Quick-freezing cabinet monitoring and adjusting system for food quick-freezing

Abstract

The invention discloses a quick-freezing cabinet monitoring and adjusting system for food quick-freezing, which belongs to the technical field of quick-freezing cabinet adjustment and comprises an information acquisition module, an information analysis module and a quick-freezing mode adjusting module. Wherein the information acquisition module is used for acquiring related data of food placed in a quick-freezing cabinet and operation data of the quick-freezing cabinet to obtain an influence parameter data set; the information analysis module is used for calculating a comprehensive state value in the quick-freezing cabinet according to the influence parameter data set; and the quick-freezing mode adjusting module is used for selecting a quick-freezing mode according to the comprehensive state value and dynamically adjusting working parameters of the quick-freezing cabinet. Through intelligent information acquisition, analysis and adjustment functions, real-time monitoring and dynamic adjustment of the state of food in the quick-freezing cabinet are realized, and the safety and quality of the food are effectively guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of quick-freezing cabinet adjustment, and in particular to a quick-freezing cabinet monitoring and adjustment system for quick-freezing food. Background Art

[0002] With the acceleration of modern life and the development of the food industry, quick-frozen food has been widely used in the market due to its convenience, high nutritional retention and long storage period. However, traditional quick-freezing equipment and technology still have many shortcomings in ensuring the quick-freezing effect and food safety.

[0003] Traditional quick freezers often lack intelligent monitoring and adjustment functions during operation, and cannot dynamically adjust the quick freezing parameters according to factors such as the type, weight, and initial temperature of the food placed in the quick freezer. This leads to unstable quick freezing effects, and some foods may reduce their quality due to incomplete quick freezing, and even cause food safety problems. Secondly, traditional quick freezers consume a lot of energy during operation, especially when they are not fully loaded or fully loaded but the types and weights of food vary greatly, the energy waste phenomenon is particularly serious. This not only increases operating costs, but also violates the current social requirements for energy conservation, emission reduction, and green production. In addition, traditional quick freezers also have obvious shortcomings in equipment maintenance and food safety monitoring. Due to the lack of effective early warning and maintenance mechanisms, quick freezers often cannot be discovered and handled in time when failures or food quality problems occur, which may lead to serious consequences such as food damage and equipment downtime. In response to the above problems, the market urgently needs a quick freezer monitoring and adjustment system that can monitor food status in real time, dynamically adjust quick freezing parameters, optimize energy consumption, and provide an effective early warning and maintenance mechanism. Summary of the invention

[0004] To solve the above problems, the present invention provides a quick-freezing cabinet monitoring and adjustment system for food quick freezing. Through intelligent information collection, analysis and adjustment functions, real-time monitoring and dynamic adjustment of the food status in the quick-freezing cabinet are realized, effectively ensuring the safety and quality of the food.

[0005] The above objectives can be achieved through the following solutions: A quick-freezing cabinet monitoring and adjustment system for quick-freezing food comprises an information collection module, an information analysis module and a quick-freezing mode adjustment module; wherein the information collection module is used to collect relevant data of food placed in the quick-freezing cabinet and the operation data of the quick-freezing cabinet to obtain an influencing parameter data set; the information analysis module is used to calculate a comprehensive state value in the quick-freezing cabinet according to the influencing parameter data set; the quick-freezing mode adjustment module is used to select a quick-freezing mode according to the comprehensive state value and dynamically adjust the working parameters of the quick-freezing cabinet.

[0006] Furthermore, the information collection module includes: a food information collection unit, an equipment information collection unit and an information processing unit; wherein the food information collection unit is used to collect relevant data of food in the quick freezer when the quick freezer is opened, so as to obtain food information data; the equipment information collection unit is used to collect operating data of the quick freezer equipment when the quick freezer is opened, so as to obtain equipment information data; the information processing unit is respectively connected to the food information collection unit and the equipment information collection unit, so as to store the food information data and the equipment information data; the information processing unit is also used to update the stored food information data and the stored equipment information data according to the collected food information data and the collected equipment information data; the information processing unit is also used to perform feature extraction on the food information data and the equipment information data to obtain an influencing parameter data set.

[0007] Furthermore, the feature extraction of the food information data and the equipment information data to obtain the influencing parameter data set includes: preprocessing the food information data and the equipment information data to obtain a primary data set; feature extraction of the primary data set to obtain an intermediate data set; normalization of the intermediate data set to obtain an influencing parameter data set.

[0008] Furthermore, the information analysis module includes: a model building unit and a comprehensive state value calculation unit; wherein the model building unit is used to establish a state model for characterizing the comprehensive state value; the comprehensive state value calculation unit is connected to the information processing unit and the model building unit, respectively, and is used to substitute the influencing parameter data set into the state model to calculate the comprehensive state value.

[0009] Furthermore, the state model for characterizing the comprehensive state value includes: collecting historical influencing parameter data sets and historical comprehensive state evaluations to construct a historical data set; normalizing the historical data set to obtain a training data set; establishing a linear regression model; using the training data set to train the linear regression model to obtain a stable calculation model for the comprehensive state value. ,have , In the formula, is the error term coefficient, is the influencing parameter data set The regression coefficient corresponding to the influencing parameter data is is the influencing parameter data set Influencing parameter data.

[0010] Further, the quick-freezing mode adjustment module includes: a state analysis unit, a dynamic adjustment unit, an energy-saving optimization unit and an early warning maintenance unit; wherein the state analysis unit is connected to the comprehensive state value calculation unit, and is used to judge the size of the comprehensive state value and the preset first threshold and the preset second threshold; the dynamic adjustment unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using the dynamic adjustment mode when the comprehensive state value is greater than the first threshold; the energy-saving optimization unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using the energy-saving optimization mode when the comprehensive state value is less than or equal to the first threshold and greater than the second threshold; the early warning maintenance unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using the early warning maintenance mode when the comprehensive state value is less than or equal to the second threshold.

[0011] Furthermore, the use of the dynamic adjustment mode to adjust the working parameters of the quick-freezing cabinet includes: establishing and training a quick-freezing effect prediction model; inputting the food information data into the quick-freezing effect prediction model to obtain required quick-freezing effect data; and adjusting the working parameters of the quick-freezing cabinet according to the required quick-freezing effect data.

