Intelligent digital frequency conversion triple effect unit energy consumption management method and system

Abstract

The invention provides an intelligent digital frequency conversion triple effect unit energy consumption management method and system, and relates to the technical field of energy consumption management, and the method comprises the steps: obtaining the high-efficiency, medium-efficiency and low-efficiency demand information of a current user; acquiring historical high-efficiency, medium-efficiency and low-efficiency energy supply parameters of the user in historical time, and distributing to obtain high-efficiency, medium-efficiency and low-efficiency weights; frequency conversion parameters for performing frequency conversion control on the triple-effect unit are randomly configured, energy efficiency control prediction resources are configured, triple-effect control prediction is performed, and high-efficiency, medium-efficiency and low-efficiency control parameters are obtained; and according to the high-efficiency, medium-efficiency and low-efficiency weights and the high-efficiency, medium-efficiency and low-efficiency demand information, performing weighted calculation and processing on control deviations of the high-efficiency, medium-efficiency and low-efficiency control parameters to obtain frequency conversion adaptive parameters, performing iterative optimization to obtain optimal frequency conversion parameters, and performing frequency conversion energy consumption management. The technical problem that in the prior art, a frequency conversion triple-effect unit cannot dynamically adjust frequency conversion parameters according to the effect dividing requirement of a user, and then the frequency conversion control effect is affected is solved.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption management, and in particular to an intelligent digital variable frequency triple-effect unit energy consumption management method and system. Background Art

[0002] The variable frequency triple-effect chiller is a highly efficient and energy-saving device that combines cascaded energy utilization with variable frequency speed regulation technology. Its core principle is to maximize energy utilization through the coordinated operation and dynamic adjustment of three levels of efficiency. Based on temperature, variable frequency triple-effect chillers can be categorized into high, medium, and low efficiency levels. Specifically, the high efficiency (first effect) utilizes high-temperature energy to perform core functions, such as cooling. The medium efficiency (second effect) recovers excess medium-temperature energy from the high-efficiency stage and uses it for secondary needs, such as preheating domestic water to 45°C through a secondary heat exchanger. The low efficiency (third effect) recovers excess low-temperature energy from the medium-efficiency stage, achieving ultimate waste heat recovery to meet minimum requirements, such as using the remaining low-temperature heat for swimming pool heating or greenhouse insulation. This eliminates the energy waste of traditional single- or dual-effect chillers. By connecting three stages in series, a cascaded utilization logic is implemented, achieving maximum energy utilization.

[0003] However, traditional control methods usually use fixed parameters to manage the efficiency of variable frequency three-effect units, without considering the efficiency requirements of users. It is impossible to dynamically adjust the variable frequency parameters according to the efficiency requirements of users. For example, when the user focuses on high-efficiency cooling in a certain period of time, energy is still allocated according to the preset ratio, which will cause the core function to experience temperature fluctuations due to insufficient resource supply, thereby affecting the frequency conversion control effect. Summary of the Invention

[0004] The present invention aims to solve the technical problem in the prior art that variable frequency three-effect units cannot dynamically adjust frequency conversion parameters according to the user's efficiency requirements, thereby affecting the frequency conversion control effect, and provides an intelligent digital variable frequency three-effect unit energy consumption management method and system.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides an intelligent digital variable frequency triple-effect unit energy consumption management method, comprising: Obtain the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information; Obtain the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, and assign high-efficiency weights, medium-efficiency weights, and low-efficiency weights, where each energy supply parameter includes the stable energy supply time; Randomly configure frequency conversion parameters for frequency conversion control of the three-effect unit, configure energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, perform three-effect control prediction, and obtain high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; According to the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, the control deviations of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters are weightedly calculated, and the frequency conversion adaptation parameters are obtained through processing, and the optimal frequency conversion parameters are obtained through iterative optimization to perform frequency conversion energy consumption management.

[0006] In a second aspect, the present invention provides an intelligent digital variable frequency three-effect unit energy consumption management system, comprising: Data collection module, used to obtain the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information; The weight allocation module is used to obtain the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time, and allocate high-efficiency weights, medium-efficiency weights and low-efficiency weights, where each energy supply parameter includes the stable energy supply time; a parameter configuration module for randomly configuring frequency conversion parameters for frequency conversion control of a three-effect unit, configuring energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, performing three-effect control prediction, and obtaining high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; The optimization output module is used to perform weighted calculation on the control deviations of the high-efficiency control parameters, the medium-efficiency control parameters and the low-efficiency control parameters according to the high-efficiency weight, the medium-efficiency weight and the low-efficiency weight, as well as the high-efficiency demand information, the medium-efficiency demand information and the low-efficiency demand information, process the obtained frequency conversion adaptation parameters, and iteratively optimize the obtained optimal frequency conversion parameters to perform frequency conversion energy consumption management.

[0007] The beneficial effects of the present invention are: Compared with the existing technology, this application first obtains the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, obtains the user's three-efficiency demand information, and converts the user's energy demand into quantifiable and executable technical indicators, providing the necessary data basis for subsequent weight allocation, predictive control, parameter optimization and other operations. Secondly, the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time are obtained, and high-efficiency weights, medium-efficiency weights and low-efficiency weights are allocated. Among them, each energy supply parameter includes a stable energy supply time. By collecting the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time, the user's dependence on the three-level energy efficiency is quantified, and weight allocation is performed accordingly, solving the defect that traditional fixed weights cannot adapt to the user's dynamic needs. Thirdly, the frequency conversion parameters for frequency conversion control of the three-effect unit are randomly configured. According to the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters, energy efficiency control prediction resources are configured to perform three-effect control prediction, obtain high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters, and according to the historical stable energy supply situation, energy efficiency control prediction resources are allocated. The three-effect control prediction is performed through the frequency conversion three-effect controller to provide the necessary data basis for the subsequent iterative optimization of the frequency conversion parameters. Finally, according to the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, the control deviations of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters are weightedly calculated, and the frequency conversion adaptation parameters are obtained through processing, and the optimal frequency conversion parameters are obtained through iterative optimization to perform frequency conversion energy consumption management. The user's efficiency requirements, historical data and real-time control parameters are taken into consideration, and the frequency conversion adaptation parameters are calculated and generated. Then, the frequency conversion parameters are optimized through random iteration, and the optimal frequency conversion parameters are found with the adaptation parameters as the guide. The optimal frequency conversion parameters can achieve the best energy consumption control stability while meeting the user's efficiency requirements, thereby improving the frequency conversion control effect.

[0008] Through the above technical solution, this application fully considers the user's demand information for three effects, and uses the frequency conversion three-effect controller cluster to carry out three-effect control prediction for different frequency conversion parameter combinations. Then, based on the user's effect-specific demand, historical stable energy supply data and real-time control parameter deviation, a multi-dimensional weighted calculation model is constructed to generate frequency conversion adaptation parameters. Then, through a random iterative optimization strategy, the global optimal frequency conversion parameters are dynamically searched with the frequency conversion adaptation parameters as the guide, and the optimal frequency conversion parameters are finally applied to frequency conversion energy consumption management. In this way, the user's effect-specific demand is accurately matched, and the frequency conversion control effect is improved through iterative optimization of the frequency conversion parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of an intelligent digital frequency conversion triple-effect unit energy consumption management method provided by the present invention; Figure 2This is a structural diagram of an intelligent digital variable frequency three-effect unit energy consumption management system provided by the present invention.

