Environment-adaptive variable frequency cold storage capacity adjusting system

Abstract

The present application relates to the technical field of refrigeration device, especially to an environment adaptive variable frequency cold storage capacity regulation system, comprising a cold storage basic device and a host computer, the host computer is provided with a sensing prediction, scheduling decision, control execution and self-calibration module. The sensing prediction module predicts the required storage capacity curve and the predicted return air temperature curve according to the warehouse plan, the external environment temperature and the current cold load baseline, and determines the return air temperature curvature according to the return air temperature. The scheduling decision module determines the hierarchical scheduling strategy according to the storage capacity gap, and determines the timing correction amount according to the return air temperature curvature. The control execution module executes the partition wall displacement, the compressor frequency modulation and the access or exit of the modular cold storage according to the timing correction amount, and corrects the output according to the return air temperature curvature. The self-calibration module compares the actual and predicted return air temperatures and corrects the prediction parameters. The present application realizes the collaborative control of the cold storage volume and the refrigeration output and the forward-looking capacity regulation, which can reduce the operation energy consumption and improve the stability of the warehouse temperature.

Description

Technical Field

[0001] This invention relates to the field of refrigeration equipment technology, and in particular to an environmentally adaptive variable frequency cold storage capacity adjustment system. Background Technology

[0002] Cold storage facilities, as core infrastructure of cold chain logistics, account for a significant proportion of the total energy consumption of the cold chain. Current cold storage capacity adjustment methods involve either moving partitions to change the physical volume of the effective storage space or adjusting the refrigeration output of variable frequency compressors to match changes in heat load. However, these two adjustment methods operate independently in existing technologies. The position adjustment of moving partitions is usually based on manual judgment or simple capacity utilization thresholds, while the frequency control of variable frequency compressors is based on feedback adjustment based on the deviation between the current return air temperature and the set value.

[0003] Chinese Patent Publication No. CN112902541A discloses a sealing and insulation system for a variable-capacity cold storage, including a movable partition wall for dividing the cold storage space. The movable partition wall includes an insulated wall and an auxiliary insulation mechanism disposed within the insulated wall. The auxiliary insulation mechanism includes a main insulation airbag, a sealing insulation airbag, and an adjustment component. Storage slots are provided on the side walls of the insulated wall parallel to the moving direction of the insulated wall. The sealing insulation airbag is disposed in the storage slot. A receiving cavity is provided in the insulated wall, and the main insulation airbag is installed in the receiving cavity. The main insulation airbag is connected to the sealing insulation airbag. The adjustment component is used to adjust the volume of the receiving cavity. Therefore, the sealing and insulation system for a variable-capacity cold storage has the following problems: volume adjustment and refrigeration output are independent and not coordinated, resulting in a delayed refrigeration response when the storage capacity changes abruptly, causing temperature fluctuations and wasted cooling capacity. Summary of the Invention

[0004] To address this issue, the present invention provides an environmentally adaptive variable frequency cold storage capacity regulation system to overcome the problem in existing technologies where volume regulation and refrigeration output are independent and not coordinated, resulting in delayed refrigeration response during sudden changes in storage capacity, leading to temperature fluctuations and wasted cooling capacity.

[0005] To achieve the above objectives, the present invention provides an environmentally adaptive variable frequency cold storage capacity adjustment system, comprising a cold storage body, movable partitions, a variable frequency compressor, a refrigeration circuit, and a host computer, wherein the host computer further comprises: The sensing and prediction module is used to obtain the return gas temperature of the return gas pipeline of the refrigeration circuit, obtain the return gas temperature curvature, obtain the storage plan data, the external ambient temperature and the current cooling load baseline data, and predict the required storage capacity curve and the predicted return gas temperature curve in the next adjustment cycle. The scheduling decision module is used to determine whether there is a storage capacity gap and its magnitude based on the required storage capacity curve and the current available storage capacity, determine a hierarchical scheduling strategy based on the magnitude and duration of the storage capacity gap, and determine the timing correction amount for executing each hierarchical scheduling strategy based on the return gas temperature curvature. The control execution module is used to execute scheduling actions according to the hierarchical scheduling strategy and the timing correction amount. The scheduling actions include at least one of the following: adjusting the position of the movable partition wall, controlling the frequency of the variable frequency compressor, controlling the access or exit of the modular cold storage, and adjusting the output of the scheduling actions in real time according to the return gas temperature curvature. The self-calibration module is used to compare the actual change curve of the return gas temperature with the predicted return gas temperature curve, and adjust the prediction parameters of the required storage capacity curve according to the comparison results.

