Ultra-low temperature preservation and processing control method for aquatic products

Abstract

The present invention discloses a method for controlling ultra-low temperature preservation processing of aquatic products, which relates to the technical field of preservation processing of aquatic products, and specifically includes the following steps: evaluating the risk level of local oxidation of fat on the surface of high-fat aquatic products in various areas of the freezing chamber due to oxygen exposure, and dividing the various areas in the freezing chamber into low-risk areas, medium-risk areas and high-risk areas according to the evaluation results; constructing an oxidation risk adjustment mechanism according to the division results of various areas in the freezing chamber; in the process of implementing the oxidation risk adjustment mechanism, evaluating whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet expectations, and optimizing the oxidation risk adjustment mechanism according to the evaluation results. The present invention solves the problem of fat oxidation caused by oxygen exposure on the surface of high-fat aquatic products during ultra-low temperature freezing, and effectively improves the preservation effect during freezing through real-time monitoring and dynamic optimization and adjustment mechanisms.

Description

Technical Field

[0001] The invention relates to the technical field of aquatic product preservation processing, and in particular to a method for controlling aquatic product ultra-low temperature preservation processing. Background Art

[0002] Ultra-low temperature preservation of aquatic products refers to a preservation technology that rapidly cools aquatic products to extremely low temperatures (usually minus 40°C or lower) to inhibit the microbial activity, enzymatic reaction and chemical changes inside the aquatic products, and delay the process of corruption and deterioration. This method can maintain the original flavor, nutrients and texture of aquatic products for a long time, especially for the vulnerable components such as protein, fat and water in aquatic products, which has a significant protective effect. The reason why ultra-low temperature preservation requires precise control is that during the different storage and transportation processes of aquatic products, slight changes in environmental factors such as temperature and humidity will directly affect their quality and preservation effect. In an ultra-low temperature environment, improper control may cause the water in aquatic products to freeze, the cell structure to be destroyed or ice crystals to form, which will damage their taste and nutrition. The fine ultra-low temperature processing control can ensure that aquatic products are always in the best storage state throughout the preservation process by real-time monitoring and adjustment of parameters such as temperature and humidity, effectively inhibiting the growth of microorganisms and extending the shelf life. Therefore, the control of ultra-low temperature preservation processing of aquatic products is not only the key to ensuring their long-term quality, but also to meet the market's increasingly stringent requirements for the freshness and safety of aquatic products.

[0003] Existing ultra-low temperature preservation processing control technology for aquatic products usually achieves precise control through multiple temperature control systems and environmental parameter adjustment methods. First, through rapid freezing technology, aquatic products are quickly cooled to minus 40°C or lower in a short period of time to inhibit microbial growth and enzymatic hydrolysis reactions and reduce the quality loss of aquatic products. In this process, temperature changes need to be monitored and adjusted in real time to ensure uniform temperature inside the aquatic products and avoid incomplete local freezing or excessive water loss. Secondly, parameters such as humidity and atmosphere (such as oxygen content) in the refrigerated environment also need to be precisely controlled to prevent cracking, freezing damage or discoloration on the surface of aquatic products. At the same time, modern refrigeration equipment is usually equipped with an automated control system that can make real-time adjustments according to environmental changes, monitor key factors such as temperature, humidity, oxygen, and carbon dioxide concentrations through data sensors, ensure the stability of the temperature control environment during the entire storage and transportation process, and avoid cell structure damage caused by temperature fluctuations or excessive freezing. In addition, some systems have also added modified atmosphere packaging technology to further delay the oxidation and corruption of aquatic products by adjusting the composition of the gas in the package. Through these multi-level control measures, the existing ultra-low temperature preservation processing technology for aquatic products can effectively maintain the freshness, taste and nutrients of aquatic products, thereby extending their shelf life and meeting the needs of long-term transportation and storage.

[0004] The prior art has the following deficiencies:

[0005] During the ultra-low temperature freezing process, when processing aquatic products with high fat content (such as salmon and tuna), especially in the early stage of freezing, due to the uneven distribution of the cold air flow direction in the freezing chamber, the surface of the aquatic products in some areas may be exposed to a high residual oxygen concentration, thereby causing local oxidation of fat. This situation mainly occurs because the cold air flow has not yet formed a stable covering effect in the early stage of freezing, and the diffusion rate of oxygen is faster than the homogenization process of the cold air flow, resulting in the fat on the surface of the aquatic products being more easily exposed to oxygen and undergoing oxidation reactions than the inside. However, the existing ultra-low temperature preservation processing control technology for aquatic products cannot monitor the dynamic changes of oxygen concentration in real time, so it is impossible to timely detect and respond to the risk of local oxidation of fat caused by oxygen exposure on the surface of high-fat aquatic products, and it is also impossible to accurately adjust the distribution of the cold air flow to reduce the contact between oxygen and fat, thus failing to effectively curb fat oxidation. This will cause the surface color of the aquatic products to change, the smell to deteriorate, and the flavor to decline, further causing the accumulation of oxidation products generated by fat decomposition, aggravating the deterioration of texture and shelf life, and ultimately significantly reducing the quality and market value of the product.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for controlling ultra-low temperature preservation processing of aquatic products to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for controlling ultra-low temperature preservation processing of aquatic products, which specifically comprises the following steps:

[0009] During the ultra-low temperature freezing process, the freezing room used to freeze high-fat aquatic products is evenly divided into several areas, and a sensor network is deployed to obtain real-time environmental status information of each area of ​​the freezing room;

[0010] Analyze the environmental status information of each area of ​​the freezer, evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure, and divide each area of ​​the freezer into low-risk area, medium-risk area and high-risk area according to the evaluation results;

[0011] According to the division results of each area in the freezer, an oxidation risk adjustment mechanism is established, and corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively;

[0012] During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results;

[0013] Conduct real-time comprehensive analysis of environmental status information, adjustment feedback information, and preservation effect data obtained during the freezing process, continuously improve freezing strategies, and dynamically optimize oxidation risk adjustment mechanisms.

[0014] Preferably, the environmental status information of each area of ​​the freezer is analyzed, the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer caused by oxygen exposure is evaluated, and each area of ​​the freezer is divided into a low-risk area, a medium-risk area and a high-risk area according to the evaluation results, which specifically includes the following steps:

[0015] Preprocessing the acquired environmental status information of each area of ​​the freezing chamber;

[0016] Extracting oxygen concentration dynamic information and cold air flow distribution information from the pre-processed environmental status information of each area of ​​the freezer, and analyzing them after extraction to generate oxygen concentration change response coefficients and cold air flow distribution uniformity indexes for each area respectively;

[0017] An oxidation risk assessment model was constructed based on the oxygen concentration change response coefficient and cold air flow distribution uniformity index generated for each area. The oxidation risk assessment coefficient for each area was generated by weighted summation. The model was analyzed after generation to evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure. According to the assessment results, each area in the freezer was divided into low-risk, medium-risk and high-risk areas.

[0018] Preferably, the logic for obtaining the oxygen concentration change response coefficient and the cold air flow distribution uniformity index of each area is as follows:

[0019] Extract the dynamic information of oxygen concentration from the pre-processed environmental status information of each area of ​​the freezer, including the oxygen concentration, cold air flow rate and temperature change rate of each area in the freezer at different times over a period of time, and use the function to calculate the oxygen concentration in each area of ​​the freezer according to the time series. , and To express, For time point, Indicates that within a period of time Time freezer compartment The oxygen concentration in the area Indicates that within a period of time Time freezer compartment The cooling air velocity in each area, Indicates that within a period of time Time freezer compartment The temperature change rate of the region is defined as , , is a positive integer;

[0020] Calculate the oxygen concentration change response coefficient of each area. The specific calculation formula is as follows:

[0021]

[0022] In the formula, For the Response coefficient of oxygen concentration change in each area;

[0023] The cold air flow distribution information is extracted from the preprocessed environmental status information of each area of ​​the freezer, including the deviation angle between the cold air flow direction of each area in the freezer and the regional center reference axis at different times within a period of time and the ratio of the actual coverage of the cold air flow to the regional volume, and the function is used to calculate the cold air flow distribution information according to the time series. and To express, Indicates that within a period of time Freezer The deviation angle between the cold air flow direction of each area and the central reference axis of the area, Indicates that within a period of time Freezer The ratio of the actual coverage of cold airflow in a region to the volume of the region;

[0024] Calculate the cold air flow distribution uniformity index of each area. The specific calculation formula is as follows:

[0025]

[0026] In the formula, For the The cold air flow distribution uniformity index of a region.

