Intelligent motor remote monitoring method based on industrial bus

Abstract

The invention provides an intelligent motor remote monitoring method based on an industrial bus, and relates to the technical field of motor control or adjustment. The intelligent level of a motor controller is improved by constructing a technical closed loop of'future risk prediction based on digital twinning-coupling fusion decision of multi-domain risks-prospective adaptive adjustment of internal resources-dynamic deterministic broadcast of external capability '. The robustness of a motor control system in a high-dynamic and high-uncertainty industrial environment and the reliability of task execution can be enhanced, the cooperative efficiency of the whole distributed control system is effectively improved, and a solid technical basis is provided for realizing higher-order predictive maintenance and autonomous health management in a complex industrial scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of motor control or regulation, in particular to an intelligent motor remote monitoring method based on an industrial bus. BACKGROUND

[0002] In complex industrial scenarios such as high-end manufacturing, robot collaboration, and new energy, intelligent motors have evolved from traditional power execution units to key network nodes integrating perception, computing, and communication capabilities. In scenarios such as high-end manufacturing and robot collaboration, which have strict requirements for dynamic performance and system reliability, a single motor controller is required not only to accurately execute local control but also to act as an intelligent agent to efficiently collaborate with other nodes in a distributed system. Therefore, improving the autonomous regulation capabilities and the depth of information exchange of motor controllers in complex network environments has become an important development direction in this field.

[0003] Currently, motor remote monitoring technology based on industrial buses still faces several technical challenges in evolving towards higher levels of intelligence: 1. Existing technologies generally rely on monitoring current or historical operating data to assess motor status and respond. For example, the method disclosed in Chinese Patent Document CN110829937A determines the current state of the motor by collecting its current position and current signals. This control paradigm lacks the ability to anticipate potential risks (such as sudden overheating due to insulation aging) that evolve rapidly, which to some extent limits the safety margin of the system under extreme conditions.

[0004] 2. In traditional control strategies, risks in the physical domain of the motor (such as overheating and overloading) and resource status in the controller computing domain (such as processor load and memory usage) are usually evaluated independently. This separation of dimensions makes it difficult for the system to identify and quantify the compounded risks caused by the coupling of multi-domain factors. For example, a slight physical anomaly has a much higher probability of leading to system failure when computing resources are highly strained than when resources are abundant, and existing technologies lack effective means to quantify the coupling gain effect of such risks.

[0005] 3. The status information broadcast by existing controllers is usually discrete and static, such as "normal," "warning," or "fault" mode identifiers. This coarse granularity of information cannot convey the dynamic trends of the internal state of the controller to external systems. An external system cannot tell whether the state of a controller in "high load mode" is tending towards stability or on the brink of deterioration. This ambiguity in communication information hinders the upper-level system from making accurate and forward-looking collaborative decisions, and further improves the coordination efficiency and robustness of the entire distributed system.

[0006] The above information disclosed in the background section is only for the purpose of enhancing the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present application is to provide an intelligent motor remote monitoring method based on an industrial bus to solve the problems raised in the background.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The intelligent motor remote monitoring method based on the industrial bus, the specific steps comprising: S1: obtaining real-time operation data for constructing a multi-physical field digital twin model of the motor by the motor control system; Based on the multi-physical field digital twin model, the future physical state parameters of the motor within a preset time window are predicted; S2: Fusing the future physical state parameters and the resource state parameters of the motor control system, generating a task criticality index; According to the task criticality index, a forward-looking resource scheduling strategy is generated; S3: According to the forward-looking resource scheduling strategy, dynamically adjusting the resource allocation of the calculation task and the communication task in the motor control system; S4: Based on the resource allocation result after dynamic adjustment, generating a controller capability portrait representing the current external service capability of the motor control system, and broadcasting the controller capability portrait to the external system through the industrial bus.

[0009] Compared with the prior art, the present application has the following beneficial effects: by introducing a multi-physical field digital twin model; this model is not satisfied with monitoring the current state, but through real-time data driving, the future physical state evolution of the motor in the digital space is deduced, so as to quantify the potential, not yet occurred fault probability and safety margin. This realizes the fundamental change from "passive response" to "active prediction", and provides a key time window for all subsequent decisions; Through the adaptive weighted fusion mechanism based on risk coupling gain, when multiple risks are superimposed, the "task criticality index" generated can produce steep nonlinear growth, so as to more sensitively and earlier capture those compound risks that are easily ignored in a single dimension; The constructed dynamic capability portrait generation and broadcast model. The controller generates and broadcasts a multi-dimensional, dynamic capability portrait. The core of the portrait is the introduction of a "task execution certainty index", which quantifies the time-varying rate of internal comprehensive risk, revealing the "stability" and "predictability" of its future state to external systems. This enables the coordination of the entire distributed system, from simple synchronization based on discrete states to fine-grained adaptive coordination based on future stability prediction. BRIEF DESCRIPTION OF DRAWINGS

[0010] Fig. 1 Application flowchart of the present application; Fig. 2 Execution logic diagram of step S1 and step S2 of the present application; Fig. 3 Execution logic diagram of step S3 and step S4 of the present application. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0012] It can be understood that the terms "first", "second" and the like used in the present application can be used herein to describe various elements, but unless specifically stated, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0013] Example 1: Please refer to Figs. 1-3 The present application provides a technical solution: The intelligent motor remote monitoring method based on industrial bus, the specific steps include: S1: Obtain real-time operation data for constructing a multi-physical field digital twin model of the motor from the motor control system; Based on the multi-physical field digital twin model, predict the future physical state parameters of the motor within a preset time window; S2: Fuse the future physical state parameters and the resource state parameters of the motor control system to generate a task criticality index; According to the task criticality index, generate a forward-looking resource scheduling strategy; S3: Dynamically adjust the resource allocation of computing tasks and communication tasks within the motor control system according to the forward-looking resource scheduling strategy; S4: Based on the resource allocation result after dynamic adjustment, generate a controller capability portrait representing the current external service capability of the motor control system, and broadcast the controller capability portrait to the external system through the industrial bus.

[0014] Further explanation: for Fig. 1 It should be noted that: "digital twin and future state prediction" represents step S1; "multi-domain risk fusion and strategy decision" represents step S2; "internal resource dynamic self-adaptive adjustment" represents step S3; "dynamic capability image generation and broadcast" represents step S4; Real-time running data, specifically including: obtaining at least one electrical parameter; and obtaining non-electrical physical parameters reflecting the internal thermodynamics of the motor; future physical state parameters include future failure probability and system safety margin; resource state parameters include calculation time margin; Further explanation: the step of fusing and generating the task criticality index in S2 is specifically: by adaptively weighting and fusing the future failure probability, system safety margin and calculation time margin of the motor control system, the task criticality index is generated.

[0015] Further explanation: adaptive weighting fusion includes: calculating a risk coupling factor based on future physical state parameters and resource state parameters.

[0016] The risk coupling factor includes a physical risk coupling factor, which is calculated based on the combination of future failure probability and system safety margin; The risk coupling factor also includes a resource risk coupling factor, which is calculated based on the combination of future failure probability and calculation time margin; The risk coupling factor is used to dynamically adjust the preset basic weight to generate a dynamic weight; the dynamic weight is used to weight and sum the future physical state parameters and resource state parameters, and the sum result is subjected to nonlinear normalization processing to obtain the task criticality index.

[0017] Further explanation: generating a forward-looking resource scheduling strategy in S2 is specifically: dividing the numerical interval of the task criticality index into at least two threshold intervals; and presetting a corresponding resource scheduling mode for each threshold interval, the resource scheduling mode at least including a task priority adjustment strategy and a loading and unloading strategy of a functional module.

[0018] The core logic of the embodiment is to construct and calibrate a high-fidelity multi-physical field digital twin model in real time by synchronously obtaining the electrical parameters of the motor and the non-electrical physical parameters obtained through online identification technology. Secondly, the model is used to predict the potential failure probability of the motor in the future time window and the current safety margin, and to quantify the potential risk in the physical domain. The embodiment introduces an adaptive weighted fusion mechanism based on risk coupling gain, which can nonlinearly fuse the predicted risk parameters from the physical domain and the real-time resource state parameters from the computing domain to generate a task criticality index that can more sensitively reflect the composite risk scenario. Finally, according to the comparison result of the task criticality index and the preset multiple dynamic thresholds, the system can decisively switch to the optimal resource scheduling mode, which specifically defines the execution priority of different software tasks in the central processor and the dynamic loading or unloading strategy of specific functional algorithm modules. The task criticality index can produce a "steep" nonlinear growth when multiple risks are superimposed, so that risk avoidance can be achieved earlier and more decisively. In this embodiment, the key parameters involved are calculated and processed by the processor deployed inside the motor controller, which is described as follows: Electrical parameters: including motor phase current, parameter symbol , representing the instantaneous current value flowing through the motor stator winding; and motor terminal voltage, parameter symbol , representing the instantaneous voltage value applied to the motor terminal, these two parameters are the basis for representing the electromagnetic state of the motor.