[0010] In the accompanying drawings, the components represented by the reference numerals are as follows: Data collection module 11, weight distribution module 12, parameter configuration module 13, optimization output module 14. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0014] Example 1, as Figure 1 As shown, an embodiment of the present invention provides an intelligent digital variable frequency triple-effect unit energy consumption management method, comprising: S10: Obtaining the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information; The variable frequency three-effect unit is a high-efficiency energy-saving equipment that integrates energy cascade utilization and variable frequency speed regulation technology. Its core lies in maximizing energy utilization through the coordinated operation and dynamic adjustment of three-level efficiency. The variable frequency three-effect unit can be divided into three levels according to temperature: high efficiency, medium efficiency, and low efficiency. Specifically, high efficiency (first effect): using high-temperature energy to complete core functions, such as refrigeration; medium efficiency (second effect): recovering the remaining medium-temperature energy in the high-efficiency link for secondary needs, such as preheating domestic water to 45°C through the secondary heat exchanger generated during the refrigeration process; low efficiency (third effect): recovering the remaining low-temperature energy in the medium-efficiency link to achieve ultimate recovery of waste heat to meet minimum requirements, such as using the last remaining low-temperature heat for swimming pool heating or greenhouse insulation.

[0015] Furthermore, traditional control methods usually use fixed parameters to manage the efficiency of variable frequency three-effect units, without considering the efficiency requirements of users, and cannot dynamically adjust the priority according to the efficiency requirements of users.

[0016] In response to the above problems, this application obtains the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information.

[0017] Specifically, step S10 in the method includes: Obtain the current user's high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature; The high-efficiency demand temperature, the medium-efficiency demand temperature and the low-efficiency demand temperature are used as high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information.

[0018] In the embodiment of the present application, the high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature of the current user are first obtained. Specifically, the high-efficiency demand temperature is the target temperature at which the first effect uses high-temperature energy to complete its core function, the medium-efficiency demand temperature is the target temperature at which the second effect recovers the waste heat of the first effect for secondary demand, and the low-efficiency demand temperature is the target temperature at which the third effect recovers the remaining low-temperature energy of the second effect for meeting the minimum requirement. For example, the user can directly input personalized three-effect demand temperatures through a touch screen, an APP, or a central control system, for example, a high-efficiency demand temperature of 100°C, a medium-efficiency demand temperature of 60°C (such as for preheating domestic water), and a low-efficiency demand temperature of 25°C.

[0019] Secondly, the high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature are used as high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information. For example, the high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature of the current user reflect the user's current demand temperature for the three effects. Therefore, the high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature can be used as the user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information. For example, the high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature of 100°C, 60°C, and 25°C are used as the high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information, respectively.

[0020] In summary, compared to existing technologies, this application obtains the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information. This allows users to obtain the three-efficiency demand information and convert their energy demand into quantifiable and executable technical indicators, providing the necessary data foundation for subsequent weight allocation, predictive control, parameter optimization, and other operations.

[0021] S20: Obtain historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within a user's historical timeframe, and assign high-efficiency weights, medium-efficiency weights, and low-efficiency weights, wherein each energy supply parameter includes a stable energy supply time; Users' demands for the three types of energy can vary significantly and dynamically. For example, during the summer, daytime users may significantly increase their demand for high-efficiency energy supply (such as cooling), while at night they may rely more on medium-efficiency preheating (such as domestic water preheating) or low-efficiency heating (such as swimming pool insulation). Therefore, traditional fixed-parameter, differentiated-efficiency control methods are difficult to adapt to this dynamically changing demand priority, leading to problems such as insufficient core function protection and low waste heat recovery efficiency.

[0022] To address the above issues, this application obtains the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time, and allocates high-efficiency weights, medium-efficiency weights and low-efficiency weights, where each energy supply parameter includes the stable energy supply time.

[0023] Specifically, step S20 in the method includes: Obtain the cumulative efficient energy supply time of the user in the recent historical period that has provided stable and efficient energy supply as a historical efficient energy supply parameter, wherein stable and efficient energy supply includes the efficient energy supply temperature fluctuation range not exceeding a preset efficient temperature fluctuation threshold; Obtain the user's cumulative medium-efficiency energy supply time and the cumulative low-efficiency energy supply time for stable medium-efficiency energy supply and stable low-efficiency energy supply in the recent historical period as historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters; According to the time lengths of the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters and the historical low-efficiency energy supply parameters, a high-efficiency weight, a medium-efficiency weight and a low-efficiency weight are obtained by allocation calculation.

[0024] In the embodiment of the present application, the cumulative time of stable and efficient energy supply by the user in the recent historical period is first obtained as a historical efficient energy supply parameter, wherein stable and efficient energy supply includes the efficient energy supply temperature fluctuation range not exceeding a preset efficient temperature fluctuation threshold. Specifically, the recent historical time is selected as a time window, and within the time window, the cumulative time of stable and efficient energy supply with the actual efficient energy supply temperature fluctuation range being less than or equal to the preset efficient temperature fluctuation threshold is counted as the historical efficient energy supply parameter. The recent historical time can be determined comprehensively based on specific climate, location, and other factors. For example, the recent historical time can be determined as the last 7 days to improve data timeliness, or the last 30 days to filter out occasional fluctuations and capture the user's normalized energy usage pattern. Those skilled in the art can dynamically adjust it according to actual conditions. Furthermore, the preset efficient temperature fluctuation threshold is a pre-set allowable fluctuation range of the efficient temperature. The smaller the preset efficient temperature fluctuation threshold, the higher the stability requirement for the efficient function. Since the efficient function is a core function, the preset efficient temperature fluctuation threshold should be set small, for example, set to ±0.5°C. Those skilled in the art can dynamically adjust it according to actual conditions. For example, based on actual conditions, the most recent historical time is determined to be the last 7 days, and the high-efficiency temperature fluctuation threshold is preset to 100°C±0.5°C. Then, the last 7 days are used as a time window, and all cumulative high-efficiency energy supply times in the last 7 days in which the actual temperature fluctuation range of high-efficiency energy supply does not exceed 100°C±0.5°C are collected from the historical operation data of the equipment, such as 100 hours, and this is used as the historical high-efficiency energy supply parameter. The historical high-efficiency energy supply parameter reflects the user's demand for high-efficiency energy supply. The larger the historical high-efficiency energy supply parameter, the greater the user's demand for high-efficiency energy supply.

