the high-temperature area generated causes an arc to occur between electrical contact points.Monitor the internal current waveform of the device through a current sensor. Especially when the current exceeds 20A due to load expansion, observe the sudden change of the current. If the current waveform shows a sharp change and maintains a high current (exceeding 20A), then preliminarily estimate the probability of the occurrence of the arc discharge phenomenon. For the hydraulic system, the risk of arc discharge is relatively low, but for the devices driven by motors, especially the numerical control machine tools and industrial motor-driven devices, the risk of arc discharge is relatively high. For example, if the device is in an overloaded state, the current exceeds 20A, and the current fluctuation range is large, then the arc discharge phenomenon occurs. According to the arc discharge phenomenon and the short-circuit probability, when the device current is abnormal, detect whether the device will have an instantaneous high-voltage pulse phenomenon. The high-voltage pulse is an instantaneous high voltage caused by the rapid change of the current during the short circuit or arc discharge of the electrical device, which will impact the device power grid. To detect this phenomenon, use a high-voltage detector to monitor the voltage fluctuation in real time. When the current exceeds 20A and the arc discharge occurs, it causes an instantaneous voltage fluctuation inside the circuit, forming a high-voltage pulse. Detect the voltage fluctuation inside the device through a voltage sensor and identify the time point of the voltage mutation to analyze whether the instantaneous high-voltage pulse phenomenon has occurred. When the device has an instantaneous high-voltage pulse, especially when the voltage reaches above 280V, use a harmonic analyzer to detect the harmonic pollution of the device power grid. The harmonic pollution in the device power grid is caused by the current fluctuation induced by the voltage pulse. Especially when high-voltage pulse phenomena such as arc discharge or short circuit occur, it causes strong harmonic interference in the device power grid. Through the voltage sensor with high-frequency sampling, monitor the harmonic components of the voltage signal during the operation of the device, and use algorithms such as Fourier transform to analyze the high-frequency signals in the power grid, so as to judge whether there is a harmonic pollution problem in the device. For example, during the motor drive of a machine tool, when the current is overloaded and the arc discharge occurs, high-frequency harmonics appear in the device voltage. Through harmonic analysis, judge the impact of harmonic pollution on the device power grid, and then evaluate the operation stability of the device and the health status of the power grid. Combine the power grid harmonic pollution and the instantaneous high-voltage pulse phenomenon to conduct a comprehensive analysis of the transient current surge phenomenon of the device. Monitor the instantaneous current fluctuation of the device through high-frequency current and voltage sensors to identify whether there is a current surge phenomenon. Use the time-domain analysis method to analyze the current waveform during the operation of the device, especially when the arc discharge, voltage mutation, and harmonic pollution occur, whether there is a current surge phenomenon. For example, in the hydraulic system or motor device, the current surge phenomenon usually accompanies high-voltage pulses and power grid harmonic pollution, affecting the stability and lifespan of the device.

[0127] Preferably, the multi-dimensional interactive aging trend analysis of the device described in step S2 includes the following steps:

[0128] Detect the cable insulation degradation status of the transient current surge phenomenon of the device;

[0129] Calculate the insulation breakdown probability of industrial equipment based on the phenomenon of transient current surge in the equipment and the deterioration of cable insulation;

[0130] Detect the overcurrent conduction condition based on the insulation breakdown probability of industrial equipment and the phenomenon of transient current surge in the equipment;

[0131] Calculate the local heat accumulation of the equipment for the overcurrent conduction condition;

[0132] Calculate the growth trend of thermal stress for the local heat accumulation of the equipment;

[0133] Estimate the local thermal expansion of the equipment for the local heat accumulation of the equipment;

[0134] Detect the associated jamming fault of equipment parts based on the local thermal expansion of the equipment;

[0135] Analyze the deformation trend of equipment parts for the associated jamming fault of equipment parts;

[0136] Conduct multi-dimensional interactive aging trend analysis of the equipment based on the growth trend of thermal stress and the deformation trend of equipment parts to obtain the multi-dimensional interactive aging trend of the equipment.