[0019] "Motor phase current" and "motor terminal voltage" are obtained by direct measurement through high-precision Hall effect current sensors (Allegro-Microsystems-ACS720 series) and voltage divider resistor network voltage sensors integrated on the controller hardware circuit; the analog signals output by the sensors are sampled through a 16-bit resolution analog-to-digital converter (ADC), with a sampling frequency set to 20kHz, synchronized with the pulse width modulation (PWM) frequency. The original data sequence after sampling is digitally filtered through a third-order low-pass Butterworth filter, with a cutoff frequency set to 5kHz.

[0020] Non-electrical physical parameters: specifically the equivalent thermal resistance of the stator winding, parameter symbol Physically, represents the equivalent thermal resistance encountered by the stator windings of a motor as a heat source during heat transfer to the motor housing, measured in Kelvin per watt (K / W). It directly reflects the motor's heat dissipation capacity and health status. This parameter is obtained through an online parameter identification algorithm, specifically based on resistance temperature measurement and recursive least squares (RLS). The "brief pause between two motion commands" in motor control is defined as the non-torque output interval. The inverter module of the controller injects a weak DC test current of constant amplitude into the U and V phase windings of the motor, with the parameter symbol being... The current amplitude is set to 1% of the motor's rated current to ensure that the generated torque is negligible and does not cause a significant temperature rise; during this period, the voltage drop between phases U and V is measured, and its parameter symbol is... Based on Ohm's law, the instantaneous DC resistance of the winding is calculated, and its parameter symbol is... The calculation relationship is as follows equal Divide by Secondly, based on the temperature coefficient of resistance of the winding copper, which is calibrated at the motor factory, its parameter symbol is... And the winding resistance at a reference temperature of 25 degrees Celsius, the parameter symbol of which is Through linear approximation, by The current average temperature of the winding is calculated by reverse calculation, and its parameter symbol is: The relation is expressed as: Equal to reference temperature plus ( minus The difference is then divided by ( Multiply The product of ) . Finally, the controller records the total motor power loss in real time during continuous operating cycles as the input heat flow for the thermal model, and records The case temperature is measured by an NTC thermistor mounted on the case. The temperature difference between the windings and the housing is considered. This input-output data pair is fed into a recursive least squares (RLS) estimator, which identifies online and iteratively the first-order transfer function describing the dynamic relationship between the temperature difference between the windings and the housing and the power loss. The steady-state gain in this function is then determined as the equivalent thermal resistance of the stator windings. .

[0021] Injected in this embodiment The measured value was 0.1 amperes. If it is 0.15 volts, then It is 1.5 ohms. If the motor is at 25 degrees Celsius... The temperature coefficient of resistance of the copper winding is 1.3 ohms. If it is 0.00393, then The calculation is 25 + (1.5 - 1.3) / (0.00393 * 1.3) = 64.1 degrees Celsius. In the past 10 seconds, the average power loss is 20 watts, and the average temperature difference is measured to be 40 degrees Celsius, so The preliminary estimate is 40 / 20 = 2 K / W. The RLS algorithm will continue to smooth and correct this value based on new data. For the "multi-physical field digital twin model", it needs to be supplemented that: The core of the "multi-physical field digital twin model" in this embodiment is a composite model composed of an electrical loss model and a thermal network model. Its construction process is divided into two stages: offline parameter calibration and online parameter identification. The specific content is as follows: the electrical loss model is used to calculate the total power loss of the motor as the input of the thermal network model . The second-order Foster thermal network model is used to simulate the transmission path and cumulative effect of heat in the motor. The second-order Foster network structure is selected, which is composed of two groups of parallel "thermal resistance-thermal capacity" (R-C) circuits. The first group of RC circuits characterizes the heat transfer path from the motor winding heat source to the machine shell; the second group of RC circuits characterizes the heat transfer path from the machine shell to the external environment. Through motor design data and laboratory "blocked rotor temperature rise experiment" and "no-load temperature rise experiment", most of the thermal network parameters are pre-calibrated. Specifically: winding thermal capacity and machine shell thermal capacity : According to the copper mass of the motor winding, the material and mass of the core and the machine shell, the initial value is calculated by the specific heat capacity formula of the material; the winding to machine shell thermal resistance is basically unchanged after the internal structure of the motor is fixed, and is calculated according to the thermal Ohm's law by measuring the stable temperature difference between the winding and the machine shell and the motor heating power under controlled heat dissipation conditions.

[0022] The machine shell to the environment thermal resistance is a parameter that characterizes the dynamic change of the motor external heat dissipation air speed with the working condition; and is identified online; the recursive least squares method (FF-RLS) with forgetting factor is used. The implementation steps of the algorithm are as follows: The input is a set of time series data pairs, including: the total power loss of the motor at the current time ; the machine shell temperature measured by the temperature sensor attached to the surface of the motor machine shell in real time.

[0023] The total motor power loss is the accurate addition of copper loss and iron loss; the copper loss is calculated according to Joule's law by obtaining the d-q axis current components in the FOC control algorithm and multiplying the winding resistance value after real-time temperature compensation; specifically, the winding resistance value is calculated in real time by a linear temperature compensation model, the input of the model is the winding temperature estimated by the multi-physical field digital twin model in real time, and the mathematical relationship is: obtaining the temperature difference between the current winding temperature estimation value and the preset reference temperature; then, the temperature difference is multiplied by the known resistance temperature coefficient, and the product obtained is added to the numerical value, so as to obtain the temperature compensation factor; finally, the temperature compensation factor is multiplied by the resistance reference value calibrated offline at the reference temperature, and the product is the real-time resistance value represented by the compensated winding resistance value.

[0024] The iron loss is estimated by a two-dimensional lookup table calibrated in advance; the controller takes the current motor electrical angular velocity and stator flux linkage amplitude as input, obtains the corresponding iron loss value by table lookup and application of the bilinear interpolation method.

[0025] The identification algorithm task in the controller is executed once in a fixed time period. The latest total motor power loss and measurement value is substituted into the recursive formula of the FF-RLS algorithm, and the covariance matrix and parameter estimation vector are updated iteratively; the fixed time period of the embodiment is "every 1 second"; the output is the optimal estimation value of the recognized machine shell to ambient thermal resistance under the current working condition.

[0026] The controller updates the latest identified value to the parameter matrix of the second-order Foster thermal network model in real time. Thus, the high-fidelity digital twin model reflecting the current real cooling environment is constructed by real-time self-calibration.

[0027] Further, future physical state parameters: including future failure probability, the parameter symbol of which is , representing the probability that the key temperature point of the motor exceeds the safety threshold within the initial "100 milliseconds" future preset time window, which is a dimensionless [0, 1] interval value; and system safety margin, the parameter symbol of which is , representing the relative distance of the current key state parameter to its safety threshold, which is a dimensionless [0, 1] interval value; the key temperature point of the embodiment includes the winding temperature; the "future failure probability" and "system safety margin" are predicted by the second-order Foster thermal network model in the multi-physical field digital twin model; specifically, the identified and other offline calibrated thermal resistance and capacitance parameters, a second-order Foster thermal network is constructed to accurately describe the heat transfer path from the winding to the machine frame and then to the environment. The input of the network is the total power loss of the motor at the current time, and the output is the predicted average temperature of the winding and the frame temperature at the current time . The controller takes the current state as the initial condition, converts the predicted load torque in the next 100 milliseconds into a predicted power loss sequence, and takes this as the input of the thermal model. By solving the differential equation of the model, the winding temperature prediction trajectory in the next 100 milliseconds is obtained. Based on the model parameters and the uncertainty of the load prediction, a Gaussian random noise with a mean of zero and a standard deviation is superimposed on the deterministic trajectory, and the standard deviation is obtained by offline experimental calibration; by running 1000 times of Monte Carlo simulation, the number of times that the highest temperature in the prediction trajectory exceeds the safety threshold is counted, and the future failure probability is obtained by dividing the total simulation number 1000 ; the safety threshold in this embodiment is 155 degrees Celsius The following describes the acquisition of the "load torque": in this embodiment, an AR(2) model extrapolation method based on a second-order autoregressive model is used to generate and calculate the load torque TL, and the specific implementation steps are as follows: The controller opens a first-in-first-out (FIFO) queue in the memory to store the actual values of the load torque TL in the last N sampling periods. The actual value is obtained by multiplying the q-axis current command value in the FOC (Field-Oriented-Control, magnetic field oriented control) by the torque coefficient; recursive least squares (RLS) is used to fit the historical torque data in the cache queue and online identify three coefficients of the AR(2) model: the constant term c, the first-order lag coefficient a1, and the second-order lag coefficient a2. The model expression is: TL(t)=c+a1×TL(t-1)+a2×TL(t-2); where t is the index mark of the time; The current load torque value TL(t) and the torque value TL(t-1) at the previous time are used as initial conditions, and the AR(2) model with the identified coefficients is used for iterative calculation to generate a torque prediction sequence in the next 100 milliseconds. In this embodiment, the load torque at the next time TL(t+1)=c+a1×TL(t)+a2×TL(t-1) is predicted, and then TL(t+1) and TL(t) are used to predict TL(t+2), and so on. The obtained torque prediction sequence is combined with the current speed to convert the total power loss prediction sequence of the motor in the next 100 milliseconds through the electrical loss model, and this sequence is the predicted input of the thermal network model.

