Display cabinet self-adapting energy-saving control method based on distributed control

Abstract

The present application belongs to the technical field of adaptive energy-saving control, and particularly relates to a display cabinet adaptive energy-saving control method based on distributed control, steps of which comprise: building a communication module and a display cabinet to obtain the average energy consumption of the display cabinet in the region; building a data acquisition system inside and outside the display cabinet; designing a double closed loop energy-saving logic; building a display cabinet distributed control, based on the real-time energy consumption of the display cabinet, the heat temperature coefficient of the goods, and the current internal temperature of the display cabinet, to obtain the dynamic adjustment task of the local control node interaction data; obtaining the equipment operation signal characteristic data in the data storage library to establish a fault prediction model; based on the data on the cloud management node, using the fault prediction model to predict the peak value of use, and adjusting the operation strategy of the display cabinet. The present application can realize precise energy-saving regulation and control, optimize energy allocation, and reduce operating costs through the double closed loop energy-saving logic combined with regional average energy consumption calculation and energy-saving potential value sorting.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of adaptive energy-saving control, and particularly relates to a display cabinet adaptive energy-saving control method based on distributed control. BACKGROUND

[0002] In modern commercial activities, display cabinets, as key equipment for displaying and exhibiting various goods, are widely used in supermarkets, convenience stores, shopping malls, and various specialty stores. From cold drinks, dairy products, to baked goods, fresh flowers and plants, to electronic products, jewelry, and other goods, different types of goods rely on display cabinets to maintain a suitable storage environment while attracting the attention of consumers. The widespread use of display cabinets has also brought significant energy consumption problems, with the power consumption of their continuous operation accounting for a significant proportion of the total energy consumption of commercial sites.

[0003] Under the current technical development trend, energy issues are of great concern, and as high-energy-consuming equipment in the commercial field, the development of energy-saving technology for display cabinets is crucial. The rise of emerging technologies such as the Internet of Things has accelerated the process of intelligent management of commercial equipment, and integrating display cabinets into an intelligent system ensures that they remain in good operating condition.

[0004] In the prior art, CN102176146B discloses a display cabinet adaptive energy-saving control method, which belongs to an adaptive energy-saving control method. The technical solution is as follows: a display cabinet is installed with an adaptive energy-saving control device, which has a controller; a detection input circuit connected to the controller, a door switch sensor, an evaporator sensor, and a cabinet temperature sensor connected to the detection input circuit; a control output circuit connected to the controller, a compressor control circuit, a defrosting control circuit, a lighting control circuit, a fan control circuit, and an alarm control circuit connected to the control output circuit.

[0005] However, the existing technology, including the above-mentioned patent, has problems such as poor control effect, inability to accurately predict equipment failure, and inability to effectively respond to usage peaks during actual application. SUMMARY

[0006] To solve the above technical problems, the purpose of the present application is to provide a display cabinet adaptive energy-saving control method based on distributed control, which can realize precise energy-saving regulation and control, optimize energy allocation, and reduce operating costs through the combination of double-closed-loop energy-saving logic, regional average energy consumption calculation, and energy-saving potential value sorting.

[0007] The present application is implemented through the following technical solutions:

[0008] The display cabinet adaptive energy-saving control method based on distributed control includes the following steps:

[0009] S1, build a communication module and a showcase, the showcase building includes: setting a local control node, a regional edge computing node, a cloud management node collaborative framework, based on the regional edge computing node, the average energy consumption of the regional showcase is obtained;

[0010] S2, build a data acquisition system inside and outside the showcase, the data acquired by the data acquisition system includes: showcase internal environment data, showcase external environment data, equipment operation data, user behavior data;

[0011] S3, design a double closed loop energy saving logic, the internal environment is adjusted by the intelligent control valve, the external environment is adjusted by the average energy consumption of the regional showcase and the target deviation;

[0012] S4, build a showcase distributed control, based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, the current internal temperature of the showcase, the dynamic adjustment task of the local control node interaction data is obtained;

[0013] S5, obtain the equipment operation signal feature data in the data storage library, and establish a fault prediction model;

[0014] S6, based on the data on the cloud management node, the use peak is predicted by using the fault prediction model, and the operation strategy of the showcase is adjusted.

