A storage environment control system and method for ozone water washed eggs

Abstract

The application discloses an ozone water-washed egg storage environment control system and method, and particularly relates to the field of environmental parameter control, and is used for solving the problem that the existing ozone water-washed eggs are sensitive to temperature and humidity fluctuations due to changes in eggshell permeability during storage, resulting in abnormal water loss rate, uneven quality and inaccurate environmental regulation; the system is constructed by building a space grid inside the storage unit, collecting the mass change time series data and temperature and humidity information of each node water-washed egg, calculating the water loss rate and identifying the abnormal state, establishing a prediction model according to the exponential relationship between temperature and water loss rate, generating an environmental regulation strategy combined with the abnormal type, recording and forming the temperature and humidity distribution field after executing the strategy, and then completing the overall reset and parameter synchronous migration, so that the temperature and humidity distribution of the storage environment remains orderly and stable, thereby realizing accurate environmental control of the ozone water-washed eggs and improving the health and safety of the eggs during storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental parameter control, and more particularly, to a storage environment control system and method for ozone water-washed eggs. BACKGROUND

[0002] In the current fresh egg storage and preservation process, ozone water washing technology, as a new type of cleaning and sterilization method, is gradually introduced into large-scale egg processing. This technology can effectively remove harmful microorganisms and impurities on the surface of the eggshell in a short time, thereby improving the hygiene and safety of the eggs during the flow process.

[0003] However, at the same time, this processing process changes the permeability of the micro-pores on the eggshell surface, making ozone water-washed eggs more sensitive to the external environment during storage. Compared with untreated fresh eggs, ozone water-washed eggs are more likely to show accelerated water loss rate, rapid air sac inflation, or surface condensation when the temperature and humidity fluctuate. If the environment is not properly controlled, it may lead to a shorter preservation period or uneven quality of the entire batch of products in a short period of time. In practical application scenarios, ozone water-washed eggs are often stacked in large cold chain warehouses, processing workshops, or logistics distribution centers. These environments often need to maintain the stable quality of the eggs under the conditions of long time, multiple batches of alternating in and out. Due to frequent fluctuations in environmental factors, such as equipment start-stop, in-out operation, and local space airflow difference, the conventional overall constant temperature and humidity method often fails to meet the more stringent storage requirements of ozone water-washed eggs.

[0004] Therefore, for this scenario, how to achieve higher precision temperature and humidity control under large-scale storage and transportation conditions has become a core problem that ozone water-washed eggs must face in the storage and circulation process. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a storage environment control system and method for ozone water-washed eggs to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] A storage environment control method for ozone water-washed eggs, comprising the following steps:

[0008] S1, acquiring the quality change time series data of the water-washed eggs in each grid node of the storage unit, and synchronously monitoring the current environmental temperature data and environmental humidity data;

[0009] S2, calculating the water loss rate of the eggshell micro-pores based on the quality change time series data, and determining whether the current state of the water-washed eggs belongs to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality;

[0010] S3, establishing a water loss rate prediction model according to the exponential variation relationship between the ambient temperature and the water loss rate;

[0011] S4, setting the ambient temperature and humidity data adjustment direction according to the exchange rate abnormal type, and generating an environment control strategy combining the water loss rate prediction model;

[0012] S5, after the execution of the environment control strategy, recording each grid node and the corresponding ambient temperature and humidity control parameters, and resetting the storage unit as a whole based on the ambient temperature and humidity control parameter distribution;

[0013] S6, based on the reset storage unit, synchronously migrating the ambient temperature and humidity control parameters of each grid node.

[0014] In a preferred embodiment, in S1, the mass change time series data of the washed eggs in each grid node of the storage unit are obtained, and the current ambient temperature data and ambient humidity data are synchronously monitored, which specifically includes:

[0015] A spatial grid is constructed inside the storage unit, the washed eggs are uniformly arranged in each grid node, and periodic sampling is performed according to a unified time reference;

[0016] The instantaneous mass value of the washed egg and the ambient temperature and humidity value of each grid node are synchronously recorded;

[0017] The time series data collected by different grid nodes are aligned and merged using a unified timestamp mechanism, the average curve of each grid node is calculated, and the dynamic mass change time series of the washed egg is generated.

[0018] In a preferred embodiment, in S2, the water loss rate of the eggshell micropore is calculated based on the mass change time series data, and the current state of the washed egg is determined to belong to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality, which specifically includes:

[0019] The mass value at the continuous time point in the mass change time series data is obtained, the absolute value of the mass difference value of the adjacent time points is calculated and divided by the time interval, and the instantaneous water loss rate data point of each time period is obtained;

[0020] The arithmetic mean value of the ambient temperature data and the arithmetic mean value of the ambient humidity data in the corresponding time period are synchronously extracted, the instantaneous water loss rate data point is data fused with the corresponding temperature and humidity arithmetic mean value, and a time-ordered water loss rate-ambient parameter correlation sequence is formed;

[0021] A water loss rate reference line and an allowable fluctuation range are set, when the water loss rate data point in the correlation sequence continuously exceeds the upper limit of the reference line and is accompanied by an ambient temperature rising or ambient humidity decreasing trend, it is determined to be the exchange rate hyperactivity type abnormality;

[0022] When the water loss rate data points continue to be lower than the lower limit of the baseline and are accompanied by a trend of decreasing ambient temperature or increasing ambient humidity, it is determined to be an exchange rate inhibition type abnormality.