[0028] Furthermore, system safety margin The calculation logic is as follows: obtain the winding temperature at the current moment. Safety thresholds corresponding to motor insulation class And the ambient temperature at which the motor is currently operating, as measured by sensors. Calculate the actual temperature rise range, i.e. and The difference; and calculate the total allowable temperature rise range, i.e. and The difference; system safety margin It is determined as the ratio of the actual temperature rise range to the total allowable temperature rise range, and the output value range is limited to the interval [0, 1]; when equal hour, A value of 1 indicates the maximum margin; when equal hour, A value of 0 indicates that the margin has been exhausted. In this embodiment, if... , , Then the system safety margin It equals the quotient of (155-124) divided by (155-25), that is, 31 divided by 130 equals 0.238.

[0029] Resource status parameters, specifically the computation time margin, are denoted by [symbol missing]. This represents the percentage of idle time remaining in the current PWM control cycle after the processor has completed all preset hard real-time calculation tasks, relative to the total control cycle duration. It is a dimensionless value within the range [0, 1]. This parameter is directly provided by the performance monitoring module of the real-time operating system (RTOS) on the controller. At the end of the control cycle, the RTOS reads the CPU's internal high-precision timer to calculate the total idle time of the CPU during that cycle. Dividing this idle time by the total duration of the PWM control cycle yields the result. .

[0030] Risk coupling factors: including physical risk coupling factors, whose parameter symbols are as follows This is used to amplify the impact of multiple risks occurring simultaneously within the physical domain; and a resource risk coupling factor, whose parameter symbol is... This is used to amplify the impact when physical risks and computational resource constraints occur simultaneously. Both are dimensionless values ​​and are greater than or equal to 1.

[0031] For physical risk coupling factors The calculation logic is as follows: calculate the basic physical risk index, which is equal to the future failure probability. times (1 minus system safety margin ). Then, equals 1 plus the base physical risk indicator times a preset physical coupling gain coefficient .

[0032] Resource risk coupling factor is calculated as follows: calculate a base resource risk indicator, which equals future failure probability times (1 minus computation time margin ). Then, equals 1 plus the base resource risk indicator times a preset resource coupling gain coefficient . In this embodiment, , , , and . The base physical risk indicator is 0.8 x (1 - 0.1) = 0.72, and the physical risk coupling factor is 1 + 0.72 x 2.0 = 2.44. The base resource risk indicator is 0.8 x (1 - 0.2) = 0.64, and the resource risk coupling factor is 1 + 0.64 x 3.0 = 2.92. When multiple risks occur, the risk coupling factor is greater than 1, which plays a magnifying role.

[0033] For the physical coupling gain coefficient and the resource coupling gain coefficient , the following offline optimization method is used for calibration: a hardware-in-the-loop (HIL) simulation platform or a real motor test bench is established; a group of typical and representative extreme conditions are defined by experienced system engineers, and a desired task criticality indicator is set for each scenario Output value: Scenario A1 (single high thermal risk): . The desired output is approximately 0.75.

[0034] Scenario B1 (single high computation risk): . The desired output is approximately 0.65.

[0035] Scenario C1 (complex high risk): . In this scenario, the expected risk is significantly amplified, and the desired output is greater than 0.95.

[0036] Define an objective function, whose value is the actual output of the algorithm in all key scenarios Root Mean Square Error (RMSE) between the expert-set expectations . Grid-Search or more efficient Particle Swarm Optimization (PSO) algorithm is used to iteratively search within the pre-set and value range. In each iteration, a set of candidate is substituted into the algorithm, and all key scenarios are run on the platform to calculate the error value of the objective function; after sufficient iterations, the set of that minimizes the error of the objective function is found, which is the final calibration result. This result is solidified into the constant definition of the controller program.

[0037] Further, the parameter symbol of the task criticality index is , which is a normalized index that quantifies the "urgency" or "danger" of the system's current and future operation, with a value range of [0, 1]; For dividing the numerical range of the task criticality index into at least two threshold intervals; set two threshold intervals through the strategy threshold parameter composed of "low criticality threshold" and "high criticality threshold"; Strategy threshold parameter: including low criticality threshold, parameter symbol ; and high criticality threshold, parameter symbol . Both of these criticality thresholds are in the interval (0, 1) and are used to divide different intervals of the task criticality index to trigger different scheduling strategies; they are determined through offline experimental calibration, and the specific steps are as follows: build a motor test platform, run the motor under different but controllable heat dissipation conditions and load conditions, and artificially inject calculation disturbance tasks to simulate different calculation time margins . In this process, the calculated values of the task criticality index and the actual operating state of the motor are continuously recorded, including whether the overheating protection occurs, the control accuracy decreases, etc.; through actual experiments, find the maximum task criticality index value, when it exceeds this maximum task criticality index value, the system has a high probability of entering an unsafe state in a short time, and this value is calibrated as ; this embodiment sets to 0.8. Find another low-value task criticality index value, when it is lower than this "low-value task criticality index value", the system can run stably and efficiently for a long time under any test conditions, and this value is calibrated as , and the value of this embodiment is 0.3; it should be noted that "high probability" is adjusted based on actual application requirements, and this embodiment sets it to 80%; The core computing process of the embodiment is periodically executed by the processor in the controller in an independent, high-priority non-hard real-time task, specifically once every 10 milliseconds; the computing process is as follows: 1.1) The initial input is the parameters acquired by the sensor and the RTOS in real time, including: motor phase current , motor terminal voltage , case temperature , ambient temperature , and calculation time margin .

[0038] 1.2) Call the online parameter identification module, use the input electrical parameters to calculate and update the equivalent thermal resistance of the stator winding 1.3) Update the updated and real-time measured and as the latest parameters to the multi-physical field digital twin model, then call the model to calculate the future failure probability and system safety margin 1.4) According to the output and , and the input , calculate the physical risk coupling factor and the resource risk coupling factor 1.5) The , , and the calculated and are adaptively weighted and fused to calculate the final task criticality index .

[0039] 1.6) Condition judgment one: judge whether the task criticality index is greater than or equal to the high criticality threshold .

[0040] Path one is true: generate a "safety priority mode" resource scheduling strategy; the specific content of the strategy is encoded as an instruction, which is a data structure containing a mode identifier and a priority list.

[0041] Condition judgment two: if condition judgment one is false, continue to judge whether the task criticality index is less than the low criticality threshold ; path two is true: generate an "energy efficiency priority mode" resource scheduling strategy; path three is false: generate a "performance balance mode" resource scheduling strategy. The following key tasks run in the controller of the embodiment, the smaller the RTOS priority value, the higher the priority: the specific resource scheduling strategy in the three modes is defined as follows: 0.1) Set the resource scheduling strategy of "safety priority mode" as: When the task criticality index is greater than the high criticality threshold, this mode is triggered. In this mode, system survival is the only goal, and all resources are tilted towards safety and core control. Details: The priority of "safety monitoring and protection" is raised to the highest level 1, and the priority of "motor FOC core control" is maintained at the second highest level 2. The priority of "digital twin and prediction" is set to "ensure continuous risk assessment" at level 3. The priority of "external communication" is set to a lower priority of "only report the most critical state when CPU is idle" at level 10. The priority of "data recording" is lowered to "lowest, can be suspended at any time" at level 15. "Function module adjustment" unloads or suspends all unnecessary background diagnostic, data analysis and other function modules.

[0042] 0.2) Set the resource scheduling strategy of "energy efficiency priority mode" as: When the task criticality index is less than the low criticality threshold , this mode is triggered. In this mode, the system has sufficient safety margin and can execute computationally intensive advanced algorithms to optimize energy efficiency. Details: The priority of "safety monitoring and protection" is set to level 3, and the model predictive control (MPC) algorithm module is loaded and executed to replace the traditional PID controller. The priority of "motor FOC core control" is set to level 2, and more complex parameter identification algorithms are loaded. The priority of "digital twin and prediction" is set to regular monitoring at level 4. The priority of "external communication" is set to normal bandwidth at level 5. The priority of "data recording" is set to normal recording at level 10.

[0043] 0.3) The resource scheduling strategy of "performance balance mode" is: when the task criticality index is between the two thresholds, it is the default mode. Details: The priority of "safety monitoring and protection" is set to level 2, and the standard PID control algorithm is executed. The priority of "motor FOC core control" is set to level 4. The priority of "digital twin and prediction" is set to level 3. The priority of "external communication" is set to level 5. The priority of "data recording" is set to level 10.