[0025] Secondly, the user's cumulative medium-efficiency energy supply time and cumulative low-efficiency energy supply time for stable medium-efficiency energy supply and stable low-efficiency energy supply in the recent historical time are obtained as historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters. Specifically, with the aforementioned recent historical time as the time window, the actual temperature fluctuation range of the medium-efficiency energy supply in the acquisition time window is less than or equal to the preset medium-efficiency temperature fluctuation threshold, and the actual temperature fluctuation range of the low-efficiency energy supply is less than or equal to the preset low-efficiency temperature fluctuation threshold. The accumulated time is used as the historical medium-efficiency energy supply parameter and the historical low-efficiency energy supply parameter. Compared with the preset high-efficiency temperature fluctuation threshold, the preset medium-efficiency temperature fluctuation threshold and the preset low-efficiency temperature fluctuation threshold are not the energy supply core, so they can be appropriately set larger. For example, the preset medium-efficiency temperature fluctuation threshold is set to ±1°C, and the preset low-efficiency temperature fluctuation threshold is set to ±3°C. Those skilled in the art can dynamically set it according to actual conditions. Exemplarily, based on actual conditions, the most recent historical time is determined to be the last 7 days, the medium-efficiency temperature fluctuation threshold is preset to 60℃±1℃, and the low-efficiency temperature fluctuation threshold is preset to 25±3℃. Then, taking the last 7 days as the time window, all cumulative medium-efficiency and low-efficiency energy supply times in the last 7 days, in which the actual temperature fluctuation ranges of medium-efficiency energy supply and low-efficiency energy supply do not exceed 60℃±1℃ and 25±3℃, are collected from the historical operation data of the equipment, such as 60 hours and 40 hours, respectively. This is used as the historical medium-efficiency energy supply parameters and the historical low-efficiency energy supply parameters. The historical medium-efficiency energy supply parameters can reflect the user's demand for medium-efficiency energy supply. The larger the historical medium-efficiency energy supply parameters, the greater the user's demand for medium-efficiency energy supply. The historical low-efficiency energy supply parameters can reflect the user's demand for low-efficiency energy supply. The larger the historical low-efficiency energy supply parameters, the greater the user's demand for low-efficiency energy supply.

[0026] Finally, according to the time length of the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters, the high-efficiency weight, medium-efficiency weight and low-efficiency weight are allocated and calculated, where the high-efficiency weight = historical high-efficiency energy supply parameters / (historical high-efficiency energy supply parameters + historical medium-efficiency energy supply parameters + historical low-efficiency energy supply parameters), the medium-efficiency weight = historical medium-efficiency energy supply parameters / (historical high-efficiency energy supply parameters + historical medium-efficiency energy supply parameters + historical low-efficiency energy supply parameters), and the low-efficiency weight = historical low-efficiency energy supply parameters / (historical high-efficiency energy supply parameters + historical medium-efficiency energy supply parameters + historical low-efficiency energy supply parameters). The high-efficiency weight, medium-efficiency weight and low-efficiency weight are the cumulative proportion of the user's stable high-efficiency, medium-efficiency and low-efficiency energy supply time in the recent historical time. The larger the time proportion, the more the user relies on this function, and the higher the weight is. For example, when the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters, and the historical low-efficiency energy supply parameters are 100 hours, 60 hours, and 40 hours, respectively, the high-efficiency weight = 100 / (100+60+40) = 0.5, the medium-efficiency weight = 60 / (100+60+40) = 0.3, and the low-efficiency weight = 40 / (100+60+40) = 0.2. Among them, the high-efficiency weight is the largest, indicating that users are more dependent on high-efficiency energy supply.

[0027] In summary, compared to the existing technology, this application obtains the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, and assigns high-efficiency weights, medium-efficiency weights, and low-efficiency weights, where each energy supply parameter includes the stable energy supply time. In this way, by collecting the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, the user's dependence on the three levels of energy efficiency is quantified, and weights are assigned accordingly, solving the problem that traditional fixed weights cannot adapt to the user's dynamic needs.

[0028] S30: Randomly configure frequency conversion parameters for frequency conversion control of the three-effect unit, configure energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, perform three-effect control prediction, and obtain high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; Different frequency conversion parameters, such as compressor frequency and water pump speed, will directly affect the energy supply performance of the three-effect unit. Therefore, the control effect of different frequency conversion parameters can be predicted, and the optimal frequency conversion parameters can be quickly determined while ensuring that the user's three-effect needs are met.

[0029] In response to the above problems, this application randomly configures the frequency conversion parameters for frequency conversion control of the three-effect unit, configures energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters, performs three-effect control prediction, and obtains high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters.

[0030] Specifically, step S30 in the method includes: Randomly configure the frequency conversion parameters for frequency conversion control of the three-effect unit; Calculating the average of the historical high-efficiency energy supply parameter, the historical medium-efficiency energy supply parameter, and the historical low-efficiency energy supply parameter to obtain an average stable energy supply time; Calculating the ratio of the average stable energy supply time to the maximum stable energy supply time to obtain an energy efficiency control prediction resource coefficient; Obtain a variable frequency three-effect controller cluster, and randomly select a variable frequency three-effect controller with a proportion of the energy efficiency control prediction resource coefficient; The frequency conversion parameters are input into a plurality of randomly selected frequency conversion three-effect controllers, and the mean of the three-effect control prediction output results is calculated to obtain high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters.

[0031] In the embodiment of the present application, the frequency conversion parameters for frequency conversion control of the three-effect unit are first randomly configured. For example, the initial frequency conversion parameters of the three-effect unit are randomly generated, such as a compressor frequency of 30-60 Hz and a water pump speed of 1000-2000 rpm.

[0032] Next, the average of the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters, and the historical low-efficiency energy supply parameters is calculated to obtain the average stable energy supply time, where the average stable energy supply time = (historical high-efficiency energy supply parameter + historical medium-efficiency energy supply parameter + historical low-efficiency energy supply parameter) / 3. For example, when the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters, and the historical low-efficiency energy supply parameters are 100 hours, 60 hours, and 40 hours, respectively, the average stable energy supply time = (100 + 60 + 40) / 3 = 66.67 hours, reflecting the overall stability of the three-efficiency energy supply operation in the recent historical period. A longer average stable energy supply time indicates better overall stability.

[0033] Next, the ratio of the average stable energy supply time to the maximum stable energy supply time is calculated to obtain the energy efficiency control prediction resource coefficient, wherein the energy efficiency control prediction resource coefficient = average stable energy supply time / maximum stable energy supply time, and the maximum stable energy supply time is the maximum value of the historical high-efficiency energy supply parameter, the historical medium-efficiency energy supply parameter, and the historical low-efficiency energy supply parameter. For example, when the historical high-efficiency energy supply parameter, the historical medium-efficiency energy supply parameter, and the historical low-efficiency energy supply parameter are 100 hours, 60 hours, and 40 hours, respectively, and the average stable energy supply time is 66.67 hours, the maximum stable energy supply time is the historical high-efficiency energy supply parameter of 100 hours, then the energy efficiency control prediction resource coefficient = 66.67 / 100 = 0.6667. The larger the energy efficiency control prediction resource coefficient, the smaller the difference in the historical stable energy supply time of high efficiency, medium efficiency, and low efficiency, and the more stable the overall operation. Conversely, if the energy efficiency control prediction resource coefficient is small, it means that a certain efficiency energy supply time is absolutely dominant and the overall stability is poor.