[0137] In the embodiments of the present invention, by monitoring the instantaneous current change of the device, it is identified whether there is a phenomenon of current surge. On this basis, the condition of cable insulation deterioration is detected. When the cable of the device is under long-term high current flow or sudden current increase, the insulation material will deteriorate due to factors such as local heating and oxidation. A high-frequency current sensor is used to sample the device current in real time, and a temperature sensor is combined to monitor the temperature change of the cable. By analyzing the relationship between current surge and temperature change, the degree of cable insulation deterioration is estimated. The specific operation is as follows: when the current suddenly increases to exceed the rated current of the device, the surface temperature of the cable is read through the temperature sensor. Once the temperature reaches the set threshold (such as 80 °C), it is determined that the cable insulation has deteriorated. At this time, by analyzing the aging process of the cable, the trend of the decline in the insulation performance of the cable is extracted. When it is detected that the device has a transient current surge phenomenon, using the monitored cable temperature change data and combining with the degree of cable insulation aging, the probability of insulation breakdown of the industrial device is calculated. The insulation performance of the cable will gradually decline with the long-term sudden increase in current, excessive temperature, and the change of electric field strength, thereby increasing the breakdown risk. A probability model (such as a risk assessment model) is used to calculate the probability of equipment insulation breakdown by combining the actually collected current and temperature data. For example, when the current surges to more than 300 A and the surface temperature of the cable continuously exceeds 80 °C, the probability of insulation breakdown will increase significantly. By calculating the historical current data and temperature curve, the probability of breakdown under the current cable insulation condition is obtained. On the basis of analyzing the probability of cable insulation breakdown, it is further detected whether there is a phenomenon of current overlimit conduction inside the device. Current overlimit conduction means that due to insulation breakdown or overload inside the device, the current abnormally flows in the cable or circuit, causing the device components to be affected by the current. By setting up a current monitoring system, the current changes of each component inside the device are detected in real time. The specific operation is as follows: a multi-point current sensor is used to monitor the current of each electrical component in the circuit. Once the monitored current exceeds the rated value, it can be determined that the current has abnormal conduction. If the current value continuously exceeds the device design parameter (for example, exceeds 50 A), the alarm mechanism is triggered, and the current change data is recorded. When it is detected that there is a phenomenon of current overlimit conduction inside the device, the calculation of the local heat accumulation generated when the current passes through each component is started. The current will generate heat due to resistance during the conduction process inside the device. Through the built-in temperature sensor of the device, the temperature change data of the electrical components is collected in real time, and the local heat accumulation is calculated according to the current intensity and resistance value. For the current overlimit area, the heat conduction formula is used to calculate the local temperature rise, and the total amount of heat accumulation is evaluated according to the rate of temperature rise and the cumulative time. For example, if the current exceeds 50 A, at the connection of the cable or electrical component, it is expected that about 20 W of heat will be accumulated within 30 minutes, increasing the local temperature rise. With the local heat accumulation brought about by current overlimit, the device components (such as cables, connectors, etc.) will generate thermal stress due to the temperature rise. This thermal stress will cause the expansion, deformation, and even damage of the device materials.Calculate the growth trend of local thermal stress of the device based on the temperature change data and the thermal expansion coefficient of the material. During the operation of the device, especially when the current exceeds the limit, the thermal stress will intensify. By combining the temperature and material properties, calculate the thermal stress of each component of the device through thermodynamic formulas. For example, during high-load operation, the cable of the machine tool heats up rapidly due to excessive current flow, resulting in an increase in the thermal stress of its material. This trend will accumulate over a long period, leading to accelerated aging of the cable. Based on the calculated growth trend of thermal stress, further evaluate the local thermal expansion phenomenon of the device. Thermal expansion refers to the change in volume of a material when the temperature rises. By collecting the thermal expansion coefficients of each component (such as cables, insulating materials, metal joints, etc.) inside the device and combining with the local temperature change data, calculate the local thermal expansion amount of the device. When the device undergoes thermal expansion, it causes jamming faults between the device parts. Jamming faults are caused by mechanical interference or blockage phenomena due to thermal expansion between device parts, affecting the normal operation of the device. By analyzing the relative position changes of the device parts and combining the temperature and thermal expansion data, detect whether there is a jamming phenomenon. For example, if the gap between components in the motor drive part decreases due to thermal expansion, jamming will occur, causing the device to fail to rotate normally. Through the temperature change data and mechanical displacement sensors, monitor the status of each component in real time, and detect and give early warnings of jamming faults in a timely manner. For the device parts with jamming, further analyze their deformation trends. Deformation refers to the permanent geometric shape change of device parts due to long-term high temperature, mechanical stress, etc. By installing deformation sensors, monitor the deformation of device parts under high temperature and stress conditions in real time. For example, on the rotating parts of a CNC machine tool, long-term high load causes slight deformation of the rotating shaft, affecting the rotation accuracy. Use sensors such as strain gauges to monitor the deformation of parts and combine with the thermal stress data to analyze the deformation trend of the device parts. Based on the growth trend of thermal stress and the deformation trend of device parts, comprehensively analyze the multi-dimensional interaction aging trend of the device. By combining factors such as thermal stress, deformation, temperature change, and current surge, use multi-dimensional data analysis methods (such as multivariate regression analysis, principal component analysis) to predict the aging trend of the device.

