Methods, devices, equipment and storage media for coordinated scheduling of air compressor groups

Abstract

This application discloses a method, apparatus, equipment, and storage medium for collaborative scheduling of air compressor groups, relating to the field of industrial automation control technology. The method includes: acquiring the operating data of each air compressor in the air compressor group; determining the true energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor based on the operating data, under preset gas demand fluctuation conditions; dynamically adjusting preset energy efficiency weights, lifespan weights, and response weights according to real-time gas demand, and weighting and summing the true energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor to generate a comprehensive scheduling cost index; determining the scheduling priority based on the comprehensive scheduling cost index, and executing scheduling decisions according to the scheduling priority. This determines a scheduling priority that balances energy efficiency, lifespan, and response performance, ultimately achieving multi-objective dynamic coordination in air compressor group scheduling decisions.

Description

Technical Field

[0001] This application relates to the field of industrial automation control technology, and in particular to a method, apparatus, equipment and storage medium for coordinated scheduling of air compressor groups. Background Technology

[0002] In industrial production, air compressor groups, as the core power source of compressed air, are usually composed of multiple air compressors of different models and service lives. They are managed in a unified manner through a collaborative scheduling system in order to meet the air demand of production while achieving energy conservation and rational utilization of equipment life.

[0003] The scheduling system for air compressor groups primarily relies on the theoretical energy efficiency parameters of the equipment at the time of manufacture or early operating data to rank each air compressor by energy efficiency and execute start-up, shutdown, and load allocation decisions according to a fixed priority queue. However, for air compressors with long service lives, the volumetric efficiency of their core compression components gradually declines due to long-term mechanical wear. This performance degradation cannot be directly monitored by conventional temperature, vibration, and pressure sensors, and the resulting small energy consumption increases are easily masked by daily operating fluctuations. Conventional scheduling systems cannot identify this kind of hidden performance degradation and still assign excessively high scheduling priorities to such equipment based on outdated energy efficiency information. When special operating conditions occur at the production end with high-frequency and large-amplitude fluctuations in air demand, the scheduling system, in order to quickly respond to changes in demand, will frequently call up these old air compressors that are mistakenly judged as high-efficiency but are actually inefficient, leading to continuous energy waste. At the same time, it accelerates the insulation aging of their motor windings under long-term compensatory overload operation, increasing the risk of sudden failures.

[0004] Conventional technologies also attempt to assess equipment health by incorporating long-term trend analysis of equipment operating parameters. However, their assessment dimensions are relatively singular, focusing only on energy efficiency or lifespan, without integrating the equipment's actual energy efficiency, potential lifespan degradation risks, and responsiveness to production demands into a unified decision-making framework for dynamic balancing. When faced with complex and ever-changing gas demand, these technologies lack the dynamic adaptability for multi-objective coordinated scheduling, making it difficult to achieve comprehensive optimization of energy efficiency and equipment lifespan while ensuring production continuity. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, equipment, and storage medium for coordinated scheduling of air compressor groups, aiming to solve the technical problem of poor dynamic coordination in air compressor group scheduling in conventional technologies.

[0006] To achieve the above objectives, this application proposes a method for coordinated scheduling of air compressor groups, the method comprising: Obtain the operating data of each air compressor in the air compressor group; Under the preset gas demand fluctuation conditions, the actual energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor are determined based on the operating data. Based on real-time gas demand, the preset energy efficiency weight, lifespan weight, and response weight are dynamically adjusted, and the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor are weighted and summed to generate a comprehensive scheduling cost index. The scheduling priority is determined based on the comprehensive scheduling cost index, and the scheduling decision is executed based on the scheduling priority.

[0007] In one embodiment, the step of determining the true energy efficiency assessment value, life loss risk factor, and response performance factor for each air compressor based on the operating data includes: Obtain the pre-stored energy efficiency benchmark data for each air compressor; Based on the gas production and energy consumption in the operating data, calculate the actual unit gas production energy consumption and compare it with the energy efficiency benchmark data to identify energy consumption deviations. Based on the operating condition characteristic parameters in the operating data, the current operating situation is determined, and the pre-stored energy consumption fluctuation range corresponding to the operating situation is obtained. The air compressor whose energy consumption deviates beyond the energy consumption fluctuation range is taken as the target air compressor, and the energy consumption performance of the target air compressor is cross-compared with the energy consumption performance of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the performance degradation of the equipment itself. If so, the true energy efficiency assessment value is updated. The life loss risk factor is quantified based on the long-term variation trend of the operating parameters of key components in the operating data within a specific load range. The response performance factor is determined based on the response time and load adjustment rate of the response scheduling command.

[0008] In one embodiment, the step of designating the air compressor whose energy consumption deviates beyond the energy consumption fluctuation range as the target air compressor, and cross-comparing the energy consumption performance of the target air compressor with that of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the degradation of the equipment's own performance, and if so, updating the true energy efficiency assessment value includes: Obtain the pre-determined individual energy efficiency degradation rate for each air compressor; Based on the individual energy efficiency degradation rate, multiple reference air compressors are selected. The individual energy efficiency degradation rate of the reference air compressors is at a low level in the group, and the similarity between the current operating situation of the reference air compressors and the operating situation of the target air compressors meets the preset requirements. Calculate the degree of deviation of the actual energy consumption of the target air compressor; Calculate the degree of energy consumption deviation of each of the reference air compressors under operating conditions where the similarity meets the preset requirements; Perform a statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; When the statistical difference analysis results exceed the preset threshold, it is determined that the energy consumption deviation is caused by the performance degradation of the target air compressor itself, and the true energy efficiency assessment value is updated.

[0009] In one embodiment, the step of performing statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor includes: Monitor the overall operating load of the air compressor group, the frequency of air demand fluctuations, and the cumulative operating time of the target air compressor; Based on the overall operating load, the frequency of gas demand fluctuations, and the cumulative operating time, the operating stage of the air compressor group is identified. Based on the identified operational stage, a corresponding preset statistical difference judgment threshold is selected from a preset threshold set; Calculate the difference between the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; Compare the difference with the selected preset statistical difference judgment threshold; Based on the comparison results, the statistical difference analysis results are generated.

[0010] In one embodiment, the step of quantifying the lifespan loss risk factor based on the long-term variation trend of the operating parameters of key components within a specific load range in the operating data includes: Monitor the operating current and winding temperature of each air compressor motor; Divide the load into preset load ranges and calculate the sliding average values ​​of the operating current and the winding temperature in each load range; Perform linear regression on the moving average to obtain the slope of change; When the slope of change continues to exceed the preset micro-deviation threshold, the motor insulation aging acceleration index is calculated based on the deviation magnitude and duration. The life loss risk factor is obtained based on the motor insulation aging acceleration index.