[0034] Furthermore, a variable frequency three-effect controller cluster is obtained, and a variable frequency three-effect controller with a proportion of the energy efficiency control prediction resource coefficient is randomly selected, wherein the number of variable frequency three-effect controllers randomly selected from the variable frequency three-effect controller cluster = [energy efficiency control prediction resource coefficient * total number of variable frequency three-effect controllers], [] is rounded up. For example, if the total number of VFD triple-effect controllers in the VFD triple-effect controller cluster is 10 and the energy efficiency control prediction resource coefficient is 0.6667, then the number of VFD triple-effect controllers to be randomly selected is [0.6667*10] = 7. That is, 7 VFD triple-effect controllers need to be randomly selected from the VFD triple-effect controller cluster. This is because a larger energy efficiency control prediction resource coefficient indicates smaller differences in the historical stable energy supply time of the three-effect units. In other words, the three-effect unit needs to handle concurrent demands from multiple efficiency segments simultaneously. For example, when the energy efficiency control prediction resource coefficient is 0.9, the three-effect unit may alternate between high-efficiency cooling, medium-efficiency constant temperature, and low-efficiency standby control within an hour. The frequency conversion parameter adjustment for each control method is different, so more frequency conversion triple-effect controllers need to be called for prediction to improve prediction accuracy. Conversely, if the energy efficiency control prediction resource coefficient is low, it means that the energy supply time of a certain effect is absolutely dominant and the frequency conversion parameter adjustment is relatively simple. In this case, the number of frequency conversion triple-effect controllers can be appropriately reduced to improve efficiency and reduce unnecessary resource waste. In this way, the number of variable frequency three-effect controllers to be called is dynamically adjusted by predicting the resource coefficient through energy efficiency control, thus balancing the prediction accuracy and unnecessary resource waste.

[0035] Finally, the variable frequency parameters are input into a plurality of randomly selected variable frequency three-effect controllers, and the mean of the three-effect control prediction output results is calculated to obtain high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters. By calculating the mean of the output results of multiple controllers, random errors can be effectively reduced and prediction accuracy can be improved. For example, randomly configured variable frequency parameters, such as compressor frequency 30-60Hz and water pump speed 1000-2000rpm, are input into 7 randomly selected variable frequency three-effect controllers, and the three-effect control prediction output results are predicted and output respectively. Then, the mean of the three-effect control prediction output results is calculated. For example, the high-efficiency control parameter is 99.8℃, the medium-efficiency control parameter is 59.6℃, and the low-efficiency control parameter is 25.3℃ obtained by predicting and calculating the mean of the output results of the 7 variable frequency three-effect controllers.

[0036] Furthermore, the “obtaining a variable frequency triple-effect controller cluster” includes: According to the control data of the three-effect unit in the historical time, the sample frequency conversion parameter set is collected, as well as the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set and the sample low-efficiency control parameter set under the control of different sample frequency conversion parameters; Randomly dividing a preset number of groups of data within the sample variable frequency parameter set, the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set, and the sample low-efficiency control parameter set to train a first variable frequency three-effect controller; Continue to train multiple variable frequency three-effect controllers to obtain a variable frequency three-effect controller cluster.

[0037] In the embodiment of the present application, first, based on the control data of the three-effect unit in the historical time, a sample frequency conversion parameter set is collected, as well as a sample high-efficiency control parameter set, a sample medium-efficiency control parameter set, and a sample low-efficiency control parameter set under the control of different sample frequency conversion parameters. Specifically, during the historical time, different frequency conversion parameters, such as different frequencies, voltages, and current parameters, are collected as the sample frequency conversion parameter set, and the high-efficiency energy supply temperature, medium-efficiency energy supply temperature, and low-efficiency energy supply temperature under the control of different sample frequency conversion parameters are collected as the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set, and the sample low-efficiency control parameter set. Exemplarily, different frequency conversion parameters and their corresponding high-efficiency energy supply temperature, medium-efficiency energy supply temperature, and low-efficiency energy supply temperature are collected. For example, when the frequency is 30 Hz and the water pump speed is 1000 rpm, the high-efficiency energy supply temperature is 99°C, the medium-efficiency energy supply temperature is 59.5°C, and the low-efficiency energy supply temperature is 25.1°C; when the frequency is 35 Hz and the water pump speed is 1050 rpm, the high-efficiency energy supply temperature is 99.6°C, the medium-efficiency energy supply temperature is 59.8°C, and the low-efficiency energy supply temperature is 25.6°C; when the frequency is 40 Hz and the water pump speed is 1100 rpm, the high-efficiency energy supply temperature is 100.1°C, the medium-efficiency energy supply temperature is 60.2°C, and the low-efficiency energy supply temperature is 25.3°C, etc., to obtain a sample frequency conversion parameter set, a sample high-efficiency control parameter set, a sample medium-efficiency control parameter set, and a sample low-efficiency control parameter set.

[0038] Secondly, a preset number of groups of data are randomly divided within the sample frequency conversion parameter set, sample high-efficiency control parameter set, sample medium-efficiency control parameter set, and sample low-efficiency control parameter set to train the first frequency conversion three-effect controller. Exemplarily, the first frequency conversion three-effect controller can adopt a machine learning or deep learning model, such as a neural network, a random forest, a gradient boosting tree, etc., by learning the mapping relationship between the frequency conversion parameters and the three-effect control parameters, inputting the frequency conversion parameters, and predicting and outputting the three-effect control predicted output results. For example, when constructing the first frequency conversion three-effect controller through a neural network, a hybrid architecture combining a long short-term memory network (LSTM) and a multi-layer perceptron (MLP) can be adopted. The input layer receives the frequency conversion parameters, which are input into a 64-unit LSTM layer after normalization. The temporal inertia of temperature changes is captured through the hyperbolic tangent activation function. The middle layer introduces an attention mechanism to automatically weight key historical data (such as abnormal fluctuation moments). Two fully connected layers (128 / 64 units, LeakyReLU activation) are combined to achieve nonlinear transformation, effectively fitting the mapping relationship between the frequency conversion parameters and the three-effect control parameters (R2 ≥0.95), the output layer adopts an independent head structure to output high-efficiency, medium-efficiency, and low-efficiency control parameters respectively.

[0039] Exemplarily, the training process of the first variable frequency three-effect controller can be achieved through the following technical paths: 1. Data preparation: The sample variable frequency parameter set, sample high-efficiency control parameter set, sample medium-efficiency control parameter set, and sample low-efficiency control parameter set can be randomly divided into a training set, a validation set, and a test set in a ratio of 7:1.5:1.5. 2. Model training: The divided training set data is input into the model. Model parameters, such as the neural network weights and bias, are adjusted through iterative calculation to minimize the error between the three-effect control parameters output by the model and the actual sample data. The optimization objective function can be selected as the mean squared error (MSE) or the mean absolute error (MAE) to optimize the prediction accuracy of the three-effect control parameters. Stable energy supply demand can also be used as a regularization constraint to prevent the model from overfitting to unstable operating data. Finally, the deviation rate between the predicted value and the actual value is calculated on the validation set. When the prediction error is ≤±1°C and the prediction accuracy on the test set reaches above 95%, it is considered converged, and the first variable frequency three-effect controller is trained.

[0040] Finally, multiple variable frequency three-effect controllers are trained to obtain a variable frequency three-effect controller cluster. Specifically, following the same construction and training process as the first variable frequency three-effect controller, multiple variable frequency three-effect controllers are trained and combined to obtain a variable frequency three-effect controller cluster.

[0041] In summary, compared to the prior art, this application randomly configures the frequency conversion parameters for frequency conversion control of the three-effect unit. Based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, it configures energy efficiency control prediction resources, performs three-effect control prediction, and obtains high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters. In this way, based on the historical stable energy supply situation, energy efficiency control prediction resources are allocated, and three-effect control prediction is performed through the frequency conversion three-effect controller, providing the necessary data foundation for subsequent iterative optimization of the frequency conversion parameters.