[0023] In a preferred embodiment, the S3, the water loss rate prediction model is established according to the exponential change relationship between the ambient temperature and the water loss rate, specifically comprising:

[0024] Obtain the water loss rate-ambient parameter correlation sequence of the water-washed eggs in the current storage unit at different historical time periods as the original data set;

[0025] Perform separate analysis on each grid unit, plot the original data set as a temperature-water loss rate scatter plot, use statistical regression method to curve fit the scatter points, obtain the exponential change trend line reflecting the relationship between temperature change and water loss rate change, and take the trend line as the water loss rate prediction model;

[0026] The water loss rate prediction model takes the temperature value as the input parameter and predicts the water loss rate value as the output.

[0027] In a preferred embodiment, the S4, according to the exchange rate abnormality type, the ambient temperature and humidity data adjustment direction is set, and the environmental control strategy is generated in combination with the water loss rate prediction model, specifically comprising:

[0028] Read the current temperature value and the current humidity value in the water loss rate-ambient parameter correlation sequence;

[0029] If it is determined to be an exchange rate hyperactivity type abnormality, a first control strategy is generated to suppress internal gas escape, and the temperature adjustment direction of the first control strategy is negative and the humidity adjustment direction is positive;

[0030] If it is determined to be an exchange rate inhibition type abnormality, a second control strategy is generated to promote internal gas escape, and the temperature adjustment direction of the second control strategy is positive and the humidity adjustment direction is negative;

[0031] According to the water loss rate prediction model, the deviation of the predicted water loss rate corresponding to the current temperature value from the reference water loss rate is output, the ambient temperature control parameter is determined, and the temperature is adjusted;

[0032] If the target temperature adjustment value has been reached, and the deviation of the actual water loss rate from the reference water loss rate is greater than the allowable fluctuation range, then the current temperature reference value is taken as the starting point, and an iterative approximation algorithm is used to solve the humidity adjustment amount, specifically:

[0033] Set the humidity adjustment amount with fixed iteration step, and monitor the actual water loss rate under the corresponding humidity in real time in each iteration process;

[0034] When the relative error between the actual water loss rate and the benchmark water loss rate is less than the preset convergence threshold, the iteration is stopped and the current humidity is taken as the environmental humidity control parameter.

[0035] In a preferred embodiment, after the execution of the environmental regulation strategy in S5, the environmental temperature and humidity control parameters of each grid node are recorded, and the overall reset of the storage unit is performed based on the distribution of the environmental temperature and humidity control parameters, specifically including:

[0036] After the execution of the environmental regulation strategy, the environmental temperature and humidity control parameters of the water-washed eggs in each grid node are recorded, and a mapping table is established with the corresponding grid node number;

[0037] Based on the environmental temperature and humidity control parameters of each grid node, a temperature and humidity distribution field covering the entire storage unit is formed;

[0038] Global gradient analysis is performed on the distribution field to identify the comprehensive dominant gradient direction of the temperature field and the humidity field;

[0039] The dominant gradient direction is taken as the reference axis of the overall position adjustment, and all grid nodes are sorted according to the gradient size according to the reference axis to generate a continuously arranged adjustment path;

[0040] The water-washed eggs in each spatial grid are migrated and rearranged according to the adjustment path until an ordered temperature and humidity field with continuous gradient distribution is formed in the global range.

[0041] In a preferred embodiment, in S6, based on the reset storage unit, the environmental temperature and humidity control parameters of each grid node are synchronously migrated, specifically including:

[0042] The environmental temperature and humidity control parameter and grid node number mapping table is called to obtain the original control parameters corresponding to each new grid node number after migration and rearrangement, and the original environmental temperature and humidity control parameters are written into the new grid node after migration;

[0043] The environmental control instructions are issued to the new grid node to make the environmental temperature and humidity of each grid node consistent with the spatial distribution after migration.

[0044] On the other hand, the present application provides a storage environment control system for ozone water-washed eggs, comprising:

[0045] The data acquisition module is used to construct a spatial grid inside the storage unit, obtain the mass change time series data of the water-washed eggs in each grid node, and synchronously monitor the environmental temperature and humidity values to form a complete original data set;

[0046] An abnormality discrimination module is configured to calculate the water loss rate of the eggshell micropore based on the quality change time series data, and determine whether the current state belongs to the exchange rate acceleration type abnormality or the exchange rate inhibition type abnormality in combination with the temperature and humidity trend;

[0047] A prediction modeling module is configured to establish a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate, and input the temperature data to generate a corresponding predicted water loss rate result;

[0048] A strategy generation module is configured to output an environmental regulation strategy in combination with the abnormality type and the prediction model, set the temperature and humidity adjustment direction, and determine the target temperature and humidity control parameters for execution;

[0049] A space resetting module is configured to record the control parameters of each grid node after the execution of the regulation strategy, perform overall resetting of the storage unit based on the parameter distribution, and synchronize the migration of the environmental temperature and humidity parameters of each node.