[0011] In one embodiment, after the step of performing a scheduling decision based on the scheduling priority, the method further includes: Record the actual operating intensity and continuous operating duration of the scheduled air compressor during this scheduling cycle; Obtain the current life loss risk factor of the scheduled air compressor; Based on the actual operating intensity, the corresponding penalty coefficient is read from the preset penalty coefficient table; Multiply the penalty coefficient by the actual operating intensity and the continuous operating duration to obtain the cumulative lifespan loss risk; The accumulated amount of lifetime loss risk is added to the current lifetime loss risk factor to obtain the updated lifetime loss risk factor. Based on the updated life loss risk factor, the comprehensive scheduling cost index of the scheduled air compressor will be increased in the next calculation of the comprehensive scheduling cost index.

[0012] In one embodiment, before the step of determining the scheduling priority based on the comprehensive scheduling cost index and performing a scheduling decision based on the scheduling priority, the method further includes: The sum of the comprehensive scheduling cost indices of all operating air compressors under the current scheduling scheme is used as the current total scheduling cost; The simulation switches to the optimal scheduling scheme consisting of air compressors with the lowest overall scheduling cost index, and the sum of the overall scheduling cost indices is taken as the optimal scheduling total cost. Calculate the difference between the current total scheduling cost and the optimal total scheduling cost to obtain the benefit difference. A preset benefit difference threshold value is obtained, and the benefit difference threshold value is dynamically adjusted according to the fluctuation range of real-time gas demand and the rate of change of pipeline pressure. When the difference in benefits exceeds the threshold value for the difference in benefits, the step of determining the scheduling priority based on the comprehensive scheduling cost index and making a scheduling decision is executed. When the difference in benefits does not exceed the threshold value for the difference in benefits, the current scheduling scheme remains unchanged.

[0013] Furthermore, to achieve the above objectives, this application also proposes an air compressor group collaborative scheduling device, which includes: The acquisition module is used to acquire the operating data of each air compressor in the air compressor group; The determination module is used to determine the true energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor based on the operating data under the preset gas demand fluctuation conditions. The adjustment module is used to dynamically adjust the preset energy efficiency weight, lifespan weight, and response weight according to the real-time gas demand, and to perform a weighted summation of the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor to generate a comprehensive scheduling cost index. The scheduling module is used to determine the scheduling priority based on the comprehensive scheduling cost index and to execute scheduling decisions based on the scheduling priority.

[0014] In addition, to achieve the above objectives, this application also proposes an air compressor group collaborative scheduling device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the air compressor group collaborative scheduling method described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the air compressor group collaborative scheduling method described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: By acquiring the operating data of each air compressor in the air compressor group; under the preset gas demand fluctuation conditions, the actual energy efficiency assessment value, life loss risk factor, and response performance factor of each air compressor are determined based on the operating data; according to the real-time gas demand, the preset energy efficiency weight, life weight, and response weight are dynamically adjusted, and the actual energy efficiency assessment value, life loss risk factor, and response performance factor of each air compressor are weighted and summed to generate a comprehensive scheduling cost index; the scheduling priority is determined based on the comprehensive scheduling cost index, and scheduling decisions are executed according to the scheduling priority. In other words, by incorporating the three dimensions of actual energy efficiency, potential lifespan loss risk, and response performance into a unified evaluation framework, when facing high-frequency and large-amplitude fluctuations in gas demand, the scheduling strategy is adaptively matched with real-time operating conditions by dynamically adjusting the weight coefficients of the three dimensions. This enables a quantitative assessment of the current comprehensive operating cost of each air compressor. Therefore, based on the comprehensive scheduling cost index, the actual scheduling value of each air compressor in the group can be accurately ranked, thereby determining the scheduling priority that takes into account energy efficiency, lifespan, and response performance, and ultimately completing the multi-objective dynamic coordination of air compressor group scheduling decisions. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1This is a flowchart illustrating an embodiment of the air compressor group collaborative scheduling method of this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the air compressor group collaborative scheduling method of this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the air compressor group collaborative scheduling method of this application; Figure 4 This application illustrates the application of collaborative scheduling of conventional air compressor groups in a specific application scenario. Figure 5 This is a schematic diagram illustrating the application of the air compressor group collaborative scheduling method of this application in a specific application scenario; Figure 6 This is a schematic diagram of the module structure of the air compressor group coordinated scheduling device according to an embodiment of this application; Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the air compressor group collaborative scheduling method in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or device scheduling platform capable of performing the above functions. The following description uses a device scheduling platform as an example to illustrate this embodiment and the subsequent embodiments.

[0024] Based on this, embodiments of this application provide a method for collaborative scheduling of air compressor groups, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the air compressor group collaborative scheduling method of this application.

[0025] In this embodiment, the air compressor group collaborative scheduling method includes steps S10~S40: Step S10: Obtain the operating data of each air compressor in the air compressor group; It should be noted that the operational data includes, but is not limited to, parameters obtained in real-time through collection or statistical analysis, such as the air output of each air compressor under different operating loads, motor current, motor winding temperature, pipeline pressure, ambient temperature, ambient humidity, power supply voltage, vibration spectrum, and cumulative operating time. The air compressor group refers to a collection of multiple air compressors connected in parallel through a pipeline network to jointly provide compressed air to downstream production equipment. The acquisition refers to the process of transmitting the operational data to the central control unit at a preset frequency using data acquisition elements such as sensors, current transformers, and flow meters deployed on each air compressor and pipeline network.

[0026] Understandably, this step provides the data foundation for all subsequent analysis, evaluation, and scheduling decisions. By comprehensively collecting multi-dimensional parameters of each air compressor during operation, this implementation method can grasp the real-time status and historical performance of each device within the group, providing necessary information support for accurately identifying hidden performance degradation, quantifying lifespan loss risks, and assessing response capabilities.

[0027] Step S20: Under the preset gas demand fluctuation conditions, determine the actual energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor based on the operating data. It should be noted that the preset gas demand fluctuation conditions refer to pre-set operating conditions used to trigger the execution of specific scheduling logic by this method, including but not limited to the rate of decrease of pipeline pressure exceeding a preset threshold within a preset time period, or the fluctuation amplitude of gas flow exceeding a preset proportion within a preset time period. The actual energy efficiency assessment value refers to a quantitative indicator reflecting the current actual compression efficiency of the air compressor main unit after cross-comparison and scenario analysis, eliminating interference from daily operating condition fluctuations. The lifespan loss risk factor refers to a value quantified based on the long-term micro-deviation trend of key component operating parameters within a specific load range, characterizing the degree of accelerated insulation aging or remaining lifespan loss risk of components. The response performance factor refers to a quantitative indicator characterizing the air compressor's ability to meet rapidly changing production demands, determined based on the air compressor's response time to scheduling commands and load adjustment rate.