[0015] Preferably, in S1, the showcase local control node collects temperature, humidity, light intensity, compressor running current, cabinet door opening and closing times data in real time, and sends them to the regional edge computing node through the wireless communication module; the regional edge computing node collects regional power grid load rate data, and receives data uploaded by multiple showcase local control nodes; the cloud management node collects comprehensive data uploaded by each regional edge computing node, including regional showcase average energy consumption, equipment fault early warning, external energy policy information and industry energy consumption standard data; each node adjusts the data acquisition frequency and strategy parameters according to the actual energy saving effect through the bidirectional communication feedback mechanism.

[0016] Preferably, the regional edge computing node receives data uploaded by multiple showcase local control nodes, and analyzes the average energy consumption of the regional showcase The calculation formula is:

[0017] ;

[0018] Wherein: n is the total number of regional showcases, i is the number of regional showcases, is the energy consumption of the i-th showcase, is the weight factor of the i-th showcase;

[0019] The local control node transmits the collected data to the regional edge computing node in real time via a wireless communication module. At the same time, the local control node receives control commands from the regional edge computing node and adjusts the operating status of the display cabinet equipment, adjusts the compressor frequency, and switches the lighting according to the data commands, thereby affecting the average energy consumption calculation of the regional edge computing node.

[0020] The cloud management node collects average energy consumption and device operating status data uploaded by edge computing nodes in various regions, and integrates and analyzes the data.

[0021] Preferably, in step S2, a data acquisition system is built inside and outside the display case to acquire the following data based on the local control node of the display case:

[0022] The internal environmental data of the display case includes: temperature, humidity, and light intensity; the external environmental data of the display case includes: room temperature, humidity, and light cycle; the equipment operation data includes: compressor operating current, compressor operating frequency, and ventilation system operating power; and the user behavior data includes: user dwell time and number of times the cabinet door is opened and closed.

[0023] Calculate the energy consumption thresholds inside and outside the display case:

[0024] ;

[0025] in, As the energy consumption threshold, for Weighting factors This is the basic energy consumption threshold, where u is the parameter data number. for Weighting factors Let u be the value of the u-th parameter data, and m be the total number of parameter data. The local control node of the display cabinet will feed back the adjusted energy consumption data to the edge computing node of the region.

[0026] Preferably, in S3, the inner loop control adjusts the compressor operating frequency according to the intelligent control valve, as shown below:

[0027] ;

[0028] Where k is the sequence number of the control cycle. This represents the adjustment amount of the compressor's operating frequency in the k-th control cycle. This is the proportionality coefficient. Set the deviation value between the temperature and the actual temperature for the k-th control cycle. Here, l represents the integral coefficient, and l represents the summation index. To control the cumulative sum of periodic temperature deviations, The differential coefficients are... Set the deviation value of temperature and actual temperature for the k-1 control cycle.

[0029] Preferably, the outer loop control area edge computing node adjusts the temperature set value and lighting brightness parameter of the showcase local control node according to the energy consumption deviation, and adjusts the inner loop control according to the data feedback of the showcase local control node.

[0030] The calculation formula of the energy consumption deviation of the area edge computing node is:

[0031] ;

[0032] Wherein, is the deviation value, is the average energy consumption of the showcases in the area, is the energy saving target value.

[0033] Preferably, in S4, based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, and the current temperature, the energy saving task is allocated according to the following rules:

[0034] S4.1, analyze to obtain the energy saving potential value of each showcase;

[0035] The calculation formula of the energy saving potential value of the showcase is:

[0036] ;

[0037] Wherein, is the energy saving potential value of the i-th showcase, is the weight factor of is the maximum energy consumption value allowed by the showcase, is the current actual energy consumption value of the i-th showcase, is the heat stability coefficient of the goods stored in the i-th showcase, is the weight factor of is the pre-set temperature threshold value of the showcase, is the current actual temperature of the showcase, is the weight factor of ;

[0038] S4.2, sort the showcases in descending order of energy saving potential value, and preferentially allocate energy saving tasks to the showcases with large energy saving potential values.

