Storage environment control system and method for eggs washed by ozone water

Abstract

The invention discloses a storage environment control system and method for ozone water-washed eggs, particularly relates to the field of environment parameter control, and aims to solve the problems of abnormal water loss rate, non-uniform quality and inaccurate environment regulation and control caused by the fact that existing ozone water-washed eggs are sensitive to temperature and humidity fluctuation due to eggshell permeability change in the storage process. The method comprises the following steps: constructing a space grid in a storage unit, collecting quality change time sequence data and temperature and humidity information of washed eggs at each node, calculating a water loss rate and judging an abnormal state, establishing a prediction model according to an index relationship between temperature and the water loss rate, and generating an environment regulation and control strategy in combination with an abnormal type. After the strategy is executed, a temperature and humidity distribution field is recorded and formed, and then overall resetting and synchronous parameter migration are completed, so that temperature and humidity distribution of the storage environment is kept orderly and stable, accurate environment control over the eggs washed with ozone is achieved, and sanitation and safety of the eggs in the storage process are improved.

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 link, ozone water washing technology as a new type of cleaning and sterilization method is gradually introduced into large-scale egg processing process. 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 egg in the flow process.

[0003] However, at the same time, the processing process changes the permeability of the eggshell micro-pores, making the 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 link. 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: A storage environment control method for ozone water-washed eggs, comprising the following steps: S1, acquiring quality change time series data of water-washed eggs in each grid node of a storage unit, and synchronously monitoring current environmental temperature data and environmental humidity data; 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; S3, establishing a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate; S4, according to the exchange rate abnormal type, set the environmental temperature, humidity data adjustment direction, combined with the water loss rate prediction model to generate the environmental regulation strategy; S5, after the execution of the environmental regulation strategy, record each grid node and the corresponding environmental temperature, humidity control parameters, and reset the storage unit based on the environmental temperature, humidity control parameter distribution; S6, based on the reset storage unit, the environmental temperature and humidity control parameter synchronization migration is carried out for each grid node.

[0007] 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 acquired, and the current environmental temperature data and environmental humidity data are synchronously monitored, which specifically includes: A spatial grid is constructed inside the storage unit, the washed eggs are uniformly arranged in each grid node, and periodic sampling is carried out according to a unified time reference; The instantaneous mass value of the washed egg and the environmental temperature and humidity value are synchronously recorded in each grid node; The time series data collected by different grid nodes are aligned and merged by 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.

[0008] 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: The mass value at the continuous time point in the mass change time series data is acquired, 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; The arithmetic mean value of the environmental temperature data and the arithmetic mean value of the environmental 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 the water loss rate-environmental parameter correlation sequence in time sequence is formed; The water loss rate baseline and the allowable fluctuation range are set, when the water loss rate data point in the correlation sequence continuously exceeds the upper limit of the baseline and is accompanied by the trend of rising environmental temperature or decreasing environmental humidity, it is determined to be the exchange rate hyperactivity type abnormality; When the water loss rate data point continuously falls below the lower limit of the baseline and is accompanied by the trend of decreasing environmental temperature or increasing environmental humidity, it is determined to be the exchange rate inhibition type abnormality.

[0009] In a preferred embodiment, in S3, the water loss rate prediction model is established according to the exponential change relationship between the environmental temperature and the water loss rate, which specifically includes: obtaining a series of water loss rate-environment parameter correlations of the water-washed eggs in the current storage unit at different historical time periods as a raw data set; analyzing each grid unit separately, drawing the raw data set as a temperature-water loss rate scatter plot, using statistical regression method to curve fit the scatter points, obtaining an exponential trend line reflecting the relationship between temperature change and water loss rate change, and taking the trend line as a water loss rate prediction model; The water loss rate prediction model takes temperature value as input parameter and predicts water loss rate value as output.

