Agricultural product refrigeration house temperature and humidity control method and system

Abstract

The invention discloses an agricultural product refrigeration house temperature and humidity control method and system, relates to the field of agricultural product refrigeration house temperature and humidity control, is used for reasonably controlling the temperature and humidity of an agricultural product refrigeration house, and comprises the following steps: obtaining a state data packet sent by an agricultural product refrigeration house local control system, and the receiving time of the state data packet, the state data packet comprises temperature and humidity data of the refrigeration house, a generation timestamp of the temperature and humidity data and temperature and humidity regulation and control action information executed by the local control system; the temperature and humidity data comprises temperature and humidity; calculating the data transmission delay of the state data packet according to the generation timestamp and the receiving time; on the basis of the data transmission delay, the temperature and humidity data and the temperature and humidity regulation and control action information, the temperature and humidity state of the refrigeration house at the current moment is calculated through a preset physical model; according to the calculated temperature and humidity state, a load balancing decision is made, and the load balancing decision is used for controlling the temperature and humidity of the refrigeration house.

Description

Technical Field

[0001] The present invention relates to the field of temperature and humidity control of agricultural product cold storage, and in particular to a temperature and humidity control method and system for agricultural product cold storage. Background Art

[0002] In a distributed cold chain network, temperature and humidity control in agricultural product cold storage and network-wide load balancing are key to ensuring agricultural product quality and operational efficiency. However, when there are variations in communication link quality within the network, especially when some nodes experience significant and uncertain data transmission delays due to geographic or technical limitations, the node status data received by the central management platform may become outdated.

[0003] Making load balancing decisions based on this varying timeliness of data can cause the central platform to issue erroneous control instructions that don't align with the nodes' actual physical needs, leading to abnormal temperature and humidity within the warehouse and even increased operating costs. More seriously, the system can mistakenly attribute negative physical feedback caused by communication delays to node performance issues, creating a self-reinforcing cycle of misjudgment. Summary of the Invention

[0004] The present invention provides a method for controlling the temperature and humidity of a cold storage for agricultural products, which is used to reasonably control the temperature and humidity of the cold storage for agricultural products.

[0005] In the first aspect, in order to solve the above-mentioned technical problems, the present invention provides a temperature and humidity control method for an agricultural product cold storage, comprising: obtaining a status data packet sent by a local control system of the agricultural product cold storage and the reception time of the status data packet, the status data packet containing the temperature and humidity data of the cold storage, the generation timestamp of the temperature and humidity data, and the temperature and humidity control action information executed by the local control system; the temperature and humidity data include temperature and humidity; according to the generation timestamp and the reception time, the data transmission delay of the status data packet is calculated; based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, a preset physical model is used to infer the temperature and humidity status of the cold storage at the current moment; according to the inferred temperature and humidity status, a load balancing decision is made, and the load balancing decision is used to control the temperature and humidity of the cold storage.

[0006] Optionally, based on data transmission delay, temperature and humidity data, and temperature and humidity control action information, a preset physical model is used to infer the temperature and humidity status of the cold storage at the current moment, including: determining whether there is a thermodynamic anomaly in the cold storage, which is used to characterize the abnormal increase in heat load in the cold storage; if there is no thermodynamic anomaly in the cold storage, based on data transmission delay, temperature and humidity data, and temperature and humidity control action information, a preset physical model is used to infer the temperature and humidity status of the cold storage at the current moment.

[0007] Optionally, determining whether there is a thermodynamic anomaly in the cold storage includes: obtaining type information of the goods in the cold storage; determining, based on the type information of the goods, the standard operating power consumption of the refrigeration system required for the cold storage to maintain the goods at a specific temperature; monitoring the actual operating power consumption of the cold storage; determining that there is a thermodynamic anomaly in the cold storage when the actual operating power consumption is greater than or equal to a preset range of the standard operating power consumption for a duration longer than a preset duration; determining that there is no thermodynamic anomaly in the cold storage when the actual operating power consumption is greater than or equal to a preset range of the standard operating power consumption for a duration less than or equal to a preset duration.

[0008] Optionally, a preset physical model is used to infer the temperature and humidity status of the cold storage at the current moment, including: obtaining the actual operating power consumption of the cold storage when the cold storage is in the temperature maintenance operating state; calculating the expected operating power consumption required for the cold storage in the temperature maintenance operating state based on the set temperature of the cold storage, the external ambient temperature and the physical model; comparing the actual operating power consumption with the expected operating power consumption; when the actual operating power consumption is continuously greater than or equal to the preset deviation range of the expected operating power consumption, determining that there is a deviation between the parameters in the physical model and the actual physical characteristics of the cold storage; updating the parameters in the physical model based on the deviation; and using the updated physical model to infer the temperature and humidity status of the cold storage at the current moment.

[0009] Optionally, based on the deviation, the parameters in the physical model are updated, including: continuously obtaining a deviation sequence between the actual operating power consumption and the expected operating power consumption; analyzing the dynamic characteristics of the deviation sequence; identifying the main contribution of the deviation based on the dynamic characteristics of the deviation sequence; the main contribution of the deviation includes at least one of the following: sudden additional heat load, attenuation of cold storage insulation performance, and periodic auxiliary heating operation; based on the main contribution of the deviation, the parameters in the physical model are updated; wherein, when the main contribution of the deviation is identified as attenuation of cold storage insulation performance, the parameters related to insulation performance in the physical model are adjusted; when the main contribution of the deviation is identified as sudden additional heat load, the parameters related to the heat load of goods in the warehouse are adjusted; when the main contribution of the deviation is identified as periodic auxiliary heating operation, the update of the physical model parameters is suspended and an abnormal alarm is triggered.

