Energy-saving control method and system for refrigeration equipment

Abstract

The present invention relates to the technical field related to refrigeration energy-saving control, and specifically includes an energy-saving control method and system for refrigeration equipment, including: based on a refrigeration equipment unit, realizing the interlocking of air valve, compressor and blower status through control logic; formulating an initial strategy to manage multiple microclimate spaces according to refrigeration demand after startup; combining video surveillance to capture personnel trajectories, analyze behavior patterns, generate local customized control strategies, and formulate comprehensive refrigeration control strategies to solve the technical problem of being unable to accurately match refrigeration demand and equipment operating status due to lack of real-time perception and analysis of actual operating conditions, thereby causing unnecessary energy waste. The global refrigeration demand balance is considered, while taking into account local areas, and the refrigeration intensity is flexibly adjusted. The accurate matching between refrigeration demand and equipment operating status optimizes the allocation of refrigeration resources, effectively reduces energy waste, improves overall operating efficiency, and achieves the energy-saving, intelligent and efficient management technical effects of refrigeration equipment.

Description

Technical Field

[0001] The present invention relates to the technical field related to refrigeration energy-saving control, and in particular to an energy-saving control method and system for refrigeration equipment. Background Art

[0002] In the application areas of refrigeration equipment, such as large shopping malls, data centers, cold chain logistics, etc., due to the strict requirements on environmental conditions such as temperature and humidity, the refrigeration equipment must maintain a long-term operating state, and the energy consumption increases significantly. Conventional refrigeration equipment control often adopts simple switch control or timing control, lacks real-time perception and intelligent analysis capabilities of the actual operating conditions, and cannot accurately match the refrigeration demand with the equipment operating status. When the refrigeration equipment still maintains unnecessary load operation during non-peak hours, it cannot respond to the refrigeration demand in time when the refrigeration demand is at its peak, resulting in unnecessary energy waste.

[0003] In summary, the existing technology has a technical problem that due to the lack of real-time perception and analysis of the actual operating conditions, it is impossible to accurately match the refrigeration demand with the equipment operating status, thereby causing unnecessary energy waste. Summary of the invention

[0004] This application provides an energy-saving control system for refrigeration equipment, aiming to solve the technical problem in the prior art that the refrigeration demand and the equipment operating status cannot be accurately matched due to the lack of real-time perception and analysis of the actual operating conditions, thereby causing unnecessary energy waste.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] On the one hand, the present application provides an energy-saving control method for refrigeration equipment, wherein the method includes: based on the refrigeration equipment unit, interlocking the status of the air valve, the refrigeration compressor and the air supply fan with control logic, and the air supply fan status is used to reflect the operating status of the single fan; in a closed space, after receiving the start-up instruction corresponding to the refrigeration equipment unit, determining the overall initial control strategy corresponding to multiple refrigeration microclimate spaces, and the overall initial control strategy is used to manage the multiple refrigeration microclimate spaces formed within the rated refrigeration area corresponding to the refrigeration equipment unit; connecting the video monitoring equipment to capture the movement trajectory of the personnel, and collecting the behavior of the target personnel in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the movement trajectory of the personnel Monitoring data set; constructing a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network; the behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target personnel in the refrigeration microclimate space, and the local customized control strategies are used to manage the refrigeration microclimate space where the target personnel is currently located; through the overall initial control strategy and the local customized control strategy, the comprehensive refrigeration control strategy of the refrigeration equipment unit is determined, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing the user comfort of the target personnel.

[0007] On the other hand, the present application provides an energy-saving control system for refrigeration equipment, wherein the system includes: a control interlocking module, which is used to interlock the status of air valves, refrigeration compressors and blowers based on the refrigeration equipment unit with control logic, and the status of the blower is used to reflect the operating status of a single blower; a module for determining an overall initial control strategy, which is used to determine the overall initial control strategy corresponding to multiple refrigeration microclimate spaces in a closed space after receiving a start-up instruction corresponding to the refrigeration equipment unit, and the overall initial control strategy is used to manage multiple refrigeration microclimate spaces formed within the rated refrigeration area corresponding to the refrigeration equipment unit; a movement trajectory capture module, which is used to connect to a video surveillance device, capture the movement trajectory of a person, and collect the movement trajectory of a target person in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the movement trajectory of the person. A behavior monitoring data set; an analysis module for constructing a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network; a module for obtaining a local customized control strategy, which is used for the behavior pattern analysis model to obtain local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target person in the refrigeration microclimate space, wherein the local customized control strategy is used to manage the refrigeration microclimate space where the target person is currently located; an energy-saving control module, which is used to determine the comprehensive refrigeration control strategy of the refrigeration equipment unit through the overall initial control strategy and the local customized control strategy, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing the user comfort of the target person.

[0008] In summary, one or more technical solutions provided in the present application solve the technical problem that the lack of real-time perception and analysis of the actual operating conditions leads to the inability to accurately match the refrigeration demand with the equipment operating status, thereby causing unnecessary energy waste. Real-time control logic is implemented to achieve a close linkage between the status of air valves, compressors and blowers, ensuring that the refrigeration equipment unit can quickly and accurately adjust the operating status according to the current refrigeration demand. Combined with video surveillance technology, personnel trajectories are captured and behavior patterns are analyzed to generate local customized control strategies for each microclimate space. The initial strategy is combined with the local customized strategy to formulate a comprehensive refrigeration control strategy, considering the global refrigeration demand balance while taking into account the local area, flexibly adjusting the refrigeration intensity, and accurately matching the refrigeration demand with the equipment operating status to optimize the allocation of refrigeration resources, thereby effectively reducing energy waste, improving overall operating efficiency, and achieving the technical effect of energy-saving, intelligent and efficient management of refrigeration equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of an energy-saving control method for refrigeration equipment is provided for this application.