[0044] 1.7) The final output contains data structures with explicit scheduling mode instructions. This data structure is sent to the scheduler kernel of the RTOS, which is used to actually adjust the priority of each task and load / unload the specified functional modules at the beginning of the next scheduling period. Furthermore, this embodiment uses an adaptive weighted fusion method based on risk coupling gain; by introducing a risk coupling factor to dynamically adjust the weight, it can accurately model and quantify this nonlinear risk superposition effect, providing more timely and accurate risk assessment. Supplementary explanation for "using the risk coupling factor to dynamically adjust the preset basic weight to generate a dynamic weight": For basic weight determination: set basic weights for three basic risk parameters, including future failure probability basic weight , safety margin basic weight , and calculation time margin basic weight . These basic weights reflect the inherent importance of each parameter when there is no coupling effect; their values are obtained by a group of "5 motor design engineers and 5 embedded system engineers" field experts comparing and scoring the relative importance of each risk factor, constructing a judgment matrix, and calculating the maximum eigenvalue corresponding to the eigenvector. This embodiment determines a set of values as follows: , , . In each calculation period, the dynamic weight is obtained by multiplying the basic weight by the calculated risk coupling factor: the dynamic weight of future failure probability is equal to multiplied by the physical risk coupling factor .

[0045] The dynamic weight of safety margin is equal to multiplied by the physical risk coupling factor . The dynamic weight of calculation time margin is equal to multiplied by the resource risk coupling factor . Calculate the weighted risk comprehensive value, whose parameter symbol is . Its calculation logic is: multiplied by the future failure probability , plus multiplied by (1 minus the system safety margin ), plus multiplied by (1 minus ).

[0046] Limit the final output to the interval [0, 1], use the improved logistic S-shaped function, i.e. Sigmoid function, to normalize , and get the final task criticality index The calculation logic is: equal to 1 divided by 1 plus the natural constant e raised to the power of (-k times the Subtract ) times the value of the task criticality index. Among them, the parameter k controls the steepness of the curve, and the parameter controls the center offset of the curve. These two parameters are determined by offline experimental calibration to ensure that the corresponding function has the most appropriate response sensitivity near the calibrated and .

[0047] The following detailed implementation is described: When the output value of the task criticality index tends to 1, it indicates that the motor control system determines that the integrated state of the motor and its control unit tends to be critical or dangerous; this is the result of the combined effect of "future potential physical failure, current exhausted safety margin, and tense computing resources" and has produced nonlinear coupling effect. The value of the task criticality index that tends to 1 is a strong signal that the system must immediately take the highest priority risk mitigation measures, including forced switching to the "safety first mode", sacrificing part of the performance at the cost of ensuring the core safety of the system.

[0048] When the output value of the task criticality index tends to 0, it indicates that the motor control system determines that the running state of the motor and its control unit is highly safe, stable and resourceful. This indicates that the probability of future failure is lower, the current safety margin is sufficient, and the idle time of the processor is more. The index value that tends to 0 provides a decision basis for the system to execute the "energy efficiency first mode".

[0049] The final output of the task criticality index is determined by the adaptive weighted fusion mechanism of the three key input parameters in this embodiment. The influence relationship of each parameter on the output value and its physical logic rationality are analyzed as follows: The future failure probability and the task criticality index have a positive correlation; when other parameters remain unchanged, increases, which directly leads to the monotonic increase of the weighted risk comprehensive value . At the same time, increases will also nonlinearly amplify the dynamic weight of itself and other related risk items through the calculation of the physical risk coupling factor and the resource risk coupling factor , further accelerating the growth of . Finally, through the monotonic increasing Sigmoid function, it leads to The increase in the probability of overheating failure in the near future, as predicted by the digital twin model, indicates a higher potential danger to the system and suggests that the system's operating state should be considered more "critical".

[0050] System safety margin With task criticality index There is a negative correlation between them; in fusion computing, what directly participates in the weighting is "1 minus the system safety margin". "This item. Therefore, when other parameters remain unchanged, The reduction will result in "1 minus the system safety margin". The addition of items, in turn, makes And the final Monotonically increasing. This indicates that the closer the current state of the system is to its safety boundary, the smaller the system's safety margin and the higher the degree of danger of the system.

[0051] Calculate time margin With task criticality index There is a negative correlation between them; what directly participates in the weighting is "1 minus "This item. When other parameters remain unchanged, The decrease of will result in "1 minus The addition of items thus makes And the final Monotonically increasing. This design reveals that controllers lacking sufficient computational power to execute complex diagnostic, predictive, and control algorithms will experience a significant decrease in their ability to cope with unforeseen circumstances, even if their physical objects are currently in a good state, thus resulting in a higher overall system risk.

[0052] To quantitatively verify the significant advancements of the "Adaptive Weighted Fusion Method Based on Risk Coupling Gain" proposed in this embodiment compared to existing technologies, a set of comparative experiments was designed. The experiments were conducted on a high-precision motor durability test bench, simulating various typical operating conditions by precisely controlling the load, heat dissipation conditions, and controller background computation tasks. This experiment aims to verify the core innovation of this embodiment: the risk coupling effect. Therefore, multiple scenarios ranging from single risk to complex risk were set up. The experiment will compare the outputs of the two methods: Method 1 (this embodiment), using adaptive weighted fusion to calculate the "task criticality index". Method 2 (Comparative Technique) uses the traditional linear weighted fusion calculation to obtain the "linear task criticality index," which does not include risk coupling factors. By comparing the output differences of the two methods in different scenarios, the improvement in early warning sensitivity and accuracy of this embodiment in complex risk scenarios can be clearly demonstrated. The comparative experiments are shown in the table below:

[0053] The linear task criticality index is calculated by the comparative technology, and the calculation method is that the future failure probability, (1 minus system safety margin) and (1 minus calculation time margin) are directly weighted and summed using the preset basic weight, and then normalized by the same Sigmoid function as the embodiment. This method represents the conventional technical solution without considering the risk coupling effect.

[0054] Based on the benchmark verification under a single risk scenario, in scene one (regular smooth running), all input risk parameters are at a low level, and the average of the task criticality index represented by the output indicators of the two methods is consistent, and the value is low. This proves that the embodiment will not produce false alarms in the safe working condition, which is consistent with the prior art behavior; In scene two (single hot risk accumulation) and scene three (single calculation resource shortage), the average of the task criticality index represented by the output indicators of the embodiment is 0.49 and 0.42 respectively, which is higher than the average of the corresponding output indicators of the comparative technology 0.40 and 0.36. In a single risk, the risk coupling factor of the embodiment will have a slight amplification effect, which reflects higher risk sensitivity; Based on the core advantage demonstration under a composite risk scenario, compare scene four and scene two, three: Scene four simulates the situation where hot risk and calculation resource risk occur at the same time. The output indicator of the comparative technology (experiment D-1 group: 0.56) is equal to the simple superposition of the risk increments in scene two and scene three (0.39-0.18+0.35≈0.56), which reflects the essence of linear fusion, that is, the simple addition of risks; The output indicator of the embodiment (experiment D-1 group: 0.82) is much higher than its output in a single risk scenario, and is significantly higher than the output of the comparative technology. Specifically, compared with 0.56 of the comparative technology, the output indicator value of the embodiment is increased by 46.4%. The increase ratio is (the index of the embodiment-the index of the comparative technology) / the index of the comparative technology=(0.82-0.56) / 0.56≈0.464. This data proves the effectiveness of the "risk coupling gain" design of the embodiment. When multiple risks occur at the same time, the real danger faced by the system is synergistically amplified rather than simply added. The embodiment successfully captures and quantifies this nonlinear effect by calculating and applying the physical risk coupling factor (1.420) and the resource risk coupling factor (2.000), so that the task criticality index can "jump" to a higher level. In practical applications, the index value of 0.82 will trigger the highest level of "safety priority mode", while the 0.56 of the comparative technology still keeps the system in the "performance balance mode", thus missing the risk avoidance opportunity.

[0055] The data of the first group and the second group of each scene are compared: in all scenes, the data of two independent experiments (the first group and the second group) show high consistency. In the key scene four, the output indicators of the two experiments are close to 0.82 and 0.84, respectively. This proves that the algorithm of the embodiment has high determinacy and stability, and the output result is reliable and reproducible, meeting the high reliability requirement of the industrial control field.

[0056] Further description: the step of dynamically adjusting the resource allocation includes: according to the preset resource scheduling mode corresponding to the resource scheduling strategy, calling the kernel service of the real-time operating system to modify the execution priority of at least one computing task and one communication task; The step of generating the controller capability profile includes: calculating by using a dynamic capability profile generation model; the input of the dynamic capability profile generation model includes the current resource scheduling mode determined by the resource scheduling strategy and the resource state parameter.