[0010] Optionally, analyzing the dynamic characteristics of the deviation sequence includes: smoothing the deviation sequence to obtain a smoothed deviation sequence; analyzing the trend of the smoothed deviation sequence; identifying instantaneous fluctuations of the deviation sequence; and judging the dynamic characteristics of the deviation sequence based on the trend and instantaneous fluctuations.

[0011] Optionally, identifying the main contribution of the deviation includes: analyzing the trend, amplitude and change frequency of the deviation sequence; if the dynamic characteristics indicate that the trend of the deviation sequence is a slowly rising trend and the amplitude is within a preset range, then determining that the main contribution of the deviation is the attenuation of the cold storage insulation performance; if the dynamic characteristics indicate that the amplitude change of the deviation sequence within a preset time period is greater than a change threshold, then determining that the main contribution of the deviation is a sudden additional heat load; if the dynamic characteristics indicate that the change frequency of the deviation sequence is a periodic change, then determining that the main contribution of the deviation is a periodic auxiliary heating operation.

[0012] Optionally, based on the main contribution of the deviation, the parameters in the physical model are updated, including: based on the deviation between the actual operating power consumption and the expected operating power consumption, an optimization algorithm is used to perform iterative calculations to obtain iterative calculation results; based on the iterative calculation results, the parameters in the physical model corresponding to the main contribution of the deviation are adjusted; wherein the parameter adjustment rules are used to define the step size and convergence conditions of the optimization algorithm.

[0013] Optionally, the method also includes: obtaining the amplitude of the deviation between the actual operating power consumption and the expected operating power consumption; adjusting the step size of the optimization algorithm according to the amplitude of the deviation; obtaining the duration or change rate of the deviation between the actual operating power consumption and the expected operating power consumption; and defining the convergence condition according to the duration or change rate of the deviation.

[0014] In a second aspect, the present invention provides a temperature and humidity control system for a cold storage for agricultural products, which is used to control the temperature and humidity of a cold storage for agricultural products. The system comprises: The data packet acquisition module is used to obtain the status data packet sent by the local control system of the agricultural product cold storage and the reception time of the status data packet. The status data packet contains the temperature and humidity data of the cold storage, the generation timestamp of the temperature and humidity data, and the temperature and humidity control action information executed by the local control system; the temperature and humidity data includes temperature and humidity; A transmission delay calculation module, used to calculate the data transmission delay of the status data packet based on the generation timestamp and the reception time; The temperature and humidity status estimation module is used to estimate the temperature and humidity status of the cold storage at the current moment based on data transmission delay, temperature and humidity data, and temperature and humidity control action information, using a preset physical model; The load balancing decision module is used to make load balancing decisions based on the estimated temperature and humidity conditions. The load balancing decision is used to control the temperature and humidity of the cold storage.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present application provides a method and system for controlling the temperature and humidity of a cold storage for agricultural products. This method compensates for data transmission delays and uses a physical model to infer the real-time temperature and humidity status of the cold storage, thereby avoiding erroneous decisions caused by outdated data. It has the advantages of effectively ensuring the quality of agricultural products and improving operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for controlling temperature and humidity in a cold storage for agricultural products provided by an embodiment of the present invention; Figure 2 This is a flow chart of another method for controlling temperature and humidity in a cold storage for agricultural products provided by an embodiment of the present invention; Figure 3 The present invention provides a schematic diagram of the structure of a temperature and humidity control system for a cold storage for agricultural products. DETAILED DESCRIPTION

[0017] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0018] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0019] The following specific examples will be used to introduce and explain in detail a method for controlling temperature and humidity in a cold storage for agricultural products provided in the embodiments of the present application.

[0020] Reference Figure 1 The present invention provides a method for controlling temperature and humidity in a cold storage for agricultural products, comprising the following steps: S1, obtain the status data packet sent by the local control system of the agricultural product cold storage and the receiving time of the status data packet.

[0021] The status data packet contains the cold storage's temperature and humidity data, the timestamp of when the data was generated, and information about the temperature and humidity control actions performed by the local control system. The temperature and humidity data includes temperature and humidity. The timestamp of when the data was generated by the local control system is the time it was recorded.

[0022] As one possible implementation, the local control system of the agricultural products cold storage can periodically (for example, every 5 minutes) generate a status data packet and send it to the cold storage temperature and humidity control system. In response, the cold storage temperature and humidity control system receives the status data packet sent by the local control system. Upon receiving the data packet, the cold storage temperature and humidity control system can immediately record the current system time to obtain the time the status data packet was received.

[0023] It should be noted that the data packet can be encapsulated in JSON format, which includes fields such as "temperature": 4.5, "humidity": 85, "timestamp": 1678886400 (Unix timestamp, indicating the time when the data was generated), and "control_actions": {"compressor_on": true, "fan_speed": "medium"}.

[0024] In one example, the format for recording the current system time may be "reception_time":1678886415.