[0012] Furthermore, the use of the energy-saving optimization mode to adjust the working parameters of the quick freezer includes: establishing and training a power consumption prediction model; inputting the equipment information data and the food information data into the power consumption prediction model to obtain energy-saving power consumption prediction data; and adjusting the working parameters of the quick freezer according to the energy-saving power consumption prediction data.

[0013] Furthermore, the use of the early warning maintenance mode to adjust the working parameters of the quick freezer includes: analyzing the food data information to obtain food quality indicators; analyzing the equipment data information to obtain equipment operating status indicators; judging whether the food quality indicators or the equipment operating status indicators meet the preset requirements; if the food quality indicators do not meet the preset requirements, adjusting the working parameters of the quick freezer according to the food data information; if the equipment operating status indicators do not meet the preset requirements, adjusting the working parameters of the quick freezer according to the food data information; if both the food quality indicators and the equipment operating status indicators meet the preset requirements, switching to the energy-saving optimization mode to adjust the working parameters of the quick freezer.

[0014] Furthermore, the quick freezing mode adjustment module further includes: an alarm unit; wherein the alarm unit is connected to the early warning maintenance unit, and is used to issue an alarm message when the food quality index or the equipment operation status index does not meet the preset requirements.

[0015] Compared with the prior art, the present invention has the following advantages: 1. The present invention can monitor the state of food in the quick-freezing cabinet and the operation data of the quick-freezing cabinet in real time through the coordinated work of the information collection module, the information analysis module and the quick-freezing mode adjustment module, and dynamically adjust the quick-freezing parameters according to these data to ensure that the food achieves the best quick-freezing effect; 2. The present invention intelligently selects the most suitable quick-freezing mode according to factors such as the type, weight, initial temperature of the food, and the operating status of the quick-freezing cabinet, thereby effectively avoiding the deterioration of food quality and safety problems caused by incomplete quick-freezing; 3. While ensuring the quick freezing effect, the present invention can dynamically adjust the working parameters of the quick freezing cabinet through the energy-saving optimization mode, such as reducing the refrigeration power, adjusting the wind speed, etc., to achieve the purpose of energy saving and consumption reduction, thereby reducing operating costs; 4. The present invention can timely discover food quality problems and abnormal equipment operation status through the early warning maintenance mode, and send out alarm information through the alarm unit to remind the operator to take timely measures to avoid food damage and equipment failure, thereby ensuring the stable operation of the quick freezer.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a framework diagram of a quick-freezing cabinet monitoring and regulating system for quick-freezing food according to an embodiment of the present invention.

[0019] Figure 2 The present invention is a schematic structural diagram of a quick-freezing cabinet monitoring and regulating system for quick-freezing food according to an embodiment of the present invention.

[0020] Figure 3 It is a workflow diagram of the dynamic adjustment mode of an embodiment of the present invention.

[0021] Figure 4 It is a working flow chart of the energy-saving optimization mode of an embodiment of the present invention.

[0022] Figure 5 It is a workflow diagram of the early warning maintenance mode of an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 An embodiment of the present invention provides a quick-freezing cabinet monitoring and adjustment system for quick-freezing food. Through intelligent information collection, analysis and adjustment functions, real-time monitoring and dynamic adjustment of the food status in the quick-freezing cabinet are achieved, effectively ensuring the safety and quality of the food.

[0025] like Figure 2 As shown, the system of this embodiment specifically includes: an information collection module, an information analysis module and a quick freezing mode adjustment module; wherein, The information collection module is used to collect relevant data of food placed in the quick-freezing cabinet and the operation data of the quick-freezing cabinet to obtain an influencing parameter data set; Specifically, relevant data of food placed in the quick freezer (such as food type, weight, initial temperature, shelf life, etc.) and operating data of the quick freezer (such as temperature, humidity, operating time, etc.) are collected, and these data are integrated into an influencing parameter data set.

[0026] For example, when the quick-freeze cabinet door is opened, the information collection module starts working, and the food information collection unit will capture the type, weight and initial temperature of the fish fillets placed in the cabinet; this can be done by scanning the QR code on the food, manually entering food-related information, etc.; at the same time, the equipment information collection unit will record the current temperature, humidity and running time of the quick-freeze cabinet; after this information is integrated, it forms an influencing parameter data set, providing a basis for subsequent analysis.

[0027] The information analysis module is used to calculate the comprehensive state value in the quick-freezing cabinet according to the influencing parameter data set; Specifically, based on the influencing parameter data set, a comprehensive state value in the quick-freezing cabinet is calculated through a specific algorithm or model; this comprehensive state value reflects the overall state of the food during the quick-freezing process, including information on the quick-freezing effect, food quality, etc.

[0028] Exemplarily, the information analysis module will use a previously established state model (such as a linear regression model) and substitute the influencing parameter data set into the model for calculation to obtain the comprehensive state value of the fish fillets in the current quick-freezer; this value may vary depending on factors such as the type of fish fillets, weight, initial temperature, shelf life, and operating conditions of the quick-freezer.

[0029] The quick freezing mode adjustment module is used to select the quick freezing mode according to the comprehensive state value and dynamically adjust the working parameters of the quick freezing cabinet.

[0030] Specifically, according to the comprehensive status value, the most suitable quick-freezing mode is selected, and the working parameters of the quick-freezing cabinet (such as temperature, wind speed, etc.) are dynamically adjusted to ensure that the food achieves the best quick-freezing effect.

[0031] For example, if the comprehensive status value indicates that the current quick-freezing effect is not ideal (such as the temperature in the center of the fish fillet drops too slowly), the quick-freezing mode adjustment module will select the dynamic adjustment mode to improve the refrigeration efficiency and wind speed of the quick-freezing cabinet to speed up the quick-freezing of the fish fillet; if the comprehensive status value indicates that the current quick-freezing effect is good and the energy consumption is high, it will switch to the energy-saving optimization mode to reduce energy consumption while maintaining food quality.