[0010] Figure 2 A schematic diagram of the structure of an energy-saving control system for refrigeration equipment is provided for this application.

[0011] Description of reference numerals: control interlocking module M100, module for determining overall initial control strategy M200, movement trajectory capturing module M300, analysis module M400, module for obtaining local customized control strategy M500, energy-saving control module M600. DETAILED DESCRIPTION

[0012] Embodiment 1

[0013] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides an energy-saving control method for refrigeration equipment, wherein the method comprises:

[0014] S1: Based on the refrigeration equipment unit, the air valve, refrigeration compressor and air supply fan status are interlocked by control logic, and the air supply fan status is used to reflect the operating status of a single fan.

[0015] Interlocking control logic, specifically, the air valve is a key component for regulating the air volume and wind direction of the refrigeration equipment unit. By controlling the opening of the air valve, the flow and distribution of the refrigeration air can be accurately controlled to meet the needs of different refrigeration microclimate spaces. In the interlocking control logic, the opening of the air valve will be automatically adjusted according to the operating status of the blower and the workload of the refrigeration compressor. The refrigeration compressor is the heart of the refrigeration equipment unit, responsible for compressing the refrigerant and releasing heat to produce a refrigeration effect. The operating status of the refrigeration compressor (such as start and stop, speed adjustment, etc.) will directly affect the refrigeration capacity and energy consumption of the refrigeration system. In the interlocking control logic, the operation of the refrigeration compressor will be based on a comprehensive judgment of multiple factors such as refrigeration demand, ambient temperature, refrigerant pressure, etc., and coordinated with the status of the blower and the air valve. The blower is responsible for delivering cooling air to each cooling microclimate space. The operating status (such as speed, start and stop, etc.) directly affects the flow and speed of the cooling air. In the interlocking control logic, the status of the blower will reflect the operating status of the single fan in real time, including its speed, air volume and other parameters. At the same time, the operating status of the blower will also be automatically adjusted according to the cooling demand and the opening of the air valve to ensure that the cooling air can be evenly and efficiently distributed to each cooling microclimate space.

[0016] The control logic is used to interlock the status of the air valve, refrigeration compressor and air supply fan. Correspondingly, the interlocking control instructions include adjusting the opening of the air valve, adjusting the operating load and speed of the refrigeration compressor, controlling the start and stop and speed of the air supply fan, etc., to provide support for subsequent analysis.

[0017] S2: In a closed space, after receiving a start-up instruction corresponding to a refrigeration equipment unit, an overall initial control strategy corresponding to a plurality of refrigeration microclimate spaces is determined, wherein the overall initial control strategy is used to manage a plurality of refrigeration microclimate spaces formed within a rated refrigeration area corresponding to the refrigeration equipment unit.

[0018] In a closed space, when the refrigeration equipment unit receives the start-up command, a comprehensive environmental assessment of the closed space is conducted, including measuring key parameters such as temperature, humidity, and air flow rate. The initial environmental conditions of each area (i.e., the refrigeration microclimate space) are evaluated to understand the differences and potential needs between the areas. The refrigeration demand of each refrigeration microclimate space is predicted based on factors such as the purpose of the closed space, the density of people, and the heat load of the equipment. Changes in refrigeration demand in different time periods (such as day and night, weekdays and weekends) and various emergencies (such as gathering of people, equipment failure, etc.) are considered.

[0019] Plan the allocation of cooling resources according to the cooling demand and the performance parameters of the refrigeration equipment units (such as cooling capacity, air supply volume, etc.); determine the key control parameters such as the air volume and temperature set point required for each cooling microclimate space; based on the above analysis, formulate an overall initial control strategy, which should clearly define the control objectives, control methods and priorities of each cooling microclimate space, taking into account the balance between energy saving and comfort.

[0020] In actual application, the overall initial control strategy is applied to the refrigeration equipment unit to start the refrigeration process; the environmental conditions and control effects of each refrigeration microclimate space are monitored in real time to meet the preset control objectives; the overall initial control strategy is adjusted and optimized based on the monitoring results and feedback from actual conditions. In particular, when formulating the overall initial control strategy, the particularity and complexity of the enclosed space should be fully considered, such as the impact of factors such as space layout, personnel flow, and equipment distribution on the refrigeration effect; through continuous data collection and analysis, problems are discovered and improved in a timely manner.

[0021] S3: Connecting a video surveillance device to capture a person's movement trajectory, and collecting a behavior monitoring data set of a target person in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the person's movement trajectory.

[0022] According to the size, layout and light conditions of the refrigeration microclimate space, select appropriate video surveillance equipment (such as cameras, infrared sensors, etc.) for installation, ensure that the equipment can cover all areas that need to be monitored, and the installation location will not interfere with the normal activities of personnel; connect the video surveillance equipment to the network to ensure the stability and real-time data transmission, and use wired or wireless connection, depending on the site conditions and network environment; configure the video surveillance software or platform, set the equipment parameters (such as resolution, frame rate, storage method, etc.), and divide and mark the monitoring area.

[0023] Use video analysis software or algorithms to process video streams to identify and track people in the video, which usually involves image processing, machine learning or deep learning technologies, such as background subtraction, target detection, target tracking, etc.; through continuous video frame analysis, the identified person location information is connected to construct the movement trajectory of the person, which requires processing and analysis of data such as timestamps and location coordinates; the initially constructed trajectory is smoothed, error corrected and denoised to improve the accuracy and reliability of the trajectory.