[0028] Understandably, when gas demand is detected to be entering a special operating condition of high frequency and large fluctuations, this step no longer relies on the theoretical parameters of the equipment at the time of manufacture or outdated historical data. Instead, it uses real-time collected operating data to conduct an independent quantitative assessment of each air compressor from three dimensions: actual energy efficiency, potential life loss, and response speed. This provides an accurate and real-time basis for decision-making in subsequent multi-objective dynamic trade-offs.

[0029] Step S30: Based on real-time gas demand, dynamically adjust the preset energy efficiency weight, lifespan weight, and response weight, and perform a weighted sum of the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor for each air compressor to generate a comprehensive scheduling cost index. It should be noted that the real-time air demand refers to the immediate demand for compressed air at the production end, reflected by the current pipeline pressure change rate, air flow rate, and its fluctuation characteristics. The preset energy efficiency weight, lifespan weight, and response weight are three pre-configured adjustable coefficients used to control the contribution ratio of the actual energy efficiency assessment value, the lifespan loss risk factor, and the response performance factor to the comprehensive scheduling cost index. Dynamic adjustment refers to the process of automatically increasing or decreasing the weight of a certain dimension according to preset adjustment rules based on the changing characteristics of the real-time air demand. The comprehensive scheduling cost index is a single numerical indicator generated through a weighted summation method, used to uniformly measure the comprehensive scheduling cost of each air compressor under current operating conditions.

[0030] Understandably, this step is crucial for achieving dynamic coordination of multiple objectives. Unlike related technologies that use fixed weights or single ranking indicators, this step integrates the evaluation factors of three dimensions into a comparable comprehensive scheduling cost index, and flexibly adjusts the influence of each factor according to real-time gas demand. This allows the scheduling system to achieve an adaptive trade-off between pursuing energy efficiency, protecting equipment lifespan, and ensuring production response, thereby generating a scheduling basis that best matches the current operating conditions.

[0031] Step S40: Determine the scheduling priority based on the comprehensive scheduling cost index, and execute the scheduling decision based on the scheduling priority.

[0032] It should be noted that the scheduling priority refers to the priority call queue formed by sorting the air compressors in the air compressor group according to the comprehensive scheduling cost index from low to high. The lower the index, the lower the comprehensive scheduling cost of the air compressor under the current operating conditions, and the higher the priority of being called. The scheduling decisions include, but are not limited to, starting an air compressor, stopping an air compressor, performing loading or unloading operations on an air compressor, and allocating target loads to each operating air compressor.

[0033] Understandably, this step transforms the generated comprehensive scheduling cost index into directly executable operational instructions. By directly linking scheduling priority with the comprehensive scheduling cost index, this implementation ensures that scheduling decisions always prioritize the air compressor that is optimal under the current operating conditions. This minimizes the overall operating cost while meeting production air demand, completing a closed loop from data analysis to execution control.

[0034] Optionally, prior to step S40, the coordinated scheduling of the air compressor group also includes: The sum of the comprehensive scheduling cost indices of all operating air compressors under the current scheduling scheme is used as the current total scheduling cost; The simulation switches to the optimal scheduling scheme consisting of air compressors with the lowest overall scheduling cost index, and the sum of the overall scheduling cost indices is taken as the optimal scheduling total cost. Calculate the difference between the current total scheduling cost and the optimal total scheduling cost to obtain the benefit difference. A preset benefit difference threshold value is obtained, and the benefit difference threshold value is dynamically adjusted according to the fluctuation range of real-time gas demand and the rate of change of pipeline pressure. When the difference in benefits exceeds the threshold value for the difference in benefits, the step of determining the scheduling priority based on the comprehensive scheduling cost index and making a scheduling decision is executed. When the difference in benefits does not exceed the threshold value for the difference in benefits, the current scheduling scheme remains unchanged.

[0035] It should be noted that the current scheduling scheme refers to the actual scheduling configuration currently in effect, which determines which air compressors are operating and their respective load allocations. The current total scheduling cost refers to the sum of the comprehensive scheduling cost indices of all air compressors in operation under the current scheduling scheme. The optimal scheduling scheme refers to the theoretical scheduling configuration obtained by selecting the combination of air compressors with the lowest cost, based on the comprehensive scheduling cost indices of each air compressor in ascending order, while meeting current air demand, and strictly according to the scheduling priority. The optimal total scheduling cost refers to the sum of the comprehensive scheduling cost indices of all operating air compressors under the optimal scheduling scheme. The benefit difference refers to the difference between the current total scheduling cost and the optimal total scheduling cost, reflecting the theoretical comprehensive scheduling cost savings that can be obtained by switching from the current scheduling scheme to the optimal scheduling scheme. The preset benefit difference threshold is a pre-set cost-saving threshold used to determine whether a scheduling switch is worthwhile. The dynamic adjustment of the benefit difference threshold means that the benefit difference threshold is not a fixed value, but is automatically adjusted according to the fluctuation range of real-time gas demand and the rate of change of pipeline pressure according to preset rules. When the fluctuation range of gas demand is large or the rate of change of pipeline pressure is fast, the benefit difference threshold is lowered to improve the system's responsiveness to demand changes; when gas demand is stable, the benefit difference threshold is raised to prioritize maintaining the stable operation of the current scheduling scheme and avoid additional losses caused by frequent equipment start-ups and shutdowns. Maintaining the current scheduling scheme unchanged means that when the benefit difference amount does not reach the benefit difference threshold, the system abandons the scheduling decision to switch to the optimal scheduling scheme and continues to operate using the current scheduling scheme.

[0036] Understandably, this implementation method introduces a benefit difference assessment and threshold judgment step after generating the comprehensive scheduling cost index and determining the scheduling priority accordingly, before executing the scheduling decision. This implementation method does not immediately execute a scheduling switch every time a difference is found between the theoretically optimal solution and the current solution. Instead, it first calculates the amount of comprehensive scheduling cost savings that can be achieved after the switch and compares it with a benefit difference threshold value that is dynamically adjusted according to real-time operating conditions. Only when the expected cost savings are sufficiently large and exceed the benefit difference threshold value is the scheduling switch actually executed; otherwise, the current solution remains unchanged. This implementation method effectively avoids the back-and-forth oscillation of scheduling priorities and repeated start-ups and shutdowns of equipment caused by frequent small fluctuations in the comprehensive scheduling cost index of each air compressor in the system. While ensuring a sensitive response to changes in gas demand, it also takes into account the stability of the scheduling system and the continuity of equipment operation, reducing the additional mechanical wear and control system overhead caused by excessively frequent scheduling switches.

[0037] This embodiment provides a collaborative scheduling method for air compressor groups. By incorporating three dimensions—actual energy efficiency, potential lifespan loss risk, and response performance—into a unified evaluation framework, it achieves adaptive matching between scheduling strategies and real-time operating conditions when facing high-frequency, large-amplitude fluctuations in air demand. This enables a quantitative assessment of the current comprehensive operating cost of each air compressor. Therefore, based on the comprehensive scheduling cost index, the actual scheduling value of each air compressor within the group can be accurately ranked, thereby determining the scheduling priority that takes into account energy efficiency, lifespan, and response performance. Ultimately, this completes a multi-objective, dynamically coordinated air compressor group scheduling decision.

