A power frequency air compressor unit control method and related equipment for multi-machine cooperation

Abstract

This application relates to the field of unit control technology, and provides a control method and related equipment for multi-unit coordinated industrial frequency air compressor units. The method includes: acquiring operating data of the industrial frequency air compressor units to be controlled; performing combined prediction based on the operating data to obtain predicted air consumption values ​​for the industrial frequency air compressor units; constructing operating condition characteristics based on the operating data and predicted air consumption values, and identifying the operating condition type of the industrial frequency air compressor units based on the operating condition characteristics; constructing an optimization objective function, and minimizing the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the unit; and controlling each industrial frequency air compressor in the unit according to the predicted state of each compressor. This method can improve the reliability and group control capability of industrial frequency air compressor unit control.

Description

Technical Field

[0001] This application relates to the field of unit control technology, and in particular to a control method and related equipment for a multi-unit coordinated industrial frequency air compressor unit. Background Technology

[0002] Compressed air is a widely used power source in industrial production. Industrial frequency air compressors dominate due to their reliable structure and ease of maintenance, but their constant speed relies solely on loading / unloading to maintain pipeline pressure. Under frequent load fluctuations, prolonged periods of no-load operation and frequent loading / unloading often occur. According to relevant research and industry surveys, the no-load rate of air compressor systems in manufacturing is generally between 28% and 42%, and even exceeds 55% in intermittent production enterprises. Furthermore, most existing industrial frequency air compressor units use a single-unit independent control method for loading and unloading, which presents the following technical problems in actual operation: (1) Multiple machines are controlled independently, resulting in serious energy waste during no-load operation: Existing solutions mostly rely on individual units to independently determine loading and unloading based on the pressure at the unit itself or the end of the pipeline network, lacking a global collaborative scheduling mechanism. Under low load conditions, multiple units are prone to simultaneously entering an idling state, resulting in a large amount of ineffective energy consumption. Studies have shown that the efficiency of traditional screw compressor motors is only 85%, with idling losses accounting for 15%-20% and peak current losses reaching 30%, resulting in extremely serious energy waste.

[0003] (2) Control lag and frequent switching: Existing solutions rely solely on feedback regulation based on pipeline end pressure, resulting in long control lag times. Sudden load changes can lead to a cycle of "sudden pressure drop → collective unit loading → pressure overshoot → collective unit unloading," exacerbating equipment wear and causing significant pipeline pressure fluctuations, failing to meet the gas requirements of high-precision production processes. Existing group control solutions with predictive capabilities often employ simple linear prediction, lacking accuracy and exhibiting coarse-grained operating condition classification, making them highly susceptible to misjudgments leading to frequent loading and unloading.

[0004] (3) Mainstream energy-saving retrofits are costly, have poor adaptability, and pose equipment risks: Currently, the mainstream energy-saving retrofit solution for air compressors in the industry is frequency conversion retrofitting, which involves installing a frequency converter to replace the fixed-frequency motor with a variable-frequency speed control motor, thereby achieving dynamic matching between exhaust volume and air load. However, this solution has three major problems: 1. High retrofit costs: the cost of retrofitting a single unit with frequency converters can easily reach tens of thousands of yuan, and the investment payback period is long. The energy-saving benefits of small-scale air compressor units cannot cover the retrofit costs. 2. Poor adaptability: The insulation level and heat dissipation system of the old industrial frequency air compressor motor are not designed for variable frequency operation. The harmonics generated by the addition of the frequency converter will accelerate the aging of the motor insulation. When running at low frequency, the heat dissipation air volume will be reduced sharply, which can easily lead to the motor overheating and tripping or even burning out. 3. Limited energy-saving effect: Most frequency converter retrofits only achieve soft start and have not been optimized for multi-unit coordination. In scenarios where multiple units operate in parallel, the problem of multiple units being idle at the same time cannot be solved.

[0005] (4) Lack of group control and coordination capabilities: While some existing air compressor group control solutions exist, most focus on pressure regulation optimization of a single unit and simple start-stop timing control, only achieving the basic function of "starting and stopping alternately according to pressure upper and lower limits," lacking systematic coordinated scheduling of multiple industrial frequency air compressor units. Load prediction often uses simple linear regression or long-cycle big data prediction, which cannot adapt to the second-level nonlinear fluctuation characteristics of industrial gas consumption, resulting in insufficient prediction accuracy. Operating condition identification is mostly based on single-dimensional threshold division, with coarse granularity, which is prone to misjudgment leading to invalid switching. It cannot achieve predictive control that "accurately predicts load in advance and dynamically matches unit operating status," and cannot fundamentally solve the problems of no-load energy consumption and control lag.

[0006] It is evident that current control methods for industrial frequency air compressor units suffer from poor reliability and group control capabilities. Summary of the Invention

[0007] This application provides a control method and related equipment for multi-machine collaborative industrial frequency air compressor units, which can solve the problems of poor reliability and group control capability of industrial frequency air compressor units.

[0008] In a first aspect, embodiments of this application provide a control method for a multi-machine coordinated industrial frequency air compressor unit, the control method comprising: Acquire the operating data of the industrial frequency air compressor unit to be controlled; Based on the combined prediction of the operating data of the industrial frequency air compressor unit, the predicted air consumption of the industrial frequency air compressor unit is obtained. Operating condition characteristics are constructed based on operational data and predicted gas consumption, and the operating condition type of the industrial frequency air compressor unit is identified based on these characteristics. An optimization objective function is constructed, and the optimization objective function is minimized according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit; the optimization objective function is used to describe the operating cost of the industrial frequency air compressor unit; Based on the predicted status of each industrial frequency air compressor, control is performed on each industrial frequency air compressor unit.