[0024] According to business needs, the target person is identified, and the target person is any person in the closed space; after the target person enters the refrigerated microclimate space, his / her behavior data is collected, including movement trajectory, residence time, activity area, interaction with other objects, etc.; the collected behavior data is organized into a structured data set, including fields such as timestamp, location information, behavior type, etc. At the same time, the data is classified, labeled and pre-processed as needed for subsequent analysis and application.

[0025] S4: Constructing a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network.

[0026] The behavior pattern analysis model includes a stand-alone single space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network, wherein the stand-alone single space behavior pattern analysis network (SSI-BPAN, Stand-alone Single Space Behavior Pattern Analysis Network) is used to analyze the behavior pattern of a target person in a single refrigeration microclimate space; receive a behavior monitoring data set in any refrigeration microclimate space, including the target person's movement trajectory, residence time, activity type and other information; use machine learning or deep learning algorithms to extract behavior features, such as frequently visited areas, residence time distribution, activity patterns, etc.; identify significant behavior patterns through methods such as clustering, classification or sequence analysis; use historical data to train the model to improve the accuracy and robustness of pattern recognition; and provide a behavior pattern analysis report in a single refrigeration microclimate space, including common behavior patterns, abnormal behavior detection, etc.

[0027] The Multi-Space Interactive Behavior Pattern Analysis Network (MSI-BPAN) is used to analyze the movement and interaction behaviors of target personnel between different cooling microclimate spaces; it receives output data from multiple SSI-BPANs, as well as cross-space behavior monitoring data sets (such as the movement trajectory of target personnel between different spaces); it uses graph theory, network science and other methods to establish a relationship model between cooling microclimate spaces, taking into account factors such as spatial distance, connectivity, and functional complementarity; it analyzes the target personnel's interactive behaviors such as movement paths, residence time, and activity conversion between multiple spaces; it combines single-space behavior patterns and cross-space interactive behaviors to identify more complex overall behavior patterns; and it provides a behavioral pattern analysis report across cooling microclimate spaces, including spatial interaction patterns, flow patterns of target personnel, etc.

[0028] S5: The behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target person in the refrigeration microclimate space, and the local customized control strategies are used to manage the refrigeration microclimate space where the target person is currently located.

[0029] According to the target personnel's current cooling microclimate space and their behavior patterns, adjust the space's environmental parameters such as temperature and humidity in real time to meet immediate needs; based on the target personnel's historical behavior patterns and current trends, predict the target users' corresponding behavior paths and needs in the future, and adjust the environmental parameters of the relevant cooling microclimate spaces in advance; on the premise of meeting the personnel's comfort, achieve efficient energy utilization by optimizing the operation strategies of different cooling microclimate spaces (such as adjusting the load distribution, start and stop time of the refrigeration unit, etc.); formulate adaptive environmental control strategies for the target personnel to improve their satisfaction and comfort.

[0030] S6: Determine the comprehensive refrigeration control strategy of the refrigeration equipment unit through the overall initial control strategy and the local customized control strategy, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing user comfort of the target personnel.

[0031] The overall initial control strategy is used as the basic framework to provide global guidance and basic settings for the entire refrigeration system. The overall initial control strategy takes into account the rated refrigeration area of ​​the refrigeration equipment unit, the basic requirements of each refrigeration microclimate space, and the initial environmental conditions. The local customized control strategy is used as a supplement and refinement of the overall strategy. The local customized control strategy is based on the specific behavior patterns of the target personnel in each refrigeration microclimate space, and fine-tunes the environmental parameters in a specific space.

[0032] Use comfort indicators (such as temperature, humidity, wind speed, and comprehensive evaluation of air quality) to monitor the comfort level of the refrigeration microclimate space where the target personnel are located; set comfort thresholds to ensure that environmental parameters always remain within an acceptable range; reduce energy consumption by optimizing the operating parameters of the refrigeration equipment units (such as the operating frequency of the compressor, the speed of the fan, the flow rate of cooling water, etc.) while meeting the comfort requirements; use predictive control algorithms to predict future refrigeration needs based on historical data and real-time monitoring results, and adjust the equipment status in advance to match these needs; implement intelligent scheduling strategies to allocate refrigeration resources based on the real-time load conditions and priorities of each refrigeration microclimate space.

[0033] Real-time monitoring of environmental parameters of each refrigeration microclimate space and the behavior patterns of target personnel; triggering the adjustment mechanism immediately when it is detected that the environmental parameters deviate from the comfort threshold or the behavior patterns of target personnel change; fine-tuning the environmental parameters of the specific space to restore the comfort level according to the provisions of the local customized control strategy, and at the same time, feeding back the adjustment results to the overall initial control strategy so that the operating strategy of the entire refrigeration system can be globally adjusted when necessary.

[0034] In summary, by integrating the overall initial control strategy with the local customized control strategy and seeking a balance between comfort and energy saving, a comprehensive refrigeration control strategy for refrigeration equipment units is determined, which can effectively improve user comfort, reduce energy consumption, improve system efficiency, and promote sustainable development.

[0035] Furthermore, the present application method also includes:

[0036] The cooling water pump is controlled in linkage with the refrigeration equipment unit. The operating time of each cooling water pump is accumulated in time rotation. The cooling water pump with the short accumulated operating time is started first each time, and the cooling water pump with the long accumulated operating time is selected when it is stopped. The cooling water pump corresponds to the refrigeration equipment unit one by one. The abnormal operating status of multiple cooling water pumps is analyzed. The multiple cooling water pumps serve as backup for each other. When any cooling water pump fails, a fault mutual backup strategy is generated. The fault mutual backup strategy is used to quickly respond to cooling water pump failures.