[0027] Preferably, the response coefficients for changes in oxygen concentration in each region are generated and cold air flow distribution uniformity index Construct an oxidation risk assessment model and generate oxidation risk assessment coefficients for each region by weighted summation , the specific calculation formula is: ,in and are the oxygen concentration change response coefficients for each area and cold air flow distribution uniformity index The weight coefficient of

[0028] The oxidation risk assessment factors for each region will be generated Compared with the pre-set oxidation risk assessment factor threshold range The comparison was conducted, and the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure was evaluated based on the comparison results. Based on the evaluation results, each area in the freezer was divided into low-risk area, medium-risk area and high-risk area. The specific comparison analysis and division are as follows:

[0029] like , the risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is low risk, and this area is classified as a low risk area;

[0030] like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is medium risk, and this area is classified as a medium risk area;

[0031] like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is high, and this area is classified as a high-risk area.

[0032] Preferably, an oxidation risk adjustment mechanism is constructed according to the division results of each area in the freezer, specifically: according to the division results of the low-risk area, the medium-risk area and the high-risk area, different cold air flow adjustment, temperature control and equipment scheduling parameters are set respectively to form an oxidation risk adjustment mechanism; the oxidation risk adjustment mechanism is based on the oxidation risk assessment coefficient of each area and the performance of the refrigeration equipment, and automatically determines the method and amplitude of cold air flow adjustment and temperature adjustment through pre-set rules;

[0033] Corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively. Specifically: in low-risk areas, use the standard cold airflow adjustment parameters in the oxidation risk adjustment mechanism to reduce the fluctuation of cold airflow and reduce the frequency of temperature adjustment; in medium-risk areas, maintain the standard cold airflow and temperature adjustment parameters in the oxidation risk adjustment mechanism, and do not change the current cold airflow distribution and temperature control strategies; in high-risk areas, use the enhanced cold airflow adjustment parameters in the oxidation risk adjustment mechanism to increase the frequency of cold airflow and improve the temperature control accuracy, so as to reduce oxygen exposure and reduce the risk of fat oxidation.

[0034] Preferably, during the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet expectations, and optimize the oxidation risk adjustment mechanism according to the evaluation results, which specifically includes the following steps:

[0035] During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time and pre-processed after acquisition;

[0036] Extracting oxygen concentration adjustment information and cold air flow optimization information from the pre-processed adjustment feedback information of each area of ​​the freezer compartment, and analyzing them after extraction to generate oxygen concentration optimization coefficients and cold air flow optimization response coefficients for each area respectively;

[0037] An adjustment evaluation model is constructed for the generated oxygen concentration optimization coefficient and cold airflow optimization response coefficient of each area to generate the adjustment coefficient of each area. After the generation, an analysis is performed to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results.

[0038] Preferably, the logic for obtaining the oxygen concentration optimization coefficient and the cold air flow optimization response coefficient of each area is as follows:

[0039] The oxygen concentration adjustment information and cold air flow optimization information are extracted from the pre-processed adjustment feedback information of each area of ​​the freezer, wherein the oxygen concentration adjustment information includes the oxygen concentration of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, the oxygen concentration change rate, and the pre-set oxygen concentration of each area of ​​the freezer; the cold air flow optimization information includes the cold air flow rate, the temperature change rate, and the pre-set cold air flow rate of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, and are calibrated as , , , , and , Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration in the area Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration change rate in each area is Indicates the freezer compartment Preset oxygen concentration in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The cooling air velocity in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The temperature change rate of the region, Indicates the freezer compartment Pre-set cooling air flow rates for each zone, , , and are all positive integers;

[0040] Calculate the oxygen concentration optimization coefficient of each area. The specific calculation formula is as follows:

[0041]

[0042] In the formula, For the Optimization coefficient of oxygen concentration in each area;

[0043] Calculate the cold airflow optimization response coefficient of each area. The specific calculation formula is as follows:

[0044]

[0045] In the formula, For the Optimize the response factor of cold airflow in each zone.

[0046] Preferably, the oxygen concentration optimization coefficient for each generated area is and cold airflow optimization response factor Construct an adjustment assessment model and generate adjustment coefficients for each region through weighted summation , the specific calculation formula is: ,in and Optimize the oxygen concentration coefficient for each area and cold airflow optimization response factor The weight coefficient of

[0047] The adjustment coefficients for each region are generated The adjustment coefficient thresholds for each area are set in advance Compare and evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each region can meet expectations based on the comparison results, and optimize the oxidation risk adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows:

[0048] like , the oxidation risk adjustment mechanism cannot achieve the expected adjustment effect on the area, and the oxidation risk adjustment mechanism needs to be optimized, including: adjusting the distribution and flow rate of the cold air flow, increasing the cold air flow rate and changing the direction of the cold air flow to ensure uniform oxygen distribution; optimizing temperature control, increasing temperature control accuracy, and avoiding temperature fluctuations that lead to accelerated oxidation reactions; adjusting the oxygen concentration setting value according to the evaluation results to make it consistent with the freezing environment of the area and reduce the risk of oxygen exposure; strengthening the responsiveness of the refrigeration equipment and improving the sensitivity of equipment adjustment;

[0049] like The oxidation risk adjustment mechanism can achieve the expected adjustment effect on each region, and there is no need to optimize the oxidation risk adjustment mechanism.

[0050] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0051] 1. The present invention effectively solves the risk of local oxidation of surface fat of high-fat aquatic products due to oxygen exposure during ultra-low temperature freezing through a series of precise monitoring and adjustment mechanisms. First, by deploying a sensor network in the freezer, the oxygen concentration, cold air flow rate and temperature data of each area are acquired in real time. Combined with data stream processing and real-time feedback, changes in the freezing environment can be dynamically monitored. This data-driven monitoring method enables precise control of the environmental status of each area in the freezer, and can promptly detect oxidation risks caused by uneven cold air flow or excessive oxygen concentration, and respond in real time. Through refined data collection and analysis, it is ensured that the freezing conditions in each area meet the preset optimization standards, thereby reducing the probability of oxidation reactions and improving the preservation effect of aquatic products.

[0052] 2. The present invention evaluates factors such as changes in oxygen concentration and uniformity of cold air flow distribution in real time, combines a weighted sum model to generate an adjustment coefficient for each area, and compares it with a preset threshold to determine whether the freezing strategy needs to be further optimized. The advantage of this mechanism is that it can flexibly adjust factors such as cold air flow rate, temperature control, and oxygen concentration according to the freezing environment in different areas to achieve precise regulation and control during the freezing process. For low-risk areas, standard cold air flow and temperature control strategies are maintained; for medium-risk areas, existing strategies are maintained; and for high-risk areas, oxygen exposure is minimized and fat oxidation is reduced by enhancing cold air flow regulation and improving temperature control accuracy. This dynamically optimized adjustment strategy can be continuously optimized during the freezing process and flexibly adjusted based on real-time feedback to ensure that the freezing environment in each area is always in the best condition.

[0053] 3. The present invention can not only dynamically optimize the oxidation risk adjustment mechanism, but also continuously improve the freezing strategy according to the evaluation results by conducting a real-time comprehensive analysis of the environmental data, adjustment feedback and preservation effects obtained during the freezing process. By combining adaptive control and machine learning algorithms, the strategy can be automatically adjusted according to changes in the freezing environment and the characteristics of aquatic products to ensure that the oxidation risk is controlled within an acceptable range. This continuous optimization process enables the refrigeration equipment to operate efficiently, while greatly improving the quality of aquatic products and extending their shelf life. Overall, the technical effect of the present invention is to achieve precise environmental control, dynamic adjustment and flexible optimization during the freezing process, greatly improving the application effect of ultra-low temperature freezing technology in the preservation of aquatic products, and has significant market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0055] Figure 1 The figure is a flow chart of the ultra-low temperature fresh-keeping processing control method of aquatic products of the present invention. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0057] The present invention provides Figure 1 The ultra-low temperature preservation processing control method of aquatic products shown in the figure specifically comprises the following steps:

[0058] During the ultra-low temperature freezing process, the freezing room used to freeze high-fat aquatic products is evenly divided into several areas, and a sensor network is deployed to obtain real-time environmental status information of each area of ​​the freezing room;

[0059] To achieve the regional division of the freezer, a virtual regional division model based on a three-dimensional space gridding algorithm can be constructed through software. First, a digital model of the three-dimensional space is constructed using the geometric parameters of the freezer (such as length, width, and height). In this model, by inputting grid division parameters (such as the number of regions, region size, uniformity standard, etc.), the algorithm automatically divides the freezer into several spatial grid regions. During the division process, advanced segmentation algorithms (such as octree segmentation algorithms or equal volume segmentation algorithms) can be used to ensure the uniformity of the regions, and each region is assigned a unique identifier for subsequent management. By simulating the distribution of cold air flow and the placement density of aquatic products, the grid division can be further optimized so that the regional division is not only uniform but also meets the requirements of the freezing process. All these steps can be completed through software operation and data calculation without actually changing the physical structure of the freezer.