[0050] The technical effects and advantages of the storage environment control system and method for ozone water-washed eggs according to the present application are as follows:

[0051] In view of the characteristics of the enhanced permeability of the eggshell surface after ozone treatment, a more accurate environmental regulation mechanism than the traditional constant temperature and humidity method is provided, thereby effectively avoiding the problems of abnormal water loss rate and quality difference caused by temperature and humidity fluctuations. By constructing a spatial grid inside the storage unit and collecting quality, temperature and humidity data in real time for each node, the system can dynamically identify the state changes of eggs at different positions and quickly respond to the regulation, realizing differentiated control from the whole to the local. With the help of the water loss rate prediction model and the abnormality discrimination logic, the abnormal trend of gas exchange can be found in advance and the corresponding regulation strategy can be output, so that the temperature and humidity adjustment is more in line with the actual needs of the egg body, thereby delaying the air bag inflation and slowing down the egg white liquefaction speed. Further, through overall position adjustment and synchronization of control parameters, the present method can make the temperature and humidity field in the storage environment continuous and orderly, avoiding the quality unevenness caused by over-drying or over-wetting in local areas.

[0052] The present method not only improves the stability and consistency of ozone water-washed eggs in large-scale storage and flow process, but also reduces the energy consumption caused by excessive start and stop of environmental equipment, improves the fine level of storage management, and ensures that the products maintain high commodity value and safety in long-term storage and multi-link flow process. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The figure is a schematic diagram of the storage environment control method for ozone water-washed eggs according to the present application;

[0054] Figure 2 The figure is a schematic diagram of the storage environment control system structure for ozone water-washed eggs according to the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0056] Embodiment 1

[0057] Figure 1 A storage environment control method for ozone water washed eggs is provided, which comprises the following steps:

[0058] S1, acquiring the mass change time series data of the washed eggs in each grid node of the storage unit, and synchronously monitoring the current environmental temperature data and the environmental humidity data;

[0059] S2, calculating the water loss rate of the eggshell micropore based on the mass change time series data, and determining whether the current state of the washed egg belongs to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality;

[0060] S3, establishing a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate;

[0061] S4, setting the adjustment direction of the environmental temperature and humidity data according to the exchange rate abnormality type, and generating an environmental control strategy in combination with the water loss rate prediction model;

[0062] S5, after the execution of the environmental control strategy is completed, recording each grid node and the corresponding environmental temperature and humidity control parameters, and resetting the storage unit as a whole based on the distribution of the environmental temperature and humidity control parameters;

[0063] S6, based on the reset storage unit, synchronously migrating the environmental temperature and humidity control parameters of each grid node.

[0064] In S1, the mass change time series data of the washed eggs in each grid node of the storage unit is acquired, and the current environmental temperature data and the environmental humidity data are synchronously monitored.

[0065] The grid is divided according to the geometric spatial structure inside the storage unit, and the storage space is divided into a plurality of cubic units with clear boundaries by adopting a three-dimensional coordinate mode, each cubic unit being a spatial grid node. The division mode of the grid needs to be set in combination with the volume of the storage unit and the number of eggs stored, so as to uniformly distribute the washed eggs to each grid node, and the number of eggs placed in each node is kept close to uniform, avoiding the influence of the representative data due to the excessively high local stacking density. After the arrangement is completed, a collection device is arranged in each grid node to ensure that the egg information of each node can be collected at the same time point. The time interval of the periodic sampling needs to be determined in advance, and an excessively long interval may lead to data loss, and an excessively short interval will increase the collection and processing burden. In this embodiment, the sampling period is set to be once every 10 minutes, and the sampling frequency can be uniformly controlled by the device clock, and the collection time of all nodes is kept strictly synchronized. In order to avoid the time difference caused by the delay of individual node collection, the collection process needs to be bound to a unified reference clock signal, which can be issued by the centralized controller of the storage unit to ensure that all nodes perform the sampling task at the same time point. A comprehensive and time-unified sampling system is established inside the entire storage unit.

[0066] In each grid node, a quality measurement unit and a temperature and humidity measurement unit are configured. The quality measurement unit is used to obtain the overall quality value of the washed eggs in the node in real time, and the measurement mode is to weigh the node tray as a whole to ensure the stability and representativeness of the data. In actual deployment, a precise electronic weighing device is arranged below each tray, and the weighing precision is set to 0.01 g, which can meet the detection needs of the weight change of eggs caused by slow water loss during storage. The temperature and humidity measurement unit is arranged near the eggs in each node to ensure that the obtained values can truly reflect the microenvironment conditions around the eggs. The temperature measurement precision is set to 0.1℃, and the humidity measurement precision is set to 1%RH, both of which can cover the monitoring range required for storage. During the collection process, the weighing and temperature and humidity measurement are kept synchronous to ensure that the weight, temperature and humidity data collected each time have the same time identifier, avoiding the asynchronous data. In order to prevent accidental measurement errors, each collection result needs to be subjected to a stability test, and if the difference between the single measurement value and the previous two values is more than 5%, it is determined as an abnormal point, and the collection device re-collects once in the same period until the result meeting the stability requirement is obtained.

