Self-adaptive dynamic pressure regulating and energy saving system and method based on pneumatic equipment

Abstract

The invention relates to the technical field of industrial automation, and discloses a self-adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment, and the system comprises a state monitoring module which is used for constructing an operation state matrix of the pneumatic equipment; the pressure control module is used for generating a dynamic pressure control vector of the pneumatic equipment; the risk judgment module is used for calculating a real-time operation risk coefficient of the pneumatic equipment; the hierarchical regulation and control module is used for establishing a hierarchical regulation and control strategy set of the pneumatic equipment; the control feedback module is used for generating a self-adaptive control parameter sequence of the dynamic voltage regulation instruction and the state early warning instruction; and the scheme output module is used for integrating the dynamic pressure regulating instruction, the state early warning instruction, the hierarchical regulation and control strategy set and the self-adaptive control parameter sequence and outputting a self-adaptive dynamic pressure regulating and energy saving scheme of the pneumatic equipment. The energy utilization efficiency and the operation stability of the pneumatic equipment can be improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and in particular to an adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment. Background Technology

[0002] Adaptive dynamic pressure regulation energy saving of pneumatic equipment refers to the process of real-time regulation of air pressure supply and consumption of pneumatic equipment by integrating pressure sensors, intelligent control units, dynamic pressure regulation modules and energy consumption statistics terminals. This is to accurately monitor changes in equipment operating conditions, load demand fluctuations and compressed air pressure loss, aiming to improve the matching of air pressure supply and energy utilization efficiency of pneumatic equipment.

[0003] Currently, the commonly used pressure regulation method for pneumatic equipment is the fixed-speed air supply method. This method continuously supplies air to the pneumatic equipment by driving it at a preset fixed speed, as seen in applications 202222501043.0 and 201721246871.7. Neither of these methods includes real-time feedback adjustment components (such as pressure sensors or dynamic regulating valves) that are linked to the end-user equipment. Consequently, they cannot adjust the air supply pressure or flow rate based on real-time load fluctuations such as changes in the refueling rate of the die-casting machine or the amount of fuel injected by the vehicle engine. During low-load or intermittent working periods, a large amount of compressed air overflows or is released without participating in effective operations, resulting in a significant reduction in the energy utilization efficiency of the pneumatic equipment. Summary of the Invention

[0004] The present invention provides an adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment, the main purpose of which is to improve the energy utilization efficiency and operational stability of pneumatic equipment.

[0005] To achieve the above objectives, the present invention provides an adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment, comprising: The status monitoring module is used to collect main pipeline pressure data, actuator displacement data and ambient temperature data of the pneumatic equipment in order to construct the operating status matrix of the pneumatic equipment; The pressure control module is used to generate dynamic pressure regulation commands and status warning commands for the pneumatic equipment based on the operating status matrix, and to parse the pressure setpoint sequence and regulation rate parameters in the dynamic pressure regulation commands to generate the dynamic pressure control vector of the pneumatic equipment. The risk assessment module is used to extract the performance degradation rate, sealing performance index and abnormal vibration frequency band from the status warning command to generate the status monitoring vector of the pneumatic equipment, and calculate the real-time operating risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the status monitoring vector. The graded control module is used to determine the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector, and to establish a graded control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient. The control feedback module is used to collect system response data and risk evolution data of the pneumatic equipment in real time after applying the graded control strategy set, so as to generate an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command. The scheme output module is used to integrate the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set, and the adaptive control parameter sequence, and output the adaptive dynamic pressure regulation energy-saving scheme of the pneumatic equipment.

[0006] Optionally, generating the dynamic pressure regulation command for the pneumatic equipment based on the operating state matrix includes: Based on the operating state matrix, the current operating mode features and state degradation trend features of the pneumatic equipment are extracted; Calculate the optimal target pressure value of the pneumatic equipment under the current operating mode characteristics; Identify the performance degradation factors and performance offset compensation amounts in the aforementioned state degradation trend characteristics; Based on the performance degradation factor and the performance offset compensation amount, the optimal target pressure value is compensated and corrected to generate the reference pressure command of the pneumatic equipment. Acquire real-time load disturbance data of the pneumatic equipment, and analyze the disturbance frequency and amplitude characteristics in the real-time load disturbance data; Based on the disturbance frequency and the amplitude characteristics, a feedforward pressure regulation compensation command for the pneumatic equipment is generated; The reference pressure command and the feedforward pressure adjustment compensation command are combined to generate the dynamic pressure adjustment command for the pneumatic equipment.

[0007] Optionally, generating a status warning command for the pneumatic equipment based on the operating status matrix includes: Construct a health assessment system for the aforementioned operational status matrix; Calculate the real-time deviation of each health assessment indicator in the health assessment system; Identify the abnormal evolution patterns and early signs of failure of the health assessment indicators; Based on the real-time deviation, the abnormal evolution mode, and the fault precursor characteristics, the status warning level and warning content of the pneumatic equipment are determined; By combining the status warning level and the warning content, a status warning command for the pneumatic equipment is generated.

[0008] Optionally, the health assessment system for constructing the operating status matrix includes: Identify the target evaluation component corresponding to the operating state matrix; The pressure stability data, displacement accuracy data, and temperature drift data related to the target evaluation component are extracted from the operating status matrix, and the real-time performance data of the target evaluation component is collected simultaneously. The pressure stability data is used to generate the pressure fluctuation entropy value of the target evaluation component; The positioning deviation index in the displacement accuracy data is parsed out, and the time-domain correlation analysis of the positioning deviation index and the pressure fluctuation entropy value is performed to output the time-domain correlation strength coefficient. Based on a preset scoring mapping function, the time-domain correlation strength coefficient is converted into a mechanical transmission health score of the target evaluation component; Collect historical equipment fault data corresponding to the target evaluation component, and combine the temperature drift data and the historical equipment fault data to calculate the thermal degradation coefficient corresponding to the target evaluation component; Based on the real-time performance data, the efficiency degradation rate of the target evaluation component is identified; Calculate the performance degradation risk index of the target evaluation component based on the thermal degradation coefficient and the efficiency decay rate; A health assessment system for the operating state matrix is ​​established by combining the mechanical transmission health score, the performance degradation risk index, and the pressure fluctuation entropy value.

[0009] Optionally, establishing a hierarchical control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient includes: Based on the real-time energy-saving optimization value and the real-time operating risk coefficient, a two-dimensional decision space for the pneumatic equipment is constructed. The two-dimensional decision space is divided into a safe operation zone, an energy-saving optimization zone, a risk warning zone, and an emergency intervention zone. Using the real-time energy-saving optimization value and the real-time operation risk coefficient, define the boundary thresholds corresponding to the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the boundary thresholds, differentiated control rules are set for the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the differentiated control rules, dynamic control instructions are generated for the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the two-dimensional decision space, the differentiated control rules, and the dynamic control commands, a hierarchical control strategy set for the pneumatic equipment is established.

[0010] Optionally, calculating the real-time operational risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the state monitoring vector includes: Identify the pressure time-domain trajectory parameters and the adjustment aggression index in the dynamic pressure control vector; Extract the component safety margin and system damping characteristic parameters from the state monitoring vector; Based on the pressure time-domain trajectory parameters and the component safety margin, calculate the overload risk probability of the pneumatic equipment; Based on the adjustment aggression index and the system damping characteristic parameters, the probability of instability risk of the pneumatic equipment is calculated; Real-time monitoring of the energy efficiency coefficient and frequency of abnormal events of the pneumatic equipment; Combining the overload risk probability, the instability risk probability, the energy efficiency coefficient, and the frequency of abnormal events, the real-time operating risk coefficient of the pneumatic equipment is calculated using the following formula:

[0011] in, Indicates the real-time operational risk coefficient. Indicates the probability of overload risk. Indicates the probability of instability risk. Indicates the energy efficiency coefficient. This indicates the rated energy efficiency reference value. Indicates the frequency of abnormal events. Indicates the reference threshold for abnormal frequency. Indicates the continuous operating time of pneumatic equipment. Indicates the performance half-life of pneumatic equipment. This represents the time decay coefficient.

[0012] Optionally, determining the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector includes: Based on the dynamic pressure control vector, the real-time load demand and pressure supply redundancy of the pneumatic equipment are identified. Calculate the minimum target energy consumption of the pneumatic equipment based on the real-time load demand and the pressure supply redundancy. The pressure regulation rate and pressure holding time are extracted from the dynamic pressure control vector; Based on the pressure regulation rate and the pressure holding time, the dynamic energy consumption characteristics of the pneumatic equipment are analyzed. Construct an energy consumption optimization model among the minimum target energy consumption value, the dynamic energy consumption characteristics, and the pressure supply redundancy; Based on the energy consumption optimization model, the energy loss rate of the pneumatic equipment under different pressure settings is calculated; Based on the energy loss rate, the real-time energy-saving optimization value corresponding to the pneumatic equipment is determined.

[0013] Optionally, the real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the graded control strategy set to generate the adaptive control parameter sequence of the dynamic pressure regulation command and the status warning command includes: Extract the instruction tracking error and dynamic characteristic parameters from the system response data; Identify risk trend characteristics and anomaly patterns in the risk evolution data; The control performance of the pneumatic device is evaluated based on the command tracking error and the dynamic characteristic parameters. Based on the evaluation results of the control performance, the controller parameter adjustment amount of the dynamic voltage regulation command is generated; Based on the risk trend characteristics and the anomaly patterns, the risk warning sensitivity of the pneumatic equipment is determined; Based on the aforementioned risk warning sensitivity, the warning threshold adjustment amount for the status warning instruction is defined; By combining the controller parameter adjustment amount and the early warning threshold adjustment amount, an adaptive control parameter sequence for the dynamic voltage regulation command and the status early warning command is generated.

[0014] Optionally, the process of collecting main pipeline pressure data, actuator displacement data, and ambient temperature data of the pneumatic equipment to construct an operating state matrix for the pneumatic equipment includes: Extract the time-domain and frequency-domain features of the main pipeline pressure data to generate the pressure state feature vector of the pneumatic equipment; Based on the displacement data of the actuator, analyze the motion trajectory of the actuator in the pneumatic device; Calculate the instantaneous velocity and acceleration of the actuator in the motion trajectory to form the displacement state feature vector of the pneumatic device; The temperature fluctuation rate and heat load index in the ambient temperature data are analyzed, and based on the temperature fluctuation rate and heat load index, the efficiency attenuation coefficient and heat compensation amount of the key components in the pneumatic equipment are determined. Based on the efficiency attenuation coefficient and the heat compensation amount, a temperature influence feature vector of the pneumatic equipment is generated; Identify the multiphysics coupling relationship among the pressure state feature vector, the displacement state feature vector, and the temperature effect feature vector; Based on the multi-physics coupling relationship, the pressure state feature vector, the displacement state feature vector, and the temperature influence feature vector are subjected to feature fusion processing to obtain a fused feature vector. The operating state matrix of the pneumatic equipment is constructed using the fused feature vectors.