[0060] The deployment of sensor networks can be achieved through the collaborative design of software and hardware layout. First, according to the aforementioned grid areas, the algorithm is used to analyze the possibility of environmental changes in each area and the dynamic characteristics of oxygen concentration, temperature and humidity, and airflow distribution, and the sensor installation points in each area are determined. Then, the layout density of sensors is optimized through software to ensure that the information of all areas in the freezer can be covered without cross-interference. In actual deployment, wireless sensor networks (WSNs) can be used to collect environmental status information in real time by installing oxygen concentration sensors, temperature and humidity sensors, and airflow velocity sensors. These sensors are connected to the software control platform through the Internet of Things technology to form a centralized monitoring network. All sensor data are transmitted to the software analysis end through a high-precision acquisition module for real-time processing. After the deployment is completed, the working status of each sensor is self-checked through the software, and a dynamic perception mechanism is established to ensure the accuracy and stability of the sensor data.

[0061] To achieve "real-time acquisition of environmental status information in each area of ​​the freezer", it can be done through the coordinated operation of the sensor network and the software analysis system. First, the sensors deployed in various areas of the freezer (such as oxygen concentration sensors, temperature and humidity sensors, airflow velocity sensors, etc.) establish a stable wireless connection with the central control system through the Internet of Things protocol (such as LoRa or ZigBee) to collect environmental parameters in real time. The sensors will send the collected data regularly and transmit it to the software platform through the gateway device for centralized processing. The software system continuously collects environmental status information from sensors in each area through the real-time data stream receiving module, and groups and marks the data according to the timestamp to ensure the timeliness and integrity of the data. Subsequently, the software preprocesses the collected data, including denoising, outlier detection and packet loss compensation, to improve the accuracy and reliability of the data. The environmental status information of each area is automatically mapped to the three-dimensional model of the virtual freezer to intuitively display the dynamic changes of oxygen concentration, temperature and humidity and airflow distribution in the area. At the same time, the software realizes millisecond-level refresh of the environmental status of the freezer through the set sampling frequency and data update mechanism to ensure the real-time nature of the monitoring results. During the entire process, the software can also dynamically monitor the working status of the sensor, such as automatically detecting low battery or signal interruption, to ensure the continuous operation of the system and the integrity of the data flow. In this way, the environmental status information of each area in the freezer can be obtained in real time and accurately, providing an accurate data basis for subsequent analysis and adjustment.

[0062] The main purpose of doing this is to solve the core problem of "the inability to monitor the dynamic changes of oxygen concentration in the freezer in real time" in the current technology, and to provide basic support for solving the risk of local fat oxidation caused by oxygen exposure on the surface of high-fat aquatic products. Dividing the freezer into several uniform areas and deploying a sensor network for each area can effectively capture the dynamic changes of the environmental state in the freezer, refine the spatial granularity of monitoring, and provide accurate data support for subsequent risk assessment. In addition, by implementing regional division and sensor deployment through software, the distribution of cold air flow and monitoring accuracy can be dynamically optimized to ensure that the environmental status of each area of ​​the freezer is always within a controllable range, thereby achieving early detection and accurate response to fat oxidation risks. Ultimately, this method can not only make up for the shortcomings of existing technologies in monitoring and response, but also comprehensively improve the effect of frozen preservation of aquatic products through refined management, extend the shelf life of products, and enhance market value.

[0063] Analyze the environmental status information of each area of ​​the freezer, evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure, and divide each area of ​​the freezer into low-risk area, medium-risk area and high-risk area according to the evaluation results;

[0064] In this embodiment, the environmental status information of each area of ​​the freezer is analyzed, the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer caused by oxygen exposure is evaluated, and each area in the freezer is divided into a low-risk area, a medium-risk area and a high-risk area according to the evaluation results, which specifically includes the following steps:

[0065] Preprocessing the acquired environmental status information of each area of ​​the freezing chamber;

[0066] The environmental status information of each area of ​​the freezer obtained by preprocessing is to improve the accuracy, consistency and effectiveness of the data, and ensure the reliability of subsequent analysis, parameter calculation and risk assessment. In the complex environment of the freezer, the data collected by the sensor may be interfered by noise, the timestamps are not synchronized or there are outliers, so the following preprocessing is required: first, the dynamic information of oxygen concentration and cold air flow distribution information collected is denoised by filtering algorithms (such as low-pass filtering or Kalman filtering) to eliminate short-term anomalies caused by sensor noise or environmental fluctuations; secondly, the collected data of different sensors are synchronized to a unified time axis through timestamp alignment technology to solve the time series differences between the data; then, statistical methods (such as based on the triple standard deviation method or IQR method) are used to detect and eliminate outliers to avoid abnormal data affecting the calculation results, and interpolation methods are used to fill the gaps in the data after elimination to ensure the integrity of the data. Through these preprocessing operations, the data quality can be effectively improved, providing a reliable basic support for subsequent analysis and oxidation risk assessment.

[0067] Extracting oxygen concentration dynamic information and cold air flow distribution information from the pre-processed environmental status information of each area of ​​the freezer, and analyzing them after extraction to generate oxygen concentration change response coefficients and cold air flow distribution uniformity indexes for each area respectively;

[0068] Extracting the dynamic information of oxygen concentration and cold air flow distribution information from the environmental status information of each area of ​​the pre-processed freezer can be achieved by combining software with data processing algorithms to parse the multi-dimensional environmental status information into the required specific information categories. First, the software automatically extracts key fields such as oxygen concentration-related data and cold air flow velocity by marking the category labels of the pre-processed data (such as oxygen concentration, cold air flow velocity, direction deviation, etc.) using the feature screening algorithm. Subsequently, for the dynamic information of oxygen concentration, the software dynamically analyzes the time series, including calculating the concentration change rate, second-order derivative, and fluctuation characteristics, and generates a complete data set related to the dynamic change of oxygen. For the cold air flow distribution information, the software constructs a cold air flow intensity and distribution model by parsing the field data of cold air flow velocity, direction deviation, and coverage time ratio, and extracts the coverage characteristics of cold air flow in each area. At the same time, the software uses the spatial coordinates of the regional division to match the above-extracted numerical data with the spatial position of each area to ensure that the extraction results correspond to the regional division information one by one. Through this process, the extracted dynamic information of oxygen concentration and cold air flow distribution information can be directly input into the subsequent calculation module to provide data support for generating the oxygen concentration change response coefficient and the cold air flow distribution uniformity index.

[0069] An oxidation risk assessment model was constructed based on the oxygen concentration change response coefficient and cold air flow distribution uniformity index generated for each area. The oxidation risk assessment coefficient for each area was generated by weighted summation. The model was analyzed after generation to evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure. According to the assessment results, each area in the freezer was divided into low-risk, medium-risk and high-risk areas.

[0070] In this embodiment, the logic for obtaining the oxygen concentration change response coefficient and the cold air flow distribution uniformity index of each area is as follows:

[0071] Extract the dynamic information of oxygen concentration from the pre-processed environmental status information of each area of ​​the freezer, including the oxygen concentration, cold air flow rate and temperature change rate of each area in the freezer at different times over a period of time, and use the function to calculate the oxygen concentration in each area of ​​the freezer according to the time series. , and To express, For time point, Indicates that within a period of time Time freezer compartment The oxygen concentration in the area Indicates that within a period of time Time freezer compartment The cooling air velocity in each area, Indicates that within a period of time Time freezer compartment The temperature change rate of the region is defined as , , is a positive integer;

[0072] To obtain three types of data in the freezer, namely oxygen concentration, cold air flow rate and temperature change rate, an integrated sensor network can be deployed and combined with software for data collection, synchronization and processing. First, oxygen concentration can be collected in real time by oxygen sensors. Sensors usually use electrochemical or optical principles to measure oxygen concentration. These sensors are installed in key locations in various areas of the freezer (such as near the surface of aquatic products and the inlet of cold air flow) to capture the dynamic changes of oxygen. Secondly, the cold air flow rate can be monitored in real time by wind speed sensors (such as hot wire anemometers or ultrasonic sensors) installed in different areas of the freezer. These sensors can measure the speed and direction of cold air flow and provide dynamic distribution information of cold air flow. Finally, the temperature change rate is measured by temperature sensors. Temperature sensors should be installed at multiple points in the freezer (such as locations with large temperature gradients). The temperature change rate is obtained by continuously collecting temperature data and calculating the rate of temperature change over time. The data of all sensors are connected to the centralized control system through the Internet of Things (IoT) technology. The software system is responsible for real-time data collection, synchronization and storage, and preliminary preprocessing (such as timestamp alignment, data denoising, etc.). In this way, the oxygen concentration, cold air flow rate and temperature change rate data of each area can be obtained efficiently and accurately, providing reliable data support for subsequent analysis and evaluation.