[0042] S40: Based on the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, the control deviations of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters are weightedly calculated, and the frequency conversion adaptation parameters are obtained through processing, and the optimal frequency conversion parameters are obtained through iterative optimization to perform frequency conversion energy consumption management.

[0043] The above steps obtain the user's demand information for high efficiency, medium efficiency, and low efficiency, as well as the high efficiency, medium efficiency, and low efficiency control parameters under the frequency conversion parameter control. Therefore, the frequency conversion parameters can be scored accordingly. Through iterative optimization, the frequency conversion parameters with the highest score are output to perform frequency conversion energy consumption management.

[0044] In response to the above problems, this application performs weighted calculation on the control deviations of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters based on the high-efficiency weights, medium-efficiency weights and low-efficiency weights, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, processes to obtain the frequency conversion adaptation parameters, and iteratively optimizes to obtain the optimal frequency conversion parameters to perform frequency conversion energy consumption management.

[0045] Specifically, step S40 in the method includes: Obtain the average high-efficiency energy supply information, average medium-efficiency energy supply information, and average low-efficiency energy supply information of users in the recent historical period; Calculating the similarities between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, and performing weighted calculation using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain the frequency conversion accurate adaptation parameter; Calculating the differences between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, to obtain a high-efficiency control deviation, a medium-efficiency control deviation, and a low-efficiency control deviation; Calculating the fluctuation amplitudes of the high-efficiency control deviation, the medium-efficiency control deviation, and the low-efficiency control deviation and the average high-efficiency energy supply information, the average medium-efficiency energy supply information, and the average low-efficiency energy supply information respectively, and performing weighted calculations using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain a frequency conversion deviation fluctuation parameter, and calculating a frequency conversion fluctuation adaptation parameter; The frequency conversion adaptation parameter is calculated based on the frequency conversion accuracy adaptation parameter and the frequency conversion fluctuation adaptation parameter.

[0046] In the embodiment of the present application, the average high-efficiency energy supply information, average medium-efficiency energy supply information, and average low-efficiency energy supply information of the user for stable energy supply in the recent historical time are first obtained, wherein the recent historical time can be comprehensively determined based on specific climate, location and other factors, such as determining it as the last 7 days or the last 10 days. Those skilled in the art can dynamically adjust it according to actual conditions. For example, based on historical operation data, the average high-efficiency energy supply temperature, average medium-efficiency energy supply temperature, and average low-efficiency energy supply temperature of the user for stable energy supply in the last 7 days are collected. For example, 99.6°C, 59.5°C, and 25.1°C are collected and obtained as the average high-efficiency energy supply information, average medium-efficiency energy supply information, and average low-efficiency energy supply information.

[0047] Secondly, respectively calculate the similarity between the high-efficiency control parameters, medium-efficiency control parameters and inefficient control parameters and the high-efficiency demand information, medium-efficiency demand information and inefficient demand information, and use the high-efficiency weight, medium-efficiency weight and inefficient weight for weighted calculation to obtain the frequency conversion accurate adaptation parameter, wherein the similarity can be obtained by calculating the Euclidean distance, cosine similarity, etc. between the high-efficiency, medium-efficiency and inefficient control parameters and the demand information. The similarity reflects the degree of deviation between the high-efficiency, medium-efficiency and inefficient control parameters under the current frequency conversion parameter control and the user's ideal high-efficiency, medium-efficiency and inefficient demand information. The frequency conversion accurate adaptation parameter = high-efficiency weight * high-efficiency similarity + medium-efficiency weight * medium-efficiency similarity + low-efficiency weight * low-efficiency similarity. For example, under the current variable frequency parameter control, the high-efficiency control parameters, the medium-efficiency control parameters, and the low-efficiency control parameters are 99.8°C, 59.6°C, and 25.3°C, respectively; the user's high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information are 100°C, 60°C, and 25°C, respectively; the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight are 0.5, 0.3, and 0.2, respectively; and the Euclidean distances between the high-efficiency, medium-efficiency, and low-efficiency control parameters and the demand information are calculated, for example, |99.8°C-100°C|=0.2, |59.6°C-60°C|=0.4, | 25.3℃-25℃|=0.3 is used as the similarity between the high-efficiency control parameters, the medium-efficiency control parameters, and the low-efficiency control parameters and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information. Then, the frequency conversion accurate adaptation parameter is weightedly calculated based on the similarity and the weight. For example, the frequency conversion accurate adaptation parameter = 0.5*0.2+0.3*0.4+0.2*0.3=0.28. The frequency conversion accurate adaptation parameter can reflect the overall matching accuracy of the user's target demand under the current frequency conversion parameter control. The larger the frequency conversion accurate adaptation parameter, the better the current frequency conversion parameters.

[0048] Thirdly, the differences between the high-efficiency control parameter, medium-efficiency control parameter and low-efficiency control parameter and the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information are calculated respectively to obtain the high-efficiency control deviation, medium-efficiency control deviation and low-efficiency control deviation. For example, under the current frequency conversion parameter control, the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters are 99.8℃, 59.6℃ and 25.3℃ respectively, and the user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information are 100℃, 60℃ and 25℃ respectively. Then the high-efficiency control deviation = |99.8℃-100℃| = 0.2, the medium-efficiency control deviation = |59.6℃-60℃| = 0.4, and the low-efficiency demand information = |25.3℃-25℃| = 0.3. These deviations directly reflect the degree of deviation between the three-effect temperature under the current frequency conversion parameter control and the user's target demand temperature, and also represent the degree of control change that may occur in the future to achieve the user's target demand temperature, that is, the frequency conversion control fluctuation that may be further generated. The larger the deviation, the greater the deviation between the three-effect temperature under the current frequency conversion parameter control and the user's target demand temperature, and the greater the frequency conversion control fluctuation that may be further generated to achieve the user's target demand temperature.

[0049] Furthermore, the fluctuation amplitudes of the high-efficiency control deviation, the medium-efficiency control deviation and the low-efficiency control deviation and the average high-efficiency energy supply information, the average medium-efficiency energy supply information and the average low-efficiency energy supply information are calculated respectively, and the high-efficiency weight, the medium-efficiency weight and the low-efficiency weight are used for weighted calculation to obtain the frequency conversion deviation fluctuation parameter, and the frequency conversion fluctuation adaptation parameter is calculated, wherein, the high-efficiency fluctuation amplitude = high-efficiency control deviation / average high-efficiency energy supply information, the medium-efficiency fluctuation amplitude = medium-efficiency control deviation / average medium-efficiency energy supply information, the low-efficiency fluctuation amplitude = low-efficiency control deviation / average low-efficiency energy supply information, the frequency conversion deviation fluctuation parameter = high-efficiency weight * high-efficiency fluctuation amplitude + medium-efficiency weight * medium-efficiency fluctuation amplitude + low-efficiency weight * low-efficiency fluctuation amplitude, and the frequency conversion fluctuation adaptation parameter = 1-frequency conversion deviation fluctuation parameter. For example, the high-efficiency control deviation, medium-efficiency control deviation and low-efficiency control deviation are 0.2, 0.4 and 0.3 respectively, the average high-efficiency energy supply information, the average medium-efficiency energy supply information and the average low-efficiency energy supply information are 99.6℃, 59.5℃ and 25.1℃ respectively, the high-efficiency weight, medium-efficiency weight and low-efficiency weight are 0.5, 0.3 and 0.2 respectively, then the high-efficiency fluctuation range = 0.2 / 99.6 = 0.002, the medium-efficiency fluctuation range = 0.4 / 59.5 = 0.0067, and the low-efficiency fluctuation range = 0.3 / 25.1=0.012, frequency conversion deviation fluctuation parameter=0.5*0.002+0.3*0.0067+0.2*0.012=0.0054, frequency conversion fluctuation adaptation parameter=1-0.0054=0.9946. In this way, the frequency conversion fluctuation adaptation parameter can reflect the possible control fluctuations that may occur in the future to achieve the user's target temperature. The smaller the frequency conversion fluctuation adaptation parameter, the smaller the future control fluctuations, that is, the more stable the control, which can avoid the user experience being affected by the sharp fluctuations of the frequency conversion parameters.