[0006] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity adjustment system, the sensing and prediction module is configured to collect the return gas temperature value of the return gas pipeline of the refrigeration circuit according to a preset sampling period, and process the inbound plan data, external ambient temperature and current cold load baseline data to generate the equivalent heat load time series curve for the next adjustment period, and map the required storage capacity curve for the corresponding time period based on the equivalent heat load time series curve. The sensing and prediction module performs a second derivative on the time series of the return gas temperature to obtain the return gas temperature curvature.

[0007] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity regulation system, the sensing and prediction module is configured to: use the current cold load baseline data as the basic heat load reference value; determine the inbound heat load increment for the next regulation cycle based on the inbound time node and cargo scale in the inbound plan data; determine the enclosure heat transfer heat load increment based on the external ambient temperature combined with the set temperature difference between inside and outside the cold storage and the heat transfer parameters of the cold storage enclosure structure; the sensing and prediction module superimposes the basic heat load reference value and each heat load increment to generate an equivalent heat load time series curve; and determines the predicted return gas temperature corresponding to the end of each preset sampling cycle based on the equivalent heat load time series curve, generating the predicted return gas temperature curve.

[0008] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity adjustment system, the sensing and prediction module uses the ratio of each heat load value in the equivalent heat load time-series curve to the rated heat load carrying threshold as the required effective storage capacity value, and generates the required storage capacity curve by arranging all the required effective storage capacity values ​​in time sequence.

[0009] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity adjustment system, the scheduling decision module obtains the required storage capacity curve at the end of each preset sampling period in the next adjustment cycle, calculates the gap between the storage capacity demand value at the end of each preset sampling period and the current available storage capacity value, accumulates the product of each gap and the sampling interval to obtain the gap integral value, and determines whether there is a storage capacity gap and the corresponding gap magnitude in the adjustment cycle based on the gap integral value. Specifically, if the gap integral value exceeds a preset allowable integral threshold, it is determined that there is a storage capacity gap.

[0010] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity regulation system, the scheduling decision module is configured as follows: If the gap integral value does not exceed the preset allowable integral threshold, it is determined to be a negligible gap, and the hierarchical scheduling strategy is to adjust the position of the movable partition and / or control the frequency of the variable frequency compressor. When the gap integral value exceeds the preset allowable integral threshold but does not exceed N times the preset allowable integral threshold, it is determined to be a medium-level gap. The hierarchical scheduling strategy is to adjust the position of the movable partition, control the frequency of the variable frequency compressor, and connect it to a single modular cold storage. In response to the gap integral value exceeding N times the preset allowable integral threshold, it is determined to be a large-scale gap. The hierarchical scheduling strategy is to adjust the position of the movable partition, control the frequency of the variable frequency compressor, and connect at least two modular cold storage units. Where N is a positive integer greater than 1.

[0011] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity regulation system, the scheduling decision module obtains the return gas temperature curvature of each preset sampling period within the current regulation period, calculates the average absolute deviation, determines the amplitude of the timing correction amount based on the value of the average absolute deviation, and determines the asymmetric direction of the curvature value distribution, and determines the direction of the timing correction amount based on the asymmetric direction.

[0012] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity adjustment system, the control execution module is configured to determine the start time of each scheduling action based on the amplitude of the timing correction amount, and to determine the offset direction of the start time of each scheduling action based on the direction of the timing correction amount. The control execution module starts executing each scheduling action according to the corrected start time, and dynamically corrects the output parameters of each scheduling action according to the real-time collected return gas temperature curvature.

[0013] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity regulation system, the control execution module dynamically corrects the output parameters of each scheduling action based on the real-time collected return air temperature curvature in the following way: In response to the positive curvature of the return gas temperature, the target frequency of the variable frequency compressor is increased to maintain the current displacement of the movable partition wall; Conversely, reducing the target frequency of the variable frequency compressor increases the expansion displacement of the movable partition.

[0014] As a preferred technical solution for an environmentally adaptive variable frequency cold storage capacity regulation system, the self-calibration module obtains the return gas temperature at the end of each sampling cycle within the previous regulation cycle and constructs the actual change curve of the return gas temperature in chronological order. The difference between the actual change curve and the predicted return gas temperature curve is calculated to obtain the deviation sequence. The cumulative error value is determined based on the deviation sequence, and the heat load increment is corrected to correct the equivalent heat load time series curve.