[0073] Calculate the oxygen concentration change response coefficient of each area. The specific calculation formula is as follows:

[0074]

[0075] In the formula, For the Response coefficient of oxygen concentration change in each area;

[0076] Calculate the response coefficient of oxygen concentration change The purpose of this calculation is to quantify the relationship between the change in oxygen concentration in each area of ​​the freezer and the change in cold airflow and temperature, and then evaluate the potential risk of oxygen exposure to local oxidation of fat. Rate of change , capturing the fluctuation range of oxygen concentration per unit time, which reflects the dynamic changes of oxygen in the area. Next, the cold air flow rate Rate of change with temperature Combined, the effects of cold airflow and temperature on oxygen concentration fluctuations are obtained, reflecting the impact of environmental factors on oxygen distribution. In order to eliminate the amplification effect of cold airflow and temperature changes on the overall energy of the system, the denominator in the formula is standardized by the square sum of the cold airflow velocity and the temperature change rate, ensuring that only the responsiveness of the oxygen concentration change itself is reflected. This coupling effect is integrated within the internal structure, and the oxygen concentration change response coefficient of each area is finally obtained. , which provides a quantitative assessment of the oxidative risk of the freezing environment and helps to more precisely control the oxygen concentration and temperature during the freezing process, thereby reducing the risk of fat oxidation.

[0077] No. Response coefficient of oxygen concentration change in each region The size of directly reflects the response of oxygen concentration in the area to cold airflow and temperature changes, which in turn affects the risk level of local oxidation of fat on the surface of high-fat aquatic products in the area due to oxygen exposure. When the value of is large, it means that the oxygen concentration in the area fluctuates violently, and the cold airflow and temperature changes have a strong impact on the oxygen distribution, which usually means that there is a higher risk of oxygen exposure in the area, resulting in more significant fat oxidation reactions and increasing the possibility of local oxidation. Therefore, the oxygen concentration change response coefficient The higher the value, the higher the oxidation risk level of the area in the freezer, indicating that high-fat aquatic products in this area are more likely to undergo fat oxidation due to fluctuations in oxygen concentration. The value indicates that the oxygen concentration in this area is stable, the cold airflow and temperature changes have little impact on the oxygen distribution, and the oxidation risk is low. Through the calculation of this coefficient, a quantitative basis can be provided for the risk level assessment of each area in the freezer, helping to optimize the freezing environment and reduce oxidation damage.

[0078] The cold air flow distribution information is extracted from the preprocessed environmental status information of each area of ​​the freezer, including the deviation angle between the cold air flow direction of each area in the freezer and the regional center reference axis at different times within a period of time and the ratio of the actual coverage of the cold air flow to the regional volume, and the function is used to calculate the cold air flow distribution information according to the time series. and To express, Indicates that within a period of time Freezer The deviation angle between the cold air flow direction of each area and the central reference axis of the area, Indicates that within a period of time Freezer The ratio of the actual coverage of cold airflow in a region to the volume of the region;

[0079] In the freezer, the deviation angle between the cold airflow direction and the regional center reference axis and the ratio of the actual cold airflow coverage to the regional volume can be achieved by installing an appropriate sensor network and combining it with the software system for collection and analysis. First, the deviation angle between the cold airflow direction and the regional center reference axis can be obtained by installing a wind speed sensor or an airflow sensor. The sensor records the direction of the cold airflow and calculates the deviation angle of the cold airflow by comparing it with the regional center reference axis (usually the reference direction of the freezer design). The sensor is usually equipped with a direction detection module, which can measure the spatial direction of the airflow through a three-dimensional sensor system, align these data with the geometric model of the freezer to accurately calculate the deviation angle. The data will be recorded in time series and input into the control system, and the software will calculate the dynamic changes of the deviation angle by calculating these data. Secondly, the ratio of the actual cold airflow coverage to the regional volume can be obtained by analyzing the coverage of the cold airflow in a specific area. This can be achieved by installing airflow velocity sensors and temperature sensors to evaluate the distribution of cold airflow in the area. By measuring the cold airflow velocity and temperature changes by sensors, the regional volume covered by the cold airflow can be inferred. The software system calculates the actual volume covered by the cold airflow by analyzing the velocity and temperature gradient of the airflow in real time, combined with the geometric volume of the area, and compares it with the total volume of the area to obtain the ratio of the cold airflow coverage to the area volume. After time synchronization, these data will be converted into time series information for subsequent analysis. In this way, the software can monitor and calculate the distribution of cold airflow in real time, providing basic data for the calculation of the cold airflow distribution uniformity index.

[0080] Calculate the cold air flow distribution uniformity index of each area. The specific calculation formula is as follows:

[0081]

[0082] In the formula, For the The cold air flow distribution uniformity index of a region.

[0083] Calculate the cold air flow distribution uniformity index The purpose of this method is to evaluate the uniformity of the cold air flow in each area of ​​the freezer, and then understand the coverage effect of the cold air flow on the surface of the aquatic products. First, the molecular part is calculated by Ratio of cold airflow coverage to area volume The product of reflects the strength and actual effect of the cold air flow in this area. represents the strength of the cold air flow, and The coverage efficiency of the cold airflow in the area is quantified. The product of the two is calculated in the time period By integrating within the range, we can get the total coverage of the cold airflow in the area during the entire time period. The denominator is calculated by calculating the cold airflow direction deviation The absolute value of the sine of , and in the time period The uniformity of the cold air flow direction is quantified by integrating the sine value of the directional deviation. The greater the deviation of the cold air flow direction, the more uneven the cold air flow coverage in the area, which may cause the cold air flow to fail to effectively cover the surface of the aquatic product, thus affecting the freezing effect. By taking the absolute value of the sine value of the directional deviation and integrating it, we can quantify the impact of this unevenness on the overall effect of the cold air flow. Finally, the cold air flow distribution uniformity index It is a ratio calculation of the effect of cold air flow in the area and the influence of directional deviation. The larger the result, the more uniform the distribution of cold air flow and the better the cold air flow coverage in the area, thereby optimizing the freezing environment and reducing the risk of oxidation.

[0084] No. The uniformity index of cold air flow distribution in each area The size of is directly related to the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure. If the temperature is high, it means that the distribution of cold air flow in the area is relatively uniform, and the cold air flow can cover the surface of the aquatic product more evenly, reducing the accumulation of oxygen in local areas, thereby effectively reducing the risk of oxygen exposure and fat oxidation. When it is low, it indicates that the cold airflow is unevenly distributed in the area, which may lead to insufficient cold airflow in some areas and high oxygen concentration in some local areas, thus accelerating the oxidation reaction of fat. Through the calculation of this index, it is possible to evaluate whether the cold airflow in each area of ​​the freezer effectively covers the surface of aquatic products, and then predict the risk of local fat oxidation caused by oxygen exposure in the area. In short, the uniformity of cold airflow distribution directly affects the level of oxidation risk. Areas with poor uniformity have higher risks, and areas with better uniformity have lower risks.

[0085] In this embodiment, the response coefficients of the oxygen concentration changes in each region are generated. and cold air flow distribution uniformity index Construct an oxidation risk assessment model and generate oxidation risk assessment coefficients for each region by weighted summation , the specific calculation formula is: ,in and are the oxygen concentration change response coefficients for each area and cold air flow distribution uniformity index The weight coefficient of

[0086] The construction of the oxidation risk assessment model generates the oxidation risk assessment coefficient for each area by weighted summing the oxygen concentration change response coefficient and the cold air flow distribution uniformity index. Specifically, the model adopts a weighted summation method, that is, and Combined, the comprehensive oxidation risk assessment factor for each area is calculated. The formula is: , in this formula, and are weight coefficients, which determine the importance of the oxygen concentration change response coefficient and the cold air flow distribution uniformity index in risk assessment. Specifically, The weight coefficient reflects the direct impact of the fluctuation of oxygen concentration on the oxidation risk. The larger the value, the more significant the impact of the change in oxygen concentration on the oxidation risk. The weight coefficient reflects the impact of the uniformity of cold air flow on risk assessment. The larger the value, the more important the uniform distribution of cold air flow is in reducing oxidation risk. By adjusting these two weight coefficients, we can optimize the assessment model according to actual conditions, so that the oxidation risk assessment coefficient can more accurately reflect the oxidation risk of each area, thereby guiding the regulation and optimization of the freezing environment.

[0087] The oxidation risk assessment factors for each region will be generated Compared with the pre-set oxidation risk assessment factor threshold range The comparison was conducted, and the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure was evaluated based on the comparison results. Based on the evaluation results, each area in the freezer was divided into low-risk area, medium-risk area and high-risk area. The specific comparison analysis and division are as follows:

[0088] like , the risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is low risk, and this area is classified as a low risk area;

[0089] This situation means that the freezing environment in this area is relatively stable, the oxygen concentration fluctuates less, and the cold air flow is evenly distributed. For the surface of high-fat aquatic products, the risk of oxygen exposure is low, so the possibility of local oxidation of fat is low. The oxidation risk in this area is low and it is classified as a low-risk area. This means that the freezing control effect in this area is good, which can effectively extend the shelf life of aquatic products and reduce the quality loss caused by oxidation.