[0067] After the data collection of each grid node is completed, the data of different nodes is aligned on a unified time axis. The implementation of the unified timestamp mechanism is that all collected data is provided with a time identifier generated by a central reference clock, and the time identifier is recorded in seconds as the minimum unit. When the collected data of each node is summarized, the time identifier is used as the alignment reference, and the data at the same time point is arranged in the same row to form a standardized time sequence matrix. If some nodes fail to successfully collect data at a certain time, the average value of the two valid sampling points before and after the node is used to fill in the missing data, ensuring the continuity of the overall data. After alignment, the time sequence data of each grid node is merged, that is, the arithmetic mean of all instantaneous quality values in a sampling period is calculated to obtain the average quality value of the node; at the same time, the average values of temperature and humidity are calculated in the same way. The average curve of the quality change of each node is drawn through the average results of consecutive sampling periods, and the curve can reflect the water loss trend of the eggs during storage. Finally, all the average curves are integrated into a complete dynamic time sequence, which contains the average quality value, temperature value and humidity value change trajectory of each grid node at different time points.

[0068] In S2, the water loss rate of the eggshell micropore is calculated based on the quality change time sequence data, and it is determined whether the current state of the water-washed egg belongs to the exchange rate accelerated abnormality or the exchange rate inhibited abnormality.

[0069] After the collection of the quality change time sequence data is completed, the quality values recorded by each grid node at different sampling times are extracted one by one. According to the unified time reference, the quality records of adjacent two time points are read from the data sequence in sequence. By comparing the quality difference between the two consecutive time points, the quality reduction amount of the eggs in the time period can be obtained. Since the water-washed eggs mainly show gradual evaporation of water during storage, the absolute value of the quality difference can directly reflect the water loss in the time period. Then, the quality difference and the corresponding sampling time interval are subjected to ratio operation to obtain the instantaneous water loss rate data point of the time period. In the implementation process, the sampling time interval is a fixed value, which is set to 10 minutes in this embodiment, and the rate calculation maintains a unified time scale. In order to ensure the stability of the calculation results, all the water loss rate data points are arranged in time sequence to form a complete instantaneous water loss rate sequence. The sequence reflects the dynamic change of the water loss rate of the eggs in different time periods.

[0070] After obtaining the sequence of instantaneous water loss rates, the environmental conditions in the same time period are extracted to realize the correspondence between the water loss rate and the environmental temperature and humidity. The specific method is to take the temperature data and humidity data collected in each time window as the same sampling interval, and the representative temperature value and the representative humidity value of the time period are obtained by arithmetic average. The average calculation method here is to add all the measured temperature values in the window, and then divide by the number of measurements, and the humidity is the same. In this way, the occasional deviation of single-point measurement can be eliminated, and the obtained environmental data can stably represent the overall environmental level in the time period. Then, the instantaneous water loss rate data points in the time period are associated and paired with the corresponding temperature average value and humidity average value, and finally a water loss rate-environmental parameter correlation sequence arranged in time sequence is formed. Each data entry in the sequence contains an instantaneous water loss rate value, a set of environmental temperature and humidity values, thereby establishing the correspondence between the water loss rate and the environmental conditions on the time axis.

[0071] After obtaining the water loss rate-environmental parameter correlation sequence, it is judged whether the current state of the water-washed eggs is abnormal by the baseline method. The setting of the baseline is based on the results of long-term statistical analysis. In this embodiment, the water loss rate range in the normal state is set to be between 0.05% and 0.1%, that is, in a sampling period, the mass of the water-washed eggs should not decrease by more than 0.1% of the initial mass, and should not be lower than 0.05%. In order to tolerate environmental fluctuations, the allowable fluctuation range is set to ±0.01% in this embodiment. When the water loss rate data points of a plurality of consecutive time periods in the correlation sequence are higher than the upper limit of the baseline, and the corresponding environmental parameters show that the temperature is in an upward trend or the humidity is in a downward trend, it is determined that the state belongs to the exchange rate hyperactivity type of abnormality, indicating that the water loss rate of the egg body is too fast; when the water loss rate data points of a plurality of consecutive time periods in the correlation sequence are lower than the lower limit of the baseline, and the corresponding environmental parameters show that the temperature is in a downward trend or the humidity is in an upward trend, it is determined that the state belongs to the exchange rate inhibition type of abnormality, indicating that the gas exchange of the egg body surface is excessively limited.

[0072] In S3, a water loss rate prediction model is established according to the exponential change relationship between the environmental temperature and the water loss rate.

[0073] During different historical periods of the storage unit's operation, each grid node is used as the analysis unit. Records of the correlation between the water loss rate and corresponding environmental parameters for each grid node are continuously collected and saved, ultimately forming the original dataset. The correlation record between the water loss rate and environmental parameters involves pairing the instantaneous water loss rate value obtained from quality monitoring with the corresponding values ​​obtained from temperature and humidity measurements within each sampling time window, forming a complete set of data entries. Each data entry includes a time label, water loss rate value, and temperature and humidity parameters, reflecting the state of the eggs during that time period. To ensure data representativeness and stability, the sampling period needs to cover multiple different operating conditions in daily operations, such as during continuous operation of cooling equipment, frequent opening and closing of storage unit doors, and long-term stable storage. This embodiment sets the sampling cycle to once every 10 minutes, with a historical time range of 30 consecutive days, ultimately forming a dataset containing tens of thousands of entries. In the original dataset, the data for each grid node is stored independently, preserving the correspondence between eggs and environmental conditions at different spatial locations. These historical data are marked with a unified time reference to ensure traceability according to chronological order and spatial distribution during subsequent analysis. In this way, the original dataset comprehensively reflects the dynamic correspondence between the water loss behavior of ozone-washed eggs under different conditions inside the storage unit and environmental conditions.