[0032] Exemplarily, the quick-freezing mode adjustment module is provided with a state analysis unit, a dynamic adjustment unit, an energy-saving optimization unit and a warning maintenance unit; when the comprehensive state value is greater than a preset first threshold value, the state analysis unit determines that the current quick-freezing effect is not good, and then triggers the dynamic adjustment unit; the dynamic adjustment unit accelerates the temperature drop process of the food by adjusting the refrigeration power, wind speed and other parameters of the quick-freezing cabinet; for example, the refrigeration power is increased to the maximum power, and the wind speed is increased at the same time to ensure that the food reaches the required freezing temperature in a short time; when the comprehensive state value is between the preset first threshold value and the second threshold value, the energy-saving optimization unit is activated; the unit optimizes the working parameters of the quick-freezing cabinet, such as reducing the refrigeration power and adjusting the wind speed, etc. Ensure quick-freezing effect while reducing energy consumption; for example, according to the type and weight of food, automatically adjust the refrigeration power to a suitable level that is energy-saving and ensures quick-freezing effect; and when the comprehensive status value is less than or equal to the second threshold, the early warning maintenance unit will issue an alarm to prompt the operator to check the operating status of the quick-freezing cabinet or the quality of the food; for example, if it is found that the equipment information data shows that a sensor of the quick-freezing cabinet has a fault, the early warning maintenance unit will immediately issue an alarm message and recommend corresponding maintenance or replacement operations; the quick-freezing cabinet monitoring and adjustment system realizes real-time monitoring and dynamic adjustment of the food status in the quick-freezing cabinet through intelligent information collection, analysis and adjustment functions, effectively ensuring the safety and quality of food.

[0033] Furthermore, the information collection module includes: a food information collection unit, an equipment information collection unit and an information processing unit; wherein, The food information collection unit is used to collect relevant data of the food in the quick-freezing cabinet when the quick-freezing cabinet is opened to obtain food information data; Specifically, the food information collection unit may include a barcode scanner installed on the outside of the quick freezer and a food temperature collection device installed inside the quick freezer. When the quick freezer door is opened, the barcode scanner can quickly obtain key information such as the type, weight, and shelf life of the food by scanning the barcode, RFID tag on the food packaging, or using image recognition technology; this information is crucial for the subsequent calculation of the comprehensive state value and the selection of the quick freezing mode; at the same time, key information such as the type, weight, and shelf life of the food can also be provided through manual input; the food temperature collection device can collect the temperature of the food placed in the quick freezer, which is of great significance for the subsequent adjustment of the mode of the quick freezer.

[0034] Exemplarily, a barcode scanner can be installed at the entrance of a quick freezer. Before a box of frozen chicken is placed in the quick freezer, the barcode of the food can be scanned using the barcode scanner to immediately identify the type, weight and shelf life (e.g., 6 months) of the box of chicken. This information is transmitted in real time to an information processing unit for storage and subsequent processing.

[0035] The equipment information collection unit is used to collect the operation data of the quick-freezing cabinet equipment when the quick-freezing cabinet is opened to obtain equipment information data; Specifically, the equipment information collection unit is installed inside the quick freezer. The equipment information collection unit includes a temperature collection device inside the quick freezer and a monitoring unit for the quick freezer equipment. The equipment information collection unit is responsible for real-time monitoring of various operating data in the quick freezer, including temperature, humidity, wind speed, oxygen content, and the operating status of the refrigeration system; these data reflect the working efficiency of the quick freezer and the environmental conditions in which the food is located.

[0036] Illustratively, during the quick-freezing process, the equipment information acquisition unit continuously monitors the temperature inside the quick-freezing cabinet; if the temperature deviates from the preset quick-freezing temperature range (such as -30°C to -25°C), the unit will immediately capture this change and transmit the relevant data to the information processing unit for further analysis.

[0037] The information processing unit is connected to the food information collection unit and the device information collection unit respectively, and is used to store the food information data and the device information data; the information processing unit is also used to update the stored food information data and the stored device information data according to the collected food information data and the collected device information data; The information processing unit is further used to extract features from the food information data and the equipment information data to obtain an influencing parameter data set.

[0038] Specifically, the information processing unit is the core component of the information collection module, which is responsible for receiving, storing, updating and processing data from the food information collection unit and the equipment information collection unit; by extracting and preprocessing features of these data, the information processing unit can generate an influencing parameter data set, providing a basis for subsequent information analysis and quick freezing mode adjustment.

[0039] Exemplarily, after the information processing unit receives the data transmitted by the food information collection unit and the equipment information collection unit, it first stores and updates the data; then, it uses a feature extraction algorithm to process these data, extracts parameters that have a significant impact on the quick-freezing effect, food quality and equipment operating status (such as food type, shelf life, quick-freezer temperature, etc.), and forms an influencing parameter data set; these data sets will be transmitted to the information analysis module for further analysis and calculation.

[0040] Furthermore, the feature extraction of the food information data and the equipment information data to obtain the influencing parameter data set includes: Preprocessing the food information data and the equipment information data to obtain a primary data set; Specifically, data preprocessing is the first step in feature extraction, which aims to clean and normalize data and eliminate noise and redundant information; this process includes steps such as data cleaning, deduplication, and missing value processing to ensure the accuracy of subsequent feature extraction.

[0041] For example, the received food information data may contain abnormal values ​​(such as unreasonable shelf life, weight out of range, etc.) caused by scanning errors or transmission failures; the information processing unit identifies and corrects these abnormal values ​​through data cleaning steps to ensure the accuracy and consistency of the data; at the same time, for missing values ​​that may exist in the equipment information data (such as missing temperature data within a certain period of time), the information processing unit uses interpolation or other algorithms to fill in the missing values ​​to ensure the integrity of the data.

[0042] Performing feature extraction on the primary data set to obtain an intermediate data set; Specifically, feature extraction is one of the core tasks of the information processing unit; it aims to extract characteristic parameters that have a significant impact on the quick-freezing effect, food quality and equipment operating status from the preprocessed data; these characteristic parameters will serve as the basis for subsequent information analysis and quick-freezing mode adjustment.