[0038] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S20, steps S01 to S06 are also included: Step S01: Obtain the pre-stored energy efficiency benchmark data for each air compressor; Step S02: Calculate the actual unit gas production energy consumption based on the gas production and energy consumption in the operating data, and compare it with the energy efficiency benchmark data to identify energy consumption deviations; Step S03: Determine the current operating scenario based on the operating condition characteristic parameters in the operating data, and obtain the pre-stored energy consumption fluctuation range corresponding to the operating scenario; Step S04: The air compressor whose energy consumption deviates beyond the energy consumption fluctuation range is taken as the target air compressor, and the energy consumption performance of the target air compressor is cross-compared with the energy consumption performance of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the performance degradation of the equipment itself. If so, the true energy efficiency assessment value is updated. Step S05: Quantify the life loss risk factor based on the long-term change trend of the operating parameters of key components in the operating data within a specific load range; Step S06: Determine the response performance factor based on the response time and load adjustment rate of the response scheduling instruction.

[0039] It should be noted that the pre-stored energy efficiency benchmark data refers to the mapping relationship data between air production and corresponding energy consumption recorded by each air compressor under multiple stable load points after its initial use or major overhaul. Its storage format includes, but is not limited to, discrete data point tables or fitted curve functions. The actual unit air production energy consumption refers to the electrical energy consumed by the air compressor to produce one unit volume of compressed air under the current operating state, calculated based on real-time collected air production and energy consumption data. The energy consumption deviation refers to the difference or ratio between the actual unit air production energy consumption and the theoretical energy consumption value corresponding to the same or similar load points in the energy efficiency benchmark data. The operating condition characteristic parameters include, but are not limited to, parameters reflecting the current external operating conditions of the air compressor, such as operating load, ambient temperature, pipeline pressure change rate, ambient humidity, and power supply voltage. The current operating scenario refers to classifying the current operating state of the air compressor group into pre-defined typical operating scenarios according to the operating condition characteristic parameters and preset classification rules, including, but not limited to, nighttime low-load stable operation scenario, daytime high-load fluctuating operation scenario, and emergency start-up scenario for a specific production line. The energy consumption fluctuation range refers to the upper and lower limits of normal energy consumption fluctuations for each air compressor under each operating scenario, based on historical operating data under its healthy state. The target air compressor refers to a specific air compressor whose energy consumption deviation continuously exceeds the energy consumption fluctuation range and is preliminarily determined to potentially exhibit performance degradation. The cross-comparison refers to synchronously comparing the energy consumption deviation of the target air compressor with the energy consumption performance of other air compressors of the same model or with similar service life and in healthy state within the air compressor group under the same or similar operating scenarios, to eliminate common fluctuation factors in the current operating scenario. The equipment's own performance degradation refers to the decrease in volumetric efficiency of the air compressor's core compression components due to mechanical wear, distinct from energy consumption deviations caused by operating scenario fluctuations or changes in auxiliary system efficiency. The specific load range refers to several intervals in which the air compressor's operating load is divided according to a preset ratio. The long-term trend refers to the direction of continuous change obtained by regression analysis of the sliding average of the operating parameters of the key components within the preset time window under the specific load range. The key components include, but are not limited to, the air compressor motor. The response time of the dispatch command refers to the time required from the dispatch system issuing the start command to the air compressor's air output reaching the preset ratio of the rated air output. The load adjustment rate refers to the time required from the dispatch system issuing the load or unload command to the air compressor's air output or motor current stabilizing at the target load.

[0040] Understandably, this implementation method uses energy efficiency benchmark data as a reference, compares the real-time calculated unit gas production energy consumption with the benchmark to initially identify energy consumption deviations, and introduces operating scenario identification and energy consumption fluctuation range as secondary filters to effectively eliminate the interference of daily operating condition fluctuations on the judgment of energy consumption deviations. When the energy consumption deviation exceeds the normal fluctuation range of the corresponding scenario, this implementation method does not directly determine it as equipment performance degradation, but cross-compares the energy consumption performance of the target air compressor with other healthy air compressors in the group under similar scenarios to confirm whether the deviation is equipment-specific, thereby accurately identifying the actual energy efficiency decline caused by the equipment's own performance degradation, and updating the actual energy efficiency assessment value accordingly. At the same time, this implementation method analyzes the long-term micro-deviation trend of key component operating parameters within a specific load range to quantify the life loss risk factor; and determines the response performance factor by measuring the air compressor's response time to dispatch commands and load adjustment rate. Through the above three parallel evaluation paths, this implementation method provides accurate and reliable three-dimensional input for the calculation of the comprehensive dispatch cost index.

[0041] In one feasible implementation, the step of designating the air compressor whose energy consumption deviates beyond the energy consumption fluctuation range as the target air compressor, and cross-comparing the energy consumption performance of the target air compressor with that of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the degradation of the equipment's own performance, and if so, updating the true energy efficiency assessment value includes: Obtain the pre-determined individual energy efficiency degradation rate for each air compressor; Based on the individual energy efficiency degradation rate, multiple reference air compressors are selected. The individual energy efficiency degradation rate of the reference air compressors is at a low level in the group, and the similarity between the current operating situation of the reference air compressors and the operating situation of the target air compressors meets the preset requirements. Calculate the degree of deviation of the actual energy consumption of the target air compressor; Calculate the degree of energy consumption deviation of each of the reference air compressors under operating conditions where the similarity meets the preset requirements; Perform a statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; When the statistical difference analysis results exceed the preset threshold, it is determined that the energy consumption deviation is caused by the performance degradation of the target air compressor itself, and the true energy efficiency assessment value is updated.

[0042] It should be noted that the pre-determined individual energy efficiency degradation rate for each air compressor refers to a pre-calibrated percentage value reflecting the degree of degradation of its current energy efficiency relative to its own health status, obtained through periodic health checks or historical operating data analysis. The reference air compressor refers to an air compressor selected from the air compressor group for comparison with the target air compressor in terms of energy consumption performance. Its selection criteria include, but are not limited to, an individual energy efficiency degradation rate that is at a low level within the group and a current operating condition similar to that of the target air compressor. An individual energy efficiency degradation rate at a low level within the group indicates that the air compressor's energy efficiency degradation is relatively minor within the group and can serve as a health reference benchmark for judging whether other air compressors experience specific degradation. A current operating condition similar to that of the target air compressor means that the reference air compressor's operating load, ambient temperature, pipeline pressure change rate, and other operating condition characteristics are within the same or similar range as those of the target air compressor. The actual energy consumption deviation of the target air compressor refers to the deviation between the measured unit gas production energy consumption of the target air compressor under the current operating conditions and the theoretical energy consumption value at the corresponding load point in the energy efficiency benchmark data. The energy consumption deviation of the reference air compressor refers to the deviation between the measured unit gas production energy consumption of each reference air compressor under a similar operating condition to the target air compressor and the theoretical energy consumption value at the corresponding load point in its own energy efficiency benchmark data. The statistical difference analysis includes, but is not limited to, performing hypothesis testing on a sample set consisting of the energy consumption deviation of the target air compressor and the energy consumption deviation of multiple reference air compressors to determine whether they come from the same distribution, thereby determining whether the deviation of the target air compressor is statistically significant. The preset threshold refers to a pre-set statistical significance level used to determine whether the results of the statistical difference analysis are sufficient to support the specificity of the energy consumption deviation of the target air compressor. The determination that the energy consumption deviation is caused by the performance degradation of the target air compressor itself means that when the statistical difference analysis result exceeds the preset threshold, it is confirmed that the energy consumption deviation of the target air compressor is not caused by the general fluctuation of the current operating situation, but by the performance degradation of its own mechanical components. At this time, the update of the true energy efficiency assessment value is triggered.