[0090] like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is medium risk, and this area is classified as a medium risk area;

[0091] This situation indicates that there is a certain fluctuation in the freezing environment in this area. The oxygen concentration and cold air flow distribution are relatively uniform, but there is still a certain fluctuation or unevenness. For high-fat aquatic products, the risk of oxygen exposure is at a medium level, and the possibility of local oxidation is relatively high. The risk in this area is at a medium level, and the freezing conditions need to be further optimized to reduce oxidation damage and improve the preservation of aquatic products.

[0092] like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is high, and this area is classified as a high-risk area.

[0093] This indicates that there are high oxygen concentration fluctuations in this area and uneven distribution of cold airflow, resulting in excessive oxygen exposure in some areas. This makes it more likely that fat on the surface of high-fat aquatic products will be locally oxidized, which in turn reduces the quality and flavor of the product. The oxidation risk in this area is high, and immediate measures need to be taken, such as adjusting cold airflow or temperature control, to reduce the oxidation risk and ensure that the quality of the aquatic products is not compromised.

[0094] The pre-set threshold interval of the oxidation risk assessment coefficient can be determined by historical data analysis, experimental data or simulation, and can be dynamically optimized by software. First, the software can collect a large amount of historical oxygen concentration, cold air flow distribution, temperature change and other data from various areas in the freezer, and perform statistical analysis on these data to find out the distribution characteristics of the oxidation risk coefficient in different areas. Through cluster analysis or quantile analysis, the software can identify the common range of oxidation risk assessment coefficients, so as to preliminarily set a reasonable threshold interval. Furthermore, the experimental data can be combined with regression analysis of sample data under different oxidation conditions to fit a risk coefficient interval based on changes in environmental conditions (such as oxygen concentration, cold air flow distribution, etc.). The software can continuously adjust these thresholds based on the environmental data collected in real time, and use optimization algorithms such as machine learning to correct the threshold interval in real time according to the latest changes in the freezing environment to adapt to changes in different seasons, equipment performance or other external factors. Through these methods, the software can automatically and accurately determine and adjust the threshold interval of the oxidation risk assessment coefficient to ensure the accuracy and flexibility of risk assessment.

[0095] According to the division results of each area in the freezer, an oxidation risk adjustment mechanism is established, and corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively;

[0096] In this embodiment, according to the division results of each area in the freezer, an oxidation risk adjustment mechanism is constructed, specifically: according to the division results of the low-risk area, the medium-risk area and the high-risk area, different cold air flow adjustment, temperature control and equipment scheduling parameters are set respectively to form an oxidation risk adjustment mechanism; the oxidation risk adjustment mechanism is based on the oxidation risk assessment coefficient of each area and the performance of the refrigeration equipment, and automatically determines the method and amplitude of cold air flow adjustment and temperature adjustment through pre-set rules;

[0097] The realization of "building an oxidation risk adjustment mechanism based on the division results of each area in the freezer" can be achieved through an integrated automatic control system, which dynamically adjusts the cold air flow, temperature control and equipment scheduling based on real-time monitoring data and preset rules. First, the sensor network collects data such as oxygen concentration, cold air flow rate and temperature change rate in each area of ​​the freezer in real time. The software system inputs these data into the oxidation risk assessment model to generate an oxidation risk assessment coefficient for each area. Based on these assessment coefficients, the software system automatically identifies low-risk areas, medium-risk areas and high-risk areas according to the preset risk area division rules. According to the risk level of different areas, the software system automatically adjusts the cold air flow adjustment parameters, temperature control parameters and equipment scheduling to achieve optimization. For example, the low-risk area can maintain standard cold air flow and temperature control parameters, while the high-risk area increases the frequency of cold air flow adjustment and improves the temperature control accuracy. The software can also automatically determine the frequency and method of equipment scheduling through the scheduling algorithm to ensure that the effect of cold air flow and temperature adjustment in the high-risk area is maximized. The implementation of the entire adjustment mechanism depends on the real-time feedback of the control system. The software dynamically adjusts various parameters according to the current state of the freezing environment to minimize the oxidation risk.

[0098] Corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively. Specifically: in low-risk areas, use the standard cold airflow adjustment parameters in the oxidation risk adjustment mechanism to reduce the fluctuation of cold airflow and reduce the frequency of temperature adjustment, so as to maintain a stable freezing environment; in medium-risk areas, maintain the standard cold airflow and temperature adjustment parameters in the oxidation risk adjustment mechanism, and do not change the current cold airflow distribution and temperature control strategies; in high-risk areas, use the enhanced cold airflow adjustment parameters in the oxidation risk adjustment mechanism to increase the frequency of cold airflow and improve the temperature control accuracy, so as to reduce oxygen exposure and reduce the risk of fat oxidation.

[0099] The realization of different adjustment measures for low-risk areas, medium-risk areas and high-risk areas can be completed through a dynamic environmental adjustment system controlled by software. First, the software system automatically evaluates the oxidation risk of each area based on the real-time collected data such as oxygen concentration, cold air flow rate and temperature change, and divides the area into low-risk area, medium-risk area and high-risk area according to the risk assessment results. Next, for different risk areas, the software will automatically adjust the cold air flow and temperature control strategy according to the set adjustment rules. In the low-risk area, due to the low oxidation risk, the fluctuation of cold air flow is small and the temperature is relatively stable. The software will use standard cold air flow adjustment parameters to reduce the fluctuation of cold air flow and reduce the frequency of temperature adjustment to ensure that the freezing environment remains stable. The purpose of this is to avoid energy waste and system over-response caused by over-adjustment while maintaining sufficient freezing effect. In the medium-risk area, the adjustment parameters of cold air flow and temperature are kept at standard settings, that is, no major adjustments are made. The oxidation risk here is at a medium level, and maintaining the current cold air flow distribution and temperature control strategy helps to balance the management of freezing effect and oxidation risk. The software will maintain a standard control strategy in this area to ensure that the freezing effect is not affected by frequent adjustments while avoiding unnecessary energy consumption. In high-risk areas, due to the higher risk of oxidation, the software system will increase the frequency of cold airflow and improve the accuracy of temperature control based on the enhanced cold airflow adjustment parameters. This adjustment can increase the coverage frequency of cold airflow, ensure better protection of the surface of high-fat aquatic products, reduce oxygen exposure, and reduce the risk of fat oxidation. In addition, improving the accuracy of temperature regulation can help better control temperature fluctuations in the freezer, further reducing the chance of oxidation reactions. This regional adjustment strategy is automatically controlled by the software system, and can flexibly adjust environmental parameters in different areas according to actual risk requirements, thereby optimizing the freezing effect and minimizing the risk of oxidation. In this way, the software achieves refined management of the freezer environment, ensuring that the freezing process in each area is both energy-saving and effective.

[0100] During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results;

[0101] In this embodiment, during the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezing chamber is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

[0102] During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time and pre-processed after acquisition;

[0103] During the implementation of the oxidation risk adjustment mechanism, real-time feedback information on the adjustment of each area of ​​the freezer can be obtained through a sensor network installed in the freezer. These sensors include oxygen concentration sensors, temperature sensors, and cold air flow rate sensors, which are used to measure the oxygen concentration, temperature changes, and cold air flow rate of each area, respectively. The sensors can continuously collect data and transmit the data to the central processing system through the Internet of Things technology. The central system receives real-time data from different areas through wireless networks (such as Wi-Fi or LoRa) and summarizes them. In order to ensure the timeliness and accuracy of the data, the system can set a certain time interval (such as every minute or every second) for data collection. At the same time, using real-time data stream processing technology (such as Apache Kafka or RabbitMQ), it can ensure that the data is transmitted and processed in real time while being collected, which is convenient for subsequent analysis.

[0104] After obtaining real-time data, data preprocessing is a very important step to improve the quality of the data and ensure the accuracy and reliability of subsequent analysis. First, data preprocessing needs to solve the problem of noise interference. Common noise sources may include sensor errors, environmental interference, etc. Therefore, the first step of data preprocessing is usually filtering, such as applying a low-pass filter to remove high-frequency noise, or using a sliding average method to smooth the data to eliminate instantaneous fluctuations. Secondly, the preprocessing process also includes data standardization or normalization, converting different types of data (such as oxygen concentration, temperature, and cold air flow rate) to a unified scale for effective comparison and calculation. Finally, outlier detection is also required to eliminate outliers caused by sensor failure or data transmission problems to ensure that the final data set is stable and reliable. These preprocessing operations can be implemented through specialized data processing software tools (such as the Pandas library in Python) and real-time data streaming platforms (such as Apache Flink or Spark Streaming).