[0015] Compared to the prior art, this invention, by collecting main pipeline pressure data, actuator displacement data, and ambient temperature data of the pneumatic equipment, constructs an operating state matrix for the pneumatic equipment. This clarifies the correlation between the dynamic changes in the operating parameters of the pneumatic equipment and the actual needs of the end-user equipment, as well as the influence mechanism of ambient temperature on the stability of the air source pressure. This allows for targeted optimization of the pneumatic equipment's air supply regulation logic. Furthermore, by generating dynamic pressure regulation commands for the pneumatic equipment based on the operating state matrix, this invention upgrades the traditional fixed-speed air supply mode of pneumatic equipment to a demand-driven dynamic control mode, precisely linking the pneumatic... The dynamic matching relationship between equipment operating status and terminal gas demand enhances the ability to control energy consumption and pressure stability during gas supply regulation. This embodiment of the invention generates status warning commands for the pneumatic equipment based on the operating status matrix, accurately linking the operating status of the pneumatic equipment with energy waste risks. This ensures timely risk identification and targeted pre-control measures, while providing advance risk guidance for the early execution of dynamic pressure regulation commands, enhancing the energy management capabilities and operational stability of the pneumatic equipment during low-load or intermittent working periods. Furthermore, this embodiment of the invention calculates... The real-time operational risk coefficient of the pneumatic equipment can establish a precise correlation between the dynamic pressure regulation process of the pneumatic equipment and the health status of key components and energy loss risks. It accurately identifies hidden risk areas such as overflow losses caused by pressure regulation deviations and additional energy consumption caused by component performance degradation, helping to improve the dynamic control capability and operational stability of the pneumatic equipment's energy utilization efficiency. In this embodiment of the invention, by determining the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector, the traditional extensive fixed pressure regulation mode of the pneumatic equipment can be upgraded to an adaptive dynamic precise pressure regulation mode, accurately linking the real-time air supply pressure demand of the pneumatic equipment with dynamic... The variation law of the pressure control vector enhances the ability of the adaptive dynamic pressure regulation process to control the real-time matching degree of air pressure and the dynamic control of energy consumption. Furthermore, the embodiments of the present invention establish a hierarchical control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient. This can accurately match the dynamic load demand and safe operating boundary of the pneumatic system, realize the transformation from constant pressure air supply mode to adaptive control of "safety-energy efficiency" synergistic optimization, effectively solve the overflow loss and low energy efficiency problems caused by ignoring the changes in terminal demand in the traditional fixed speed air supply method, and strengthen the real-time dynamic correlation control of the pneumatic system's energy supply and load demand.This invention, through real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the hierarchical control strategy set, generates an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command. This organically integrates the real-time operational feedback of the pneumatic equipment with pressure regulation and energy-saving requirements. The dynamically updated adaptive control parameter sequence enhances the accuracy of the pressure regulation command and the timeliness of the warning command, improving the energy efficiency and operational safety of the pneumatic equipment during adaptive dynamic pressure regulation. Finally, by integrating the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set, and the adaptive control parameter sequence, this invention outputs an adaptive dynamic pressure regulation energy-saving scheme for the pneumatic equipment. This achieves a dynamic balance between optimal energy consumption and controllable risk under different load scenarios, while avoiding insufficient energy-saving effects due to reliance on fixed pressure regulation parameters or operational safety hazards caused by a lack of risk linkage mechanisms, significantly improving the energy efficiency and operational stability of the pneumatic equipment. Therefore, this invention provides an adaptive dynamic pressure regulation energy-saving system and method for pneumatic equipment, which can improve the energy efficiency and operational stability of pneumatic equipment. Attached Figure Description

[0016] Figure 1 A functional block diagram of an adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment provided in an embodiment of the present invention; Figure 2 An adaptive dynamic pressure regulation energy-saving control flowchart of an adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment is provided as an embodiment of the present invention; Figure 3 This is a flowchart illustrating an adaptive dynamic pressure regulation energy-saving method based on pneumatic equipment, provided as an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0020] In practice, the server-side equipment deployed in the pneumatic-based adaptive dynamic pressure regulation energy-saving system may consist of one or more devices. This pneumatic-based adaptive dynamic pressure regulation energy-saving system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this pneumatic-based adaptive dynamic pressure regulation energy-saving system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this pneumatic-based adaptive dynamic pressure regulation energy-saving system can be understood as software deployed on a cloud node, used to provide pneumatic-based adaptive dynamic pressure regulation energy-saving services to various user terminals. Alternatively, this pneumatic-based adaptive dynamic pressure regulation energy-saving system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this pneumatic-based adaptive dynamic pressure regulation energy-saving system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide pneumatic-based adaptive dynamic pressure regulation energy-saving services to various user terminals.

[0021] In terms of implementation, the adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment and the user terminal are mutually adaptable. That is, if the adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment is implemented as a website, then the user terminal is implemented as a webpage; or if the adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0022] Reference Figure 1 The image shows an adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment provided in an embodiment of the present invention. The system includes: The adaptive dynamic pressure regulation energy-saving system 100 based on pneumatic equipment described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a server, server cluster, etc., based on adaptive dynamic pressure regulation energy saving of pneumatic equipment), or it can be developed into a website. Depending on the functions implemented, the adaptive dynamic pressure regulation energy-saving system 100 based on pneumatic equipment includes a status monitoring module 101, a pressure control module 102, a risk assessment module 103, a graded regulation module 104, a control feedback module 105, and a scheme output module 106.

[0023] In this embodiment of the invention, in the tracking of adaptive dynamic pressure regulation energy saving based on pneumatic equipment, each of the above modules can be implemented independently and called by other modules. Here, "called" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the adaptive dynamic pressure regulation energy saving system based on pneumatic equipment provided by this embodiment of the invention, the applicable scope of the adaptive dynamic pressure regulation energy saving architecture based on pneumatic equipment can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the adaptive dynamic pressure regulation energy saving system based on pneumatic equipment. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0024] The following describes, with reference to specific embodiments, each component and specific workflow of the adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment.

[0025] The status monitoring module 101 is used to collect main pipeline pressure data, actuator displacement data and ambient temperature data of the pneumatic equipment to construct the operating status matrix of the pneumatic equipment.

[0026] This invention collects main pipeline pressure data, actuator displacement data, and ambient temperature data of pneumatic equipment to construct an operating state matrix of the pneumatic equipment. This clarifies the correlation between the dynamic changes of the pneumatic equipment's operating parameters and the actual needs of the end-user equipment, as well as the influence mechanism of ambient temperature on the stability of the gas source pressure. This allows for targeted optimization of the pneumatic equipment's gas supply regulation logic.

[0027] The term "pneumatic equipment" refers to a general term for equipment that uses compressed air as a power source to achieve energy transfer and mechanical motion control. This includes, but is not limited to, pneumatic compressors (such as screw and piston air compressors), pneumatic control systems (including air source processing units and valve islands), and supporting pneumatic actuators (such as cylinders and pneumatic motors). Its core function is to provide compressed air that meets pressure and flow requirements to end-user equipment (such as pneumatic manipulators, pneumatic tools, and pneumatic components in automated production lines). The term "main pipeline pressure data" refers to the real-time pressure change data collected by pressure sensors on the core pipeline connecting the pneumatic compressor outlet, the air source processing unit, and the end-user equipment inlet. The term "actuator displacement data" refers to the displacement data of the actuators in the pneumatic equipment that directly perform mechanical motion (such as the piston rod of a cylinder, the output shaft of a pneumatic motor, etc.). The positional change information of the valve core (e.g., the valve core of the actuated valve) can be collected by displacement sensors (such as magnetostrictive displacement sensors and grating displacement sensors); the ambient temperature data refers to the temperature information of the external environment and the working environment of the core components in which the pneumatic equipment operates, including the ambient air temperature of the equipment installation area (e.g., the room temperature of the compressor room), the surface temperature of the pneumatic main pipeline, the inlet / outlet temperature of the compressed air cooling unit, and the working environment temperature of the actuator, etc., which can be collected by temperature sensors. The ambient temperature data includes real-time temperature values, temperature change trends, and temperature fluctuation ranges; the operating state matrix refers to a multi-dimensional, time-series data matrix model constructed based on the collected main pipeline pressure data, actuator displacement data, and ambient temperature data. Its core is to transform the discrete operating parameters of the pneumatic equipment into a structured mathematical representation.

[0028] As an embodiment of the present invention, the step of collecting main pipeline pressure data, actuator displacement data, and ambient temperature data of the pneumatic equipment to construct an operating state matrix of the pneumatic equipment includes: Extract the time-domain and frequency-domain features of the main pipeline pressure data to generate the pressure state feature vector of the pneumatic equipment; Based on the displacement data of the actuator, analyze the motion trajectory of the actuator in the pneumatic device; Calculate the instantaneous velocity and acceleration of the actuator in the motion trajectory to form the displacement state feature vector of the pneumatic device; The temperature fluctuation rate and heat load index in the ambient temperature data are analyzed, and based on the temperature fluctuation rate and heat load index, the efficiency attenuation coefficient and heat compensation amount of the key components in the pneumatic equipment are determined. Based on the efficiency attenuation coefficient and the heat compensation amount, a temperature influence feature vector of the pneumatic equipment is generated; Identify the multiphysics coupling relationship among the pressure state feature vector, the displacement state feature vector, and the temperature effect feature vector; Based on the multi-physics coupling relationship, the pressure state feature vector, the displacement state feature vector, and the temperature influence feature vector are subjected to feature fusion processing to obtain a fused feature vector. The operating state matrix of the pneumatic equipment is constructed using the fused feature vectors.

[0029] The time-domain features refer to the statistical characteristics and trends of the main pipeline pressure data over time. These can be obtained by calculating local statistics using a sliding window method (window size set to 1 second) combined with peak detection algorithms commonly used in time-domain analysis (such as threshold-based peak extraction, with the threshold set to the average ± 10%). The frequency-domain features refer to parameters reflecting the frequency distribution characteristics of the pressure signal after the time-domain data of the main pipeline pressure has been transformed to the frequency domain using mathematical methods such as Fourier transform. The pressure state feature vector is a one-dimensional array composed of one or more time-domain features and one or more frequency-domain features as vector elements, combined in a specific order. The actuator refers to the device in a pneumatic system that converts the pressure energy of compressed air into mechanical motion and performs work, such as a pneumatic cylinder, pneumatic motor, pneumatic gripper, or the drive part of a pneumatic valve. The motion trajectory refers to a continuous path formed by the position of the displacement component of the actuator (such as the piston rod of a cylinder) changing over time within its range of motion. For example, for a cylinder, its motion trajectory can be described as: The time is at 0mm (origin). Move until the target distance is 100mm (the endpoint). The time interval returns to 0mm; the instantaneous velocity refers to the rate of change of displacement of the actuator within a small time interval; the acceleration refers to the rate of change of velocity of the actuator within a small time interval; the displacement state feature vector is a one-dimensional array composed of feature parameters extracted from motion trajectory, instantaneous velocity, and instantaneous acceleration data, used to characterize the smoothness, response speed, positioning performance, and other dynamic characteristics of the actuator's motion; the temperature fluctuation rate refers to the absolute value or amplitude of the change in ambient temperature per unit time, for example, if the temperature rises from 25℃ to 30℃ in one minute, the fluctuation rate is 5℃ / min; the heat load index is a parameter index that quantifies the degree of heat load borne by key components based on ambient temperature data and the heat dissipation characteristics of key components of pneumatic equipment, and its calculation formula is: Where k is the heat dissipation coefficient, A is the heat dissipation area, and ΔT is the temperature difference corresponding to the component. For example, the inlet temperature of the cooling unit of a pneumatic compressor is 25℃, the outlet temperature is 35℃, the heat dissipation area of ​​the cooling unit is 0.5m², and the heat dissipation coefficient is... The heat load index can then be calculated as follows: The key components refer to parts in a pneumatic system whose performance is particularly sensitive to temperature changes, and whose efficiency degradation significantly affects the performance of the entire system, such as O-rings, cylinder barrels and pistons, pressure regulating valves, and pneumatic motors. The efficiency degradation coefficient is a proportional factor used to quantify the degree to which the working efficiency (such as friction efficiency and sealing efficiency) of a key component (such as a cylinder) decreases due to temperature effects. The value range is usually [0,1] (the closer the coefficient is to 1, the smaller the efficiency degradation; the closer it is to 0, the greater the efficiency degradation). The thermal compensation amount refers to the quantitative value of adjusting the working parameters (such as pressure and flow rate) of the key component to offset the negative impact of ambient temperature changes on the performance of the key components of the pneumatic equipment. For example, the input for a pneumatic pressure regulating valve at 25°C is 0. A 7MPa air source pressure can output a stable pressure of 0.6MPa. When the ambient temperature drops to 15℃, due to the increased friction of the valve core, the output pressure drops to 0.55MPa under the same input pressure. The input pressure needs to be increased by 0.05MPa to restore the output pressure to 0.6MPa. Therefore, 0.05MPa is the thermal compensation amount. The multi-physics coupling relationship refers to the interaction and mutual influence between the pressure field (corresponding to the main pipeline pressure data), the mechanical motion field (corresponding to the actuator displacement data), and the temperature field (corresponding to the ambient temperature data) of the pneumatic equipment. The fused feature vector refers to a single high-dimensional vector formed by integrating the pressure state feature vector, displacement state feature vector, and temperature influence feature vector based on the multi-physics coupling relationship and through a feature fusion algorithm.