[0043] Understandably, this implementation method incorporates a mechanism for selecting a reference air compressor and conducting statistical difference analysis to scientifically and quantitatively identify the true cause of the target air compressor's energy consumption deviation. By selecting a reference air compressor within the group with a low energy efficiency degradation rate and operating conditions similar to the target air compressor, the degree of energy consumption deviation between the two is calculated and compared. This implementation method effectively eliminates interference from group energy consumption fluctuations caused by overall changes in the current operating conditions. Only when the statistical difference analysis results show that the energy consumption deviation of the target air compressor is significantly different from that of the reference air compressor can it be confirmed that the deviation is caused by the equipment's own performance degradation, and the true energy efficiency assessment value is updated accordingly. This implementation method avoids misjudgments that may be caused by a single threshold judgment or simple comparison, improves the accuracy and reliability of the true energy efficiency assessment value, and provides a reliable input basis for the subsequent calculation of the comprehensive scheduling cost index.

[0044] In one feasible implementation, the step of performing statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor includes: Monitor the overall operating load of the air compressor group, the frequency of air demand fluctuations, and the cumulative operating time of the target air compressor; Based on the overall operating load, the frequency of gas demand fluctuations, and the cumulative operating time, the operating stage of the air compressor group is identified. Based on the identified operational stage, a corresponding preset statistical difference judgment threshold is selected from a preset threshold set; Calculate the difference between the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; Compare the difference with the selected preset statistical difference judgment threshold; Based on the comparison results, the statistical difference analysis results are generated.

[0045] It should be noted that the overall operating load refers to the proportion of the total air output or total power of all operating air compressors in the air compressor group at the current moment to the group's rated total air output or rated total power. The air demand fluctuation frequency refers to the frequency of changes in pipeline pressure or air flow exceeding a preset range per unit time. The cumulative operating time refers to the total duration of load-bearing operation of the target air compressor since its first commissioning or most recent major overhaul. The operating stage refers to a preset stage into which the life cycle or production rhythm of the air compressor group is currently divided based on indicators such as the overall operating load, the air demand fluctuation frequency, and the cumulative operating time. This includes, but is not limited to, the group break-in period, the group stable operation period, the group aging period, and the seasonal peak operation period. The preset threshold set refers to a set of statistical difference judgment thresholds pre-set for each of the operating stages. The thresholds corresponding to different operating stages can be set to different values ​​to adapt to the differences in equipment operating characteristics at each stage. The preset statistical difference judgment threshold refers to a critical value used to determine whether there is a significant difference between the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor under the currently identified operating stage. The difference amount refers to the difference or ratio between the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor, reflecting the absolute or relative difference in their deviation levels.

[0046] Understandably, this implementation dynamically correlates the judgment threshold for statistical difference analysis with the operating stage of the air compressor group. Since the energy consumption performance and fluctuation characteristics of the air compressor group exhibit systematic differences at different operating stages, using a fixed threshold may lead to misjudgments when the group as a whole enters its aging period, or be overly sensitive during the break-in period. This implementation identifies the current operating stage by monitoring the overall operating load of the group, the frequency of air demand fluctuations, and the cumulative operating time of the target air compressor. It then selects a statistical difference judgment threshold suitable for that stage from a preset threshold set and compares the differences. This allows the judgment criteria for performance degradation to be dynamically adjusted according to the actual operating status of the group, thereby improving the accuracy and robustness of the statistical difference analysis results.

[0047] In one feasible implementation, the step of quantifying the life loss risk factor based on the long-term variation trend of the operating parameters of key components within a specific load range in the operating data includes: Monitor the operating current and winding temperature of each air compressor motor; Divide the load into preset load ranges and calculate the sliding average values ​​of the operating current and the winding temperature in each load range; Perform linear regression on the moving average to obtain the slope of change; When the slope of change continues to exceed the preset micro-deviation threshold, the motor insulation aging acceleration index is calculated based on the deviation magnitude and duration. The life loss risk factor is obtained based on the motor insulation aging acceleration index.

[0048] It should be noted that the operating current refers to the current value on the three-phase power lines of the motor, which is collected in real time by a current transformer, including but not limited to the average current and the real-time values ​​of the current in each phase. The winding temperature refers to the temperature value collected in real time by a temperature measuring element embedded inside the motor winding, which includes but is not limited to a PT100 resistance temperature detector. The preset load range refers to dividing the operating load of the air compressor into multiple continuous ranges according to the percentage of air production or motor current, with each range corresponding to a specific operating intensity range. The moving average value refers to the smoothed value obtained by performing a moving average calculation on the operating current and winding temperature collected under each load range within a preset time window, used to filter out instantaneous fluctuations and extract stable trend components. The linear regression refers to a statistical method of fitting a straight line to the sequence data of the moving average value changing over time. The slope of change refers to the slope of the fitted straight line obtained by the linear regression, which characterizes the average rate and direction of change of the operating current or the winding temperature per unit time. The preset micro-deviation threshold refers to a pre-set critical value for the slope of a small change, lower than the traditional fault alarm threshold, used to detect early signs of slow degradation of equipment parameters before they reach alarm levels. "Continuously exceeding" means that the slope of change remains greater than the preset micro-deviation threshold for multiple consecutive preset statistical periods. The deviation amplitude refers to the difference between the actual sliding average value of the operating current or the winding temperature and the health status reference value. The duration refers to the cumulative time for the deviation amplitude to remain above a preset level. The motor insulation aging acceleration index is a quantitative indicator characterizing the acceleration of the aging rate of motor insulation materials relative to the normal aging rate, calculated comprehensively based on the deviation amplitude, the duration, and preset deviation parameter weighting factors. The weighting factor is an importance coefficient pre-set according to the type of deviation parameter, used to distinguish the different degrees of influence of operating current deviation and winding temperature deviation on insulation aging.

