Energy-saving temperature control system for refrigeration of cold storage

Abstract

The application discloses an energy-saving temperature control system for cold storage refrigeration and relates to the technical field of energy management, comprising a thermal disturbance data acquisition module, a disturbance feature extraction module, a false triggering identification module, an energy consumption fluctuation analysis module and an energy-saving temperature control adjustment module; the thermal disturbance data acquisition module acquires the whole-process operation data of the opening and closing rhythm of the door body, the internal temperature change, the humidity change and the energy fluctuation during the operation of the cold storage, and generates a cold storage thermal disturbance time series data set; through the acquisition of the opening and closing rhythm of the door body, the temperature and humidity change and the energy fluctuation data, the disturbance response index is established, and the dynamic optimization of the refrigeration control is realized. The system identifies the false triggering period caused by the temperature signal lag, reduces the frequent start and stop of the compressor; through the silent acquisition, the reverse sampling, the delay response and the pulse slow-release adjustment, the refrigeration action is synchronized with the door body rhythm, the temperature control is stabilized, the energy consumption is reduced and the equipment operation life is prolonged.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to an energy-saving temperature control system for cold storage refrigeration. Background Technology

[0002] An energy-saving temperature control system for cold storage refrigeration refers to a comprehensive control system that achieves synergistic optimization of refrigeration effect and energy utilization efficiency by real-time monitoring and dynamic adjustment of temperature, humidity, load changes, and equipment energy consumption during cold storage operation. This system typically integrates key components such as temperature sensors, pressure monitoring, flow detection, intelligent control algorithms, and execution control devices. By analyzing the changing trends of heat load within the cold storage, it automatically adjusts the compressor start-up and shutdown rhythm, refrigerant flow distribution, and evaporator heat exchange intensity, thereby meeting the stable temperature requirements of the storage environment while reducing ineffective operation and energy waste of refrigeration equipment. Furthermore, this system can perform predictive control by combining external environmental parameters, door opening and closing frequency, and the thermal characteristics of stored goods, enabling refined energy-saving management of the cold storage at different operating stages.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, cold storage energy-saving temperature control systems typically rely on real-time feedback signals from temperature sensors to dynamically adjust the refrigeration process. However, during operation, when the doors are frequently opened and closed, the system often fails to capture the rapid changes in thermal disturbance in a timely manner. Because the opening and closing of the doors causes hot external air to be rapidly drawn into the storage room, the temperature sensor detects a sudden temperature rise within a very short time. The system may misinterpret this as an overall temperature increase, repeatedly triggering refrigeration commands and causing the compressor to start and stop frequently. This process creates continuous convection of hot and cold air masses inside the cold storage, causing oscillations in the energy consumption curve and disrupting the temperature control balance. Prolonged operation not only increases energy consumption but also easily leads to problems such as delayed temperature regulation, unstable cold source load, and fatigue operation of refrigeration equipment, reducing the overall energy-saving effect and operational reliability of the system.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an energy-saving temperature control system for cold storage refrigeration to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an energy-saving temperature control system for cold storage refrigeration, comprising a thermal disturbance data acquisition module, a disturbance feature extraction module, a false trigger identification module, an energy consumption fluctuation analysis module, and an energy-saving temperature control adjustment module:

[0008] The thermal disturbance data acquisition module collects the entire process operation data of the cold storage, including the door opening and closing rhythm, internal temperature changes, humidity changes, and energy fluctuations, and generates a cold storage thermal disturbance time series dataset to characterize the dynamic impact of door actions on changes in internal environmental parameters.

[0009] The disturbance feature extraction module performs segmented comparison operations based on the cold storage thermal disturbance time series dataset to extract the temperature rise amplitude at the moment the door is opened and establish a disturbance response index to record the temperature rise rhythm features caused by the opening and closing of the door.

[0010] The false trigger identification module determines the time interval of temperature signal response lag based on the disturbance response index, detects abnormal temperature change points within the lag response interval, and generates a compressor start-stop abnormal record table to identify false trigger periods in the control logic.

[0011] The energy consumption fluctuation analysis module traces the energy consumption time trajectory based on the compressor start-stop anomaly record table, performs correlation analysis on the false trigger period and energy consumption curve, determines the concentrated area of ​​energy consumption fluctuation, and forms a high-frequency false judgment window to locate the distribution characteristics of abnormal energy consumption.

[0012] The energy-saving temperature control module performs dynamic energy-saving adjustment control for high-frequency misjudgment windows. It sets a silent sampling time interval before the door opens, performs reverse sampling during the thermal disturbance stage caused by the door opening and closing, and introduces delayed response and pulse slow release adjustment during the sampling process to synchronize the cooling action with the door opening and closing rhythm, stabilize temperature changes and reduce energy consumption fluctuations during the thermal disturbance stage.

[0013] Preferably, the steps for generating the cold storage thermal disturbance time series dataset are as follows:

[0014] Establish a door action time sequence while the cold storage is in continuous operation, continuously record the time nodes of door opening, full opening, closing and full closing, and correspond them with a unified time axis to form a door action record sequence;

[0015] The entire process of collecting temperature and humidity changes inside the cold storage is based on the door action record sequence as a time index. The data from each collection point is synchronously labeled with the door action sequence to form a correspondence between door actions and environmental changes.

[0016] Energy parameters such as compressor operating power, refrigerant circulation flow rate, and heat exchange temperature difference are collected along the same time axis and aligned with temperature and humidity change curves to obtain the energy response process.

[0017] By time-calibrating and integrating the door movement time series, temperature and humidity change data, and energy parameters, a cold storage thermal disturbance time series dataset with temporal continuity and multi-dimensional parameter synchronization is generated, which is used to characterize the dynamic impact of door movement on changes in cold storage environmental parameters.