[0030] Optionally, the efficiency decay coefficient of key components in the pneumatic equipment can be obtained through controlled variable experiments. For example, using ambient temperature, component operating time, and air source impurity content as variables, and the actual compression efficiency (for compressor cylinders), pressure control accuracy (for pressure regulating valves), and sealing performance index (for actuator seals) of key components as evaluation indicators, the significance of each variable's influence on efficiency decay can be determined through variance analysis of multiple sets of experimental data. By combining this with a linear regression model to fit the correlation equation between efficiency and variables, the efficiency decay coefficient under different operating conditions can be calculated. The multi-physics coupling relationship between the pressure state feature vector, the displacement state feature vector, and the temperature influence feature vector can be obtained using partial least squares methods. The Multiplicative Regression (PLSR) algorithm determines the pneumatic equipment's operating state matrix by considering the relationship between the main pipeline pressure fluctuation amplitude, actuator load, ambient temperature change rate, and feature vector parameters in historical tests. The operating state matrix of the pneumatic equipment can be constructed using sliding window technology. The core parameters are the fused feature vector (10-30 dimensions), data acquisition time interval (0.1s-1s), and matrix update frequency (1min-10min). The constraints are the temporal characteristics, data integrity (≥99%), and parameter correlation accuracy (correlation coefficient ≥0.9) in the historical data. The fused feature vector is arranged in a time series, and the matrix elements are dynamically updated through a sliding window. After data standardization, a structured operating state matrix is ​​constructed.

[0031] The pressure control module 102 is used to generate dynamic pressure regulation commands and status warning commands for the pneumatic equipment based on the operating status matrix, and to parse the pressure setpoint sequence and adjustment rate parameters in the dynamic pressure regulation commands to generate the dynamic pressure control vector of the pneumatic equipment.

[0032] This invention, through generating dynamic pressure regulation commands for pneumatic equipment based on the operating state matrix, can upgrade the traditional fixed-speed air supply mode of pneumatic equipment to a demand-driven dynamic control mode. It accurately correlates the operating state of the pneumatic equipment with the dynamic matching relationship of the end-user's air demand, strengthening the control over energy consumption and pressure stability during the air supply regulation process. The dynamic pressure regulation command refers to a control signal generated by the control system for real-time adjustment of the main or branch pressure setpoint of the pneumatic equipment. For example, if the operating state matrix shows that the actuator is in a pressure-maintaining standby state in the current cycle and does not require high pressure, the dynamic pressure regulation command will lower the main pressure from the conventional 0.6 MPa to 0.3 MPa to reduce the air compressor's energy consumption.

[0033] As an embodiment of the present invention, generating the dynamic pressure regulation command of the pneumatic equipment based on the operating state matrix includes: Based on the operating state matrix, the current operating mode features and state degradation trend features of the pneumatic equipment are extracted; Calculate the optimal target pressure value of the pneumatic equipment under the current operating mode characteristics; Identify the performance degradation factors and performance offset compensation amounts in the aforementioned state degradation trend characteristics; Based on the performance degradation factor and the performance offset compensation amount, the optimal target pressure value is compensated and corrected to generate the reference pressure command of the pneumatic equipment. Acquire real-time load disturbance data of the pneumatic equipment, and analyze the disturbance frequency and amplitude characteristics in the real-time load disturbance data; Based on the disturbance frequency and the amplitude characteristics, a feedforward pressure regulation compensation command for the pneumatic equipment is generated; The reference pressure command and the feedforward pressure adjustment compensation command are combined to generate the dynamic pressure adjustment command for the pneumatic equipment.

[0034] The current operating mode features refer to the set of core features extracted from the operating state matrix that characterize the working mode of the pneumatic equipment at the current moment. For example, when the equipment is in different modes such as high-speed propulsion, low-speed precise positioning, pressure holding, and return stroke, the current operating mode features of the pneumatic equipment may include parameters such as current motion speed, average load pressure, and valve core opening. The state degradation trend features refer to features extracted from the historical time series data of the operating state matrix that characterize the gradual deterioration of system performance (such as efficiency, sealing performance, and friction characteristics) over time, such as the monthly increasing trend of cylinder friction and the slow increasing trend of seal leakage rate. The optimal target pressure value refers to the optimal pressure setpoint calculated based on a preset pneumatic system dynamics model in the current operating mode to meet the power requirements of the actuator. The performance degradation factor refers to a proportional coefficient used to quantify the degree of degradation of key performance indicators (such as mechanical efficiency and sealing efficiency) of pneumatic equipment relative to its initial state. It can be determined using a performance degradation model, such as an exponential degradation model, and its calculation formula is as follows: ,in, (where k is the performance degradation factor, k is the degradation rate constant, and t is the running time); the performance offset compensation amount refers to the pressure adjustment value that needs to be added or reduced above the optimal target pressure value to offset the output deviation caused by equipment performance degradation. For example, if the theoretical optimal pressure is 0.6MPa to enable a new cylinder to achieve a stroke of 100mm, but currently 0.66MPa is required to achieve the same stroke due to degradation, then the performance offset compensation amount is +0.06MPa; the compensation correction processing refers to the real-time, online adjustment calculation process of the optimal target pressure value based on the performance degradation factor and the performance offset compensation amount to generate a reference pressure command that can adapt to the current health state of the equipment and is more in line with actual needs; the reference pressure command refers to the command generated after compensation correction processing to guide the pneumatic equipment to maintain the basic air supply pressure under stable operating conditions; the real-time load disturbance Data refers to the data of external forces or torques that are collected in real time by sensors, acting on the actuator and causing unexpected changes in its output force. For example, if a terminal pneumatic manipulator is originally gripping a 5kg workpiece (stable load) and suddenly switches to gripping a 10kg workpiece, causing the main pipeline pressure to drop instantly from 0.605MPa to 0.58MPa and the flow rate to increase from 0.5m³ / min to 0.7m³ / min, then the real-time data of the sudden pressure drop and flow rate increase is the real-time load disturbance data. The disturbance frequency refers to the frequency of the periodic change of the load disturbance signal in the real-time load disturbance data. The amplitude characteristic refers to the maximum deviation of the load disturbance signal from the stable value in the real-time load disturbance data. The feedforward pressure regulation compensation command refers to the pressure regulation command generated in advance based on the disturbance frequency and amplitude characteristics of the real-time load disturbance data to offset the impact of load disturbance on the air supply pressure.

[0035] Optionally, the optimal target pressure value of the pneumatic equipment under the current operating mode characteristics can be achieved by inverse calculation based on a preset pneumatic system dynamics model. For example, using the current operating mode characteristics (such as target speed and load mass) as input, the theoretical pressure value that meets the ideal power requirements is calculated through the system inverse dynamics model, and the model accuracy is verified by the goodness of fit (such as the error norm between the model output and the expected output). The feedforward pressure adjustment compensation command of the pneumatic equipment can be generated using a frequency domain feedforward control algorithm. For example, using the disturbance frequency and amplitude characteristics as input, the compensation pressure value is calculated through a preset disturbance-pressure transfer function model (such as a frequency domain compensation filter), and the compensation parameters are optimized based on the amplitude frequency characteristics and phase frequency characteristics to achieve optimal feedforward control for disturbance suppression.

[0036] Furthermore, by generating a status warning command for the pneumatic equipment based on the operating status matrix, this embodiment of the invention can accurately correlate the operating status of the pneumatic equipment with the risk of energy waste, ensuring the timeliness of risk identification and the pertinence of pre-control measures. Simultaneously, it provides advance risk guidance for the early execution of dynamic pressure regulation commands, enhancing the energy management capability and operational stability of the pneumatic equipment during low-load or intermittent working periods. The status warning command refers to a signal or message generated by the system to indicate that the current or future health status of the pneumatic equipment deviates from the normal range and to alert to potential fault risks. For example, if the operating status matrix shows a sustained and slow increase in the low-frequency energy component in the frequency domain characteristics of the pressure status, indicating a possible early minor leak in the system, the system will generate a low-severity warning command: "Warning code W101: System airtightness has decreased; planned inspection recommended."

[0037] As an embodiment of the present invention, generating the status warning command of the pneumatic equipment based on the operating status matrix includes: Construct a health assessment system for the aforementioned operational status matrix; Calculate the real-time deviation of each health assessment indicator in the health assessment system; Identify the abnormal evolution patterns and early signs of failure of the health assessment indicators; Based on the real-time deviation, the abnormal evolution mode, and the fault precursor characteristics, the status warning level and warning content of the pneumatic equipment are determined; By combining the status warning level and the warning content, a status warning command for the pneumatic equipment is generated.

[0038] The health assessment system refers to a multi-dimensional indicator framework constructed based on the operating principles and failure modes of pneumatic equipment to quantitatively assess the overall health status of the equipment. The health assessment indicators are parameters used to directly quantify a specific operating characteristic of the equipment within each dimension of the health assessment system, such as pressure fluctuation amplitude and pressure recovery time under the pressure stability dimension; displacement velocity deviation and action response delay under the actuator dimension; and compressor energy consumption deviation and valve sealing leakage under the component performance dimension. The real-time deviation refers to the difference between the real-time monitoring value of the health assessment indicator and the preset normal benchmark value (or normal...). The deviation from the normal range (the range of pressure fluctuations) can be positive or negative (positive deviation is above the reference value, negative deviation is below the reference value). For example, if the normal reference value for pressure fluctuation amplitude is 0.02 MPa and the real-time monitoring value is 0.05 MPa, then the real-time deviation = (0.05 - 0.02) / 0.02 × 100% = 150% (severe positive deviation); if the normal range for displacement velocity deviation is ±5 mm / s and the real-time monitoring value is -3 mm / s (actual velocity is below the rated velocity), then the real-time deviation = (-3 - 0) / 5 × 100% = -60% (moderate negative deviation, 0 is within the normal range). The abnormal evolution pattern refers to the regular trend of the real-time deviation of the health assessment index over time, which is used to reflect the evolution path of the index from normal to abnormal state; the fault precursor features refer to the characteristic signals that accompany the abnormal evolution of the health assessment index and are directly related to specific faults. For example, when the deviation of the pressure fluctuation amplitude of the main pipeline exceeds 100%, and is accompanied by the deviation of the pressure regulating valve response delay exceeding 50% and the valve body temperature rising by 5°C, the fault precursor features are: pressure fluctuation + valve response delay + valve body temperature rise, which can correspond to the valve core sticking fault of the pressure regulating valve. When the displacement speed deviation of the actuator exceeds 80%, accompanied by abnormal noise from the cylinder exhaust port and a filter pressure difference exceeding 0.1 MPa in the air source treatment unit, the precursor characteristics are: displacement deviation + abnormal exhaust noise + large filter pressure difference, which can correspond to a cylinder intake blockage fault. The status warning level refers to the warning severity level divided according to the real-time deviation of the health assessment indicators, the deterioration rate of the abnormal evolution mode, and the severity of the fault precursor characteristics. The warning content refers to the specific information generated based on the status warning level and fault precursor characteristics, including the warning object, the cause of the abnormality, the risk impact, and the handling suggestions.