[0025] S2. Calculate the data transmission delay of the status data packet based on the generation timestamp and the reception time.

[0026] Among them, data transmission delay refers to the time difference between the status data packet being generated by the local control system and being received by the central management platform.

[0027] As a possible implementation manner, the system may calculate a difference between a generation timestamp and a reception time, and determine the difference as a data transmission delay of the status data packet.

[0028] S3. Based on the data transmission delay, temperature and humidity data, and temperature and humidity control action information, the preset physical model is used to calculate the temperature and humidity status of the cold storage at the current moment.

[0029] Among them, the preset physical model refers to a mathematical or logical model that describes the heat and moisture transfer inside the cold storage and the impact of equipment operation on temperature and humidity. It can be implemented using a thermodynamic model, a heat and mass transfer model, an empirical model, or a statistical model based on historical data. Its purpose is to simulate the dynamic process of temperature and humidity changes in the cold storage over time, and combine historical data and control actions to predict the future or current status of the cold storage.

[0030] Among them, the temperature and humidity control action information refers to the operation records performed by the local control system to adjust the temperature and humidity of the cold storage, such as the start and stop of the refrigeration compressor, the fan operation mode, the start and stop of the dehumidifier, etc. Its purpose is to serve as the input of the physical model to reflect the impact of local control on the state of the cold storage and improve the accuracy of the state estimation.

[0031] As a possible implementation method, the system can input data transmission delay, temperature and humidity data, and temperature and humidity control action information into a preset physical model. The preset physical model can process data transmission delay, temperature and humidity data, and temperature and humidity control action information to infer the temperature and humidity status of the cold storage at the current moment.

[0032] In one example, the model takes as input temperature and humidity data (4.5°C, 85% humidity), a 15-second data transmission delay, and a local control action (compressor on, fan at medium speed). The model considers that within 15 seconds, the cold storage temperature will further drop due to the continued operation of the compressor. Furthermore, taking into account factors such as the respiration heat of the goods within the warehouse, the model infers that the actual temperature of the cold storage at that moment may have dropped to 4.2°C, with a humidity of 84%. Finally, the load balancing decision module uses the inferred temperature of 4.2°C and humidity of 84%, combined with the real-time status of other cold storage facilities, to perform global optimization. For example, it determines whether to assign new incoming orders to that cold storage facility or adjust the operating strategy of its refrigeration equipment to achieve energy optimization and load balancing across the entire cold chain network.

[0033] As another possible implementation method, in order to improve the accuracy of the inference results, the system can determine whether there are thermodynamic anomalies in the cold storage. If there are no thermodynamic anomalies in the cold storage, the temperature and humidity status of the cold storage at the current moment can be inferred using a preset physical model based on data transmission delay, temperature and humidity data, and temperature and humidity control action information.

[0034] It should be noted that thermodynamic anomaly is used to characterize the abnormal increase in heat load in cold storage.

[0035] Abnormal heat load increase means that under normal operating conditions, the heat entering the cold storage is significantly higher than the heat expected by the physical model due to unplanned events or changes in the external environment. Specifically, it can be caused by factors such as newly entered high-temperature goods, the storage door being left open for a long time, heat leakage caused by equipment failure, or a sudden increase in the external ambient temperature.

[0036] In some preferred embodiments, specifically, to determine whether a cold storage facility has thermodynamic anomalies, the instantaneous rate of change of the internal temperature of the cold storage facility may be continuously monitored. For example, if the temperature of the cold storage facility rises by more than a preset threshold (e.g., 2°C) within a short period of time (e.g., 5 minutes) and no refrigeration equipment failure or shutdown alarms are received, it can be determined that the cold storage facility may have a thermodynamic anomaly. This typically indicates an abnormal increase in heat load, such as the sudden arrival of a large amount of high-temperature goods or the door being left open for an extended period of time.

[0037] In a specific embodiment, if the system detects a temperature rise rate exceeding 0.5°C per minute for more than three minutes, it may determine that a thermodynamic anomaly exists in the cold storage. In this case, to prevent the physical model from generating inaccurate estimates of temperature and humidity during this abnormal state, the system may suspend the temperature and humidity estimation based on the physical model and trigger an anomaly alarm, prompting the operator to inspect the cold storage. Conversely, if the temperature change rate remains within a normal range during continuous monitoring, or if there are fluctuations but they do not reach the preset anomaly threshold, it can be determined that no thermodynamic anomaly exists in the cold storage. Under normal operating conditions, the system continues to accurately estimate the current temperature and humidity conditions of the cold storage using the preset physical model based on data transmission delays, temperature and humidity data, and temperature and humidity control action information obtained from the cold storage local control system. For example, the system may determine that the average temperature of the cold storage over the past 10 minutes was 4°C and the humidity was 75%, the data transmission delay was 2 minutes, and the refrigeration equipment was operating at 70% power during this period. The physical model integrates this data with the cold storage's structural parameters and thermodynamic properties to calculate the actual temperature and humidity inside the cold storage at the current moment, such as 3.8°C and 76% humidity. This approach ensures that model-based calculations are performed only when the cold storage's thermodynamic conditions are stable and predictable, ensuring accurate results.