[0010] In a preferred embodiment, in S4, according to the exchange rate abnormal type, the environmental temperature and humidity data adjustment direction are set, and the environmental control strategy is generated in combination with the water loss rate prediction model, which specifically includes: reading the current temperature value and the current humidity value in the water loss rate-environment parameter correlation sequence; If it is determined as an exchange rate hyperactivity type, a first control strategy is generated to inhibit internal gas diffusion, and the temperature adjustment direction of the first control strategy is negative and the humidity adjustment direction is positive; If it is determined as an exchange rate inhibition type, a second control strategy is generated to promote internal gas diffusion, and the temperature adjustment direction of the second control strategy is positive and the humidity adjustment direction is negative; 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 environmental temperature control parameter is determined, and the temperature is adjusted; If the actual water loss rate deviates from the reference water loss rate by more than the allowable fluctuation range after reaching the target temperature adjustment value, 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: Setting the humidity adjustment amount with fixed iteration step, the actual water loss rate under the corresponding humidity is monitored in real time in each iteration process; 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 is stopped and the current humidity is taken as the environmental humidity control parameter.

[0011] In a preferred embodiment, in S5, after the execution of the environmental control strategy is completed, the environmental temperature and humidity control parameters of each grid node are recorded, and the storage unit is overall reset based on the environmental temperature and humidity control parameter distribution, which specifically includes: After the execution of the environmental control strategy is completed, 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; 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; performing global gradient analysis on the distribution field to identify the comprehensive dominant gradient direction of the temperature field and the humidity field; taking the dominant gradient direction as the reference axis of the overall position adjustment, sorting all the grid nodes according to the gradient size according to the reference axis to generate a continuously arranged adjustment path; migrating and rearranging the ozone-washed eggs in each spatial grid according to the adjustment path until an ordered temperature and humidity field with continuous gradient distribution is formed in the global range.

[0012] In a preferred embodiment, the S6, based on the reset storage unit, the environment temperature and humidity control parameter synchronization migration of each grid node specifically includes: calling the environment temperature and humidity control parameter and grid node number mapping table, obtaining the original control parameter corresponding to each new grid node number after migration and rearrangement, and writing the original environment temperature and humidity control parameter into the new grid node after migration; issuing environment control instructions to the new grid node to make the environment temperature and humidity of each grid node consistent with the spatial distribution after migration.

[0013] On the other hand, the present application provides an ozone-washed egg storage environment control system, comprising: The data acquisition module is used for constructing a spatial grid inside the storage unit, obtaining the mass change time series data of the ozone-washed eggs in each grid node, and synchronously monitoring the environment temperature and humidity values to form a complete original data set; The abnormality discrimination module is used for calculating the water loss rate of the eggshell micropore based on the mass change time series data, and determining whether the current state belongs to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality in combination with the temperature and humidity trend; The prediction modeling module is used for establishing a water loss rate prediction model according to the exponential change relationship between the environment temperature and the water loss rate, inputting the temperature data to generate the corresponding predicted water loss rate result; The strategy generation module is used for combining the abnormal type and the prediction model to output the environment regulation strategy, setting the temperature and humidity adjustment direction, and determining the target temperature and humidity control parameter for execution; The spatial reset module is used for recording the control parameters of each grid node after the execution of the regulation strategy, performing overall reset of the storage unit based on the parameter distribution, and synchronously migrating the environment temperature and humidity parameters of each node.

[0014] The technical effects and advantages of the ozone-washed egg storage environment control system and method of the present application are: According to the characteristics of the enhanced permeability of the eggshell surface after ozone treatment, a more accurate environmental control 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 space 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 control, realizing differentiated control from the whole to the local. With the help of the water loss rate prediction model and abnormality discrimination logic, the abnormal trend of gas exchange can be found in advance and the corresponding control 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 sac expansion and slowing down the egg white liquefaction speed. Further, by adjusting the overall position and synchronously migrating the control parameters, the method can make the temperature and humidity field in the storage environment continuous and orderly, avoiding uneven quality caused by over-drying or over-wetting in local areas.