[0057] Further description: the task execution determinacy index is contained in the controller capability profile; the task execution determinacy index is calculated based on the time variation rate of the task criticality index representing the comprehensive risk state of the controller; The step of broadcasting the controller capability profile includes: encapsulating the data of the controller capability profile into a process data object (PDO) of an industrial bus protocol and periodically sending.

[0058] The following specific implementation description is made on the above content: the core logic of the embodiment is that the prospective resource scheduling strategy generated by the upper decision module is accurately converted into the deterministic adjustment of the priority of the computing and communication tasks in the controller by calling the kernel service of the real-time operating system (RTOS), so as to ensure the priority execution right of the key task in resource competition. The embodiment constructs a dynamic capability profile generation model. The model not only depends on the current resource scheduling mode, but also further integrates the root cause of mode switching, that is, the bottom physical risk and the computing resource state parameter, to dynamically and real-timely calculate and generate a multi-dimensional controller capability profile. The profile not only contains the conventional expected response time and available data bandwidth, but also introduces the task execution determinacy index, which quantifies the time variation rate of the “task criticality index” corresponding to the internal comprehensive risk index to represent the stability of the future state of the controller. The dynamic capability profile is formatted and broadcast to the external system through the industrial bus, so that the entire distributed system can be upgraded from simple state synchronization to cooperative self-adaptation based on future stability prediction, to produce system-level robustness and cooperative efficiency improvement.

[0059] In the embodiment, the controller capability profile and its constituent parameters are calculated and encapsulated by the processor deployed in the motor controller, which is specifically described as follows: Current resource scheduling mode: its parameter symbol is , is an enumeration type state identifier, used to explicitly indicate the macro resource scheduling strategy in which the controller is currently located. Its value domain at least includes: "safety priority mode", "performance balance mode", "energy efficiency priority mode"; the value of the current resource scheduling mode is directly determined by the "forward-looking resource scheduling strategy" generated by the previous step S2; Expected instruction response time: its parameter symbol is , which is a numerical value in milliseconds (ms), representing the expected maximum time from when the controller receives the high-level motion instruction issued by the external PLC system to when the instruction is fully executed and reflected in the motor output; the high-level motion instruction in this embodiment includes position closed-loop control instruction; the expected instruction response time Specifically, an "algorithm-benchmark response time mapping table" is pre-stored in the controller firmware, which is calibrated through offline benchmarking and records the core calculation time required by different control algorithms to complete a control cycle under the nominal load of the processor. The parameter symbol of the core calculation time is . The "different control algorithms" in this embodiment include PID, MPC; After the current resource scheduling mode is determined, the corresponding core calculation time is obtained according to the "algorithm-benchmark response time mapping table" loaded under this mode. Then, the expected instruction response time is calculated as follows: multiplied by the dynamic response time modulation factor; the dynamic response time modulation factor is equal to 1 plus the difference between (1 minus the current calculation time margin ) multiplied by the preset response time sensitivity coefficient . The response time sensitivity coefficient is calibrated through offline stress testing and reflects the degree of influence of CPU load on instruction execution delay; in this embodiment, the of the PID algorithm is 0.8 ms through table lookup. The current is 0.2, indicating that the CPU is busy, is calibrated as 0.5. Then the dynamic response time modulation factor is 1+(1-0.2)×0.5=1.4. The finally calculated is 0.8ms×1.4=1.12ms.

[0060] Available data processing bandwidth: its parameter symbol is , which is a numerical value in kilobits per second, representing the effective data transmission rate that the controller can ensure for non-core communication tasks including "remote monitoring data reporting" under the current resource allocation strategy; the available data processing bandwidth The calculation logic is that a "task priority-benchmark bandwidth mapping table" is pre-stored in the controller firmware, which is calibrated by offline benchmarking, and records the theoretical maximum bandwidth that a communication task can obtain when different priorities are allocated to the communication task under the condition of a certain system bus bandwidth, and the parameter symbol is . After the current resource scheduling mode is determined, the system obtains according to the "task priority-benchmark bandwidth mapping table" set for the communication task under the resource scheduling mode. Then, the available data processing bandwidth is multiplied by the current calculation time margin . It is represented that even if the communication task priority is high, if the CPU has no idle time, that is, the calculation time margin tends to 0, the data cannot be effectively processed and transmitted, and therefore the available bandwidth also tends to 0. In the "safety priority mode" of the present embodiment, the communication task priority is lowered to the lowest, and the table lookup obtains 20 kbps. If 0.22 at this time, the available data processing bandwidth is 20 kbps x 0.22 = 4.4 kbps.

[0061] For the acquisition of the "algorithm-benchmark response time mapping table" and the "task priority-benchmark bandwidth mapping table", taking the acquisition of the "algorithm-benchmark response time mapping table" as an example: a benchmarking platform is built, the hardware core of which is an AURIX-TC397 series microcontroller of Infineon Company, which runs at a frequency of 300 MHz and is equipped with a program flash memory of 4 MB and an SRAM of 1.7 MB. The software environment of the platform is ETAS RTA-OS based on the OSEK / VDX standard, configured as a fixed-priority preemptive scheduling kernel; the calibration process is performed. Each control algorithm to be calibrated (including basic PID control algorithm, PID algorithm with feedforward compensation, model predictive control (MPC) algorithm) is packaged as an independent test task with the highest priority. Through the test host program, the test task is called ten thousand times in a loop. Before and after each call, the number of clock cycles consumed by the task execution is accurately recorded by reading the 64-bit CPU cycle counter (Cycle-Counter-Register) of the TC397 kernel. In order to ensure that the measurement is pure calculation time, all interrupts are disabled during the test, and it is ensured that the data is in the cache. Data processing and table building are performed. For the ten thousand clock cycle samples collected for each algorithm, statistical filtering is performed to remove the highest and lowest 5% of data points to exclude the influence of accidental system jitter. Then, the arithmetic mean of the remaining 90% samples is calculated. The average clock cycle number is divided by the clock frequency of the processor 300 MHz to obtain the core calculation time , in milliseconds.

[0062] Finally, all algorithms and their corresponding benchmark response times are organized into a lookup table containing two columns (algorithm identifier, value) and solidified in the read-only program storage area of the controller.

[0063] Further, the task execution certainty index: its parameter symbol is , is a normalized value with the value range in the interval (0, 1]. It represents the measure of the sustainability and stability of the controller's expected instruction response time and available data processing bandwidth and other capability indicators; this parameter is generated by a prediction model based on the time derivative of the internal risk state, whose theoretical basis comes from the analysis of the trend of system state changes in the stability theory of control systems; the specific determination logic is: the controller caches at least the task criticality index of the previous calculation period. Then, the time rate of change of the current task criticality index relative to the previous moment is calculated, whose parameter symbol is , and the calculation method is to subtract from the current task criticality index ; and divide this difference by the time interval between the two calculations; this time rate of change reflects the speed of deterioration or improvement of the system risk state; the task execution certainty index is mapped by applying a negative exponential function, specifically represented as equals the (negative times the absolute value of ) power of the natural constant e. Wherein, is the preset certainty decay coefficient, with the physical unit of seconds (s), calibrated through offline experiments, used to adjust the sensitivity of the index to the risk change rate. This function ensures that when the risk stability representation tends to 0, the task execution certainty index tends to 1; when the risk changes dramatically, the task execution certainty index tends to 0.

[0064] The current task criticality index of this embodiment is 0.82, while the 10ms ago is 0.62, and the time interval is 0.01 seconds. Then is (0.82-0.62) / 0.01=20. If is calibrated to 0.1, then the task execution certainty index The calculation is e^(-0.1x|20|)=e^(-2)≈0.135. This low task execution certainty index warns the external system that although the controller is still working, its state is unstable and can enter a worse mode or fail at any time.

[0065] Controller capability profile: its parameter symbol is , which is a structured data set used to encapsulate and broadcast all the above-mentioned capability parameters. Its data structure is defined as a record or structure containing four fields: .

[0066] The core calculation process of the above steps S3 and S4 is periodically executed by the medium-priority task responsible for system management in the controller, and the embodiment is executed every 20 milliseconds. Its calculation process is as follows: 2.1) The starting input is the proactive resource scheduling strategy generated in the previous step S2, and the real-time updated internal state parameters, including the calculation time margin and the current and historical task criticality indicators .

[0067] 2.2) Analyze the input resource scheduling strategy to determine the current resource scheduling mode ; according to the current resource scheduling mode , query the preset "mode-task priority mapping table".

[0068] In a specific embodiment, the motor control system is built-in real-time operating system (RTOS), which is responsible for scheduling and managing multiple software tasks concurrently executed in the controller. The resource scheduling mechanism defines a set of key tasks contained in the motor controller, which are managed by the RTOS. The task set at least includes: 1. Safety monitoring task: the function of this task is to inspect the key state of the controller hardware at a high frequency, such as detecting whether there is a serious electrical fault such as overcurrent, overvoltage, etc., so as to execute the highest priority hardware protection action in case of emergency.