[0018] Preferably, the steps for establishing the disturbance response index are as follows:

[0019] When the cold storage thermal disturbance time series dataset is completed, the time interval between the time point when the door starts to open and the time point when the door is completely closed is determined by taking the time series of the door opening and closing rhythm as a reference. The temperature curve is then segmented and divided into a stable phase, a changing phase and a recovery phase.

[0020] Based on the temperature change curves of each segment, the temperature change trend in the early stage of door opening is analyzed to determine the time point when the temperature starts to rise and the time point when the temperature peaks, and the temperature increment between the two is calculated to obtain the temperature rise amplitude at the moment the door opens.

[0021] After determining the temperature rise rate, analyze the temperature response speed, temperature recovery trend and fluctuation duration in each time period to form a temperature rise rhythm characteristic curve.

[0022] Using time index as a unified reference, the temperature rise amplitude, temperature rise rate, fluctuation duration and temperature recovery process are recorded to form a disturbance response index, which is used to characterize the temperature rise rhythm characteristics caused by door movement.

[0023] Preferably, the steps for generating the compressor start-stop anomaly record table are as follows:

[0024] Using the door opening time and temperature rise start point recorded in the disturbance response index as a reference, the time interval of temperature signal response lag is determined, and the start and end times of the lag response are marked on the time axis to form a complete time range.

[0025] The temperature change process is continuously analyzed within a defined time period to identify the time points where the rate of temperature change changes abruptly and record them as abnormal temperature change points to reflect the inconsistency between the temperature signal and the air temperature change.

[0026] Using the compressor operating time series as a reference, each abnormal temperature change point is matched with the compressor start-up and shutdown events to form a start-up and shutdown event dataset containing time correspondence.

[0027] The time matching results are organized, and a compressor start-stop anomaly record table indexed by time is established to record the temperature response lag time segment, abnormal temperature change point, and start-stop event time, which is used to identify the false triggering period in the control logic.

[0028] Preferably, each abnormal temperature change point recorded in the compressor start-stop abnormality record table includes the corresponding door action type, temperature change amplitude, lag duration, compressor start-stop status, and energy consumption status. Multiple abnormal events within the same operating cycle are arranged in chronological order to present the correspondence between temperature signal response lag and compressor start-stop behavior.

[0029] The preferred high-frequency misjudgment window formation process is as follows:

[0030] Based on the time index in the compressor start-stop anomaly record table, the time node of each abnormal start-stop event is mapped to the energy consumption time series of cold storage operation, and the compressor operating power, evaporator heat exchange intensity and refrigerant circulation flow change curves are synchronized to a unified time axis.

[0031] After the time mapping is completed, the power curve, cooling load curve and energy transfer curve within the energy change range before and after each false triggering time are analyzed to identify the energy offset caused by the false triggering and record the energy response process.

[0032] By overlaying and comparing the energy consumption curves of multiple false triggering periods, the time areas with concentrated energy fluctuation amplitude and high frequency are identified, and the energy consumption fluctuation cluster area is marked.

[0033] A high-frequency misjudgment window is established with the energy consumption fluctuation cluster area as the core range, and the time series of false trigger events, energy fluctuation amplitude, duration and recovery time nodes are recorded to locate the abnormal distribution characteristics of energy consumption.

[0034] Preferably, when establishing a high-frequency misjudgment window, the energy consumption fluctuation analysis module merges the energy fluctuation intervals according to time continuity and marks the time series of the false triggering event and the change in energy fluctuation amplitude on the same time axis. By synchronously recording the duration of energy fluctuation and the recovery time node, a continuous time-series structure of abnormal energy consumption distribution is formed to reflect the energy fluctuation aggregation characteristics caused by false triggering events in the operation of cold storage.

[0035] Preferably, for high-frequency misjudgment windows, energy-saving dynamic adjustment control is implemented. A silent sampling time interval is set before the door opens, and reverse sampling is performed during the thermal disturbance stage. Delay response and pulse slow release adjustment are introduced during the sampling process to synchronize the cooling action with the door opening and closing rhythm. The steps are as follows:

[0036] Before entering the high-frequency misjudgment window, a silent acquisition time interval is set according to the time pattern of the door opening and closing rhythm and the time information in the compressor start-stop abnormal record table. Within this interval, temperature, humidity and energy consumption parameters are continuously acquired and the cooling action is stopped to control the judgment.

[0037] During the door opening phase, a reverse sampling method is implemented, using previously collected stable temperature data as a time reference to delay the processing of temperature change trends in order to avoid triggering cooling action by a sudden temperature rise signal.

[0038] When the door closes, a delayed response operation is introduced. A delay time interval is set to record temperature and humidity changes and pause the refrigeration start and stop commands, so that the cold storage air temperature gradually returns to a stable distribution.

[0039] At the end of the delayed response time, pulse slow-release regulation is performed to adjust the cooling intensity in stages to gradually restore the cooling process, so that the temperature curve is consistent with the thermal disturbance decay curve and the energy-saving dynamic control cycle is completed.

[0040] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0041] This invention achieves precise characterization of thermal disturbances by synchronously collecting and analyzing the door opening and closing rhythm, temperature and humidity changes, and energy fluctuations throughout the entire operation of a cold storage facility. This allows the control logic to proactively adjust the refrigeration response before and after door movement. By establishing a dynamic correlation between the disturbance response index and the energy consumption time trajectory, the system can effectively identify false triggering periods caused by temperature signal lag, fundamentally reducing frequent compressor start-stop cycles, making the refrigeration operation more stable and continuous, and improving the stability of cold storage temperature control and energy utilization.