[0043] For example, in the food information data, the information processing unit identifies key characteristic parameters such as food type, shelf life, and weight through feature extraction algorithms; these parameters are crucial for evaluating the quick-freezing requirements of food and predicting food quality; at the same time, in the equipment information data, the information processing unit extracts environmental characteristic parameters such as temperature, humidity, and wind speed, as well as equipment status characteristic parameters such as the refrigeration system and circulation system; these characteristic parameters together constitute the key factors affecting the quick-freezing effect and equipment operating efficiency.

[0044] The intermediate data set is normalized to obtain an influencing parameter data set.

[0045] Specifically, normalization is to scale the extracted feature parameter values ​​to the same scale range to ensure that different feature parameters have equal weights in subsequent calculations; this process helps to improve the accuracy and stability of the model.

[0046] For example, assume that during the feature extraction process, the information processing unit extracts characteristic parameters such as food shelf life (unit: day), weight (unit: kilogram) and equipment temperature (unit: Celsius); due to the different units and value ranges of these parameters, direct calculation may result in distorted results; therefore, the information processing unit uses a normalization algorithm to scale the values ​​of these characteristic parameters to the range of 0-1 (or other suitable ranges); in this way, different characteristic parameters can have the same weight and influence in the subsequent comprehensive state value calculation and quick-freezing mode selection; the information processing unit generates an influencing parameter data set by preprocessing, feature extraction and normalization of food information data and equipment information data; these data sets provide an accurate and reliable basis for subsequent information analysis and quick-freezing mode adjustment.

[0047] Furthermore, the information analysis module includes: a model building unit and a comprehensive state value calculation unit; wherein, The model building unit is used to build a state model for representing the comprehensive state value; Specifically, the model building unit is the basis of the information analysis module and is responsible for constructing a state model used to characterize the comprehensive state inside the freezer; the model is a mathematical or statistical model that can predict or evaluate the comprehensive state value inside the freezer based on an influencing parameter data set (such as food type, shelf life, freezer temperature, etc.); the model building unit can use historical data, domain knowledge and machine learning algorithms to train and optimize the model to ensure its accuracy and reliability.

[0048] Exemplarily, the model building unit may use machine learning algorithms such as multivariate linear regression, support vector machine, neural network, etc. to construct the state model; for example, if a multivariate linear regression model is used, the model building unit will first collect a large amount of historical data, including food quality assessment results under different food types, shelf life, quick freezer temperature and other conditions; then, it uses these data to train the model to find out the key factors and their weights that affect the comprehensive state in the quick freezer; finally, a multivariate linear regression equation is obtained that can predict the comprehensive state value in the quick freezer based on the current influencing parameter data set.

[0049] The comprehensive state value calculation unit is connected to the information processing unit and the model establishment unit respectively, and is used to substitute the influencing parameter data set into the state model to calculate the comprehensive state value.

[0050] Specifically, the comprehensive state value calculation unit is the core of the information analysis module. It is responsible for interacting with the information processing unit and the model building unit, substituting the influencing parameter data set into the state model for calculation, and thus obtaining the comprehensive state value of the quick-freezing cabinet; this value is a quantitative indicator used to reflect the current quick-freezing effect, quality status and potential risk of deterioration of the food; the higher the comprehensive state value, the better the quick-freezing effect of the food and the more guaranteed the quality.

[0051] Exemplarily, it is assumed that the model building unit has constructed a multivariate linear regression model to predict the comprehensive state value in the quick freezer; when new food is placed in the quick freezer, the information processing unit will collect relevant data and perform preprocessing, feature extraction and normalization to generate an influencing parameter data set; then, the comprehensive state value calculation unit substitutes these parameters into the multivariate linear regression equation for calculation to obtain a specific comprehensive state value; for example, if the calculation result is 0.85 (assuming that the comprehensive state value ranges from 0 to 1), it indicates that the current comprehensive state in the quick freezer is good and the quick freezing effect is satisfactory; the information analysis module realizes an accurate assessment of the state of food in the quick freezer through the collaborative work of the model building unit and the comprehensive state value calculation unit. This assessment result provides an important basis for the subsequent quick freezing mode adjustment, which helps to ensure the safety and quality of food and extend the shelf life.

[0052] Furthermore, the establishing of a state model for characterizing the comprehensive state value includes: Collect historical impact parameter data sets and historical comprehensive status evaluations to construct historical data sets; Specifically, a large amount of historical data should be collected. These data should include data sets of influencing parameters of the quick freezer under different operating conditions (such as food type, shelf life, quick freezer temperature, humidity, wind speed and other environmental parameters, as well as equipment operating status parameters, etc.) and the corresponding comprehensive status evaluation results within the quick freezer; these evaluation results can be comprehensive indicators obtained through expert evaluation, equipment monitoring data or laboratory testing, reflecting the overall condition of the internal environment of the quick freezer.

[0053] Normalizing the historical data set to obtain a training data set; Specifically, since the units and value ranges of different influencing parameters may be different, in order to eliminate the impact of these differences on model training, the historical data set needs to be normalized and the values ​​of all influencing parameters should be scaled to the same scale range (such as between 0 and 1 or between -1 and 1) so that the model can learn the relationship between the parameters more effectively.

[0054] Build a linear regression model; The linear regression model is trained using the training data set to obtain a stable calculation model. ,have , In the formula, is the error term coefficient, is the influencing parameter data set The regression coefficient corresponding to the influencing parameter data is is the influencing parameter data set Influencing parameter data.

[0055] Specifically, the linear regression model is a commonly used prediction model, which assumes that there is a linear relationship between the target variable (in this case, the comprehensive state value S) and the influencing parameter data set; the normalized historical data set (i.e., the training data set) is input into the linear regression model, and the model is trained by minimizing the difference between the predicted value and the true value (such as using the mean square error as the loss function); during the training process, the model will continuously adjust the regression coefficient so that the predicted comprehensive state value is as close as possible to the true comprehensive state evaluation in the historical data.