[0015] The 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 product maintains high commodity value and safety in long-term storage and multi-link flow process. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A schematic diagram of a storage environment control method for ozone water-washed eggs of the present application; Figure 2 A schematic diagram of the structure of a storage environment control system for ozone water-washed eggs of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely 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. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Embodiment 1 Figure 1 A storage environment control method for ozone water-washed eggs of the present application is given, which includes the following steps: S1, acquiring the mass 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; S2, calculating the water loss rate of the eggshell micropore based on the mass change time series data, and discriminating whether the current state of the water-washed egg belongs to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality; S3, establishing a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate; S4, according to the exchange rate anomaly type, set the environmental temperature, humidity data adjustment direction, combined with the water loss rate prediction model to generate environmental control strategy; S5, after the execution of the environmental control strategy, record each grid node and the corresponding environmental temperature, humidity control parameters, and reset the storage unit based on the environmental temperature, humidity control parameter distribution; S6, based on the reset storage unit, synchronize the environmental temperature and humidity control parameters of each grid node.

[0019] 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 environmental temperature data and environmental humidity data are synchronously monitored.

[0020] The storage unit is divided into grid according to the geometric space structure, and the storage space is divided into a plurality of cubic units with clear boundaries in a three-dimensional coordinate mode, each cubic unit being a spatial grid node. The grid division method needs to be set in combination with the volume of the storage unit and the number of eggs placed, so that the washed eggs are evenly distributed to each grid node, and the number of eggs placed in each node is kept close to uniform, avoiding the influence of data representativeness due to high local stacking density. After the arrangement, collecting devices are 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 periodic sampling needs to be determined in advance, and too long interval may lead to data loss, and too short interval will increase the collection and processing burden. In this embodiment, the sampling period is set to every 10 minutes, and the sampling frequency can be uniformly controlled by the device clock, and the collection time of all nodes is 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 sampling tasks at the same time point. A comprehensive and time-unified sampling system is established inside the whole storage unit.

[0021] 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 water-washed eggs in the node in real time. The measurement method is to weigh the entire node tray 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 the eggs caused by slow water loss during storage. The temperature and humidity measurement unit is deployed 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°C, and the humidity measurement precision is set to 1% RH, which can cover the monitoring range required for storage. During the collection process, the weighing and temperature and humidity measurement are performed synchronously to ensure that the weight, temperature and humidity data collected each time have the same time stamp, avoiding the situation of out-of-sync data. In order to prevent accidental measurement errors, each collection result needs to be subjected to a stability test. If the difference between the single measurement value and the previous and subsequent two times exceeds 5%, it is determined as an abnormal point, and the collection device re-collects once in the same cycle until the result meeting the stability requirement is obtained.

[0022] After completing the data collection of each grid node, the data of different nodes are aligned on a unified time axis. The implementation method of the unified timestamp mechanism is that all collected data are 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 to ensure the continuity of the overall data. After the alignment is completed, 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 the continuous sampling periods, which 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.

[0023] 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 hyperactivity type abnormality or the exchange rate inhibition type abnormality.

[0024] After the acquisition of the mass change time series data is completed, the mass values recorded by each grid node at different sampling time points are extracted one by one. According to a unified time reference, the mass records of adjacent two time points are read from the data sequence in turn. By comparing the mass difference between the two consecutive time points, the mass reduction amount of the eggs occurring 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 mass difference can directly reflect the water loss in the time period. Then, the mass 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 uniform 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.

[0025] After obtaining the instantaneous water loss rate sequence, 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 same time window as the sampling interval, and the temperature data and humidity data collected in each time window are respectively subjected to arithmetic average to obtain the representative temperature value and the representative humidity value of the time period. The average calculation method here is to add all the measured temperature values in the window and divide by the number of measurements, and the humidity is the same. In this way, the occasional deviation that may exist in single-point measurement can be eliminated, and it is ensured that the obtained environmental data can stably represent the overall environmental level in the time period. Then, the instantaneous water loss rate data point of the time period and the corresponding temperature average value and humidity average value are associated and paired to finally form a water loss rate-environmental parameter correlation sequence arranged in time sequence. Each data entry in the sequence contains an instantaneous water loss rate value, a set of environmental temperature and humidity values, thereby establishing a correspondence between the water loss rate and the environmental conditions on the time axis.

