A method for energy efficiency regulation of a continuous material processing plant based on a running state determination

Abstract

The application discloses a kind of based on running state determination continuous material processing equipment energy efficiency regulation and control method, it is related to industrial automation and process control technical field, this method includes: real-time acquisition equipment operating parameter, material state parameter and environmental parameter, form multisource operation data set, obtain the load state, health state and efficiency state of equipment, form the safety operation constraint boundary of current time, optimization is carried out by heuristic optimization algorithm, obtain optimal energy efficiency operating parameter set.Energy efficiency regulation and control instruction sequence is generated, control response deviation is calculated and corrected, and the parameter value range in threshold parameter and energy efficiency evaluation function is adjusted by periodic statistical analysis.The application realizes continuous material processing equipment energy consumption optimization by the closed-loop regulation and control method of multisource data fusion, state determination rule chain and comprehensive energy efficiency evaluation, improves the stability of system operation, control precision and working condition adaptability, enhances the reliability of long-term operation.

Description

Technical Field

[0001] This application relates to the field of industrial automation and process control technology, and in particular to a method for energy efficiency regulation of continuous material handling equipment based on operating status determination. Background Technology

[0002] In industrial applications of continuous material handling equipment, such as conveying, mixing, crushing, and sorting processes, the equipment typically operates continuously for extended periods. Its operating efficiency and energy consumption directly impact overall production costs and equipment lifespan. Existing control methods often rely on fixed process parameters or adjustments based on single feedback quantities, making it difficult to simultaneously optimize energy consumption and manage equipment health. This can easily lead to problems such as localized load fluctuations, unstable energy consumption, and accelerated equipment wear.

[0003] In addition, existing technologies typically lack the ability to comprehensively judge and classify multi-source operating status information during the operation parameter control process. They also fail to adequately consider the coordination relationship between multi-dimensional parameters such as operating speed, material flow rate, and equipment health status. They often rely on fixed thresholds or single index adjustments set by experience, resulting in a weak adaptability of the control process to changes in operating conditions and difficulty in achieving refined energy efficiency control and stable regulation under continuous operating conditions. Summary of the Invention

[0004] This application provides an energy efficiency control method for continuous material handling equipment based on operational status determination. It employs a multi-source operational parameter acquisition and hierarchical status determination mechanism, a physical mechanism-based energy efficiency evaluation function construction method, a parameter optimization strategy under constraints, and a closed-loop feedback correction control method. This enables dynamic optimization and refined control of the operational parameters of continuous material handling equipment, achieving coordinated control of energy consumption and equipment health status during operation, improving equipment operational stability and energy efficiency, and reducing energy consumption per unit of material and cumulative equipment lifespan loss.

[0005] This application provides an energy efficiency control method for continuous material handling equipment based on operational status determination, comprising: deploying a sensor acquisition unit on the continuous material handling equipment to collect equipment operating parameters, material state parameters, and environmental parameters in real time, forming a multi-source operational dataset; performing feature extraction and time-series analysis on the multi-source operational dataset, and determining the feature quantities step by step based on a preset state determination rule chain to obtain the equipment's load state, health state, and efficiency state; based on the load state and health state, combined with the equipment's physical limit parameters and a preset safety redundancy coefficient, constructing a safety constraint space for the equipment's operating parameters, forming a safety operational constraint boundary at the current moment; within the safety operational constraint boundary, constructing an energy efficiency evaluation function based on physical mechanisms, using operating speed, material flow rate, and equipment health score as inputs, and unit material energy consumption and cumulative equipment lifespan loss as parameters. The optimization objective is to obtain the optimal set of energy-efficient operating parameters through a heuristic optimization algorithm. Based on this optimal set, a target operating parameter sequence is constructed. Then, according to the parameter deviation between the current equipment operating state and the target operating parameter sequence, combined with preset parameter adjustment rules and adjustment step size constraints, an energy efficiency control command sequence is generated. This sequence is then sent to the equipment actuators for execution, and real-time operating data after control is collected. The control response deviation is calculated, and subsequent control commands are dynamically corrected based on this deviation. At the end of each control cycle, a comparative analysis of energy consumption data before and after control is performed to calculate energy efficiency improvement indicators. The operating data for the current cycle is stored in a historical dataset, and the threshold parameters in the state determination rule chain and the parameter value ranges in the energy efficiency evaluation function are adjusted through periodic statistical analysis. Attached Figure Description

[0006] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0007] Figure 1 This is a schematic flowchart illustrating an energy efficiency control method for continuous material handling equipment based on operational status determination, provided in an embodiment of this application. Detailed Implementation

[0008] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0011] This application provides an energy efficiency control method for continuous material handling equipment based on operating status determination, such as... Figure 1 As shown, the method includes: S1: Deploy sensor acquisition units on continuous material handling equipment to collect equipment operating parameters, material status parameters and environmental parameters in real time, forming a multi-source operating dataset.

[0012] Specifically, sensor acquisition units are deployed on continuous material handling equipment to acquire multi-dimensional status information in real time during equipment operation. These sensor acquisition units may include, but are not limited to, speed sensors, flow sensors, pressure sensors, temperature sensors, and vibration sensors, used to collect equipment operating parameters, material state parameters, and environmental parameters, respectively. The equipment operating parameters may include parameters characterizing the equipment's working state, such as operating speed, motor load, current, or torque; the material state parameters may include parameters reflecting the material conveying and processing characteristics, such as material flow rate, particle size distribution, or concentration; and the environmental parameters may include external condition parameters affecting the equipment's operational stability, such as ambient temperature, humidity, or dust concentration. Through the coordinated acquisition of data from these multiple types of sensors, a multi-source operational dataset covering the entire equipment operation process can be formed. During data acquisition, various sensors synchronously collect data according to a unified sampling period, and the data acquisition module performs timestamp marking and preliminary integration processing, thereby ensuring the consistency and comparability of data from different sources in the time dimension, providing a reliable data foundation for subsequent operational status determination and energy efficiency control. The above methods enable multi-dimensional, real-time sensing of the operating status of continuous material handling equipment. This effectively overcomes the problem of incomplete information caused by relying on single parameters or partial information in existing technologies, providing data support for subsequent multi-dimensional comprehensive analysis and refined energy efficiency control, thereby improving the accuracy and reliability of system regulation. For example, in conveyor-type continuous material handling equipment, the conveyor belt speed can be obtained through a speed sensor, the material throughput per unit time can be obtained through a flow sensor, and the drive motor load can be obtained through a current sensor. Combined with ambient temperature parameters, the operating conditions of the equipment can be comprehensively characterized, thus forming a complete multi-source operating dataset.

[0013] S2: Perform feature extraction and time series analysis on the multi-source running dataset, and make step-by-step judgments on the feature quantities based on the preset state judgment rule chain to obtain the load status, health status and performance status of the device.

[0014] Furthermore, step S2 also includes: S21. Perform time synchronization processing and outlier removal on the multi-source operation dataset, and use a sliding time window to resample each operation parameter in segments to form a time-series data sequence under a unified time scale; S22. Based on the time-series data sequence, extract multi-dimensional features to characterize the device's operating status. The multi-dimensional features include amplitude features characterizing load changes, fluctuation features characterizing operational stability, time-series gradient features characterizing state change trends, and unit-time energy consumption features characterizing energy consumption levels; S23. Normalize each of the multi-dimensional features, and map continuous features to corresponding state determination intervals according to preset feature interval mapping rules to form standardized feature parameters; S24. Based on the standardized feature parameters, perform step-by-step determination according to a preset state determination rule chain, and output the device's load status, health status, and performance status in sequence, wherein the subsequent state determination is constrained and corrected based on the previous determination result.