[0042] This invention, after identifying high-frequency misjudgment windows, establishes a time-synchronized relationship between refrigeration action and door opening / closing rhythm by setting a silent acquisition zone, implementing reverse sampling, and introducing delayed response and pulse slow-release regulation. This control method allows refrigeration behavior to avoid the transient phase of thermal disturbances, maintaining controlled temperature fluctuations during disturbances and preventing energy waste caused by ineffective refrigeration starts. By coordinating the refrigeration rhythm with the door movement pattern, intelligent energy-saving operation of the cold storage is achieved in scenarios with frequent opening and closing, effectively extending equipment life and reducing operating energy consumption. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0044] Figure 1 This is a schematic diagram of a module of an energy-saving temperature control system for cold storage refrigeration according to the present invention. Detailed Implementation

[0045] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0046] This invention provides, for example Figure 1 The energy-saving temperature control system for cold storage refrigeration shown includes a thermal disturbance data acquisition module, a disturbance feature extraction module, a false trigger identification module, an energy consumption fluctuation analysis module, and an energy-saving temperature control adjustment module.

[0047] The thermal disturbance data acquisition module collects the entire process operation data of the cold storage, including the door opening and closing rhythm, internal temperature changes, humidity changes, and energy fluctuations, and generates a cold storage thermal disturbance time series dataset to characterize the dynamic impact of door actions on changes in internal environmental parameters.

[0048] To construct a time-series dataset of cold storage thermal disturbances that characterizes the dynamic impact of door movements on changes in internal environmental parameters, the entire process is based on the acquisition of physical parameters, time correlation, environmental state synchronization, and energy response records during actual cold storage operation. The entire data construction is achieved through continuous data acquisition and time-series processing. The specific implementation steps are as follows:

[0049] While the cold storage is in continuous operation, a time sequence of door movements is established based on the door's operational rhythm. Specifically, all actions occurring during a complete opening and closing process are continuously recorded, including the time when the door begins to open, the time when it is fully open, the time when it begins to close, and the time when it is fully closed. Each action node corresponds to a unified timeline and is ordered according to the actual operating sequence of the cold storage, thus forming a continuous sequence of door movement records. During the recording process, the duration of door opening, the interval between openings, and the time the door remains stationary after closing are simultaneously marked, ensuring complete traceability of the door's movement rhythm over time. In this way, each stage of door movement can be correlated with the cold storage's operating state on the timeline, providing a unified time reference for subsequent environmental parameter collection.

[0050] After establishing the door action time series, synchronous data collection of temperature and humidity changes inside the cold storage was conducted, using the door action sequence as a time index. To ensure the continuity of the collected data, multiple temperature and humidity sampling points were set up inside the cold storage, allowing the air temperature and humidity conditions in different spatial areas of the cold storage to be recorded simultaneously. The temperature and humidity data at each sampling point were time-labeled with the door action time series, thus establishing a correspondence between door action and environmental changes on the timeline. Temperature and humidity data were continuously collected throughout the entire time period—before the door opens, during the door opening process, and after the door closes—to reflect the dynamic changes in the air inside the cold storage under different door states. This clearly shows the direction of cold air flow, the changing trend of the temperature gradient, and the trajectory of humidity changes caused by the influx of outside air when the door begins to open, thereby establishing a complete mapping relationship between the air parameters inside the cold storage and changes in door action at the data level.

[0051] After obtaining time-series data on temperature and humidity changes, the dynamic changes of energy parameters inside the cold storage are further collected along the same time axis. These energy parameters include the electrical power data during compressor operation, the refrigerant flow rate in the circulation pipeline, and the temperature difference changes between the evaporator and condenser during heat exchange. Each energy parameter is collected synchronously based on a time index consistent with the door's movement time series. By mapping the energy change curves one-to-one with the door's movement time axis and the temperature and humidity change curves, the impact of door opening and closing on the overall energy state of the cold storage can be revealed. When the door is opened, outside air enters the cold storage, and internal cold air is lost, changing the load state of the refrigeration system. At this time, the compressor's operating power, refrigerant flow rate, and heat exchange intensity will fluctuate accordingly. By continuously recording these parameters, the entire process of the cold storage's energy response can be obtained over time, forming a time-series curve of energy changes with door movement, thus establishing a direct correspondence between energy changes and environmental disturbances.

[0052] The acquired door action time series, temperature and humidity change data, and energy fluctuation data are uniformly time-calibrated and integrated to generate a cold storage thermal disturbance time series dataset with temporal continuity and multi-dimensional parameter synchronization. During the integration process, the door action sequence is first used as the main time axis, aligning the temperature and humidity change data and energy fluctuation data in chronological order. Subsequently, through precise matching of time points, each time node can simultaneously reflect the door status, internal temperature and humidity status, and energy operation status, thus forming a synchronous mapping relationship between multi-source data. This time series dataset uses time as the core index, integrating door opening and closing behavior, environmental parameter changes, and energy response processes into a continuous dynamic data sequence. This allows the cold storage operation status at any given time to reflect the dynamic correlation between door actions and internal environmental changes. This dataset can fully present the formation, diffusion, and attenuation process of thermal disturbances during door opening and closing in the cold storage, revealing the delayed characteristics of temperature changes, the response period of humidity fluctuations, and the dynamic trajectory of energy consumption, providing fundamental data support for subsequent disturbance feature identification, energy consumption pattern analysis, and energy-saving control strategy optimization.

[0053] The disturbance feature extraction module performs segmented comparison operations based on the cold storage thermal disturbance time series dataset to extract the temperature rise amplitude at the moment the door is opened and establish a disturbance response index to record the temperature rise rhythm features caused by the opening and closing of the door.