[0026] After obtaining the sequence of the correlation between the water loss rate and the environmental parameters, the current state of the water-washed eggs is determined whether it is abnormal by the baseline method. The setting of the baseline is based on the long-term statistical analysis results, and 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 fluctuation range allowed in this embodiment is ±0.01%. 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 surface is excessively limited.

[0027] 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.

[0028] In different historical time periods of the storage unit operation, the correlation records of the water loss rate and the corresponding environmental parameters of each grid node are continuously collected and saved, and finally the original data set is formed. The so-called correlation record of the water loss rate and the environmental parameters is that in each sampling time window, the instantaneous water loss rate value obtained from the quality monitoring link is paired with the corresponding values obtained from the temperature and humidity measurement link to form a complete data entry. Each data entry contains a time label, a water loss rate value, and temperature and humidity parameters, which reflect the state of the egg product in that time period. In order to ensure the representativeness and stability of the data, the sampling time period needs to cover multiple different working conditions in daily operation, such as the continuous operation stage of the cooling equipment, the frequent opening and closing stage of the storage unit, and the long-time stable storage stage, etc. In this embodiment, the sampling period is set to be 10 minutes once, and the historical time period range is continuously 30 days, finally forming a data set containing tens of thousands of entries. In the original data set, the data of each grid node is stored independently, retaining the corresponding relationship between the egg product and the environmental conditions at different spatial positions. These historical data are marked with a unified time reference, ensuring that they can be traced according to the time sequence and spatial distribution in the subsequent analysis process. In this way, the original data set fully reflects the dynamic corresponding relationship between the water loss behavior of the ozone water-washed eggs and the environmental conditions under different conditions inside the storage unit.

[0029] The data of each grid cell is independently analyzed to avoid confusion of different node data caused by spatial differences. Specifically, for each grid node, all data entries collected by the node in the historical time period are sorted in chronological order, and the extracted temperature value and water loss rate value are used as the main variables to construct the corresponding relationship set between temperature and water loss rate. Taking the temperature value as the horizontal coordinate and the water loss rate value as the vertical coordinate, 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 different environmental conditions, it is necessary to ensure that the sample size is sufficient so that the trend of water loss rate change with temperature change can be clearly shown on the scatter plot. During plotting, each scatter point retains its source node and time label, which facilitates tracing or excluding abnormal data in subsequent analysis.

[0030] After obtaining the scatter plot, the scatter data is curve-fitted by statistical regression method to extract the mathematical relationship between temperature change and water loss rate change. In this embodiment, exponential regression is selected as the fitting method, that is, it is assumed that the water loss rate increases exponentially with the increase of temperature. The specific process of fitting is as follows: all data points on the scatter plot are preprocessed to eliminate abnormal points with a deviation greater than twice the average value relative to adjacent data points. Subsequently, least squares regression is performed on the preprocessed data points to obtain a smooth exponential trend line. The trend line is judged for convergence by comparing the residual sum of squares, and when the residual is less than the preset threshold, the fitting is confirmed to be completed. The residual threshold is set to 0.05 to ensure that the fitted curve can well fit the data point distribution. After completing the fitting of the exponential trend line, the trend line is used as the core content of the water loss rate prediction model. The function definition of the prediction model is that when the input parameter is the current environmental temperature value of a grid node, the model can output the predicted water loss rate value under the temperature condition. In order to ensure the universality of the model, an exponential trend line is independently established for each grid node as the prediction model of the node.

[0031] In S4, according to the exchange rate abnormal type, the environmental temperature and humidity data adjustment direction are set, and the environmental control strategy is generated in combination with the water loss rate prediction model.