[0015] Specifically, based on the obtained multi-source operational dataset, feature extraction and time-series analysis are performed on the dataset. The extracted features are then progressively evaluated based on a pre-defined state determination rule chain to obtain the equipment's load status, health status, and performance status. The multi-source operational dataset undergoes preprocessing. Time synchronization processing aligns data from different sources to a unified time base, and outliers that may exist during the acquisition process are identified and removed. Simultaneously, a sliding time window is used to resample each operational parameter in segments, reconstructing data from different sampling frequencies on a unified time scale to form a continuous and consistent time-series data sequence, thereby improving the stability and reliability of subsequent analysis. Based on this, multi-dimensional features characterizing the equipment's operational status are extracted from the time-series data sequence. These multi-dimensional features include: amplitude features reflecting the degree of load change, fluctuation features characterizing operational stability, time-series gradient features describing parameter change trends, and energy consumption per unit time measuring energy consumption levels. These multi-dimensional features are obtained through statistical calculations of the time-series data within the sliding time window. Among them, amplitude features are obtained by extracting the maximum, minimum, and average values ​​of operating parameters within a time window; fluctuation features are obtained by calculating the fluctuation amplitude or dispersion of operating parameters; time-series gradient features are obtained by calculating the parameter changes between adjacent time sampling points, used to characterize the parameter change trend; and energy consumption per unit time features are calculated from equipment power and material processing volume. These features are combined in a preset order to form a multi-dimensional feature vector, used to characterize the comprehensive operating state of the equipment within the current time window. Through the joint extraction of multi-dimensional features, a multi-faceted characterization of the equipment's operating state is achieved. Subsequently, each of the multi-dimensional features is normalized, and according to a preset feature interval mapping rule, continuously changing features are mapped to discrete state determination intervals, forming standardized feature parameters. The preset feature interval mapping rule is used to map continuously changing features to discrete state determination intervals. The mapping rule includes the interval division method for each feature, the corresponding state label, and the interval boundary threshold, wherein each feature is divided into multiple value intervals, each corresponding to a different operating state. The division of the feature intervals can be determined based on the equipment's rated operating parameters, design limits, and process operating specifications. Simultaneously, statistical analysis of typical operating condition data from the equipment's historical operation is used to obtain the distribution range of each feature quantity under different operating states, thereby determining the interval boundary thresholds. Furthermore, safety threshold intervals can be set for key feature quantities based on equipment safety operation requirements to effectively identify abnormal operating states. Through the aforementioned feature interval mapping rules, continuous feature quantities can be transformed into standardized feature parameters, providing a unified input for the subsequent state determination rule chain and improving the consistency and accuracy of the state determination process.This processing method eliminates the influence of dimensional differences between different characteristic quantities on the judgment results, improving the consistency of the judgment process. After obtaining standardized characteristic parameters, a step-by-step judgment process is performed according to a preset state judgment rule chain. Specifically, after obtaining standardized characteristic parameters, each characteristic parameter is judged step-by-step according to a preset state judgment rule chain. The step-by-step judgment process is executed sequentially according to a preset order, including load state judgment, health state judgment, and performance state judgment. Based on the amplitude characteristic parameters characterizing load changes, the current load state of the equipment is judged. Specifically, the amplitude characteristic parameters are compared with preset load range thresholds. When the characteristic parameters fall into different ranges, the corresponding output is a light load, normal load, or high load state. After obtaining the load state, the health state of the equipment is judged using the load state as a constraint. Specifically, based on the fluctuation characteristic parameters characterizing operational stability and the time-series gradient characteristic parameters, combined with the judgment threshold range corresponding to the load state, a comprehensive judgment is made on the operational stability and potential wear of the equipment, thereby outputting a health state result. After obtaining the load state and health state, the performance state of the equipment is judged based on the energy consumption characteristic parameters per unit time. Specifically, under the joint constraints of load and health status, energy consumption characteristic parameters are matched within intervals to determine whether the current operation is in a high-efficiency, average, or inefficient state. By introducing a hierarchical constraint mechanism, subsequent state determinations can be corrected based on the results of previous determinations, improving the overall accuracy and stability of the determination. This achieves hierarchical and multi-dimensional identification of the operating status of continuous material handling equipment, effectively overcoming the inaccuracy problems caused by relying on only a single parameter or simple threshold judgment in existing technologies. It provides a reliable basis for subsequent safety constraint construction and energy efficiency optimization, thereby improving the precision and adaptability of the overall system control. For example, in conveying continuous material handling equipment, amplitude characteristics can be extracted from motor current and speed data to characterize the load level, fluctuation characteristics can be extracted from vibration signals to assess operational stability, and time-series gradient characteristics can be extracted from flow rate changes to reflect material change trends. Combined with energy consumption characteristics per unit time, a comprehensive analysis is performed, and finally, the current equipment is output as a combination of high load, slight wear, and high-efficiency operation through a state determination rule chain.

[0016] S3: Based on the load status and health status, combined with the equipment physical limit parameters and preset safety redundancy coefficient, construct a safety constraint space for the equipment operating parameters to form the safety operating constraint boundary at the current moment.

[0017] Furthermore, step S3 also includes: S31. Based on the real-time degradation rate of the health status and the fluctuation frequency characteristics of the load status, a preset redundancy coefficient mapping rule is invoked to determine the adaptive safety margin corresponding to each key operating parameter; the adaptive safety margin is nonlinearly adjusted according to the health degradation rate and load fluctuation frequency according to a preset attenuation rule; S32. The coupling weight relationship between each operating parameter is extracted, and the multi-dimensional operating parameters are decoupled and transformed to obtain relatively independent parameter dimensions; using the physical limit parameters of the equipment as the benchmark limit, and on the basis of superimposed adaptive safety margin, a multi-dimensional limiting processing method is used to construct the initial safety constraint envelope; S33. Based on the multi-source operating dataset, a sliding time... The window fits the trend of operating parameters and calculates the parameter evolution trend within a preset time domain. Based on the approximation between the parameter evolution trend and the initial safety constraint envelope, a safety margin is calculated. When the safety margin is lower than a preset threshold, the initial safety constraint envelope is shrunk and adjusted along each parameter dimension to generate a safe operating constraint boundary. S34: The safe operating constraint boundary and the equipment process limit are checked for interval consistency, and conflicting regions that exceed the limit are eliminated. The remaining feasible regions are integrated to form a continuous feasible operating domain. The range of values ​​of each operating parameter corresponding to the feasible operating domain at the current time is extracted and output as the safe operating constraint boundary to the subsequent energy efficiency optimization steps.

[0018] Furthermore, step S33 also includes: S33-1. Using the current control cycle as a benchmark, extract the operating parameter sequence within a historical sliding time window of a preset length; perform weighted trend fitting processing on the operating parameter sequence to suppress instantaneous fluctuation interference, and extract the benchmark rate of change and trend change characteristics of each operating parameter; S33-2. Based on the benchmark rate of change and trend change characteristics, perform trend extrapolation of the operating parameters in the time dimension to generate a discrete parameter evolution sequence covering a preset prediction time domain; calculate the boundary distance of each parameter value in the discrete parameter evolution sequence relative to the initial safety constraint envelope, and take the minimum boundary distance of each parameter dimension as the current safety margin. Quantity; S33-3, compare the current safety margin with the preset dynamic warning threshold. When the current safety margin is lower than the dynamic warning threshold, trigger the constraint boundary contraction mechanism. Calculate the dynamic contraction coefficient corresponding to each operating parameter based on the difference between the safety margin and the warning threshold and the predicted time domain length. The dynamic contraction coefficient satisfies the following: it is inversely proportional to the deviation of the safety margin from the preset warning threshold; it is positively correlated with the predicted time domain length; and it is subject to weighted modulation by the rate of change of the operating parameters. Based on the dynamic contraction coefficient, the initial safety constraint envelope is proportionally contracted along each parameter dimension to obtain the safe operating constraint boundary.