[0054] The temperature rise instantaneously upon door opening is obtained from the cold storage thermal disturbance time-series dataset, and a disturbance response index reflecting the rhythmic characteristics of temperature rise caused by door opening and closing is established. The entire process is based on the obtained cold storage thermal disturbance time-series dataset. Through continuous segmented analysis and dynamic comparison of the correlation between temperature changes, door state changes, and energy responses in the time series, disturbance response information with the ability to describe temperature rise patterns is formed. The specific implementation steps are as follows:

[0055] After establishing the time-series dataset of thermal disturbances in the cold storage, time reference intervals for segmented comparison were determined based on the time series of door opening and closing rhythms. Specifically, the initial reference point was the time when the door began to open, and the termination point was the time when the door was completely closed. Within this time interval, the temperature curve was segmented for extraction. To ensure the synchronization between door movement and temperature changes, the stable phase before door opening, the changing phase during door opening, and the recovery phase after door closing were each divided into independent data segments. Each data segment contains a complete record of temperature, humidity, and energy changes, and is consistent with the time information of door opening and closing states, thus providing a unified time framework for the subsequent identification of temperature rise amplitude. In this way, temperature change segments that completely correspond to door movements can be formed on the timeline of cold storage operation, providing clear boundary conditions for temperature change feature extraction.

[0056] After segmenting the time period, the temperature change curve for each segment is used as a basis for continuous analysis of the temperature change over time to determine the initial state and peak response of the temperature change at the moment the door opens. Specifically, during the initial opening phase of the door, the air is affected by the entrainment of external heat, causing a rapid rise in internal temperature. By continuously tracking the temperature change trend within this time period, the time point when the temperature begins to rise and the time point when the temperature reaches its peak can be determined, and the temperature increment between the two can be calculated. This temperature increment is the magnitude of the temperature rise caused by the moment the door opens. In determining this temperature rise magnitude, the temperature change curve is simultaneously correlated with the door's movement time information to ensure that the phased characteristics of the temperature change are consistent with the door's movement phase, thereby avoiding analytical errors caused by data misalignment. This temperature rise magnitude not only reflects the transient heat input caused by the inflow of external air but also reflects the intensity of the air stratification changes inside the cold storage, providing a basic numerical basis for the subsequent quantification of disturbance characteristics.

[0057] After determining the temperature rise rate, the analysis continues based on the synchronization between the door's movement rhythm and temperature changes. This involves a comprehensive analysis of the temperature response speed, temperature recovery trend, and duration of temperature fluctuations within each time period to establish a temperature rise rhythm characteristic. This process uses a time series as the main thread, completely reconstructing the temperature change process within the door's movement cycle into three parts: a heating phase, a stabilizing phase, and a recovery phase. Recording the correspondence between the heating phase and the door's opening duration reveals the time delay pattern of the temperature rise process; recording the temperature fluctuation range during the stabilizing phase reflects the amplitude of temperature fluctuations caused by air mixing; and recording the temperature drop time during the recovery phase demonstrates the continuous process of heat dissipation within the cold storage. The data from these three phases together constitute the temperature rise rhythm characteristic curve, ensuring that the heat disturbance caused by each door movement is recorded in both time and temperature amplitude dimensions.

[0058] After obtaining the characteristic curves of temperature rise amplitude and temperature rise rhythm, a disturbance response index is established using a time index as a unified reference. This index centers on the time point of door operation, centrally recording the temperature rise amplitude, temperature rise rate, fluctuation duration, and temperature recovery process at that moment in a unified index table. By classifying and numbering the disturbance responses within different door operation cycles, a complete record structure describing the temperature response patterns of the cold storage during multiple opening and closing processes is formed. This index not only preserves the temperature change characteristics caused by each door operation but also includes the distribution pattern of thermal disturbances within the cold storage over time. Through continuous updating and expansion of the disturbance response index, a time-series archive reflecting the overall thermal disturbance characteristics of the cold storage can be formed, laying a data foundation for subsequent analysis of the correspondence between door operations and energy consumption.

[0059] The false trigger identification module determines the time interval of temperature signal response lag based on the disturbance response index, detects abnormal temperature change points within the lag response interval, and generates a compressor start-stop abnormal record table to identify false trigger periods in the control logic.

[0060] Based on the disturbance response index, the time interval of temperature signal response lag is determined, and anomalies in temperature changes are detected within this time interval. This creates a compressor start-stop anomaly record table that accurately identifies false triggering periods in the refrigeration control logic. The entire process is based on the established disturbance response index, synchronously linking the cold storage door action time information, temperature change process, and compressor operating status. Through time interval division, temperature curve analysis, abnormal change point extraction, and start-stop event recording, a complete false triggering identification and recording process is gradually constructed. The specific implementation steps are as follows:

[0061] After the disturbance response index is constructed, the initial time range for the temperature signal response lag is determined by referencing the door opening time and the corresponding temperature rise start point recorded in the index. Specifically, when the door is open, outside air enters the cold storage, causing a temperature change, but the signal collected by the temperature sensor will be delayed. To accurately determine this delay, the temperature change trend is analyzed time-by-time along the temperature change curve, starting from the time the door begins to open. The first point where the temperature curve changes from stable to rising is identified, and this point is determined as the start time of the temperature signal response. The analysis continues along the time axis until the temperature curve enters a sustained rising state and the temperature increase stabilizes; this point is taken as the end time of the lag response. Thus, the entire time interval from the inflow of outside air after the door opens to the time when the sensor signal begins to reflect the temperature change constitutes the time segment of the temperature signal response lag. In this process, the division of the time segment not only considers the rhythm of the door's movement but also maps the physical response process of the temperature signal to the time nodes, ensuring that the time range of the lag segment has a complete start and end definition.