[0105] Extracting oxygen concentration adjustment information and cold air flow optimization information from the pre-processed adjustment feedback information of each area of ​​the freezer compartment, and analyzing them after extraction to generate oxygen concentration optimization coefficients and cold air flow optimization response coefficients for each area respectively;

[0106] When extracting the oxygen concentration adjustment information and cold air flow optimization information from the adjustment feedback information of each area of ​​the pre-processed freezer, the operation can be performed through the data processing platform. First, the pre-processed data is stored in a database, such as a SQL database, a NoSQL database, or a real-time streaming data storage system (such as Apache Kafka or Apache Flink). In order to extract the oxygen concentration adjustment information, fields related to oxygen concentration (such as oxygen concentration, concentration change rate, etc.) can be filtered out from the stored data and filtered according to a preset time range. This information is usually indexed by a timestamp field and aggregated or smoothed by time period to extract the change data at each time point. Similarly, cold air flow optimization information can be obtained by extracting fields such as cold air flow rate, preset cold air flow rate, and temperature change rate. The data platform can extract the required specific data through query operations (such as SQL queries or stream data processing) and pass it to the analysis module. The extracted data can be further analyzed, the response coefficient can be calculated, or a report can be generated through a data analysis tool. This process can be fully implemented through the data flow platform and the database query engine to ensure the efficiency and accuracy of data processing.

[0107] An adjustment evaluation model is constructed for the generated oxygen concentration optimization coefficient and cold airflow optimization response coefficient of each area to generate the adjustment coefficient of each area. After the generation, an analysis is performed to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results.

[0108] In this embodiment, the logic for obtaining the oxygen concentration optimization coefficient and the cold air flow optimization response coefficient of each area is as follows:

[0109] The oxygen concentration adjustment information and cold air flow optimization information are extracted from the pre-processed adjustment feedback information of each area of ​​the freezer, wherein the oxygen concentration adjustment information includes the oxygen concentration of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, the oxygen concentration change rate, and the pre-set oxygen concentration of each area of ​​the freezer; the cold air flow optimization information includes the cold air flow rate, the temperature change rate, and the pre-set cold air flow rate of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, and are calibrated as , , , , and , Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration in the area Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration change rate in each area is Indicates the freezer compartment Preset oxygen concentration in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The cooling air velocity in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The temperature change rate of the region, Indicates the freezer compartment Pre-set cooling air flow rates for each zone, , , and are all positive integers;

[0110] To obtain the above-mentioned quantitative data (oxygen concentration, rate of change of oxygen concentration, preset oxygen concentration, cold air flow rate, temperature change rate and preset cold air flow rate), it is first necessary to install a multi-sensor network in the freezer. The oxygen concentration can be monitored in real time by an oxygen concentration sensor (such as an electrochemical sensor or an infrared sensor), and the data is transmitted to a central processing system. By setting the frequency of timed acquisition (such as once per second or once per minute), the oxygen concentration data at different time points over a period of time can be obtained. After obtaining the oxygen concentration data, the rate of change of oxygen concentration can be obtained by calculating the difference between the oxygen concentrations at adjacent time points and dividing it by the time interval. In order to obtain the preset oxygen concentration, it is necessary to store it in a database for real-time adjustment and comparison based on the design of the freezer and the preset target of the oxidation risk adjustment mechanism.

[0111] Similarly, cold air flow rate data can be obtained through cold air flow rate sensors (such as thermal flow meters or pitot tubes), which are usually installed in key locations in the freezer to ensure that the cold air flow rate data can be captured in real time. The temperature change rate can be obtained through temperature sensors (such as RTD sensors or thermocouples). The data system will record the temperature changes at a certain time interval and calculate the temperature change rate. The preset cold air flow rate is set in advance and stored in the system based on the design parameters or adjustment targets of the freezer. These real-time collected data are transmitted to the central database or cloud platform for storage and processing through the Internet of Things platform or wireless sensor network (such as Wi-Fi, Zigbee or LoRa). Data stream processing tools (such as Apache Kafka or Apache Flink) are used to ensure that the data can be transmitted and analyzed in time after collection, so as to monitor the changes in the environment in the freezer in real time and provide accurate data support for subsequent risk assessment.

[0112] Calculate the oxygen concentration optimization coefficient of each area. The specific calculation formula is as follows:

[0113]

[0114] In the formula, For the Optimization coefficient of oxygen concentration in each area;

[0115] Calculate the oxygen concentration optimization coefficient in each area The purpose of the formula is to quantify the effect of changes in oxygen concentration on the oxidative risk adjustment mechanism. The numerator calculates the combined effect of oxygen concentration deviation, oxygen concentration change rate, and cold air flow rate, reflecting how these factors work together to cause changes in oxidative risk. Specifically, Indicates the difference between the actual oxygen concentration in the area and the preset concentration, multiplied by (absolute value of the rate of change of oxygen concentration) and (cold air flow rate), the effects of oxygen concentration change and cold air flow rate on the oxidation reaction can be weighted. The oxygen concentration change rate indicates the speed of change of oxygen concentration, and the cold air flow rate affects the uniform distribution of oxygen. The combination of the two determines the intensity of oxygen exposure and the degree of acceleration of the oxidation reaction. It is used to normalize the fluctuation of oxygen concentration change rate and cooling gas flow rate to ensure that the actual impact of oxygen concentration change on oxidation risk can be fairly measured under different environments. It reflects the oxygen concentration response effect during the adjustment process, and provides a basis for the optimization of the adjustment mechanism.

[0116] No. Optimization factor of oxygen concentration in each area The size of directly reflects the sensitivity and response intensity of oxygen concentration changes to the adjustment effect during the implementation of the oxidation risk adjustment mechanism in this area. When it is higher, it means that the oxygen concentration in the area fluctuates greatly or the cold airflow has a stronger impact on the distribution of oxygen concentration, indicating that the oxidation risk adjustment mechanism has a more significant effect on the area, which may lead to the acceleration or slowing down of the oxidation reaction, and the effect of the adjustment strategy is more obvious. A value of 0 indicates that the fluctuation of oxygen concentration in this area is small or the adjustment mechanism fails to effectively respond to the change of oxygen concentration, indicating that the adjustment effect of the oxidation risk adjustment mechanism on this area has not met expectations, and it may be necessary to optimize control strategies such as cold air flow or temperature regulation. Therefore, the oxygen concentration optimization coefficient The size of is a key indicator to measure whether the oxidative risk adjustment mechanism is effective in achieving the expected effect. A high value indicates that the mechanism adjustment is successful, while a low value indicates that further optimization is needed.

[0117] Calculate the cold airflow optimization response coefficient of each area. The specific calculation formula is as follows:

[0118]

[0119] In the formula, For the Optimize the response factor of cold airflow in each zone.

[0120] Optimize the response factor of cold airflow in each area The purpose of the formula design is to quantify the impact of cold air flow optimization on the implementation of the oxidation risk adjustment mechanism. The numerator is calculated by calculating the cold air flow rate deviation With oxygen concentration And the temperature change rate The effect of cold airflow optimization on oxidation risk adjustment was evaluated by multiplying Indicates the real-time cold air flow rate in the area. The difference between the preset cooling air flow rate for the area reflects the effect of the cooling air flow adjustment. and , we can further combine the effects of oxygen concentration and temperature changes on oxidation risk, and comprehensively consider how the cold air flow rate and environmental factors work together to affect the rate of oxidation reaction. The fluctuation of cold air flow rate and temperature change are standardized to eliminate the interference of their fluctuations on the calculation results, ensuring that the effect of cold air flow optimization is fairly evaluated. Through this formula, the actual effect of cold air flow optimization adjustment can be reflected, which helps to evaluate whether the oxidation risk adjustment mechanism has achieved the expected goal and guide further optimization.

[0121] No. Optimized response factor for cooling airflow in each zone The size of directly reflects the response effect of cold air flow adjustment on the oxidation risk adjustment mechanism. When it is higher, it means that the difference between the cold air flow rate deviation and the preset value is larger, and the cold air flow adjustment is more closely related to the oxygen concentration and temperature changes. This indicates that the cold air flow optimization has a significant regulatory effect on the oxidation reaction in this area and can effectively reduce the oxidation risk, thereby making the effect of the oxidation risk adjustment mechanism more significant. The value indicates that the cold airflow is properly optimized and adjusted, and the adjustment effect has reached the expected level, which can effectively control oxygen exposure and temperature fluctuations and slow down the oxidation reaction. The value indicates that the effect of cold airflow optimization is poor, and the changes in oxygen concentration and temperature cannot be effectively adjusted, resulting in the inability to effectively control the oxidation risk. This shows that the oxidation risk adjustment mechanism in this area has failed to achieve the expected effect, and the cold airflow adjustment strategy needs to be further optimized.