[0009] Optionally, based on the operating data of the industrial frequency air compressor unit, a combined prediction is performed to obtain the predicted air consumption value of the industrial frequency air compressor unit, including: Linear prediction based on operational data yields the initial gas consumption forecast. Calculate the residual correction value and the physical quantity compensation value; The initial predicted air consumption value, residual correction value, and physical quantity compensation value are coupled and superimposed to obtain the predicted air consumption value of the power frequency air compressor unit.

[0010] Optionally, the operational data includes the current real-time gas consumption and pressure of the pipeline network; Linear prediction based on operational data yields initial gas consumption forecasts, including: Through the formula:

[0011] Calculate the initial gas consumption forecast. ; in, , , , All are weighting coefficients. Indicates the current time Real-time gas consumption in the pipeline network Indicates the current time Real-time pressure of the pipeline network, This indicates the target pressure set for the pipeline network. Indicates time difference, Indicates the current time The rate of change of pipeline pressure; Calculate the residual correction value and physical quantity compensation value, including: Through the formula:

[0012] Calculate residual correction value ; in, Represents the development coefficient. Indicates the gray action quantity. Indicates the residual prediction step size. This indicates the residual gas consumption. Through the formula:

[0013] Calculate the compensation value of physical quantities ; in, Indicates equipment attenuation compensation. Indicates temperature and pressure compensation. Indicates pipeline volume compensation:

[0014]

[0015]

[0016] in, This represents the exhaust gas attenuation coefficient per unit hour. This indicates the total cumulative operating time of the unit. This represents the gas consumption sampling value. Indicates standard temperature. Indicates real-time temperature. This indicates the total volume of the plant's pipeline network. It represents standard atmospheric pressure.

[0017] Optionally, the operating data includes the rated discharge capacity of each industrial frequency air compressor in the industrial frequency air compressor unit; Operating condition characteristics are constructed based on operational data and gas consumption forecasts, including: Calculate the flow rate change rate and the normalized pressure rate change rate based on the operating data and gas consumption forecast. Average load rate of computer group based on operational data; The operating condition characteristics are obtained by combining the flow rate change rate, normalized pressure rate change rate, and unit average load rate into a vector.

[0018] Optionally, the flow rate change rate can be calculated based on operating data and gas consumption forecasts, including: Through the formula:

[0019] Calculate the rate of change of flow ; Calculating the normalized rate of change of pressure includes: Through the formula:

[0020] Calculate the normalized rate of change of pressure ; The average load rate of the computer group based on operational data includes: Through the formula:

[0021] Average load rate of computer group ; in, Indicates the first The rated discharge capacity of a single industrial frequency air compressor Indicates the first Loading indicator for a single industrial frequency air compressor.

[0022] Optionally, the operating condition type of the power frequency air compressor unit can be identified based on operating condition characteristics, including: Calculate the comprehensive evaluation score of the industrial frequency air compressor unit based on the operating condition characteristics; Based on the comprehensive evaluation score and the preset score range, the operating condition type of the industrial frequency air compressor unit is identified.

[0023] Optionally, the objective function to be optimized is:

[0024] in, Indicates total cost. , , All are weights. This represents the total energy consumption of the system. This represents the energy consumption normalization benchmark. Indicates the number of times the unit status is switched. This represents the normalization benchmark for handover frequency. Indicates the degree of uneven wear. This represents the normalized benchmark for wear deviation.

[0025] Optionally, the objective function is minimized according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit, including: Define constraints by operating condition type; Under the constraints, the optimization objective function is minimized to obtain the initial predicted state of each power frequency air compressor. For each industrial frequency air compressor, if the initial predicted state of the industrial frequency air compressor meets the unloading switching waiting time condition, the current state of the industrial frequency air compressor is taken as the predicted state; if the initial predicted state of the industrial frequency air compressor does not meet the unloading switching waiting time condition, the initial predicted state of the industrial frequency air compressor is taken as the predicted state.

[0026] Secondly, embodiments of this application provide a multi-machine coordinated industrial frequency air compressor unit control device, including: The acquisition module is used to acquire the operating data of the industrial frequency air compressor unit to be controlled; The prediction module is used to perform combined predictions based on the operating data of the industrial frequency air compressor unit to obtain the predicted air consumption value of the industrial frequency air compressor unit. The module is used to construct operating condition characteristics based on operating data and gas consumption forecasts, and to identify the operating condition type of the power frequency air compressor unit based on the operating condition characteristics. The solution module is used to construct the optimization objective function and minimize the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit; the optimization objective function is used to describe the operating cost of the industrial frequency air compressor unit; The control module is used to control each industrial frequency air compressor in the industrial frequency air compressor unit based on the predicted status of each industrial frequency air compressor.

[0027] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned multi-machine collaborative industrial frequency air compressor unit control method.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned multi-machine collaborative power frequency air compressor unit control method.