[0038] Understandably, sudden external heat inputs not fully accounted for by the physical model can occur during cold storage operation, such as a large amount of newly arrived high-temperature agricultural products or the door being left open for extended periods. These conditions can cause significant deviations between the actual heat load of the cold storage and the heat load estimated by the physical model based on historical data and preset parameters. Directly inputting data from these abnormal conditions into the physical model for estimation, the model would fail to accurately capture these external disturbances, resulting in inaccurate estimates of the temperature and humidity conditions. Therefore, this solution first determines whether the cold storage has a thermodynamic anomaly. This anomaly clearly indicates an abnormal increase in heat load. This determination allows the system to identify whether the cold storage is currently in an abnormal thermodynamic state. Only after confirming that the cold storage is not in an abnormal state does the system use the preset physical model to estimate the temperature and humidity conditions at the current moment, based on data transmission delays, temperature and humidity data, and temperature and humidity control action information obtained from the local control system. This processing logic ensures that the physical model only performs estimations when the cold storage is in a relatively stable and predictable thermodynamic environment, thereby preventing abnormal heat loads from interfering with the estimation results. By combining it with steps such as obtaining data transmission delay, temperature and humidity data, and temperature and humidity control action information, this solution can more reliably utilize these real-time data and accurately predict the actual temperature and humidity status of the cold storage while eliminating external interference.

[0039] S4. Make load balancing decisions based on the estimated temperature and humidity conditions.

[0040] Among them, the load balancing decision is used to control the temperature and humidity of the cold storage.

[0041] As a possible implementation method, based on the inferred temperature and humidity conditions, the system can use a load balancing algorithm (for example, an optimization algorithm based on minimizing network-wide energy consumption and balancing equipment wear) to comprehensively consider information such as each node's current actual load, remaining cooling capacity, equipment health, and the priority of the warehousing tasks to be assigned, and calculate the optimal resource allocation plan.

[0042] For example, if calibration reveals that the temperature at remote Node A has returned to normal and has spare cooling capacity, while nearby Node B is idle but far from a new task, the system might decide to assign the new task to Node A rather than mistakenly assigning it to Node B or the even more distant Node C, thus avoiding unnecessary transportation costs and cargo risk. Ultimately, the system generates specific control instructions (such as "Node A compressor maintains current operating frequency" or "Node A accepts new incoming warehouse task").

[0043] Through the above technical solution, this method effectively solves the problem of obsolete status data caused by communication delays in distributed agricultural product cold storage networks. By accurately calculating the data transmission delay and incorporating it into the calculation process of the temperature and humidity status of the cold storage, the system can obtain real-time information that is closer to the actual physical state of the cold storage, overcoming the deviation caused by the traditional method of making decisions based on lagged data. This enables the central management platform to make more accurate and timely load balancing decisions, avoiding the issuance of erroneous control instructions that do not meet the actual needs of the cold storage due to information distortion. Therefore, this method can significantly improve the accuracy and response speed of cold storage temperature and humidity control, effectively ensure the quality of agricultural products during storage, while optimizing the overall operational efficiency of the cold chain network, reducing unnecessary energy consumption and operating costs, and fundamentally avoiding the system from misinterpreting negative physical feedback caused by communication delays as node performance problems, thereby forming a self-reinforcing misjudgment cycle.

[0044] One possible design is Figure 2 As shown, the system can determine whether there is a thermodynamic anomaly in the cold storage based on the following steps: S101. Obtain the type information of the goods in the cold storage.

[0045] Among them, the type information of goods refers to the types and characteristic data of agricultural products or other items stored in the cold storage, such as vegetables, fruits, meat, seafood, etc., as well as their physical properties such as respiratory heat, specific heat capacity, and water content. Its purpose is to provide accurate input for the subsequent calculation of standard operating power consumption.

[0046] As a possible implementation method, when the goods are transported into the cold storage, the system can automatically identify the type of goods by scanning the barcode or RFID tag on the goods packaging.

[0047] As another possible implementation, the operator may also manually input the main types of goods currently stored in the cold storage through the cold storage management interface, and the system may then obtain type information of the goods in the cold storage based on the manually input content.

[0048] S102. Determine, based on the cargo type information, the standard operating power consumption of the refrigeration system of the cold storage required to maintain the cargo at a specific temperature.

[0049] Among them, standard operating power consumption refers to the theoretical or empirical amount of electrical energy that the refrigeration system should consume to maintain a specific temperature when storing a specific type of goods in a cold storage under specific temperature conditions.

[0050] As a possible implementation method, the system can establish a preset mapping relationship, and determine the operating power consumption that has a mapping relationship with the type information of the goods and the specific temperature from the preset mapping relationship, and use the operating power consumption as the standard operating power consumption.

[0051] It should be noted that the preset mapping relationship includes the mapping relationship between the type information of different goods, different temperatures and different standard operating power consumption.

[0052] S103: Monitor the actual operating power consumption of the cold storage.

[0053] As a possible implementation method, a smart meter or power consumption sensor can be installed on the power supply line of the cold storage. The system can monitor the actual operating power consumption of the cold storage through the smart meter or power consumption sensor installed on the power supply line of the cold storage refrigeration system.

[0054] S104: When the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption for a duration longer than a preset duration, it is determined that a thermodynamic anomaly exists in the cold storage.

[0055] The preset range and preset duration can be set as needed. For example, the preset range can be 10% of the standard operating power consumption, and the preset duration can be 10 minutes.