[0039] Optionally, the abnormal evolution pattern of the health assessment index can be identified using a time-series pattern recognition algorithm, such as using the Dynamic Time Warping (DTW) algorithm to compare the real-time deviation time-series curve with the historical fault evolution curve to determine the matching evolution pattern; the fault precursor features of the health assessment index can be identified using a fault feature extraction algorithm, such as using a wavelet packet decomposition algorithm to decompose the pressure, temperature, and vibration signals under abnormal conditions and extract the fault feature frequencies; the status warning command of the pneumatic equipment can be generated using a fuzzy comprehensive evaluation-rule base matching combination algorithm, such as first using a fuzzy comprehensive evaluation algorithm to use the real-time deviation level, abnormal evolution speed, and fault precursor intensity as fuzzy input variables to calculate the comprehensive evaluation value and map it to the warning level; then calling a preset warning rule base to match the warning level with the fault precursor features, thereby determining the warning object, abnormal cause, risk impact, and handling suggestions, and finally generating a complete status warning command.

[0040] As another embodiment of the present invention, the health assessment system for constructing the operating state matrix includes: Identify the target evaluation component corresponding to the operating state matrix; The pressure stability data, displacement accuracy data, and temperature drift data related to the target evaluation component are extracted from the operating status matrix, and the real-time performance data of the target evaluation component is collected simultaneously. The pressure stability data is used to generate the pressure fluctuation entropy value of the target evaluation component; The positioning deviation index in the displacement accuracy data is parsed out, and the time-domain correlation analysis of the positioning deviation index and the pressure fluctuation entropy value is performed to output the time-domain correlation strength coefficient. Based on a preset scoring mapping function, the time-domain correlation strength coefficient is converted into a mechanical transmission health score of the target evaluation component; Collect historical equipment fault data corresponding to the target evaluation component, and combine the temperature drift data and the historical equipment fault data to calculate the thermal degradation coefficient corresponding to the target evaluation component; Based on the real-time performance data, the efficiency degradation rate of the target evaluation component is identified; Calculate the performance degradation risk index of the target evaluation component based on the thermal degradation coefficient and the efficiency decay rate; A health assessment system for the operating state matrix is ​​established by combining the mechanical transmission health score, the performance degradation risk index, and the pressure fluctuation entropy value.

[0041] The target evaluation component refers to a core physical component selected from pneumatic equipment whose operating state has a critical impact on the overall performance and reliability of the equipment, such as a pneumatic actuator cylinder, a precision pneumatic servo valve, a pressure regulating valve, and a guide device for a gripping mechanism. The pressure stability data refers to a time-series data set describing the pressure variation characteristics over time at the main pipeline of the pneumatic system or the inlet of the target evaluation component. The displacement accuracy data refers to the time-series data extracted from the operating state matrix, showing the deviation between the actual and theoretical displacements of the moving parts in the target evaluation component (limited to components containing moving parts, such as actuator cylinders and valve cores). The temperature drift data refers to the data extracted from the operating state matrix of the target evaluation component... The data includes the offset of the component's body temperature and the ambient temperature over time; the real-time performance data refers to parameter data directly reflecting the core functional performance of the component, collected in real time during its operation. For example, if the target evaluation component is a pneumatic compressor, the real-time performance data includes real-time exhaust volume, real-time specific power, real-time compression efficiency, and real-time dehumidification capacity; if the target evaluation component is an actuator cylinder, the data includes real-time actuation speed and real-time output force; the pressure fluctuation entropy value is a quantitative index calculated using an entropy algorithm based on pressure stability data, used to characterize the degree of disorder in pressure fluctuations. The larger the entropy value, the more irregular and unstable the pressure fluctuations; the smaller the entropy value, the more... The better the regularity and stability, the better; the positioning deviation index refers to the statistical index extracted from displacement accuracy data, which quantitatively characterizes the degree of deviation between the actual positioning position and the theoretical positioning position of the moving parts of the target evaluation component (such as cylinder piston rod, valve core); the time domain correlation strength coefficient refers to the quantitative coefficient obtained by analyzing the correlation between the positioning deviation index and the pressure fluctuation entropy value in the time dimension, and the value range is usually [-1,1]. The higher the coefficient, the stronger the synchronicity of the changes between the two, and the closer the correlation between the health status of mechanical transmission and pressure stability; the scoring mapping function refers to a predefined mathematical function or set of rules used to map the continuous value of the time domain correlation strength coefficient to a specific scoring interval. The mechanical transmission health score refers to a standardized score obtained by converting the time-domain correlation strength coefficient through a scoring mapping function. This score is used to quantitatively assess the health status of the mechanical transmission system of the target evaluation component (including mechanical transmission structures such as cylinder piston rods and valve transmission mechanisms). The score typically ranges from 0 to 100 (a higher score indicates better mechanical transmission health; a lower score suggests the transmission system is more likely to have wear, jamming, or other problems). The equipment historical fault data refers to a collection of information recorded during the past operation of the equipment containing the target evaluation component, including the fault occurrence time, fault type, fault cause, pre-fault temperature drift data, pre-fault performance parameter changes, fault handling measures, and fault impact.The thermal degradation coefficient is a quantitative index calculated based on the temperature drift data of the target evaluation component and historical equipment failure data. It characterizes the degree of performance degradation caused by temperature changes. The value range is typically [0,1] (the closer the coefficient is to 1, the less severe the thermal degradation and the less affected the component's performance is by temperature; the closer the coefficient is to 0, the more severe the thermal degradation and the greater the affected the component's performance is by temperature). For example, at a reference temperature of 25°C, the thermal degradation coefficient is 1.0; when the ambient temperature rises to 50°C, the coefficient may become 2.5, indicating that at this high temperature, the degradation rate of the component is... 2.5 times the baseline; the efficiency decay rate refers to the relative rate or proportion at which the current efficiency of the target evaluation component decreases relative to its rated efficiency or initial efficiency. For example, if the rated efficiency is 90% and the current efficiency is 81%, then the efficiency decay rate is (90%-81%) / 90%=10%; the performance degradation risk index is a quantitative indicator calculated by combining the thermal degradation coefficient and efficiency decay rate of the target evaluation component using a weighted summation algorithm. It is used to assess the degree of risk of severe performance degradation or failure of the component in the future. The higher the value, the greater the risk.

[0042] Optionally, the scoring mapping function can be constructed using a piecewise linear interpolation method based on expert experience rules. For example, using the time-domain correlation strength coefficient as input, and according to the preset health status threshold intervals (e.g., [0,0.3] for healthy, (0.3,0.7] for warning, and (0.7,1.0] for fault), the input values ​​of each interval are mapped to the corresponding scoring output intervals (e.g., [80,100], [60,80], [0,60]) through a linear transformation function, and finally a mechanical transmission health score is generated. The thermal degradation coefficient corresponding to the target evaluation component can be calculated using the Arrhenius acceleration model combined with the regression analysis method of equipment historical fault data. For example, using the temperature-failure time series in the equipment historical fault data as a sample, the activation energy parameter in the Arrhenius equation is fitted by the nonlinear least squares method to establish a relationship model between temperature and failure rate. Then, the currently monitored temperature drift data is substituted into the model to calculate the thermal acceleration factor relative to the standard reference temperature as the thermal degradation coefficient.

[0043] This invention, through parsing the pressure setpoint sequence and adjustment rate parameter in the dynamic pressure regulation command, generates a dynamic pressure control vector for the pneumatic equipment. This improves the matching accuracy between the pneumatic equipment's air supply pressure and the real-time demand of the terminal, enhances the dynamic response time of the pressure regulation process to load changes, and strengthens the dynamic pressure control vector's ability to accurately avoid overflow losses during air source delivery.

[0044] The pressure setpoint sequence refers to the time series of pressure target values ​​that are dynamically adjusted according to changes in the load of the terminal gas-using equipment, as parsed from the dynamic pressure regulation command. This sequence defines the trajectory and target of system pressure change that should be followed within a future time period. For example, a pressure setpoint sequence of [0.5, 0.55, 0.6, 0.6, 0.58] MPa within a control cycle indicates that the system pressure should first rise from 0.5 MPa to 0.6 MPa in the next few control cycles, maintain this position for a period of time, and then slightly decrease to 0.58 MPa. The adjustment rate parameter refers to the quantitative indicator used to constrain the speed of pressure adjustment of pneumatic equipment, parsed from the dynamic pressure regulation command. For example, in scenarios with a sudden increase in terminal load (such as a robotic arm switching from standby to gripping, with the load rate increasing from 10% to 8%), the adjustment rate parameter may be used to adjust the pressure. The adjustment rate parameter can be set to: a pressure rise rate of 0.02 MPa / s to ensure that the pressure can rise from 0.52 MPa to 0.605 MPa within (0.605-0.52) / 0.02=4.25s, meeting the pressure requirements of the gripping action; in scenarios with sudden load drops (such as switching from gripping to standby, with the load rate dropping from 80% to 10%), the parameter can be set to a pressure drop rate of 0.01 MPa / s to ensure that the pressure drops smoothly to 0.52 MPa within (0.605-0.52) / 0.01=8.5s, avoiding pipeline vibration and seal damage caused by sudden pressure drops; the dynamic pressure control vector refers to a structured, multi-dimensional vector that integrates the pressure setpoint sequence and the adjustment rate parameter, which can directly drive pneumatic equipment to perform pressure control.

[0045] Optionally, the dynamic pressure control vector of the pneumatic equipment can be generated by a model predictive control (MPC) algorithm. For example, with system pressure error, actuator tracking error and energy consumption as optimization objectives, and the pressure setpoint in the future finite time domain as the decision variable, the optimal pressure setpoint sequence and its corresponding adjustment rate constraint can be solved simultaneously by solving a constrained quadratic programming problem, and then integrated and encapsulated into a dynamic pressure control vector.

[0046] The risk determination module 103 is used to extract the performance degradation rate, sealing performance index and vibration abnormal frequency band from the status warning command to generate the status monitoring vector of the pneumatic equipment, and calculate the real-time operating risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the status monitoring vector.

[0047] This invention extracts the performance degradation rate, sealing performance index, and abnormal vibration frequency band from the status warning command to generate a status monitoring vector for the pneumatic equipment. This vector can accurately match the correlation between the performance degradation status of key components of the pneumatic equipment and energy consumption. By leveraging the multi-parameter collaborative monitoring capability of the vector, it adapts the dynamic mapping relationship between the component status and energy consumption of the pneumatic system, providing accurate status basis for subsequent equipment operation and maintenance strategy adjustments and air supply parameter optimization.