[0039] ​​Preferably, in S5, the fault prediction model takes the device operation signal as an important input, the current signal is processed to extract the mean, variance, peak value, and pulse factor characteristics, and the characteristics reflect the fluctuation and energy distribution of the current signal in the time scale to obtain the stability of the device operation; meanwhile, the current signal is converted from the time domain to the frequency domain by means of the frequency domain Fourier transform analysis means, and the main frequency, harmonic frequency, and corresponding amplitude characteristics are extracted, and the frequency domain characteristics help to reveal the vibration characteristics and potential mechanical fault signs during the device operation;

[0040] The Fourier transform formula is:

[0041] ;

[0042] where t is the time variable, is a continuous signal in the frequency domain, is the representation of the signal in the frequency domain, j is the imaginary unit, h is the angular frequency, is a complex exponential function.

[0043] Preferably, in the fault prediction model, for parameter updating of the fault prediction model, after the maintenance personnel complete the maintenance work on the showcase, the new device operation data of the showcase, the information of the repaired and replaced parts, and the adjusted internal and external environment data of the showcase are fed back to the fault prediction model, and the fault prediction model optimizes and adjusts the parameters in the fault prediction model based on the new data by using the parameter updating algorithm in machine learning, so that the model can continuously and accurately predict the future faults of the showcase and adapt to the state changes after the maintenance of the showcase.

[0044] The parameter updating algorithm formula is:

[0045] ;

[0046] where, is the value of the parameter to be optimized after the z+1th iteration, is the value of the parameter in the zth iteration, is the learning rate, is the objective function about the gradient of the parameter .

[0047] Preferably, in step S6, the cloud management node continuously collects and stores energy consumption data, operating parameters, environmental parameters, and historical peak usage data of each display cabinet within the area. Using time series analysis algorithms combined with machine learning technology, it predicts peak usage for a specific time period in the future. When a peak usage is predicted to occur, the cloud management node sends an instruction to the regional edge computing node. The regional edge computing node adjusts the equipment operation strategy of the display cabinet in advance based on the instruction and the actual situation of the local display cabinet. Specific adjustments include: reducing the cooling intensity of the display cabinet, adjusting the brightness and on-time of the lighting system, and optimizing the start-stop logic of the compressor. At the same time, the regional edge computing node monitors the operating status of the display cabinet in real time and dynamically adjusts the operating strategy based on feedback information.

[0048] The present invention has the following beneficial effects:

[0049] This invention achieves precise energy-saving regulation, optimizes energy allocation, and reduces operating costs by combining dual-closed-loop energy logic with regional average energy consumption calculation and energy-saving potential value ranking. Through collaborative communication between local, regional, and cloud nodes, and processing of multi-source data, it enables real-time monitoring, precise control, and intelligent management of the display cabinet's operating status. The fault prediction model uses equipment operation signal analysis to identify potential fault risks in advance, ensuring stable equipment operation, reducing downtime losses, and extending service life. The cloud management node predicts peak usage, enabling the display cabinet to better cope with peak periods, avoid excessive energy consumption and malfunctions, and improve energy efficiency and equipment reliability. Attached Figure Description

[0050] Figure 1 This is a flowchart of the method of the present invention;

[0051] Figure 2 This is a flowchart of the energy consumption control and data processing of the display cabinet in an embodiment of the present invention;

[0052] Figure 3 This is a flowchart illustrating the display cabinet fault prediction and peak usage response in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0054] like Figure 1 As shown, the adaptive energy-saving control method for display cabinets based on distributed control includes the following steps:

[0055] S1. Set up the communication module and display cabinet. The display cabinet setup includes: setting up a collaborative framework of local control nodes, regional edge computing nodes, and cloud management nodes. Based on the regional edge computing nodes, the average energy consumption of the display cabinets in the region is obtained.

[0056] The showcase local control node collects temperature, humidity, light intensity, compressor operating current, and cabinet door opening and closing data in real time, and sends the data to the regional edge computing node through a wireless communication module.

[0057] The regional edge computing node collects data such as power grid load rate in the region, and receives data uploaded by multiple showcase local control nodes.

[0058] The cloud management node collects comprehensive data uploaded by the regional edge computing nodes, including average energy consumption of showcases in the region, device failure warning, external energy policy information, and industry energy consumption standard data. The nodes adjust data collection frequency and strategy parameters according to actual energy-saving effect through a bidirectional communication feedback mechanism.