[0074] Data from each grid cell is analyzed independently to avoid data confusion between different nodes due to spatial differences. Specifically, for each grid node, all data entries collected within the historical time period are organized chronologically, and the extracted temperature and water loss rate values ​​are used as the primary variables to construct a set of correspondences between temperature and water loss rate. With temperature as the x-axis and water loss rate as the y-axis, all historical data points are plotted on a two-dimensional coordinate graph to form a temperature-water loss rate scatter plot. Since the distribution of data points may be discrete under different time periods and environmental conditions, it is necessary to ensure a sufficient number of samples to clearly show the trend of water loss rate changing with temperature on the scatter plot. During the plotting process, the source node and time label of each scatter point are retained to facilitate tracing the source or excluding outlier data in subsequent analysis.

[0075] After obtaining the scatter plot, a statistical regression method is used to fit the scatter data to extract the mathematical relationship between temperature change and water loss rate change. This embodiment uses exponential regression as the fitting method, assuming that the water loss rate increases exponentially with rising temperature. The specific fitting process is as follows: all data points on the scatter plot are preprocessed to remove outliers that deviate significantly from the overall distribution. Outliers are defined as those whose deviation from their neighboring data points exceeds twice the average value. Then, least squares regression is performed on the preprocessed data points to obtain a smooth exponential trend line. This trend line is converged by comparing the sum of squared residuals; fitting is considered complete when the residuals are less than a preset threshold. The residual threshold is set to 0.05 to ensure that the fitted curve closely matches the data point distribution. After fitting the exponential trend line, this trend line is used as the core of the water loss rate prediction model. The prediction model is defined as follows: when the input parameter is the current ambient temperature value of a certain grid node, the model can output the predicted water loss rate value under that temperature condition. To ensure the model's universality, an independent exponential trend line is established for each grid node, serving as the prediction model for that node.

[0076] In step S4, based on the abnormal type of exchange rate, the direction of adjustment for ambient temperature and humidity data is set, and an environmental control strategy is generated by combining the water loss rate prediction model.

[0077] The environmental parameters at the current moment are obtained from the associated sequence, serving as the basis for generating the control strategy. Specifically, the latest set of data entries is extracted from the stored data sequence, containing the temperature and humidity values ​​at the corresponding time point. The extraction method is to directly call the data record with the current time stamp, ensuring that the data is synchronized with the water loss rate status determination. When the determination result is an over-rate anomaly, it indicates that the washed egg is currently in a state of excessive water loss. To prevent the internal gas from escaping too quickly, the ambient temperature needs to be lowered and the ambient humidity increased. Therefore, the control strategy sets the temperature adjustment direction to negative, meaning the target temperature is lower than the current value, while the humidity adjustment direction is positive, meaning the target humidity is higher than the current value. When the determination result is a rate-inhibition anomaly, it indicates that the washed egg is in a state of hindered gas exchange, requiring the temperature to be increased and the humidity to be decreased to promote gas exchange. Therefore, the control strategy sets the temperature adjustment direction to positive, meaning the target temperature is higher than the current value, while the humidity adjustment direction is negative, meaning the target humidity is lower than the current value. The generation of control strategies not only provides direction, but also includes binding with current environmental parameters to ensure that calculations can be made based on the actual values ​​of current temperature and humidity when executing control. The generation of each control strategy relies on real-time monitoring data and anomaly detection results.

[0078] After generating the temperature and humidity control direction, the water loss rate prediction model is invoked, using the current temperature value as input to obtain the predicted water loss rate under that temperature condition. Then, the predicted water loss rate is compared with a preset baseline water loss rate, and the difference between the two is calculated. The baseline water loss rate is set before implementation to 0.05%-0.1%, for example, a mass decrease of no more than 0.08% of the initial value per hour. When the predicted water loss rate is higher than the baseline value, it indicates that the current temperature level will cause excessive water loss. In this case, the specific amount of temperature reduction needed is determined based on the difference, for example, a decrease of 0.2℃ for every 0.01% increase. When the predicted water loss rate is lower than the baseline value, it indicates that the current temperature level will cause excessive water loss. In this case, the specific amount of temperature increase needed is determined based on the difference, for example, an increase of 0.2℃ for every 0.01% decrease. Through this difference conversion relationship, a clear temperature control parameter can be obtained, that is, the specific amount of increase or decrease needed based on the current temperature. Subsequently, this control parameter is combined with the control direction to form the final temperature adjustment command, which is directly applied to the temperature control of the storage unit. Continue monitoring after temperature adjustment.

[0079] After temperature adjustment is completed and the target temperature value is reached, the difference between the actual water loss rate and the baseline water loss rate is checked again. If the difference is still greater than the allowable fluctuation range, humidity adjustment is performed. During implementation, the environmental conditions at the target temperature are used as the new reference state, and the environmental humidity value at the target temperature is used as the initial reference humidity, which is also used as the starting point for subsequent iterative adjustments. A clear iteration step size is set, and the step size is fixed to ensure the stability and controllability of the adjustment process. In this embodiment, the humidity adjustment step size is set to increase or decrease by 2%RH each time. In each iteration, the environmental humidity is adjusted according to the step size, and after the adjustment is completed, a stabilization period is waited for. In this embodiment, the stabilization period is set to 10 minutes. At the end of the stabilization period, the actual water loss rate data of each grid node in the current storage unit is collected and compared with the baseline water loss rate. In this way, each iteration not only changes the environmental humidity but also monitors the water loss rate, thus forming a closed-loop process of "adjustment-observation-comparison".