[0138] Preferably, the prediction of the dynamic precision decay of the device in step S2 includes:

[0139] Predict the dynamic electromagnetic radiation interference caused by the transient current surge phenomenon of the device;

[0140] Detect the electromagnetic radiation reverse interference of the dynamic electromagnetic radiation interference;

[0141] Predict the sensor reading error of the electromagnetic radiation reverse interference;

[0142] Detect the increase in the associated voidage of the device parts according to the multi-dimensional interaction aging trend of the device;

[0143] Evaluating the loosening trend of equipment parts by the growth of the associated void fraction of equipment parts;

[0144] Conducting equipment operation vibration detection based on the loosening trend of equipment parts and the growth of the associated void fraction of equipment parts to obtain equipment operation vibration data;

[0145] Calculating the resonance probability of equipment parts for the equipment operation vibration data and the loosening trend of equipment parts;

[0146] Measuring the attenuation status of equipment operation stability according to the resonance probability of equipment parts;

[0147] Estimating the dynamic accuracy attenuation of the equipment based on the attenuation status of equipment operation stability and the sensor reading error to obtain equipment dynamic accuracy attenuation data.

[0148] In the embodiments of the present invention, when the device is under high load or transient current surges, electromagnetic radiation interference will be generated. In this step, by installing a current sensor and an electromagnetic radiation detector on the device, the current fluctuations and radiation conditions of the device are monitored in real time. In actual operation, when the device current exceeds a preset threshold (for example, exceeding 120% of the rated current), the current sensor will record the surge data of the current, and at the same time, the electromagnetic radiation detector will track and record the radiation intensity caused by the current surge. The radiation interference of the device is usually proportional to the duration of the current surge. Specifically, a frequency range of 30 Hz to 3 GHz is used to detect the radiation interference. The electromagnetic radiation reverse interference will affect the control system and sensor performance of the device. By installing electromagnetic shielding and electromagnetic interference detection devices around the device. In the experiment, an electromagnetic interference sensor with high sensitivity (for example, an error detection ability of 0.1 mV) is selected to measure the reverse interference signal radiated during the operation of the device. This signal will return to the electrical device through the grounding system or the circuit of the device, forming an electromagnetic interference source. By detecting the reverse interference signal, the change of the electromagnetic environment of the device can be understood in real time, and its impact on the accuracy of the device can be estimated. The electromagnetic radiation reverse interference will affect the accuracy of the sensor, resulting in distorted device data. In this step, using the electromagnetic interference model and combining with the actual electromagnetic interference detection data, the impact on the device sensor readings is evaluated. In the experiment, by simulating the interference effects of different electromagnetic radiation intensities on the sensor and recording the changes in sensor errors, voltage sensors and pressure sensors are used to simulate the operation of the device in an electromagnetic interference environment, record the measurement errors and analyze the impact of the errors on the dynamic accuracy of the device. During the aging process of the device, the interaction between parts will cause the porosity between parts to increase. This step requires analyzing the contact wear situation between parts by long-term monitoring of the operation state of the device, combining the working temperature, load changes and material aging model of the device. In the experiment, by monitoring the operation cycle of the device, a laser scanning measurement is used to measure the surface morphology changes of the device parts. By comparing the porosity changes between the device parts (for example, using microscopic scanning technology), the growth trend of the associated porosity of the parts is evaluated, so as to estimate the aging process of the device. As the porosity increases, the parts will become loose. By installing sensors (such as displacement sensors, acceleration sensors, etc.) at key parts of the device, the relative displacement of the parts is monitored in real time. If the porosity increases to a certain extent, the relative movement between the device parts will intensify, resulting in a loosening phenomenon. In the experiment, by analyzing the displacement data of each part during the operation of the device and combining with the data of the porosity increase, the loosening trend of the parts is evaluated. When the device parts become loose, it will cause unstable vibrations during the operation of the device. This step requires real-time detection by vibration sensors to monitor whether the device generates abnormal vibrations. In the experiment, the vibration data during the operation of the device is collected by vibration sensors, and combined with the loosening trend model analysis, the vibration frequency range of the device is estimated.Through vibration spectrum analysis, the running stability and fault potential of the device are effectively evaluated. During the operation of the device, part resonance phenomenon occurs. Resonance usually occurs when the device parts match their natural frequencies. According to the vibration data and the trend of part looseness, by calculating the resonance frequencies of each component of the device and combining the vibration data for resonance probability analysis. In the experiment, by calculating the frequency response function (FRF) of the device, the probability of part resonance occurrence is predicted, providing a basis for fault warning. This process relies on advanced spectrum analysis tools such as spectrum analyzers and vibration analysis software. The vibration of the device at the resonance frequency will exacerbate the wear of the parts, resulting in a decline in the running stability of the device. This step calculates the stability attenuation of the device by real-time monitoring the vibration and resonance conditions of the device and combining the experimental data. By analyzing the acceleration and displacement data of the device, the trend of stability attenuation during long-term operation of the device can be evaluated. In the experiment, through long-term monitoring data, the algorithm predicts the stability attenuation rate of the device, and combines the running stability attenuation of the device and the reading error of the sensor to estimate the dynamic accuracy attenuation of the device. This process requires inputting the data obtained in the previous steps into the accuracy attenuation model and comprehensively evaluating in combination with factors such as the operating environment, load, and temperature of the device. In the experiment, by integrating all vibration data, resonance data, and sensor error data, the dynamic accuracy attenuation of the device is estimated through a data fusion algorithm.