[0015] Compared with existing technologies, the advantages of this invention are that by combining the storage plan, external ambient temperature, and current cold load baseline through a sensing and prediction module, the required storage capacity and return gas temperature change trends are predicted. The cumulative impact of the storage capacity gap is quantified by gap integration and matched with a hierarchical scheduling strategy. Simultaneously, the scheduling start-up sequence and real-time output parameters are corrected based on the return gas temperature curvature. This achieves coordinated linkage and forward-looking control of cold storage volume adjustment and refrigeration output. It effectively avoids temperature fluctuations caused by lag in refrigeration response when storage capacity changes suddenly. It can also call up adjustment methods such as movable partitions, variable frequency compressors, and modular cold storage as needed, reducing unnecessary equipment start-ups and shutdowns and cold energy waste. At the same time, by comparing the actual return gas temperature with the predicted value and correcting the prediction parameters in a closed loop, the system's adaptability to changing operating conditions is continuously improved. Ultimately, it significantly reduces the energy consumption of cold storage operation while ensuring storage temperature stability. Attached Figure Description

[0016] Figure 1 This is a flowchart of an environmentally adaptive variable frequency cold storage capacity adjustment system according to an embodiment of the present invention; Figure 2 This is a structural diagram of the cold storage body, movable partition wall, and modular cold storage according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figure 1 and Figure 2 As shown, this invention provides an environmentally adaptive variable frequency cold storage capacity adjustment system, including a cold storage body, movable partitions, a variable frequency compressor, a refrigeration circuit, and a host computer. The host computer further includes: The sensing and prediction module is used to obtain the return gas temperature of the return gas pipeline of the refrigeration circuit, obtain the return gas temperature curvature, obtain the storage plan data, the external ambient temperature and the current cooling load baseline data, and predict the required storage capacity curve and the predicted return gas temperature curve in the next adjustment cycle. The scheduling decision module is used to determine whether there is a storage capacity gap and its magnitude based on the required storage capacity curve and the current available storage capacity, determine a hierarchical scheduling strategy based on the magnitude and duration of the storage capacity gap, and determine the timing correction amount for executing each hierarchical scheduling strategy based on the return gas temperature curvature. The control execution module is used to execute scheduling actions according to the hierarchical scheduling strategy and the timing correction amount. The scheduling actions include at least one of the following: adjusting the position of the movable partition wall, controlling the frequency of the variable frequency compressor, controlling the access or exit of the modular cold storage, and adjusting the output of the scheduling actions in real time according to the return gas temperature curvature. The self-calibration module is used to compare the actual change curve of the return gas temperature with the predicted return gas temperature curve, and adjust the prediction parameters of the required storage capacity curve according to the comparison results.

[0022] In implementation, the movable partition wall is installed inside the cold storage unit and is an insulated partition component that can move back and forth along the direction of the unit. The movable partition wall divides the internal space of the cold storage unit into an effective storage area and an unused area. By changing the position of the movable partition wall within the unit, the volume of the effective storage area can be increased or decreased, achieving active adjustment of the storage capacity. Sealing structures are provided between the edges of the movable partition wall and the side walls, top, and bottom of the unit, ensuring a tight seal with the unit walls after it is moved into place. The drive mechanism of the movable partition wall is connected to a host computer via control signals, and the drive mechanism performs displacement actions in response to position commands output by the host computer.

[0023] For example, the movable partition can be an electric sliding insulated door or a movable partition, such as the L-series electric cold storage sliding door, the CLTD type electric sliding door, or the DDYM electric sliding insulated door equipped with a rack and pinion drive. The movable cold storage folding partition is composed of multiple panels spliced ​​together, which can realize the adjustment of the internal area of ​​the storage.

[0024] The variable frequency compressor provides variable capacity refrigerant compression power for the cold storage. The refrigeration circuit is a vapor compression refrigeration cycle pipeline that includes a compressor, condenser, throttling device, and evaporator. The operating frequency of the variable frequency compressor is controlled by frequency commands output from the host computer. The return gas pipeline of the refrigeration circuit is used to return the refrigerant vapor from the evaporator outlet to the compressor suction port. A temperature sensor is installed on this pipeline to collect the return gas temperature data and upload it to the host computer.

[0025] Modular cold storage units are standardized, mobile refrigeration units independent of the main cold storage building, each with its own complete insulated enclosure and refrigeration system. There is no rigid connection between the modular cold storage unit and the main cold storage unit; its internal temperature can be remotely set and monitored by a host computer via a communication network. Connecting a modular cold storage unit refers to powering it on and enabling its temperature control within the host computer's monitoring range; deactivating it refers to powering it off, shutting it down, and removing it from the monitoring range. Modular cold storage units can be pre-positioned within the factory area for backup, or leased from external sources and transported to a designated location for use.

[0026] In this implementation, the cold storage body, movable partition wall, variable frequency compressor, refrigeration circuit, and modular cold storage can be any of the existing technologies, and no specific limitation is made here.