[0056] S105: When the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption and the duration thereof is less than or equal to the preset duration, determine that there is no thermodynamic abnormality in the cold storage.

[0057] In some embodiments, in order to use a preset physical model to calculate the temperature and humidity state of the cold storage at the current moment, the present application further includes the following steps: S201. When the cold storage is in a temperature-maintaining operating state, obtain the actual operating power consumption of the cold storage.

[0058] Among them, the temperature maintenance operation state means that the internal temperature of the cold storage has reached or is close to the set target temperature, and the refrigeration system is mainly operated in an operation mode for the purpose of maintaining this temperature, rather than drastically cooling or heating. Specifically, it can mean that the refrigeration equipment of the cold storage is in intermittent operation or low-power operation to offset a small amount of heat load. Its purpose is to provide a stable benchmark operating condition to accurately evaluate the energy consumption performance of the cold storage.

[0059] S202: Calculate the expected operating power consumption required for the cold storage to maintain the temperature according to the set temperature of the cold storage, the external ambient temperature, and the physical model.

[0060] Among them, the expected operating power consumption refers to the energy that the cold storage should theoretically consume when operating at a temperature-maintained state, based on the theoretical physical model of the cold storage, under given set temperature, external ambient temperature and other conditions.

[0061] As a possible implementation method, the system can input the set temperature of the cold storage and the external ambient temperature into the physical model. The physical model can calculate the expected operating power consumption based on preset heat load calculation formula, equipment energy efficiency ratio and other parameters.

[0062] S203: Compare the actual operating power consumption with the expected operating power consumption.

[0063] As a possible implementation method, the system may periodically (for example, every 5 minutes) read the values ​​of the actual operating power consumption and the expected operating power consumption and calculate the difference between the two values ​​for comparison.

[0064] S204: When the actual operating power consumption is continuously greater than or equal to a preset deviation range of the expected operating power consumption, it is determined that there is a deviation between the parameters in the physical model and the actual physical characteristics of the cold storage.

[0065] Among them, the actual physical characteristics of the cold storage refer to the real physical properties of the cold storage in actual operation, such as its actual thermal insulation performance, equipment efficiency, sealing, etc. Specifically, it refers to the actual heat load changes caused by factors such as aging of the cold storage structure over time, degradation of insulation material performance, and poor sealing of doors and windows. Its purpose is to reflect the performance changes that may occur in the cold storage during long-term operation.

[0066] The deviation refers to the difference between the actual operating power consumption and the expected operating power consumption. Specifically, it refers to the difference between the actual power consumption and the expected power consumption. Its purpose is to quantify the degree of inconsistency between the physical model prediction and the actual performance of the cold storage.

[0067] In one example, when the actual operating power consumption exceeds a preset deviation range of the expected operating power consumption (for example, set to ±10% of the expected operating power consumption) for 30 consecutive minutes or longer, the system will determine that there is a deviation between the parameters in the physical model and the actual physical characteristics of the cold storage.

[0068] S205: Update the parameters in the physical model according to the deviation.

[0069] As a possible implementation method, the system can use optimization algorithms such as Kalman filtering or least squares method to iteratively adjust the parameters related to thermal insulation performance and equipment efficiency in the physical model according to the deviation between the actual operating power consumption and the expected operating power consumption, such as the overall heat transfer coefficient U value of the cold storage or the COP value of the refrigeration equipment, until the deviation between the model-predicted power consumption and the actual power consumption converges to a preset threshold.

[0070] As another possible implementation method, the system can use an optimization algorithm to perform iterative calculations based on the deviation between the actual operating power consumption and the expected operating power consumption to obtain iterative calculation results; based on the iterative calculation results, the parameters in the physical model corresponding to the main contributions to the deviation are adjusted.

[0071] The step size and convergence conditions of the optimization algorithm are defined by the parameter adjustment rules. Parameter adjustment rules refer to a set of pre-defined guidelines that guide the operation of the optimization algorithm and the parameter adjustment process.

[0072] In one example, the system calculates the gradient of the deviation between actual and expected operating power consumption under the current physical model parameters. This gradient indicates the direction of parameter adjustment. Based on this gradient direction and a preset step size, a small adjustment is then made to the thermal insulation performance parameters. For example, if the deviation indicates a decline in thermal insulation performance, the thermal insulation coefficient can be appropriately increased. This adjustment process is iterative, with the deviation and gradient recalculated with each iteration, and the parameters adjusted again based on the new gradient direction and step size. The parameter adjustment rule can be defined as follows: the initial step size can be set to a small value to ensure the stability of the initial adjustment. As the number of iterations increases or the magnitude of the deviation decreases, the step size can be dynamically adjusted. For example, an adaptive step size strategy can be implemented to automatically reduce the step size as the optimal solution is approached, thereby improving convergence accuracy. The convergence condition can be defined as the optimization algorithm terminating when the deviation between the actual and expected operating power consumption is less than a preset small threshold after multiple consecutive iterations, or when a preset maximum number of iterations is reached.

[0073] In this way, the insulation performance parameters in the physical model can be precisely adjusted to more accurately reflect the current insulation conditions of the cold storage. Similarly, if it is identified that the main contribution to the deviation is a sudden additional heat load, the system can use a similar optimization algorithm to adjust the parameters in the physical model related to the heat load of the goods in the warehouse to adapt to the new heat load conditions.