[0049] Understandably, this embodiment provides a method for quantifying lifespan loss risk based on long-term micro-deviation trend analysis. Unlike related technologies that rely on fixed threshold alarms, this embodiment does not wait for operating parameters to reach alarm limits. Instead, it extracts the slow changing trends of operating current and winding temperature over a long period by dividing the load range and using moving average processing. When the slope of change continuously exceeds a preset micro-deviation threshold, even if the absolute values ​​of the parameters are still within the safe range, this embodiment calculates the motor insulation aging acceleration index based on the magnitude and duration of the deviation, and obtains the lifespan loss risk factor accordingly. This method of preemptively capturing weak degradation signals allows the scheduling system to detect the accumulation of equipment lifespan loss risk in advance, thereby proactively avoiding excessive use of such equipment in scheduling decisions and achieving preventative equipment protection.

[0050] Based on the first and second embodiments of this application, the same or similar content as the above embodiments in the third embodiment of this application can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 After step S40, the air compressor group collaborative scheduling method further includes steps S1 to S6: Step S1: Record the actual operating intensity and continuous operating duration of the scheduled air compressor during this scheduling cycle; Step S2: Obtain the current life loss risk factor of the scheduled air compressor; Step S3: Based on the actual operating intensity, read the corresponding penalty coefficient from the preset penalty coefficient table; Step S4: Multiply the penalty coefficient by the actual operating intensity and the continuous operating duration to obtain the accumulated lifespan loss risk. Step S5: Add the accumulated lifetime loss risk amount to the current lifetime loss risk factor to obtain the updated lifetime loss risk factor. Step S6: Based on the updated life loss risk factor, increase the comprehensive scheduling cost index of the scheduled air compressor in the next calculation of the comprehensive scheduling cost index.

[0051] It should be noted that the current scheduling cycle refers to the time period from the start of the current scheduling decision to the triggering of the next scheduling decision. The actual operating intensity refers to the operating load level of the scheduled air compressor within the current scheduling cycle, including but not limited to average load rate, peak load rate, or load fluctuation amplitude expressed as a percentage of rated load. The continuous operating time refers to the cumulative length of time the scheduled air compressor maintains a loaded operating state within the current scheduling cycle. The preset penalty coefficient table refers to a pre-configured data table storing the mapping relationship between different operating intensities and corresponding penalty coefficients. The penalty coefficient is used to weight and amplify the impact of high-load or overload operating behavior on lifespan loss; the higher the operating intensity, the larger the corresponding penalty coefficient. The accumulated lifespan loss risk refers to the increase in lifespan loss caused by actual operating intensity and time within the current scheduling cycle, calculated by multiplying the penalty coefficient, the actual operating intensity, and the continuous operating time. The multiplication means using the penalty coefficient as a weighting factor and multiplying it with the actual operating intensity and the continuous operating time to comprehensively reflect the accelerating effect of operating intensity and time on lifespan loss. The updated lifespan loss risk factor refers to the value obtained by adding the accumulated lifespan loss risk to the current lifespan loss risk factor, reflecting the latest lifespan loss risk status of the equipment. Increasing the comprehensive scheduling cost index of the scheduled air compressor means that in the next calculation of the comprehensive scheduling cost index, the increased value of the updated lifespan loss risk factor leads to a corresponding increase in the weighted summation result, thereby reducing the priority of the air compressor in subsequent scheduling.

[0052] Understandably, this implementation constructs a negative feedback closed-loop mechanism after scheduling execution. When an air compressor is scheduled to perform a high-load or long-term operation task, this implementation does not restore its life loss risk factor to its original state after the scheduling ends. Instead, it calculates an accumulated life loss risk based on the actual operating intensity and continuous operating duration, multiplied by a preset penalty coefficient, and permanently adds this to the air compressor's life loss risk factor. The updated life loss risk factor will automatically increase the air compressor's scheduling cost in the next calculation of the comprehensive scheduling cost index, thereby reducing its priority in subsequent scheduling. This implementation enables the scheduling system to have memory capabilities, allowing for quantitative tracking of the long-term consequences of each scheduling decision. It prevents certain air compressors from being repeatedly over-called due to their excellent short-term response performance, achieving balanced management of equipment life loss while meeting production continuity requirements.

[0053] For example, this application provides a method for collaborative scheduling of air compressor groups, the core of which lies in introducing a comprehensive scheduling cost index to guide the group's operational decisions. This index is not a simple energy efficiency ranking, but rather a weighted evaluation of the actual energy efficiency performance, potential lifespan loss risk, and response performance of each air compressor. This allows the scheduling system to adaptively balance the three objectives of ensuring production response, saving energy, and extending equipment lifespan based on this index.

[0054] The central control unit continuously collects operating data such as motor current, air output, and pipeline pressure for each air compressor under different operating loads. For each air compressor, a comprehensive performance test is conducted after its first use or after a major overhaul, recording its air output and corresponding energy consumption data at multiple stable load points to establish energy efficiency benchmark data under healthy conditions.

[0055] During operation, the system calculates the actual unit energy consumption of each air compressor in real time and compares it with the theoretical energy consumption value at the corresponding load point in the energy efficiency benchmark data to identify energy consumption deviations. To distinguish between equipment performance degradation and daily operating condition fluctuations, the system analyzes operating condition characteristic parameters such as operating load, ambient temperature, and pipeline pressure change rate, categorizes the current operating state into preset typical operating conditions, and obtains the preset energy consumption fluctuation range corresponding to the current operating condition. When the energy consumption deviation continues to exceed this energy consumption fluctuation range, the system designates the corresponding air compressor as the target air compressor and cross-compares its energy consumption performance with that of other healthy air compressors in the group under similar conditions to confirm whether the deviation is caused by equipment performance degradation. If so, its true energy efficiency assessment value is updated.

[0056] Simultaneously, the system continuously monitors the operating current and winding temperature of each air compressor motor. Dividing the load into preset ranges, it calculates the moving average of the operating current and winding temperature within each range, and obtains the slope of change through linear regression analysis of the moving average. When the slope of change continuously exceeds a preset micro-deviation threshold, the system calculates the motor insulation aging acceleration index based on the deviation magnitude and duration, thereby quantifying the lifespan loss risk factor. Furthermore, the system records the response time and load adjustment rate of each air compressor to dispatch commands, determining the response performance factor.

[0057] Based on this, the system dynamically adjusts the preset energy efficiency weight, lifespan weight, and response weight according to real-time gas demand. It then performs a weighted sum of the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor for each air compressor to generate a comprehensive dispatch cost index. Dispatch priorities are determined according to the comprehensive dispatch cost index from low to high, and start / stop and load allocation decisions are executed based on this priority.