[0050] Finally, the frequency conversion adaptation parameter is calculated based on the frequency conversion accuracy adaptation parameter and the frequency conversion fluctuation adaptation parameter, where the frequency conversion adaptation parameter = the frequency conversion accuracy adaptation parameter + the frequency conversion fluctuation adaptation parameter. For example, when the frequency conversion accuracy adaptation parameter is 0.28 and the frequency conversion fluctuation adaptation parameter is 0.9946, the frequency conversion adaptation parameter = 0.28 + 0.9946 = 1.2746. The frequency conversion accuracy adaptation parameter can reflect the matching accuracy between the actual triple-effect temperature under the current frequency conversion parameter control and the user's target temperature requirement, and the frequency conversion fluctuation adaptation parameter can reflect the future control stability. Through fusion calculation, the frequency conversion adaptation parameter is finally obtained. The frequency conversion adaptation parameter comprehensively reflects the matching degree of the current frequency conversion control parameters to the user's requirements and the operational stability. The larger the frequency conversion adaptation parameter, the better the current frequency conversion control parameters are, the more they can meet the user's target temperature requirement, and the stronger the future operational stability.

[0051] Furthermore, the “iterative optimization to obtain optimal frequency conversion parameters and perform frequency conversion energy consumption management” includes: Continue to randomly configure frequency conversion parameters, process and obtain frequency conversion adaptation parameters, and perform iterative optimization; After the optimization converges, the optimal frequency conversion parameters corresponding to the maximum value of the frequency conversion adaptation parameters are obtained to perform frequency conversion energy consumption management.

[0052] In the embodiment of the present application, iterative optimization can be used to find the optimal frequency conversion parameters guided by the adaptation parameters, and frequency conversion energy consumption management can be performed accordingly. Specifically: First, continue to randomly configure the frequency conversion parameters, process and obtain the frequency conversion adaptation parameters, and perform iterative optimization. For example, based on the current frequency conversion parameters, continue to randomly generate new frequency conversion parameters. Then, configure energy efficiency control prediction resources, perform three-effect control prediction, and then calculate the frequency conversion accuracy adaptation parameters and frequency conversion fluctuation adaptation parameters separately. Based on these, the frequency conversion adaptation parameters are fused and calculated to obtain the frequency conversion adaptation parameters. In this way, according to the logic of frequency conversion parameters → calculate frequency conversion adaptation parameters → adjust frequency conversion adaptation parameters, continuous iterative optimization is carried out, gradually narrowing the frequency conversion parameter search range and making the frequency conversion parameters evolve in a more optimal direction.

[0053] Secondly, after the optimization converges, the optimal frequency conversion parameters corresponding to the maximum value of the frequency conversion adaptive parameters are obtained, and frequency conversion energy consumption management is performed. For example, during the iterative process, when the change in the frequency conversion adaptive parameters is less than a preset threshold, that is, when the optimization effect stabilizes and no longer significantly improves, the optimization process is considered to have converged. At this point, the frequency conversion parameters corresponding to the maximum value of the frequency conversion adaptive parameters during the iterative process are found to be the optimal frequency conversion parameters. The optimal frequency conversion parameters can achieve optimal energy consumption control stability while meeting the user's target temperature. The preset threshold can be set based on the temperature control accuracy of the three-effect unit. For example, when the accuracy requirement is extremely high, the threshold should be set to a small value, such as 0.01% to 0.1%, to ensure that the parameters converge to a high-precision solution. For ordinary civilian scenarios with low accuracy requirements, the threshold can be relaxed to 0.5% to 1% to improve optimization efficiency. Furthermore, the optimal frequency conversion parameters are applied to frequency conversion energy consumption management to optimize the three-effect energy distribution, reduce ineffective energy loss, and achieve efficient energy consumption management of the three-effect unit.

[0054] In summary, compared to the prior art, this application performs a weighted calculation of the control deviations of the high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters based on the high-efficiency weights, medium-efficiency weights, and low-efficiency weights, as well as the high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information, processes the obtained frequency conversion adaptation parameters, and iteratively optimizes the obtained optimal frequency conversion parameters to perform frequency conversion energy consumption management. In this way, the user's efficiency requirements, historical data, and real-time control parameters are taken into consideration, the frequency conversion adaptation parameters are calculated and generated, and then the frequency conversion parameters are optimized through random iterations. The optimal frequency conversion parameters are found with the adaptation parameters as the guide. The optimal frequency conversion parameters can achieve the best energy consumption control stability while meeting the user's efficiency requirements, thereby improving the frequency conversion control effect.

[0055] In summary, the embodiments of the present application have at least the following technical effects: Compared to existing technologies, this application first obtains the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information. In this way, the user's three-efficiency demand information is obtained, and the user's energy demand is converted into quantifiable and executable technical indicators, providing the necessary data foundation for subsequent weight allocation, predictive control, parameter optimization, and other operations.

[0056] Secondly, this application obtains the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, and assigns high-efficiency weights, medium-efficiency weights, and low-efficiency weights, where each energy supply parameter includes the stable energy supply time. In this way, by collecting the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, the user's dependence on the three levels of energy efficiency is quantified, and weights are assigned accordingly, solving the problem that traditional fixed weights cannot adapt to users' dynamic needs.

[0057] Thirdly, this application randomly configures the frequency conversion parameters for the three-effect unit to perform frequency conversion control. Based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, it configures energy efficiency control prediction resources, performs three-effect control prediction, and obtains high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters. In this way, based on the historical stable energy supply situation, energy efficiency control prediction resources are allocated, and three-effect control prediction is performed through the frequency conversion three-effect controller, providing the necessary data foundation for the subsequent iterative optimization of the frequency conversion parameters.

[0058] Finally, this application calculates the control deviation of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters based on the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, processes to obtain the frequency conversion adaptation parameters, and iteratively optimizes to obtain the optimal frequency conversion parameters to perform frequency conversion energy consumption management. In this way, the user's efficiency requirements, historical data and real-time control parameters are taken into consideration, the frequency conversion adaptation parameters are calculated and generated, and then the frequency conversion parameters are optimized through random iteration. The optimal frequency conversion parameters are found with the adaptation parameters as the guide. The optimal frequency conversion parameters can achieve the best energy consumption control stability while meeting the user's efficiency requirements, thereby improving the frequency conversion control effect.