[0029] The above-mentioned solution in this application has the following beneficial effects: In the embodiments of this application, the operating data of the industrial frequency air compressor unit to be controlled is acquired, and then combined prediction is performed based on the operating data to obtain the predicted air consumption value of the industrial frequency air compressor unit. Operating condition characteristics are then constructed based on the operating data and the predicted air consumption value, and the operating condition type of the industrial frequency air compressor unit is identified based on the operating condition characteristics. An optimization objective function is then constructed, and the optimization objective function is minimized according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the unit. Finally, each industrial frequency air compressor in the unit is controlled according to its predicted state. The combined prediction based on actual operating data avoids the deficiency of single linear prediction in capturing nonlinear fluctuations, improves the accuracy of the predicted air consumption value of the unit, effectively reflects the operating condition of the industrial frequency air compressor unit, constructs an optimization objective function, and solves for the predicted states of multiple industrial frequency air compressors according to the operating condition type, realizing coordinated loading and unloading of multiple units, ensuring efficient operation of the unit, and effectively improving the reliability and group control capability of the industrial frequency air compressor unit.

[0030] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating a multi-machine coordinated industrial frequency air compressor unit control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the working condition identification process provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of a multi-machine coordinated industrial frequency air compressor unit control device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0033] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0034] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0035] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0036] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0037] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0038] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0039] To address the issues of poor reliability and group control capability in existing industrial frequency air compressor unit control systems, this application provides a multi-unit collaborative industrial frequency air compressor unit control method. This method acquires the operating data of the industrial frequency air compressor units to be controlled, then performs combined predictions based on the operating data to obtain predicted air consumption values ​​for the units. Next, it constructs operating condition characteristics based on the operating data and predicted air consumption values, identifies the operating condition types of the industrial frequency air compressor units based on these characteristics, constructs an optimization objective function, and minimizes the objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the unit. Finally, it controls each industrial frequency air compressor in the unit based on its predicted state. Among them, combined prediction based on actual operating data avoids the shortcomings of single linear prediction in capturing nonlinear fluctuations, improves the accuracy of the predicted air consumption of the unit, effectively reflects the operating conditions of the industrial frequency air compressor unit, constructs an optimized objective function and solves the predicted state of multiple industrial frequency air compressors according to the operating condition type, realizes the coordinated loading and unloading of multiple machines, ensures the efficient operation of the unit, and effectively improves the reliability and group control capability of the industrial frequency air compressor unit.

[0040] The following is an exemplary description of the multi-machine coordinated industrial frequency air compressor unit control method provided in this application.

[0041] like Figure 1 As shown, the multi-machine coordinated industrial frequency air compressor unit control method provided in this application includes the following steps: Step 11: Obtain the operating data of the industrial frequency air compressor unit to be controlled.

[0042] The aforementioned industrial frequency air compressor unit includes multiple industrial frequency air compressors, which are air compressors driven by industrial industrial frequency power supplies. The aforementioned operating data includes the real-time air consumption of the pipeline network, the real-time pressure of the pipeline network, the rated discharge capacity of each industrial frequency air compressor, the operating current, and operating environment data such as temperature.

[0043] In some embodiments of this application, the aforementioned industrial frequency air compressor unit to be controlled can be an industrial frequency air compressor unit used in automobile manufacturing plants or chemical production plants. Operating data can be acquired using sensors and other equipment.

[0044] For example, for a power frequency air compressor unit consisting of three air compressors, a 0.2-level pressure sensor, a 1-level vortex flow sensor, and a PT100 temperature sensor are installed at the outlet of the air storage tank in the main compressed air pipeline of the plant. The pressure sensor has a range of 0~1.6MPa and outputs a 4-20mA analog signal; the flow sensor has a range of 0~40m³ / min and outputs a 4-20mA analog signal; and the temperature sensor has a range of -20~120℃ and outputs a 4-20mA analog signal. All sensors are connected to the analog input module of the edge energy-saving controller through shielded cables to ensure the accuracy of data transmission in environments with strong electromagnetic interference.

[0045] An AC0-100A openable current sensor and a dry contact acquisition module for operating status are installed on each of the three air compressors. The current sensor collects the real-time operating current of the unit for power input to the computer. The dry contact acquisition module collects the unit's operating / stop, loading / unloading, and fault alarm status signals. The unit status acquisition module communicates with the edge energy-saving controller via Modbus-TCP industrial Ethernet with a communication cycle of 100ms.

[0046] An industrial-grade programmable logic controller (PLC) is used as the edge energy-saving controller. It is equipped with a central processing unit (CPU) module, 8 analog input modules, 16 digital input modules, 16 digital output modules, and an Ethernet communication module. It has dual-machine redundancy backup function, and the computing power can meet the model calculation and control logic execution at a frequency of 1Hz. The controller is installed in the control cabinet of the air compressor room in the plant area and connected to the plant's existing uninterruptible power supply (UPS) to ensure normal operation and alarm after power failure.

[0047] The digital output module of the edge energy-saving controller connects to the original loading / unloading control circuit and start / stop control circuit of each air compressor through relay dry contacts. The dry contacts are completely electrically isolated from the original control circuit of the air compressor. The original overload, over-temperature, over-pressure and other protection logic of the air compressor is not modified. Control commands are only issued under the premise that the original protection circuit of the air compressor is normal. When the air compressor triggers the original protection, the controller immediately cuts off all control commands to the unit to prioritize the safety of the equipment.

[0048] Efficiency tests were conducted on three air compressors under a rated discharge pressure of 0.72 MPa, with test load rates of 25%, 50%, 75%, and 100%. The real-time input power and real-time discharge volume of each unit were recorded at each load rate, and the energy consumption per unit discharge volume was calculated (calculation formula: energy consumption per unit discharge volume = real-time input power / real-time discharge volume, unit: kW). m - ³ The lower the energy consumption per unit of exhaust volume, the higher the unit's operating efficiency.