[0149] Preferably, step S3 includes the following steps:

[0150] Step S31: Calculate the device error accumulation based on the device dynamic accuracy attenuation data, thereby obtaining the device error accumulation data;

[0151] Step S32: Detect the deviation of the device motion trajectory based on the device error accumulation data and the device dynamic accuracy attenuation data, thereby obtaining the device motion trajectory deviation;

[0152] Step S33: Estimate the fault risk of the industrial device according to the device motion trajectory deviation and the device error accumulation data, obtaining the industrial device fault risk;

[0153] Step S34: Estimate the attenuation state of the industrial product quality according to the industrial device fault risk data and the device motion trajectory deviation, obtaining the industrial device product quality attenuation data.

[0154] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0155] Step S31: Calculate the device error accumulation based on the device dynamic accuracy attenuation data, thereby obtaining the device error accumulation data;

[0156] In the embodiments of the present invention, the dynamic precision decay data of the equipment is mainly obtained by real-time monitoring of parameters such as the operating state of the equipment, the reading error of the sensor, and the vibration of the equipment, and the dynamic precision decay data of the equipment during long-term operation is collected. Specialized measuring tools, such as high-precision laser interferometers and displacement sensors, are used to real-time monitor the errors generated during the movement of the equipment. Then, based on these dynamic precision data, the cumulative error calculation formula is used to superimpose the errors of the equipment. During specific calculations, devices such as acceleration sensors and force sensors are used to real-time collect the error data during the movement of the equipment, and the errors generated in each cycle are accumulated. The error accumulation data of the equipment calculated by this method.

[0157] Step S32: Based on the equipment error accumulation data and the equipment dynamic precision decay data, perform equipment motion trajectory offset detection to obtain the equipment motion trajectory offset;

[0158] In the embodiments of the present invention, the equipment motion trajectory offset is usually jointly affected by equipment error accumulation and dynamic precision decay. In this step, based on the equipment error accumulation data and dynamic precision decay data obtained in the previous step, the equipment motion trajectory is further deduced. The actual motion trajectory of the equipment is accurately tracked using the equipment motion trajectory sensors (such as laser scanners, encoders, etc.), and compared with the ideal trajectory during equipment design. The degree of equipment motion trajectory offset is obtained by calculating the offset amount in each operation cycle to obtain the trajectory offset data. During this calculation process, numerical optimization methods such as the least squares method are used to reduce errors and ensure the accuracy of the offset data. Through these detections, the equipment motion trajectory offset is obtained.

[0159] Step S33: According to the equipment motion trajectory offset and the equipment error accumulation data, perform industrial equipment failure risk estimation to obtain the industrial equipment failure risk;

[0160] In the embodiments of the present invention, the equipment motion trajectory offset and error accumulation data are key indicators for judging the industrial equipment failure risk. The assessment of the failure risk depends on the correlation between the equipment motion offset and the error. Combining the equipment error accumulation data and motion trajectory offset data obtained in the previous steps, reliability analysis methods (such as Weibull analysis, Monte Carlo simulation, etc.) are used for failure risk prediction. During specific implementation, the offset data and error data of the equipment in different working states are input into the failure risk model, and combined with the historical operation data of the equipment, the risk assessment is carried out. For example, when using reliability analysis, a threshold is set. When the maximum allowable offset of the equipment motion trajectory exceeds a certain value, the probability of failure will increase significantly. According to the prediction of the model, the equipment failure risk value is obtained, thereby evaluating the potential failure risk in its current state.

[0161] Step S34: Estimate the quality attenuation state of industrial products based on the industrial equipment failure risk data and the deviation of the equipment motion trajectory, and obtain the industrial equipment product quality attenuation data.

[0162] In the embodiments of the present invention, changes in equipment failure risk and motion trajectory deviation will directly affect the quality of products. In this step, using the equipment failure risk data obtained from step S33, combined with the equipment motion trajectory deviation data, further calculate the quality attenuation of industrial products produced by the equipment. When the equipment has a failure risk, it usually shows quality degradation phenomena such as an increase in product size deviation and surface roughness during the production process. By comparing the theoretical quality indicators of the product with the quality of the actually produced product, a quality attenuation model is used to quantify the quality change during the operation of the equipment. This model takes into account the direct impact of equipment state changes on the product. For example, the deviation of the equipment motion trajectory leads to a decrease in the accuracy of the machined workpiece, thus causing the attenuation of product quality. In the calculation, a method combining process control data and quality control data is used to predict the quality attenuation trend during the equipment production process to obtain the product quality attenuation data.

[0163] Preferably, step S33 includes the following steps:

[0164] Step S331: Estimate the increased equipment wear data based on the equipment error accumulation data and the deviation of the equipment motion trajectory;

[0165] Step S332: Calculate the increased surface roughness data of the equipment operation based on the increased equipment wear data;

[0166] Step S333: Perform surface defect detection on the equipment operation based on the increased surface roughness data of the equipment operation to obtain the surface defect data of the equipment operation;

[0167] Step S334: Calculate the equipment collision probability of the equipment motion trajectory deviation;

[0168] Step S335: Estimate the industrial equipment failure risk based on the equipment collision probability and the surface defect data of the equipment operation.