[0059] The regional edge computing node receives data uploaded by multiple showcase local control nodes and analyzes the average energy consumption of showcases in the region , and the calculation formula is:

[0060] ;

[0061] wherein n is the total number of showcases in the region, i is the number of showcases in the region, is the energy consumption of the i-th showcase, is a weight factor.

[0062] The formulas in this embodiment can be de-dimensioned when calculating to simplify the calculation.

[0063] The local control node transmits the collected data to the regional edge computing node in real time through a wireless communication module. Meanwhile, the local control node receives control instructions issued by the regional edge computing node, adjusts the operating state of the showcase device according to the data instructions, adjusts the compressor frequency and switch lighting, and thus affects the average energy consumption calculation of the regional edge computing node.

[0064] The cloud management node collects average energy consumption and device operating state data uploaded by each regional edge computing node, and performs data integration and analysis.

[0065] The above-set is obtained from a database, and is obtained by establishing a historical calculation according to historical data , a mapping set of and its corresponding weight factor is established, and the current is obtained. In the following, , , , , All of these are obtained through a mapping set of historical data and weight factors established in the database, that is, the corresponding weight factors are obtained based on the current data.

[0066] In this embodiment, during the setup of the display cabinet system, local control nodes collect real-time data such as temperature and humidity and transmit it to regional edge computing nodes. While collecting regional power grid load rate data, the regional edge computing nodes also receive data from multiple local control nodes, analyze and calculate the average energy consumption of the display cabinets within the region, and issue instructions to the local control nodes to adjust equipment operating status to influence energy consumption. The cloud management node collects and integrates comprehensive data such as average energy consumption and equipment operating status uploaded by the regional edge computing nodes. Through a two-way communication feedback mechanism, the nodes adjust the data collection frequency and strategy parameters based on the actual energy-saving effect.

[0067] A distributed control system utilizing local control nodes, regional edge computing nodes, and cloud management nodes facilitates the collection of internal data from the display cabinet by the local control nodes. Regional edge computing nodes collect internal and external data and calculate average energy consumption. Two-way communication exists between nodes, allowing for adjustments to data collection and strategies based on energy-saving effects. Cloud management nodes integrate and analyze data to support decision-making. This system comprehensively collects data, accurately calculates energy consumption, and enables dynamic optimization, contributing to intelligent management and energy efficiency improvements for the display cabinet.

[0068] S2. Build a data acquisition system for the inside and outside of the display case. The data acquired by the data acquisition system includes: internal environmental data of the display case, external environmental data of the display case, equipment operation data, and user behavior data.

[0069] In S2, a data acquisition system for the inside and outside of the display case is built to acquire the following data based on the local control node of the display case:

[0070] The internal environmental data of the display case includes: temperature, humidity, and light intensity; the external environmental data of the display case includes: room temperature, humidity, and light cycle; the equipment operation data includes: compressor operating current, compressor operating frequency, and ventilation system operating power; and the user behavior data includes: user dwell time and number of times the cabinet door is opened and closed.

[0071] Calculate the energy consumption thresholds inside and outside the display case:

[0072] ;

[0073] in, As the energy consumption threshold, for Weighting factors Here, u represents the base energy consumption threshold, and u is the parameter data (the data acquired by the data acquisition system) number. for Weighting factors For the u-th parameter data value, m is the total number of parameter data, the local control node of the showcase feeds back the adjusted energy consumption data to the regional edge computing node.

[0074] The energy consumption threshold sets a reasonable range for the energy consumption of the showcase. When the actual energy consumption of the showcase exceeds this threshold, it indicates that its energy consumption is too high and energy-saving measures need to be taken; on the contrary, if the energy consumption is lower than the threshold, it indicates that the current energy consumption control is good, or there may be a situation of insufficient device operation.

[0075] The flow chart of the energy consumption control and data processing of the showcase is shown in Figure 2 .

[0076] In this embodiment, the local control node first collects data such as internal and external environment, device operation, user behavior, etc. of the showcase and transmits them to the regional edge computing node, then builds and trains the model to learn the relationship between each parameter factor and energy consumption, calculates the energy consumption threshold according to the formula, determines the parameter value, weight factor and basic energy consumption threshold, etc. Finally, the local control node feeds back the adjusted energy consumption data to the regional edge computing node to optimize the control strategy and adjust the parameters.