[0062] After determining the time interval of the temperature signal response lag, the temperature change process within this time interval is analyzed point by point to identify discontinuous changes in the temperature signal during the response. The temperature curve typically experiences a phase of slow to rapid increase within the lag interval, but the temperature change trend may fluctuate due to factors such as the inflow velocity of external air, disturbances in cold air distribution, and the direction of airflow inside the cold storage. During this phase, each sampling point of the temperature curve within the lag interval is continuously compared to identify the time points where the rate of temperature change abruptly changes. If the rate of temperature change suddenly increases and then suddenly decreases or exhibits abnormal fluctuations within a continuous time period, this time point is marked as an abnormal temperature change point. Each abnormal temperature change point records its occurrence time, temperature value, and corresponding door action status to reflect the inconsistency between the temperature signal acquisition response and the actual air temperature change. In this way, abnormal temperature changes caused by airflow disturbances, sensor response delays, or differences in temperature stratification within the lag interval can be clearly identified, providing specific temporal evidence and data foundation for subsequent determination of the cause of false triggering.

[0063] After identifying abnormal temperature change points, the compressor's operating time series is used as a reference to perform time-correlation matching between each abnormal temperature change point and the compressor's start-up and shutdown behavior. To achieve time correlation analysis, the start-up and shutdown times of the compressor are aligned with the temperature change timeline within the lag zone, comparing the temperature lag response triggered by each door action. During this matching process, if the time interval between a certain abnormal temperature change point and the compressor start-up or stop event is within a very short time range, it is determined that the start-up and shutdown event may have been caused by a misleading lag temperature signal. By summarizing the time correspondences within all door action cycles, a dataset of start-up and shutdown events containing time-matching information can be formed. In this dataset, each event records its corresponding door action type, abnormal temperature point location, compressor start-up and shutdown status, and temperature change direction, establishing a traceable correspondence between the temperature response lag and the compressor's operating logic. Through this time-matching analysis, compressor false triggering events caused by temperature signal delays can be distinguished throughout the entire cold storage operation, providing accurate time correlation basis for control logic optimization.

[0064] After the lag segment analysis and compressor start-stop time matching are completed, all matching results are systematically organized to form a compressor start-stop anomaly record table. This record table uses time as the primary index, uniformly recording the temperature response lag time segment, corresponding abnormal temperature change point, compressor start-stop event time, and cold storage energy consumption status for each door opening and closing cycle. Each record includes not only time information but also the temperature change amplitude, lag duration, and a detailed description of the start-stop behavior, thus comprehensively presenting the correspondence between lag response and false triggering. During the record table formation process, multiple abnormal events within the same operating cycle are arranged in chronological order, ensuring a complete reflection of false triggering phenomena occurring at different stages of the compressor. In this way, the compressor start-stop anomaly record table can demonstrate the distribution pattern of false triggering in actual cold storage operation, reveal the impact of temperature signal response lag on refrigeration control logic, and provide data support for subsequent energy consumption optimization and control rhythm adjustment.

[0065] The energy consumption fluctuation analysis module traces the energy consumption time trajectory based on the compressor start-stop anomaly record table, performs correlation analysis on the false trigger period and energy consumption curve, determines the concentrated area of ​​energy consumption fluctuation, and forms a high-frequency false judgment window to locate the distribution characteristics of abnormal energy consumption.

[0066] Based on the compressor start-stop anomaly record table, the energy consumption time trajectory is retrospectively analyzed, and the temporal correlation between the false trigger period and the energy consumption curve is analyzed to identify the concentrated area of ​​energy consumption fluctuations and form a high-frequency false judgment window. This is used to locate the characteristics of abnormal energy consumption distribution during cold storage operation. The entire process uses the compressor start-stop anomaly record table as the time reference, combined with the energy consumption change curve of the cold storage during operation, and through continuous steps such as time mapping, energy response analysis, fluctuation cluster identification, and anomaly distribution extraction, the energy consumption pattern exhibited by the cold storage before and after the false trigger event is tracked throughout the entire process. The specific implementation steps are as follows:

[0067] After the compressor start-stop anomaly log is created, the time index recorded within it is used as the core basis to map the time node of each abnormal start-stop event to the energy consumption time series of the cold storage operation. Specifically, each start-stop time data in the anomaly log is precisely aligned with the time axis of the energy consumption curve, ensuring that each false triggering event has a clear time correspondence on the energy consumption curve. During this mapping process, the compressor operating power, evaporator heat exchange intensity, and refrigerant circulation flow rate change curves are all included on the same time axis, thus ensuring that the energy consumption curve not only reflects electrical energy consumption but also includes the impact of refrigeration load changes on energy fluctuations. Through this multi-dimensional parameter synchronization method, the specific location of each false triggering event can be marked on the energy consumption time trajectory, establishing a time correspondence between the false triggering period and energy consumption fluctuations, providing a precise time framework for subsequent analysis of the impact of false triggering on energy consumption.

[0068] After establishing a correspondence between the false trigger time and the energy consumption time trajectory, a retrospective analysis is performed on the energy consumption change trend before and after each false trigger period. Specifically, taking the false trigger time as the center, energy change intervals of a certain length are selected before and after it, and the power change curve, refrigeration load change curve, and cold storage energy transfer curve within these intervals are analyzed. By observing whether the power curve shows sudden increases, sudden decreases, or frequent fluctuations before and after the false trigger, the energy consumption deviation caused by the false trigger can be identified. At the same time, the refrigerant circulation flow change is superimposed with the compressor start-stop state, and the adjustment cycle and stabilization recovery time of the refrigerant flow after each false trigger are recorded, thereby clarifying the delayed impact of the false trigger event on the energy response process. Through retrospective analysis of multiple false trigger periods, a set of time-series segments containing the fluctuation characteristics of the energy consumption curve can be formed. These segments record the complete process of energy consumption recovery after the compressor's erroneous start-stop, providing continuous data support for the determination of concentrated energy consumption fluctuation areas.