[0056] For example, assuming that there is a historical data set, the historical data set is normalized to obtain a training data set; then, the linear regression model is trained using these data to obtain the regression coefficient of each influencing parameter; for example, assuming that the regression coefficients obtained by the trained model are (these coefficients are hypothetical and need to be obtained through training in practice): the shelf life is 0.2, the quick freezer temperature is -0.15, the humidity is 0.1, and the wind speed is 0.05, so as to obtain the comprehensive state value The calculation model is , where is the normalized value of the shelf life of the food type (chicken), is the normalized value of the quick-freeze cabinet temperature, is the normalized value of humidity, is the normalized value of wind speed. When there is a new influencing parameter data set (such as the food type is chicken, the shelf life is 120 days, the quick-freeze cabinet temperature is -28℃, the humidity is 82%, and the wind speed is 1.8m / s), these values ​​(after normalization) can be substituted into the trained linear regression model to calculate the comprehensive state value. Through calculation, a specific comprehensive state value can be obtained to evaluate the overall state of all the items in the current quick-freeze cabinet. The higher this value is, the better the comprehensive state of the internal environment of the quick-freeze cabinet is.

[0057] Furthermore, the quick freezing mode adjustment module includes: a state analysis unit, a dynamic adjustment unit, an energy-saving optimization unit and an early warning maintenance unit; wherein, The state analysis unit is connected to the comprehensive state value calculation unit and is used to determine the size of the comprehensive state value and a preset first threshold and a preset second threshold; Specifically, the state analysis unit is the core part of the quick freezing mode adjustment module, which receives data from the comprehensive state value calculation unit, that is, the current comprehensive state value of the quick freezing cabinet. The unit determines which adjustment mode should be adopted by comparing the comprehensive state value with the preset first threshold and second threshold.

[0058] Exemplarily, assume that the preset first threshold is 80 (indicating that the quick-freeze cabinet is in a highly efficient but possibly slightly energy-consuming state), and the second threshold is 60 (indicating that the quick-freeze cabinet is in a relatively energy-saving but possibly less efficient state); when the comprehensive state value is 85, the state analysis unit will determine that a dynamic adjustment mode needs to be adopted; when the comprehensive state value is 70, it will determine that an energy-saving optimization mode needs to be adopted; and when the comprehensive state value is 55, it will determine that an early warning maintenance mode needs to be started.

[0059] The dynamic adjustment unit is connected to the state analysis unit and is used to adjust the working parameters of the quick-freezing cabinet using a dynamic adjustment mode when the comprehensive state value is greater than the first threshold; Specifically, when the state analysis unit determines that the comprehensive state value is greater than the first threshold, the dynamic adjustment unit will start; it dynamically adjusts the working parameters of the quick freezer (such as temperature, wind speed, compressor operating frequency, etc.) according to the current working state of the quick freezer and external environmental conditions to ensure that the quick freezer can operate efficiently while minimizing energy consumption.

[0060] For example, during the high temperature period in summer, if the comprehensive status value of the quick-freeze cabinet exceeds 80, the dynamic adjustment unit may automatically increase the operating frequency of the compressor, increase the cooling output, and adjust the wind speed to optimize the cooling distribution to ensure that the temperature inside the cabinet drops quickly and remains stable.

[0061] The energy-saving optimization unit is connected to the state analysis unit and is used to adjust the working parameters of the quick-freezing cabinet using the energy-saving optimization mode when the comprehensive state value is less than or equal to the first threshold value and greater than the second threshold value; Specifically, when the comprehensive state value is between the first threshold and the second threshold, the energy-saving optimization unit will take over the adjustment task; its goal is to reduce energy consumption as much as possible while ensuring the normal operation of the quick-freezing cabinet.

[0062] For example, in spring and autumn, when the comprehensive status value is 70, the energy-saving optimization unit may reduce the operating frequency of the compressor, reduce the cooling output, and adjust parameters such as wind speed and door opening times to achieve energy-saving effects; in addition, it may also automatically adjust the operating time of the quick-freeze cabinet based on historical data and predictive models to avoid peak electricity consumption periods.

[0063] The early warning maintenance unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using an early warning maintenance mode when the comprehensive state value is less than or equal to the second threshold.

[0064] Specifically, when the comprehensive status value is less than or equal to the second threshold, the early warning maintenance unit will be activated; this means that the quick freezer may have problems with food quality indicators or a fault state, and immediate maintenance or adjustment is required.

[0065] For example, when the comprehensive status value is 70, the energy-saving optimization unit may reduce the operating frequency of the compressor, reduce the cooling output, and adjust parameters such as wind speed and door opening times to achieve energy-saving effects; in addition, it may also automatically adjust the operating time of the quick-freeze cabinet based on historical data and predictive models to avoid peak electricity consumption periods.

[0066] The early warning maintenance unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using an early warning maintenance mode when the comprehensive state value is less than or equal to the second threshold.

[0067] Specifically, when the comprehensive status value is less than or equal to the second threshold, the early warning maintenance unit will be activated; this means that the quick freezer may have problems with food quality indicators or a fault state, and immediate maintenance or adjustment is required.

[0068] For example, if the comprehensive status value drops to 55, the early warning maintenance unit may sound an alarm and automatically adjust the operating parameters of the quick-freeze cabinet to avoid further deterioration; at the same time, it will also generate a maintenance report to remind operators to check the quick-freeze cabinet's refrigeration system, control system and other key components to detect and solve problems in a timely manner.

[0069] Furthermore, if Figure 3 As shown, the use of the dynamic adjustment mode to adjust the working parameters of the quick freezer includes: Establish and train a quick freezing effect prediction model; Specifically, it is necessary to establish a quick-freezing effect prediction model, which is based on machine learning or deep learning algorithms and is used to predict the quick-freezing effect of quick-freezing cabinets under different working parameters; the quick-freezing effect can be measured by indicators such as the time required for the center temperature of the food to drop to a safe storage temperature and the food quality loss rate; in order to train this model, it is necessary to collect a large amount of historical data, including quick-freezing effect data under different combinations of working parameters; these data can come from the actual operation records of the quick-freezing cabinet, or be obtained through experiments; then, use these data to train the model so that it can accurately predict the quick-freezing effect.