[0019] Specifically, based on the determination of equipment load and health status, and combined with the equipment's physical limit parameters and preset safety redundancy coefficients, the equipment operating parameters are constrained to construct a safety constraint space for the equipment operating parameters and form the safety operating constraint boundary at the current moment, which is used to limit the feasible range of subsequent energy efficiency regulation processes. Specifically, firstly, based on the real-time degradation rate of the equipment's health status and the fluctuation frequency characteristics of the load status, a preset redundancy coefficient mapping rule is invoked to determine the adaptive safety margin corresponding to each key operating parameter. The preset redundancy coefficient mapping rule is used to determine the adjustment ratio of the safety margin according to the equipment operating status. The mapping rule includes input state variables, redundancy coefficient interval division, and the correspondence between states and redundancy coefficients. The input state variables include parameters such as the equipment health degradation rate and load fluctuation frequency. The value of the redundancy coefficient is divided into multiple intervals according to the equipment operating status. When the equipment is in good health and the load is stable, the redundancy coefficient takes a larger value; when the equipment health status deteriorates or load fluctuations increase, the redundancy coefficient gradually decreases to tighten the safety range of the operating parameters. The mapping rules are determined based on the equipment's rated operating parameters, design limits, and safety protection specifications. Statistical analysis of equipment commissioning process and historical operating data is also performed to summarize safety margin requirements under different operating conditions, thus forming a stable and reproducible redundancy coefficient mapping relationship. Through these mapping rules, the safety margin can be adaptively adjusted according to changes in equipment operating status, improving the safety and stability of the operation process. The adaptive safety margin is adjusted according to the health degradation rate and load fluctuation frequency based on a preset attenuation rule, automatically tightening the parameter allowable range when the equipment is in poor health or experiences large load fluctuations. Furthermore, the coupling relationships between various operating parameters are extracted, and multi-dimensional operating parameters are separated, dividing mutually influencing parameters into relatively independent control dimensions. Specifically, the changing trends of each operating parameter are compared and analyzed within a sliding time window. When multiple parameters exhibit consistent change characteristics in the direction or magnitude of change, a coupling relationship is determined. Simultaneously, considering the response relationship during parameter adjustment, when a change in one parameter causes a significant change in another parameter, the coupling correlation is further confirmed. After determining the coupling relationship, based on the degree of influence of each parameter on the equipment's operating state, the coupled parameters are divided into primary control parameters and secondary parameters, with the primary control parameters serving as the adjustment benchmark. During parameter adjustment, variation constraint ranges are set for the secondary parameters to keep them within a preset range when the primary control parameters change, thereby reducing the mutual influence between parameters. Subsequently, using the equipment's physical limit parameters as benchmark limits, and based on the adaptive safety margin, amplitude limiting processing is performed on each parameter dimension to obtain the initial safety constraint range. First, the physical limit intervals corresponding to each operating parameter are obtained as the initial value range.Building upon this, an adaptive safety margin is introduced to adjust the upper and lower limits of each parameter dimension. Specifically, the upper limit of each parameter is tightened according to the safety margin, while the lower limit is correspondingly raised, causing the allowable range of the parameter to shrink inward from its original physical limit, thus forming a more conservative operating range. Further, amplitude limiting is performed on each operating parameter dimension to obtain the independent value range of each parameter in the current operating state. These value ranges are then combined to form the initial safety constraint range in a multi-dimensional parameter space. Further, based on a multi-source operating dataset, statistical analysis is performed on the changes of each operating parameter within a sliding time window to determine the changing trend of the parameter in a short time range. The safety margin in the current operating state is calculated based on the closeness between the changing trend and the initial safety constraint range. When the safety margin is lower than a preset threshold, the constraint range of the corresponding parameter dimension is tightened, thereby obtaining a forward-looking safe operating constraint boundary. The forward-looking safety operation constraint boundary refers to the parameter allowable range formed by predictively adjusting the safety operation constraint boundary based on the initial safety operation constraint boundary determined by the physical limits of the equipment, combined with the changing trend of operating parameters within a sliding time window. By analyzing the changing trends of multi-source operating data within a short time window, the direction and magnitude of change of each operating parameter in the future preset time domain are determined. Combined with the proximity of the current safety constraint boundary, the safety boundary is contracted or adjusted, thus forming a forward-looking safety operation constraint boundary. Compared with the safety constraint boundary determined based on the current state, this boundary introduces an early response mechanism to parameter changing trends, enabling the parameter constraint range to be adjusted in advance according to changes in operating state, thereby reducing the risk of operating parameters exceeding the safety boundary in a short period of time. Subsequently, the consistency of the forward-looking safety operation constraint boundary with the equipment process allowable range is verified, the portion exceeding the process limit is eliminated, and the remaining feasible range is integrated to form a continuous feasible operating interval. Finally, the allowable value range of each operating parameter at the current moment is extracted from the feasible operating interval and output as the safety operation constraint boundary to the subsequent energy efficiency optimization steps. The above processing method enables dynamic constraint control of equipment operating parameters, allowing safety constraints to adaptively adjust according to equipment health status and load changes. Simultaneously, by considering short-term trends, the constraint range is tightened in advance, effectively preventing exceedances or instability during operation. This improves system safety and continuity, and provides a stable and reliable constraint basis for subsequent parameter adjustments. For example, in continuous material handling equipment, when an increase in motor current fluctuation and vibration characteristics are detected, the upper limits of operating speed and material flow rate can be reduced according to preset redundancy rules, and the parameter range can be further tightened based on recent load change trends, thereby preventing the equipment from entering an overload operating state.Furthermore, using the current control cycle as a benchmark, a sequence of operating parameters within a historical sliding time window of a preset length is extracted. This sequence undergoes weighted trend processing, assigning higher weights to more recent data points to suppress the impact of instantaneous fluctuations on trend judgment, thereby extracting the benchmark rate of change and trend characteristics of each operating parameter. Based on this, and using the benchmark rate of change and trend characteristics, trend extrapolation is performed on each operating parameter over time to generate a discrete parameter evolution sequence covering a preset prediction time domain, used to characterize the possible change paths of the operating parameters in the short-term future. Further, the values ​​of each parameter in the discrete parameter evolution sequence are compared and analyzed with the initial safety constraint envelope. The boundary distance of the parameter value relative to the safety constraint boundary at each time node is calculated, and the minimum boundary distance is selected as the current safety margin across all prediction time ranges to characterize the most dangerous degree to which the operating parameter approaches the safety boundary. Subsequently, the current safety margin is compared with a preset dynamic warning threshold. When the safety margin is less than the dynamic warning threshold, a safety constraint boundary contraction mechanism is triggered. Under this mechanism, a dynamic contraction coefficient is calculated for each operating parameter based on the difference between the safety margin and the warning threshold, and the predicted time domain length. This dynamic contraction coefficient is inversely proportional to the difference between the safety margin and the warning threshold, positively correlated with the predicted time domain length, and weighted by the rate of change of the operating parameters to reflect the impact of different parameter change rates on safety risks. Finally, based on the dynamic contraction coefficient, the initial safety constraint envelope is proportionally contracted along each parameter dimension to obtain the safe operating constraint boundary, allowing the safety constraint range to be adjusted in advance according to short-term operating trends. This method enables early response to short-term evolution trends of operating parameters, ensuring that the safe operating constraint boundary not only reflects the current operating state but also predicts and tightens potential future exceedance risks, thereby improving the safety and stability of the system. For example, in a continuous material conveying system, when a continuous upward trend in motor current is detected within a sliding time window, trend extrapolation indicates that it may approach the safety upper limit within a short-term prediction interval. In this case, the system will tighten the operating speed and load upper limit in advance based on the decrease in the safety margin, thus preventing the equipment from entering an overload operating state.

[0020] S4: Within the safety operation constraint boundary, construct an energy efficiency evaluation function based on physical mechanisms, taking operating speed, material flow rate and equipment health score as inputs, and unit material energy consumption and equipment cumulative life loss as optimization objectives. Optimize using a heuristic optimization algorithm to obtain the optimal set of energy efficiency operating parameters.

[0021] Furthermore, step S4 also includes: S41. Based on equipment transmission dynamics and material conveying mechanism, establish a deterministic correspondence between operating speed, material flow rate, equipment health score, unit material energy consumption, and cumulative life loss; and convert the safe operation constraint boundary into a hard limit range of optimization variables; S42. Perform dimensional normalization on unit material energy consumption and cumulative life loss, and weight the normalized indicators according to the operating condition priority coefficient to construct a comprehensive energy efficiency evaluation function; and calculate the comprehensive energy efficiency evaluation value based on the comprehensive energy efficiency evaluation function, generating an initial parameter combination set within the hard limit range; S43. Perform iterative optimization calculation on the parameter combination within the hard limit range, calculate the comprehensive energy efficiency evaluation value generation by generation, perform boundary correction or penalty processing on parameter combinations that exceed the limits, and update the parameter set according to the evaluation value until convergence; S44. Select the parameter combination with the optimal comprehensive energy efficiency evaluation value from the converged parameter set, and verify the equipment physical limits and process continuity constraints to determine the optimal energy efficiency operating parameter set.

[0022] Furthermore, step S42 also includes: S42-1. Perform dimensionless processing on the unit material energy consumption index and the cumulative life loss index, and map them to the dimensionless evaluation space using a preset normalization function to obtain standardized evaluation indicators; S42-2. Determine the operating condition priority coefficient according to the importance of equipment operation under different operating conditions, and map the operating condition priority coefficient to the unit material energy consumption index and the cumulative life loss index respectively to form an index weight allocation relationship; S42-3. Based on the standardized evaluation indicators and the index weight allocation relationship, perform weighted fusion processing on the unit material energy consumption and the cumulative life loss to construct a comprehensive energy efficiency evaluation function, which is used to uniformly represent the comprehensive relationship between the equipment energy efficiency level and the degree of life loss; S42-4. Calculate the current parameter combination based on the comprehensive energy efficiency evaluation function to obtain the comprehensive energy efficiency evaluation value, and generate an initial parameter combination set within the hard limit range.

[0023] Furthermore, step S43 also includes: S43-1. For each parameter combination in the initial parameter combination set, calculate the corresponding evaluation value based on the comprehensive energy efficiency evaluation index to form an energy efficiency evaluation sequence for each parameter combination; S43-2. ​​Perform boundary constraint verification on each parameter combination. When a parameter combination exceeds the hard limit range, use the boundary truncation correction method to process it so that it meets the safe operation constraint requirements; S43-3. Based on the evaluation value, sort and update the parameter combination, and repeat the calculation and correction process from S43-1 to S43-2. ​​At the same time, judge the change range of the optimal comprehensive energy efficiency evaluation value in adjacent iteration cycles. When the change range is less than the preset threshold or the maximum number of iterations is reached, stop the iteration process.