[0048] The performance degradation rate refers to a derivative or proportional parameter extracted from condition warning commands and related historical data, used to quantify how quickly the working efficiency or output capacity of pneumatic equipment or its core components deteriorates over time. For example, if the cylinder efficiency decreases from 1% per month to 2% per month, its performance degradation rate doubles. The sealing performance index refers to a quantitative parameter extracted from condition warning commands, used to comprehensively evaluate the leakage degree of the pneumatic equipment's sealing system (such as cylinder seals, valve cores, and pipe joints). This includes internal leakage rate (the pressure drop per unit time due to poor sealing, such as kPa / min), abnormal flow increment (the compensation flow required to maintain constant pressure), and analysis based on sound or vibration signals. The resulting leakage index, etc., wherein the internal leakage rate can be calculated by monitoring the pressure drop rate during the pressure holding stage; the leakage index can be obtained by extracting the characteristic frequency band energy value in the leakage ultrasonic signal using spectral analysis; the abnormal vibration frequency band refers to a continuous frequency range determined by analyzing the vibration signal spectrum, whose energy amplitude is significantly higher than the normal baseline and is related to a specific failure mode. For example, rolling bearing failure will generate abnormal frequency bands at its characteristic frequencies (such as outer ring failure frequency, inner ring failure frequency, and rolling element passage frequency) and their harmonics; the condition monitoring vector refers to a one-dimensional array composed of multiple dimensions of condition characteristic parameters such as performance degradation rate, sealing performance index, and abnormal vibration frequency band in a specific order.

[0049] Optionally, the condition monitoring vector of the pneumatic equipment can be generated by multi-source feature fusion and dimensionality reduction algorithms. For example, using performance degradation rate, sealing performance index and abnormal vibration frequency band as input features, the high-dimensional original features are extracted and compressed by principal component analysis (PCA) or autoencoder to eliminate redundancy between features, and finally a low-dimensional, linearly independent comprehensive condition monitoring vector is formed.

[0050] Furthermore, this embodiment of the invention calculates the real-time operational risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the state monitoring vector. This allows for a precise correlation mapping between the dynamic pressure regulation process of the pneumatic equipment and the health status of key components and energy loss risks. It accurately identifies hidden risk areas such as overflow losses caused by pressure regulation deviations and additional energy consumption caused by component performance degradation. This helps improve the dynamic control capability and operational stability of the pneumatic equipment's energy utilization efficiency. The real-time operational risk coefficient is a composite evaluation index generated based on the dynamic pressure control vector and the state monitoring vector, used to quantify the probability and potential severity of performance failure or functional malfunction of the pneumatic equipment in the next time period.

[0051] As an embodiment of the present invention, the step of calculating the real-time operating risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the state monitoring vector includes: Identify the pressure time-domain trajectory parameters and the adjustment aggression index in the dynamic pressure control vector; Extract the component safety margin and system damping characteristic parameters from the state monitoring vector; Based on the pressure time-domain trajectory parameters and the component safety margin, calculate the overload risk probability of the pneumatic equipment; Based on the adjustment aggression index and the system damping characteristic parameters, the probability of instability risk of the pneumatic equipment is calculated; Real-time monitoring of the energy efficiency coefficient and frequency of abnormal events of the pneumatic equipment; The real-time operating risk coefficient of the pneumatic equipment is calculated by combining the overload risk probability, the instability risk probability, the energy efficiency coefficient, and the frequency of abnormal events.

[0052] The pressure time-domain trajectory parameters refer to a set of characteristic parameters extracted from the dynamic pressure control vector that reflect the change of main pipeline pressure of pneumatic equipment over time, including pressure rise rate (MPa / s), pressure overshoot (%), pressure fluctuation amplitude (MPa), and pressure holding time. The regulation aggression index is an indicator derived from the regulation rate parameters of the dynamic pressure control vector, quantifying the aggression of pressure regulation actions. Its value range is typically [0,1] (the closer the index is to 1, the more aggressive the regulation action, such as rapid pressure increase / decrease; the closer it is to 0, the smoother the regulation). Its calculation formula is: Aggression Index = ×(Standardized rate of change)+ ×(standardized acceleration of change), where , The weighting coefficient is used for the component safety margin. The component safety margin refers to the difference or ratio between the actual load currently borne by the pneumatic component and its designed maximum load-bearing capacity. The calculation formula is: (Actual maximum bearing pressure - Safety threshold) / Safety threshold × 100%. For example, if the actual maximum bearing pressure of the cylinder is 21 MPa and the set safety threshold is 15 MPa, then its safety margin is (21-15) / 21 × 100% = 28.6%. The system damping characteristic parameters refer to physical parameters describing the pneumatic system's ability to suppress pressure fluctuations and mechanical vibrations, including damping coefficient, decay time constant, and resonance frequency suppression capability. The overload risk probability refers to the probability that, under the current pressure time-domain trajectory, the load borne by the pneumatic component exceeds its safety margin allowable range. The statistical probability, with a value range of [0,1], indicates that the higher the value, the higher the probability of overload failure; the instability risk probability refers to the probability that, under the current adjustment aggressiveness, the pressure or motion will produce divergent oscillations due to insufficient system damping; the energy efficiency coefficient refers to the efficiency ratio of the pneumatic equipment in converting input electrical energy into effective mechanical work, and its calculation formula is: energy efficiency coefficient = output mechanical power / input electrical power; the abnormal event frequency refers to the number of abnormal working events (such as pressure drop, position deviation, overcurrent protection) that occur in the pneumatic equipment per unit time, and its calculation formula is: abnormal event frequency = number of events / time window, such as 12 abnormal pressure fluctuation events occurring in the past 1 hour, the occurrence frequency is 0.2 times / minute.

[0053] In another embodiment of the present invention, the real-time operating risk coefficient of the pneumatic equipment is calculated using the following formula:

[0054] in, Indicates the real-time operational risk coefficient. Indicates the probability of overload risk. Indicates the probability of instability risk. Indicates the energy efficiency coefficient. This indicates the rated energy efficiency reference value. Indicates the frequency of abnormal events. Indicates the reference threshold for abnormal frequency. Indicates the continuous operating time of pneumatic equipment. Indicates the performance half-life of pneumatic equipment. This represents the time decay coefficient.

[0055] In detail, the energy efficiency coefficient ( This coefficient is used to quantify the efficiency level of converting actual energy supply from pneumatic equipment into effective energy consumption at the terminal. It is assigned a specific value by analyzing the matching relationship between the energy input, air supply energy, and effective energy consumption of the terminal load. For example, in precision pneumatic machining scenarios, energy utilization efficiency is extremely important. If the effective energy consumption of the terminal machining equipment reaches 90% after the electrical energy input is converted into air supply energy, the energy efficiency coefficient can be set to 0.9 to strictly ensure efficient energy utilization. In contrast, in intermittent operation scenarios of ordinary pneumatic tools, a certain degree of energy loss is permissible. If the effective energy consumption is 60%, the coefficient can be set to 0.6, balancing cost and energy consumption while meeting operational requirements. The performance half-life of the pneumatic equipment (…) The time decay coefficient (TDC) represents the time required for the performance of pneumatic equipment to degrade to 50% of its initial value. This TDC can be obtained using a parameter fitting algorithm based on accelerated life testing. For example, in a laboratory environment, accelerated life testing is performed on pneumatic equipment to simulate the performance degradation process under high load and high frequency operation. A curve fitting tool (such as Origin's curve fitting toolbox) is used to fit the test data to obtain a curve showing the performance degradation over time, thereby determining the performance half-life. It can be used to dynamically adjust the running time of pneumatic equipment. The impact of time decay coefficient on the real-time operational risk factor (FI) can be assigned a specific value by analyzing the historical performance degradation rate of key components (such as seals and cylinders), failure probability data under different operating lengths, and the current maintenance cycle of the equipment. For example, for pneumatic compressors operating in a high-load continuous mode, the performance degradation of their components accumulates rapidly with operating time, and the sensitivity of failure risk to time is high. In this case, the time decay coefficient can be set to 0.3 to significantly enhance the amplification effect of operating time on the risk factor and ensure timely warning of end-of-life risks. On the other hand, for pneumatic manipulators operating intermittently under low load, the performance degradation of components is slow, and the sensitivity of risk to time is low. In this case, the time decay coefficient can be set to 0.1 to weaken the impact of operating time, avoid over-evaluation leading to wasted maintenance resources, and balance the accuracy of risk warning with equipment utilization efficiency. Optionally, the time decay coefficient ( The results are obtained through a nonlinear regression algorithm based on historical data of equipment performance degradation.

[0056] It should be noted that the above formula introduces the performance half-life ( ) and runtime ( The coupling term of ) through the time decay coefficient ( ) The amplifying effect of component aging on risk (e.g., compressors) =8000h, running =6000h, =0.2, then the coupling term 1 + 0.2 × (6000 / 8000) = 1.15, increasing the risk coefficient by 15%, which can achieve an integrated assessment of short-term operational risk and long-term lifespan risk; by using the energy efficiency coefficient ( Risk calculations are incorporated through an exponential function, when the actual energy efficiency ( Below the benchmark energy efficiency ( When the equipment's rated energy efficiency is 0.85, the index term will decay rapidly to amplify the risk (e.g., ...). =0.4、 =0.85, exp(−0.4 / 0.85)≈0.62), making the risk calculation results more consistent with the actual energy waste; by adjusting the overload risk weight ( ), instability risk weight ( ), abnormal event weights ( Performing Euclidean norm normalization can ensure the rationality of weight allocation under different working conditions (such as in high-precision machining scenarios, where...). =0.6、 =0.5、 =0.3, after normalization, the coefficient ≈0.84 (which avoids risk amplification and distortion caused by weighting), thus achieving condition-appropriate risk assessment.

[0057] The graded control module 104 is used to determine the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector, and to establish a graded control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient.

[0058] This invention, through determining the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector, upgrades the traditional coarse fixed pressure regulation mode of the pneumatic equipment to an adaptive dynamic precision pressure regulation mode. It accurately correlates the real-time air supply pressure demand of the pneumatic equipment with the changing patterns of the dynamic pressure control vector, strengthening the adaptive dynamic pressure regulation process's control over real-time air pressure matching and dynamic energy consumption. The real-time energy-saving optimization value refers to the energy allocation parameter calculated in real-time based on the dynamic pressure control vector, used to minimize system energy consumption while ensuring the motion performance and stability of the pneumatic equipment's actuator. For example, when the equipment is in a brief standby state, the real-time energy-saving optimization value may instruct the system to dynamically reduce the main pipeline pressure from 0.6 MPa to 0.3 MPa, thereby significantly reducing the air compressor load and leakage losses.

[0059] As an embodiment of the present invention, determining the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector includes: Based on the dynamic pressure control vector, the real-time load demand and pressure supply redundancy of the pneumatic equipment are identified. Calculate the minimum target energy consumption of the pneumatic equipment based on the real-time load demand and the pressure supply redundancy. The pressure regulation rate and pressure holding time are extracted from the dynamic pressure control vector; Based on the pressure regulation rate and the pressure holding time, the dynamic energy consumption characteristics of the pneumatic equipment are analyzed. Construct an energy consumption optimization model among the minimum target energy consumption value, the dynamic energy consumption characteristics, and the pressure supply redundancy; Based on the energy consumption optimization model, the energy loss rate of the pneumatic equipment under different pressure settings is calculated; Based on the energy loss rate, the real-time energy-saving optimization value corresponding to the pneumatic equipment is determined.