[0037] Set a time rotation cycle, such as daily or weekly, and accumulate the operating time of each cooling water pump according to the time rotation cycle; each time the cooling water pump needs to be started, first check the accumulated operating time of all pumps, and give priority to starting the pump with the shortest accumulated operating time; when a pump reaches the predetermined operating time or needs maintenance, the system will select the pump with the longest accumulated operating time to stop the operation for rotation; the cooling water pump corresponds to the refrigeration equipment unit one by one to ensure that each unit can obtain a stable cooling water flow, thereby balancing the operation of multiple cooling water pumps; when designing the system, it should be ensured that the pump capacity and performance of each unit can meet the cooling needs of the unit.

[0038] Monitor the operating status of each cooling water pump in real time, including key parameters such as current, voltage, flow, temperature, etc.; use data analysis technology to identify abnormal modes in the operation of the pump, such as current fluctuations, reduced flow, and rapid temperature increase; once an abnormality is found, immediately trigger an alarm and start the fault diagnosis program to determine the specific cause of the problem.

[0039] A mutual backup relationship is established between multiple cooling water pumps to ensure that the system can respond quickly when any water pump fails. When a water pump fails, the system immediately evaluates the capacity and performance of the remaining water pumps to determine whether they can meet the current cooling demand. If the capacity of the remaining water pumps is sufficient, the rapid response mechanism will automatically switch to the backup water pump and stop the failed water pump to ensure stable operation of the system.

[0040] By implementing the above control strategy and fault backup strategy, the operating efficiency and reliability of the cooling water pump in the refrigeration system can be significantly improved, and the system downtime caused by water pump failure can be reduced, which is of great significance for ensuring the overall performance of the refrigeration system and user comfort.

[0041] Furthermore, the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network. The method of the present application includes:

[0042] Set time period information, which includes the agreed time point of the first cycle control and the agreed time point of the second cycle control under the time limit condition; set the fixed activity area of ​​the target personnel, which includes the agreed coordinates of the first cycle control and the agreed coordinates of the second cycle control under the task condition; based on the time period information and the fixed activity area, combined with the humidity control module, perform joint cycle control, and the humidity control module is connected to the independent single-space behavior pattern analysis network communication.

[0043] Define different time periods according to actual needs, such as working days, weekends, morning and evening rush hours, etc. Set different control parameters and strategies in each time period; in each time period, set two key cycle control agreed time points, namely the first cycle control agreed time point and the second cycle control agreed time point. The first cycle control agreed time point and the second cycle control agreed time point are used to trigger the system's cycle control process, such as the adjustment of environmental parameters, the start and stop of equipment, etc.; set specific time limit conditions for each time period and cycle control agreed time point, such as time range, environmental condition threshold, etc., to ensure the accuracy and effectiveness of the control strategy.

[0044] According to the daily activity patterns and task requirements of the target personnel, their fixed activity areas in the refrigerated microclimate space are identified, including office workstations, meeting rooms, rest areas, etc.; in the fixed activity areas, the first-loop control agreed coordinates and the second-loop control agreed coordinates are set. The first-loop control agreed coordinates and the second-loop control agreed coordinates are the center points, boundary points or key position points of the fixed activity areas, which are used to guide the operation of the humidity control module in a specific area; the impact of task conditions on fixed activity areas is considered, such as meetings, training, cleaning and other tasks that lead to increased personnel density or changes in activity patterns, which requires adjustment of the humidity control strategy.

[0045] The joint cycle control of the humidity control module and the independent single-space behavior pattern analysis network further ensures that a stable communication connection is established between the humidity control module and the independent single-space behavior pattern analysis network to transmit data and instructions in real time; the analyzed target personnel behavior pattern data (such as activity trajectory, residence time, etc.) and environmental parameter data (such as current humidity, temperature, etc.) are shared with the humidity control module.

[0046] Based on the time period information and the setting of fixed activity areas, the humidity control module determines the current time period and the location of the target personnel according to the data provided by SSI-BPAN, and determines the appropriate humidity control target; at the agreed time point of the first control cycle, the humidity adjustment process is started, and the operating parameters of the refrigeration equipment or humidification / dehumidification equipment are adjusted according to the humidity demand in the fixed activity area and the behavior pattern of the target personnel; at the agreed time point of the second control cycle, the humidity control effect and the location change of the target personnel are evaluated again, and necessary adjustments are made or the current status is maintained.

[0047] Through the implementation of the above steps, the refined and intelligent control of humidity in the refrigeration microclimate space can be achieved, thereby improving the comfort of the target personnel.

[0048] Furthermore, the behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target personnel in the refrigeration microclimate space. The method of this application includes:

[0049] Based on the image monitoring device, user image information is collected, clothing type analysis is performed, and the user clothing index is obtained, and the user clothing index reflects the body temperature of the target person; based on the historical refrigeration control examples marked with comfort evaluation marks, combined with the user clothing index, an auxiliary control parameter set is extracted, and the indicators corresponding to the auxiliary control parameter set include ambient temperature, equipment operating status, and personnel distribution density; through the auxiliary control parameter set, the local customized control strategy is iteratively optimized for accuracy.

[0050] Using image monitoring equipment installed in the refrigeration microclimate space, the user's image information is collected in real time. Computer vision technology, such as deep learning algorithms, is used to analyze the collected images and identify the user's clothing type (such as short sleeves, long sleeves, coats, skirts, etc.). Based on the identified clothing type, combined with clothing material, thickness and other attributes, as well as current ambient temperature, humidity and other conditions, the user's clothing index is calculated. The user's clothing index reflects the target person's perceived temperature in the current environment.

[0051] Review historical refrigeration control instances, record user comfort feedback (such as satisfied, average, dissatisfied, etc.) under different environmental conditions, and obtain feedback through user behavior analysis and other methods; extract auxiliary control parameter sets closely related to comfort based on comfort evaluation marks and combined with user clothing index, including ambient temperature, equipment operating status (such as air conditioning, fan switch, wind speed, temperature setting, etc.), personnel distribution density, etc.; conduct in-depth analysis on the extracted auxiliary control parameter sets to find out the correlation between different parameters and comfort, and provide a basis for subsequent control strategy optimization.