[0069] 2. Motor control loop task: the function of this task is to execute the core motor current loop, speed loop, etc. closed-loop control algorithm, such as field-oriented control (FOC) algorithm, whose real-time performance and determinacy are directly related to the dynamic performance and stability of the motor.

[0070] 3. Thermal model prediction task: the function of this task is to run the preset motor multi-physical field digital twin model to calculate and predict the physical state parameters of the motor in the future time window, which is the data basis for the proactive decision of the invention.

[0071] 4. Resource scheduling decision task: the function of this task is to fuse the output of the thermal model prediction task and the resource state parameters of the system itself, calculate the task criticality index, and generate the final forward-looking resource scheduling strategy according to the index.

[0072] 5. Remote communication task: the function of this task is to be responsible for the data exchange between the controller and external devices (in this embodiment, the upper PLC or monitoring system), including but not limited to broadcasting the controller capability profile of the application.

[0073] 6. Data recording task: the function of this task is to store the key data or event log during the system operation into a non-volatile storage medium, for subsequent offline analysis or fault tracing.

[0074] Based on the above defined task set, the embodiment constructs a "mode-task priority mapping table" for accurately and deterministically mapping the macro resource scheduling mode output by the upper decision module to the execution priority configuration of each task in the underlying RTOS. In the preferred embodiment, the priority of the RTOS adopts the convention that the smaller the numerical value, the higher the priority, where 0 is the highest assignable execution priority; the mapping table is shown in the following table:

[0075] For the "safety first mode", the priority configuration aims to maximize the safety of the system and the decision response speed. In this mode, the safety monitoring task (priority 0) and the motor control loop task (priority 1) maintain their inherent highest priority to ensure basic hardware safety and motor driving. At the same time, the execution priority of the thermal model prediction task (priority 2) and the resource scheduling decision task (priority 3) is raised, which ensures that the system can update the risk state assessment and respond with the highest frequency and least delay, forming a fast risk avoidance closed loop. Correspondingly, the priority of the remote communication task (priority 15) and the data recording task (priority 25) is lowered, so as to concentrate the processor time slice resources on the core tasks directly related to safety and control.

[0076] For the "performance balanced mode", the priority configuration aims to achieve balanced performance of each function of the controller, which is the standard working state of the system under normal working conditions. In this mode, the priorities of the tasks are set to a reasonable gradient distribution. The core control task (priority 1) is still given priority, while the prediction (priority 4) and decision (priority 5) tasks are run periodically with moderate priority. At the same time, the remote communication task (priority 8) is given a high priority to ensure smooth and timely information exchange between the controller and external systems.

[0077] For the "energy efficiency priority mode", the priority configuration aims to provide resource bias for the execution of energy efficiency optimization algorithms or strategies, while ensuring basic safety and performance. In this mode, the priority of the motor control loop task can be moderately reduced (priority 2) to reserve execution time for other tasks that compute more complex energy efficiency optimization algorithms. Meanwhile, the thermal model prediction task (priority 3) and the resource scheduling decision task (priority 4) still maintain high priority, because accurate perception of the system state is a prerequisite for implementing advanced energy efficiency control strategies.

[0078] When the dynamic adjustment of resource allocation represented by step S3 of the embodiment of the application is triggered, the resource scheduling decision task in the controller determines the current resource scheduling mode. Then, the task queries the "mode-task priority mapping table" with the enumeration value or index corresponding to the mode to obtain the configuration set corresponding to the mode, which contains the new priorities of all managed tasks. Finally, through a loop or iteration process, the standard API function provided by the RTOS is called one by one to apply the queried new priority values to the corresponding task control block (TCB), so that the dynamic and deterministic adjustment of the entire system task scheduling strategy is completed within microseconds.

[0079] Further, the kernel API function of the real-time operating system (RTOS) is called to modify the priority parameters of the safety-related core computing tasks defined in the table to the highest level, and the priority parameters of the non-core communication tasks are modified to a lower level. This operation ensures that when the next RTOS clock tick arrives, the scheduler will immediately execute task preemption according to the new priority order; 2.3) Call the calculation model of , input the current and , and calculate the expected instruction response time . Call the calculation model of , input the current and , and calculate the available data processing bandwidth.

[0080] Call the "prediction model based on the time derivative of the internal risk state", input the current , and calculate the task execution certainty index .

[0081] 2.4) Encapsulate the resource scheduling mode determined in 2.2) and the four parameters of , , calculated in 2.3) into a complete controller capability profile data frame according to the pre-defined data structure. The controller capability profile The data frames are written into a dedicated shared memory region or message queue for the communication task to read; The communication task reads the latest controller capability profile from the shared memory region when executed by the scheduler The data frames, and according to the used industrial bus protocol, maps them to the specified byte offset position of the process data object (PDO). In the next bus communication cycle, the PDO containing the latest capability profile will be automatically and periodically broadcasted onto the industrial bus network.

[0082] 2.5) The final output is a standardized data message containing the controller dynamic capability profile broadcasted on the industrial bus, which can be received and parsed by any PLC, HMI or other motor controller node in the network. This embodiment uses a dynamic calculation model to make the capability profile accurately reflect the "real" capability and "future" stability of the controller in real time, providing an unprecedented depth of decision-making information for the upper system, a response time sensitivity coefficient and a deterministic decay coefficient , are determined through offline performance calibration experiments. The specific method is: a controller hardware-in-the-loop (HIL) test platform is built, the processor's calculation load and simulated motor risk state are automatically changed through scripts, and a high-precision oscilloscope and bus analyzer are used to accurately measure the actual response time and data throughput of the controller. Through nonlinear regression fitting of a large amount of input-output data, the optimal coefficient estimate value can be obtained, and it is fixed in the controller firmware.

[0083] In one preferred embodiment of the present embodiment, the value of the response time sensitivity coefficient is set to 0.5. This value is determined through the following offline stress test experiment: on the benchmark test platform, run the core control algorithm task. Run the adjustable-computing-load interference task in parallel, simulate different CPU loads by changing its loop count, so that the computing time margin decreases linearly from 0.9 to 0.1. At each value point, the actual response time of the core control algorithm task is measured one thousand times and averaged. Finally, the growth rate of the measured actual response time relative to the core computing time is linearly regressed with the value of (1 minus the current computing time margin ), and the slope of the fitted straight line is determined as the response time sensitivity coefficient . The value 0.5 is determined when the performance degradation model and the measured data have the best fitting goodness.

[0084] Further, the embodiment has the following technical logic paths, specifically: Technical logic path one: "predicting overheating risk" → trigger "safety first mode" → S3 performs resource adjustment → thermal management task gets highest priority → motor temperature is actively controlled → avoid hard protection shutdown.

[0085] When the overheating risk is predicted: it corresponds to the input premise of the S3 process, after the pre-step (S1, S2) predicts high "future failure probability " and calculates high "task criticality index ", the scheduling strategy of "safety first mode" is generated, which is the basis for the execution of S3.

[0086] The core action of S3 is to modify the priority parameters of the safety-related core computing tasks defined in the table to the highest level by calling the kernel API function of the real-time operating system (RTOS), while the priority parameters of non-core communication tasks are modified to a lower level; The core computing tasks of the embodiment include thermal management and protection monitoring tasks, which realize the technical effect of "controller actively allocating more resources for thermal management algorithm"; In preemptive RTOS, setting the priority of the task to the highest level represents unconditional CPU time slice occupation at any time, which is the most reliable implementation of "allocating more resources" in the real-time computing field. The above "technical logic path one" has the following technical effects: Optimize the thermal performance of the motor to avoid failure: the "thermal management and protection monitoring task" with the highest priority can run more timely and frequently. By performing more precise control operations, it can perform optimal dynamic current derating or more accurate control of the cooling fan speed according to real-time temperature and model prediction, thereby actively stabilizing the motor temperature below the safety threshold, fundamentally avoiding the occurrence of overheating failure.

[0087] Avoid unplanned shutdown caused by triggering traditional hard protection: traditional hard protection (including thermal relay or temperature sensor interruption) is a passive and cliff-type protection that will inevitably lead to shutdown once triggered. This scheme actively and prospectively schedules resources through S3, so that the thermal management task can intervene when the temperature is far from reaching the hard protection threshold. The motor will thus reduce the output performance, but it will not stop, thereby ensuring the continuity of production.

[0088] Technical logic path two: predict load stability → trigger "energy efficiency first mode" → S3 performs resource adjustment → high energy efficiency complex algorithm task gets high priority → motor output is smoother and power consumption is lower → energy efficiency and quality are improved.

[0089] When the load is predicted to be stable: this corresponds to another input of S3. At this time, the "task criticality index is in low position, the system decision enters "energy efficiency priority mode".