[0049] Simultaneously, the unit's no-load operating power and total loss during a single loading-unloading switch are tested: the no-load power is the input power of the unit when it is under stable no-load; the total loss during a single loading-unloading switch is the additional energy consumption of a complete loading-unloading cycle, including the energy consumption of startup impact and the energy consumption converted from mechanical wear. After conversion, it is stored in the unit parameter database, with a typical value of 0.05 kWh / cycle.

[0050] After the test is completed, the unit exhaust energy consumption under rated operating conditions (100% load rate) is sorted from low to high to establish a full operating condition efficiency library for the units, and the high-efficiency unit is identified as Unit 1, the medium-efficiency unit as Unit 2, and the low-efficiency unit as Unit 3.

[0051] The pipeline pressure control range is set at 0.7~0.74MPa (pressure fluctuation ≤±0.02MPa). ; Load feedforward prediction time Δt = 20 seconds; The interval between adding and unloading operations should be ≥30 seconds. Off-peak start interval ≥ 5 seconds; The upper limit of the pressure safety limit is 0.78 MPa, and the lower limit of the safety limit is 0.65 MPa.

[0052] Data acquisition frequency: 1Hz.

[0053] It should be noted that after obtaining the runtime data, preprocessing can be performed on it. For example: The pipeline monitoring module collects the pressure of the main pipeline in real time. ,flow ,temperature The sampling frequency is 1Hz. The unit status acquisition module collects real-time data on the operating status (running / stopping), loading status (loading / unloading), output current, cumulative running time, and fault status of three air compressors. All collected data undergoes a 5-point moving average filter to eliminate random fluctuations. Abnormal data is also removed based on the following rules: data exceeding the sensor's range by ±5%, or no data returned for three consecutive sampling cycles, is considered abnormal and removed, replaced by the average of the previous three valid data points. If abnormal data persists for more than five sampling cycles, sensor degradation logic is triggered. The pre-processed valid data is stored in the controller's local database by timestamp, serving as the historical data basis for load feedforward prediction, with a data storage period of no less than 90 days.

[0054] Step 12: Based on the operating data of the industrial frequency air compressor unit, perform combined prediction to obtain the predicted air consumption value of the industrial frequency air compressor unit.

[0055] The above gas consumption forecast values ​​are the predicted gas consumption of the industrial frequency air compressor unit at future times after the current time.

[0056] In some embodiments of this application, the step of combining and predicting the operating data of the power frequency air compressor unit to obtain the predicted air consumption value of the power frequency air compressor unit includes: The first step is to perform linear prediction based on operational data to obtain the initial gas consumption forecast.

[0057] Specifically, through the formula:

[0058] Calculate the initial gas consumption forecast. .

[0059] in, , , , All are weighting coefficients. Indicates the current time Real-time gas consumption in the pipeline network, in m³ / min. Indicates the current time Real-time pressure of the pipeline network, in MPa. This indicates the target pressure set for the pipeline network, such as 0.72 MPa. Indicates time difference, Indicates the current time The rate of change of pipeline pressure, in MPa / s.

[0060] For example, the above weight coefficients can be updated online using recursive least squares (RLS) with a 5-minute sliding window (300 sets of 1Hz data in total). The initial values ​​are set to [0.4, 0.2, 0.3, 0.1], and the forgetting factor λ = 0.95. The system automatically iterates once every 5 minutes. If the fitting error of the new weights decreases by ≥1% compared to the original weights, the original weights are replaced. Otherwise, they remain unchanged, thus achieving real-time adaptation to load fluctuations.

[0061] The second step is to calculate the residual correction value and the physical quantity compensation value.

[0062] Specifically, through the formula:

[0063] Calculate residual correction value .

[0064] in, The development coefficient represents the trend and rate of change of the residuals. Indicates the gray action quantity. Indicates the residual prediction step size. This indicates the residual gas consumption.

[0065] For example, the aforementioned development coefficient and grey action quantity can be obtained using the GM(1,1) grey prediction model, specifically: S1: Residual sequence construction: Define linear prediction residuals: ,in k For historical sampling moments; Take the residuals of the 10 most recent consecutive sampling points to form the original residual sequence: ,in This is the first sampled value of the residual sequence.

[0066] S2: One-time cumulative generation (1-AGO): The original residual sequence is accumulated once to generate the accumulated sequence:

[0067] In the formula: For the original residual sequence, the first... Each sample value; This is the k-th item in the cumulative sequence.

[0068] S3: Solving for the GM(1,1) parameters: Based on the cumulative sequence, the gray model parameters, namely the development coefficient, are solved using the least squares method. Grey action quantity The constraint development coefficient ranges from [-0.5, 0.5]. If it exceeds this range, the correction will be abandoned and the residual compensation will be 0.

[0069] Through the formula:

[0070] Calculate the compensation value of physical quantities .

[0071] in, Indicates equipment attenuation compensation. Indicates temperature and pressure compensation. Indicates pipeline volume compensation:

[0072]

[0073]

[0074] in, This represents the exhaust gas attenuation coefficient per unit hour. This indicates the total cumulative operating time of the unit. This represents the gas consumption sampling value. Indicates standard temperature. Indicates real-time temperature. This indicates the total volume of the plant's pipeline network. It represents standard atmospheric pressure.

[0075] The third step is to couple and superimpose the initial predicted air consumption value, the residual correction value, and the physical quantity compensation value to obtain the predicted air consumption value of the power frequency air compressor unit.