[0074] S206: Calculate the temperature and humidity status of the cold storage at the current moment using the updated physical model.

[0075] As a possible implementation method, the system can input data transmission delay, temperature and humidity data, and temperature and humidity control action information into the updated physical model, and infer the temperature and humidity status of the cold storage at the current moment based on the output results of the updated physical model.

[0076] In some embodiments, the deviation may be the result of the combined effect of multiple factors. Simply adjusting the parameters may cause model distortion or adjustment in the wrong direction, resulting in failure to quickly converge to the optimal solution.

[0077] To this end, in order to update the parameters in the physical model according to the deviation, the application also includes the following steps: S301 : Continuously obtain a deviation sequence between actual operating power consumption and expected operating power consumption.

[0078] S302. Analyze the dynamic characteristics of the deviation sequence.

[0079] The dynamic characteristics of the deviation sequence refer to the patterns and characteristics of the time-varying deviations between actual and expected operating power consumption. Specifically, these characteristics include the trend, amplitude, frequency, and instantaneous fluctuations of the deviation sequence. The purpose is to reveal the underlying causes of the deviations by analyzing these characteristics.

[0080] As a possible implementation method, the system can smooth the deviation sequence to obtain a smoothed deviation sequence, analyze the trend of the smoothed deviation sequence, identify the instantaneous fluctuations of the deviation sequence, and further, judge the dynamic characteristics of the deviation sequence based on the trend and instantaneous fluctuations.

[0081] For example, the system can employ moving average filtering or exponential smoothing to eliminate random noise in the data, resulting in a smoothed deviation sequence. This smoothed deviation sequence can then be subjected to trend analysis, such as through linear regression or time series decomposition, to determine its long-term trend. Transient fluctuations in the deviation sequence can also be identified, such as by setting thresholds or employing anomaly detection algorithms to capture short-term, drastic changes. Based on these trends and transient fluctuations, the dynamic characteristics of the deviation sequence can be determined.

[0082] S303. Identify the main contribution of the deviation based on the dynamic characteristics of the deviation sequence.

[0083] Among them, the main contributions of the deviation include at least one of the following: sudden additional heat load, attenuation of cold storage insulation performance, and periodic auxiliary heating operation.

[0084] As a possible implementation method, the system can analyze the trend, amplitude, and frequency of the deviation sequence to identify the main contributions of the deviation, which can be divided into the following three cases: If the dynamic characteristics indicate that the trend of the deviation sequence is a slowly increasing trend and the amplitude is within the preset range, it is determined that the main contribution of the deviation is the attenuation of the cold storage insulation performance.

[0085] If the dynamic characteristic indicates that the amplitude change of the deviation sequence within the preset time period is greater than the change threshold, it is determined that the main contribution of the deviation is the sudden additional heat load.

[0086] If the dynamic characteristic indicates that the frequency of change of the deviation sequence is periodic, it is determined that the main contribution of the deviation is the periodic auxiliary heating operation.

[0087] S304: Update the parameters in the physical model according to the main contribution of the deviation.

[0088] As a possible implementation method, when the main contribution of the identification deviation is the attenuation of the cold storage insulation performance, the system can adjust the parameters related to the insulation performance in the physical model; when the main contribution of the identification deviation is the sudden additional heat load, the system can adjust the parameters related to the heat load of the goods in the warehouse in the physical model; when the main contribution of the identification deviation is the periodic auxiliary heating operation, the system can suspend the update of the physical model parameters and trigger an abnormal alarm.

[0089] For example, when insulation performance degradation is identified, the heat transfer coefficient in the model can be adjusted; when a sudden additional heat load is identified, the cargo specific heat capacity or heat release rate in the model can be adjusted.

[0090] Unreasonable convergence conditions may cause the algorithm to stop iterating too early, making it impossible to obtain the optimal parameter adjustment results, or the iteration time may be too long, increasing the computational burden.

[0091] In this regard, the present application further proposes that the steps of adjusting the parameters corresponding to the main contribution of the deviation in the physical model include: S401: Obtain an amplitude of a deviation between actual operating power consumption and expected operating power consumption.

[0092] The magnitude of the deviation refers to the absolute size of the difference between the actual operating power consumption and the expected operating power consumption. Its purpose is to quantify the degree of deviation between the model prediction and the actual situation.

[0093] As a possible implementation manner, the system may calculate the absolute value of the difference between the actual operating power consumption and the expected operating power consumption, and use the absolute value as the amplitude of the deviation between the actual operating power consumption and the expected operating power consumption.

[0094] S402: Adjust the step size of the optimization algorithm according to the magnitude of the deviation.

[0095] The step size of the optimization algorithm refers to the adjustment amount of each parameter update during the iteration process. It can be determined by a fixed value, a dynamic adjustment factor based on gradient information, or a preset lookup table. Its purpose is to control the speed and stability of parameter updates.

[0096] As a possible implementation manner, the system may determine the step size corresponding to the amplitude of the deviation from a preset lookup table according to the amplitude of the deviation, so as to adjust the step size of the optimization algorithm.

[0097] The preset lookup table includes mapping relationships between different deviation amplitudes and different step lengths.