[0059] Through the above technical solution, this application fully considers the user's demand information for three effects, and uses the frequency conversion three-effect controller cluster to carry out three-effect control prediction for different frequency conversion parameter combinations. Then, based on the user's effect-specific demand, historical stable energy supply data and real-time control parameter deviation, a multi-dimensional weighted calculation model is constructed to generate frequency conversion adaptation parameters. Then, through a random iterative optimization strategy, the global optimal frequency conversion parameters are dynamically searched with the frequency conversion adaptation parameters as the guide, and the optimal frequency conversion parameters are finally applied to frequency conversion energy consumption management. In this way, the user's effect-specific demand is accurately matched, and the frequency conversion control effect is improved through iterative optimization of the frequency conversion parameters.

[0060] Example 2, as Figure 2 As shown, based on the same inventive concept as the intelligent digital variable frequency three-effect unit energy consumption management method provided in Example 1, the embodiment of the present invention further provides an intelligent digital variable frequency three-effect unit energy consumption management system, comprising: The data collection module 11 is used to obtain the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information; The weight allocation module 12 is used to obtain the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters, and the historical low-efficiency energy supply parameters of the user in the historical time, and allocate high-efficiency weights, medium-efficiency weights, and low-efficiency weights, wherein each energy supply parameter includes the stable energy supply time; a parameter configuration module 13 for randomly configuring frequency conversion parameters for frequency conversion control of the three-effect unit, configuring energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, performing three-effect control prediction, and obtaining high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; The optimization output module 14 is used to perform weighted calculation on the control deviations of the high-efficiency control parameters, the medium-efficiency control parameters and the low-efficiency control parameters according to the high-efficiency weight, the medium-efficiency weight and the low-efficiency weight, as well as the high-efficiency demand information, the medium-efficiency demand information and the low-efficiency demand information, process and obtain the frequency conversion adaptation parameters, and iteratively optimize to obtain the optimal frequency conversion parameters to perform frequency conversion energy consumption management.

[0061] The data acquisition module 11 is specifically used for: Obtain the current user's high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature; The high-efficiency demand temperature, the medium-efficiency demand temperature and the low-efficiency demand temperature are used as high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information.

[0062] The weight distribution module 12 is specifically configured to: Obtain the cumulative efficient energy supply time of the user in the recent historical period that has provided stable and efficient energy supply as a historical efficient energy supply parameter, wherein stable and efficient energy supply includes the efficient energy supply temperature fluctuation range not exceeding a preset efficient temperature fluctuation threshold; Obtain the user's cumulative medium-efficiency energy supply time and the cumulative low-efficiency energy supply time for stable medium-efficiency energy supply and stable low-efficiency energy supply in the recent historical period as historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters; According to the time lengths of the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters and the historical low-efficiency energy supply parameters, a high-efficiency weight, a medium-efficiency weight and a low-efficiency weight are obtained by allocation calculation.

[0063] The parameter configuration module 13 is specifically used to: Randomly configure the frequency conversion parameters for frequency conversion control of the three-effect unit; Calculating the average of the historical high-efficiency energy supply parameter, the historical medium-efficiency energy supply parameter, and the historical low-efficiency energy supply parameter to obtain an average stable energy supply time; Calculating the ratio of the average stable energy supply time to the maximum stable energy supply time to obtain an energy efficiency control prediction resource coefficient; Obtain a variable frequency three-effect controller cluster, and randomly select a variable frequency three-effect controller with a proportion of the energy efficiency control prediction resource coefficient; The frequency conversion parameters are input into a plurality of randomly selected frequency conversion three-effect controllers, and the mean of the three-effect control prediction output results is calculated to obtain high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters.

[0064] Furthermore, the “obtaining a variable frequency triple-effect controller cluster” includes: According to the control data of the three-effect unit in the historical time, the sample frequency conversion parameter set is collected, as well as the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set and the sample low-efficiency control parameter set under the control of different sample frequency conversion parameters; Randomly dividing a preset number of groups of data within the sample variable frequency parameter set, the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set, and the sample low-efficiency control parameter set to train a first variable frequency three-effect controller; Continue to train multiple variable frequency three-effect controllers to obtain a variable frequency three-effect controller cluster.

[0065] The optimization output module 14 is specifically configured to: Obtain the average high-efficiency energy supply information, average medium-efficiency energy supply information, and average low-efficiency energy supply information of users in the recent historical period; Calculating the similarities between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, and performing weighted calculation using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain the frequency conversion accurate adaptation parameter; Calculating the differences between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, to obtain a high-efficiency control deviation, a medium-efficiency control deviation, and a low-efficiency control deviation; Calculating the fluctuation amplitudes of the high-efficiency control deviation, the medium-efficiency control deviation, and the low-efficiency control deviation and the average high-efficiency energy supply information, the average medium-efficiency energy supply information, and the average low-efficiency energy supply information respectively, and performing weighted calculations using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain a frequency conversion deviation fluctuation parameter, and calculating a frequency conversion fluctuation adaptation parameter; The frequency conversion adaptation parameter is calculated based on the frequency conversion accuracy adaptation parameter and the frequency conversion fluctuation adaptation parameter.

[0066] Furthermore, the “iterative optimization to obtain optimal frequency conversion parameters and perform frequency conversion energy consumption management” includes: Continue to randomly configure frequency conversion parameters, process and obtain frequency conversion adaptation parameters, and perform iterative optimization; After the optimization converges, the optimal frequency conversion parameters corresponding to the maximum value of the frequency conversion adaptation parameters are obtained to perform frequency conversion energy consumption management.

[0067] In summary, the embodiments of the present application have at least the following technical effects: Compared with the existing technology, this application first obtains the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information through the data acquisition module, obtains the user's three-efficiency demand information, and converts the user's energy demand into quantifiable and executable technical indicators, providing the necessary data basis for subsequent weight allocation, predictive control, parameter optimization and other operations. Secondly, through the weight allocation module, the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time are obtained, and high-efficiency weights, medium-efficiency weights and low-efficiency weights are allocated, wherein each energy supply parameter includes a stable energy supply time. By collecting the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time, the user's dependence on the three-level energy efficiency is quantified, and weight allocation is performed accordingly, solving the defect that traditional fixed weights cannot adapt to the user's dynamic needs. Thirdly, through the parameter configuration module, the frequency conversion parameters for the frequency conversion control of the three-effect unit are randomly configured. According to the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters, the energy efficiency control prediction resources are configured, and the three-effect control prediction is performed to obtain high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters. According to the historical stable energy supply situation, the energy efficiency control prediction resources are allocated, and the three-effect control prediction is performed through the frequency conversion three-effect controller, providing the necessary data basis for the subsequent iterative optimization of the frequency conversion parameters. Finally, through the optimization output module, according to the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, the control deviation of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters is weightedly calculated, and the frequency conversion adaptation parameters are obtained through processing, and the optimal frequency conversion parameters are obtained through iterative optimization to perform frequency conversion energy consumption management. The user's efficiency requirements, historical data and real-time control parameters are taken into consideration, and the frequency conversion adaptation parameters are calculated and generated. Then, the frequency conversion parameters are optimized through random iteration, and the optimal frequency conversion parameters are found with the adaptation parameters as the guide. The optimal frequency conversion parameters can achieve the best energy consumption control stability while meeting the user's efficiency requirements, thereby improving the frequency conversion control effect. In this way, the user's efficiency requirements are accurately matched, and the frequency conversion control effect is improved through iterative optimization of the frequency conversion parameters.