[0169] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0170] Step S331: Estimate the increased equipment wear data based on the equipment error accumulation data and the deviation of the equipment motion trajectory;

[0171] In the embodiments of the present invention, the increased wear of the device is directly related to error accumulation and movement trajectory deviation. Based on the device error accumulation data and movement trajectory deviation data obtained in the foregoing steps, data fitting and analysis methods are used to estimate the increased wear of the device. In this step, the device error accumulation data reflects the gradual misalignment of the device during long-term operation, and the deviation of the movement trajectory indicates the decline in the position accuracy of the moving parts. These factors lead to increased friction, thereby accelerating the wear of the device parts. Using the rolling contact fatigue theory and wear prediction model, a mathematical model of increased wear is established by integrating dynamic load and offset data. During the calculation process, the operation data collected by high-precision sensors (such as vibration sensors, force sensors, etc.) and special wear test equipment are used to calculate the increased wear data of the device components under different working conditions, which reflects the wear degree of each component of the device and the trend of increased wear.

[0172] Step S332: Calculate the increased data of the surface roughness of the device operation based on the increased wear data of the device;

[0173] In the embodiments of the present invention, the increased wear of the device will cause the increase of the surface roughness of its operation surface, which in turn affects the processing accuracy and product quality of the device. In this step, according to the increased wear data of the device, a surface roughness prediction model is used to calculate the increased data of the surface roughness of the device operation, and high-precision measuring tools such as surface profilometers are used to obtain the surface roughness data of the device operation components. Then, combined with the increased wear data, the growth trend of the surface roughness is estimated. This calculation is based on factors such as the thickness of the wear layer, the hardness of the surface material, and the contact pressure of the components, and corresponding physical formulas or experience-based models are used for calculation. By comparing the surface roughness differences before and after the device wear, the increased data of the surface roughness of the device operation can be accurately obtained.

[0174] Step S333: Perform surface defect detection on the device operation surface according to the increased data of the surface roughness of the device operation to obtain the surface defect data of the device operation;

[0175] In the embodiments of the present invention, the increase in the surface roughness of the device is often accompanied by the generation of surface defects, such as scratches, cracks, depressions, etc. In this step, combining the device surface roughness growth data calculated in the previous steps, defect detection of the device operating surface is carried out through surface defect detection technology. The specific method includes using high-definition image processing technology and non-contact surface detection instruments (such as laser scanners, optical microscopes, etc.) to scan the device surface, and comparing the scan results with the standard surface topography data to detect abnormal surface features. During the scanning process, the surface roughness increase area is analyzed through image processing software to identify surface defects, and the types, positions, and severities of the defects are recorded. Through high-resolution detection equipment, surface defect information can be accurately obtained, and the potential impact of the defects on the device operation can be further quantified to obtain device operation surface defect data.

[0176] Step S334: Calculate the device collision probability of the device motion trajectory deviation;

[0177] In the embodiments of the present invention, based on the device motion trajectory deviation data obtained through the foregoing steps, combined with the geometric model and kinematic analysis of the device, the probability of device component collision is calculated. The specific method is to use kinematic simulation technology to simulate the collision situation during the device operation, and combined with the trajectory deviation amount of the device, calculate the probability of collision occurrence. During this process, a collision detection algorithm is adopted to analyze the motion path of the device under the deviation condition based on the device motion trajectory and the relative position relationship between components, and determine the probability of collision. This process also takes into account the influence of the device working load, the gap between components, and the external environment (such as temperature change, etc.) on the collision probability. Through multiple simulations and experiments, the probability value of the device collision is obtained.

[0178] Step S335: Estimate the industrial device failure risk based on the device collision probability and the device operation surface defect data.

[0179] In the embodiments of the present invention, based on the device collision probability data and the device operation surface defect data obtained through the foregoing steps, a risk assessment model is used to estimate the failure risk. Device collision may cause problems such as component damage and structural failure, while surface defects exacerbate the wear of the device, further increasing the failure risk. By combining these two sets of data, technologies such as fault tree analysis (FTA) or failure mode and effect analysis (FMEA) are used for comprehensive evaluation. In this step, the collision probability and the severity of the surface defect are used as input parameters and input into the failure risk estimation model. According to the model calculation results, the overall failure risk level of the device is obtained, and the time or type of device failure is predicted.