[0080] During the humidity iteration process, a convergence check is performed on the adjustment results to determine whether the preset target requirements have been met. Convergence is determined by comparing the error between the currently monitored actual water loss rate and the baseline water loss rate. When the error is less than a convergence threshold, iteration stops and the current humidity value is locked as the final environmental humidity control parameter. The convergence threshold is consistent with the previously set baseline allowable fluctuation range. When the difference between the actual water loss rate and the baseline water loss rate is less than this value, convergence is considered complete. Once this condition is met, iteration is immediately terminated to prevent over-adjustment of the environmental humidity.

[0081] In step S5, after the environmental control strategy is executed, each grid node and its corresponding environmental temperature and humidity control parameters are recorded, and the storage unit is reset as a whole based on the distribution of the environmental temperature and humidity control parameters.

[0082] After the environmental control strategy is executed within the storage unit, the status of each grid node is recorded so that subsequent operations can be based on complete historical data. Specifically, for each grid node, the ambient temperature and humidity values ​​at the end of the control process are read and bound to the node's number, generating a uniquely identified record. For example, when there are 120 grid nodes within the storage unit, the temperature, humidity, and node number of each node are recorded one by one, ultimately forming a table containing 120 data entries. This table serves as a mapping table between temperature and humidity control parameters and node numbers, establishing a clear parameter attribution relationship to ensure that parameter mismatches do not occur during subsequent location adjustments. The mapping table is stored in three columns: node number, current temperature, and current humidity.

[0083] After the mapping table is established, the temperature and humidity control parameters of all nodes are integrated to form a distribution field covering the entire storage unit. Specifically, based on the three-dimensional coordinate system of the storage unit, the node number is mapped to its actual spatial location, and the corresponding temperature and humidity values ​​are assigned to its spatial coordinates. In this way, the temperature and humidity data within the entire storage unit are converted into a distribution map with spatial attributes. The distribution map presents an overall spatial field composed of multiple node points, each point corresponding to a temperature and humidity parameter. To make the distribution field more intuitive and easier for subsequent calculations, interpolation processing is performed on the node data, i.e., calculating transition values ​​between adjacent nodes according to certain weights, thereby generating a continuous temperature and humidity distribution in space. The interpolation method can be a distance-weighted average; in this embodiment, the interpolation range is set to within twice the distance between adjacent nodes. After interpolation processing, the temperature and humidity distribution field of the entire storage unit can clearly show the differences in environmental conditions in different areas.

[0084] After forming the temperature and humidity distribution field, a global gradient analysis is performed to determine the most dominant change directions in the temperature and humidity fields. Specifically, the temperature and humidity differences between adjacent nodes are calculated and mapped onto spatial coordinate axes to generate temperature and humidity gradient vectors. Then, the gradient vectors of all nodes are aggregated to obtain a comprehensive gradient field covering the entire region. This comprehensive gradient field reflects the overall trend of temperature and humidity changes within the storage unit, indicating the direction of most significant change. To identify the dominant direction, the temperature and humidity gradients are weighted and merged. In this embodiment, the default weight ratio is set to 1:1. The specific weight ratio needs to be set comprehensively based on storage conditions to ensure a reasonable influence of temperature and humidity in the judgment. Through vector synthesis, a comprehensive dominant gradient direction is obtained, representing the most significant change axis of the overall temperature and humidity field. In actual calculations, if the sum of the temperature and humidity differences along the major axis of the storage unit is the largest, then that major axis direction is identified as the dominant direction. After the dominant direction is identified, it serves as a reference axis for overall position adjustment, guiding subsequent node sorting and migration.

[0085] All nodes are projected onto the dominant direction and arranged according to their positional order within that direction. Then, using the node temperature and humidity parameters as a sorting reference, they are arranged sequentially from low to high or high to low gradient magnitude, generating a continuous adjustment path. Once the path is determined, the positions of the water-washed eggs in each node are migrated one by one in this order: water-washed eggs in high-temperature, high-humidity areas are gradually moved to areas closer to low-temperature, low-humidity areas, while those in low-temperature, low-humidity areas are moved to high-temperature, high-humidity areas. The migration can be accomplished using a transport mechanism, ensuring that each migration operation is performed in the correct path order, avoiding node skipping or duplication. During the migration, the parameter binding relationships in the mapping table need to be updated simultaneously; that is, when a water-washed egg in a node is migrated to a new position, its original temperature and humidity control parameters are synchronously written into the new node number. The migration operation continues until all water-washed eggs in all nodes have been rearranged. After completion, the water-washed eggs inside the entire storage unit form a continuous and orderly distribution in space. That is, the temperature and humidity along the dominant direction show a gradual increasing or decreasing trend, eliminating the original local chaotic state, and finally forming an orderly temperature and humidity field with a continuous gradient distribution in the global range.

[0086] In step S6, based on the reset storage unit, the ambient temperature and humidity control parameters of each grid node are synchronously migrated.