[0024] Specifically, within the safety operation constraint boundary, a physical mechanism-based energy efficiency evaluation function is constructed. This function uses operating speed, material flow rate, and equipment health score as inputs, and unit material energy consumption and cumulative equipment lifespan loss as optimization objectives. An optimization heuristic method is used to calculate the optimal energy efficiency operating parameter set. Based on the equipment transmission dynamics characteristics and material conveying process mechanism, a correspondence is established between operating speed, material flow rate, equipment health score, unit material energy consumption, and cumulative equipment lifespan loss. The safety operation constraint boundary obtained in step S3 is then mapped to the value restriction range of each operating parameter, serving as a hard constraint condition in the subsequent optimization process. Based on the transmission dynamics of the equipment, the functional relationship between driving power, operating speed, and load is determined, where driving power is positively correlated with operating speed and material load. Combining the energy consumption mechanism during material conveying, the energy consumption per unit time is expressed as a function of driving power and operating time. Energy consumption is normalized using material flow rate to obtain the expression for energy consumption per unit material. Based on the stress state and wear mechanism of key equipment components, a mapping relationship between equipment operating parameters and lifespan loss is established. The equipment health score is used as a correction factor characterizing the current degree of equipment degradation and introduced into the lifespan loss calculation process, allowing lifespan loss to dynamically adjust with changes in operating speed, load level, and health status. Furthermore, the safe operation constraint boundary obtained in step S3 is converted into value range constraints for each operating parameter. That is, upper and lower limits are set for parameters such as operating speed, material flow rate, and driving output, forming a set of parameter value ranges. In the subsequent optimization process, range constraint judgment is performed on all candidate parameter combinations. When a parameter value exceeds the specified range, it is eliminated or corrected, thereby achieving a hard constraint on the optimization process. The unit material energy consumption and cumulative equipment lifespan loss are normalized, and the normalized indicators are weighted and combined according to a preset operating condition priority coefficient to construct a comprehensive energy efficiency evaluation function. Based on the comprehensive energy efficiency evaluation function, an initial set of parameter combinations is generated within the hard constraints. The parameter combination set is iteratively optimized within the hard constraints, and the corresponding comprehensive energy efficiency evaluation value is calculated in each iteration. When a parameter combination exceeds the constraint range, boundary correction or penalty evaluation adjustment is performed, and the parameter combinations are sorted and updated according to the comprehensive energy efficiency evaluation value until a preset convergence condition is met. The parameter combination with the optimal comprehensive energy efficiency evaluation value is selected from the set of parameter combinations that meet the convergence condition, and this parameter combination is verified by equipment physical limit constraints and process continuity constraints to determine the final optimal energy efficiency operating parameter set.For the physical limit constraint verification of the equipment, based on the equipment design parameters and operating specifications, the physical limit value range corresponding to each operating parameter is obtained, including the upper limit of operating speed, the upper limit of drive output capacity, and the upper limit of material handling capacity. Each operating parameter in the optimal parameter combination is compared with its corresponding physical limit value range. When any operating parameter exceeds its physical limit range, an over-limit judgment is performed on the parameter combination, and the over-limit parameter is truncated or discarded according to the preset correction rules. For the process continuity constraint verification, based on the stable operation requirements of the continuous material handling process, the change range between the optimal parameter combination and the current actual operating parameters of the equipment is calculated, including the change rate of operating speed, the change rate of material flow rate, and the change rate of drive output. When any change rate exceeds the preset change threshold, it is determined that the parameter combination does not meet the process continuity requirements, and it is smoothed or a suboptimal parameter combination is selected. Furthermore, after passing the physical limit constraint and process continuity constraint verification, the parameter combination that satisfies all constraint conditions is determined as the final optimal energy efficiency operating parameter set. Through the implementation of step S4 and its sub-steps S41 to S44, an energy efficiency evaluation function based on physical mechanisms is constructed under the constraint of safe operation boundary. Combined with a heuristic optimization strategy, the operating parameters are globally optimized, so that the equipment can achieve the optimal balance between unit material energy consumption and equipment life loss under the premise of meeting safe operation conditions. This effectively reduces the overall energy consumption of the system and extends the service life of the equipment, significantly improving the operational stability and energy efficiency of the continuous material processing process.

[0025] Furthermore, the unit material energy consumption index and cumulative lifespan loss index are subjected to dimensionless processing, and a preset normalization function is used to map the index to a dimensionless evaluation space to obtain standardized evaluation indicators. The normalization function can employ an interval linear mapping method to transform each index value to a preset interval range, thereby eliminating the influence of different dimensions on the evaluation results. The specific implementation process of dimensionless processing of the unit material energy consumption index and cumulative lifespan loss index and mapping them to a dimensionless evaluation space is as follows: First, the reference value ranges for the unit material energy consumption index and cumulative lifespan loss index are determined respectively. The reference value ranges are determined through the equipment design parameter range, the safe operation constraint boundary obtained in step S3, or the historical operation statistical range, and are used to limit the minimum and maximum reference values ​​of each index. Second, for different types of evaluation indicators, corresponding normalization mapping methods are selected. For indicators where "the smaller the index value, the better the performance" (including the unit material energy consumption index and cumulative lifespan loss index), a reverse interval linear normalization function is used to process the original index values ​​to map them to a dimensionless interval. Further, the unit material energy consumption index... With cumulative lifespan loss index Normalization calculations were performed separately to obtain standardized evaluation indicators, specifically: ： ； ;in, The normalized dimensionless evaluation index ， These represent the lower and upper limits of the reference range for energy consumption per unit of material, respectively. , These are the lower and upper limits of the reference range for the lifespan loss index. Furthermore, when the original index value exceeds the reference range, boundary saturation processing is applied to limit the normalization result to a preset dimensionless range. Specifically, when the index value is greater than the upper limit, the normalization result is 0; when the index value is less than the lower limit, the normalization result is 1. Finally, a standardized evaluation index with unified dimensions and a consistent value range is obtained for subsequent weighted fusion calculations. Through the above-mentioned unified dimension processing and interval linear normalization mapping, energy consumption and lifespan loss indices, which originally had different dimensions and large differences in magnitude, are converted into unified dimensionless evaluation indices. This effectively eliminates the impact of dimensional differences on the comprehensive evaluation results and improves the stability and comparability of multi-index fusion calculations. Based on the differences in equipment operating objectives under different operating conditions, an operating condition priority coefficient is determined, and the operating condition priority coefficient is allocated to the unit material energy consumption index and the cumulative life loss index respectively, forming a corresponding index weight allocation relationship; wherein, under high load or high energy consumption conditions, the weight of the unit material energy consumption index is increased, and under low equipment health status or life-sensitive conditions, the weight of the life loss index is increased. Based on the load status, health status, and performance status obtained in step S2, the current operating conditions of the equipment are classified and determined. These operating conditions include at least high load, low load, good health, and health degradation conditions. Specifically, a high load condition is defined as a load rate exceeding a preset load threshold, and a health degradation condition is defined as a health degradation condition when the equipment health score is below a preset health threshold. Next, operating condition priority coefficients are generated based on the operating condition determination results. These coefficients characterize the relative importance of energy consumption optimization targets and lifespan protection targets under the current operating conditions. In some implementations, these priority coefficients can be determined through rule mapping. For example, under high load or high energy consumption conditions, the priority coefficient of the unit material energy consumption index is increased; under health degradation or lifespan-sensitive conditions, the priority coefficient of the cumulative lifespan loss index is increased. Further, the operating condition priority coefficients are converted into weight allocation coefficients, and the unit material energy consumption index and the cumulative lifespan loss index are normalized and weighted, so that the sum of all weights is 1. Specifically: ;in, As the weight of the unit material energy consumption index, As the weight of the lifespan loss index, This refers to the priority coefficient under the corresponding operating condition. Furthermore, in some embodiments, the priority coefficient can be dynamically adjusted based on continuous operating condition parameters, such as load rate. With health score Constructing functional relationships: ;in, Load factor (between 0 and 1) Assess your health score (between 0 and 1). This is a preset adjustment coefficient. The adjustment coefficient... This is used to adjust the influence of load rate and equipment health status on the weighting of indicators. Its value is determined by preset rules. Based on equipment design parameters and operation control objectives, the importance of energy consumption optimization objectives and lifespan protection objectives is initially set, thereby determining the adjustment coefficient. α and β The baseline value; where, when energy saving is the priority objective of the system, it should be appropriately increased. α When the system prioritizes device protection, the value of [value] should be appropriately increased. β The value of is determined; secondly, during the system debugging phase, different values ​​are obtained through trial operation analysis of typical operating conditions. α、β The energy consumption level and equipment operational stability under the selected value combinations are assessed, and the parameter combination that optimizes the overall energy efficiency index and meets the operational stability requirements is chosen as the set value of the adjustment coefficient. Furthermore, in some embodiments, to ensure the smoothness of the adjustment process, the adjustment coefficient... α、β Limited to a preset value range, for example α、β ∈[0.1, 1.0], and remains constant during operation or takes segmented values ​​within different operating condition ranges. This is achieved by adjusting the coefficient. α、β By implementing preset and segmented control, the impact of load changes and equipment health status on weight allocation can be controlled, thereby improving the adaptability and stability of the comprehensive energy efficiency evaluation function under different operating conditions. Finally, the calculated weight coefficients are... and The unit material energy consumption index and the cumulative lifespan loss index are respectively assigned to form an index weight allocation relationship, which is used for the subsequent construction of the comprehensive energy efficiency evaluation function. Based on the standardized evaluation index and the index weight allocation relationship, the unit material energy consumption and the cumulative lifespan loss are weighted and fused to construct a comprehensive energy efficiency evaluation function, which is used to uniformly represent the comprehensive relationship between the equipment energy efficiency level and the degree of lifespan loss. The specific implementation process of weighted fusion processing of unit material energy consumption and cumulative lifespan loss to construct a comprehensive energy efficiency evaluation function based on the standardized evaluation index and the index weight allocation relationship is as follows: According to the dimensionless standardized evaluation index obtained in step S42-1, the standardized value of unit material energy consumption is obtained respectively. Standardized value of cumulative equipment lifespan loss , wherein and The values ​​are all limited to the range of [0,1], and the larger the value, the better the performance; based on the working condition priority coefficient determined in step S42-2, the weight of the unit material energy consumption index is calculated. Weighting of life loss index And it is normalized to meet the requirements. Furthermore, a weighted fusion approach is adopted to construct a comprehensive energy efficiency evaluation function, combining the standardized evaluation indicators with their corresponding weights to obtain the comprehensive energy efficiency evaluation value. The comprehensive energy efficiency evaluation value is used to uniformly characterize the overall performance between the energy efficiency level and the degree of lifespan loss of the equipment, and the larger the evaluation value, the better the overall performance. Subsequently, the corresponding comprehensive energy efficiency evaluation value is calculated for each group of candidate operating parameter combinations, and the parameter combinations are sorted and screened according to the evaluation values ​​to determine the preferred parameter set for subsequent optimization calculations. In some embodiments, the standardized evaluation index can also be nonlinearly transformed and then weighted and fused to enhance the ability to distinguish different performance ranges, thereby improving the adaptability and stability of the comprehensive evaluation function under complex operating conditions. Based on the comprehensive energy efficiency evaluation function, the evaluation calculations are performed on each candidate parameter combination to obtain the corresponding comprehensive energy efficiency evaluation value, and an initial parameter combination set is generated within the hard limit range as the initial solution space for subsequent iterative optimization. The specific implementation process of evaluating and calculating each candidate parameter combination based on the comprehensive energy efficiency evaluation function and generating an initial parameter combination set is as follows: First, within the hard limit range constructed in step S3, based on the value range of key operating parameters such as operating speed, material flow rate, and drive output, an initial candidate parameter combination set is generated using an equal-step discretization method or an interval sampling method. Each candidate parameter combination satisfies that each operating parameter does not exceed the corresponding physical limit constraint and safe operation constraint boundary. Second, each candidate parameter combination is substituted into the comprehensive energy efficiency evaluation function constructed in step S4, and its corresponding comprehensive energy efficiency evaluation value is calculated to characterize the comprehensive performance of the parameter combination between energy consumption level and lifespan loss. Further, all candidate parameter combinations are sorted according to the comprehensive energy efficiency evaluation value, and parameter combinations with evaluation values ​​lower than the preset screening threshold or not meeting the operating condition constraints are removed. Subsequently, the remaining parameter combinations that meet the constraints and have better evaluation values ​​are summarized to form an initial parameter combination set, which serves as the initial solution space for the subsequent iterative optimization process. The initial solution space simultaneously satisfies the hard limit range constraint and the comprehensive energy efficiency evaluation screening condition. Through the implementation of steps S42-1 to S42-4, the unit material energy consumption and equipment cumulative lifespan loss indicators, which originally had different dimensions, are uniformly mapped to a dimensionless evaluation space. Furthermore, by dynamically weighting and integrating these indicators based on operating condition priorities, a comprehensive energy efficiency evaluation function that simultaneously reflects energy efficiency levels and equipment health status is constructed. This achieves a unified single-objective expression for multi-objective optimization problems, significantly improving the stability and convergence efficiency of the parameter optimization process. For example, when the equipment is operating under high load, the weight of the unit material energy consumption indicator is set to 0.7, and the weight of the cumulative lifespan loss indicator is set to 0.3. When the equipment health score is below a preset threshold, the weight of the lifespan loss indicator is increased to 0.6 to reduce the risk of further equipment damage. Through this dynamic weight adjustment mechanism, coordinated optimization between energy consumption control and equipment protection is achieved.