[0027] In practice, the warehousing plan data includes the planned warehousing quantity, warehousing categories, and their corresponding thermophysical parameters for the next adjustment cycle.

[0028] There is a factory-calibrated correspondence between the operating frequency and cooling capacity of the variable frequency compressor. After obtaining the current operating frequency, the host computer maps the current cooling capacity output value through this correspondence, which serves as the baseline data for the current cooling load. If the operating current is also obtained, the mapping result can be verified.

[0029] The sensing and prediction module uses the current cold load baseline data as the basic heat load benchmark value. It converts the inbound quantity at each time point in the inbound plan data into an inbound heat load increment based on the specific heat capacity and inbound temperature of the corresponding product category. The difference between the external ambient temperature and the set temperature inside the cold storage is multiplied by the heat transfer coefficient of the enclosure structure and the product of the heat transfer area to determine the enclosure heat transfer load increment. The basic heat load benchmark value and each heat load increment are added point-by-point along the same time dimension to generate an equivalent heat load time-series curve covering the next adjustment cycle. The heat transfer coefficient is calculated by dividing the thermal conductivity of the insulation material on each surface of the cold storage body by the material thickness, and the heat transfer area is determined by the design geometry of the cold storage body.

[0030] The sensing and prediction module also continuously performs second-order differentiation on the time series of return air temperature within the sampling period to obtain the return air temperature curvature. The return air temperature curvature characterizes the changing trend of the return air temperature rate, reflecting the dynamic acceleration of the cooling load within the cold storage. The sign of the curvature reflects the direction of the acceleration of the return air temperature change. This curvature value is continuously updated at sampling period intervals.

[0031] The sampling period is determined by using the time from the start-up of the cold storage system to the steady-state of the return gas temperature as the system's thermal inertia time constant. A preliminary value for the sampling period is set at 1 / 10 to 1 / 5 of this time constant. The system is then run under different sampling periods, and the temperature fluctuation within the cold storage is evaluated. The sampling period corresponding to the smallest temperature fluctuation is then determined as the preset sampling period. For example, when the system's thermal inertia time constant is 60 seconds, the sampling period can be selected as a fixed value within the range of 6 to 12 seconds.

[0032] The adjustment period is the length of time it takes for the system to perform one complete perception prediction, scheduling decision and self-calibration cycle, which includes an integer number of complete preset sampling periods. The typical value of the adjustment period is 5 minutes to 30 minutes, which includes tens to hundreds of sampling periods and can be configured according to the thermal inertia of the cold storage and the frequency of load fluctuations.

[0033] The thermal inertia time constant is obtained through on-site testing after the cold storage is built: under stable operating conditions, the compressor frequency (or set temperature) is changed, and the change in return gas temperature is continuously recorded from the moment of change. The time corresponding to when the temperature change reaches 63.2% of the total change is taken as the thermal inertia time constant. Alternatively, it can be identified by using the step response segment of historical operating data during normal system operation.

[0034] The heat load of a cold storage facility can be decomposed into three independent components: the current steady-state load, the instantaneous load brought in by incoming goods, and the continuous load introduced from the external environment. The current cold load baseline is the steady-state component, the increment of the incoming heat load is a pulse-like component corresponding to the time node of the incoming goods, and the increment of the enclosure heat transfer load is a periodic component synchronized with the changes in the external ambient temperature. By superimposing these three components point by point and aligning them on the time axis, the dynamic change curve of the total heat load in the future period can be obtained. After mapping the heat load curve to the predicted return air temperature curve and the required storage capacity curve, the former is used by the self-calibration module to verify the prediction deviation, and the latter is used by the scheduling decision module to determine the storage capacity gap.

[0035] The sensing and prediction module uses the current cold load baseline data as the basic heat load benchmark value. Based on the planned warehousing quantity, warehousing category, and warehousing temperature at each time node in the warehousing plan data, it calculates the warehousing heat load increment for the corresponding time node according to the specific heat capacity of each category of goods. The external heat carried by the goods when they enter the warehouse will be released inside the warehouse, constituting an additional heat load. At the same time, the difference between the external ambient temperature and the set temperature inside the warehouse is multiplied by the product of the heat transfer coefficient and heat transfer area of ​​the enclosure structure to obtain the enclosure heat transfer load increment. This increment reflects the rate at which external heat is transferred into the warehouse through the enclosure structure, and it fluctuates with the seasonality and daily variation of the external ambient temperature.

[0036] The sensing and prediction module converts the heat load values ​​at the end of each preset sampling period into corresponding predicted return air temperature values ​​based on the mapping relationship between heat load values ​​and return air temperature, generating a predicted return air temperature curve aligned with the time axis of the equivalent heat load time series curve. During actual cold storage operation, multiple sets of heat load values ​​and corresponding return air temperature data under different heat load conditions are collected, and a mapping relationship formula between the two is established through curve fitting.