[0076] Specifically, through the formula:

[0077] Calculate the predicted gas consumption value .

[0078] Step 13: Construct operating condition characteristics based on operating data and predicted gas consumption, and identify the operating condition type of the power frequency air compressor unit based on the operating condition characteristics.

[0079] The above operating condition types are used to describe the load change status of the industrial frequency air compressor unit, which can be load surge, load increase, recovery buffer, load stability, load decrease buffer, load decrease or load drop.

[0080] In some embodiments of this application, the steps of constructing operating condition characteristics based on operating data and predicted gas consumption, and identifying the operating condition type of the industrial frequency air compressor unit based on these operating condition characteristics, include: The first step is to calculate the flow rate change rate and the normalized pressure rate change rate based on the operating data and the gas consumption forecast.

[0081] Specifically, through the formula:

[0082] Calculate the rate of change of flow .

[0083] Through the formula:

[0084] Calculate the normalized rate of change of pressure .

[0085] The second step is to analyze the average load rate of the computer group based on the operating data.

[0086] Specifically, through the formula:

[0087] Average load rate of computer group .

[0088] in, Indicates the first The rated discharge capacity of a single industrial frequency air compressor Indicates the first Loading indicator for a single industrial frequency air compressor.

[0089] The third step is to combine the flow rate change rate, normalized pressure rate change rate, and unit average load rate into a vector to obtain the operating condition characteristics.

[0090] The fourth step is to calculate the comprehensive evaluation score of the industrial frequency air compressor unit based on the operating conditions.

[0091] Specifically, through the formula:

[0092] Calculate the overall evaluation score .

[0093] in, This represents the triangular membership function, with an output interval of [-1, 1].

[0094] The fifth step is to identify the operating condition type of the industrial frequency air compressor unit based on the comprehensive evaluation score and the preset score range.

[0095] For example, the operating condition type of the industrial frequency air compressor unit is determined based on the score range of the comprehensive evaluation score, and the corresponding load index is also determined. The preset score range is shown in Table 1.

[0096] Table 1

[0097] Operating condition identification process as follows Figure 2 As shown in the figure, this diagram illustrates a complete algorithm flow for predicting air consumption and classifying operating conditions for air compressor units: The system takes real-time air consumption, pressure change rate, support pressure, and historical average air consumption as inputs, and merges them through three paths: the original injection curve, feedback prediction residual correction, and multi-step gain compensation, to output the predicted air consumption. Subsequently, it undergoes slope optimization calculation and fuzzy comprehensive discrimination calculation in three time periods, and finally completes the classification of seven operating conditions, dividing the unit's operating status into seven levels: load pressure holding W=3, load rising W=2, rising buffer W=1, load stable W=0, falling buffer W=-1, load falling W=-2, and load unloading W=-3, providing a quantitative basis for determining operating conditions for subsequent unit joint control and scheduling.

[0098] Step 14: Construct the optimization objective function and minimize the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit.

[0099] The objective function described above describes the operating cost of the industrial frequency air compressor unit. The predicted state described above describes the state of the industrial frequency air compressor at future times after the current time, such as shutdown, no-load, or full-load.

[0100] In some embodiments of this application, the steps of constructing the optimization objective function and minimizing the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit include: The first step is to construct the objective function for optimization.

[0101] Specifically, the objective function to be optimized is:

[0102] in, Indicates total cost. , , All are weights (energy consumption weights) Switching frequency weight Wear leveling weight All three conditions are met. + + =1, the weighting ratio can be adjusted according to on-site needs). This represents the total energy consumption of the system. , For the first Energy consumption per unit displacement of air compressor Air compressor valid load indicator, air compressor status ( When (full load gas production) take 1, stop the machine ( ), unloaded ( When ), take 0. This represents the energy consumption normalization benchmark. This indicates the number of times the unit status is switched (the total number of times the loading / unloading and start-up / stopping actions occur in this unit change compared to the previous control cycle. Each loading, unloading, start-up, and shutdown is counted as one switch. This is used to constrain the mechanical shock and electrical loss caused by frequent actions. The smaller the value, the fewer the unit loading / unloading actions and the lower the mechanical shock and electrical loss). This represents the normalization benchmark for handover frequency. This indicates the degree of uneven wear (the standard deviation of the cumulative running time of each air compressor; the smaller the value, the more even the running time of each unit, the more synchronous the aging of the equipment, and the longer the overall service life of the entire unit). , No. Total cumulative runtime of the air compressor, in hours. (The arithmetic mean of the cumulative running time of the 3 air compressors). The normalized reference for wear deviation can be 100h.

[0103] The second step is to define constraints based on the operating condition type.

[0104] For example, constraints are defined based on the load index M corresponding to the operating condition type. The constraints include: Capacity constraint: The sum of the rated exhaust volumes of all fully loaded units exceeds the predicted gas consumption. To ensure sufficient gas supply; Exclusive no-load constraint: Under non-abrupt operating conditions (|M|≤2), at most one unit is in no-load state, avoiding ineffective energy consumption waste caused by multiple units being no-loaded at the same time; Minimum interval constraint: The interval between two loading and unloading operations for a single unit must be ≥30 seconds, and repeated switching within a short period of time is prohibited; Priority constraint: When the load increases, high-efficiency units are started first; when the load decreases, inefficient units are stopped first. Safety constraints: Faulty units must be forcibly shut down and must not be included in the dispatch queue; equipment safety must be prioritized.

