A high-efficiency refrigeration unit control system and method for deep-sea fish

Abstract

The invention discloses a high-efficiency refrigeration unit control system and method for deep-sea fish, and relates to the technical field of equipment control. The invention calculates the impact values ​​of all environmental data on refrigeration efficiency, extracts the environmental data that affects the refrigeration efficiency as key influencing factors, extracts two classification nodes, and uses the two classification nodes to classify the key influencing factors into primary factors, secondary factors, and tertiary factors; collects all key influencing factors and refrigeration parameters of historical refrigeration units during operation, and constructs a relationship mapping model between the key influencing factors and the refrigeration parameters; uses the impact values ​​of key influencing factors of different levels to calculate the corresponding refrigeration parameter adjustment rate of each level of key influencing factors; detects the data values ​​of the key influencing factors in real time when the deep-sea fish are stored, and uses a change threshold to make judgments and early warnings; and selects the corresponding refrigeration parameter adjustment rate according to the level of the early warning key influencing factor to adjust the refrigeration parameter to a target value.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to a high-efficiency refrigeration unit control system and method for deep-sea fish. Background Art

[0002] Deep-sea fish inhabit the low-temperature, high-pressure deep-sea environment. Once caught and removed from their original habitat, they are highly susceptible to spoilage at room temperature. To maintain the quality and taste of deep-sea fish and extend their shelf life, their temperature must be quickly lowered and maintained at a suitable low temperature. This places high demands on the performance and control of refrigeration units. With increasing attention to energy and environmental issues, the refrigeration industry faces the dual pressures of energy conservation and environmental protection. Deep-sea fish refrigeration units must maintain cooling efficiency while minimizing energy consumption and environmental impact. This can be achieved by employing environmentally friendly refrigerants and optimizing refrigeration system operating parameters. Furthermore, with the continuous expansion of modern fishery operations, the demand for automated and intelligent control of refrigeration systems is increasing. Efficient control systems can enable remote monitoring, fault diagnosis, and automatic adjustment of refrigeration units, improving production efficiency, reducing labor costs, and ensuring stable refrigeration system operation.

[0003] However, in today's refrigeration preservation of deep-sea fish, since the transportation of deep-sea fish often requires crossing long latitudes and distances, the external environmental data changes greatly during transportation and preservation. The refrigeration parameters during current refrigeration preservation are relatively fixed and cannot be automatically adjusted according to environmental changes. When the refrigeration parameters are manually adjusted, the adjustment rate is relatively arbitrary, which greatly damages the preservation quality of deep-sea fish. Summary of the Invention

[0004] The object of the present invention is to provide a high-efficiency refrigeration unit control system and method for deep-sea fish, so as to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for controlling an efficient refrigeration unit for deep-sea fish, the method comprising the following steps:

[0007] S100, collecting all environmental data and refrigeration efficiency of the historical refrigeration unit during operation, calculating the impact of all environmental data on the refrigeration efficiency, and extracting the environmental data that affects the refrigeration efficiency as key influencing factors;

[0008] Furthermore, the specific steps for extracting environmental data that affect refrigeration efficiency as key influencing factors are as follows:

[0009] S101. Collect all historical environmental data and refrigeration efficiency of the refrigeration unit during operation, use refrigeration efficiency as the dependent variable and environmental data as the variable, and construct a multiple linear regression model of environmental data and refrigeration efficiency. The model formula is:

[0010] y=β0+β1×x1+β2×x2+...+β n ×x n ;

[0011] In the formula, y represents the cooling efficiency, x1, x2, ...x n represents the 1st, 2nd, ..., nth environmental data, β0 represents the constant term in the model, β1, β2, ..., β n The coefficients representing the 1st, 2nd, ..., nth environmental data;

[0012] S102, collect all the environmental data and refrigeration efficiency of the refrigeration unit during operation for m times as observation values, and set the observation value of the i-th environmental data as {x i1 、x i2 、x i3 ,...x im}, where x i1 、x i2 、x i3 ,...x im Represents the value of the i-th environmental data collected for the 1st, 2nd, 3rd, ...mth time. Use all the observation values ​​of the n types of environmental data to construct the independent variable observation matrix. Set the first column of the observation matrix to 1, corresponding to the constant term in the multivariate linear regression model. The number of elements in the independent variable observation matrix is ​​m × (n + 1). Use the m-times collected cooling efficiency to construct the variable observation matrix. The number of elements in the variable observation matrix is ​​m. Use the variable observation matrix and the independent variable observation matrix to calculate the coefficient matrix. The formula is:

[0013] β′=(X T X) -1 X T Y;

[0014] In the formula, β' represents the coefficient matrix, X represents the independent variable observation matrix, and X T represents the transposed matrix of the independent variable observation matrix, Y represents the variable observation matrix; β'=[β0, β1, β2, ..., β n ] T ;

[0015] S103, extract β1, β2, ..., β n As the impact value of the corresponding type of environmental data, the impact value threshold is manually set to β threshold , when |β i |>β thresholdWhen , the corresponding environmental data is judged as the key influencing factor G, and the key influencing factor set is obtained after judging the impact values ​​of all environmental data.