[0026] Furthermore, for each parameter combination in the initial parameter combination set, based on the comprehensive energy efficiency evaluation function constructed in step S4, the corresponding comprehensive energy efficiency evaluation value is calculated to form an energy efficiency evaluation sequence for each parameter combination. Each parameter combination corresponds to a unique evaluation value, used to characterize the comprehensive performance level of the combination in terms of unit material energy consumption and cumulative lifetime loss. Boundary constraint verification is performed on each parameter combination, comparing each operating parameter with the hard limit range obtained in step S3. When any operating parameter exceeds the corresponding limit range, boundary truncation correction is performed on that parameter, adjusting it to the corresponding upper or lower limit value, thereby ensuring that the corrected parameter combination meets the safe operation constraint requirements and preventing illegal parameters from participating in subsequent iterative calculations. The parameter combinations are sorted and updated based on the comprehensive energy efficiency evaluation values ​​of each parameter combination. The parameter combinations with better evaluation values ​​are selected as the candidate set for the next iteration cycle. Simultaneously, the evaluation calculation and boundary correction processes S43-1 to S43-2 are repeated. After each iteration cycle, the change in the optimal comprehensive energy efficiency evaluation value between two adjacent iteration cycles is calculated. When the change is less than a preset convergence threshold or the maximum number of iterations is reached, the iteration process is deemed converged and the iteration calculation is stopped, outputting the current optimal parameter combination set. Through the iterative optimization and boundary constraint correction mechanism in steps S43-1 to S43-3, the parameter optimization process gradually approaches the optimal solution while satisfying the equipment's safe operation constraints. Simultaneously, the convergence judgment condition effectively avoids invalid iterations, improving optimization efficiency and computational stability, and ensuring that the final output parameter combination reaches a locally or globally optimal state in the sense of comprehensive energy efficiency evaluation.

[0027] S5: Construct a target operating parameter sequence based on the optimal energy efficiency operating parameter set, and generate an energy efficiency control command sequence based on the parameter deviation between the current equipment operating status and the target operating parameter sequence, combined with preset parameter adjustment rules and adjustment step size constraints.

[0028] Furthermore, step S5 of this application also includes: S51. The optimal energy efficiency operating parameter set is expanded sequentially according to the control cycle. Combining the maximum allowable rate of change of the actuator and the physical characteristics of acceleration and deceleration, rate constraints are applied to the parameter changes in adjacent control cycles to generate a continuously changing target operating parameter sequence. A gradual transition constraint is set for the initial stage of the target operating parameter sequence to suppress parameter abrupt changes in the initial control cycle. S52. The current equipment operating state parameters are obtained, and the current equipment operating state parameters are time-aligned with the target parameters of the corresponding control cycle in the target operating parameter sequence. The instantaneous parameter deviation of each control dimension is calculated, and based on adjacent control cycles… S53. Determine the deviation trend based on the deviation change of the control cycle; S54. Based on the instantaneous parameter deviation and deviation trend, call the preset parameter adjustment rule library to generate a basic adjustment amount; perform single-cycle step size constraint processing on the basic adjustment amount, and perform limit correction when the basic adjustment amount exceeds the preset allowable adjustment range to generate a constrained adjustment increment; S556. Superimpose the constrained adjustment increment onto the current operating parameters to generate the expected setting parameters for the next control cycle; convert the expected setting parameters into control instructions that can be recognized by the actuator, and encapsulate and time-sequence mark them according to the control cycle to generate an energy efficiency control instruction sequence and output it to the execution module.