[0090] "controller actively loads and executes more energy-efficient complex algorithm": S3 mechanism is a general framework, according to the current resource scheduling mode , query the pre-set "mode-task priority mapping table". In the "energy efficiency priority mode", the mapping table will raise the priority of the pre-embedded "including model predictive control MPC" task, while reducing the priority of the simple PID control algorithm task or making it dormant. This mode-based dynamic adjustment of task priority is the specific implementation of "active loading and execution"; the above "technical logic path two" has the following technical effects: "reduces its own running power consumption": MPC complex algorithm can significantly reduce copper loss and iron loss by predicting the future state of the motor and performing more global and optimized voltage and current vector control, thereby directly achieving energy saving and consumption reduction under the premise of meeting the same torque output; Technical logic path three: downstream motor executes step S3 due to safety risk and capacity reduction → downstream motor executes step S4 generates and broadcasts an image containing this "capacity reduction" information → upstream PLC receives and parses the image → PLC actively adjusts its own strategy to adapt to downstream changes → avoids system conflicts and ensures overall line efficiency.

[0091] "when the upstream main controller (PLC) receives the capability image": the final output of S4 is a standardized data packet containing the controller's dynamic capability image broadcast on the industrial bus, and the PLC is its pre-set receiver. "learn that the selected motor has reduced capacity for safety": when the controller enters "safety priority mode", S4 will dynamically calculate new capability parameters. The image received by the PLC will clearly see that: the current resource scheduling mode field becomes "safety priority mode". The example of this embodiment is: the expected instruction response time increases from 0.8ms to 1.12ms; the available data processing bandwidth drops from 100kbps to 4.4kbps. These three quantitative indicators together constitute a description of the "capacity reduction" state.

[0092] The above "technical logic path three" has the technical effect of "proactively adjusting the global production rhythm or process parameters", specifically when high-frequency coordinated motion with the motor is needed, by reading that has become 1.12ms, and then actively adjusting its waiting or synchronization timing from 0.8ms to 1.2ms, thereby adapting to the changes in the downstream motor and avoiding mechanical interference or product scrap due to timing mismatch.

[0093] The introduced "task execution determinism index" To achieve the following technical effects; Technical Logic Path Four: Multiple motors broadcast their respective dynamic capability profiles → The PLC not only knows "what it can do now," but also uses the task execution determinism index Knowing whether the future state is stable → the PLC assigns critical tasks to the most stable node → the system avoids the risk of assigning tasks to nodes that are about to fail → the system's robustness is improved.

[0094] "The main controller performs real-time, intelligent task redistribution and load balancing based on the capability profiles broadcast by all motor nodes in the network." "Intelligent" is reflected in the deterministic index of task execution. In its application. Task execution certainty index. Based on "task criticality index" The "time rate of change" of the node is calculated. High value, and Its task execution certainty index It will approach 1. This indicates that although the node is heavily loaded, its state is controllable and stable. The other node's... When the value is relatively low, but The characteristics are rising rapidly. "This indicates that it is much larger than the certainty index of its task execution" It will approach 0. The node's state is unstable and is sliding towards a dangerous edge; The technical effect of the fourth technical logic path is to assign critical tasks to nodes with stronger "capability profiles"; "stronger" refers to prioritizing nodes with a higher certainty index for task execution. The highest node. The PLC will assign high-precision, long-cycle machining tasks to tasks with a deterministic execution index. Nodes approaching 1 are selected to ensure their state does not change abruptly during task execution. This relates to the task execution determinism index. For nodes approaching zero, the PLC will proactively isolate them or assign only monitoring tasks that are allowed to fail, thereby achieving intelligent load balancing based on future stability predictions.

[0095] The following detailed implementation instructions are provided for the above content: When the task execution determinism index The closer a value is to 1, the more stable the overall risk state within the controller; this state signifies the criticality index representing the risk. It changes little over a continuous time period, i.e., its rate of change over time. tends to 0. This indicates that the system is in either a low-risk steady state or a high-risk stable overload state. In this state, the capability profile broadcasted by the controller to the outside world contains higher reliability and higher predictability in expected instruction response time and available data processing bandwidth.

[0096] When the task execution certainty index tends to 0, the comprehensive risk state inside the controller is more unstable or is in more intense dynamic change; this state represents the task criticality index has a greater increase or decrease amplitude, and the absolute value of the time rate of change is greater. In this state, the current capability profile broadcasted by the controller to the outside world is only a transient snapshot, which is invalid at the next moment. Therefore, the reliability and predictability of the profile are lower, and the external system receiving this signal regards the controller node as a more unreliable or more uncertain unit.

[0097] The only direct input parameter affecting the task execution certainty index is the time rate of change of the task criticality index . The absolute value of the time rate of change of the task criticality index and the final output task execution certainty index have a nonlinear, monotonically decreasing negative correlation; the predictability of the control system depends on the rate of change of its state.

[0098] When the absolute value of the time rate of change starts to increase from 0, it indicates that the system state starts to change, and its uncertainty increases, so the task execution certainty index follows. A negative exponential function is used for mapping, which has the inherent characteristic that when the independent variable is small, the dependent variable task execution certainty index drops rapidly; when the independent variable is large, the dependent variable drops slowly and tends to 0. This makes the index sensitive to changes in the system state from “stable” to “small fluctuations”, and can timely capture early signs of instability, while for a system that is already in intense fluctuations, it stably outputs a low certainty signal close to 0.

[0099] To quantitatively verify the beneficial effects of the method of the embodiment in improving the coordination efficiency and robustness of the distributed control system, the following comparative experiments are designed. The experiment is carried out in an industrial bus network environment including one main controller (PLC) and multiple motor controller nodes. The comparative technical solution is “prior art”, that is, the motor controller can only broadcast its current running mode to the PLC, and cannot provide the task execution certainty index. The specific data of the controller state broadcast and system decision comparison experiment under different working conditions are shown in the following table:

[0100] System task allocation success rate: a system-level performance indicator, defined as the percentage of control instructions issued by the master controller (PLC) to the entire network that are successfully received and completed on time by the target motor controller nodes within a unit of time. A decrease in this indicator is caused by instruction response timeouts or nodes going offline due to faults.

[0101] Comparison of Scenario One and Scenario Two: In Scenario Two (steady overload), the controller enters "safety first mode" due to high load. For the prior art, the PLC can only observe the "safety first mode" signal and will conservatively reduce task allocation to the node, causing the system task allocation success rate to drop from nearly 100% to 85.2%. However, the PLC of the present embodiment receives not only the mode signal but also a task execution certainty index of 0.9 or higher. This informs the PLC that, although the node is highly loaded, its state is completely stable. Therefore, the PLC continues to allocate non-critical, simple tasks to it, avoiding unnecessary load shifting and maintaining the system task allocation success rate at a high level of 98% or higher. In the steady overload scenario, the present embodiment improves the system task allocation success rate by (98.35%-85.2%) / 85.2%≈15.4% compared to the prior art. This data strongly demonstrates that the present embodiment can accurately distinguish between "high load" and "unstable", improving resource utilization of the system under extreme conditions.

[0102] Comparison of Scenario Two and Scenario Three: In both scenarios, the controller broadcasts "safety first mode" to the outside. For the prior art, the PLC cannot distinguish between these two fundamentally different "safety first" states, so it will adopt the same conservative load reduction strategy as in Scenario Two. However, Scenario Three is a sudden, rapidly deteriorating overheating process, and this conservative strategy is not enough to avoid the impending failure, resulting in a large number of tasks failing to execute due to the node's impending overheating shutdown, and the system task allocation success rate dropping to 44.5%. In contrast, in the present embodiment, the PLC receives a task execution certainty index of 0.23 in Scenario Three. This signal serves as a decisive early warning, and the PLC immediately determines the node as a "high-risk unstable node" and executes the highest priority avoidance strategy, i.e., immediately stops allocating any new tasks to it and attempts to migrate the existing tasks. This proactive avoidance action enables the system to successfully avoid the loss of instructions and execution failures caused by the sudden shutdown of a single node, and the system task allocation success rate is maintained at 99% or higher. The task execution certainty index unique to the present embodiment provides a new information dimension for predicting future state stability for the upper-level control system, enhancing the robustness and continuity of task execution of the entire distributed control system.

[0103] To transform the quantized output of the task execution certainty index into an executable, standardized operation strategy for the upper-layer master controller (PLC), the present embodiment defines the following application interval division. The standard is based on a large amount of experimental data analysis and combined with expert experience in the field of industrial automation for system stability. The core idea is that the higher the task execution certainty index , the higher the trust of the PLC for the node, and the more aggressive the task allocation strategy; on the contrary, the lower the task execution certainty index , the more decisive the avoidance and intervention strategy of the PLC. The application interval division is specifically shown in the following table:

[0104] In the preferred embodiment, the intelligent motor controller node and the remote PLC master node are physically independent and separate hardware entities. The only connection between them is established through the deterministic industrial field bus. The industrial bus is a protocol that supports periodic and deterministic data exchange, such as Ether-CAT, Profinet-IRT, or CANopen. This physical separation and connection through the bus constitute the physical basis for "remote" monitoring. For example, the remote PLC can be installed in the central control cabinet, while the intelligent motor controller is directly integrated into the motor equipment body on the production line, and the spatial distance between them can be tens of meters or even farther. To efficiently and deterministically transmit the "controller capability portrait " generated in step S4 to the remote PLC, the present embodiment adopts a standardized data encapsulation mechanism.