[0180] Preferably, step S4 includes the following steps:

[0181] Step S41: Evaluate the decline in equipment performance using the industrial equipment failure risk and industrial equipment product quality degradation data to obtain equipment performance decline data;

[0182] Step S42: Transmit the equipment performance decline data and industrial equipment failure risk to the digital control system, and conduct an assessment of industrial equipment operation defects based on the digital control system to obtain industrial equipment operation defect data;

[0183] Step S43: Conduct an abnormal warning for industrial equipment based on the industrial equipment defect data in the digital control system, and adjust the industrial equipment parameters according to the industrial equipment abnormal warning, thereby generating industrial equipment adjustment parameters.

[0184] In the embodiments of the present invention, in combination with the equipment failure risk data and equipment product quality attenuation data obtained in the foregoing steps, a comprehensive evaluation model is used to evaluate the decline in equipment performance. This evaluation model comprehensively considers the functional degradation of each component of the equipment by using the weighted average method based on the weighted data of equipment failure risk and quality attenuation. The equipment failure risk data is derived from various sensor data monitored during the operation of the equipment, including parameters such as the temperature, pressure, and vibration of the equipment. These data are collected in real time through high-frequency sensors (such as pressure sensors, temperature sensors, acceleration sensors, etc.) at a frequency of more than 100 Hz. The equipment product quality attenuation data is comprehensively analyzed based on the data of increased surface roughness of the equipment, surface defect detection results, and increased wear data obtained in the foregoing steps. Data normalization processing is performed to ensure that data in different dimensions have the same calculation weight. Then, a regression analysis method or a fault tree analysis (FTA) technique is used to quantitatively evaluate the decline in equipment performance, and the performance decline trend of each equipment component is calculated. This trend is obtained through weighted calculation, where the influence coefficients of failure risk and product quality attenuation on equipment performance are set according to historical data and expert experience. The obtained equipment performance decline data reflects the overall operating state of the equipment. After the equipment performance decline data and industrial equipment failure risk data are transmitted to the digital control system, the digital control system performs real-time processing and evaluation of these data through a real-time data reception and analysis module. This control system includes an industrial Internet platform and a cloud computing architecture, which can receive real-time data from various devices, and perform data transmission, storage, and analysis. Through an industrial Internet of Things (IIoT) gateway device, the equipment performance decline data and failure risk data are transmitted to the cloud platform. The digital control system on the platform uses a multi-dimensional analysis model to integrate and process the data. This digital control system applies machine learning algorithms (such as support vector machines, decision tree algorithms, etc.) to analyze the operating state of the equipment and identify existing operating defects. Based on the historical data, failure risk, performance decline data, and operating parameters of the equipment, the digital control system can detect potential failure points and operating defects of the equipment. For example, the system discovers that the temperature of the equipment rises abnormally or the vibration amplitude exceeds the normal range, and correlates with the equipment failure risk data to evaluate whether there are defects such as damage to key components or performance decline in the equipment. In this way, the system can give defect evaluation data of the equipment, including the location, severity of the defect, and the impact on the overall operating performance of the equipment. According to the evaluation results of the equipment defect data by the digital control system, the system will trigger an abnormal warning for industrial equipment. The warning mechanism relies on threshold judgment. When certain parameters (such as vibration, temperature, etc.) during the operation of the equipment exceed the set threshold, the control system will automatically generate a warning message. The digital control system will analyze the historical failure data and defect data of the equipment through a machine learning model, predict the type of abnormality occurring in the current equipment, and classify and warn according to different types of failures.This warning message will send an alarm to the operator through the user interface or directly through the device monitoring center, prompting potential problems with the device and taking timely measures. Based on these warning messages, the system will automatically adjust the parameters of industrial equipment according to the defect assessment results of the equipment. The operating parameters of the equipment (such as the rotational speed of the drive motor, feed speed, pressure, etc.) will be adjusted according to the real-time state of the equipment, thus avoiding the occurrence of faults or slowing down the fault process of the equipment. The adjustment process is based on the optimization algorithm built into the digital control system and is adjusted in real time by combining the equipment performance degradation and fault risk data. The adjustment process involves the instruction output to the equipment controller (such as the PLC control system) to ensure that the equipment can operate within the predetermined safety range and avoid serious faults. For example, if the system detects abnormal vibration or excessive load of the equipment, the control system will automatically lower the working load of the equipment or reduce the movement speed, thereby reducing the risk of equipment damage. The digital control system ensures that the equipment can respond quickly and make adjustments in abnormal states through efficient data communication and control algorithms. The equipment adjustment parameters include but are not limited to motor power, transmission system speed, cooling system flow rate, etc. Through the optimized adjustment of these parameters, the operating stability of the equipment is restored, the fault risk is effectively reduced, and the industrial equipment adjustment parameters are generated.