[0060] Wherein, the real-time load requirement refers to the instantaneous output power or force required by the pneumatic equipment actuator to complete the current specific process action; the pressure supply redundancy refers to the difference between the current actual pressure supply value of the pneumatic equipment and the minimum pressure supply value required to meet the real-time load requirement; the minimum target energy consumption value refers to the minimum energy consumption theoretically achievable during equipment operation, under the premise of meeting the real-time load requirement of the pneumatic equipment, ensuring operational performance (such as action speed and positioning accuracy), and not damaging the service life of the equipment; the pressure regulation rate refers to the average rate of change of the pressure value from the current value to the target value when the pressure supply system of the pneumatic equipment responds to load changes or pressure regulation commands, for example, a certain pneumatic stamping... When switching workpiece specifications, the working pressure needs to be adjusted from 0.5MPa to 0.3MPa. The pressure sensor records that it took 0.8s for the pressure to drop from 0.5MPa to 0.3MPa. Therefore, the pressure adjustment rate = (0.5MPa - 0.3MPa) ÷ 0.8s = 0.25MPa / s. The pressure holding time refers to the duration for which the pneumatic equipment maintains the pressure value within the target pressure range (usually allowing ±5% deviation) after pressure adjustment to ensure stable operation (e.g., pneumatic grippers maintain clamping force, cylinders maintain extended state to complete processing). The dynamic energy consumption characteristic refers to the energy consumption of the pneumatic equipment during dynamic operation processes such as pressure adjustment, pressure holding, and load changes. The energy consumption optimization model refers to a mathematical programming model that takes minimizing the total energy consumption of the system as the objective function, real-time load demand, minimum target energy consumption value, dynamic energy consumption characteristics, and pressure supply redundancy as inputs, and pressure setpoint as the decision variable. The different pressure settings refer to a series of discrete or continuous target pressure values ​​set during the adaptive dynamic pressure regulation process of pneumatic equipment, based on the energy consumption optimization model, to verify the energy consumption effect of different pressure supply schemes. For example, the minimum pressure supply value for a certain pneumatic handling equipment to meet real-time load demand is 0.3 MPa, and the equipment's safe pressure range is 0.1 MPa to 0.8 MPa. In the energy consumption optimization model, different pressure settings are set, including 0.3MPa, 0.35MPa, 0.4MPa, 0.45MPa, and 0.5MPa (intervals of 0.05MPa). By calculating the energy loss rate under each pressure setting, the optimal pressure setting with the lowest energy consumption can be determined (e.g., if the calculation shows that the energy loss rate is lowest at 0.35MPa, then this value is the optimal pressure setting). The energy loss rate refers to the ratio of the actual energy consumption of the pneumatic equipment per unit time to the minimum target energy consumption value under a certain pressure setting. For example, assuming the input power is 800W under a certain pressure setting and the minimum target energy consumption value is 754W, then the energy loss rate is (800-754) / 800≈5.75%.

[0061] Optionally, the energy consumption optimization model among the minimum target energy consumption value, the dynamic energy consumption characteristics, and the pressure supply redundancy can be constructed using a constrained multi-objective optimization algorithm. For example, with minimizing the total system energy consumption as the objective function, and the upper limit of the pressure supply redundancy and the dynamic energy consumption characteristics (the relationship between the adjustment rate and energy consumption) as constraints, a linear weighted method is used to transform multiple objectives into a single-objective optimization problem, which is then solved using gradient descent or a genetic algorithm. The minimum target energy consumption value of the pneumatic equipment can be obtained using an energy consumption benchmark calculation algorithm. For example, based on real-time load demand (F) and equipment operating parameters (such as operating speed v and stroke s), the minimum mechanical work required for the equipment to complete a single operation is first calculated using the formula W=F×s, and then combined with the equipment transmission efficiency (…). ), power source efficiency (such as motor efficiency) air compressor efficiency ), using formula =W÷( × × )( It is calculated based on the minimum target energy consumption value.

[0062] Furthermore, this embodiment of the invention establishes a hierarchical control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient. This enables precise matching of the dynamic load demand and safe operating boundary of the pneumatic system, achieving an adaptive control transformation from a constant-pressure air supply mode to a "safety-energy efficiency" synergistic optimization. This effectively solves the overflow loss and low energy efficiency problems caused by ignoring changes in terminal demand in the traditional fixed-speed air supply method, and strengthens the real-time dynamic correlation control between the energy supply and load demand of the pneumatic system. The hierarchical control strategy set refers to a predefined set of multi-level, multi-mode pneumatic equipment control commands based on a quantitative matching relationship between the real-time energy-saving optimization value and the real-time operating risk coefficient. Its core is to automatically select and execute the control strategy most suitable for the current operating condition based on the real-time calculated energy-saving optimization value and risk coefficient, through preset rules.

[0063] As an embodiment of the present invention, the step of establishing a graded control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient includes: Based on the real-time energy-saving optimization value and the real-time operating risk coefficient, a two-dimensional decision space for the pneumatic equipment is constructed. The two-dimensional decision space is divided into a safe operation zone, an energy-saving optimization zone, a risk warning zone, and an emergency intervention zone. Using the real-time energy-saving optimization value and the real-time operation risk coefficient, define the boundary thresholds corresponding to the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the boundary thresholds, differentiated control rules are set for the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the differentiated control rules, dynamic control instructions are generated for the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone; Based on the two-dimensional decision space, the differentiated control rules, and the dynamic control commands, a hierarchical control strategy set for the pneumatic equipment is established.

[0064] The two-dimensional decision space refers to a Cartesian coordinate plane with the real-time operational risk coefficient as the X-axis and the real-time energy-saving optimization value as the Y-axis, used to map the complex operating state of pneumatic equipment into visualized two-dimensional coordinate points. The safe operation zone refers to the area in the two-dimensional decision space with a low operational risk coefficient and a high energy-saving optimization value. Within this zone, the system operates stably and reliably, with sufficient energy-saving adjustment space. For example, it is defined as a zone with a risk coefficient ≤ 0.3 and an energy-saving optimization value ≥ 0.6, where proactive energy-saving strategies can be implemented. The optimized energy-saving zone refers to the area in the two-dimensional decision space with a low operational risk coefficient but a moderate energy-saving optimization value. Within this zone, the system operates stably but the energy-saving space is limited, requiring a balance between energy saving and performance. For example, a zone with a risk coefficient ≤ 0.3 and 0.3 ≤ energy-saving optimization value < 0.6 requires robust energy-saving measures. The risk warning zone refers to the area in the two-dimensional decision space with a high operational risk coefficient and arbitrary energy-saving optimization values ​​(high, medium, or low). The system operation carries potential risks, and safety and stability should be the primary objectives. For example, in areas with a risk coefficient > 0.7, a conservative operating strategy should be adopted regardless of the energy-saving optimization value. The emergency intervention zone refers to areas in the two-dimensional decision space with extremely high operational risk coefficients. In these areas, the system faces serious safety threats and requires immediate emergency protection measures. For example, in areas with a risk coefficient > 0.9, the highest level alarm and emergency handling should be triggered. The boundary threshold refers to the critical values ​​used to define the range of the safe operation zone, energy-saving optimization zone, risk warning zone, and emergency intervention zone in the two-dimensional decision space. The differentiated control rules refer to the targeted control logic and operating standards formulated based on the characteristics of different areas (safe operation zone, energy-saving optimization zone, etc.) in the two-dimensional decision space. The dynamic control instructions refer to specific executable control commands generated based on real-time energy-saving optimization values, real-time operational risk coefficients, and the differentiated control rules corresponding to the area, including operating parameters such as pressure setpoints, adjustment rates, and equipment start / stop.

[0065] Optionally, the two-dimensional decision space can be divided into a safe operation zone, an optimized energy-saving zone, a risk warning zone, and an emergency intervention zone by combining clustering analysis based on historical operating data with expert experience rules. For example, using the risk coefficient and energy-saving optimization value in historical operating data as sample points, the K-means clustering algorithm can be used to identify the natural clustering areas of the data points. Then, the clustering boundaries can be corrected by combining expert experience to finally determine the geometric boundaries of each zone. The dynamic control commands for the safe operation zone, the optimized energy-saving zone, the risk warning zone, and the emergency intervention zone can be generated by a regional adaptive optimization algorithm based on model predictive control (MPC). For example, different objective functions and constraints can be used in different zones: minimizing energy consumption in the safe operation zone, optimizing energy efficiency in the optimized energy-saving zone, stabilizing the system in the risk warning zone, and protecting safety in the emergency intervention zone. By solving the region-specific optimization problem, the most suitable control command sequence for the current zone state can be generated in real time.

[0066] The control feedback module 105 is used to collect system response data and risk evolution data of the pneumatic equipment after applying the graded control strategy set in real time, so as to generate an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command.

[0067] This invention, through real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the hierarchical control strategy set, generates an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command. This organically integrates the real-time operation feedback of the pneumatic equipment with the pressure regulation and energy-saving requirements. The dynamically updated adaptive control parameter sequence enhances the accuracy of the pressure regulation command and the timeliness of the warning command, thereby improving the energy utilization efficiency and operational safety of the pneumatic equipment during the adaptive dynamic pressure regulation process.

[0068] The system response data refers to the real-time measurement dataset reflecting the dynamic characteristics and performance output of the pneumatic equipment after executing the graded control strategy set, including data such as the tracking error of the pressure setpoint, the response time of the actuator displacement, the recovery rate of the main pipeline pressure, the amplitude of flow fluctuation, and the overshoot of the cylinder movement speed; the risk evolution data refers to the characteristic data sequence characterizing the trend of the system's operational risk state over time after executing the graded control strategy set, including characteristic data such as the slope of the risk coefficient change, the increment of the frequency of abnormal events, the acceleration of the performance degradation rate, and the decay trend of the sealing performance index; the adaptive control parameter sequence refers to a set of parameters dynamically adjusted and generated based on the system response data and risk evolution data, used to optimize the performance of subsequent dynamic pressure regulation commands and status warning commands.

[0069] As an embodiment of the present invention, the real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the graded control strategy set to generate the adaptive control parameter sequence of the dynamic pressure regulation command and the status warning command includes: Extract the instruction tracking error and dynamic characteristic parameters from the system response data; Identify risk trend characteristics and anomaly patterns in the risk evolution data; The control performance of the pneumatic device is evaluated based on the command tracking error and the dynamic characteristic parameters. Based on the evaluation results of the control performance, the controller parameter adjustment amount of the dynamic voltage regulation command is generated; Based on the risk trend characteristics and the anomaly patterns, the risk warning sensitivity of the pneumatic equipment is determined; Based on the aforementioned risk warning sensitivity, the warning threshold adjustment amount for the status warning instruction is defined; By combining the controller parameter adjustment amount and the early warning threshold adjustment amount, an adaptive control parameter sequence for the dynamic voltage regulation command and the status early warning command is generated.