[0105] The "controller capability portrait" is solidified as a structured data object. In embodiments based on Ether-CAT or CANopen bus, the data object is mapped as a process data object (Process-Data-Object, PDO). Define "Tx-PDO" as a transmit process data object, and its internal data structure is as follows: Byte 0: Current resource scheduling mode mapped as an 8-bit unsigned integer (UINT8).

[0106] Bytes 1-2: Expected instruction response time mapped as a 16-bit unsigned integer (UINT16) with units of microseconds.

[0107] Bytes 3-4: Available data processing bandwidth mapped as a 16-bit unsigned integer (UINT16) with units of KB / s.

[0108] Bytes 5-8: Task execution certainty index The mapping is either a 32-bit floating point number (FLOAT), or a 32-bit integer (DINT) rounded to the nearest integer after being multiplied by 1000 to save bandwidth.

[0109] The intelligent motor controller is configured to autonomously broadcast the encapsulated PDO data frame to the industrial bus at a fixed, pre-set "every 10 milliseconds" cycle without the need for a request from the host station. This "publish-subscribe" mode ensures that the remote PLC can continuously, quasi-real-time receive the latest capability profile of each controller node, forming the data flow basis for remote monitoring.

[0110] The remote PLC host station as a monitoring end, its internal application program contains a special logic module for receiving, analyzing and utilizing the "controller capability profile" PDO broadcast from each motor controller node. The decision logic of the remote end at least includes: The PLC maintains a real-time updated state table in its memory for each motor controller node on the network. The content of the table is the complete mirror of the latest broadcast "controller capability profile" of each node.

[0111] The task scheduler of the PLC will first query the state mirror of the target node before issuing new, computation-intensive or time-sensitive motion control instructions.

[0112] If the expected instruction response time of the node and the available data processing bandwidth both meet the requirements of the instruction, the task is normally issued.

[0113] If not, the PLC will automatically re-route the task to another node in the network with more abundant capabilities, or put it in the waiting queue, thereby achieving dynamic and forward-looking load balancing at the system level.

[0114] The diagnosis and alarm logic module of the PLC continuously monitors the "soft state" indicators in the profile.

[0115] When the current resource scheduling mode of the node switches from "performance balance" to "safety first", the remote monitoring interface of the PLC will immediately pop up a "warning" message, prompting "Node X has entered resource contraction mode, please pay attention". At the same time, the scheduling logic of the PLC will automatically reduce the task allocation priority of the node.

[0116] When the task execution determinacy index of the node is lower than the pre-set "threshold", the PLC will trigger a higher level "unstable state" alarm, and can execute pre-set risk avoidance strategies, such as safely pausing the production process it controls, or migrating its tasks to a redundant backup node.

[0117] The calculation logic involved in the present application can use regression analysis algorithms in machine learning to analyze the internal trends and relationships of the collected parameters to establish a mathematical model. This process can be implemented with professional computing tools such as Python's Scikit-learn library or R language environment. To ensure the effectiveness and accuracy of the model, cross-validation techniques will be used to evaluate the performance of the model, and iterative optimization will be combined with continuous feedback to ensure that the calculation content can truly reflect the inherent laws of objective data. In all calculation processes, to eliminate the influence of different physical dimensions and ensure that the data is compared and analyzed on the same scale, the input parameters in each formula are dimensionless. The dimensionless techniques used include but are not limited to Min-Max-Normalization or Z-Score standardization.

[0118] It should be emphasized that the foregoing examples are merely intended to illustrate the preferred implementation of the present application, and are not intended to limit the scope of protection of the present application. Based on the technical solutions, spirit and principles disclosed by the present application, those of ordinary skill in the art can make various forms of modifications, equivalent replacements or improvements. Any such variation that does not deviate from the technical solution of the present application shall fall within the scope of protection of the claims of the present application. In addition, the technical features of each embodiment in the specification can be combined arbitrarily without causing technical contradictions. All possible and non-contradictory combinations shall be considered as the scope disclosed in the specification; the present application also provides a computer readable storage medium having computer program instructions stored thereon.

Claims

1. A method for remote monitoring of intelligent motor based on industrial bus, characterized in that, The specific steps include: S1: obtaining real-time running data for constructing a motor multi-physical field digital twin model by a motor control system; Based on the multi-physical field digital twin model, the future physical state parameters of the motor within a preset time window are predicted; S2: Fusion of the future physical state parameters and the resource state parameters of the motor control system to generate a task criticality index; According to the task criticality index, a forward-looking resource scheduling strategy is generated; S3: According to the forward-looking resource scheduling strategy, dynamically adjust the resource allocation of the calculation task and the communication task in the motor control system; S4: Based on the resource allocation result after dynamic adjustment, a controller capability portrait representing the current external service capability of the motor control system is generated, and the controller capability portrait is broadcasted to the external system through the industrial bus.

2. The industrial bus-based intelligent motor remote monitoring method according to claim 1, characterized in that: The real-time running data specifically includes: obtaining at least one electrical parameter; and obtaining non-electrical physical parameters reflecting the internal thermodynamics of the motor; The future physical state parameters include future failure probability and system safety margin; the resource state parameters include calculation time margin; The step of fusion and generation of the task criticality index in S2 is specifically: By adaptively weighting fusion of the future failure probability, system safety margin and calculation time margin of the motor control system, the task criticality index is generated.

3. The industrial bus-based intelligent motor remote monitoring method according to claim 2, characterized in that: The adaptive weighting fusion includes: calculating a risk coupling factor based on the future physical state parameters and resource state parameters; The risk coupling factor includes a physical risk coupling factor calculated based on the combination of future failure probability and system safety margin; The risk coupling factor also includes a resource risk coupling factor calculated based on the combination of future failure probability and calculation time margin; The risk coupling factor is used to dynamically adjust the preset basic weight to generate a dynamic weight; The dynamic weight is used to weight sum the future physical state parameters and resource state parameters, and the sum result is subjected to nonlinear normalization processing to obtain the task criticality index.

4. The industrial-bus-based intelligent motor remote monitoring method according to claim 3, characterized in that: In S2, the forward-looking resource scheduling strategy is generated, which is specifically: Divide the numerical interval of the task criticality index into at least two threshold intervals; And preset a corresponding resource scheduling mode for each threshold interval, the resource scheduling mode at least includes task priority adjustment strategy.

5. The industrial-bus-based intelligent motor remote monitoring method according to claim 4, characterized in that: For "dividing the numerical interval of the task criticality index into at least two threshold intervals", two threshold intervals are formed by "low criticality threshold" and "high criticality threshold" strategy threshold parameters; Condition judgment one: whether the task criticality index is greater than or equal to the high criticality threshold; Path one is true: generate a "safety priority mode" resource scheduling strategy; Condition judgment two: if condition judgment one is false, continue to judge whether the task criticality index is less than the low criticality threshold; Path two is true: generate an "energy efficiency priority mode" resource scheduling strategy; Path three is false: generate a "performance balance mode" resource scheduling strategy.

6. The industrial bus-based intelligent motor remote monitoring method according to claim 5, characterized in that: The greater the output value of the task criticality index, the more the comprehensive state of the motor and its control unit tends to be critical, and the more unstable the running state is.

7. The industrial-bus-based intelligent motor remote monitoring method according to claim 6, characterized in that: The resource allocation of the computing task and the communication task in the motor control system is dynamically adjusted, including: according to the preset resource scheduling mode corresponding to the resource scheduling strategy, calling the kernel service of the real-time operating system to modify the execution priority of at least one computing task and one communication task; The generation step of the controller capability profile includes: calculating by using a dynamic capability profile generation model; The input of the dynamic capability profile generation model includes the current resource scheduling mode and the resource state parameter.

8. The industrial-bus-based intelligent motor remote monitoring method according to claim 7, characterized in that: The controller capability profile contains a task execution certainty index. The task execution certainty index is calculated based on the time variation rate of the task criticality index representing the comprehensive risk state inside the controller; The step of broadcasting the controller capability profile to the external system through the industrial bus includes: encapsulating the data of the controller capability profile as a process data object of the industrial bus protocol and periodically sending it.

9. The intelligent motor remote monitoring method based on the industrial bus according to claim 8, characterized in that: The greater the task execution certainty index, the more stable the comprehensive risk state inside the controller corresponding to the motor control system is.

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