[0058] Specifically, this will be illustrated through a comparative example in an industrial scenario. (Refer to...) Figure 4In a group of air compressors, one compressor has been in service for a long time. Due to mechanical wear, its volumetric efficiency has subtly decreased, requiring more electricity to produce the same amount of compressed air. However, this slight efficiency drop is masked by fluctuations in daily air demand. Under the relevant scheduling technology, this compressor, due to its high theoretical energy efficiency parameters at the time of manufacture, has consistently ranked high in the priority queue. When air demand fluctuates frequently and significantly, the system will frequently call upon this compressor for a rapid response, leading to energy waste and accelerated aging of its motor insulation.

[0059] Using this solution, refer to Figure 5 The system identified energy consumption deviations, combined with operational context and cross-comparison, confirming the actual performance degradation of the air compressor and lowering its actual energy efficiency assessment value. Simultaneously, by analyzing the long-term micro-deviation trends of its motor operating current and winding temperature, it discovered accelerated insulation aging and assigned a higher lifespan loss risk factor. Under fluctuating gas demand conditions, the system adjusts weights based on real-time demand, and the calculated comprehensive scheduling cost index prioritizes this older air compressor lower than other air compressors in the group with higher energy efficiency and lower lifespan risk, thus avoiding over-utilization. If the air compressor needs to be temporarily used as a supplement in an emergency, the system records its operating intensity and applies a punitive acceleration to its lifespan loss risk factor to limit its frequency of use in subsequent scheduling.

[0060] In one optional implementation, the cross-comparison process incorporates a reference air compressor selection and statistical difference analysis mechanism. The system acquires the pre-determined individual energy efficiency degradation rate for each air compressor and selects multiple reference air compressors whose individual energy efficiency degradation rates are at a low level within the group and whose current operating conditions are similar to the target air compressor. The energy consumption deviation of the target air compressor and each reference air compressor under similar operating conditions is calculated separately, and statistical difference analysis is performed on both. When the analysis results exceed a preset threshold, it is determined that the energy consumption deviation is caused by the performance degradation of the target air compressor itself, and its true energy efficiency assessment value is updated. The judgment threshold for this statistical difference analysis can be dynamically selected based on the overall operating load of the group, the frequency of gas demand fluctuations, and the operating stage identified by the cumulative operating time; different operating stages correspond to different preset thresholds.

[0061] In an optional implementation, the method further includes a scheduling decision stability management mechanism. Before executing a scheduling decision, the system simulates the sum of the comprehensive scheduling cost indices of all operating air compressors under the current scheduling scheme as the current total scheduling cost. Simultaneously, it simulates switching to the optimal scheduling scheme, which consists of air compressors with the lowest current comprehensive scheduling cost index, as the optimal total scheduling cost. The difference between the two is calculated to obtain the benefit difference. The system obtains a preset benefit difference threshold, which is dynamically adjusted based on the real-time fluctuation range of gas demand and the rate of change of pipeline pressure. Scheduling switching is only executed when the benefit difference exceeds this threshold; otherwise, the current scheduling scheme remains unchanged to avoid frequent equipment start-ups and shutdowns due to minor cost fluctuations.

[0062] In an optional implementation, the method further includes an adaptive protection mechanism for transient operation risk based on partial discharge monitoring. The system installs a high-frequency current sensor in the motor power supply circuit to collect high-frequency components in the current at a high sampling rate. Partial discharge events are identified and their energy levels are assessed using background noise dynamic suppression and event correlation spectrum analysis techniques. Detected partial discharge events are timestamped with transient operating states to label transient-induced partial discharge events. A transient partial discharge risk index is calculated based on the event's frequency, energy, and duration. When this index exceeds a dynamically adjusted risk threshold, the system activates a load buffer mode, adjusts the motor's lifespan loss risk factor to reduce its scheduling priority, and imposes a cooling-off period restriction, preventing the motor from performing high-intensity transient operations again during the cooling-off period.

[0063] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the air compressor group collaborative scheduling method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0064] This application also provides a coordinated scheduling device for air compressor groups; please refer to [reference needed]. Figure 6 The air compressor group coordinated scheduling device includes: The acquisition module 10 is used to acquire the operating data of each air compressor in the air compressor group; The determination module 20 is used to determine the actual energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor based on the operating data under the preset gas demand fluctuation conditions. The adjustment module 30 is used to dynamically adjust the preset energy efficiency weight, lifespan weight and response weight according to the real-time gas demand, and to perform a weighted summation of the actual energy efficiency assessment value, lifespan loss risk factor and response performance factor of each air compressor to generate a comprehensive scheduling cost index. The scheduling module 40 is used to determine the scheduling priority based on the comprehensive scheduling cost index and to execute scheduling decisions based on the scheduling priority.

[0065] The air compressor group collaborative scheduling device provided in this application, employing the air compressor group collaborative scheduling method in the above embodiments, can solve the technical problem of poor dynamic coordination in air compressor group scheduling in conventional technologies. Compared with the prior art, the beneficial effects of the air compressor group collaborative scheduling device provided in this application are the same as those of the air compressor group collaborative scheduling method provided in the above embodiments, and other technical features in the air compressor group collaborative scheduling device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0066] This application provides an air compressor group collaborative scheduling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the air compressor group collaborative scheduling method in the above embodiment 1.

[0067] The following is for reference. Figure 7 The diagram illustrates a structural schematic suitable for implementing the air compressor group collaborative scheduling device in the embodiments of this application. The air compressor group collaborative scheduling device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, tablets, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital televisions and desktop computers. Figure 7 The air compressor group coordinated scheduling device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0068] like Figure 7As shown, the air compressor group coordinated scheduling device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the air compressor group coordinated scheduling device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the air compressor group coordination equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an air compressor group coordination equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0069] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0070] The air compressor group collaborative scheduling device provided in this application, employing the air compressor group collaborative scheduling method in the above embodiments, can solve the technical problem of poor dynamic coordination in air compressor group scheduling in conventional technologies. Compared with the prior art, the beneficial effects of the air compressor group collaborative scheduling device provided in this application are the same as those of the air compressor group collaborative scheduling method provided in the above embodiments, and other technical features in this air compressor group collaborative scheduling device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0071] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0073] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the air compressor group coordinated scheduling method in the above embodiments.

[0074] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0075] The aforementioned computer-readable storage medium may be included in the air compressor group coordination and scheduling equipment; or it may exist independently and not be assembled into the air compressor group coordination and scheduling equipment.

[0076] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the air compressor group coordinated scheduling device, cause the air compressor group coordinated scheduling device to: acquire the operating data of each air compressor in the air compressor group; Under the preset gas demand fluctuation conditions, the actual energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor are determined based on the operating data. Based on real-time gas demand, the preset energy efficiency weight, lifespan weight, and response weight are dynamically adjusted, and the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor are weighted and summed to generate a comprehensive scheduling cost index. The scheduling priority is determined based on the comprehensive scheduling cost index, and the scheduling decision is executed based on the scheduling priority.