[0070] The command tracking error refers to the deviation between the actual output physical quantities such as pressure and rotational speed of the pneumatic equipment and the preset target value of the command during the execution of a dynamic pressure regulation command. For example, if the preset target pressure of a pneumatic equipment's dynamic pressure regulation command is 0.4 MPa, and the actual output pressure of the equipment is collected by the pressure sensor 10 seconds after the command is executed, then the command tracking error at this time = |0.38 MPa - 0.4 MPa| = 0.02 MPa. The dynamic characteristic parameter refers to the characteristic parameter of the pneumatic equipment outputting parameters as a function of time when responding to a dynamic pressure regulation command. The data includes response time, overshoot, and attenuation ratio; the risk trend characteristics refer to the regular characteristics extracted from the risk evolution data that reflect the changes in the operating risk of pneumatic equipment over time, including the rate of change, direction of change (increasing / decreasing / stable), and fluctuation frequency of the risk coefficient; the abnormal patterns refer to abnormal data patterns in the risk evolution data that deviate from the normal operating pattern, including sudden risk jumps (such as a sudden increase in the risk coefficient in a short period of time), periodic abnormal fluctuations (such as a risk peak occurring every 30 minutes), and persistently abnormally high levels (such as a risk coefficient that is consistently higher than the normal range). The control performance refers to the ability of pneumatic equipment to accurately track commands and maintain stable output parameters when executing dynamic pressure regulation commands. The evaluation result refers to the quantitative judgment result of the pneumatic equipment's control performance obtained through a control performance evaluation algorithm, including the overall performance level (e.g., excellent / good / medium / poor), the compliance status of each individual indicator (e.g., tracking error meets standards, overshoot does not meet standards), and performance weakness analysis (e.g., excessively long response time leading to overall performance degradation). The controller parameter adjustment amount refers to the adjustment of the controller's core parameters based on the control performance evaluation result to optimize the control effect of dynamic pressure regulation commands. The specific numerical value or range to be adjusted; the risk warning sensitivity refers to the sensitivity of the pneumatic equipment's status warning system to risk changes (including small fluctuations in risk coefficients, risk trend reversals, and the emergence of potential abnormal patterns) in risk evolution data; the warning threshold adjustment amount refers to the specific numerical value of adjusting the original warning threshold (such as risk alert threshold, emergency intervention threshold) based on risk trend characteristics and abnormal pattern analysis results to improve the accuracy of status warning instructions, including the threshold increase (e.g., from 0.3 to 0.35) or decrease (e.g., from 0.6 to 0.55).

[0071] Optionally, the control performance of the pneumatic equipment can be evaluated using a weighted comprehensive evaluation function based on command tracking error (such as integral of absolute error, IAE) and dynamic characteristic parameters (such as rise time and overshoot). The risk warning sensitivity of the pneumatic equipment can be identified using a fuzzy logic algorithm. For example, using a fuzzy logic system, the trend slope and anomaly severity are taken as input, and reasoning is performed through a preset fuzzy rule base (IF-THEN) to output a sensitivity level that changes continuously from low to high, thereby dynamically defining the urgency of the warning based on the real-time evolution of the risk. The adaptive control of the dynamic pressure regulation command and the status warning command... The control parameter sequence can be generated based on a parameter collaborative optimization algorithm that integrates multi-source data. For example, different optimization objectives and parameter constraints can be adopted for different instruction types: in the optimization of dynamic voltage regulation instruction parameters, the objective is to maximize the energy consumption reduction rate, and the constraint is that the control performance score is ≥0.7; in the optimization of status warning instruction parameters, the objective is to maximize the warning accuracy, and the constraint is that the risk warning sensitivity index is ≥0.8. By solving the optimization problem of "maximizing the objective function + satisfying the constraint", the collaborative combination of controller parameter adjustment and warning threshold adjustment is obtained, and an adaptive control parameter sequence that adapts to the current dual needs of equipment control and warning is generated in real time.

[0072] The output module 106 is used to integrate the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set and the adaptive control parameter sequence, and output the adaptive dynamic pressure regulation energy-saving scheme of the pneumatic equipment.

[0073] This invention integrates the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set, and the adaptive control parameter sequence to output an adaptive dynamic pressure regulation energy-saving scheme for pneumatic equipment. This scheme can achieve a dynamic balance between optimal energy consumption and controllable risk for pneumatic equipment under different load scenarios. At the same time, it avoids insufficient energy-saving effect due to reliance on fixed pressure regulation parameters or operational safety hazards caused by the lack of risk linkage mechanism, thereby significantly improving the energy utilization efficiency and operational stability of pneumatic equipment.

[0074] The adaptive dynamic pressure regulation energy-saving scheme refers to a comprehensive pressure regulation energy-saving execution scheme constructed based on pneumatic equipment operating data (such as load demand, energy consumption data, and risk parameters) by integrating dynamic pressure regulation commands, status warning commands, hierarchical control strategy sets, and adaptive control parameter sequences. Its core is to extract dynamic pressure regulation command templates, status warning command types, hierarchical control strategies, and initial adaptive control parameters that match the current operating condition feature vector from the pneumatic equipment's real-time operating database. The scheme integration module then structures these elements, clarifying command triggering conditions, strategy call priorities, and parameter association rules. Based on this, an initial energy-saving scheme is generated, and operating condition adaptation data is obtained through pneumatic equipment trial operation feedback to drive real-time scheme updates.

[0075] For example, 40 production cycles (each cycle being 8 hours) on an automotive welding production line were selected. A pneumatic gripper driven by a Festo cylinder (model DSBC-32-100-PPVA-N3) was used as the controlled object. The operation of the device was recorded for 20 production cycles before and after using the adaptive dynamic pressure regulation energy-saving scheme described in this invention. In terms of hardware configuration, an SMC pressure sensor (model PSE530), a Siemens S7-1200 PLC as the edge controller, and a Festo solenoid valve (model VUVG-L14-MT-M5-1P3) as the actuator were used. The quantitative indicators to be observed during the operation included average energy consumption, pressure stability accuracy, and the number of false alarms. The comparative data are as follows:

[0076] Conclusion: The table above clearly shows that the adaptive dynamic pressure regulation energy-saving scheme described in this invention is significantly better than before in terms of reducing average energy consumption, improving pressure stability accuracy, and reducing the number of false alarms. This verifies the effectiveness of the method of this invention in improving the energy utilization efficiency and operational stability of pneumatic equipment.

[0077] To gain a more intuitive understanding of the application and logical presentation of the adaptive dynamic pressure regulation energy-saving scheme in the energy-saving control of pneumatic equipment, please refer to... Figure 2The diagram illustrates an adaptive dynamic pressure regulation energy-saving control flowchart of an adaptive dynamic pressure regulation energy-saving system based on pneumatic equipment, provided by an embodiment of the present invention. The flowchart clearly demonstrates the correlation between each stage of the energy-saving scheme generation within the overall control system. The data acquisition layer is the key data source for the subsequent generation of dynamic pressure regulation commands and status warning commands. The intelligent decision-making layer intuitively presents the generation logic of dynamic pressure control vectors and status monitoring vectors, providing a clear data flow path for calculating real-time operational risk coefficients and determining real-time energy-saving optimization values. The optimization execution layer further demonstrates the generation correlation between the hierarchical control strategy set and the adaptive control parameter sequence. It should be noted that the linear representation of each stage in the diagram is essentially a concise representation of the core correlation of "data input - logic processing - scheme output" energy-saving control for pneumatic equipment. In actual scenarios, the complexity of coordination between each stage is far greater than what is shown in the diagram. This flowchart serves only as an intuitive reference for the core control logic, assisting in understanding the complete generation path of the adaptive dynamic pressure regulation energy-saving scheme from data acquisition to practical application.

[0078] Compared to the prior art, this invention, by collecting main pipeline pressure data, actuator displacement data, and ambient temperature data of the pneumatic equipment, constructs an operating state matrix for the pneumatic equipment. This clarifies the correlation between the dynamic changes in the operating parameters of the pneumatic equipment and the actual needs of the end-user equipment, as well as the influence mechanism of ambient temperature on the stability of the air source pressure. This allows for targeted optimization of the pneumatic equipment's air supply regulation logic. Furthermore, by generating dynamic pressure regulation commands for the pneumatic equipment based on the operating state matrix, this invention upgrades the traditional fixed-speed air supply mode of pneumatic equipment to a demand-driven dynamic control mode, precisely linking the pneumatic... The dynamic matching relationship between equipment operating status and terminal gas demand enhances the ability to control energy consumption and pressure stability during gas supply regulation. This embodiment of the invention generates status warning commands for the pneumatic equipment based on the operating status matrix, accurately linking the operating status of the pneumatic equipment with energy waste risks. This ensures timely risk identification and targeted pre-control measures, while providing advance risk guidance for the early execution of dynamic pressure regulation commands, enhancing the energy management capabilities and operational stability of the pneumatic equipment during low-load or intermittent working periods. Furthermore, this embodiment of the invention calculates... The real-time operational risk coefficient of the pneumatic equipment can establish a precise correlation between the dynamic pressure regulation process of the pneumatic equipment and the health status of key components and energy loss risks. It accurately identifies hidden risk areas such as overflow losses caused by pressure regulation deviations and additional energy consumption caused by component performance degradation, helping to improve the dynamic control capability and operational stability of the pneumatic equipment's energy utilization efficiency. In this embodiment of the invention, by determining the real-time energy-saving optimization value of the pneumatic equipment based on the dynamic pressure control vector, the traditional extensive fixed pressure regulation mode of the pneumatic equipment can be upgraded to an adaptive dynamic precise pressure regulation mode, accurately linking the real-time air supply pressure demand of the pneumatic equipment with dynamic... The variation law of the pressure control vector enhances the ability of the adaptive dynamic pressure regulation process to control the real-time matching degree of air pressure and the dynamic control of energy consumption. Furthermore, the embodiments of the present invention establish a hierarchical control strategy set for the pneumatic equipment based on the real-time energy-saving optimization value and the real-time operating risk coefficient. This can accurately match the dynamic load demand and safe operating boundary of the pneumatic system, realize the transformation from constant pressure air supply mode to adaptive control of "safety-energy efficiency" synergistic optimization, effectively solve the overflow loss and low energy efficiency problems caused by ignoring the changes in terminal demand in the traditional fixed speed air supply method, and strengthen the real-time dynamic correlation control of the pneumatic system's energy supply and load demand.This invention, through real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the hierarchical control strategy set, generates an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command. This organically integrates the real-time operational feedback of the pneumatic equipment with pressure regulation and energy-saving requirements. The dynamically updated adaptive control parameter sequence enhances the accuracy of the pressure regulation command and the timeliness of the warning command, improving the energy efficiency and operational safety of the pneumatic equipment during adaptive dynamic pressure regulation. Finally, by integrating the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set, and the adaptive control parameter sequence, this invention outputs an adaptive dynamic pressure regulation energy-saving scheme for the pneumatic equipment. This achieves a dynamic balance between optimal energy consumption and controllable risk under different load scenarios, while avoiding insufficient energy-saving effects due to reliance on fixed pressure regulation parameters or operational safety hazards caused by a lack of risk linkage mechanisms, significantly improving the energy efficiency and operational stability of the pneumatic equipment. Therefore, this invention provides an adaptive dynamic pressure regulation energy-saving system and method for pneumatic equipment, which can improve the energy efficiency and operational stability of pneumatic equipment.

[0079] like Figure 3 The diagram shown is a flowchart illustrating an adaptive dynamic pressure regulation energy-saving method based on pneumatic equipment according to an embodiment of the present invention. In this embodiment, the adaptive dynamic pressure regulation energy-saving method based on pneumatic equipment includes: S1. Collect the main pipeline pressure data, actuator displacement data and ambient temperature data of the pneumatic equipment to construct the operating status matrix of the pneumatic equipment; S2. Based on the operating state matrix, generate dynamic pressure regulation commands and status warning commands for the pneumatic equipment, and parse out the pressure setpoint sequence and regulation rate parameters in the dynamic pressure regulation commands to generate the dynamic pressure control vector of the pneumatic equipment. S3. Extract the performance degradation rate, sealing performance index and vibration abnormal frequency band from the status warning command to generate the status monitoring vector of the pneumatic equipment. Calculate the real-time operating risk coefficient of the pneumatic equipment based on the dynamic pressure control vector and the status monitoring vector. S4. Based on the dynamic pressure control vector, determine the real-time energy-saving optimization value of the pneumatic equipment, and establish a hierarchical control strategy set for the pneumatic equipment according to the real-time energy-saving optimization value and the real-time operating risk coefficient. S5. Real-time acquisition of system response data and risk evolution data of the pneumatic equipment after applying the graded control strategy set, in order to generate an adaptive control parameter sequence for the dynamic pressure regulation command and the status warning command; S6. Integrate the dynamic pressure regulation command, the status warning command, the hierarchical control strategy set, and the adaptive control parameter sequence to output the adaptive dynamic pressure regulation energy-saving scheme of the pneumatic equipment.