[0069] After analyzing energy consumption time segments, the energy consumption curves of multiple false triggering periods are comprehensively compared to identify time areas with high energy fluctuation amplitude and frequency. Specifically, the energy change trends of each time period are overlaid and compared to find concentrated intervals where energy consumption changes periodically increase or decrease. When multiple false triggering periods show overlapping energy consumption curve fluctuations within a similar time range, it indicates that this time area may be a concentrated area of ​​energy consumption fluctuations. In this process, the synchronous change characteristics of power curves, flow curves, and temperature curves are compared over time. If there is a continuous deviation between power change and temperature response within the same time period, this time period is marked as an energy consumption fluctuation cluster. In this way, concentrated areas of energy consumption fluctuations can be identified on the operating timeline of the cold storage, making energy anomalies traceable in the time dimension. The implementation of this step makes energy consumption changes no longer discrete single events, but restored to a dynamic process with continuous distribution characteristics, laying the foundation for the formation of subsequent high-frequency false judgment windows.

[0070] After identifying concentrated energy consumption fluctuation areas, high-frequency false alarm windows are formed around these areas, and the energy consumption anomaly distribution characteristics within them are extracted. The establishment of high-frequency false alarm windows is based on the clustering characteristics of false triggering events in the energy consumption time series. Continuous or adjacent energy consumption fluctuation intervals are merged into a unified energy consumption anomaly interval, which is then represented continuously on the time axis. Each high-frequency false alarm window includes the time series of the false triggering event, the corresponding energy fluctuation amplitude, the fluctuation duration, and the time node for recovery to stability. Through statistical analysis of these windows, the concentrated distribution characteristics of false triggering events at the energy consumption level during cold storage operation can be determined. For example, when frequent compressor starts and stops are concentrated in a certain operating period, the energy curve will form dense peak-valley changes during that period. The formation process of the high-frequency false alarm window records this concentrated phenomenon of energy fluctuation. By sorting multiple high-frequency false alarm windows by time, the temporal pattern of energy consumption anomalies within the cold storage's operating cycle can be further revealed. This process not only reveals the intrinsic connection between false triggering and energy consumption fluctuations but also provides a foundation for subsequent energy-saving adjustments.

[0071] The energy-saving temperature control module performs energy-saving dynamic adjustment control for high-frequency misjudgment windows. It sets a silent sampling time interval before the door opens, performs reverse sampling during the thermal disturbance stage caused by the opening and closing of the door, and introduces delayed response and pulse slow release adjustment during the sampling process to synchronize the cooling action with the door opening and closing rhythm, stabilize temperature changes and reduce energy consumption fluctuations during the thermal disturbance stage.

[0072] Energy-saving dynamic adjustment and control are achieved within the high-frequency misjudgment window, ensuring that the cooling action is synchronized with the door opening and closing rhythm. During thermal disturbance phases, stable temperature control and reduced energy consumption fluctuations are achieved. The entire process is time-based, with door movement patterns, temperature signal response characteristics, and cooling action rhythm as its core elements. A complete dynamic control process is constructed through time pre-control, data sampling and adjustment, response delay control, and slow-release cooling adjustment steps. Specific implementation steps are as follows:

[0073] Before the high-frequency misjudgment window arrives, a silent data acquisition time interval is set based on the timing pattern of the door opening and closing rhythm and the time information in the compressor start-stop anomaly record table. The goal of this stage is to establish a time buffer in advance to prevent transient thermal disturbances caused by outside air entering the cold storage when the door is about to open from being immediately collected by the temperature sensor and triggering the refrigeration action. In specific implementation, the period before the door opens is defined as the silent acquisition interval, using the point when the door is about to open as the benchmark. Within this interval, the acquisition of temperature, humidity, and energy consumption parameters continues, but the acquired signals are only used for data recording and do not participate in the refrigeration action control judgment. In this way, false actions caused by brief signal fluctuations can be eliminated before the door opens, allowing the refrigeration unit to remain stable before the thermal disturbance actually forms. The setting of the silent acquisition interval ensures that the control logic remains in an observation state before outside hot air enters the cold storage, providing clean initial conditions for data acquisition and action judgment in the subsequent thermal disturbance stage, keeping the temperature control response separate from the door movement process in time, thereby preventing the refrigeration unit from being triggered to operate before the door is fully opened.

[0074] After the door is actually opened, the air inside the cold storage begins to experience thermal disturbance due to the entrainment of outside air. At this time, the temperature changes rapidly and transiently. To ensure that the temperature data reflects the overall thermal equilibrium rather than local transient changes, a reverse sampling method is implemented at this stage. Specifically, when the door is open, the temperature acquisition does not directly use the instantaneous temperature signal as the control basis. Instead, it uses previously acquired stable temperature data as a time reference, delaying the processing of temperature change trends so that the control logic does not immediately respond to sudden temperature rise signals. During the reverse sampling process, temperature acquisition continues, but the control action is based on the sampled data from a certain period of time ago. In this way, the sudden temperature rise signal caused by the rapid influx of air when the door is first opened can be effectively filtered out, thereby preventing the refrigeration unit from being frequently triggered in the early stages of thermal disturbance. The reverse sampling process cancels out the direction of the cold storage temperature signal acquisition with the direction of thermal disturbance change, smoothing the data in the time dimension. This makes the temperature feedback more reflective of the true trend of the overall cold storage environment, providing an accurate basis for temperature changes in the delayed response stage.