[0077] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0079] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0080] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described air compressor group coordinated scheduling method, which can solve the technical problem of poor dynamic coordination in air compressor group scheduling in conventional technology. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the air compressor group coordinated scheduling method provided in the above embodiments, and will not be repeated here.

[0081] All acquisition of signals, information, or actions in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the relevant device owner.

[0082] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for coordinated scheduling of air compressor groups, characterized in that, The method includes: Obtain the operating data of each air compressor in the air compressor group; Under the preset gas demand fluctuation conditions, the actual energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor are determined based on the operating data. Based on real-time gas demand, the preset energy efficiency weight, lifespan weight, and response weight are dynamically adjusted, and the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor are weighted and summed to generate a comprehensive scheduling cost index. The scheduling priority is determined based on the comprehensive scheduling cost index, and the scheduling decision is executed based on the scheduling priority.

2. The method as described in claim 1, characterized in that, The steps for determining the true energy efficiency assessment value, life loss risk factor, and response performance factor for each air compressor based on the operational data include: Obtain the pre-stored energy efficiency benchmark data for each air compressor; Based on the gas production and energy consumption in the operating data, calculate the actual unit gas production energy consumption and compare it with the energy efficiency benchmark data to identify energy consumption deviations. Based on the operating condition characteristic parameters in the operating data, the current operating situation is determined, and the pre-stored energy consumption fluctuation range corresponding to the operating situation is obtained. The air compressor whose energy consumption deviates beyond the energy consumption fluctuation range is taken as the target air compressor, and the energy consumption performance of the target air compressor is cross-compared with the energy consumption performance of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the performance degradation of the equipment itself. If so, the true energy efficiency assessment value is updated. The life loss risk factor is quantified based on the long-term variation trend of the operating parameters of key components in the operating data within a specific load range. The response performance factor is determined based on the response time and load adjustment rate of the response scheduling command.

3. The method as described in claim 2, characterized in that, The step of designating air compressors whose energy consumption deviates beyond the energy consumption fluctuation range as target air compressors, and cross-comparing the energy consumption performance of the target air compressors with that of other air compressors in the air compressor group to confirm whether the energy consumption deviation is caused by the degradation of the equipment's own performance, and if so, updating the true energy efficiency assessment value includes: Obtain the pre-determined individual energy efficiency degradation rate for each air compressor; Based on the individual energy efficiency degradation rate, multiple reference air compressors are selected. The individual energy efficiency degradation rate of the reference air compressors is at a low level in the group, and the similarity between the current operating situation of the reference air compressors and the operating situation of the target air compressors meets the preset requirements. Calculate the degree of deviation of the actual energy consumption of the target air compressor; Calculate the degree of energy consumption deviation of each of the reference air compressors under operating conditions where the similarity meets the preset requirements; Perform a statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; When the statistical difference analysis results exceed the preset threshold, it is determined that the energy consumption deviation is caused by the performance degradation of the target air compressor itself, and the true energy efficiency assessment value is updated.

4. The method as described in claim 3, characterized in that, The step of performing statistical difference analysis on the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor includes: Monitor the overall operating load of the air compressor group, the frequency of air demand fluctuations, and the cumulative operating time of the target air compressor; Based on the overall operating load, the frequency of gas demand fluctuations, and the cumulative operating time, the operating stage of the air compressor group is identified. Based on the identified operational stage, a corresponding preset statistical difference judgment threshold is selected from a preset threshold set; Calculate the difference between the energy consumption deviation of the target air compressor and the energy consumption deviation of the reference air compressor; Compare the difference with the selected preset statistical difference judgment threshold; Based on the comparison results, the statistical difference analysis results are generated.

5. The method as described in claim 2, characterized in that, The step of quantifying the life loss risk factor based on the long-term variation trend of the operating parameters of key components in the operating data within a specific load range includes: Monitor the operating current and winding temperature of each air compressor motor; Divide the load into preset load ranges and calculate the sliding average values ​​of the operating current and the winding temperature in each load range; Perform linear regression on the moving average to obtain the slope of change; When the slope of change continues to exceed the preset micro-deviation threshold, the motor insulation aging acceleration index is calculated based on the deviation magnitude and duration. The life loss risk factor is obtained based on the motor insulation aging acceleration index.

6. The method as described in claim 1, characterized in that, After the step of performing a scheduling decision based on the scheduling priority, the method further includes: Record the actual operating intensity and continuous operating duration of the scheduled air compressor during this scheduling cycle; Obtain the current life loss risk factor of the scheduled air compressor; Based on the actual operating intensity, the corresponding penalty coefficient is read from the preset penalty coefficient table; Multiply the penalty coefficient by the actual operating intensity and the continuous operating duration to obtain the cumulative lifespan loss risk; The accumulated amount of lifetime loss risk is added to the current lifetime loss risk factor to obtain the updated lifetime loss risk factor. Based on the updated life loss risk factor, the comprehensive scheduling cost index of the scheduled air compressor will be increased in the next calculation of the comprehensive scheduling cost index.

7. The method as described in claim 1, characterized in that, Before the step of determining the scheduling priority based on the comprehensive scheduling cost index and executing the scheduling decision based on the scheduling priority, the method further includes: The sum of the comprehensive scheduling cost indices of all operating air compressors under the current scheduling scheme is used as the current total scheduling cost; The simulation switches to the optimal scheduling scheme consisting of air compressors with the lowest overall scheduling cost index, and the sum of the overall scheduling cost indices is taken as the optimal scheduling total cost. Calculate the difference between the current total scheduling cost and the optimal total scheduling cost to obtain the benefit difference. A preset benefit difference threshold value is obtained, and the benefit difference threshold value is dynamically adjusted according to the fluctuation range of real-time gas demand and the rate of change of pipeline pressure. When the difference in benefits exceeds the threshold value for the difference in benefits, the step of determining the scheduling priority based on the comprehensive scheduling cost index and making a scheduling decision is executed. When the difference in benefits does not exceed the threshold value for the difference in benefits, the current scheduling scheme remains unchanged.

8. A collaborative scheduling device for air compressor groups, characterized in that, The device includes: The acquisition module is used to acquire the operating data of each air compressor in the air compressor group; The determination module is used to determine the true energy efficiency assessment value, life loss risk factor and response performance factor of each air compressor based on the operating data under the preset gas demand fluctuation conditions. The adjustment module is used to dynamically adjust the preset energy efficiency weight, lifespan weight, and response weight according to the real-time gas demand, and to perform a weighted summation of the actual energy efficiency assessment value, lifespan loss risk factor, and response performance factor of each air compressor to generate a comprehensive scheduling cost index. The scheduling module is used to determine the scheduling priority based on the comprehensive scheduling cost index and to execute scheduling decisions based on the scheduling priority.

9. A collaborative scheduling device for air compressor groups, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the air compressor group coordinated scheduling method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the air compressor group collaborative scheduling method as described in any one of claims 1 to 7.

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