[0016] By collecting all environmental data and cooling efficiency data from historical refrigeration unit operations, we can fully understand the various factors that may affect cooling efficiency and avoid missing important information. Calculating the impact of all environmental data on cooling efficiency and extracting key influencing factors helps identify key areas of focus and provides a basis for subsequent cooling system optimization. These key factors can be controlled and adjusted in a targeted manner to improve cooling efficiency.

[0017] S200, analyzing the impact values ​​of all key influencing factors, extracting two classification nodes, and using the two classification nodes to classify the key influencing factors into primary factors, secondary factors, and tertiary factors;

[0018] Furthermore, the specific steps of using two classification nodes to classify key influencing factors into primary factors, secondary factors, and tertiary factors are as follows:

[0019] S201, the absolute value of all impact values ​​in the key impact factor set |β i | Sort from small to large, calculate the absolute value difference of adjacent influence values, compare and judge all the differences, extract the maximum difference and the second largest difference, and set the absolute value of the two influence values ​​corresponding to the maximum difference to be [β before , β after ], β before A classification node J1 is extracted from the second largest difference value, and a classification node J2 is extracted from the second largest difference value;

[0020] S202. Filter the two extracted classification nodes to obtain first-level nodes and second-level nodes. The formula is:

[0021] J frist =max{J1, J2}

[0022] J second =min{J1, J2};

[0023] In the formula, J first represents the first-level node, J second Represents the secondary node; use the two nodes to judge all key influencing factors. In the key influencing factor set, when |β i | <J second When J second ≤|β i |≤J first When |β i |>J firstWhen , it is judged as a first-level factor; after judging all key influencing factors in the key influencing factor set, three levels of factor sets are obtained.

[0024] Analyzing the impact values ​​of key influencing factors and extracting classification nodes, classifying key influencing factors into different levels, facilitates more detailed management and research of factors. Factors at different levels may require different management strategies and control measures, facilitating more targeted optimization.

[0025] S300, extracting the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collecting all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and constructing a relationship mapping model between the key influencing factors and refrigeration parameters;

[0026] Furthermore, the specific steps for constructing the relationship mapping model between key influencing factors and refrigeration parameters are as follows:

[0027] S301. Extract the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collect all key influencing factors G and refrigeration parameters P of the refrigeration unit during historical operation; divide the collected key influencing factors and refrigeration parameters into a training set, a validation set, and a test set; and standardize all collected data;

[0028] S302, constructing the structure of the mapping model, specifically: setting an input layer, the number of input layer nodes is the number of key influencing factor change values ​​collected, setting an output layer, the number of output layer nodes is the number of refrigeration parameters collected, setting a hidden layer, and manually setting the number of hidden layer nodes; using ReLU in the hidden layer and a linear activation function in the output layer; using machine learning to train the mapping model to obtain a specific relationship mapping model, the formula is:

[0029] P j =f(G1, G2, ..., G k ; W);

[0030] In the formula, P j represents the jth cooling parameter, G1, G1, ..., G1 represents the 1st, 2nd, ..., kth key influencing factors, and W represents the model weight parameter; the model weight parameter is obtained by machine training fitting.

[0031] By collecting key influencing factors and refrigeration parameters and constructing a relationship mapping model, we can clearly reveal the inherent connections between these factors. This helps us gain a deeper understanding of the refrigeration system's operating mechanisms and provides theoretical support for subsequent parameter adjustment and optimization. This model allows us to predict the changing trends of refrigeration parameters based on changes in key influencing factors, enabling us to make parameter adjustments in advance, achieve optimal control of the refrigeration system, and improve cooling performance and efficiency.

[0032] S400, calculating the cooling parameter adjustment rate corresponding to each level of key influencing factor using the influence values ​​of the key influencing factors at different levels;

[0033] Furthermore, the specific steps of calculating the refrigeration parameter adjustment rate corresponding to each level of key influencing factors using the influence values ​​of the key influencing factors of different levels are as follows:

[0034] S401. Collect historical records of abnormalities of deep-sea fish during refrigeration storage, extract the key influencing factors with the largest changes in each record, mark them as abnormal records caused by the corresponding key influencing factors, judge and count the key influencing factors in the records to obtain the total number of abnormal records at each level, and calculate the level weight of the key influencing factors at each level. The formula is:

[0035]

[0036] In the formula, Le v Indicates the level weight of the key impact factor of the vth level, C v Indicates the total number of abnormal records in the vth level, C z Indicates the total number of all abnormal records collected; calculate the level weights of the three levels of key influencing factors in sequence;

[0037] S402. Calculate the adjustment rate of the cooling parameters for the key influencing factors at each level using the formula:

[0038]

[0039] In the formula, V v represents the refrigeration parameter adjustment rate of the key influencing factor of the vth level, |βp| v represents the mean impact value of all key impact factors in the vth level, It represents the sum of the impact values ​​of all key influencing factors in the vth level; the same calculation results in the adjustment rates of the refrigeration parameters of the three levels of key influencing factors are V1, V2 and V3 respectively.