[0075] To prevent short-term temperature signal fluctuations caused by air remixing, fluid disturbance, and residual heat diffusion after the door closes, a delayed response is introduced during sampling and control. Specifically, after detecting complete door closure, the refrigeration process is not immediately resumed; instead, a delay time interval is set. During this delay, temperature and humidity changes are recorded, but no start / stop commands are issued to the refrigeration unit. This allows sufficient time for the air inside the cold storage to re-establish a stable temperature hierarchy after the door closes, restoring a uniform temperature distribution and preventing false triggering due to incomplete air equilibrium. The delayed response time interval allows the cold storage to maintain a natural recovery period after the door closes, allowing the temperature signal to gradually return to a stable state. In this way, the compressor start / stop logic can be separated from the actual thermal disturbance process, preventing repeated control responses caused by residual temperature differences after door closure and achieving a continuous and smooth transition of temperature changes.

[0076] To further stabilize the internal temperature of the cold storage and suppress energy consumption fluctuations after the delayed response time, a pulse-release regulation process is introduced during the refrigeration restart phase. Specifically, after the delayed response phase, the refrigeration operation does not immediately start at full power, but adjusts the refrigeration intensity in stages, allowing the refrigeration process to recover gradually. In the initial stage, the refrigeration unit operates with a lower cooling capacity output, causing the internal temperature of the cold storage to gradually decrease. When the temperature approaches the target control range, the refrigeration intensity is gradually increased until it returns to normal refrigeration operation. This staged control of the cooling capacity release process avoids a new round of energy consumption fluctuations caused by sudden temperature changes. The pulse-release regulation process ensures that the temperature recovery curve matches the thermal disturbance decay curve, maintaining temperature stability and energy balance during the recovery process. In this stage, the rhythm of the refrigeration operation is perfectly matched with the door opening and closing rhythm. When the door returns to a stationary state, the temperature, humidity, and energy consumption data return to stable operating levels, thus completing a full energy-saving dynamic control cycle.

[0077] This invention achieves precise characterization of thermal disturbances by synchronously collecting and analyzing the door opening and closing rhythm, temperature and humidity changes, and energy fluctuations throughout the entire operation of a cold storage facility. This allows the control logic to proactively adjust the refrigeration response before and after door movement. By establishing a dynamic correlation between the disturbance response index and the energy consumption time trajectory, the system can effectively identify false triggering periods caused by temperature signal lag, fundamentally reducing frequent compressor start-stop cycles, making the refrigeration operation more stable and continuous, and improving the stability of cold storage temperature control and energy utilization.

[0078] This invention, after identifying high-frequency misjudgment windows, establishes a time-synchronized relationship between refrigeration action and door opening / closing rhythm by setting a silent acquisition zone, implementing reverse sampling, and introducing delayed response and pulse slow-release regulation. This control method allows refrigeration behavior to avoid the transient phase of thermal disturbances, maintaining controlled temperature fluctuations during disturbances and preventing energy waste caused by ineffective refrigeration starts. By coordinating the refrigeration rhythm with the door movement pattern, intelligent energy-saving operation of the cold storage is achieved in scenarios with frequent opening and closing, effectively extending equipment life and reducing operating energy consumption.

[0079] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An energy-saving temperature control system for cold storage refrigeration, characterized in that, It includes a thermal disturbance data acquisition module, a disturbance feature extraction module, a false trigger identification module, an energy consumption fluctuation analysis module, and an energy-saving temperature control module: The thermal disturbance data acquisition module collects the entire process operation data of the cold storage, including the door opening and closing rhythm, internal temperature changes, humidity changes, and energy fluctuations, and generates a cold storage thermal disturbance time series dataset. The steps for generating the cold storage thermal disturbance time series dataset are as follows: Establish a door action time sequence while the cold storage is in continuous operation, continuously record the time nodes of door opening, full opening, closing and full closing, and correspond them with a unified time axis to form a door action record sequence; The entire process of collecting temperature and humidity changes inside the cold storage is based on the door action record sequence as a time index. The data from each collection point is synchronously labeled with the door action sequence to form a correspondence between door actions and environmental changes. Energy parameters are collected along the same time axis and aligned with the temperature and humidity change curves to obtain the energy response process. The energy parameters include compressor operating power, refrigerant circulation flow rate, and heat exchange temperature difference. The door movement time series, temperature and humidity change data and energy parameters are time-calibrated and integrated to generate a cold storage thermal disturbance time series dataset with time continuity and multi-dimensional parameter synchronization. The disturbance feature extraction module performs segmented comparison operations based on the cold storage thermal disturbance time series dataset to extract the temperature rise at the moment the door is opened and establish a disturbance response index. The false trigger identification module determines the time range of temperature signal response lag based on the disturbance response index, detects abnormal temperature change points within the lag response range, and generates a compressor start-stop abnormal record table. The energy consumption fluctuation analysis module traces the energy consumption time trajectory based on the compressor start-stop anomaly record table, performs correlation analysis on the false trigger period and energy consumption curve, determines the concentrated area of ​​energy consumption fluctuation, and forms a high-frequency false judgment window. The energy-saving temperature control module performs energy-saving dynamic adjustment control for high-frequency misjudgment windows. It sets a silent sampling time interval before the door opens, performs reverse sampling during the thermal disturbance stage caused by the opening and closing of the door, and introduces delayed response and pulse slow release adjustment during the sampling process to synchronize the cooling action with the door opening and closing rhythm, stabilize temperature changes and reduce energy consumption fluctuations during the thermal disturbance stage. Among them, the silent data collection time interval refers to the period of time before the door is about to open, which is determined by taking the time point when the door is about to open as the benchmark. Reverse sampling refers to the process where temperature acquisition continues continuously during reverse sampling, but control actions are based on sampling data from a certain period of time ago. Delayed response means that after detecting that the door is fully closed, the cooling action is not resumed immediately, but a delay time interval is set; during this delay time, the temperature and humidity change data continue to be recorded, but no start / stop command is issued to the cooling device. Pulse-release regulation means that after the delayed response phase is completed, the cooling action will not start at full power immediately, but the cooling intensity will be adjusted in stages so that the cooling process can be restored in a gradual manner.