[0032] The acquisition of the correlation sequence acquires the environmental parameters at the current time as the basis for generating the control strategy. The specific process is: in the stored data sequence, the latest group of data entries containing temperature values and humidity values at the corresponding time points are extracted. The extraction method is to directly call the data record with the time tag as the current time, ensuring that the data is synchronized with the water loss rate state discrimination. When the discrimination result is a rate-enhanced abnormality, it indicates that the current water-washed egg is in an excessive water loss state, and to prevent the internal gas from escaping too quickly, the environmental temperature needs to be lowered and the environmental humidity needs to be increased. Therefore, the control strategy sets the temperature adjustment direction to be negative, i.e., the target temperature is lower than the current value, and the humidity adjustment direction to be positive, i.e., the target humidity is higher than the current value. When the discrimination result is a rate-suppressed abnormality, it indicates that the water-washed egg is in a gas exchange blocked state, and the temperature needs to be raised and the humidity needs to be lowered to promote gas exchange. Therefore, the control strategy sets the temperature adjustment direction to be positive, i.e., the target temperature is higher than the current value, and the humidity adjustment direction to be negative, i.e., the target humidity is lower than the current value. The generation of the control strategy not only gives the direction, but also includes the binding with the current environmental parameters, ensuring that the execution of the control can be based on the actual values of the current temperature and humidity. Each generation of the control strategy relies on real-time monitoring data and abnormality discrimination results.

[0033] After generating the temperature and humidity control direction, the water loss rate prediction model is called, with the current temperature value as the input to obtain the predicted water loss rate value under the temperature condition. Then, the predicted water loss rate is compared with the preset reference water loss rate to calculate the difference between them. The reference water loss rate is set to 0.05%-0.1% before implementation, for example, set to a decrease of no more than 0.08% of the initial value per hour. When the predicted water loss rate is higher than the reference value, it indicates that the current temperature level will cause excessive water loss, and the specific value of temperature decrease is determined according to the difference amplitude, for example, 0.2°C for each 0.01% increase; when the predicted water loss rate is lower than the reference value, it indicates that the current temperature level causes slow water loss, and the specific value of temperature increase is determined according to the difference amplitude, for example, 0.2°C for each 0.01% decrease. Through this difference conversion relationship, a clear temperature control parameter can be obtained, i.e., the specific amplitude that needs to be increased or decreased based on the current temperature. Then, the control parameter is combined with the control direction to form the final temperature adjustment instruction, which directly acts on the temperature control link of the storage unit. After executing the temperature adjustment, the monitoring state continues.

[0034] After the temperature adjustment has been completed and the target temperature value has been reached, the difference between the actual water loss rate and the reference water loss rate is rechecked. If the difference is still greater than the allowed fluctuation range, humidity adjustment is performed. In the implementation process, the environmental conditions at the time when the target temperature is reached are taken as the new reference state, the environmental humidity value at the target temperature is taken as the initial reference humidity, and it is taken as the starting point for subsequent iterative adjustment. A clear iteration step size is set, and the size of 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 be increased or decreased by 2% RH each time. At each iteration, the environmental humidity is adjusted according to the step size, and after the adjustment is completed, a stabilization period is waited for, which is set to 10 minutes in this embodiment. At the end of the stabilization period, the actual water loss rate data of each grid node in the storage unit is collected and compared with the reference water loss rate. In this way, each iteration not only changes the environmental humidity, but also accompanies a water loss rate monitoring, thereby forming a closed loop process of "adjustment-observation-comparison".

[0035] In the humidity iteration process, the adjustment result is judged for convergence to determine whether the preset target requirement is reached. The convergence judgment method is to compare the error between the actual water loss rate and the reference water loss rate monitored at present, and when the error is less than the convergence threshold value, the iteration is stopped and the current humidity value is locked as the final environmental humidity control parameter. The convergence threshold value is consistent with the previously set reference line allowed fluctuation range, and when the difference between the actual water loss rate and the reference water loss rate is less than the value, it is judged that the convergence is completed. Once this condition is met, the iteration is terminated immediately to prevent over-adjustment of the environmental humidity.

[0036] In S5, after the execution of the environmental regulation strategy is completed, the environmental temperature and humidity control parameters of each grid node are recorded, and the storage unit is overall reset based on the environmental temperature and humidity control parameter distribution.

[0037] After the execution of the environmental regulation strategy is completed inside the storage unit, the state of each grid node is recorded so that subsequent operations can be connected based on complete historical data. The specific method is: on each grid node, the environmental temperature value and humidity value of the node at the end of the regulation are read, which are bound with the number of the node to generate a record entry with a unique identifier. For example, when there are 120 grid nodes inside the storage unit, the temperature, humidity and node number of each node are recorded one by one, and finally a table containing 120 data is formed. The table is a mapping table between the temperature and humidity control parameters and the node number, which establishes a clear parameter attribution relationship to ensure that parameter mismatch does not occur in the subsequent position adjustment process. The mapping table storage format is: node number, current temperature, current humidity three columns.