[0077] Using neural network, integrating internal and external environment, device operation and user behavior data to calculate energy consumption threshold, which is conducive to comprehensively covering the factors affecting energy consumption, and using weight factor to accurately calculate the threshold, providing scientific and quantitative indicators for energy-saving control. At the same time, the data feedback between the local control node and the regional edge computing node forms a closed loop, enhancing the pertinence and flexibility of energy-saving control, and automatically adapting to changes in operating conditions, so that the showcase can maintain good energy-saving performance in complex environments.

[0078] S3, design double closed loop energy-saving logic, internal environment adjusts compressor by intelligent control valve, external environment adjusts local control parameters according to the deviation of average energy consumption of showcases in the region from the target.

[0079] The inner loop control adjusts the compressor operating frequency by the intelligent control valve, which is represented as:

[0080] ;

[0081] Where k is the serial number of the control period, is the adjustment amount of the compressor operating frequency in the k-th control period, is the proportional coefficient, is the deviation value of the set temperature and the actual temperature in the k-th control period, is the integral coefficient, l is the summation index, is the cumulative sum of the temperature deviation in the control period, is the differential coefficient, Set the temperature deviation value of the k-1th control cycle.

[0082] The outer loop control area edge computing node adjusts the temperature set value and lighting brightness parameter of the showcase local control node according to the energy consumption deviation, and adjusts the inner loop control according to the data feedback of the showcase local control node.

[0083] The calculation formula of the energy consumption deviation of the area edge computing node is:

[0084] ;

[0085] Wherein, is the deviation value, is the average energy consumption of the showcases in the area, is the energy saving target value.

[0086] In this embodiment, the implementation of the double closed loop energy saving logic is that the inner loop control obtains the temperature deviation data and determines the control parameter in each control cycle, substitutes the calculation formula to calculate the compressor running frequency adjustment amount and adjusts the frequency. The outer loop control calculates the deviation of the average energy consumption of the showcases in the area and the energy saving target value, adjusts the temperature set value and the lighting brightness parameter of the local control node according to this, and optimizes the inner loop control according to the feedback of the local control node. The inner loop and the outer loop cooperate to realize the energy saving of the showcases.

[0087] The inner loop accurately adjusts the compressor running frequency through the proportional, integral and differential coefficients according to the temperature deviation in the showcase, optimizes the refrigeration energy consumption. The outer loop intelligently adjusts the local control parameter according to the deviation of the average energy consumption of the showcases in the area and the energy saving target, realizes the overall energy saving, and the inner loop and the outer loop cooperate to enhance the stability and adaptability of the system, reduce the manual intervention, improve the intelligent level of the energy saving control of the showcase, and improve the energy utilization efficiency.

[0088] S4, build a distributed control of the showcase, based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, the current temperature of the showcase, obtain the dynamic adjustment task of the interactive data of the local control node.

[0089] Based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, the current temperature, the energy saving task is allocated according to the following rules:

[0090] S4.1, analyze to obtain the energy saving potential value of each showcase.

[0091] The calculation formula of the energy saving potential value of the showcase is:

[0092] ;

[0093] Wherein, is the energy saving potential value of the i th showcase, is Weighting factors This represents the maximum allowable energy consumption value for the display case. Let i be the current actual energy consumption value of the i-th display case. Let be the thermal stability coefficient of the goods stored in the i-th display case. for Weighting factors The temperature threshold is preset for the display case. The actual current temperature of the display case. for Weighting factors.

[0094] S4.2 Sort the display cases in descending order of their energy-saving potential value, and prioritize assigning energy-saving tasks to display cases with high energy-saving potential values.

[0095] In this embodiment, the local control node collects real-time energy consumption, current temperature, and other data of the display cabinets, and obtains pre-set relevant parameters. The data and weighting factors are then substituted into a formula to calculate the energy-saving potential value of each display cabinet. The display cabinets are sorted from largest to smallest energy-saving potential value, and energy-saving tasks are assigned preferentially to display cabinets with higher energy-saving potential values.