[0029] Specifically, the optimal energy efficiency operating parameter set is expanded in time according to the control cycle to form a target operating parameter sequence corresponding to each control cycle; combined with the maximum allowable rate of change of the actuator and the physical characteristics of the equipment acceleration and deceleration, the parameter changes between adjacent control cycles are rate-constrained to ensure that the parameter changes meet the physical executability requirements; according to the control cycle setting parameters of the equipment control system, each operating parameter (including operating speed, material flow rate, and drive output, etc.) in the optimal energy efficiency operating parameter set is discretized according to the time dimension, dividing a complete operating cycle into multiple continuous control cycles, and assigning a corresponding target parameter value to each control cycle, thereby forming a target operating parameter sequence; wherein, for the optimal parameter set in non-time series form, a uniform distribution method or a piecewise interpolation method is used to map each parameter to each control cycle, so that the target parameters between adjacent control cycles show a continuous changing trend. To ensure that parameter changes meet the physical feasibility of the actuator, rate constraint processing is applied to parameter changes between adjacent control cycles to obtain the maximum permissible rate of change parameter of the actuator. This maximum permissible rate of change characterizes the maximum parameter change amplitude that the actuator can withstand within a unit control cycle and is determined by the equipment's factory parameters or calibration parameters. The change in each operating parameter between adjacent control cycles is calculated, and this change is compared with the maximum permissible rate of change. When the change exceeds the permissible range, it is proportionally reduced to meet the actuator's change capability constraints. For example, the rate constraint processing also incorporates the equipment's acceleration and deceleration physical characteristics for correction. For instance, acceleration constraints are introduced for operating speed parameters, and flow inertia constraints are introduced for flow rate parameters, ensuring that parameter changes meet the dynamic response characteristics of the equipment during actual operation. Simultaneously, gradual transition constraints are set at the beginning of the target operating parameter sequence to avoid abrupt parameter changes in the initial control phase and improve system operational stability. The gradual transition constraint is used to smooth the initial stage of the target operating parameter sequence to avoid impact on the actuator due to parameter abrupt changes in the initial control phase. The specific implementation process is as follows: First, determine the initial control cycle segment of the target operating parameter sequence, which is the first N control cycles of the target operating parameter sequence, where N is a preset gradual transition length parameter, determined based on the equipment's dynamic response time or the control system's sampling period. Second, for each control cycle within the initial control cycle segment, perform staged scaling on the difference between the target operating parameters and the current actual operating parameters to form a gradually approximating gradual parameter sequence. Specifically, for the first Nth control cycle... i There are n control cycles (i=1 to N), and the target parameter is... The current actual parameters are Then the parameters after the gradient Generate as follows: ;in, For the first iGradual target parameters for each control cycle The gradient coefficient is... These are the current actual operating parameters. The optimal energy efficiency target parameter; further, the gradient coefficient The parameter setting is gradually increased with the control cycle, so that the parameter change shows a monotonically smooth growth trend, for example: Linear increments are used for general stationary control scenarios, while square increments are used for control scenarios with a smoother initial phase and accelerated convergence later. A rate limit constraint can also be applied to the gradual transition process. When the parameter change between two adjacent control cycles exceeds a preset threshold, the current gradual coefficient is corrected to meet the maximum allowable rate of change requirement of the actuator, thus ensuring that the gradual transition process remains within the executable range. Ultimately, through the above gradual transition processing, the target operating parameter sequence exhibits a smooth transition from the current operating state to the target operating state at the start of control, effectively avoiding equipment shocks or system oscillations caused by parameter mutations, and improving the stability and safety of the control system. The current equipment operating state parameters are obtained, and these parameters are time-aligned with the target parameters of the corresponding control cycle in the target operating parameter sequence to calculate the instantaneous parameter deviations of each control dimension. Simultaneously, based on the changes in deviation values ​​between adjacent control cycles, the parameter deviation change trend is determined to characterize the dynamic evolution characteristics of the system's current deviation from the optimal operating state. Based on the instantaneous parameter deviation and its changing trend, a preset parameter adjustment rule library is invoked to generate a basic adjustment amount. This preset parameter adjustment rule library is a pre-built set of rules used to output the corresponding basic adjustment amount based on the equipment operating parameter deviation and its changing trend. The rule library is stored in the form of a piecewise rule table or piecewise function, where each rule at least contains a mapping relationship between the parameter deviation range, the changing trend type, and the corresponding adjustment intensity. The basic adjustment amount is subjected to single-control-cycle step-size constraint processing. When the basic adjustment amount exceeds the preset allowable adjustment range, a limit correction is performed, thereby generating a constrained adjustment increment that satisfies the execution constraints. The specific implementation process of single-control-cycle step-size constraint processing on the basic adjustment amount is as follows: First, the basic adjustment amount output by the preset parameter adjustment rule library is obtained. The basic adjustment amount is used to characterize the theoretical adjustment range that each operating parameter should make within the current control cycle; secondly, based on the physical response capability of the actuator and the control system settings, the maximum allowable adjustment range within a single control cycle is determined. The maximum allowable adjustment range is jointly limited by the maximum rate of change of the actuator, the driving capability, and the system sampling period; furthermore, the basic adjustment amount is compared dimension by dimension, and when the basic adjustment amount exceeds the corresponding maximum allowable adjustment range in any control dimension, a limit correction process is performed, specifically including: if Then let ;like Then let Otherwise, keep the original value unchanged. ;in, This is the constrained adjustment increment after constraint processing; in some implementations, to avoid frequent truncation of the adjustment amount in the critical interval, a scaling correction mechanism can also be introduced, that is, when the basic adjustment amount exceeds the allowable range, it is smoothed and compressed as follows: Alternatively, a proportional compression method may be used: This is used to maintain consistency in the adjustment direction while reducing the magnitude of sudden changes. Finally, the constrained adjustment increment, after amplitude limiting correction, is output to the actuator for parameter updates in the next control cycle, ensuring that the control commands remain within the physical limits that the actuator can withstand, thus avoiding equipment shock or control instability due to excessive adjustment. The constrained adjustment increment is then superimposed on the current operating parameters to obtain the expected setting parameters for the next control cycle. These expected setting parameters are converted into control commands recognizable by the actuator, encapsulated and timestamped according to the control cycle, forming a time-series energy efficiency control command sequence, which is then output to the execution module for execution. The optimal energy-efficient operating parameter set is transformed into continuous time-series control commands that satisfy both equipment physical constraints and execution constraints, achieving a smooth mapping from optimization results to engineering execution commands. The process of determining the optimal energy-efficient operating parameter set is as follows: First, within the safe operating constraint boundary constructed in step S3 and the hard limit range determined in step S4, a set of candidate operating parameter combinations that satisfy both equipment physical constraints and execution constraints is generated. Each candidate operating parameter combination includes at least key operating parameters such as operating speed, material flow rate, and drive output. Second, each candidate operating parameter combination is substituted into the comprehensive energy efficiency evaluation function constructed in step S4 to calculate the corresponding comprehensive energy efficiency evaluation value, which is used to characterize the comprehensive performance level of the parameter combination between unit material energy consumption and cumulative equipment lifespan loss. Further, based on the comprehensive energy efficiency evaluation... The evaluation process ranks all candidate parameter combinations, selecting the parameter combination with the highest evaluation value or the top N parameter combinations with relatively good evaluation values ​​to form an initial optimal parameter set, where N is a preset number of preferred combinations. In some implementations, an iterative screening method can also be used. After each round of screening, the parameter combinations with relatively good evaluation values ​​are retained, and new candidate parameter combinations are regenerated within their neighborhood. The evaluation calculation and ranking screening are repeated until the change in the optimal comprehensive energy efficiency evaluation value in adjacent iteration cycles is less than a preset threshold or the maximum number of iterations is reached, at which point the iteration stops, and the optimal energy efficiency operating parameter set is finally obtained. Through the above method, the optimal energy efficiency operating parameter set is deterministically calculated by the comprehensive energy efficiency evaluation function under the premise of satisfying safe operation constraints and execution constraints, thereby ensuring its feasibility and engineering applicability. At the same time, by introducing rate constraints, deviation feedback, and step size limitation mechanisms, abrupt changes in control parameters are effectively avoided, improving the stability and control accuracy of system operation, and enabling the equipment to gradually approach the optimal energy efficiency state during dynamic operation.

[0030] S6: The energy efficiency control command sequence is sent to the equipment actuator for execution, and the operating data after control is collected in real time. The control response deviation is calculated, and the subsequent control commands are dynamically corrected based on the response deviation.

[0031] Furthermore, step S6 of this application also includes: S61. The energy efficiency control command sequence is sent to the equipment actuator in segments according to the control cycle, and the actual response data of the actuator is collected simultaneously. The actual response data is filtered to obtain the actual response trajectory. ,in: ； in, The raw response data collected. S62, The actual response trajectory is a moving average filter operator; With the target running parameter sequence Perform time alignment and calculate instantaneous response deviation. With cumulative response deviation ,in: ; Based on the gradient of instantaneous response deviation change S63. Determine the response characteristic category, which includes fast convergence, hysteresis following, or oscillatory overshoot; S64. Determine the basic compensation coefficient based on the response characteristic category. And calculate the adaptive compensation coefficient. Its expression is: ;in: ; ;in, These are adaptive compensation coefficients used to adjust the intensity of command corrections. This is determined by the preset rule table corresponding to the response characteristic category. For instantaneous response deviation, To accumulate response bias, As a dynamic memory factor, The memory decay coefficient, This is the weighting adjustment coefficient. As an enforcement agency constraint correction factor, The adaptive compensation coefficient is used to determine the dead zone and saturation suppression coefficient, and is then used for subsequent control command correction; S64, the adaptive compensation coefficient is used to determine the dead zone and saturation suppression coefficient. It acts on the unexecuted energy efficiency control command segment to generate a correction command segment. The calculation formula is: ;in, Original, unexecuted control instructions. The instruction deviation correction amount is used to replace the unexecuted part of the original energy efficiency control instruction sequence with the correction instruction segment, and the result is verified based on the safe operation constraint boundary. If the constraint condition is met, the updated energy efficiency control instruction sequence is output; if the constraint condition is not met, the process reverts to the previous safe parameter point and records the interception information.

[0032] Specifically, according to the timestamp in the energy efficiency control command sequence generated in step S5, the command sequence is divided into discrete command segments consistent with the sampling period of the control system. At the arrival of each control cycle, the corresponding command segment is sent to the actuator via an industrial communication interface or fieldbus protocol, enabling the actuator to receive and execute control commands periodically. Secondly, during the actuator's response, operating data is collected in real time by sensors deployed at key nodes of the actuator. This operating data includes at least operating speed feedback values, material flow feedback values, and drive output feedback values, forming a raw actual response data sequence. Further, to eliminate sensor noise and instantaneous fluctuations during execution, the raw actual response data sequence is filtered. This filtering can employ moving average filtering or exponential smoothing filtering, weighting adjacent sampling points to suppress high-frequency disturbance signals, thereby obtaining a continuous and smooth actual response trajectory. In some embodiments, the filtering window length is set according to the control cycle sampling frequency and equipment response delay characteristics to achieve a balance between smoothness and real-time response. The energy efficiency control command sequence is sent to the equipment actuators in segments according to the control cycle, and the actual response data of the actuators is collected simultaneously. The actual response data is then filtered to obtain the actual response trajectory. ,in: ； in, The raw response data collected. The moving average filter operator is used to calculate the actual response trajectory. With the target running parameter sequence Perform time alignment and calculate instantaneous response deviation. With cumulative response deviation ,in: ; Based on the gradient of instantaneous response deviation change S63. Determine the response characteristic category, which includes fast convergence, hysteresis following, or oscillatory overshoot; S64. Determine the basic compensation coefficient based on the response characteristic category. And calculate the adaptive compensation coefficient. Its expression is: ;in: ; ;in, These are adaptive compensation coefficients used to adjust the intensity of command corrections. This is determined by the preset rule table corresponding to the response characteristic category. For instantaneous response deviation, To accumulate response bias, As a dynamic memory factor, The memory decay coefficient, This is the weighting adjustment coefficient. As an enforcement agency constraint correction factor, The adaptive compensation coefficient is used to determine the dead zone and saturation suppression coefficient, and is then used for subsequent control command correction; S64, the adaptive compensation coefficient is used to determine the dead zone and saturation suppression coefficient. It acts on the unexecuted energy efficiency control command segment to generate a correction command segment. The calculation formula is: ;in, Original, unexecuted control instructions. The instruction deviation correction amount is used to replace the unexecuted part of the original energy efficiency control instruction sequence with the correction instruction segment, and the result is verified based on the safe operation constraint boundary. If the constraint condition is met, the updated energy efficiency control instruction sequence is output; if the constraint condition is not met, the process reverts to the previous safe parameter point and records the interception information.