[0068] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0073] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent digital frequency conversion triple-effect unit energy consumption management method, characterized in that: The method comprises: Obtain the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information; Obtain the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, and assign high-efficiency weights, medium-efficiency weights, and low-efficiency weights, where each energy supply parameter includes the stable energy supply time; Randomly configure frequency conversion parameters for frequency conversion control of the three-effect unit, configure energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, perform three-effect control prediction, and obtain high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; According to the high-efficiency weight, medium-efficiency weight and low-efficiency weight, as well as the high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information, the control deviations of the high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters are weightedly calculated, and the frequency conversion adaptation parameters are obtained through processing, and the optimal frequency conversion parameters are obtained through iterative optimization to perform frequency conversion energy consumption management.

2. The energy consumption management method of the intelligent digital frequency conversion triple-effect unit according to claim 1 is characterized in that: Obtain the current user's high-efficiency demand information, medium-efficiency demand information, and low-efficiency demand information, including: Obtain the current user's high-efficiency demand temperature, medium-efficiency demand temperature, and low-efficiency demand temperature; The high-efficiency demand temperature, the medium-efficiency demand temperature and the low-efficiency demand temperature are used as high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information.

3. The energy consumption management method of the intelligent digital frequency conversion triple-effect unit according to claim 1 is characterized in that: Obtain the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters within the user's historical time, and assign high-efficiency weights, medium-efficiency weights, and low-efficiency weights, including: Obtain the cumulative efficient energy supply time of the user in the recent historical period that has provided stable and efficient energy supply as a historical efficient energy supply parameter, wherein stable and efficient energy supply includes the efficient energy supply temperature fluctuation range not exceeding a preset efficient temperature fluctuation threshold; Obtain the user's cumulative medium-efficiency energy supply time and the cumulative low-efficiency energy supply time for stable medium-efficiency energy supply and stable low-efficiency energy supply in the recent historical period as historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters; According to the time lengths of the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters and the historical low-efficiency energy supply parameters, a high-efficiency weight, a medium-efficiency weight and a low-efficiency weight are obtained by allocation calculation.

4. The energy consumption management method of the intelligent digital frequency conversion triple-effect unit according to claim 1 is characterized in that: Randomly configure the frequency conversion parameters for the frequency conversion control of the three-effect unit, configure the energy efficiency control prediction resources according to the historical high-efficiency energy supply parameters, the historical medium-efficiency energy supply parameters, and the historical low-efficiency energy supply parameters, perform three-effect control prediction, and obtain high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters, including: Randomly configure the frequency conversion parameters for frequency conversion control of the three-effect unit; Calculating the average of the historical high-efficiency energy supply parameter, the historical medium-efficiency energy supply parameter, and the historical low-efficiency energy supply parameter to obtain an average stable energy supply time; Calculating the ratio of the average stable energy supply time to the maximum stable energy supply time to obtain an energy efficiency control prediction resource coefficient; Obtain a variable frequency three-effect controller cluster, and randomly select a variable frequency three-effect controller with a proportion of the energy efficiency control prediction resource coefficient; The frequency conversion parameters are input into a plurality of randomly selected frequency conversion three-effect controllers, and the mean of the three-effect control prediction output results is calculated to obtain high-efficiency control parameters, medium-efficiency control parameters and low-efficiency control parameters.

5. The intelligent digital frequency conversion triple-effect unit energy consumption management method according to claim 4 is characterized in that: Get the variable frequency three-effect controller cluster, including: According to the control data of the three-effect unit in the historical time, the sample frequency conversion parameter set is collected, as well as the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set and the sample low-efficiency control parameter set under the control of different sample frequency conversion parameters; Randomly dividing a preset number of groups of data within the sample variable frequency parameter set, the sample high-efficiency control parameter set, the sample medium-efficiency control parameter set, and the sample low-efficiency control parameter set to train a first variable frequency three-effect controller; Continue to train multiple variable frequency three-effect controllers to obtain a variable frequency three-effect controller cluster.

6. The intelligent digital frequency conversion triple-effect unit energy consumption management method according to claim 1 is characterized in that: According to the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight, as well as the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, weighted calculation of control deviations of the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter is performed to obtain a variable frequency adaptation parameter, including: Obtain the average high-efficiency energy supply information, average medium-efficiency energy supply information, and average low-efficiency energy supply information of users in the recent historical period; Calculating the similarities between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, and performing weighted calculation using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain the frequency conversion accurate adaptation parameter; Calculating the differences between the high-efficiency control parameter, the medium-efficiency control parameter, and the low-efficiency control parameter and the high-efficiency demand information, the medium-efficiency demand information, and the low-efficiency demand information, respectively, to obtain a high-efficiency control deviation, a medium-efficiency control deviation, and a low-efficiency control deviation; Calculating the fluctuation amplitudes of the high-efficiency control deviation, the medium-efficiency control deviation, and the low-efficiency control deviation and the average high-efficiency energy supply information, the average medium-efficiency energy supply information, and the average low-efficiency energy supply information respectively, and performing weighted calculations using the high-efficiency weight, the medium-efficiency weight, and the low-efficiency weight to obtain a frequency conversion deviation fluctuation parameter, and calculating a frequency conversion fluctuation adaptation parameter; The frequency conversion adaptation parameter is calculated based on the frequency conversion accuracy adaptation parameter and the frequency conversion fluctuation adaptation parameter.

7. The intelligent digital frequency conversion triple-effect unit energy consumption management method according to claim 1 is characterized in that: Iterative optimization obtains the optimal frequency conversion parameters and performs frequency conversion energy consumption management, including: Continue to randomly configure frequency conversion parameters, process and obtain frequency conversion adaptation parameters, and perform iterative optimization; After the optimization converges, the optimal frequency conversion parameters corresponding to the maximum value of the frequency conversion adaptation parameters are obtained to perform frequency conversion energy consumption management.

8. An intelligent digital frequency conversion three-effect unit energy consumption management system, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: Data collection module, used to obtain the current user's high-efficiency demand information, medium-efficiency demand information and low-efficiency demand information; The weight allocation module is used to obtain the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters and historical low-efficiency energy supply parameters within the user's historical time, and allocate high-efficiency weights, medium-efficiency weights and low-efficiency weights, where each energy supply parameter includes the stable energy supply time; a parameter configuration module for randomly configuring frequency conversion parameters for frequency conversion control of a three-effect unit, configuring energy efficiency control prediction resources based on the historical high-efficiency energy supply parameters, historical medium-efficiency energy supply parameters, and historical low-efficiency energy supply parameters, performing three-effect control prediction, and obtaining high-efficiency control parameters, medium-efficiency control parameters, and low-efficiency control parameters; The optimization output module is used to perform weighted calculation on the control deviations of the high-efficiency control parameters, the medium-efficiency control parameters and the low-efficiency control parameters according to the high-efficiency weight, the medium-efficiency weight and the low-efficiency weight, as well as the high-efficiency demand information, the medium-efficiency demand information and the low-efficiency demand information, process the obtained frequency conversion adaptation parameters, and iteratively optimize the obtained optimal frequency conversion parameters to perform frequency conversion energy consumption management.

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