[0070] For example, assuming that the quick freezing effect is measured by the time required for the core temperature of the food to drop to a safe storage temperature, a quick freezing effect prediction model can be established, and the prediction formula for the prediction model can be , In the formula, For the The time required for the food core temperature to drop to the safe storage temperature in the working parameter combination of the quick freezer is is the activation function, is the weight matrix corresponding to the input feature vector, is the input feature vector (including food type, weight, initial temperature, etc.), is the weight matrix corresponding to the quick-freezer working parameters, For the The characteristic vector of the working parameter combination of the quick freezer, is the bias term; during the training phase, a large amount of data was collected on the relationship between parameters such as freezer temperature, wind speed, food type and initial temperature and the time required for the center temperature of food to drop to a safe storage temperature; by repeatedly adjusting the weight matrix of the model, a model was finally obtained that can accurately predict the time required for the center temperature of food to drop to a safe storage temperature.

[0071] Inputting the food information data into the quick-freezing effect prediction model to obtain required quick-freezing effect data; Specifically, in actual applications, when new food needs to be placed in a quick-freeze cabinet for quick freezing, the food information data (such as type, weight, initial temperature, etc.) needs to be input into the quick-freezing effect prediction model. The model will predict the quick-freezing effects under different working parameter combinations based on this information and the preset working parameter combinations of the quick-freeze cabinet.

[0072] Adjust the working parameters of the quick-freezing cabinet according to the required quick-freezing effect data.

[0073] Specifically, after obtaining the required quick-freezing effect data, the dynamic adjustment unit will adjust the working parameters of the quick-freezing cabinet according to these data to achieve the best quick-freezing effect; this usually involves adjusting the temperature, wind speed and other parameters of the quick-freezing cabinet to ensure that the food can be reduced to a safe storage temperature in the shortest time while maintaining the best food quality.

[0074] For example, assuming that the quick-freezing effect is measured by the time required for the center temperature of the food to drop to a safe storage temperature, a batch of fresh fish meat is to be quick-frozen, and the information data of the fish meat (such as the type of cod, the weight is 500 grams, and the initial temperature is 4°C) is input into the quick-freezing effect prediction model. The model will quickly calculate the time required for the center temperature of the food to drop to a safe storage temperature under different combinations of temperature and wind speed. For example, the temperature setting is -30°C, -25°C, and -20°C, and the wind speed setting is low, medium, and high. , , , , , , , , The characteristic vector of the combination of working parameters of the nine quick-freezers; the information data of the fish meat is input into the prediction model to obtain , , , , , , , , The time required for the core temperature of the nine corresponding foods to drop to the safe storage temperature is compared, and the characteristic vector of the working parameter combination corresponding to the minimum required time is selected. Assume is the minimum value, then The corresponding temperature and wind speed are the required working parameters, that is, the required quick-freezing effect data. The dynamic adjustment unit will immediately adjust the working parameters of the quick-freezing cabinet to this combination and start the quick-freezing process. During the quick-freezing process, the dynamic adjustment unit will also continue to monitor the quick-freezing effect and make fine adjustments as needed to ensure the best quick-freezing effect. In this way, the dynamic adjustment mode can be used to ensure that the quick-freezing cabinet can achieve the best quick-freezing effect under different food types and initial conditions, thereby improving food quality and safety while optimizing energy consumption and efficiency.

[0075] Furthermore, if Figure 4 As shown, the use of the energy-saving optimization mode to adjust the working parameters of the quick-freezing cabinet includes: Establish and train a power consumption prediction model; Specifically, in the energy-saving optimization mode, it is first necessary to establish a power consumption prediction model; this model is also based on machine learning or deep learning algorithms, and is used to predict the power consumption of the quick freezer under different working parameter combinations; the goal of the power consumption prediction model is to find the working parameter combination with the lowest power consumption while maintaining a certain quick freezing effect; in order to train this model, it is necessary to collect a large amount of historical data, including the power consumption data of the quick freezer under different working parameters and food combinations; these data can come from the actual operation records of the quick freezer, or be obtained through special experiments; through training, the model can learn the relationship between working parameters and power consumption.

[0076] For example, assuming that a power consumption prediction model is established, the prediction formula for the model can be , In the formula, For the The power consumption data of the working parameter combination of the quick freezer is is the activation function, is the weight matrix corresponding to the input feature vector, is the input feature vector (including food type, weight, initial temperature, etc.), is the weight matrix corresponding to the quick-freezer working parameters, For the The characteristic vector of the working parameter combination of the quick freezer, is the bias term; during the training phase, the power consumption data of the quick freezer under different temperatures, wind speeds, food types and weights were collected; through training, the model determined the weight matrix corresponding to the quick freezer working parameters and the weight matrix corresponding to the input feature vector.

[0077] Inputting the device information data and the food information data into the power consumption prediction model to obtain energy-saving power consumption prediction data; Specifically, in actual applications, when it is necessary to use the energy-saving optimization mode to adjust the working parameters of the quick-freezing cabinet, the current food information data (such as type, weight, initial temperature, etc.) needs to be input into the power consumption prediction model; the model will predict the power consumption under different working parameter combinations based on these data, and find the working parameter combination with the lowest power consumption and quick-freezing effect that meets the requirements.

[0078] The operating parameters of the quick-freezing cabinet are adjusted according to the energy-saving power consumption prediction data.

[0079] Specifically, after obtaining the energy-saving power consumption prediction data, the energy-saving optimization unit will adjust the working parameters of the quick-freezing cabinet according to these data to achieve the goal of energy saving and consumption reduction; this usually involves lowering the temperature of the quick-freezing cabinet, adjusting the wind speed, optimizing the number and time of door opening, and other measures to reduce power consumption as much as possible while ensuring the quick-freezing effect.

[0080] For example, suppose a batch of fresh fish meat is to be quick-frozen. The information data of the fish meat (such as the type of cod, the weight of 500 grams, and the initial temperature of 4°C) is input into the power consumption prediction model. The model will quickly calculate the power consumption of the quick-freezing cabinet for the fish meat under different combinations of temperature and wind speed, that is, the energy-saving power consumption prediction data; for example, the temperature is set to -30°C and -25°C, and the wind speed is set to low and medium. , , , The characteristic vector of the combination of working parameters of the four quick-freezers; the information data of the fish meat is input into the prediction model to obtain , , , Four corresponding energy-saving power consumption prediction data, by comparing the size of the four energy-saving power consumption prediction data, select the characteristic vector of the working parameter combination corresponding to the smallest energy-saving power consumption prediction data, assuming is the minimum value, then The corresponding temperature and wind speed are the required operating parameters; for example The corresponding temperature and wind speed are -25℃ and low wind speed, then the energy-saving optimization unit will immediately adjust the working parameters of the quick-freezing cabinet to this combination and start the quick-freezing process; during the quick-freezing process, the energy-saving optimization unit will also continue to monitor the power consumption and quick-freezing effect, and make fine adjustments as needed to ensure that energy saving and consumption reduction are achieved while maintaining the quick-freezing effect; in this way, the energy-saving optimization mode can ensure that the quick-freezing cabinet minimizes power consumption while maintaining a certain quick-freezing effect, thereby achieving the purpose of energy saving and consumption reduction.