[0096] By combining real-time energy consumption, product thermal coefficient, and current temperature to calculate energy-saving potential, display cases are prioritized and energy-saving tasks are assigned to those with high potential. This not only precisely optimizes energy allocation and improves energy efficiency but also enhances the system's adaptability to changes in actual conditions, facilitating resource management for administrators. Prioritizing energy-saving tasks for display cases with high potential achieves highly efficient energy saving under distributed control, while simultaneously ensuring product quality and display effectiveness, thus enhancing the consumer shopping experience.

[0097] S5. Obtain device operation signal characteristic data from the data repository and establish a fault prediction model.

[0098] The fault prediction model uses equipment operation signals as important inputs. The current signal undergoes signal processing to extract mean, variance, peak value, and impulse factor features. Based on these features, the fluctuation and energy distribution of the current signal over time are reflected, thus obtaining the stability of equipment operation. At the same time, using frequency domain Fourier transform analysis, the current signal is converted from the time domain to the frequency domain to extract the dominant frequency, harmonic frequencies, and corresponding amplitude features. The frequency domain features help reveal the vibration characteristics and potential mechanical fault signs during equipment operation.

[0099] The Fourier transform formula is:

[0100] ;

[0101] Where t is the time variable, It is a continuous signal in the frequency domain. is the representation of the signal in the frequency domain, j is the imaginary unit, h is the angular frequency, is the complex exponential function.

[0102] In the fault prediction model, for the parameter update of the fault prediction model, after the maintenance personnel complete the maintenance work on the showcase, the new equipment operation data of the showcase, the information of the repaired and replaced parts, the adjusted internal environment data and the external environment data of the showcase are fed back to the fault prediction model, and the fault prediction model optimizes and adjusts based on the parameters in the fault prediction model according to the new data by using the parameter update algorithm in machine learning. The model can accurately predict the future failure of the showcase and adapt to the state change of the showcase after maintenance.

[0103] The parameter update algorithm formula is:

[0104] ;

[0105] wherein, is the value of the parameter to be optimized after the z+1th iteration, is the value of the parameter at the zth iteration, is the learning rate, is the objective function about the gradient of the parameter .

[0106] In this embodiment, the showcase equipment operation signal is collected, the mean value and other features reflecting the operation stability are extracted by time domain processing of the current signal, and the main frequency and other features revealing potential faults are extracted by frequency domain analysis through Fourier transform. The model is selected, the features are extracted as input for training to establish a fault prediction model. When the equipment is maintained, the maintenance personnel feed back the new data, and the model adjusts the parameters according to the parameter update algorithm to adapt to the state change of the equipment; through model evaluation and continuous improvement, the accuracy of fault prediction is continuously improved to ensure stable operation of the showcase equipment.

[0107] By analyzing the time domain and frequency domain of the equipment operation current signal, extracting multiple features, constructing a fault prediction model, and accurately capturing the early signals of equipment failure. At the same time, combined with the data feedback after equipment maintenance, the parameter update algorithm is used to dynamically optimize the model parameters, so that they adapt to the state change of the equipment. This mechanism can predict failures in advance, reduce downtime losses, optimize maintenance strategies, and prolong the service life of the equipment, with accuracy, dynamic adaptability and economy.

[0108] S6, based on the data on the cloud management node, using the fault prediction model to predict the use peak, adjusting the operation strategy of the showcase.

[0109] The cloud management node continuously collects and stores energy consumption data, operating parameters, environmental parameters, and historical usage peak data of each showcase in the region, uses time series analysis algorithms, and combines machine learning techniques to predict the usage peak in a specific future time period.

[0110] A fault prediction and usage peak response flowchart is shown in Figure 3

[0111] When a usage peak is predicted, the cloud management node sends instructions to the regional edge computing node, which adjusts the operating strategy of the showcase in advance according to the instructions and the actual situation of the local showcase.

[0112] The specific adjustment methods include: moderately reducing the refrigeration intensity of some non-critical showcases, adjusting the brightness and opening time of the lighting system, optimizing the start-stop logic of the compressor, and at the same time, the regional edge computing node monitors the operating state of the showcase in real time and dynamically adjusts the operating strategy according to the feedback information.