[0052] According to the analysis results of the auxiliary control parameter set, the local customized control strategy is adjusted. For example, the cooling intensity of different areas is dynamically adjusted according to the user's clothing index and the population distribution density; the start and stop strategies of the refrigeration equipment are optimized according to the ambient temperature and the equipment operating status; the adjusted control strategy is applied to the actual environment, and the user's comfort feedback and new image data are continuously collected; through continuous iterative optimization, the accuracy and adaptability of the control strategy are gradually improved.

[0053] An intelligent decision support system is established, integrating modules such as image monitoring, clothing type analysis, and comfort evaluation to realize automated and intelligent management of the refrigeration system. The control strategy is automatically adjusted based on real-time data and historical experience to ensure that the refrigeration microclimate space always remains in the optimal comfort state. This not only improves the energy-saving effect of the refrigeration system, but also significantly improves the comfort and satisfaction of the target personnel, and realizes precision optimization and iteration of local customized control strategies.

[0054] Furthermore, the local customized control strategy is iterated for precision optimization through the auxiliary control parameter set. The method of the present application includes:

[0055] Through the auxiliary control parameter set, energy consumption data is used as a correlation constraint indicator to obtain the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor; based on the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor, energy consumption change analysis is performed to obtain the energy consumption change sensitivity; based on the energy consumption change sensitivity, weight distribution adjustment is performed during the precision optimization process of the local customized control strategy.

[0056] By analyzing the relationship between historical energy consumption data and ambient temperature, the impact of ambient temperature changes on energy consumption is calculated. The ambient temperature-energy consumption correlation factor is used to reflect how much energy the refrigeration system needs to consume to maintain the set comfort level at different ambient temperatures. The energy consumption under different equipment operating states (such as cooling power, wind speed, working mode, etc.) is studied to calculate the contribution of each state to energy consumption. The equipment operating state-energy consumption correlation factor is used to reflect the energy consumption of refrigeration equipment under different equipment operating states. Considering the impact of personnel distribution density on refrigeration demand, the energy consumption changes under different personnel densities are analyzed. The personnel distribution density-energy consumption correlation factor is used to reflect the energy consumption of refrigeration equipment under different personnel densities. Generally, densely populated areas require stronger cooling effects, thereby consuming more energy.

[0057] Based on the ambient temperature-energy consumption correlation factor, the equipment operating status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor, an energy consumption change analysis is performed. By simulating the energy consumption under different ambient temperatures, equipment operating status, and personnel distribution density, the sensitivity of energy consumption changes is calculated, which helps to predict how adjusting the control strategy will affect energy consumption under specific conditions.

[0058] In the process of precision optimization of local customized control strategies, weight distribution is adjusted according to the sensitivity of energy consumption changes. Parameters with high sensitivity to energy consumption changes (such as the operating status of equipment under high energy consumption) should be given higher weights so that they can be given priority in the optimization process; a comprehensive optimization strategy should be formulated in combination with comfort evaluation and user clothing index; energy consumption can be reduced by adjusting the operating status of equipment and optimizing the distribution of cooling intensity (based on the density of personnel distribution) while maintaining user comfort; a real-time monitoring system is established to dynamically adjust the weight distribution in the control strategy according to the current ambient temperature, equipment operating status and personnel distribution density. At the same time, feedback data is collected to continuously optimize energy consumption related factors and weight distribution schemes.

[0059] Through the above method, while ensuring user comfort, the energy consumption of the refrigeration system is effectively reduced, which helps to improve the overall performance of the system and contribute to energy conservation and emission reduction.

[0060] Furthermore, the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network. The method of the present application includes:

[0061] In the closed space, the expected user flow and expected length of stay corresponding to the multiple refrigeration microclimate spaces are analyzed in combination with a time series analysis algorithm; based on the expected user flow and expected length of stay corresponding to the multiple refrigeration microclimate spaces, high-density areas and low-density areas are identified; based on the high-density areas and low-density areas, a multi-objective optimization algorithm is used to balance the allocation of refrigeration resources corresponding to the multiple refrigeration microclimate spaces.

[0062] It is necessary to collect historical data, including the user flow (number of people entering and leaving) and the length of stay of users in each cooling microclimate space in different time periods; use time series analysis algorithms (such as ARIMA, LSTM, etc.) to analyze the collected historical data to identify the seasonal, cyclical and trend characteristics of user flow and length of stay; based on the analysis results, build a prediction model to predict the expected user flow and expected length of stay in the future. The prediction results will provide an important basis for subsequent resource allocation.

[0063] According to the layout and function of the cooling microclimate space, the overall space is divided into multiple areas; the predicted user flow and length of stay are allocated to each area, and the expected user density of each area (i.e. the average number of users per unit time) is calculated; according to the level of user density, the area is divided into high-density area and low-density area. High-density areas require more cooling resources to maintain user comfort, while low-density areas relatively reduce the investment in cooling resources.

[0064] Set multiple optimization goals, such as reducing overall energy consumption, improving user comfort, and reducing the operating costs of refrigeration equipment. Multiple optimization goals conflict with each other, and a multi-objective optimization algorithm is needed to find the optimal solution. Based on the identification results of high-density and low-density areas, a cooling resource allocation strategy is formulated. For example, in high-density areas, the operating power or density of refrigeration equipment is increased to ensure sufficient cooling capacity; in low-density areas, the operating power of refrigeration equipment is appropriately reduced or some equipment is shut down to save energy.