[0081] Furthermore, if Figure 5 As shown, the use of the early warning maintenance mode to adjust the working parameters of the quick freezer includes: Analyzing the food data information to obtain food quality indicators; Specifically, in the early warning maintenance mode, the system will first analyze the food data information, including the type of food, initial state, storage time, etc., to derive the quality indicators of the food; these indicators may include the freshness, acidity, moisture content, etc. of the food, which can reflect the current storage status of the food.

[0082] For example, assuming that a batch of strawberries are being quick-frozen, by analyzing the initial temperature of the strawberries, the temperature change curve during the freezing process, and the mass loss rate after thawing, the quality indicators of the strawberries can be obtained, such as whether the freezing speed is too fast, resulting in excessive ice crystals, and whether the mass loss rate is within an acceptable range.

[0083] Analyze the equipment data information to obtain equipment operation status indicators; Specifically, it is necessary to analyze the equipment data information of the quick freezer to obtain the operating status indicators of the equipment; these indicators may include the operating status of the compressor, the temperature of the condenser and evaporator, wind speed, the number and time of door openings, power consumption, etc., which reflect the working performance and efficiency of the quick freezer.

[0084] For example, by analyzing the real-time data of the quick-freeze cabinet, the operating status indicators of the equipment can be obtained, such as whether the compressor is operating normally, whether the condenser temperature is too high, whether the wind speed is stable, whether the door is opened frequently, etc.

[0085] Determine whether the food quality index or the equipment operation status index meets the preset requirements; Specifically, it is necessary to determine whether the food quality indicators and equipment operating status indicators meet the preset requirements or standards; these requirements may be based on food safety standards, equipment manufacturers' recommendations or empirical data; the system will compare the obtained food quality indicators with the preset quality standards to determine whether the food has begun to deteriorate or is about to deteriorate.

[0086] For example, it is set that the freezing speed of strawberries should be within a certain range, the mass loss rate should not exceed 5%, and the compressor of the quick freezer should operate normally and the condenser temperature should not exceed the set value; then, the actual indicators are compared with these requirements.

[0087] If the food quality index does not meet the preset requirements, adjusting the working parameters of the quick-freezing cabinet according to the food data information; Specifically, the system will immediately start the early warning maintenance program to delay the spoilage of food by adjusting parameters such as temperature, humidity, and oxygen content in the freezer; for example, lowering the temperature can slow the growth rate of microorganisms, lowering the humidity can reduce water evaporation and frost, and reducing the oxygen content can inhibit the reproduction of aerobic microorganisms.

[0088] For example, if it is found that the meat stored in the freezer begins to have an odor, the system may automatically lower the temperature further to below -20°C and adjust the humidity controller to reduce the humidity in the cabinet, while starting vacuum packaging or nitrogen packaging to reduce the oxygen content, thereby extending the shelf life of the meat.

[0089] If the equipment operation status indicator does not meet the preset requirement, adjusting the working parameters of the quick-freezing cabinet according to the food data information; Specifically, if the equipment operating status indicators do not meet the preset requirements, such as compressor failure or high condenser temperature, it is also necessary to adjust the working parameters of the quick freezer according to the equipment data information, such as shutting down the faulty compressor, increasing the heat dissipation of the condenser, etc., to ensure the normal operation of the equipment.

[0090] For example, if a compressor of a quick-freezing cabinet is found to be faulty, the faulty compressor can be immediately shut down, and a spare compressor (if any) can be started, and maintenance personnel can be notified to perform maintenance.

[0091] If the food quality index and the equipment operation status index both meet the preset requirements, the energy-saving optimization mode is switched to adjust the working parameters of the quick freezer.

[0092] Specifically, if the food quality indicators and equipment operating status indicators meet the preset requirements, it means that the current quick freezer is in good working condition and the food quality is guaranteed. At this time, it can be switched to the energy-saving optimization mode to further reduce energy consumption.

[0093] For example, if the quality indicators of strawberries and the equipment operating status indicators meet the requirements, the quick freezer can be switched to energy-saving optimization mode to reduce energy consumption by lowering the temperature, adjusting the wind speed and other measures; in this way, the early warning maintenance mode can ensure that the quick freezer can reduce energy consumption and failure risks as much as possible while maintaining food quality and normal operation of the equipment.

[0094] Furthermore, the quick freezing mode adjustment module further includes: an alarm unit; wherein, The alarm unit is connected to the early warning maintenance unit, and is used to issue an alarm message when the food quality index or the equipment operation status index does not meet the preset requirements.

[0095] Specifically, the main function of the alarm unit is to monitor the output results of the early warning maintenance unit; when the early warning maintenance unit detects that the food quality indicators or equipment operating status indicators do not meet the preset requirements, the alarm unit will immediately trigger the alarm mechanism and send an alarm message to the operator through sound, light signals, display prompts, or sending text messages, emails, etc. The alarm information may include the following key contents: Alarm type, indicating whether it is a food quality problem or an equipment operating status problem; specific indicators, providing specific indicator values ​​that do not meet the preset requirements, such as the central temperature of the food is too high, the compressor of the equipment is faulty, etc.; recommended measures, according to the nature of the problem, provide preliminary recommended measures, such as adjusting the working parameters of the quick freezer, contacting maintenance personnel, etc.

[0096] For example, suppose a batch of seafood is stored in a quick-freezer. The early warning maintenance unit discovers through monitoring that the freezing speed of the seafood is too slow, resulting in the failure of the center temperature of the food to drop to a safe range in time. At this time, the alarm unit will immediately trigger an alarm, emit sound and light signals, and display an alarm message on the display screen: "Food quality problem! The seafood is about to expire, and it is recommended to take it out as soon as possible." After seeing the alarm message, the operator can immediately take out the seafood for processing.