[0037] The sensing and prediction module divides the heat load value at the end of each preset sampling period in the equivalent heat load time series curve by the rated heat load carrying capacity threshold to obtain the required effective storage capacity value at the end of each preset sampling period. This value is then arranged in chronological order to generate the required storage capacity curve. The rated heat load carrying capacity threshold is the maximum heat load value that a unit effective storage capacity of the cold storage can withstand per unit time, obtained by dividing the rated refrigeration capacity of the cold storage refrigeration system by the effective storage volume.

[0038] It is understandable that storage capacity demand fluctuates continuously within the adjustment cycle, and gaps may appear in some periods but not in others. If the impact of storage capacity gaps on cold storage operations depends on both the magnitude and duration of the gap, using an integral accumulation method can integrate the impact of these two dimensions into a single quantitative indicator, which better reflects the cumulative effect of gaps in actual operation and the degree of cumulative impact of gaps on cold storage operations.

[0039] The current available storage capacity is the sum of the effective storage volume formed by the movable partitions within the main cold storage and the available storage volume of the modular cold storage units that have been connected. After the modular cold storage units complete the connection or disconnection operation, the system updates the value of the current available storage capacity in real time.

[0040] The scheduling decision module obtains the required storage capacity curve at the end of each preset sampling period within the next adjustment cycle, calculates the difference between the storage capacity demand value at the end of each preset sampling period and the baseline available storage capacity, and takes the positive difference as the gap amount at that moment; if the difference is negative or zero, the gap amount is zero. Each gap amount is multiplied by the sampling interval to obtain the gap area within that sampling interval. The gap areas of all sampling intervals are accumulated to obtain the gap integral value for the entire adjustment cycle.

[0041] The preset allowable integral threshold is calibrated by collecting operational data for multiple complete adjustment cycles with different cargo volumes and external temperature conditions under normal cold storage operation. The gap integral value within each cycle is recorded, along with whether any abnormal events occur during that cycle, such as goods being unable to enter the cold storage or the internal temperature exceeding the allowable range. The maximum gap integral value without any abnormal events is used as the upper limit benchmark, multiplied by a safety factor less than 1 (e.g., 0.8) to determine the preset allowable integral threshold. N is a positive integer greater than 1, and its value is determined based on the number of modular cold storage units that the system can utilize. For example, when configuring a single modular cold storage unit, N can be selected as 2; when configuring multiple modular cold storage units, the value of N increases accordingly.

[0042] Before determining the magnitude of the storage capacity shortage, the scheduling decision module first verifies the adjustable margin of the movable partition and the number of currently available modular cold storage units to confirm the remaining execution space for each adjustment method. When the shortage integral value does not exceed the preset allowable integral threshold, it is determined to be a negligible shortage, and the scheduling decision module outputs the lowest intensity graded scheduling strategy, only adjusting the position of the movable partition and / or controlling the frequency of the variable frequency compressor; when the shortage integral value exceeds the preset allowable integral threshold but does not exceed N times the threshold, it is determined to be a medium-level shortage, and a single modular cold storage unit is connected based on adjusting the partition and compressor frequency; when the shortage integral value exceeds N times the threshold, it is determined to be a large-level shortage, and at least two modular cold storage units are connected.

[0043] Understandably, adjusting the position of movable partitions does not change the total volume of the cold storage, but only alters the ratio of effective to idle volume; its adjustment limit is constrained by the total cold storage volume. Modular cold storage, on the other hand, can overcome the physical limitations of the total volume, achieving an actual increase in total storage capacity. Using both methods in tiers based on the size of the gap avoids unnecessary modular cold storage connections in small gap scenarios, reducing operating costs and equipment wear, while ensuring sufficient expansion methods are available for large gap scenarios.

[0044] Correspondingly, the system sets an exit threshold and exit determination duration below the preset allowable integral threshold to create a scheduling hysteresis to avoid frequent start-ups and shutdowns. When the gap integral value remains below the exit threshold for a period of one adjustment cycle, it is determined that the storage capacity gap has fallen below the corresponding level, and the modular cold storage is gradually exited in reverse order of access; when the gap falls below the medium level, a single modular cold storage is exited, and when it falls below the negligible level, all modular cold storages are exited. The exit process is synchronized with the contraction of the moving partition and the frequency reduction of the compressor to maintain stable storage temperature.

[0045] When the movable partition wall has reached its maximum expansion limit and there are no remaining modular cold storage units available, the system triggers a capacity over-limit warning. At the same time, it increases the upper limit of the operating frequency of the variable frequency compressor to enhance the refrigeration output and prioritizes the temperature control stability of the goods already in the warehouse, serving as a fallback mechanism for boundary conditions.