[0065] Use multi-objective optimization algorithms (such as NSGA-II, MOEA / D, etc.) to optimize the cooling resource allocation strategy, consider the trade-offs between multiple objectives, and generate a set of Pareto optimal solutions for decision makers to choose; establish a real-time monitoring system to dynamically adjust the cooling resource allocation strategy based on actual user flow, length of stay, cooling effect and other feedback data, and collect user feedback and comfort evaluation data to continuously optimize the resource allocation strategy. More scientific and reasonable management of cooling resources in closed spaces to achieve efficient use of energy and improve user comfort.

[0066] Furthermore, to balance the allocation of cooling resources corresponding to the multiple cooling microclimate spaces, the method of the present application includes:

[0067] An overcooling penalty constraint is set, wherein the overcooling penalty constraint includes a temperature threshold; based on the overcooling penalty constraint, an overcooling risk index is added in the cooling resource allocation process; and cooling resource allocation competition optimization is performed through the overcooling risk index.

[0068] In the process of cooling resource allocation, in order to avoid energy waste and user discomfort caused by over-cooling (i.e. "overcooling"), an overcooling penalty constraint is set, and an overcooling risk index is introduced to guide the optimal allocation of cooling resources. Specifically, a reasonable temperature threshold is set according to the design requirements of the cooling microclimate space, user comfort and energy-saving goals. The temperature threshold is the lowest temperature value that should not be lower than during the cooling process to avoid the occurrence of overcooling; when the temperature of a cooling microclimate space is lower than the set temperature threshold, the overcooling penalty constraint will be triggered; the specific forms of penalty include simulation of reduced efficiency of refrigeration equipment, reduction of the cooling resource allocation weight of the area, etc.

[0069] Based on the overcooling penalty constraint, an overcooling risk index is defined for each cooling microclimate space. The overcooling risk index is used to quantify the risk of overcooling faced by the space in the future. The calculation of the overcooling risk index takes into account multiple factors, such as the difference between the current temperature and the temperature threshold, the expected user flow, the expected length of stay, the operating efficiency of the refrigeration equipment, etc. Through mathematical modeling or machine learning algorithms, the overcooling risk of each space under different cooling resource allocation schemes is predicted, and the corresponding overcooling risk index is calculated accordingly.

[0070] In the process of cooling resource allocation, a competition mechanism is introduced to improve the fairness of cooling resource allocation; each cooling microclimate space competes for resource allocation based on its overcooling risk index and other optimization objectives (such as comfort, energy efficiency, etc.); a multi-objective optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) or a rule-based heuristic algorithm is used to iteratively optimize the cooling resource allocation scheme. In each iteration, the overcooling risk index and other optimization objectives of each space are considered, and the optimal solution or satisfactory solution that meets all objectives is found by adjusting the operating status of the refrigeration equipment, cooling capacity allocation and other parameters; a real-time monitoring system is established to monitor the key parameters of the cooling microclimate space, such as temperature, humidity, user flow, etc. in real time; based on monitoring data and user feedback, the overcooling penalty constraint, the calculation method of the overcooling risk index and the cooling resource allocation strategy are dynamically adjusted. At the same time, the accuracy and efficiency of cooling resource allocation are improved by continuously learning and optimizing algorithm parameters.

[0071] Through the above steps, under the premise of ensuring user comfort and energy-saving goals, overcooling in the refrigeration process can be effectively avoided and the utilization efficiency of refrigeration resources can be improved.

[0072] In summary, the beneficial effects of the embodiments of the present application are:

[0073] 1. Through control logic, the status of key components such as air valves, refrigeration compressors and blowers are interlocked to achieve coordinated and optimized operation among various components, improve the overall operating efficiency of the refrigeration equipment unit, and significantly reduce energy consumption.

[0074] 2. According to the actual load changes of the refrigeration equipment unit, the intelligent adjustment mechanism is used to adjust the air valve opening, refrigeration compressor operating frequency, blower speed and other parameters in real time, so that the equipment can maintain the best operating state under different loads and achieve the purpose of energy saving and consumption reduction.

[0075] 3. Through real-time feedback of the blower status, the operating status can be accurately judged and potential faults can be discovered in time. At the same time, the operating parameters of other components can be adjusted according to the blower status, thereby further improving the cooling effect and reducing energy consumption.

[0076] 4. In a closed space, the estimated user flow and estimated length of stay of multiple cooling microclimate spaces are analyzed by combining a time series analysis algorithm; based on the estimated user flow and estimated length of stay of multiple cooling microclimate spaces, high-density areas and low-density areas are identified; based on the high-density areas and low-density areas, a multi-objective optimization algorithm is used to balance the allocation of cooling resources corresponding to multiple cooling microclimate spaces. This allows for more scientific and reasonable management of cooling resources in closed spaces, achieving efficient use of energy and improved user comfort.

[0077] Embodiment 2

[0078] Based on the same inventive concept as the energy-saving control method for refrigeration equipment in the aforementioned embodiment, Figure 2 As shown, an embodiment of the present application provides an energy-saving control system for refrigeration equipment, wherein the system includes:

[0079] The control interlocking module M100 is used to interlock the status of the air valve, the refrigeration compressor and the blower based on the refrigeration equipment unit by control logic, and the blower status is used to reflect the operating status of a single blower;

[0080] Determine the overall initial control strategy module M200, which is used to determine the overall initial control strategy corresponding to multiple refrigeration microclimate spaces after receiving the start-up instruction corresponding to the refrigeration equipment unit in the closed space, and the overall initial control strategy is used to manage the multiple refrigeration microclimate spaces formed within the rated refrigeration area corresponding to the refrigeration equipment unit;

[0081] The movement trajectory capture module M300 is used to connect to the video monitoring equipment, capture the movement trajectory of the personnel, and collect the behavior monitoring data set of the target personnel in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the movement trajectory of the personnel;

[0082] An analysis module M400 is used to construct a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network;

[0083] Obtaining a local customized control strategy module M500, which is used for the behavior pattern analysis model to obtain local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target person in the refrigeration microclimate space, and the local customized control strategy is used to manage the refrigeration microclimate space where the target person is currently located;

[0084] The energy-saving control module M600 is used to determine the comprehensive refrigeration control strategy of the refrigeration equipment unit through the overall initial control strategy and the local customized control strategy, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing the user comfort of the target personnel.