[0087] After completing the spatial relocation and overall rearrangement within the storage unit, the environmental control parameters of each grid node are synchronously updated to ensure that the correspondence between the parameters and the egg locations remains consistent. The specific process is as follows: A previously established mapping table is invoked, which records the environmental temperature and humidity control parameters corresponding to each grid node number before the migration. Based on the migration path, the original location number corresponding to each new node number is obtained, precisely locating the control parameters that the new node should inherit. Subsequently, the temperature and humidity control values ​​from the original nodes are written one by one into the new node numbers after migration, ensuring that each node at a new location has control parameters that match the historical state of the eggs.

[0088] After the control parameters of all nodes have been synchronized and migrated, these parameters are formally issued as execution commands, enabling the environmental control facilities of each grid node to operate according to the latest parameter requirements. Specifically, each new node is numbered, the previously written temperature and humidity values ​​are read, and these are issued as control targets to the corresponding node's regulating equipment. Upon receiving the commands, the regulating equipment adjusts the local environment according to the parameter requirements.

[0089] This avoids interference caused by excessive differences in parameter allocation between adjacent grid nodes, and prevents unnecessary fluctuations in the surrounding nodes when a node performs humidification or cooling, thereby keeping the temperature and humidity field within the entire storage unit coordinated and orderly.

[0090] Example 2

[0091] The difference between Embodiment 2 and Embodiment 1 is that this embodiment introduces a storage environment control system for ozone-washed eggs.

[0092] Figure 2 A schematic diagram of a storage environment control system for ozone-washed eggs is provided according to the present invention. The ozone-washed egg storage environment control system includes:

[0093] The data acquisition module is used to construct a spatial grid inside the storage unit, acquire time-series data on the quality changes of washed eggs in each grid node, and simultaneously monitor the ambient temperature and humidity values ​​to form a complete raw dataset.

[0094] The anomaly detection module is used to calculate the water loss rate of eggshell micropores based on time-series data of quality changes, and to determine whether the current state belongs to the type of anomaly of increased exchange rate or the type of anomaly of inhibited exchange rate, in combination with temperature and humidity trends.

[0095] The predictive modeling module is used to establish a water loss rate prediction model based on the exponential relationship between ambient temperature and water loss rate, and to generate corresponding predicted water loss rate results by inputting temperature data.

[0096] The strategy generation module is used to combine the anomaly type and the prediction model to output environmental control strategies, set the direction of temperature and humidity adjustment, and determine the target temperature and humidity control parameters for execution.

[0097] The spatial reset module is used to record the control parameters of each grid node after the control strategy is executed, perform an overall reset of the storage unit based on the parameter distribution, and synchronously migrate the environmental temperature and humidity parameters of each node.

[0098] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0103] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling the storage environment of eggs washed with ozone water, characterized in that, Includes the following steps: S1. Obtain the time-series data of the quality change of washed eggs in each grid node of the storage unit, and simultaneously monitor the current ambient temperature and humidity data. S2. Calculate the water loss rate of eggshell micropores based on time series data of quality changes, and determine whether the current state of the washed egg belongs to the abnormality of hyperexchange rate or the abnormality of inhibition of exchange rate. S3. Based on the exponential relationship between ambient temperature and water loss rate, establish a water loss rate prediction model; S4. Based on the abnormal type of exchange rate, set the adjustment direction of ambient temperature and humidity data, and generate an environmental control strategy by combining the water loss rate prediction model. S5. After the environmental control strategy is executed, record each grid node and its corresponding environmental temperature and humidity control parameters, and reset the storage unit as a whole based on the distribution of environmental temperature and humidity control parameters. S6. Based on the reset storage unit, synchronize and migrate the ambient temperature and humidity control parameters for each grid node; In step S5, after the environmental control strategy is executed, each grid node and its corresponding environmental temperature and humidity control parameters are recorded, and the storage unit is reset as a whole based on the distribution of environmental temperature and humidity control parameters. This specifically includes: After the environmental control strategy is completed, record the environmental temperature and humidity control parameters of the water-washed eggs in each grid node, and establish a mapping table with the corresponding grid node number; Based on the ambient temperature and humidity control parameters of each grid node, a temperature and humidity distribution field covering the entire storage unit is formed. A global gradient analysis is performed on the distribution field to identify the combined dominant gradient direction of the temperature and humidity fields; Using the dominant gradient direction as the reference axis for overall position adjustment, all grid nodes are sorted according to gradient magnitude based on the reference axis to generate a continuously arranged adjustment path; The washed eggs in each spatial grid are migrated and rearranged according to the adjustment path until an ordered temperature and humidity field with a continuous gradient distribution is formed globally.

2. The method for controlling the storage environment of eggs washed with ozone water according to claim 1, characterized in that, In step S1, acquiring the time-series data of the quality change of washed eggs in each grid node of the storage unit, and synchronously monitoring the current ambient temperature and humidity data, specifically includes: A spatial grid is constructed inside the storage unit, and the washed eggs are evenly distributed among the grid nodes, and periodic sampling is performed according to a unified time reference. Each grid node synchronously records the instantaneous mass value of the washed egg and the ambient temperature and humidity values; By using a unified timestamp mechanism, time-series data collected from different grid nodes are aligned and merged, and the average curve of each grid node is calculated to generate a time-series sequence of dynamic quality changes in washed eggs.