2. The energy-saving temperature control system for cold storage refrigeration according to claim 1, characterized in that, The steps for building a disturbance response index are as follows: When the cold storage thermal disturbance time series dataset is completed, the time interval between the time point when the door starts to open and the time point when the door is completely closed is determined by taking the time series of the door opening and closing rhythm as a reference. The temperature curve is then segmented and divided into a stable phase, a changing phase and a recovery phase. Based on the temperature change curves of each segment, the temperature change trend in the early stage of door opening is analyzed to determine the time point when the temperature starts to rise and the time point when the temperature peaks, and the temperature increment between the two is calculated to obtain the temperature rise amplitude at the moment the door opens. After determining the temperature rise rate, analyze the temperature response speed, temperature recovery trend and fluctuation duration in each time period to form a temperature rise rhythm characteristic curve. Using time index as a unified reference, the disturbance response index is formed by recording the temperature rise magnitude, temperature rise rate, fluctuation duration, and temperature recovery process.

3. The energy-saving temperature control system for cold storage refrigeration according to claim 2, characterized in that, The steps for generating the compressor start / stop anomaly record table are as follows: Using the door opening time and temperature rise start point recorded in the disturbance response index as a reference, the time interval of temperature signal response lag is determined, and the start and end times of the lag response are marked on the time axis to form a complete time range. The temperature change process is continuously analyzed within a defined time period to identify the time points where the rate of temperature change changes abruptly and record them as abnormal temperature change points to reflect the inconsistency between the temperature signal and the air temperature change. Using the compressor operating time series as a reference, each abnormal temperature change point is matched with the compressor start-up and shutdown events to form a start-up and shutdown event dataset containing time correspondence. Organize the time matching results and establish a compressor start-stop anomaly record table indexed by time, recording the temperature response lag time range, abnormal temperature change points, and start-stop event times.

4. The energy-saving temperature control system for cold storage refrigeration according to claim 3, characterized in that, Each abnormal temperature change point recorded in the compressor start-stop anomaly record table includes the corresponding door action type, temperature change amplitude, lag duration, compressor start-stop status, and energy consumption status. Multiple abnormal events within the same operating cycle are arranged in chronological order to present the correspondence between temperature signal response lag and compressor start-stop behavior.

5. The energy-saving temperature control system for cold storage refrigeration according to claim 3, characterized in that, The formation process of the high-frequency false positive window is as follows: Based on the time index in the compressor start-stop anomaly record table, the time node of each abnormal start-stop event is mapped to the energy consumption time series of cold storage operation, and the compressor operating power, evaporator heat exchange intensity and refrigerant circulation flow change curves are synchronized to a unified time axis. After the time mapping is completed, the power curve, cooling load curve and energy transfer curve within the energy change range before and after each false triggering time are analyzed to identify the energy offset caused by the false triggering and record the energy response process. By overlaying and comparing the energy consumption curves of multiple false triggering periods, the time areas with concentrated energy fluctuation amplitude and high frequency are identified, and the energy consumption fluctuation cluster area is marked. A high-frequency misjudgment window is established with the energy consumption fluctuation cluster area as the core range, and the time sequence of the mis-triggered event, the energy fluctuation amplitude, the duration and the recovery time node are recorded.

6. The energy-saving temperature control system for cold storage refrigeration according to claim 5, characterized in that, When establishing a high-frequency false alarm window, the energy consumption fluctuation analysis module merges the energy fluctuation intervals according to time continuity and marks the time series of false trigger events and the changes in energy fluctuation amplitude on the same time axis. By synchronously recording the duration of energy fluctuations and the recovery time nodes, a continuous time-series structure of abnormal energy consumption distribution is formed.

7. The energy-saving temperature control system for cold storage refrigeration according to claim 5, characterized in that, To address the high-frequency misjudgment window, energy-saving dynamic adjustment control is implemented. A silent sampling time interval is set before the door opens, and reverse sampling is performed during the thermal disturbance phase. Delay response and pulse slow-release adjustment are introduced during the sampling process to synchronize the cooling action with the door opening and closing rhythm. The steps are as follows: Before entering the high-frequency misjudgment window, a silent acquisition time interval is set according to the time pattern of the door opening and closing rhythm and the time information in the compressor start-stop abnormal record table. Within this interval, temperature, humidity and energy consumption parameters are continuously acquired and the cooling action is stopped to control the judgment. During the door opening phase, a reverse sampling method is implemented, using previously collected stable temperature data as a time reference to delay the processing of temperature change trends in order to avoid triggering cooling action by a sudden temperature rise signal. When the door closes, a delayed response operation is introduced. A delay time interval is set to record temperature and humidity changes and pause the refrigeration start and stop commands, so that the cold storage air temperature gradually returns to a stable distribution. At the end of the delayed response time, pulse slow-release regulation is performed to adjust the cooling intensity in stages to gradually restore the cooling process, so that the temperature curve is consistent with the thermal disturbance decay curve and the energy-saving dynamic control cycle is completed.

Images