[0085] Furthermore, the energy-saving control system for refrigeration equipment is used to perform the following method:

[0086] The cooling water pump is linked to the refrigeration equipment unit for control, and the running time of each cooling water pump is accumulated in time rotation. The cooling water pump with the shortest cumulative running time is started first each time it is started, and the cooling water pump with the longest cumulative running time is selected when it is stopped. The cooling water pump corresponds to the refrigeration equipment unit one by one;

[0087] An abnormal operation status analysis is performed on a plurality of cooling water pumps, the plurality of cooling water pumps serving as backup for each other, and when any cooling water pump fails, a fault backup strategy is generated, the fault backup strategy being used to quickly respond to the cooling water pump failure.

[0088] Furthermore, the analysis module M400 is used to perform the following method:

[0089] Setting time period information, wherein the time period information includes a first cycle control agreed time point and a second cycle control agreed time point under a time limit condition;

[0090] Setting a fixed activity area for the target person, wherein the fixed activity area includes the first cycle control agreed coordinates and the second cycle control agreed coordinates under the task condition;

[0091] Combined cycle control is performed based on the time period information and the fixed activity area in combination with a humidity control module, and the humidity control module is communicatively connected with the independent single-space behavior pattern analysis network.

[0092] Furthermore, the local customized control strategy module M500 is used to execute the following method:

[0093] Collecting user image information based on image monitoring equipment, analyzing clothing types, and obtaining a user clothing index, wherein the user clothing index reflects the body temperature of the target person;

[0094] Based on the historical refrigeration control examples marked with comfort evaluation marks, combined with the user's clothing index, an auxiliary control parameter set is extracted, and the indicators corresponding to the auxiliary control parameter set include ambient temperature, equipment operating status, and personnel distribution density;

[0095] The local customized control strategy is iterated for precision optimization through the auxiliary control parameter set.

[0096] Furthermore, the local customized control strategy module M500 is also used to execute the following method:

[0097] By using the auxiliary control parameter set, the energy consumption data is used as the correlation constraint index to obtain the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor;

[0098] Based on the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor, energy consumption change analysis is performed to obtain energy consumption change sensitivity;

[0099] Based on the energy consumption change sensitivity, weight distribution adjustment is performed during the accuracy optimization process of the local customized control strategy.

[0100] Furthermore, the analysis module M400 is used to perform the following method:

[0101] In the enclosed space, the estimated user flow and estimated length of stay corresponding to the plurality of cooling microclimate spaces are analyzed in combination with a time series analysis algorithm;

[0102] identifying high-density areas and low-density areas based on the expected user flows and expected lengths of stay corresponding to the plurality of refrigerated microclimate spaces;

[0103] Based on the high-density area and the low-density area, a multi-objective optimization algorithm is used to balance the allocation of cooling resources corresponding to the multiple cooling microclimate spaces.

[0104] Furthermore, the analysis module M400 is also used to execute the following method:

[0105] Setting an overcooling penalty constraint, wherein the overcooling penalty constraint includes a temperature threshold;

[0106] Based on the overcooling penalty constraint, an overcooling risk index is added during the cooling resource allocation process;

[0107] The overcooling risk index is used to optimize the competition for refrigeration resource allocation.

[0108] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0109] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. An energy-saving control method for refrigeration equipment, characterized in that: The method comprises: Based on the refrigeration equipment unit, the air valve, refrigeration compressor and air supply fan status are interlocked by control logic, and the air supply fan status is used to reflect the operating status of a single fan; In a closed space, after receiving a start-up instruction corresponding to a refrigeration equipment unit, an overall initial control strategy corresponding to a plurality of refrigeration microclimate spaces is determined, wherein the overall initial control strategy is used to manage a plurality of refrigeration microclimate spaces formed within a rated refrigeration area corresponding to the refrigeration equipment unit; Connecting a video surveillance device to capture a person's movement trajectory, and collecting a behavior monitoring data set of a target person in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the person's movement trajectory, where the target person is any person in the closed space; Constructing a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network; The behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target person in the refrigeration microclimate space, and the local customized control strategies are used to manage the refrigeration microclimate space where the target person is currently located; Determining a comprehensive refrigeration control strategy of the refrigeration equipment unit through the overall initial control strategy and the local customized control strategy, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing user comfort of the target personnel; The behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network, including: Setting time period information, wherein the time period information includes a first cycle control agreed time point and a second cycle control agreed time point under a time limit condition; Setting a fixed activity area for the target person, wherein the fixed activity area includes the first cycle control agreed coordinates and the second cycle control agreed coordinates under the task condition; Based on the time period information and the fixed activity area, combined cycle control is performed in combination with a humidity control module, the humidity control module being communicatively connected to the independent single space behavior pattern analysis network; The behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target personnel in the refrigeration microclimate space, including: Collecting user image information based on image monitoring equipment, analyzing clothing types, and obtaining a user clothing index, wherein the user clothing index reflects the body temperature of the target person; Based on the historical refrigeration control examples marked with comfort evaluation marks, combined with the user's clothing index, an auxiliary control parameter set is extracted, and the indicators corresponding to the auxiliary control parameter set include ambient temperature, equipment operating status, and personnel distribution density; By using the auxiliary control parameter set, the local customized control strategy is iterated for precision optimization; The method of iteratively optimizing the accuracy of the local customized control strategy through the auxiliary control parameter set includes: By using the auxiliary control parameter set, the energy consumption data is used as the correlation constraint index to obtain the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor; Based on the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor, energy consumption change analysis is performed to obtain energy consumption change sensitivity; Based on the energy consumption change sensitivity, weight distribution adjustment is performed during the accuracy optimization process of the local customized control strategy.