[0185] The present invention also provides a digital control system for performing the digital control method as described above. The digital control system includes:

[0186] An industrial equipment simulation module for obtaining an industrial equipment object; constructing an industrial equipment simulation model based on the industrial equipment object, and performing industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data;

[0187] A dynamic precision decay prediction module for analyzing the equipment transient current surge phenomenon according to the industrial equipment simulation data, and performing a multi-dimensional interaction aging trend analysis of the equipment according to the equipment transient current surge phenomenon, and predicting the dynamic precision decay of the equipment by combining the multi-dimensional interaction aging trend of the equipment and the equipment transient current surge phenomenon to obtain equipment dynamic precision decay data;

[0188] A product quality decay state prediction module for estimating the industrial equipment fault risk based on the equipment dynamic precision decay data, and predicting the industrial product quality decay state according to the industrial equipment fault risk to obtain industrial equipment product quality decay data;

[0189] An industrial equipment parameter adjustment module for transmitting the industrial equipment fault risk and industrial equipment product quality decay data to the digital control system, performing industrial equipment anomaly warning according to the digital control system, and performing industrial equipment parameter adjustment according to the industrial equipment anomaly warning to generate industrial equipment adjustment parameters.

[0190] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital control method, characterized in that, It includes the following steps: Step S1: Obtain an industrial equipment object; construct an industrial equipment simulation model based on the industrial equipment object, and perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data; Step S2: Analyze the phenomenon of sudden increase in transient current of the equipment based on the industrial equipment simulation data, and perform multi-dimensional interaction aging trend analysis of the equipment according to the phenomenon of sudden increase in transient current of the equipment. Estimate the dynamic accuracy decay of the equipment by combining the multi-dimensional interaction aging trend of the equipment and the phenomenon of sudden increase in transient current of the equipment to obtain equipment dynamic accuracy decay data; Step S3: Estimate the fault risk of industrial equipment based on the equipment dynamic accuracy decay data, and estimate the attenuation state of industrial product quality according to the industrial equipment fault risk to obtain industrial equipment product quality decay data; Step S4: Transmit the industrial equipment fault risk and industrial equipment product quality decay data to the digital control system, issue an abnormal warning for industrial equipment according to the digital control system, and adjust the industrial equipment parameters according to the industrial equipment abnormal warning to generate industrial equipment adjustment parameters.

2. The digital control method according to claim 1, wherein Step S1 includes the following steps: Step S11: Set the pressure measurement range of the pressure sensor to 0 - 100 bar, set the minimum pressure change to 1 mV / bar, and set the sampling frequency to 1000 Hz; Step S12: Set the voltage measurement range of the voltage sensor to 0 - 250 V, set the minimum accuracy to ±1%, and set the sampling frequency to 2000 Hz; Step S13: Obtain an industrial equipment object; collect the operating state of the industrial equipment object by the pressure sensor and the voltage sensor; Step S14: Conduct an industrial equipment function analysis based on the industrial equipment object to obtain industrial equipment function data; Step S15: Construct an industrial equipment dynamic simulation model based on the industrial equipment function data and the industrial equipment operating state; Step S16: Perform industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data.

3. The digital control method according to claim 2, characterized in that, Step S15 includes the following steps: Step S151: Count the equipment operation cycle of the industrial equipment operating state; Step S152: Calculate the industrial equipment operating current flow according to the equipment operation cycle; Step S153: Calculate the industrial equipment power consumption according to the industrial equipment operating current flow; Step S154: Collect the equipment dynamic response characteristics using the industrial equipment function data; Step S155: Evaluate the initial equipment operation state based on the equipment dynamic response characteristics and the industrial equipment function data, and record the initial equipment operation state data; Step S156: Construct an industrial equipment dynamic simulation model using the initial equipment operation state data and the industrial equipment power consumption.

4. The digital control method according to claim 1, characterized in that, The analysis of the phenomenon of sudden increase in transient current of the equipment described in Step S2 includes: Count the industrial equipment simulation operation duration with a rated power exceeding 80% in the industrial equipment simulation data; Detect the industrial equipment load expansion when the industrial equipment simulation operation duration exceeds 1000 h; Predict the industrial equipment short-circuit probability when the industrial equipment load expands to 20%. Judge whether the current exceeds 20A based on the short - circuit probability of industrial equipment, and estimate the arc discharge phenomenon when the current exceeds 20A to obtain the estimated data of the arc discharge phenomenon; Detect the instantaneous high - voltage pulse of the industrial equipment short - circuit probability detection device according to the estimated data of the arc discharge phenomenon and the short - circuit probability of the industrial equipment; Judge whether the voltage exceeds 280V based on the instantaneous high - voltage pulse of the device, and analyze the harmonic pollution of the device power grid when the voltage exceeds 280V to obtain the harmonic pollution data of the device power grid Analyze the phenomenon of sudden increase in transient current of the device according to the harmonic pollution data of the device power grid and the instantaneous high - voltage pulse of the device.