[0038] 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 number of each node is mapped to its actual position in space, and the temperature and humidity values corresponding to the node are assigned to its spatial coordinate point. In this way, the temperature and humidity data inside the entire storage unit is converted into a distribution map with spatial attributes. The distribution map presents a whole spatial field composed of multiple node points, each point corresponding to a temperature and humidity parameter. In order to make the distribution field more intuitive and facilitate subsequent calculations, the node data is interpolated, that is, the transition value is calculated between adjacent nodes according to a certain weight, thereby generating a continuous temperature and humidity distribution in space. The interpolation method can choose a weighted average based on distance, and in this embodiment, the interpolation range is set to within twice the distance of adjacent nodes. After interpolation, the temperature and humidity distribution field of the entire storage unit can clearly show the differences in environmental state of different regions.

[0039] After the temperature and humidity distribution field is formed, a global gradient analysis is performed to determine the main change direction in the temperature field and humidity field. The specific process is as follows: calculate the temperature difference and humidity difference between adjacent nodes, and map these differences to the spatial coordinate axes to generate temperature gradient vectors and humidity gradient vectors. Then, the gradient vectors of all nodes are summarized to obtain a comprehensive gradient field covering the entire domain. The comprehensive gradient field can reflect the overall change trend of the temperature and humidity inside the storage unit, that is, in which direction the change is most obvious. In order to determine the dominant direction, the temperature gradient and humidity gradient need to be combined with a certain weight, and in this embodiment, the default weight ratio is 1:1. The specific weight ratio needs to be set in combination with the storage condition restrictions to ensure that the influence of temperature and humidity on the judgment is reasonable. Through vector synthesis, a comprehensive dominant gradient direction is obtained, which represents the most significant change axis of the temperature and humidity field. In actual calculation, if the sum of the temperature difference and humidity difference in the long axis direction of the storage unit is the largest, then the long axis direction is identified as the dominant direction. The dominant direction is used as the reference axis for overall position adjustment to guide the subsequent node sorting and migration.

[0040] All nodes are projected onto the dominant direction and arranged in order of their positions in this direction. Then, the temperature and humidity parameter values of the nodes are arranged in order of gradient size from low to high or from high to low as a reference for sorting, generating a continuous adjustment path. After the path is determined, the water-washed egg positions in each node are migrated one by one in this order, that is, the water-washed eggs in the high-temperature and high-humidity area are gradually migrated to the low-temperature and low-humidity area, and the water-washed eggs in the low-temperature and low-humidity area are migrated to the high-temperature and high-humidity area. The migration can be completed by a carrying mechanism, ensuring that each migration operation is performed in the order of the path to avoid jumping or repeating nodes. During the migration process, the parameter binding relationship in the mapping table needs to be updated simultaneously, that is, when a node's water-washed egg is migrated to a new position, its original temperature and humidity control parameters are written into the new node number. The migration operation continues until all the water-washed eggs in the nodes are rearranged. After completion, the water-washed eggs in the entire storage unit form a continuous and orderly distribution state 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 continuous gradient distribution in the global range.

[0041] In S6, based on the reset storage unit, the environmental temperature and humidity control parameters of each grid node are synchronously migrated.

[0042] After the spatial position migration and overall rearrangement in the storage unit are completed, the environmental control parameters of each grid node are synchronously updated to ensure that the parameter and egg position correspondence remain consistent. The specific process is as follows: the previously established mapping table is called, which records the environmental temperature and humidity control parameters of each grid node number before migration. According to the migration path, the original position number corresponding to each new node number is obtained, and the control parameters that the new node should inherit are accurately located. Then, the temperature control value and humidity control value in the original node are written into the new node number after migration one by one, so that each new position node has control parameters matching the historical state of the eggs.