[0122] In this embodiment, the oxygen concentration optimization coefficient of each generated area is and cold airflow optimization response factor Construct an adjustment assessment model and generate adjustment coefficients for each region through weighted summation , the specific calculation formula is: ,in and Optimize the oxygen concentration coefficient for each area and cold airflow optimization response factor The weight coefficient of

[0123] In order to build an adjustment evaluation model for the generated oxygen concentration optimization coefficient and cold air flow optimization response coefficient of each area, and generate the adjustment coefficient of each area by weighted summation, it is first necessary to combine the oxygen concentration optimization coefficient and cold air flow optimization response coefficient of each area on the data platform and perform weighted summation of the two coefficients. The specific implementation method is that the system calculates the oxygen concentration optimization coefficient and cold air flow optimization response coefficient of each area according to the oxygen concentration change and cold air flow optimization of each area. and , and apply the pre-set weight coefficients and Weight them. Weight coefficient and They respectively represent the importance of the oxygen concentration optimization coefficient and the cold air flow optimization response coefficient to the overall adjustment effect. Specifically, reflects the degree of effect of changes in oxygen concentration on oxidative risk adjustment, while It reflects the influence of cold air flow optimization on oxidation reaction control. By adjusting these two weight coefficients, the focus on oxygen concentration and cold air flow can be flexibly strengthened or weakened according to the specific needs of different areas, thereby ensuring the flexibility and accuracy of the adjustment mechanism. Finally, the generated adjustment coefficient It provides a quantitative basis for the oxidation risk adjustment effect of each area, which can be further compared with the preset adjustment coefficient threshold to achieve dynamic optimization adjustment.

[0124] The adjustment coefficients for each region are generated The adjustment coefficient thresholds for each area are set in advance Compare and evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each region can meet expectations based on the comparison results, and optimize the oxidation risk adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows:

[0125] like , the oxidation risk adjustment mechanism cannot achieve the expected adjustment effect on the area, and the oxidation risk adjustment mechanism needs to be optimized, including: adjusting the distribution and flow rate of the cold air flow, increasing the cold air flow rate and changing the direction of the cold air flow to ensure uniform oxygen distribution; optimizing temperature control, increasing temperature control accuracy, and avoiding temperature fluctuations that lead to accelerated oxidation reactions; adjusting the oxygen concentration setting value according to the evaluation results to make it consistent with the freezing environment of the area and reduce the risk of oxygen exposure; strengthening the responsiveness of the refrigeration equipment and improving the sensitivity of equipment adjustment;

[0126] This situation means that the oxidation risk adjustment mechanism failed to effectively respond to the oxidation risk in this area, and the adjustment effect did not meet expectations. This indicates that the oxygen concentration, cold air flow rate or temperature control in this area during the freezing process were not adequately regulated, and the oxidation reaction was not effectively controlled, which may lead to fat oxidation, flavor loss or reduced preservation effect. In this case, it is necessary to optimize the oxidation risk adjustment mechanism, including adjusting the cold air flow distribution, improving the temperature adjustment accuracy, resetting the oxygen concentration and other measures to ensure that the adjustment mechanism can adapt to the specific environment in the freezer, reduce oxidation risks and improve product quality.

[0127] To optimize the oxidation risk adjustment mechanism, there are many ways to achieve it, including: First, by optimizing the distribution and flow rate of the cold air flow, an intelligent cold air flow adjustment system can be used, which can monitor the temperature and oxygen concentration of each area in real time, and adjust the cold air flow rate and direction through algorithms to ensure that the cold air flow is evenly distributed and avoid excessive oxygen concentration in local areas due to uneven air flow. The direction and intensity of the cold air flow can be dynamically adjusted by combining computational fluid dynamics simulation and real-time sensor feedback to ensure that the oxygen is evenly distributed in the freezer. Secondly, the temperature control can be optimized by fine-tuning the temperature control system, using high-precision temperature sensors and PID control algorithms to monitor and adjust the temperature in the freezer in real time to avoid excessive local temperature fluctuations and prevent excessive temperature from accelerating oxidation reactions. Then, adjust the oxygen concentration set value according to the evaluation results. The system can use an adaptive control algorithm to automatically adjust the preset oxygen concentration standard according to the needs of different areas in the freezer to ensure that the oxygen concentration meets the freezing requirements of the area and reduce the risk of oxygen exposure. Finally, the responsiveness of the refrigeration equipment can be enhanced by upgrading the control system to enable it to have the ability of rapid response and high sensitivity adjustment to ensure that the refrigeration equipment can make rapid adjustments when the environment changes. These optimization measures are achieved by combining the IoT platform and real-time data stream analysis to ensure that various parameters in the freezer can be adjusted in real time to avoid the adverse effects of oxidation reactions and ensure the quality and shelf life of aquatic products.

[0128] like The oxidation risk adjustment mechanism can achieve the expected adjustment effect on each region, and there is no need to optimize the oxidation risk adjustment mechanism.

[0129] This situation means that the adjustment effect of the oxidation risk adjustment mechanism in this area has reached the expected level and the adjustment target has been achieved. This shows that factors such as oxygen concentration, cold air flow rate, and temperature control in this area have been effectively adjusted, and the oxidation reaction has been effectively suppressed, thereby ensuring the quality and preservation of aquatic products. In this case, the oxidation risk adjustment mechanism does not need to be further optimized, and the current adjustment strategy can be maintained to ensure that the environmental conditions in each area of ​​the freezer meet the requirements, reduce oxidation risks, and extend the shelf life of the product.

[0130] The "pre-set adjustment coefficient thresholds for each area" can be determined through data analysis and simulation experiments. First, the historical freezer environment data and the data on the implementation of the oxidation risk adjustment mechanism can be used as a basis to determine the performance range of each area under different control conditions by analyzing the relationship between oxygen concentration, cold air flow rate and temperature changes in different areas and oxidation risk. Using statistical methods, such as regression analysis or cluster analysis, the key factors affecting the adjustment effect can be extracted from a large amount of experimental data, and the adjustment coefficient thresholds for each area can be obtained based on these data. In order to ensure that the set thresholds are widely applicable, a simulation modeling method can be used to build a multivariate model to predict the optimal adjustment coefficient range for each area by simulating different environments and adjustment strategies. Through optimization algorithms, such as particle swarm optimization or genetic algorithms, the thresholds can be adjusted in multiple experiments until the most suitable settings for the current freezing environment and oxidation risk control are found. In addition, the threshold setting can also be adjusted dynamically. Combined with real-time monitoring data, through an adaptive adjustment mechanism, the threshold can be optimized as the freezing environment and product type change, ensuring the continued effectiveness of the adjustment mechanism. This process can be fully implemented in software through a data analysis platform and optimization algorithms, thereby achieving precise control.

[0131] Conduct real-time comprehensive analysis of environmental status information, adjustment feedback information, and preservation effect data obtained during the freezing process, continuously improve freezing strategies, and dynamically optimize oxidation risk adjustment mechanisms.

[0132] To achieve real-time comprehensive analysis of environmental status information, adjustment feedback information, and preservation effect data obtained during the freezing process, and to continuously improve freezing strategies and dynamically optimize the oxidation risk adjustment mechanism, an integrated data analysis platform can be established, combined with real-time data stream processing and machine learning algorithms. First, environmental status information (such as oxygen concentration, cold air flow rate, temperature change, etc.) and adjustment feedback information (such as oxygen concentration optimization coefficient, cold air flow optimization response coefficient, etc.) are collected in real time through the sensor network and transmitted to the data platform. The platform uses data stream processing technology (such as ApacheKafka or Apache Flink) to parallel process, filter, and aggregate real-time data to ensure the timeliness and accuracy of the data. Then, these data are analyzed by applying machine learning models (such as regression analysis, support vector machine, or reinforcement learning) to identify the association between different parameters in the freezing process and predict the impact of different freezing strategies on the preservation effect. Based on the real-time analysis results, the system can automatically adjust the freezing strategy, such as optimizing cold air flow, adjusting temperature control or oxygen concentration settings, thereby effectively reducing oxidation risks. The dynamic optimization of this process not only relies on the retrospective analysis of historical data, but also continuously adjusts and optimizes under the feedback of real-time data through the adaptive control mechanism, so that the oxidation risk adjustment mechanism can always maintain high efficiency under changing freezing conditions. The realization of this process ensures that the oxidation risk adjustment mechanism is continuously improved and optimized throughout the freezing cycle, improving the preservation effect and product quality of aquatic products.

[0133] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0134] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above 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 process or function described in the embodiment of the present application is generated in whole or in part. 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 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 by wired or wireless (such as infrared, wireless, microwave, etc.). 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 or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0135] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0136] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] 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 who is familiar with the present technical field can easily think of changes or substitutions 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 based on the protection scope of the claims.