[0105] Meanwhile, multi-machine scheduling follows three principles: "single-machine peak shaving, multiple machines at full load, and exclusive operation." Single-unit peak shaving: Under normal operating conditions, only one unit is allowed to be in no-load peak shaving state, while the remaining operating units maintain full-load gas production to avoid multiple units operating in the inefficient range at the same time; Full load of multiple units: When the load demand is large, high-efficiency units are prioritized to run at full load, while low-efficiency units are started and stopped as needed to ensure that the operating units are always in the high-efficiency range. Exclusive operation: Under non-abrupt operating conditions (|M|≤2), only one unit is allowed to be in an unloaded state in the entire pipeline network; under abrupt operating conditions (|M|=3), this constraint can be temporarily broken to prioritize the stability of pipeline network pressure.

[0106] The third step is to minimize the objective function under the constraints to obtain the initial predicted state of each power frequency air compressor.

[0107] For example, multiple candidate sets (including candidate predicted states for each industrial frequency air compressor) are generated, and the sets that do not meet the constraints are eliminated. The sets that meet the constraints are taken as feasible candidate sets. Each of the remaining feasible candidate sets is substituted into the above optimization objective function for calculation. The candidate predicted state in the feasible candidate set with the minimum comprehensive cost F is taken as the initial predicted state of the industrial frequency air compressor.

[0108] The fourth step is to take the current state of the industrial frequency air compressor as the predicted state when the initial predicted state of the industrial frequency air compressor meets the unloading and switching waiting time condition, and take the initial predicted state of the industrial frequency air compressor as the predicted state when the initial predicted state of the industrial frequency air compressor does not meet the unloading and switching waiting time condition.

[0109] For example, the above-mentioned unloading / switching wait time condition is: , To predict the load recovery time, For optimal waiting time:

[0110] in, This represents the single loading and unloading loss of the power frequency air compressor in its initial predicted state, converted into electrical energy (kWh) by the integral of the measured loading current impact, and then converted into equivalent energy consumption. This represents the no-load power, measured when the unit is under stable no-load conditions, and predicts the load recovery time. If there is no rebound within 20 seconds from the predicted flow sequence, then take 20 seconds.

[0111] Step 15: Control each industrial frequency air compressor in the industrial frequency air compressor unit according to the predicted status of each industrial frequency air compressor.

[0112] Specifically, using controllers and other devices, the state of each industrial frequency air compressor in the industrial frequency air compressor unit is switched to the corresponding predicted state. When switching states, the corresponding execution timing mode can be matched according to the load index mentioned above to determine the action execution order, interval parameters and initial triggering time.

[0113] For example, the controller receives the predicted state in real time, compares it with the current real-time state matrix of the unit, and calculates the list of actions to be executed for each unit. Then, combined with the load trend index M, it selects the corresponding execution timing mode, converting the actions into hardware control signals for the programmable logic controller (PLC) digital output module (DO). All state changes follow a two-level safety process of "no-load first, then shutdown" to avoid damage to equipment during load shutdown. Closed-loop correction only adjusts the triggering timing of the actions, without changing the action content, peak shaving / observation intervals, unit start / stop priorities, or target states.

[0114] The controller first compares the target state matrix with the real-time state matrix of each unit to obtain the actions to be executed for each unit, as shown in Table 2: Table 2

[0115] If there are no actions to be performed after calculation (smooth / transitional conditions), the current output state is held in place, and the closed-loop correction logic continues to run to prepare for the next action.

[0116] Based on the load index M, match the corresponding execution timing pattern to determine the action execution order, interval parameters, and initial triggering timing: Standard load-output timing (M=2): Loading actions are performed sequentially from highest to lowest unit efficiency; the loading interval between two adjacent units is 5 seconds (staggered start-up to avoid current surges caused by multiple motors being connected to the grid simultaneously). Standard unloading output timing (M=-2): Unloading actions are performed sequentially from low to high unit efficiency; a single unit first disconnects the loading signal to enter an unloaded state, observes for 30 seconds, and then disconnects the start / stop signal to stop the unit (two-stage unloading, using the gas buffer volume of the pipeline network to prevent gas supply collapse); the unloading interval between two adjacent units is 30 seconds; by default, the first unit is started to unload immediately after the target status is received.

[0117] Mutation urgent output timing (|M|=3): All similar actions are executed concurrently; peak-shifting intervals and observation intervals are temporarily reset to zero without delay; the system is executed immediately upon receiving the target status, prioritizing the safety of pipeline pressure.

[0118] Transitional / stable operating condition (|M|≤1): There are no pending actions; the current output state is latched, and only pressure and state changes are continuously monitored.

[0119] Based on the real-time pressure and pressure change rate of the main pipeline, the advance / delay of loading / unloading actions is dynamically calculated using a two-factor formula to correct the initial triggering timing, eliminate feedforward prediction residuals, and improve pressure control accuracy. This step operates in real-time at a frequency of 1Hz, continuously updating the action triggering timer parameters to achieve continuous closed-loop correction.

[0120] ① Loading timing is dynamically adjusted When the pipeline pressure is below the target lower limit and shows a downward trend, calculate the loading advance to trigger the loading action in advance, offset the control lag, and prevent the pressure from falling below the lower limit:

[0121] in (Bias Gain) (Gain based on rate of change), with a maximum advance of no more than 10 seconds. The larger the pressure deviation and the faster the pressure drop, the earlier the loading should be initiated. When the pipeline pressure is close to the lower limit but the rate of decrease is extremely slow, calculate the loading delay, with a maximum delay of 5 seconds, to avoid unnecessary loading switching. (The gain coefficient is a recommended value; it can be adjusted on-site in actual applications. The adjustment principle is to maximize energy saving while ensuring that the pressure does not exceed the limit.) ② Dynamic adjustment of unloading timing When the pipeline pressure exceeds the target upper limit and shows an upward trend, calculate the unloading lead time to trigger the unloading action in advance, quickly suppressing the pressure rise and preventing overpressure.