[0033] S7: After each regulation cycle ends, a comparative analysis is performed on the energy consumption data before and after regulation to calculate the energy efficiency improvement index. The operating data of the current operating cycle is stored in the historical dataset. The threshold parameters in the state determination rule chain and the parameter value range in the energy efficiency evaluation function are adjusted through periodic statistical analysis.

[0034] Specifically, after each control cycle ends, energy consumption data before and after the control cycle is acquired. This energy consumption data includes energy consumption per unit time, energy consumption per unit material, or cumulative energy consumption. Data within corresponding time windows before and after the control cycle are aligned to ensure temporal consistency in comparative analysis. Secondly, an energy efficiency improvement index is calculated based on the aligned energy consumption data. This index characterizes the degree of energy consumption improvement of the current control strategy relative to the pre-control state, and its calculation method can be the relative change rate or difference ratio of energy consumption per unit material before and after the control. Further, the operating data of the current operating cycle is stored in a historical dataset. The system includes at least operational status data, energy consumption data, and control command data, which are structured and stored according to timestamps to form a data foundation for subsequent statistical analysis. Subsequently, periodic statistical analysis is performed on the historical dataset, including frequency statistics of energy consumption distribution characteristics and status determination results under different operating conditions, to identify the changing trends of parameter distribution during long-term operation. Finally, based on the statistical analysis results, the threshold parameters in the status determination rule chain and the parameter value ranges in the energy efficiency evaluation function are adjusted to adapt to changes in the current equipment operating status, thereby improving the accuracy of status determination and the stability of energy efficiency evaluation. In some embodiments, the parameter adjustment method is an adaptive correction method based on historical data quantiles or mean drift to avoid excessive influence of single abnormal data on the rule parameters. Through the above process, dynamic optimization of rule parameters and evaluation function parameters driven by control effect feedback is achieved, improving the stability and adaptability of the system's long-term operation.

[0035] This application embodiment achieves quantitative evaluation and dynamic optimization of equipment energy efficiency trends by continuously collecting and uniformly modeling multi-source operating data of continuous material handling equipment in different operating cycles, combined with a comparative analysis mechanism of energy consumption data before and after regulation. In the specific implementation process, firstly, after the end of each regulation cycle, energy consumption data before and after regulation within the corresponding cycle is acquired, and the data is time-aligned and operating condition matched to ensure that the comparative analysis is based on the same operating scenario; then, based on the aligned energy consumption data, an energy efficiency improvement index is calculated to characterize the degree of energy-saving effect change of the current regulation strategy relative to the initial operating state, and this index is used as an important basis for evaluating the current regulation effect; furthermore, the operating status data, regulation command data, and energy consumption data within each regulation cycle are structured and stored to form a historical dataset, and continuously accumulated according to the time series to ensure that the data has complete temporal continuity and operating condition coverage; based on this, the historical data... The system performs periodic statistical analysis on the data, calculating the energy consumption distribution characteristics, frequency of state determination results, and parameter fluctuation range under different operating conditions to identify the drift trend and stable range of system parameters during long-term operation. Based on the statistical analysis results, the threshold parameters in the state determination rule chain are adaptively adjusted to reflect changes in the actual operating state of the equipment. Simultaneously, the parameter value range in the energy efficiency evaluation function is corrected to improve the evaluation function's adaptability to different operating conditions. In some embodiments, the adjustment of the threshold parameters and evaluation function parameters adopts methods based on historical data quantile statistics or moving average drift correction to avoid the impact of a single abnormal cycle on the overall parameter system. Through the above methods, this application embodiment realizes a dynamic update mechanism for rule parameters and evaluation models based on operational data feedback, enabling the system to maintain high accuracy in energy efficiency evaluation and reliability in state determination during long-term operation, while improving the stability of equipment operation and the adaptability of control strategies. Compared with the prior art, the embodiments of this application have at least the following beneficial effects: This application constructs an energy efficiency control method for continuous material handling equipment based on operating status determination, and integrates equipment operation data acquisition, status determination, energy efficiency evaluation, parameter optimization and control command generation into a unified collaborative design, realizing a closed-loop control mechanism from data perception to control execution, effectively avoiding the problems of control lag and inconsistent energy efficiency optimization caused by the fragmentation of each link in the traditional method.In practical applications, the comprehensiveness and accuracy of equipment status identification are improved through unified processing and feature extraction of multi-source operating data. A hierarchical status determination mechanism based on rule chains enables step-by-step analysis of load status, health status, and performance status, making the expression of equipment operating status clearer. By constructing a comprehensive energy efficiency evaluation function and introducing a weight allocation mechanism, a unified quantitative representation of energy consumption and lifespan loss is achieved, thereby enhancing the coordination capability of multi-objective optimization. Simultaneously, by transforming the optimized parameter set into a sequence of control instructions with time-series characteristics and combining it with the physical constraints of the actuator for smooth control, the impact of parameter mutations on equipment operation is effectively avoided, improving the stability of the control process. Furthermore, by periodically statistically analyzing historical operating data and adaptively correcting the status determination rules and evaluation function parameters, the system possesses self-learning and self-optimization capabilities under long-term operating conditions, significantly improving the system's adaptability, robustness, and overall energy efficiency under complex operating conditions. In summary, the embodiments of this application not only improve the energy efficiency control accuracy and operational stability of continuous material handling equipment, but also significantly enhance the system's real-time perception and adaptive adjustment capabilities to changes in multi-source operating states, achieving energy consumption optimization and synergistic control of lifespan loss under both physical and execution constraints. By constructing an integrated closed-loop mechanism for multi-source data acquisition, state determination, and energy efficiency evaluation, the control strategy can be dynamically modified according to the operating state, thereby avoiding the control lag problem caused by statically fixed parameters in traditional methods. Simultaneously, the combination of historical data-driven parameter correction improves the system's long-term adaptability and stability, achieving a dynamic balance between energy efficiency improvement and equipment protection.

[0036] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for energy efficiency control of continuous material handling equipment based on operating status determination, characterized in that, The method includes: S1. Deploy sensor acquisition units on continuous material handling equipment to collect equipment operating parameters, material status parameters and environmental parameters in real time, forming a multi-source operating dataset. S2. Perform feature extraction and time series analysis on the multi-source operation dataset, and make step-by-step judgments on the feature quantities based on the preset state judgment rule chain to obtain the load status, health status and performance status of the device. S3. Based on the load status and health status, combined with the equipment physical limit parameters and preset safety redundancy coefficient, construct the safety constraint space of the equipment operating parameters to form the safety operating constraint boundary at the current moment. S4. Within the safety operation constraint boundary, construct an energy efficiency evaluation function based on physical mechanism, taking operating speed, material flow rate and equipment health score as inputs, and unit material energy consumption and equipment cumulative life loss as optimization objectives. Optimize using a heuristic optimization algorithm to obtain the optimal energy efficiency operation parameter set. S5. Construct a target operating parameter sequence based on the optimal energy efficiency operating parameter set, and generate an energy efficiency control command sequence based on the parameter deviation between the current equipment operating state and the target operating parameter sequence, combined with preset parameter adjustment rules and adjustment step size constraints. S6. Send the energy efficiency control command sequence to the equipment actuator for execution, collect the operating data after control in real time, calculate the control response deviation, and dynamically correct the subsequent control commands based on the response deviation; S7. After each regulation cycle ends, a comparative analysis is performed on the energy consumption data before and after regulation to calculate the energy efficiency improvement index. The operating data of the current operating cycle is stored in the historical dataset. The threshold parameters in the state determination rule chain and the parameter value range in the energy efficiency evaluation function are adjusted through periodic statistical analysis.

2. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 1, characterized in that, Feature extraction and time-series analysis are performed on the multi-source operational dataset, and the feature quantities are judged step by step based on a preset state judgment rule chain to obtain the device's load status, health status, and performance status, including: S21. Perform time synchronization processing and outlier removal on the multi-source running dataset, and use a sliding time window to resample each running parameter in segments to form a time-series data sequence under a unified time scale. S22. Based on the time-series data sequence, extract multi-dimensional feature quantities to characterize the operating state of the equipment. The multi-dimensional feature quantities include amplitude feature quantities characterizing load changes, fluctuation feature quantities characterizing operating stability, time-series gradient feature quantities characterizing state change trends, and unit-time energy consumption feature quantities characterizing energy consumption levels. S23. Normalize each of the multidimensional features and map the continuous features to the corresponding state determination interval according to the preset feature interval mapping rule to form standardized feature parameters. S24. Based on the standardized feature parameters, perform step-by-step judgments according to the preset state judgment rule chain, and output the load status, health status and performance status of the device in sequence. The subsequent state judgment is constrained and corrected based on the previous judgment result.

3. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 1, characterized in that, Based on the load and health status, combined with the equipment's physical limit parameters and preset safety redundancy coefficients, a safety constraint space for the equipment's operating parameters is constructed, forming the safety operating constraint boundary at the current moment, including: S31. Based on the real-time degradation rate of the health status and the fluctuation frequency characteristics of the load status, a preset redundancy coefficient mapping rule is invoked to determine the adaptive safety margin corresponding to each key operating parameter; the adaptive safety margin is nonlinearly adjusted according to the health degradation rate and load fluctuation frequency according to a preset attenuation rule. S32. Extract the coupling weight relationship between each operating parameter, decouple and transform the multidimensional operating parameters to obtain relatively independent parameter dimensions; use the physical limit parameters of the equipment as the benchmark limit, and on the basis of superimposed adaptive safety margin, construct the initial safety constraint envelope using a multidimensional limiting processing method. S33. Based on the multi-source operation dataset, a sliding time window is used to fit the trend of operation parameter changes and to estimate the parameter evolution trend in the preset time domain; according to the approximation between the parameter evolution trend and the initial safety constraint envelope, a safety margin is calculated; when the safety margin is lower than a preset threshold, the initial safety constraint envelope is shrunk and adjusted along each parameter dimension to generate a safe operation constraint boundary. S34. Perform interval consistency verification between the safe operation constraint boundary and the equipment process limit, eliminate the conflicting areas that exceed the limit, and integrate the remaining feasible areas to form a continuous feasible operating domain; extract the value range of each operating parameter corresponding to the feasible operating domain at the current time, and output it as the safe operation constraint boundary to the subsequent energy efficiency optimization steps.

4. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 3, characterized in that, Based on the multi-source operational dataset, a sliding time window is used to fit the trend of operational parameter changes, and the parameter evolution trend within a preset time domain is calculated. According to the approximation degree between the parameter evolution trend and the initial safety constraint envelope, a safety margin is calculated. When the safety margin is lower than a preset threshold, the initial safety constraint envelope is shrunk and adjusted along each parameter dimension to generate a safe operational constraint boundary, including: S33-1. Based on the current control cycle, extract the sequence of operating parameters within a historical sliding time window of a preset length; perform weighted trend fitting processing on the sequence of operating parameters to suppress instantaneous fluctuation interference and extract the benchmark rate of change and trend change characteristics of each operating parameter. S33-2. Based on the benchmark rate of change and trend change characteristics, extrapolate the operating parameters in the time dimension to generate a discrete parameter evolution sequence covering the preset prediction time domain; calculate the boundary distance of each parameter value in the discrete parameter evolution sequence relative to the initial safety constraint envelope, and take the minimum boundary distance of each parameter dimension as the current safety margin. S33-3. Compare the current safety margin with the preset dynamic warning threshold. When the current safety margin is lower than the dynamic warning threshold, trigger the constraint boundary contraction mechanism. Calculate the dynamic contraction coefficient corresponding to each operating parameter based on the difference between the safety margin and the warning threshold and the predicted time domain length. Wherein, the dynamic shrinkage coefficient satisfies: It is inversely proportional to the deviation of the safety margin from the preset warning threshold; It is positively correlated with the length of the predicted time domain; And it is subject to weighted modulation by the rate of change of operating parameters; Based on the dynamic shrinkage coefficient, the initial safety constraint envelope is proportionally shrunk along each parameter dimension to obtain the safe operation constraint boundary.

5. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 1, characterized in that, Within the defined safe operation constraints, a physical mechanism-based energy efficiency evaluation function is constructed. Using operating speed, material flow rate, and equipment health score as inputs, and unit material energy consumption and cumulative equipment lifespan loss as optimization objectives, a heuristic optimization algorithm is employed to find the optimal set of energy efficiency operating parameters, including: S41. Based on equipment transmission dynamics and material conveying mechanism, establish a deterministic correspondence between operating speed, material flow rate and equipment health score and unit material energy consumption and cumulative life loss; and convert the safe operation constraint boundary into a hard limit range of optimization variables; S42. The unit material energy consumption and cumulative life loss are normalized, and the normalized indicators are weighted and combined according to the working condition priority coefficient to construct a comprehensive energy efficiency evaluation function; and the comprehensive energy efficiency evaluation value is calculated based on the comprehensive energy efficiency evaluation function, and an initial parameter combination set is generated within the hard limit range. S43. Within the hard limit range, perform iterative optimization calculations on the parameter combinations, calculate the comprehensive energy efficiency evaluation value generation by generation, perform boundary correction or penalty processing on parameter combinations that exceed the limits, and update the parameter set according to the evaluation value until convergence. S44. Select the parameter combination with the best comprehensive energy efficiency evaluation value from the converged parameter set, and verify the physical limits of the equipment and the process continuity constraints to determine the optimal energy efficiency operating parameter set.

6. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 5, characterized in that, The construction of the comprehensive energy efficiency evaluation function includes: S42-1. The unit material energy consumption index and the cumulative life loss index are processed to unify the dimensions, and the preset normalization function is used to map them to the dimensionless evaluation space to obtain the standardized evaluation index. S42-2. Determine the priority coefficient of the operating condition based on the importance of the equipment operation under different operating conditions, and assign the priority coefficient of the operating condition to the unit material energy consumption index and the cumulative life loss index respectively to form an index weight allocation relationship. S42-3. Based on the standardized evaluation indicators and the weight allocation relationship of the indicators, the unit material energy consumption and cumulative life loss are weighted and integrated to construct a comprehensive energy efficiency evaluation function, which is used to uniformly characterize the comprehensive relationship between the energy efficiency level and the degree of life loss of equipment. S42-4. Calculate the current parameter combination based on the comprehensive energy efficiency evaluation function to obtain the comprehensive energy efficiency evaluation value, and generate an initial parameter combination set within the hard limit range.

7. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 5, characterized in that, Within the hard limit range, iterative optimization calculations are performed on the parameter combinations, calculating the comprehensive energy efficiency evaluation value generation by generation. Boundary correction or penalty processing is performed on parameter combinations that exceed the limits, and the parameter set is updated according to the evaluation values ​​until convergence, including: S43-1. For each parameter combination in the initial parameter combination set, calculate the corresponding evaluation value based on the comprehensive energy efficiency evaluation index to form the energy efficiency evaluation sequence of each parameter combination. S43-2. ​​Perform boundary constraint verification on each parameter combination. When the parameter combination exceeds the hard limit range, use the boundary truncation correction method to make it meet the safe operation constraint requirements. S43-3. Based on the evaluation value, sort and update the parameter combination, and repeat the calculation and correction process from S43-1 to S43-2. ​​At the same time, judge the change range of the optimal comprehensive energy efficiency evaluation value in adjacent iteration cycles. When the change range is less than the preset threshold or the maximum number of iterations is reached, stop the iteration process.

8. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 1, characterized in that, Based on the optimal energy efficiency operating parameter set, a target operating parameter sequence is constructed. Then, according to the parameter deviation between the current equipment operating state and the target operating parameter sequence, and combined with preset parameter adjustment rules and adjustment step size constraints, an energy efficiency control command sequence is generated, including: S51. The optimal energy efficiency operating parameter set is expanded in time according to the control cycle. Combined with the maximum allowable rate of change of the actuator and the physical characteristics of acceleration and deceleration, the parameter changes of adjacent control cycles are subjected to rate constraint processing to generate a continuously changing target operating parameter sequence. Gradual transition constraints are set for the initial stage of the target operating parameter sequence to suppress parameter mutations in the initial control cycle. S52. Obtain the current device operating status parameters, and perform time alignment matching between the current device operating status parameters and the target parameters of the corresponding control cycle in the target operating parameter sequence. Calculate the instantaneous parameter deviation of each control dimension, and determine the deviation change trend based on the deviation change amount of adjacent control cycles. S53. Based on the instantaneous parameter deviation and the deviation change trend, call the preset parameter adjustment rule library to generate a basic adjustment amount; perform single-cycle step size constraint processing on the basic adjustment amount; when the basic adjustment amount exceeds the preset allowable adjustment range, perform amplitude limit correction to generate a constrained adjustment increment. S54. The constrained adjustment increment is superimposed on the current operating parameters to generate the expected setting parameters for the next control cycle; the expected setting parameters are converted into control instructions that can be recognized by the actuator, and encapsulated and time-marked according to the control cycle to generate an energy efficiency control instruction sequence and output to the execution module.

9. The energy efficiency control method for continuous material handling equipment based on operating status determination as described in claim 1, characterized in that, The energy efficiency control command sequence is sent to the equipment actuator for execution, and the operating data after control is collected in real time. The control response deviation is calculated, and subsequent control commands are dynamically corrected based on the response deviation, including: S61. The energy efficiency control command sequence is sent to the equipment actuator in segments according to the control cycle, and the actual response data of the actuator is collected simultaneously. The actual response data is filtered to obtain the actual response trajectory. ,in: ； in, The raw response data collected. It is a moving average filter operator; S62, the actual response trajectory With the target running parameter sequence Perform time alignment and calculate instantaneous response deviation. With cumulative response deviation ,in: ； ； Based on the gradient of instantaneous response deviation change Determine the category of response characteristics, which includes fast convergence, hysteresis follower, or oscillatory overshoot; S63. Determine the basic compensation coefficient according to the response characteristic category. And calculate the adaptive compensation coefficient. Its expression is: ； in: ； ； in, These are adaptive compensation coefficients used to adjust the intensity of command corrections. This is determined by the preset rule table corresponding to the response characteristic category. For instantaneous response deviation, To accumulate response bias, As a dynamic memory factor, The memory decay coefficient, This is the weighting adjustment coefficient. As an enforcement agency constraint correction factor, The dead zone and saturation suppression coefficients are used, and the adaptive compensation coefficients are used for subsequent control command corrections. S64, The adaptive compensation coefficient It acts on the unexecuted energy efficiency control command segment to generate a correction command segment. The calculation formula is: ； in, Original, unexecuted control instructions. Instruction deviation correction amount; The corrected instruction segment replaces the unexecuted part of the original energy efficiency control instruction sequence, and is verified based on the safe operation constraint boundary; in, If the constraints are met, the updated energy efficiency control instruction sequence will be output. If the constraints are not met, the system will revert to the previous safety parameter point and record the interception information.

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