[0046] The mean absolute deviation of the return air temperature curvature quantifies the severity of load fluctuations. The larger the mean absolute deviation, the more severe the fluctuation of the return air temperature curvature within that cycle, the more frequent or larger the dynamic changes in the cold storage heat load, and the more unstable the current thermal state of the system. The asymmetry of the curvature value distribution in the time dimension can reflect the time offset direction of the load peak, which can be used to determine the offset direction of the scheduling start-up time.

[0047] When the return air temperature curvature fluctuates drastically, the thermal inertia state of the cold storage changes frequently. If scheduling actions are executed according to the standard time, the uncertainty of the system's thermal state may cause the scheduling effect to deviate from expectations. For example, if the modular cold storage is connected before the heat load backflow trend has ended, the superposition of excess cooling capacity and heat load backflow may exacerbate temperature fluctuations. In this case, the execution time of the scheduling action needs to be significantly offset from the standard time to avoid periods of drastic changes in the thermal state or to place the scheduling action within a more favorable thermal inertia state window. When the average absolute deviation is small, it indicates that the system is in a relatively stable thermal state, and the amplitude of the timing correction is correspondingly reduced or even approaches zero. Execution according to the standard time is sufficient to meet the requirements.

[0048] The direction of the timing correction is determined based on the asymmetry of the return gas temperature curvature distribution. Specifically, the scheduling decision module calculates the skewness of all return gas temperature curvature values ​​within the adjustment cycle. A positive skewness indicates that the curvature distribution is positively skewed, meaning that the number or magnitude of extreme values ​​of positive curvature (corresponding to accelerated heating) is greater than that of negative curvature (corresponding to accelerated cooling). In this case, the overall trend of the return gas temperature is biased towards heating, and the direction of the timing correction is determined to be the advance direction. That is, the scheduling action is executed ahead of the standard time to complete the expansion or cooling action before the heating trend worsens, seizing the advance window of heat load backlash. A negative skewness indicates that the curvature distribution is negatively skewed, and the overall trend of the return gas temperature is biased towards cooling. The direction of the timing correction is determined to be the delay direction. That is, the scheduling action is executed late compared to the standard time, so that new actions are intervened only after the excess cooling inertia is fully released, avoiding temperature overshoot caused by the superposition of cooling.

[0049] The target position of the movable partition wall is determined by converting the difference between the current available storage capacity and the required storage capacity into the partition wall displacement. The target frequency of the variable frequency compressor is determined by adjusting the current operating frequency according to a preset frequency increase or decrease step. The access or exit of the modular cold storage is handled by on / off commands. During the displacement of the movable partition wall, the system appropriately increases the compressor frequency based on the total displacement distance and the expected movement time, maintaining this increased frequency throughout the displacement to compensate for cold leakage. After the displacement ends and the partition wall is sealed in place, the compressor frequency returns to the target set value.

[0050] The standard scheduling time is the default start time of the hierarchical scheduling strategy, i.e., the sampling time when the gap integral value first triggers the corresponding magnitude threshold, serving as the time reference for timing correction. The timing correction amount is used to adjust the standard scheduling time. The magnitude of the timing correction amount is determined by the average absolute deviation of the return gas temperature curvature. The average absolute deviation reflects the overall dispersion of the curvature value within a regulation cycle. A larger value indicates frequent or severe fluctuations in the system's heat load within the current cycle. In this case, the timing of scheduling intervention needs to be significantly offset relative to the standard time to avoid periods of drastic thermal changes or to seize a more favorable execution window. The direction of the timing correction amount is determined by the asymmetry of the curvature value distribution. Its physical logic is as follows: a positive skewness indicates that the extreme values ​​of positive curvature dominate, and the system as a whole shows a heating trend, requiring early execution to seize the initiative; a negative skewness indicates the opposite, with the system as a whole showing a cooling trend, allowing for delayed execution to wait for the inertial release of cooling capacity. The control execution module starts execution after offsetting the standard scheduling time according to the timing correction amount.

[0051] The return gas temperature curvature is also used to correct the output parameters of the scheduling action in real time. It can be understood that, due to the difference in time scales, the timing correction is based on the statistical characteristics of the curvature over a complete adjustment cycle, affecting the macroscopic initiation timing decision; while the real-time correction of the output parameters is based on the curvature value at the current sampling moment, affecting the microscopic adjustment of the execution intensity. The corresponding value of the curvature reflects the direction of acceleration for heating or cooling at this moment, rather than the cumulative trend over the cycle; the two do not conflict on the time scale.