[0113] In this embodiment, the cloud management node first continuously collects and stores data such as showcase energy consumption, operation, environment, and historical peak, and then uses time series analysis algorithms and machine learning techniques to predict future usage peaks. When a peak is predicted, the cloud sends instructions to the regional edge computing node, which adjusts the operating strategy of the showcase, such as refrigeration intensity, lighting system, and compressor start-stop logic, in combination with the actual situation, and monitors the operating state in real time, dynamically optimizes according to feedback, and balances energy saving and normal operation.

[0114] Using the cloud management node data to predict usage peaks and adjust equipment operating strategies, using time series analysis and machine learning to accurately predict electricity peaks, and guiding the regional edge computing node to optimize refrigeration, lighting, compressor, and other equipment parameters in advance to achieve energy peak shifting; at the same time, dynamic monitoring and feedback ensure stable operation, which not only reduces energy consumption and saves costs, but also realizes intelligent management driven by data, and can flexibly adapt to multiple scene requirements, balance energy saving and business operation.​

Claims

1. A method for adaptive energy saving control of a display cabinet based on distributed control, characterized in that, The method comprises the following steps: S1, build a communication module and a showcase, the showcase building comprises: setting a local control node, a regional edge computing node, a cloud management node collaborative framework, based on the regional edge computing node, the average energy consumption of the regional showcase is obtained; The local control node of the showcase collects temperature, humidity, light intensity, compressor operating current, and cabinet door opening and closing data in real time, and sends them to the regional edge computing node through the wireless communication module; the regional edge computing node collects regional power grid load rate data, and receives data uploaded by multiple local control nodes of the showcase; The regional edge computing node receives data uploaded from the plurality of showcase local control nodes, and analyzes to obtain average energy consumption of the showcases in the region The calculation formula is: ； wherein: n is the total number of display cases in the area, i is the number of the display case in the area, is the self energy consumption of the ith display case, is the weight factor of the ith display case, is the weight factor of the ith display case. The local control node transmits the collected data to the regional edge computing node in real time through the wireless communication module; at the same time, the local control node receives the control instructions issued by the regional edge computing node, adjusts the operating state of the showcase equipment according to the data instructions, adjusts the compressor frequency and switches the lighting, and then affects the average energy consumption calculation of the regional edge computing node; The cloud management node collects the average energy consumption and equipment operating state data uploaded by each regional edge computing node, and performs data integration and analysis; S2, build a data acquisition system inside and outside the showcase, the data acquisition system obtains data including: showcase internal environment data, showcase external environment data, equipment operation data, and user behavior data; S3, design a double-loop energy-saving logic, the internal environment adjusts the compressor through an intelligent control valve, and the external environment adjusts the local control parameters according to the average energy consumption of the regional showcase and the target deviation; S4, build a distributed control of the showcase, based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, and the current internal temperature of the showcase, the dynamic adjustment task of the local control node interaction data is obtained; S5, obtain the equipment operation signal feature data in the data storage library, and establish a fault prediction model; S6, based on the data on the cloud management node, the fault prediction model is used to predict the use peak value, and the operation strategy of the showcase is adjusted.

2. The distributed control based self-adaptive energy saving control method for a display cabinet according to claim 1, characterized in that, In the S1, the cloud management node collects comprehensive data uploaded by each regional edge computing node, including the average energy consumption of the regional showcase, the equipment fault early warning, the external energy policy information, and the industry energy consumption standard data; The nodes adjust the data acquisition frequency and strategy parameters according to the actual energy-saving effect through a bidirectional communication feedback mechanism.

3. The distributed control based self-adaptive energy saving control method for display cases according to claim 2, characterized in that, In the S2, the data acquisition system inside and outside the showcase is built, and the following data is obtained based on the local control node of the showcase: The internal environment data of the showcase includes temperature, humidity, and light intensity, the external environment data of the showcase includes room temperature, humidity, and light period, the equipment operation data includes compressor operating current, compressor operating frequency, and ventilation system operating power, and the user behavior data includes user stay duration and cabinet door opening and closing times; The internal and external energy consumption thresholds of the showcase are calculated: ； wherein, is an energy consumption threshold, is is a weight factor, is a basic energy consumption threshold, u is a parameter data number, is is a weight factor, is the u-th parameter data value, m is the total number of parameter data, and the showcase local control node feeds back the adjusted energy consumption data to the regional edge computing node.