[0043] After the control parameters of all nodes are synchronously migrated, these parameters are officially issued as execution instructions, so that the environmental control-related facilities of each grid node can work according to the latest parameter requirements. The specific method is as follows: for each new node number, read the temperature value and humidity value that have been written, and issue them as control targets to the corresponding node's adjustment equipment. After receiving the instructions, the adjustment equipment adjusts the local environment according to the parameter requirements.

[0044] In this way, interference caused by too large difference in parameter allocation of adjacent grid nodes is avoided, unnecessary fluctuation influence on surrounding nodes when a node performs humidification or cooling is prevented, and the temperature and humidity field in the whole storage unit is kept coordinated and orderly.

[0045] Embodiment 2 Embodiment 2 of the present application is different from embodiment 1 in that the present embodiment introduces a storage environment control system for ozone water washed egg.

[0046] Figure 2 A structural schematic diagram of the ozone water washed egg storage environment control system is given, and the ozone water washed egg storage environment control system comprises: The data acquisition module is used for constructing a space grid inside the storage unit, acquiring mass change time series data of the water washed eggs in each grid node, and synchronously monitoring environmental temperature and humidity values to form a complete original data set. The abnormality discrimination module is used for calculating the water loss rate of the eggshell micropore based on the mass change time series data, and determining 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. The prediction modeling module is used for establishing a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate, inputting the temperature data to generate a corresponding predicted water loss rate result. The strategy generation module is used for combining the abnormality type and the prediction model to output an environmental regulation strategy, setting the temperature and humidity adjustment direction, and determining the target temperature and humidity control parameters for execution. The space resetting module is used for recording the control parameters of each grid node after the regulation strategy is executed, performing overall resetting of the storage unit based on the parameter distribution, and synchronously migrating the environmental temperature and humidity parameters of each node.

[0047] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the nearest real situation, and the preset parameters and threshold values in the formula are set by the person skilled in the art according to the actual situation.

[0048] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of 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, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0049] Those skilled in the art can appreciate that the modules and algorithm steps of the 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 the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0050] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0051] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0052] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0053] In addition, the functional modules in each embodiment of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0054] If the functions are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0056] Finally, the above is only the preferred embodiment of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for controlling the storage environment of ozone water washed eggs, characterized by, The method comprises the following steps: S1, obtaining 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 environmental humidity data; 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; S3, establishing a water loss rate prediction model according to the exponential change relationship between the environmental temperature and the water loss rate; 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; 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; S6, synchronously migrating the environmental temperature and humidity control parameters of each grid node based on the reset storage unit.

2. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S1, the mass change time series data of the washed eggs in each grid node of the storage unit is obtained, and the current environmental temperature data and environmental humidity data are synchronously monitored, which specifically comprises: 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; The instantaneous mass value of the washed egg and the environmental temperature and humidity values are synchronously recorded in each grid node; By using a unified timestamp mechanism, the time series data collected by different grid nodes are aligned and merged, the average curve of each grid node is calculated, and a dynamic mass change time series sequence of the washed egg is generated.

3. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S2, the water loss rate of the eggshell micropore is calculated based on the mass change time series data, and whether the current state of the washed egg belongs to the exchange rate hyperactivity type abnormality or the exchange rate inhibition type abnormality is determined, which specifically comprises: The mass values at consecutive time points in the mass change time series data are obtained, the absolute values of the mass difference values of adjacent time points are calculated and divided by the time interval, and the instantaneous water loss rate data points of each time period are obtained; The arithmetic mean value of the environmental temperature data and the arithmetic mean value of the environmental humidity data in the corresponding time period are synchronously extracted, the instantaneous water loss rate data points are data fused with the corresponding arithmetic mean values of the temperature and humidity, and a water loss rate-environmental parameter correlation sequence in time sequence is formed; A water loss rate reference line and an allowable fluctuation range are set, when the water loss rate data points in the correlation sequence continuously exceed the upper limit of the reference line and are accompanied by an environmental temperature rising or environmental humidity decreasing trend, the exchange rate hyperactivity type abnormality is determined; When the water loss rate data points continuously fall below the lower limit of the reference line and are accompanied by an environmental temperature falling or environmental humidity rising trend, the exchange rate inhibition type abnormality is determined.

4. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S3, the water loss rate prediction model is established according to the exponential change relationship between the environmental temperature and the water loss rate, which specifically comprises: The water loss rate-environmental parameter correlation sequences of the washed eggs in the current storage unit in different historical time periods are obtained as an original data set; The original data set is plotted as a temperature-water loss rate scatter plot, the scatter points are curve fitted by using a statistical regression method, an exponential trend line reflecting the relationship between temperature change and water loss rate change is obtained, and the trend line is used as a water loss rate prediction model; The water loss rate prediction model takes temperature values as input parameters and predicts water loss rate values as output.

5. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S4, according to the exchange rate abnormal type, the environmental temperature and humidity data adjustment direction are set, and the environmental control strategy is generated in combination with the water loss rate prediction model, which specifically comprises: reading the current temperature value and the current humidity value in the water loss rate-environmental parameter correlation sequence; if it is determined to be an exchange rate hyperactivity type, a first control strategy for inhibiting internal gas diffusion is generated, and the temperature adjustment direction of the first control strategy is negative and the humidity adjustment direction is positive; if it is determined to be an exchange rate inhibition type, a second control strategy for promoting internal gas diffusion is generated, and the temperature adjustment direction of the second control strategy is positive and the humidity adjustment direction is negative; 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 environmental temperature control parameter is determined, and the temperature is adjusted; If the target temperature adjustment value has been reached, if the deviation of the actual water loss rate from the reference water loss rate is greater than the allowable fluctuation range, 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: setting the humidity adjustment amount with fixed iteration step, and monitoring the actual water loss rate under the corresponding humidity in real time in each iteration process; 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 is stopped and the current humidity is taken as the environmental humidity control parameter.

6. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S5, after the execution of the environmental control strategy is completed, the environmental temperature and humidity control parameters of each grid node are recorded, and the storage unit is overall reset based on the environmental temperature and humidity control parameter distribution, which specifically comprises: After the execution of the environmental control strategy is completed, 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; 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; Performing global gradient analysis on the distribution field, identifying the comprehensive dominant gradient direction of the temperature field and the humidity field; Taking the dominant gradient direction as the reference axis of overall position adjustment, sorting all grid nodes according to the gradient size according to the reference axis to generate a continuous arrangement adjustment path; Migrate and rearrange the water-washed eggs in each spatial grid according to the adjustment path until an ordered temperature and humidity field with continuous gradient distribution is formed in the global range.

7. The method of claim 1, wherein the ozone water washing egg storage environment control method is characterized by, In S6, based on the reset storage unit, the environmental temperature and humidity control parameters of each grid node are synchronized and migrated, which specifically comprises: Call the environmental temperature and humidity control parameter and grid node number mapping table to obtain the original control parameters corresponding to each new grid node number after migration and rearrangement, and write the original environmental temperature and humidity control parameters into the migrated new grid node. The environment control instruction is issued to the new grid nodes, so that the environment temperature and humidity of each grid node are consistent with the spatial distribution after migration.

8. An ozone water washed egg storage environment control system for implementing the ozone water washed egg storage environment control method according to any one of claims 1 to 7, characterized by, Comprise: The data acquisition module is used for constructing a spatial grid inside the storage unit, acquiring the mass change time series data of the washed eggs in each grid node, and synchronously monitoring the environment temperature and humidity values to form a complete original data set; The abnormality discrimination module is used for calculating the water loss rate of the eggshell micropore based on the mass change time series data, and determining 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; The prediction modeling module is used for establishing a water loss rate prediction model according to the exponential change relationship between the environment temperature and the water loss rate, inputting the temperature data to generate the corresponding predicted water loss rate result; The strategy generation module is used for combining the abnormality type and the prediction model to output the environment regulation strategy, setting the temperature and humidity adjustment direction, and determining the target temperature and humidity control parameters for issuing and execution; The space resetting module is used for recording the control parameters of each grid node after the execution of the regulation strategy, performing the overall reset of the storage unit based on the parameter distribution, and synchronously migrating the environment temperature and humidity parameters of each node.

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