[0122] in , The maximum lead time should not exceed 8 seconds. The greater the pressure overshoot and the faster the rise, the earlier the unloading timing should be. When the pipeline pressure is close to the upper limit but the rise rate is extremely slow, calculate the unloading delay, with a maximum delay of 4 seconds, to avoid unnecessary unloading switching. (The gain coefficient is a recommended value; it can be adjusted on-site in actual applications. The adjustment principle is to maximize energy saving while ensuring that the pressure does not exceed the limit.) ③ Mechanism of action of correction amount The calculated dynamic correction value is directly applied to the action trigger timer: Loading actions: Initial trigger timing advanced Seconds (negative values ​​will be delayed); Uninstallation actions: Initial trigger timing advanced Seconds (negative values ​​will be delayed); Only the start timing of the entire action sequence is adjusted, while the peak-shifting interval and observation interval between actions remain unchanged and are not affected by closed-loop correction. If an action has already started, the correction amount will no longer be applied to this action, but will only affect the trigger timing of the next action.

[0123] Once the action trigger timer finishes its countdown, DO hardware control signals are output sequentially according to the matched timing pattern, driving the air compressor to execute the corresponding action, ultimately bringing the unit to the target state matrix. During execution, the unit current and pipeline pressure feedback are monitored in real time. If a unit fault or abnormal pressure occurs, subsequent actions are immediately suspended and safety degradation logic is triggered.

[0124] In some embodiments of this application, to ensure the absolute safety of gas supply for factory production, the edge energy-saving controller is equipped with a multi-level fault-tolerant mechanism. All degradation adopts a smooth switching method, that is, switching occurs after the current control cycle ends, without interrupting the ongoing loading and unloading actions, thus avoiding equipment impact. Sensor data anomaly: When a sensor returns no data for 5 consecutive sampling cycles or the data fluctuates beyond the range, the combined prediction and fuzzy evaluation algorithm is automatically paused, and the system smoothly degrades to the traditional "single-point pressure upper and lower limit feedback control" to prioritize ensuring that the pipeline network has gas available; after the sensor recovers to normal and stabilizes for 10 consecutive cycles, it automatically switches back to the collaborative control mode.

[0125] Unit sudden failure trip: If a sudden failure is detected in a running industrial frequency air compressor, the controller will ignore the loading and unloading switching interval constraints, immediately start the standby unit ranked first in the efficiency library to replenish air, and at the same time cut off any instructions sent to the faulty unit and push an alarm.

[0126] Controller-unit communication interruption: If the controller fails to respond to communication with a unit for three consecutive data acquisition cycles, it is determined to be a communication interruption. The unit is immediately removed from the collaborative scheduling queue and automatically switched to its original independent pressure control mode (pressure upper and lower limits consistent with system settings), without affecting the collaborative operation of other units. A communication interruption alarm is simultaneously pushed out. Once communication is restored and stabilized for 10 consecutive data acquisition cycles, the unit is automatically reinstated into the collaborative scheduling queue.

[0127] The following is an exemplary description of the multi-machine coordinated industrial frequency air compressor unit control device provided in this application.

[0128] like Figure 3 As shown, this application embodiment provides a multi-machine collaborative industrial frequency air compressor unit control device 300, which includes: The acquisition module 301 is used to acquire the operating data of the industrial frequency air compressor unit to be controlled; Prediction module 302 is used to perform combined prediction based on the operating data of the industrial frequency air compressor unit to obtain the predicted air consumption value of the industrial frequency air compressor unit. The module 303 is used to construct operating condition characteristics based on operating data and gas consumption forecasts, and to identify the operating condition type of the power frequency air compressor unit based on the operating condition characteristics. The solver module 304 is used to construct the optimization objective function and minimize the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit; the optimization objective function is used to describe the operating cost of the industrial frequency air compressor unit; The control module 305 is used to control each industrial frequency air compressor in the industrial frequency air compressor unit according to the predicted state of each industrial frequency air compressor.

[0129] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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 as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0131] like Figure 4 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0132] Specifically, when the processor D100 executes the computer program D102, it acquires the operating data of the industrial frequency air compressor unit to be controlled, then performs combined prediction based on the operating data to obtain the predicted air consumption value of the industrial frequency air compressor unit, then constructs operating condition characteristics based on the operating data and the predicted air consumption value, identifies the operating condition type of the industrial frequency air compressor unit based on the operating condition characteristics, then constructs an optimization objective function, and minimizes the optimization objective function according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit, and finally controls each industrial frequency air compressor in the industrial frequency air compressor unit according to the predicted state of each industrial frequency air compressor. Among them, combined prediction based on actual operating data avoids the shortcomings of single linear prediction in capturing nonlinear fluctuations, improves the accuracy of the predicted air consumption of the unit, effectively reflects the operating conditions of the industrial frequency air compressor unit, constructs an optimized objective function and solves the predicted state of multiple industrial frequency air compressors according to the operating condition type, realizes the coordinated loading and unloading of multiple machines, ensures the efficient operation of the unit, and effectively improves the reliability and group control capability of the industrial frequency air compressor unit.