[0080] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0081] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0082] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive dynamic pressure regulating energy saving system based on pneumatic devices, characterized by, The system comprises: a state monitoring module for collecting main pipeline pressure data, actuator displacement data and environmental temperature data of the pneumatic device to construct an operating state matrix of the pneumatic device; a pressure control module for generating dynamic pressure regulating instructions and state early warning instructions of the pneumatic device according to the operating state matrix, resolving pressure set value sequences and regulating rate parameters in the dynamic pressure regulating instructions to generate a dynamic pressure control vector of the pneumatic device; a risk determination module for extracting performance degradation rate, sealing performance index and vibration abnormal frequency band in the state early warning instructions to generate a state monitoring vector of the pneumatic device, and calculating a real-time operating risk coefficient of the pneumatic device according to the dynamic pressure control vector and the state monitoring vector; a hierarchical regulation module for determining a real-time energy saving optimization value of the pneumatic device based on the dynamic pressure control vector, and establishing a hierarchical regulation strategy set of the pneumatic device according to the real-time energy saving optimization value and the real-time operating risk coefficient; a control feedback module for collecting system response data and risk evolution data of the pneumatic device after applying the hierarchical regulation strategy set to generate adaptive control parameter sequences of the dynamic pressure regulating instructions and the state early warning instructions; a scheme output module for integrating the dynamic pressure regulating instructions, the state early warning instructions, the hierarchical regulation strategy set and the adaptive control parameter sequences to output an adaptive dynamic pressure regulating and energy saving scheme of the pneumatic device.

2. An adaptive dynamic pressure regulating and energy saving system based on pneumatic equipment as claimed in claim 1, wherein, The generating of the dynamic pressure regulating instructions of the pneumatic device according to the operating state matrix comprises: extracting current operating modal characteristics and state degradation trend characteristics of the pneumatic device based on the operating state matrix; calculating an optimal target pressure value of the pneumatic device under the current operating modal characteristics; identifying performance degradation factors and performance offset compensation amounts in the state degradation trend characteristics; compensating and correcting the optimal target pressure value according to the performance degradation factors and the performance offset compensation amounts to generate a reference pressure instruction of the pneumatic device; obtaining real-time load disturbance data of the pneumatic device and resolving disturbance frequency and amplitude characteristics in the real-time load disturbance data; generating a feedforward pressure regulating compensation instruction of the pneumatic device based on the disturbance frequency and the amplitude characteristics; fusing the reference pressure instruction and the feedforward pressure regulating compensation instruction to generate the dynamic pressure regulating instructions of the pneumatic device.

3. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The generating of the state early warning instructions of the pneumatic device according to the operating state matrix comprises: constructing a health degree evaluation system of the operating state matrix; calculating real-time deviation degrees of each health degree evaluation index in the health degree evaluation system; identifying abnormal evolution modes and failure precursor characteristics of the health degree evaluation indexes; determining a state early warning level and early warning content of the pneumatic device according to the real-time deviation degrees, the abnormal evolution modes and the failure precursor characteristics; generating the state early warning instructions of the pneumatic device in combination with the state early warning level and the early warning content.

4. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 3, wherein, The constructing of the health degree evaluation system of the operating state matrix comprises: Identify the target evaluation component corresponding to the running state matrix; Extract the pressure stability data, displacement accuracy data and temperature drift data related to the target evaluation component from the running state matrix, and synchronously collect real-time performance data of the target evaluation component; Generate the pressure fluctuation entropy value of the target evaluation component using the pressure stability data; Analyze the positioning deviation index in the displacement accuracy data and perform time-domain correlation analysis processing of the positioning deviation index and the pressure fluctuation entropy value, and output the time-domain correlation strength coefficient; Convert the time-domain correlation strength coefficient into the mechanical transmission health score of the target evaluation component based on the pre-set scoring mapping function; Collect device historical failure data corresponding to the target evaluation component, and combine the temperature drift data and the device historical failure data to calculate the thermal degradation coefficient corresponding to the target evaluation component; Identify the efficiency attenuation rate of the target evaluation component based on the real-time performance data; Calculate the performance degradation risk index of the target evaluation component according to the thermal degradation coefficient and the efficiency attenuation rate; Establish the health degree evaluation system of the running state matrix by combining the mechanical transmission health score, the performance degradation risk index and the pressure fluctuation entropy value.

5. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The establishment of the hierarchical regulation strategy set of the pneumatic device according to the real-time energy-saving optimization value and the real-time running risk coefficient includes: Constructing a two-dimensional decision space of the pneumatic device according to the real-time energy-saving optimization value and the real-time running risk coefficient; Divide the two-dimensional decision space into a safe operation area, an optimized energy-saving area, a risk warning area and an emergency intervention area; Define the boundary threshold values corresponding to the safe operation area, the optimized energy-saving area, the risk warning area and the emergency intervention area by using the real-time energy-saving optimization value and the real-time running risk coefficient; Based on the boundary threshold values, set the differentiated regulation rules of the safe operation area, the optimized energy-saving area, the risk warning area and the emergency intervention area; Generate dynamic regulation instructions for the safe operation area, the optimized energy-saving area, the risk warning area and the emergency intervention area according to the differentiated regulation rules; Based on the two-dimensional decision space, the differentiated regulation rules and the dynamic regulation instructions, establish the hierarchical regulation strategy set of the pneumatic device.

6. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The calculation of the real-time running risk coefficient of the pneumatic device according to the dynamic pressure control vector and the state monitoring vector includes: Identify the pressure time-domain trajectory parameters and adjustment aggressiveness index in the dynamic pressure control vector; Extract the element safety margin and system damping characteristic parameters in the state monitoring vector; Calculate the overload risk probability of the pneumatic device according to the pressure time-domain trajectory parameters and the element safety margin; Calculate the instability risk probability of the pneumatic device based on the adjustment aggressiveness index and the system damping characteristic parameters; Real-time monitor the energy efficiency coefficient and the frequency of abnormal events of the pneumatic device; The real-time operation risk coefficient of the pneumatic device is calculated by the following formula based on the overload risk probability, the instability risk probability, the energy efficiency coefficient, and the abnormal event occurrence frequency: wherein, represents a real-time running risk coefficient, represents an overload risk probability, represents an instability risk probability, represents an energy efficiency coefficient, represents a rated energy efficiency reference value, represents an abnormal event occurrence frequency, represents an abnormal frequency reference threshold value, represents a continuous running time of the pneumatic device, represents a performance half-life of the pneumatic device, represents a time decay coefficient.

7. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The real-time energy-saving optimization value of the pneumatic device is determined based on the dynamic pressure control vector, including: Based on the dynamic pressure control vector, the real-time load demand and pressure supply redundancy of the pneumatic device are identified; According to the real-time load demand and the pressure supply redundancy, the minimum target energy consumption value of the pneumatic device is calculated; The pressure adjustment rate and pressure holding time are extracted from the dynamic pressure control vector; Based on the pressure adjustment rate and the pressure holding time, the dynamic energy consumption characteristics of the pneumatic device are analyzed; An energy consumption optimization model is constructed among the minimum target energy consumption value, the dynamic energy consumption characteristics, and the pressure supply redundancy; According to the energy consumption optimization model, the energy loss rate of the pneumatic device under different pressure settings is calculated; Based on the energy loss rate, the real-time energy-saving optimization value corresponding to the pneumatic device is determined.

8. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The system response data and risk evolution data of the pneumatic device after applying the hierarchical control strategy set are collected in real time to generate the adaptive control parameter sequence of the dynamic pressure regulation instruction and the state warning instruction, including: The instruction tracking error and dynamic characteristic parameters in the system response data are extracted; The risk trend characteristics and abnormal patterns in the risk evolution data are identified; Based on the instruction tracking error and the dynamic characteristic parameters, the control performance of the pneumatic device is evaluated; Through the evaluation result of the control performance, the controller parameter adjustment amount of the dynamic pressure regulation instruction is generated; According to the risk trend characteristics and the abnormal patterns, the risk warning sensitivity of the pneumatic device is determined; Based on the risk warning sensitivity, the warning threshold adjustment amount of the state warning instruction is defined; The adaptive control parameter sequence of the dynamic pressure regulation instruction and the state warning instruction is generated by fusing the controller parameter adjustment amount and the warning threshold adjustment amount.

9. An adaptive dynamic pressure regulating and energy saving system based on pneumatic devices as claimed in claim 1, wherein, The main pipeline pressure data, actuator displacement data, and environmental temperature data of the pneumatic device are collected to construct the operating state matrix of the pneumatic device, including: The time domain features and frequency domain features of the main pipeline pressure data are extracted to generate the pressure state feature vector of the pneumatic device; Based on the actuator displacement data, the motion trajectory of the actuator in the pneumatic device is analyzed; The instantaneous speed and acceleration of the actuator in the motion trajectory are calculated to form the displacement state feature vector of the pneumatic device; The temperature fluctuation rate and thermal load index in the environmental temperature data are analyzed, and based on the temperature fluctuation rate and the thermal load index, the efficiency attenuation coefficient and thermal compensation amount of the key elements in the pneumatic device are determined; According to the efficiency attenuation coefficient and the thermal compensation amount, the temperature influence feature vector of the pneumatic device is generated; The multi-physical field coupling relationship among the pressure state feature vector, the displacement state feature vector, and the temperature influence feature vector is identified; Based on the multi-physical field coupling relationship, the pressure state feature vector, the displacement state feature vector and the temperature influence feature vector are subjected to feature fusion processing to obtain a fusion feature vector; An operation state matrix of the pneumatic device is constructed through the fusion feature vector.

10. An adaptive dynamic pressure regulating energy saving method based on pneumatic equipment, using the adaptive dynamic pressure regulating energy saving system based on pneumatic equipment according to any one of claims 1-9, characterized in that, The method comprises: Collecting main pipeline pressure data, actuator displacement data and environmental temperature data of the pneumatic device to construct an operation state matrix of the pneumatic device; According to the operation state matrix, generating a dynamic pressure regulating instruction and a state early warning instruction of the pneumatic device, and analyzing a pressure set value sequence and a regulating rate parameter in the dynamic pressure regulating instruction to generate a dynamic pressure control vector of the pneumatic device; Extracting a performance degradation rate, a sealing performance index and a vibration abnormal frequency band in the state early warning instruction to generate a state monitoring vector of the pneumatic device, and calculating a real-time operation risk coefficient of the pneumatic device according to the dynamic pressure control vector and the state monitoring vector; Based on the dynamic pressure control vector, determining a real-time energy saving optimization value of the pneumatic device, and establishing a hierarchical regulation and control strategy set of the pneumatic device according to the real-time energy saving optimization value and the real-time operation risk coefficient; Real-time collecting system response data and risk evolution data of the pneumatic device after applying the hierarchical regulation and control strategy set to generate an adaptive control parameter sequence of the dynamic pressure regulating instruction and the state early warning instruction; Integrating the dynamic pressure regulating instruction, the state early warning instruction, the hierarchical regulation and control strategy set and the adaptive control parameter sequence to output an adaptive dynamic pressure regulating and energy saving scheme of the pneumatic device.

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