[0052] When the curvature is positive, the correction direction is to increase the compressor frequency while keeping the partition wall expansion displacement constant; when the curvature is negative, the correction direction is to decrease the compressor frequency and increase the partition wall expansion displacement. Under the rated operating conditions of the cold storage system, a standard heat load step disturbance (e.g., a one-time intake of rated capacity of room temperature goods) is applied from the stable operating state. The return gas temperature change process is recorded and the return gas temperature curvature value at each moment is calculated. The maximum absolute value of curvature during the entire response process is taken as the preset curvature reference value.

[0053] The preset maximum correction step size is a configurable parameter of the system. Under the rated operating conditions of the cold storage system, the same correction action is applied with different correction step sizes, and the temperature response curve inside the cold storage is recorded under each step size. The step size corresponding to the optimal value of the combined temperature overshoot and adjustment time is taken as the preset maximum correction step size.

[0054] It is understandable that the prediction deviation of return gas temperature is essentially the cumulative error of the heat load conversion process. By periodically comparing the measured value with the predicted value, the temperature deviation can be converted back into the heat load deviation, thereby correcting the conversion parameters of the heat load increment and forming a closed-loop self-calibration of the prediction model.

[0055] The self-calibration module initiates the calibration process after each adjustment cycle ends, obtains the actual return gas temperature at each sampling moment within the just-ended adjustment cycle, and constructs the actual change curve in chronological order; at the same time, it calls the predicted return gas temperature curve generated by the sensing and prediction module at the beginning of the adjustment cycle.

[0056] The deviation sequence is obtained by subtracting the actual temperature curve from the predicted return gas temperature curve at each sampling time. A positive deviation value indicates that the actual temperature is higher than the predicted value, i.e., the heat load prediction is too low; a negative deviation value indicates that the actual temperature is lower than the predicted value, i.e., the heat load prediction is too high.

[0057] The predicted return gas temperature curve is mapped from the equivalent heat load time series curve. Therefore, the deviation between the actual and predicted return gas temperatures essentially reflects the deviation between the predicted and actual heat load. The deviation values ​​in the deviation sequence collectively constitute the situation within this adjustment period. If the deviation values ​​are generally positive and large, it indicates that the predicted heat load is systematically too low; if they are generally negative and large, it indicates that the predicted heat load is systematically too high; if positive and negative deviations are interspersed and small, it indicates that the prediction is basically accurate and the deviation is within normal fluctuations.

[0058] The self-calibration module determines the cumulative error value based on the distribution characteristics of the deviation sequence. The larger the deviation value or the longer the deviation duration, the larger the cumulative error value. When the cumulative error value shows that the actual temperature is systematically higher than the predicted value, it indicates that the heat load prediction is too low, and the self-calibration module corrects in the direction of increasing the heat load increment. When the cumulative error value shows that the actual temperature is systematically lower than the predicted value, it indicates that the heat load prediction is too high, and the self-calibration module corrects in the direction of decreasing the heat load increment.

[0059] During the correction, the inverse operation is performed based on the heat load-return gas temperature mapping relationship pre-stored in the sensing and prediction module. The temperature deviation at each sampling time is converted into the corresponding heat load deviation value, and the heat load deviation value is used as the correction amount for the heat load increment. The larger the absolute value of the temperature deviation, the further the current heat load increment parameter deviates from the actual operating condition, and the larger the corresponding heat load correction amount. The smaller the absolute value of the deviation, the smaller the correction amplitude is, so as to avoid over-correction and prediction oscillation.