Claims

1. A method for controlling ultra-low temperature preservation of aquatic products, characterized in that: The specific steps include: During the ultra-low temperature freezing process, the freezing room used to freeze high-fat aquatic products is evenly divided into several areas, and a sensor network is deployed to obtain real-time environmental status information of each area of ​​the freezing room; Analyze the environmental status information of each area of ​​the freezer, evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure, and divide each area of ​​the freezer into low-risk area, medium-risk area and high-risk area according to the evaluation results; The environmental status information of each area of ​​the freezer is analyzed to evaluate the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer caused by oxygen exposure, and the areas in the freezer are divided into low-risk areas, medium-risk areas and high-risk areas according to the evaluation results. The specific steps include: Preprocessing the acquired environmental status information of each area of ​​the freezing chamber; Extracting oxygen concentration dynamic information and cold air flow distribution information from the pre-processed environmental status information of each area of ​​the freezer, and analyzing them after extraction to generate oxygen concentration change response coefficients and cold air flow distribution uniformity indexes for each area respectively; The logic for obtaining the oxygen concentration change response coefficient and the cold air flow distribution uniformity index of each area is as follows: Extract the dynamic information of oxygen concentration from the pre-processed environmental status information of each area of ​​the freezer, including the oxygen concentration, cold air flow rate and temperature change rate of each area in the freezer at different times over a period of time, and use the function to calculate the oxygen concentration in each area of ​​the freezer according to the time series. , and To express, For time point, Indicates that within a period of time Freezer The oxygen concentration in the area Indicates that within a period of time Freezer The cooling air velocity in each area, Indicates that within a period of time Freezer The cooling air velocity in each area, Indicates that within a period of time Freezer The temperature change rate of the region is defined as , , is a positive integer; Calculate the oxygen concentration change response coefficient of each area. The specific calculation formula is as follows: In the formula, For the Response coefficient of oxygen concentration change in each area; The cold air flow distribution information is extracted from the pre-processed environmental status information of each area of ​​the freezer, including the deviation angle between the cold air flow direction of each area in the freezer and the regional center reference axis at different times within a period of time and the ratio of the actual coverage of the cold air flow to the regional volume, and the function is used to calculate the cold air flow distribution information according to the time series. and To express, Indicates that within a period of time Freezer The deviation angle between the cold air flow direction of each area and the central reference axis of the area, Indicates that within a period of time Freezer The ratio of the actual coverage of cold airflow in a region to the volume of the region; Calculate the cold air flow distribution uniformity index of each area. The specific calculation formula is as follows: In the formula, For the The cold air flow distribution uniformity index of each area; An oxidation risk assessment model is constructed based on the generated oxygen concentration change response coefficients and cold air flow distribution uniformity indexes of each area. The oxidation risk assessment coefficients of each area are generated by weighted summation. After generation, the model is analyzed to assess the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer caused by oxygen exposure. Based on the assessment results, each area in the freezer is divided into low-risk area, medium-risk area and high-risk area. Response coefficient to changes in oxygen concentration in each generated area and cold air flow distribution uniformity index Construct an oxidation risk assessment model and generate oxidation risk assessment coefficients for each region by weighted summation , the specific calculation formula is: ,in and are the oxygen concentration change response coefficients of each area and cold air flow distribution uniformity index The weight coefficient of The oxidation risk assessment factors for each region will be generated Compared with the pre-set oxidation risk assessment factor threshold range The comparison was conducted, and the risk level of local oxidation of fat on the surface of high-fat aquatic products in each area of ​​the freezer due to oxygen exposure was evaluated based on the comparison results. Based on the evaluation results, each area in the freezer was divided into low-risk area, medium-risk area and high-risk area. The specific comparison analysis and division are as follows: like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is low risk, and this area is classified as a low risk area; like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is medium risk, and this area is classified as a medium risk area; like The risk level of local oxidation of fat on the surface of high-fat aquatic products in this area of ​​the freezer due to oxygen exposure is high risk, and this area is classified as a high-risk area; According to the division results of each area in the freezer, an oxidation risk adjustment mechanism is established, and corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively; During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results; Conduct real-time comprehensive analysis of environmental status information, adjustment feedback information, and preservation effect data obtained during the freezing process, continuously improve freezing strategies, and dynamically optimize oxidation risk adjustment mechanisms.

2. The method for controlling ultra-low temperature preservation of aquatic products according to claim 1, characterized in that: According to the division results of each area in the freezer, an oxidation risk adjustment mechanism is constructed, specifically: according to the division results of low-risk areas, medium-risk areas and high-risk areas, different cold air flow adjustment, temperature control and equipment scheduling parameters are set respectively to form an oxidation risk adjustment mechanism; the oxidation risk adjustment mechanism is based on the oxidation risk assessment coefficient of each area and the performance of the refrigeration equipment, and automatically determines the method and amplitude of cold air flow adjustment and temperature adjustment through pre-set rules; Corresponding adjustment measures are taken for low-risk areas, medium-risk areas and high-risk areas respectively. Specifically: in low-risk areas, use the standard cold airflow adjustment parameters in the oxidation risk adjustment mechanism to reduce the fluctuation of cold airflow and reduce the frequency of temperature adjustment; in medium-risk areas, maintain the standard cold airflow and temperature adjustment parameters in the oxidation risk adjustment mechanism, and do not change the current cold airflow distribution and temperature control strategies; in high-risk areas, use the enhanced cold airflow adjustment parameters in the oxidation risk adjustment mechanism to increase the frequency of cold airflow and improve the temperature control accuracy, so as to reduce oxygen exposure and reduce the risk of fat oxidation.

3. The method for controlling ultra-low temperature preservation of aquatic products according to claim 2, characterized in that: During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and optimize the oxidation risk adjustment mechanism according to the evaluation results, which specifically includes the following steps: During the implementation of the oxidation risk adjustment mechanism, the adjustment feedback information of each area of ​​the freezer is obtained in real time and pre-processed after acquisition; Extracting oxygen concentration adjustment information and cold air flow optimization information from the pre-processed adjustment feedback information of each area of ​​the freezer compartment, and analyzing them after extraction to generate oxygen concentration optimization coefficients and cold air flow optimization response coefficients for each area respectively; An adjustment evaluation model is constructed for the generated oxygen concentration optimization coefficient and cold airflow optimization response coefficient of each area to generate the adjustment coefficient of each area. After the generation, an analysis is performed to evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each area can meet the expectations, and the oxidation risk adjustment mechanism is optimized according to the evaluation results.

4. The method for controlling ultra-low temperature preservation of aquatic products according to claim 3, characterized in that: The logic for obtaining the oxygen concentration optimization coefficient and the cold air flow optimization response coefficient of each area is as follows: The oxygen concentration adjustment information and cold air flow optimization information are extracted from the pre-processed adjustment feedback information of each area of ​​the freezer, wherein the oxygen concentration adjustment information includes the oxygen concentration of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, the oxygen concentration change rate, and the pre-set oxygen concentration of each area of ​​the freezer; the cold air flow optimization information includes the cold air flow rate, the temperature change rate, and the pre-set cold air flow rate of each area of ​​the freezer at different times during a period of time during the implementation of the oxidation risk adjustment mechanism, and are calibrated as , , , , and , Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration in the area Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The oxygen concentration change rate in each area is Indicates the freezer compartment Preset oxygen concentration in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The cooling air velocity in each area, Indicates that during the implementation of the oxidation risk adjustment mechanism, Freezer The temperature change rate of the region, Indicates the freezer compartment Pre-set cooling air flow rates for each zone, , , and All are positive integers; Calculate the oxygen concentration optimization coefficient of each area. The specific calculation formula is as follows: In the formula, For the Oxygen concentration optimization coefficient for each area; Calculate the cold airflow optimization response coefficient of each area. The specific calculation formula is as follows: In the formula, For the Optimize the response factor of cold airflow in each zone.

5. The method for controlling ultra-low temperature preservation of aquatic products according to claim 4, characterized in that: Optimize the oxygen concentration coefficient for each generated area and cold airflow optimization response factor Construct an adjustment assessment model and generate adjustment coefficients for each region through weighted summation , the specific calculation formula is: ,in and Optimize the oxygen concentration coefficient for each area and cold airflow optimization response factor The weight coefficient of The adjustment coefficients for each region are generated The adjustment coefficient thresholds for each area are set in advance Compare and evaluate whether the adjustment effect of the oxidation risk adjustment mechanism on each region can meet expectations based on the comparison results, and optimize the oxidation risk adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows: like , the oxidation risk adjustment mechanism cannot achieve the expected adjustment effect on the area, and the oxidation risk adjustment mechanism needs to be optimized, including: adjusting the distribution and flow rate of the cold air flow, increasing the cold air flow rate and changing the direction of the cold air flow to ensure uniform oxygen distribution; optimizing temperature control, increasing temperature control accuracy, and avoiding temperature fluctuations that lead to accelerated oxidation reactions; adjusting the oxygen concentration setting value according to the evaluation results to make it consistent with the freezing environment of the area and reduce the risk of oxygen exposure; strengthening the responsiveness of the refrigeration equipment and improving the sensitivity of equipment adjustment; like The oxidation risk adjustment mechanism can achieve the expected adjustment effect on each region, and there is no need to optimize the oxidation risk adjustment mechanism.

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