2. The energy-saving control method for refrigeration equipment according to claim 1, characterized in that: The cooling water pump is linked to the refrigeration equipment unit for control, and the running time of each cooling water pump is accumulated in time rotation. The cooling water pump with the shortest cumulative running time is started first each time it is started, and the cooling water pump with the longest cumulative running time is selected when it is stopped. The cooling water pump corresponds to the refrigeration equipment unit one by one; An abnormal operation status analysis is performed on a plurality of cooling water pumps, the plurality of cooling water pumps serving as backup for each other, and when any cooling water pump fails, a fault backup strategy is generated, the fault backup strategy being used to quickly respond to the cooling water pump failure.

3. The energy-saving control method for refrigeration equipment according to claim 1, characterized in that: The behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network, and the method includes: In the enclosed space, the estimated user flow and estimated length of stay corresponding to the plurality of cooling microclimate spaces are analyzed in combination with a time series analysis algorithm; identifying high-density areas and low-density areas based on the expected user flows and expected lengths of stay corresponding to the plurality of refrigerated microclimate spaces; Based on the high-density area and the low-density area, a multi-objective optimization algorithm is used to balance the allocation of cooling resources corresponding to the multiple cooling microclimate spaces.

4. The energy-saving control method for refrigeration equipment according to claim 3, characterized in that: Balancing the allocation of refrigeration resources corresponding to the plurality of refrigeration microclimate spaces, the method comprising: Setting an overcooling penalty constraint, wherein the overcooling penalty constraint includes a temperature threshold; Based on the overcooling penalty constraint, an overcooling risk index is added during the cooling resource allocation process; The overcooling risk index is used to optimize the competition for refrigeration resource allocation.

5. An energy-saving control system for refrigeration equipment, characterized in that: Used to implement the energy-saving control method for refrigeration equipment according to any one of claims 1 to 4, the system comprises: A control interlocking module is used to interlock the status of the air valve, the refrigeration compressor and the blower based on the refrigeration equipment unit by control logic, and the blower status is used to reflect the operating status of a single blower; A module for determining an overall initial control strategy is used to determine an overall initial control strategy corresponding to a plurality of refrigeration microclimate spaces in a closed space after receiving a start-up instruction corresponding to a refrigeration equipment unit, wherein the overall initial control strategy is used to manage a plurality of refrigeration microclimate spaces formed within a rated refrigeration area corresponding to the refrigeration equipment unit; A movement trajectory capture module, used to connect to a video surveillance device, capture a movement trajectory of a person, and collect a behavior monitoring data set of a target person in the refrigeration microclimate space from the multiple refrigeration microclimate spaces according to the movement trajectory of the person, wherein the target person is any person in the closed space; An analysis module, used to construct a behavior pattern analysis model, wherein the behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network; Obtaining a local customized control strategy module, for the behavior pattern analysis model to obtain local customized control strategies corresponding to multiple refrigeration microclimate spaces based on a behavior monitoring data set of a target person in a refrigeration microclimate space, wherein the local customized control strategy is used to manage the refrigeration microclimate space where the target person is currently located; An energy-saving control module, used to determine a comprehensive refrigeration control strategy of the refrigeration equipment unit through the overall initial control strategy and the local customized control strategy, wherein the comprehensive refrigeration control strategy is used to perform energy-saving control without sacrificing user comfort of the target personnel; The behavior pattern analysis model includes an independent single-space behavior pattern analysis network and an interactive multi-space behavior pattern analysis network, including: Setting time period information, wherein the time period information includes a first cycle control agreed time point and a second cycle control agreed time point under a time limit condition; Setting a fixed activity area for the target person, wherein the fixed activity area includes the first cycle control agreed coordinates and the second cycle control agreed coordinates under the task condition; Based on the time period information and the fixed activity area, combined cycle control is performed in combination with a humidity control module, the humidity control module being communicatively connected to the independent single space behavior pattern analysis network; The behavior pattern analysis model obtains local customized control strategies corresponding to multiple refrigeration microclimate spaces based on the behavior monitoring data set of the target personnel in the refrigeration microclimate space, including: Collecting user image information based on image monitoring equipment, analyzing clothing types, and obtaining a user clothing index, wherein the user clothing index reflects the body temperature of the target person; Based on the historical refrigeration control examples marked with comfort evaluation marks, combined with the user's clothing index, an auxiliary control parameter set is extracted, and the indicators corresponding to the auxiliary control parameter set include ambient temperature, equipment operating status, and personnel distribution density; By using the auxiliary control parameter set, the local customized control strategy is iterated for precision optimization; The method of iteratively optimizing the accuracy of the local customized control strategy through the auxiliary control parameter set includes: By using the auxiliary control parameter set, the energy consumption data is used as the correlation constraint index to obtain the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor; Perform energy consumption change analysis based on the ambient temperature-energy consumption correlation factor, the equipment operation status-energy consumption correlation factor, and the personnel distribution density-energy consumption correlation factor to obtain energy consumption change sensitivity; Based on the energy consumption change sensitivity, weight distribution adjustment is performed during the accuracy optimization process of the local customized control strategy.

Images