[0097] For example, suppose that the compressor of the quick freezer suddenly fails, resulting in a significant decrease in the refrigeration effect. After the early warning maintenance unit detects this abnormal situation, the alarm unit will quickly issue an alarm message: "The equipment is operating abnormally! The compressor is faulty, please contact maintenance personnel immediately." After receiving the alarm, the operator can immediately contact professional maintenance personnel for maintenance to avoid further damage to the equipment or affect the quality of food; through the setting of the alarm unit, the speed mode adjustment module can more efficiently respond to food quality problems and abnormal equipment operation status, ensuring the stable operation of the quick freezer and the safe preservation of food.

[0098] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between the lines. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0099] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. A quick-freezing cabinet monitoring and regulating system for quick-freezing food, characterized in that: The system includes: an information collection module, an information analysis module and a quick freezing mode adjustment module; wherein, The information collection module is used to collect relevant data of food placed in the quick-freezing cabinet and the operation data of the quick-freezing cabinet to obtain an influencing parameter data set; The information analysis module is used to calculate the comprehensive state value in the quick-freezing cabinet according to the influencing parameter data set; The quick freezing mode adjustment module is used to select the quick freezing mode according to the comprehensive state value and dynamically adjust the working parameters of the quick freezing cabinet.

2. A quick-freezing cabinet monitoring and regulating system for quick-freezing of food according to claim 1, characterized in that: The information collection module includes: a food information collection unit, an equipment information collection unit and an information processing unit; wherein, The food information collection unit is used to collect relevant data of the food in the quick-freezing cabinet when the quick-freezing cabinet is opened to obtain food information data; The equipment information collection unit is used to collect the operation data of the quick-freezing cabinet equipment when the quick-freezing cabinet is opened to obtain equipment information data; The information processing unit is connected to the food information collection unit and the device information collection unit respectively, and is used to store the food information data and the device information data; the information processing unit is also used to update the stored food information data and the stored device information data according to the collected food information data and the collected device information data; The information processing unit is further used to extract features from the food information data and the equipment information data to obtain an influencing parameter data set.

3. A quick-freezing cabinet monitoring and regulating system for quick-freezing of food according to claim 2, characterized in that: The feature extraction of the food information data and the equipment information data to obtain the influencing parameter data set includes: Preprocessing the food information data and the equipment information data to obtain a primary data set; Performing feature extraction on the primary data set to obtain an intermediate data set; The intermediate data set is normalized to obtain an influencing parameter data set.

4. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 2, characterized in that: The information analysis module includes: a model building unit and a comprehensive state value calculation unit; wherein, The model building unit is used to build a state model for representing the comprehensive state value; The comprehensive state value calculation unit is connected to the information processing unit and the model establishment unit respectively, and is used to substitute the influencing parameter data set into the state model to calculate the comprehensive state value.

5. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 4, characterized in that: The establishing of a state model for characterizing the comprehensive state value comprises: Collect historical impact parameter data sets and historical comprehensive status evaluations to construct historical data sets; Normalizing the historical data set to obtain a training data set; Build a linear regression model; The linear regression model is trained using the training data set to obtain a stable calculation model. ,have ， In the formula, is the error term coefficient, is the influencing parameter data set The regression coefficient corresponding to the influencing parameter data is is the influencing parameter data set Influencing parameter data.

6. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 4, characterized in that: The quick freezing mode adjustment module includes: a state analysis unit, a dynamic adjustment unit, an energy-saving optimization unit and an early warning maintenance unit; wherein, The state analysis unit is connected to the comprehensive state value calculation unit and is used to determine the size of the comprehensive state value and a preset first threshold and a preset second threshold; The dynamic adjustment unit is connected to the state analysis unit and is used to adjust the working parameters of the quick-freezing cabinet using a dynamic adjustment mode when the comprehensive state value is greater than the first threshold; The energy-saving optimization unit is connected to the state analysis unit and is used to adjust the working parameters of the quick-freezing cabinet using the energy-saving optimization mode when the comprehensive state value is less than or equal to the first threshold value and greater than the second threshold value; The early warning maintenance unit is connected to the state analysis unit, and is used to adjust the working parameters of the quick-freezing cabinet using an early warning maintenance mode when the comprehensive state value is less than or equal to the second threshold.

7. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 6, characterized in that: The method of adjusting the working parameters of the quick-freezing cabinet by using the dynamic adjustment mode includes: Establish and train a quick freezing effect prediction model; Inputting the food information data into the quick-freezing effect prediction model to obtain required quick-freezing effect data; Adjust the working parameters of the quick-freezing cabinet according to the required quick-freezing effect data.

8. The quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 6, characterized in that: The use of the energy-saving optimization mode to adjust the working parameters of the quick-freezing cabinet includes: Establish and train a power consumption prediction model; Inputting the device information data and the food information data into the power consumption prediction model to obtain energy-saving power consumption prediction data; The operating parameters of the quick-freezing cabinet are adjusted according to the energy-saving power consumption prediction data.

9. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 6, characterized in that: The use of the early warning maintenance mode to adjust the working parameters of the quick freezing cabinet includes: Analyzing the food data information to obtain food quality indicators; Analyze the equipment data information to obtain equipment operation status indicators; Determine whether the food quality index or the equipment operation status index meets the preset requirements; If the food quality index does not meet the preset requirements, adjusting the working parameters of the quick-freezing cabinet according to the food data information; If the equipment operation status indicator does not meet the preset requirement, adjusting the working parameters of the quick-freezing cabinet according to the food data information; If the food quality index and the equipment operation status index both meet the preset requirements, the energy-saving optimization mode is switched to adjust the working parameters of the quick freezer.

10. A quick-freezing cabinet monitoring and regulating system for quick-freezing food according to claim 9, characterized in that: The quick freezing mode adjustment module also includes: an alarm unit; wherein, The alarm unit is connected to the early warning maintenance unit, and is used to issue an alarm message when the food quality index or the equipment operation status index does not meet the preset requirements.

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