4. The distributed control based adaptive energy saving control method for a display cabinet according to claim 1, wherein, In the S3, the inner loop control adjusts the compressor operating frequency through an intelligent control valve, which is represented as: ； wherein k is the index of the control period, is the adjustment amount of the compressor operating frequency in the kth control period, is the proportional coefficient, is the deviation value of the set temperature and the actual temperature in the kth control period, is the integral coefficient, and l is the summation index, is the cumulative sum of the temperature deviation of the control period, is the differential coefficient, is the deviation value of the set temperature and the actual temperature in the k-1th control period.

5. The distributed control based adaptive energy saving control method for a display cabinet as claimed in claim 4, wherein, The outer loop control adjusts the temperature set value and lighting brightness parameters of the local control node of the showcase according to the energy consumption deviation, and adjusts the inner loop control according to the data feedback of the local control node of the showcase; The calculation formula of the energy consumption deviation of the regional edge computing node is: ； wherein, is the bias value, is the average energy consumption of the display cases in the area, is the energy saving target value.

6. The distributed control based adaptive energy saving control method for a display cabinet according to claim 1, wherein, In S4, based on the real-time energy consumption of the showcase, the heat temperature coefficient of the goods, and the current temperature, energy-saving task allocation is performed according to the following rules: S4.1, the energy-saving potential value of each showcase is analyzed; The calculation formula of the energy-saving potential value of the showcase is: ； wherein, is the energy saving potential value of the i-th showcase, is the weight factor of is the maximum energy consumption value allowed by the showcase, is the current actual energy consumption value of the i-th showcase, is the thermal stability coefficient of the stored goods in the i-th showcase, is the weight factor of is the pre-set temperature threshold of the showcase, is the current actual temperature of the showcase, is the weight factor of is the weight factor of​​ S4.2, the showcases are sorted in descending order of energy-saving potential value, and the showcase with a larger energy-saving potential value is preferentially allocated an energy-saving task.

7. The distributed control based adaptive energy saving control method for a display cabinet as claimed in claim 1, wherein, In S5, the fault prediction model takes the device operation signal as an important input, and the current signal is processed to extract the mean, variance, peak value, and pulse factor features, which reflect the fluctuation and energy distribution of the current signal in the time scale, and obtain the stability of the device operation; at the same time, with the help of frequency domain Fourier transform analysis means, the current signal is converted from time domain to frequency domain, and the main frequency, harmonic frequency and corresponding amplitude characteristics are extracted, which helps to reveal the vibration characteristics and potential mechanical fault signs of the device operation; The Fourier transform formula is: ； where t is the time variable, is a continuous signal in the frequency domain, is the representation of the signal in the frequency domain, j is the imaginary unit, h is the angular frequency, is the complex exponential function.

8. The distributed control based adaptive energy saving control method for a display cabinet as claimed in claim 7, wherein: In the fault prediction model, for parameter updating of the fault prediction model, after the maintenance personnel complete the maintenance work of the showcase, the new device operation data of the showcase, the information of the replaced parts, the adjusted internal environment data and external environment data of the showcase are fed back to the fault prediction model, and the fault prediction model uses the parameter updating algorithm in machine learning to optimize and adjust the parameters in the fault prediction model, so that the model can accurately predict the future faults of the showcase and adapt to the state change after the maintenance of the showcase. The formula of the parameter updating algorithm is: ； wherein, is the value of the parameter to be optimized after the z+1th iteration, is the value of the parameter at the zth iteration, is the learning rate, is the objective function with respect to the parameter the gradient.

9. The distributed control based adaptive energy saving control method for a display case according to claim 1, wherein: In S6, the cloud management node continuously collects and stores the energy consumption data, operation parameters, environment parameters and historical use peak data of each showcase in the region, uses time series analysis algorithm and machine learning technology to predict the use peak in a specific period in the future, when the use peak is predicted to appear soon, the cloud management node sends instructions to the regional edge computing node, and the regional edge computing node adjusts the device operation strategy of the showcase in advance according to the instructions and the actual situation of the local showcase, and the specific adjustment methods include: reducing the refrigeration intensity of the showcase, adjusting the brightness and opening time of the lighting system, and optimizing the start-stop logic of the compressor, at the same time, the regional edge computing node monitors the operation state of the showcase in real time, and dynamically adjusts the operation strategy according to the feedback information.

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