3. The method for controlling the storage environment of eggs washed with ozone water according to claim 1, characterized in that, In step S2, the water loss rate of the eggshell micropores is calculated based on the time-series data of quality changes, and the current state of the washed egg is determined to be either an abnormality of increased exchange rate or an abnormality of inhibited exchange rate. Specifically, this includes: Obtain the mass values ​​at consecutive time points in the time series data of mass change, calculate the absolute value of the mass difference between adjacent time points and divide it by the time interval to obtain the instantaneous water loss rate data points for each time period. Simultaneously extract the arithmetic mean of ambient temperature data and the arithmetic mean of ambient humidity data within the corresponding time period, and fuse the instantaneous water loss rate data points with their corresponding temperature and humidity arithmetic mean data to form a time-ordered water loss rate-environmental parameter correlation sequence. Set a baseline for water loss rate and an allowable fluctuation range. When the water loss rate data points in the associated sequence are continuously higher than the upper limit of the baseline and accompanied by an increase in ambient temperature or a decrease in ambient humidity, they are identified as an anomaly of hyperexchange rate. When the water loss rate data point is consistently below the lower limit of the baseline and accompanied by a decreasing ambient temperature or increasing ambient humidity, it is identified as an exchange rate suppression anomaly.

4. The method for controlling the storage environment of eggs washed with ozone water according to claim 1, characterized in that, In step S3, the water loss rate prediction model is established based on the exponential relationship between ambient temperature and water loss rate, specifically including: Obtain the water loss rate-environmental parameter correlation sequence of the washed eggs in the current storage unit at different historical time periods as the original dataset; Each grid cell is analyzed separately, and the original dataset is plotted as a temperature-water loss rate scatter plot. The scatter plot is fitted with a curve using a statistical regression method to obtain an exponential trend line that reflects the relationship between temperature change and water loss rate change. This trend line is then used as a water loss rate prediction model. The water loss rate prediction model uses temperature as the input parameter and predicts the water loss rate as the output.

5. The method for controlling the storage environment of eggs washed with ozone water according to claim 1, characterized in that, In step S4, based on the type of abnormal exchange rate, the direction of adjustment for ambient temperature and humidity data is set, and an environmental control strategy is generated by combining the water loss rate prediction model. Specifically, this includes: Read the current temperature and humidity values ​​from the water loss rate-environment parameter correlation sequence; If the abnormality is determined to be an over-exchange rate, a first regulation strategy is generated with the goal of suppressing the escape of internal gas. The temperature adjustment direction of the first regulation strategy is negative and the humidity adjustment direction is positive. If the anomaly is identified as an exchange rate suppression type, a second regulation strategy is generated to promote the escape of internal gas. The temperature adjustment direction of the second regulation strategy is positive and the humidity adjustment direction is negative. Based on the water loss rate prediction model, the deviation between the predicted water loss rate and the baseline water loss rate corresponding to the current temperature value is output, and the ambient temperature control parameters are determined and the temperature is adjusted. If the target temperature adjustment value has been reached, and the deviation between the actual water loss rate and the reference water loss rate exceeds the allowable fluctuation range, then the humidity adjustment amount is calculated using an iterative approximation algorithm, starting from the current temperature reference value. Specifically: Set a humidity adjustment amount with a fixed iteration step size, and monitor the actual water loss rate under the corresponding humidity in real time during each iteration. When the relative error between the actual water loss rate and the reference water loss rate is less than the preset convergence threshold, the iteration stops and the current humidity is used as the environmental humidity control parameter.

6. The method for controlling the storage environment of eggs washed with ozone water according to claim 1, characterized in that, In step S6, the synchronization migration of environmental temperature and humidity control parameters for each grid node based on the reset storage unit specifically includes: Call the mapping table between ambient temperature and humidity control parameters and grid node numbers, obtain the original control parameters corresponding to each new grid node number after migration and rearrangement, and write the original ambient temperature and humidity control parameters into its new grid node after migration. Environmental control commands are issued to the new grid nodes to ensure that the ambient temperature and humidity of each grid node are consistent with the spatial distribution after migration.

7. A storage environment control system for ozone-washed eggs, used to implement the storage environment control method for ozone-washed eggs according to any one of claims 1-6, characterized in that, include: The data acquisition module is used to construct a spatial grid inside the storage unit, acquire time-series data on the quality changes of washed eggs in each grid node, and simultaneously monitor the ambient temperature and humidity values ​​to form a complete raw dataset. The anomaly detection module is used to calculate the water loss rate of eggshell micropores based on time-series data of quality changes, and to determine whether the current state belongs to the exponential exchange rate anomaly or the exponential exchange rate inhibition anomaly by combining temperature and humidity trends. The predictive modeling module is used to establish a water loss rate prediction model based on the exponential relationship between ambient temperature and water loss rate, and to generate corresponding predicted water loss rate results by inputting temperature data. The strategy generation module is used to combine the anomaly type and the prediction model to output environmental control strategies, set the direction of temperature and humidity adjustment, and determine the target temperature and humidity control parameters for execution. The spatial reset module is used to record the control parameters of each grid node after the control strategy is executed, perform an overall reset of the storage unit based on the parameter distribution, and synchronously migrate the environmental temperature and humidity parameters of each node.

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