[0133] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0134] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0135] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0136] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a multi-machine collaborative power frequency air compressor unit control method device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0138] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0139] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0140] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention.

Claims

1. A method for controlling a power frequency air compressor unit in a multi-machine coordination, characterized in that, include: Acquire the operating data of the industrial frequency air compressor unit to be controlled; Based on the combined prediction of the operating data of the industrial frequency air compressor unit, the predicted air consumption value of the industrial frequency air compressor unit is obtained. Operating condition characteristics are constructed based on the operating data and gas consumption prediction values, and the operating condition type of the power frequency air compressor unit is identified based on the operating condition characteristics. An optimization objective function is constructed, and the optimization objective function is minimized according to the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit; the optimization objective function is used to describe the operating cost of the industrial frequency air compressor unit; Based on the predicted state of each industrial frequency air compressor, each industrial frequency air compressor in the industrial frequency air compressor unit is controlled.

2. The control method for a power frequency air compressor unit according to claim 1, characterized in that, The step of combining and predicting the operating data of the power frequency air compressor unit to obtain the predicted air consumption value of the power frequency air compressor unit includes: Based on the operational data, a linear prediction is performed to obtain the initial gas consumption prediction value; Calculate the residual correction value and the physical quantity compensation value; The initial predicted air consumption value, the residual correction value, and the physical quantity compensation value are coupled and superimposed to obtain the predicted air consumption value of the power frequency air compressor unit.

3. The control method for a power frequency air compressor unit according to claim 2, characterized in that, The operational data includes the real-time gas consumption and pressure of the pipeline network at the current moment. The step of performing linear prediction based on the operational data to obtain the initial gas consumption prediction value includes: Through the formula: Calculate the initial gas consumption forecast. ; in, , , , All are weighting coefficients. Indicates the current time Real-time gas consumption in the pipeline network Indicates the current time Real-time pressure of the pipeline network, This indicates the target pressure set for the pipeline network. Indicates time difference, Indicates the current time The rate of change of pipeline pressure; The calculation of residual correction values ​​and physical quantity compensation values ​​includes: Through the formula: Calculate residual correction value ; in, Represents the development coefficient. Indicates the gray action quantity. Indicates the residual prediction step size. This indicates the residual gas consumption. Through the formula: Calculate the compensation value of physical quantities ; in, Indicates equipment attenuation compensation. Indicates temperature and pressure compensation. Indicates pipeline volume compensation: in, This represents the exhaust gas attenuation coefficient per unit hour. This indicates the total cumulative operating time of the unit. This represents the gas consumption sampling value. Indicates standard temperature. Indicates real-time temperature. This indicates the total volume of the plant's pipeline network. It represents standard atmospheric pressure.

4. The control method for a power frequency air compressor unit according to claim 3, characterized in that, The operating data includes the rated discharge capacity of each industrial frequency air compressor in the industrial frequency air compressor unit; The construction of operating condition characteristics based on the operating data and gas consumption forecast includes: The flow rate change rate is calculated based on the operating data and the predicted gas consumption, and the normalized pressure change rate is also calculated. Based on the aforementioned operational data, the average load rate of the computer group; The flow rate change rate, normalized pressure change rate, and unit average load rate are combined into a vector to obtain the operating condition characteristics.

5. The control method for a power frequency air compressor unit according to claim 4, characterized in that, The calculation of the flow rate change rate based on the operating data and gas consumption forecast includes: Through the formula: Calculate the rate of change of flow ; The calculation of the normalized rate of change of pressure includes: Through the formula: Calculate the normalized rate of change of pressure ; The average load rate of the computer group based on the operational data includes: Through the formula: Average load rate of computer group ; in, Indicates the first The rated discharge capacity of a single industrial frequency air compressor Indicates the first Loading indicator for a single industrial frequency air compressor.

6. The control method for a power frequency air compressor unit according to claim 1, characterized in that, The identification of the operating condition type of the power frequency air compressor unit based on the operating condition characteristics includes: Calculate the comprehensive evaluation score of the power frequency air compressor unit based on the operating condition characteristics; Based on the comprehensive evaluation score and the preset score range, the operating condition type of the industrial frequency air compressor unit is identified.

7. The control method for a power frequency air compressor unit according to claim 1, characterized in that, The optimization objective function is: in, Indicates total cost. , , All are weights. This represents the total energy consumption of the system. This represents the energy consumption normalization benchmark. Indicates the number of times the unit status is switched. This represents the normalization benchmark for handover frequency. Indicates the degree of uneven wear. This represents the normalized benchmark for wear deviation.

8. The control method for a power frequency air compressor unit according to claim 1, characterized in that, The step of minimizing the objective function based on the operating condition type to obtain the predicted state of each industrial frequency air compressor in the industrial frequency air compressor unit includes: Define constraints based on the aforementioned operating condition type; Under the constraints of the above conditions, the optimization objective function is minimized to obtain the initial predicted state of each power frequency air compressor; For each of the industrial frequency air compressors, when the initial predicted state of the industrial frequency air compressor meets the unloading switching waiting time condition, the current state of the industrial frequency air compressor is taken as the predicted state; when the initial predicted state of the industrial frequency air compressor does not meet the unloading switching waiting time condition, the initial predicted state of the industrial frequency air compressor is taken as the predicted state.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-machine collaborative power frequency air compressor unit control method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-machine collaborative power frequency air compressor unit control method as described in any one of claims 1 to 8.

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