[0060] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An environment-adaptive variable frequency cold storage capacity adjustment system, comprising a cold storage body, movable partitions, a variable frequency compressor, a refrigeration circuit, and a host computer, characterized in that, The host computer also includes: The sensing and prediction module is used to obtain the return gas temperature of the return gas pipeline of the refrigeration circuit, obtain the return gas temperature curvature, obtain the storage plan data, the external ambient temperature and the current cooling load baseline data, and predict the required storage capacity curve and the predicted return gas temperature curve in the next adjustment cycle. The sensing and prediction module is configured to collect the return gas temperature value of the return gas pipeline of the refrigeration circuit according to a preset sampling period, and process the warehouse entry plan data, external ambient temperature and current cold load baseline data to generate the equivalent heat load time series curve for the next adjustment period, and map the required warehouse capacity curve for the corresponding time period based on the equivalent heat load time series curve. The sensing and prediction module performs a second derivative on the time series of the return gas temperature to obtain the return gas temperature curvature. The sensing and prediction module is configured to: use the current cold load baseline data as the basic heat load reference value; determine the heat load increment for the next adjustment cycle based on the warehousing time node and cargo scale in the warehousing plan data; determine the heat load increment for the enclosure based on the external ambient temperature, the set temperature difference between inside and outside the cold storage, and the heat transfer parameters of the cold storage enclosure structure; superimpose the basic heat load reference value with each heat load increment to generate an equivalent heat load time series curve; determine the predicted return gas temperature at the end of each preset sampling cycle based on the equivalent heat load time series curve; and generate the predicted return gas temperature curve. The sensing and prediction module uses the ratio of each heat load value in the equivalent heat load time series curve to the rated heat load carrying threshold as the required effective storage capacity value, and generates the required storage capacity curve by taking all the required effective storage capacity values ​​in time sequence. The scheduling decision module is used to determine whether there is a storage capacity gap and its magnitude based on the required storage capacity curve and the current available storage capacity, determine a hierarchical scheduling strategy based on the magnitude and duration of the storage capacity gap, and determine the timing correction amount for executing each hierarchical scheduling strategy based on the return gas temperature curvature. The scheduling decision module obtains the required storage capacity curve for the next adjustment period at the end of each preset sampling period, calculates the gap between the storage capacity demand value at the end of each preset sampling period and the current available storage capacity value, accumulates the product of each gap and the sampling interval to obtain the gap integral value, and determines whether there is a storage capacity gap and the corresponding gap magnitude in the adjustment period based on the gap integral value. The scheduling decision module is configured to: in response to the fact that the gap integral value does not exceed the preset allowable integral threshold, determine that the gap is negligible, and the hierarchical scheduling strategy is to adjust the position of the movable partition and / or control the frequency of the variable frequency compressor; When the gap integral value exceeds the preset allowable integral threshold but does not exceed N times the preset allowable integral threshold, it is determined to be a medium-level gap. The hierarchical scheduling strategy is to adjust the position of the movable partition, control the frequency of the variable frequency compressor, and connect it to a single modular cold storage. In response to the gap integral value exceeding N times the preset allowable integral threshold, it is determined to be a large-scale gap. The hierarchical scheduling strategy is to adjust the position of the movable partition, control the frequency of the variable frequency compressor, and connect at least two modular cold storage units. Where N is a positive integer greater than 1; The scheduling decision module obtains the return gas temperature curvature of each preset sampling period within the current adjustment period, calculates the average absolute deviation, determines the amplitude of the timing correction amount based on the value of the average absolute deviation, and determines the asymmetry direction of the return gas temperature curvature distribution, and determines the direction of the timing correction amount based on the asymmetry direction. The control execution module is used to execute scheduling actions according to the hierarchical scheduling strategy and the timing correction amount. The scheduling actions include at least one of the following: adjusting the position of the movable partition wall, controlling the frequency of the variable frequency compressor, controlling the access or exit of the modular cold storage, and adjusting the output of the scheduling actions in real time according to the return gas temperature curvature. The control execution module is configured to determine the start time of each scheduling action based on the magnitude of the timing correction amount, and to determine the offset direction of the start time of each scheduling action based on the direction of the timing correction amount. The self-calibration module is used to compare the actual change curve of the return gas temperature with the predicted return gas temperature curve, and adjust the prediction parameters of the required storage capacity curve according to the comparison results. The self-calibration module performs a difference calculation between the actual change curve and the predicted return gas temperature curve to obtain a deviation sequence. Based on the deviation sequence, it determines the cumulative error value and corrects the heat load increment to correct the equivalent heat load time series curve.

2. The environmentally adaptive variable frequency cold storage capacity adjustment system according to claim 1, characterized in that, The scheduling decision module determines that there is a storage capacity gap if the gap integral value exceeds the preset allowable integral threshold.

3. The environmentally adaptive variable frequency cold storage capacity adjustment system according to claim 1, characterized in that, The control execution module starts executing each scheduling action according to the corrected start time, and dynamically corrects the output parameters of each scheduling action according to the real-time collected return gas temperature curvature.

4. The environmentally adaptive variable frequency cold storage capacity adjustment system according to claim 3, characterized in that, The control execution module dynamically corrects the output parameters of each scheduling action based on the real-time collected return gas temperature curvature in the following way: In response to the positive curvature of the return gas temperature, the target frequency of the variable frequency compressor is increased to maintain the current displacement of the movable partition wall; Conversely, reducing the target frequency of the variable frequency compressor increases the expansion displacement of the movable partition.

5. The environmentally adaptive variable frequency cold storage capacity adjustment system according to claim 1, characterized in that, The self-calibration module obtains the return gas temperature at the end of each sampling cycle within the previous adjustment cycle and constructs the actual change curve of the return gas temperature in chronological order.

Images