5. The digital control method according to claim 1, wherein The multi - dimensional interactive aging trend analysis of the device described in step S2 includes the following steps: Detect the cable insulation deterioration condition of the phenomenon of sudden increase in transient current of the device; Calculate the insulation breakdown probability of the industrial equipment according to the cable insulation deterioration condition of the phenomenon of sudden increase in transient current of the device; Detect the current over - limit conduction condition according to the insulation breakdown probability of the industrial equipment and the phenomenon of sudden increase in transient current of the device; Calculate the local heat accumulation of the device in the current over - limit conduction condition; Calculate the growth trend of thermal stress of the local heat accumulation of the device; Estimate the local thermal expansion of the device due to the local heat accumulation of the device; Detect the associated jamming fault of the device parts based on the local thermal expansion of the device; Analyze the deformation trend of the device parts of the associated jamming fault of the device parts; Conduct multi - dimensional interactive aging trend analysis of the device according to the growth trend of thermal stress and the deformation trend of the device parts to obtain the multi - dimensional interactive aging trend of the device.

6. The digital control method according to claim 1, characterized in that, The device dynamic accuracy attenuation prediction described in step S2 includes: Estimate the dynamic electromagnetic radiation interference generated by the phenomenon of sudden increase in transient current of the device; Detect the electromagnetic radiation reverse interference of the dynamic electromagnetic radiation interference; Estimate the sensor reading error of the electromagnetic radiation reverse interference; Detect the growth of the associated voidage of the device parts according to the multi - dimensional interactive aging trend of the device; Evaluate the loosening trend of the device parts of the growth of the associated voidage of the device parts; Conduct device operation vibration detection according to the loosening trend of the device parts and the growth of the associated voidage of the device parts to obtain the device operation vibration data; Calculate the resonance probability of the device parts based on the device operation vibration data and the loosening trend of the device parts; Measure the attenuation condition of the device operation stability according to the resonance probability of the device parts; Conduct device dynamic accuracy attenuation prediction based on the attenuation condition of the device operation stability and the sensor reading error to obtain the device dynamic accuracy attenuation data.

7. The digital control method according to claim 1, wherein Step S3 includes the following steps: Step S31: Calculate the device error accumulation based on the device dynamic accuracy attenuation data to obtain the device error accumulation data; Step S32: Detect the device motion trajectory deviation based on the device error accumulation data and the device dynamic accuracy attenuation data to obtain the device motion trajectory deviation; Step S33: Conduct industrial equipment failure risk estimation according to the device motion trajectory deviation and the device error accumulation data to obtain the industrial equipment failure risk; Step S34: Conduct industrial product quality attenuation state prediction according to the industrial equipment failure risk data and the device motion trajectory deviation to obtain the industrial equipment product quality attenuation data.

8. The digital control method according to claim 7, wherein Step S33 includes the following steps: Step S331: Estimate the data of increased equipment wear according to the device error accumulation data and the device motion trajectory deviation; Step S332: Calculate the increased data of the surface roughness of the equipment operation based on the data of the increased equipment wear. Step S333: Conduct defect detection on the equipment operation surface according to the growth data of the surface roughness of the equipment operation to obtain the defect data of the equipment operation surface. Step S334: Calculate the equipment collision probability of the equipment movement trajectory deviation. Step S335: Estimate the industrial equipment failure risk based on the equipment collision probability and the defect data of the equipment operation surface.

9. The digital control method according to claim 1, wherein Step S4 includes the following steps: Step S41: Conduct equipment performance degradation assessment on the industrial equipment failure risk and the industrial equipment product quality degradation data to obtain the equipment performance degradation data. Step S42: Transmit the equipment performance degradation data and the industrial equipment failure risk to the digital control system, and conduct industrial equipment operation defect assessment according to the digital control system to obtain the industrial equipment operation defect data. Step S43: Conduct industrial equipment anomaly warning on the industrial equipment defect data according to the digital control system, and adjust the industrial equipment parameters according to the industrial equipment anomaly warning to generate the industrial equipment adjustment parameters.

10. A digital control system, characterized in that, For implementing the digital control method as described in claim 1, this digital control system includes: An industrial equipment simulation module, which is used to obtain an industrial equipment object; construct an industrial equipment simulation model based on the industrial equipment object, and conduct industrial equipment simulation according to the industrial equipment simulation model to obtain industrial equipment simulation data. A dynamic accuracy degradation prediction module, which is used to analyze the equipment transient current surge phenomenon according to the industrial equipment simulation data, conduct multi-dimensional interaction aging trend analysis of the equipment according to the equipment transient current surge phenomenon, and conduct equipment dynamic accuracy degradation prediction on the equipment multi-dimensional interaction aging trend and the equipment transient current surge phenomenon to obtain the equipment dynamic accuracy degradation data. A product quality degradation state prediction module, which is used to estimate the industrial equipment failure risk based on the equipment dynamic accuracy degradation data, and conduct industrial product quality degradation state prediction according to the industrial equipment failure risk to obtain the industrial equipment product quality degradation data. An industrial equipment parameter adjustment module, which is used to transmit the industrial equipment failure risk and the industrial equipment product quality degradation data to the digital control system, conduct industrial equipment anomaly warning according to the digital control system, and adjust the industrial equipment parameters according to the industrial equipment anomaly warning to generate the industrial equipment adjustment parameters.

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