[0098] In one example, a preset lookup table defines three ranges: small deviation (0-5%), medium deviation (5%-15%), and large deviation (>15%). When a large deviation is detected, the system can adjust the optimization algorithm's step size, for example, to a larger value such as 0.1; when a medium deviation is detected, the step size can be set to a medium value such as 0.05; and when a small deviation is detected, the step size can be set to a smaller value such as 0.01. This segmented adjustment strategy ensures rapid convergence when the deviation is large and fine-tuned when the deviation is small.

[0099] S403: Obtain the duration or change rate of the deviation between the actual operating power consumption and the expected operating power consumption.

[0100] As a possible implementation manner, the system may determine the duration by recording the length of time that the deviation value continuously exceeds a certain threshold, or determine the change rate by calculating the difference of the deviation values ​​at consecutive time points.

[0101] S404: Define a convergence condition based on the duration or change rate of the deviation.

[0102] As a possible implementation approach, when the deviation persists for a long time and the rate of change is small, the convergence conditions can be appropriately relaxed to allow the algorithm to iterate more fully and obtain more accurate parameters. When the rate of change of the deviation is large, the convergence conditions can be tightened to encourage the algorithm to converge faster to cope with the rapidly changing cold storage operating environment.

[0103] In one example, if the deviation persists for more than 30 minutes and changes at a rate of less than 0.01% / minute, this indicates a relatively stable deviation. In this case, the convergence condition can be defined as the objective function value changing by less than 0.001 for five consecutive iterations, allowing the algorithm to iterate more fully. If the deviation change rate exceeds 0.05% / minute, this indicates significant deviation fluctuations. In this case, the convergence condition can be tightened, for example, to the objective function value changing by less than 0.01 for two consecutive iterations, prompting the algorithm to converge quickly and adapt to dynamic changes.

[0104] Through the above technical solution, the step size of the optimization algorithm can be adaptively adjusted according to the amplitude of the deviation between the actual operating power consumption and the expected operating power consumption, avoiding the problem of algorithm oscillation or slow convergence caused by a fixed step size. At the same time, the convergence condition can be dynamically defined according to the duration or rate of change of the deviation, ensuring that the algorithm can stop iteration with appropriate accuracy and efficiency under different operating conditions, avoiding the problem of premature stopping or excessive iteration time. This makes the adjustment process of the physical model parameters more efficient and accurate, improves the model's ability to adapt to the actual physical characteristics of the cold storage, and thus improves the accuracy of temperature and humidity estimation and the accuracy and stability of the cold storage temperature and humidity control.

[0105] like Figure 3 As shown, the embodiment of the present invention also provides a temperature and humidity control system for agricultural product cold storage. The system includes: The data packet acquisition module is used to obtain the status data packet sent by the local control system of the agricultural product cold storage and the reception time of the status data packet. The status data packet contains the temperature and humidity data of the cold storage, the generation timestamp of the temperature and humidity data, and the temperature and humidity control action information executed by the local control system; the temperature and humidity data includes temperature and humidity; A transmission delay calculation module, used to calculate the data transmission delay of the status data packet based on the generation timestamp and the reception time; The temperature and humidity status estimation module is used to estimate the temperature and humidity status of the cold storage at the current moment based on data transmission delay, temperature and humidity data, and temperature and humidity control action information, using a preset physical model; The load balancing decision module is used to make load balancing decisions based on the estimated temperature and humidity conditions. The load balancing decision is used to control the temperature and humidity of the cold storage.

[0106] Embodiments of the present invention also provide a terminal device. The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a temperature and humidity control program for an agricultural product cold storage. When the processor executes the computer program, the steps of each of the aforementioned agricultural product cold storage temperature and humidity control method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned system embodiments are implemented.

[0107] For example, a computer program may be divided into one or more modules / units, which are stored in a memory and executed by a processor to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0108] Terminal devices may be computing devices such as desktop computers, laptops, PDAs, and smart tablets. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of terminal devices and do not constitute a limitation of terminal devices. Terminal devices may include more or fewer components than those described above, or combinations of certain components, or different components. For example, terminal devices may also include input / output devices, network access devices, buses, and the like.

[0109] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the entire terminal device using various interfaces and lines.

[0110] The memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0111] If the module / unit integrated into the terminal device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunications signals.

[0112] It should be noted that the system embodiment described above is merely illustrative, in which the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without expending any creative effort.

[0113] The above specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for controlling temperature and humidity in a cold storage for agricultural products, characterized in that: The following steps are involved: Obtaining a status data packet sent by a local control system of an agricultural product cold storage and the time at which the status data packet was received, wherein the status data packet includes temperature and humidity data of the cold storage, a timestamp of generation of the temperature and humidity data, and information on temperature and humidity control actions performed by the local control system; the temperature and humidity data includes temperature and humidity; Calculating a data transmission delay of the status data packet according to the generation timestamp and the reception time; Based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, a preset physical model is used to infer the temperature and humidity state of the cold storage at the current moment; A load balancing decision is made based on the estimated temperature and humidity status, and the load balancing decision is used to control the temperature and humidity of the cold storage.

2. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 1, characterized in that: The method of calculating the temperature and humidity state of the cold storage at the current moment based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information using a preset physical model includes: Determining whether the cold storage has a thermodynamic anomaly, wherein the thermodynamic anomaly is used to indicate that the cold storage has an abnormal increase in heat load; In the case that the thermodynamic anomaly does not exist in the cold storage, the temperature and humidity state of the cold storage at the current moment is calculated based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information using a preset physical model.

3. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 2, characterized in that: Determining whether the cold storage has thermodynamic anomalies includes: Obtaining type information of goods in the cold storage; Determining, based on the cargo type information, the standard operating power consumption of the refrigeration system of the cold storage required to maintain the cargo at a specific temperature; Monitoring the actual operating power consumption of the cold storage; When the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption for a duration greater than a preset duration, determining that a thermodynamic anomaly exists in the cold storage; When the duration of the preset range in which the actual operating power consumption is greater than or equal to the standard operating power consumption is less than or equal to the preset duration, it is determined that no thermodynamic abnormality exists in the cold storage.

4. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 1, wherein: The method of calculating the temperature and humidity state of the cold storage at the current moment by using a preset physical model includes: When the cold storage is in a temperature-maintaining operating state, obtaining the actual operating power consumption of the cold storage; Calculating the expected operating power consumption required for the cold storage to maintain the temperature according to the set temperature of the cold storage, the external ambient temperature, and the physical model; comparing the actual operating power consumption with the expected operating power consumption; When the actual operating power consumption is continuously greater than or equal to a preset deviation range of the expected operating power consumption, it is determined that there is a deviation between the parameters in the physical model and the actual physical characteristics of the cold storage; updating parameters in the physical model according to the deviation; The updated physical model is used to estimate the temperature and humidity status of the cold storage at the current moment.

5. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 4, characterized in that: The updating of parameters in the physical model according to the deviation includes: Continuously obtaining a deviation sequence between the actual operating power consumption and the expected operating power consumption; analyzing the dynamic characteristics of the deviation sequence; Identify the main contribution of the deviation according to the dynamic characteristics of the deviation sequence; the main contribution of the deviation includes at least one of the following: sudden additional heat load, cold storage insulation performance degradation, and periodic auxiliary heating operation; updating parameters in the physical model according to a main contribution of the deviation; Among them, when it is identified that the main contribution of the deviation is the attenuation of the cold storage insulation performance, the parameters related to the insulation performance in the physical model are adjusted; when it is identified that the main contribution of the deviation is the sudden additional heat load, the parameters related to the heat load of the goods in the warehouse are adjusted; when it is identified that the main contribution of the deviation is the periodic auxiliary heating operation, the update of the physical model parameters is suspended and an abnormal alarm is triggered.

6. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 5, characterized in that: The analyzing the dynamic characteristics of the deviation sequence includes: performing smoothing on the deviation sequence to obtain a smoothed deviation sequence; Analyzing the trend of the smoothed deviation sequence; identifying instantaneous fluctuations in the deviation sequence; The dynamic characteristics of the deviation sequence are determined based on the trend and the instantaneous fluctuation.

7. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 6, characterized in that: The main contributions to identifying the deviation include: Analyzing the trend, amplitude and frequency of change of the deviation sequence; If the dynamic characteristic indicates that the trend of the deviation sequence is a slowly increasing trend and the amplitude is within a preset range, it is determined that the main contribution of the deviation is the attenuation of the cold storage insulation performance; If the dynamic characteristic indicates that the amplitude change of the deviation sequence within a preset time period is greater than a change threshold, determining that the main contribution of the deviation is a sudden additional heat load; If the dynamic characteristic indicates that the variation frequency of the deviation sequence is periodic, it is determined that a major contribution of the deviation is a periodic auxiliary heating operation.

8. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 5, characterized in that: The updating of parameters in the physical model according to the main contribution of the deviation includes: performing iterative calculations using an optimization algorithm based on a deviation between the actual operating power consumption and the expected operating power consumption to obtain an iterative calculation result; Adjusting parameters in the physical model corresponding to the main contribution of the deviation according to the iterative calculation results; The step size and convergence conditions of the optimization algorithm are defined according to parameter adjustment rules.

9. A method for controlling temperature and humidity in a cold storage for agricultural products according to claim 8, characterized in that: The method further comprises: Obtaining a magnitude of a deviation between the actual operating power consumption and the expected operating power consumption; adjusting the step size of the optimization algorithm according to the magnitude of the deviation; Obtaining a duration or a change rate of a deviation between the actual operating power consumption and the expected operating power consumption; The convergence condition is defined according to the duration or the rate of change of the deviation.

10. A temperature and humidity control system for agricultural product cold storage, used for temperature and humidity control of agricultural product cold storage, characterized in that: The system includes: A data packet acquisition module is used to acquire a status data packet sent by the local control system of the agricultural product cold storage and the time when the status data packet is received. The status data packet contains the temperature and humidity data of the cold storage, the timestamp of the generation of the temperature and humidity data, and the temperature and humidity control action information executed by the local control system; the temperature and humidity data includes temperature and humidity; a transmission delay calculation module, configured to calculate a data transmission delay of the status data packet according to the generation timestamp and the reception time; a temperature and humidity state estimation module, configured to estimate the temperature and humidity state of the cold storage at the current moment using a preset physical model based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information; The load balancing decision module is used to make a load balancing decision based on the calculated temperature and humidity status, and the load balancing decision is used to control the temperature and humidity of the cold storage.

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