[0040] By calculating the cooling parameter adjustment rate corresponding to each level of key influencing factors, the system can precisely adjust cooling parameters based on the degree of influence of factors at different levels. This avoids problems such as energy waste and poor cooling performance caused by blindly adjusting parameters, achieving precise and intelligent cooling parameter adjustment. A reasonable adjustment rate helps maintain the stability of the cooling system, prevents system impacts caused by overly fast or slow parameter adjustments, and extends the service life of the refrigeration equipment.

[0041] S500. Collect the data values of the key influencing factors when the refrigerated preservation of historical deep - sea fish fails, and calculate the change thresholds for each key influencing factor respectively; during the preservation of deep - sea fish, continuously detect the data values of the key influencing factors in real - time and use the change thresholds to make judgment and early warning;

[0042] Further, the specific steps for continuously detecting the data values of the key influencing factors in real - time and using the change thresholds to make judgment and early warning during the preservation of deep - sea fish are as follows:

[0043] S501. Collect the data values of the key influencing factors when the refrigerated preservation of historical deep - sea fish fails, calculate the average value and standard deviation of the data values of the key influencing factors when the refrigerated preservation of historical deep - sea fish fails respectively, and calculate the change threshold using the average value and standard deviation. The formula is:

[0044] Th=x_p±x_b;

[0045] In the formula, Th represents the change threshold of the key influencing factor, x_p represents the average value of the data values of the key influencing factor, and x_b represents the standard deviation of the data values of the key influencing factor; when the influence value of the key influencing factor is negative, the + sign is selected for the threshold formula, and when the influence value of the key influencing factor is positive, the - sign is selected for the threshold formula;

[0046] S502. Continuously detect the data values of all key influencing factors during the preservation of deep - sea fish as xs, and use the change threshold to make judgment. When the influence value of the key influencing factor is negative, the judgment rule is: when xs≥Th, it is judged that there is an abnormal risk in refrigerated preservation for early warning; when xs < Th, it is judged that the refrigerated preservation is normal; [[ID=十七]]

[0047] When the influence value of the key influencing factor is positive, the judgment rule is opposite.

[0048] S600. When the refrigeration unit issues an early warning, collect the warning key influencing factors and input them into the relationship mapping model to calculate the adjustment target value of the refrigeration parameters; select the corresponding refrigeration parameter adjustment rate according to the level of the warning key influencing factor and adjust the refrigeration parameters to the target value.

[0049] Further, the specific steps for selecting the corresponding refrigeration parameter adjustment rate according to the level of the warning key influencing factor and adjusting the refrigeration parameters to the target value are as follows:

[0050] S601. When the refrigeration unit issues an early warning, collect the warning key influencing factors and input them into the relationship mapping model to calculate the adjustment target value Pm of the refrigeration parameters, search for the real - time warning key influencing factor in the three - level factor set to obtain the level of the real - time warning key influencing factor;

[0051] The corresponding refrigeration parameter adjustment rate is selected according to the level of the real-time warning key influencing factor to adjust the refrigeration parameters in the refrigeration unit until the adjustment target value Pm is reached.

[0052] When a refrigeration unit issues an alert, a relational mapping model is used to calculate the target values ​​for the refrigeration parameters. The system then selects the appropriate adjustment rate based on the level of the key influencing factors. This allows for rapid and accurate response to refrigeration system issues. Refrigeration parameters are promptly adjusted to restore the system to normal conditions, ensuring the preservation of deep-sea fish. Selecting the adjustment rate based on factor level further optimizes the adjustment strategy, making the adjustment process more scientific and rational. This approach not only accounts for the varying impacts of different factors on refrigeration efficiency, but also ensures effective adjustment while minimizing system disruption.

[0053] A high-efficiency refrigeration unit control system for deep-sea fish, comprising a data acquisition module, a key influencing factor search module, an influencing factor classification module, a mapping model construction module, an adjustment rate calculation module, an early warning module, and an adjustment module;

[0054] The data acquisition module is used to collect all environmental data and refrigeration efficiency of the historical refrigeration unit during operation, and to collect historical records of abnormalities of deep-sea fish during refrigeration storage;

[0055] The key influencing factor search module is used to calculate the impact value of all environmental data on the refrigeration efficiency, and extract the environmental data that affects the refrigeration efficiency as the key influencing factors;

[0056] The impact factor classification module is used to analyze the impact values ​​of all key impact factors, extract two classification nodes, and use the two classification nodes to classify the key impact factors into primary factors, secondary factors, and tertiary factors;

[0057] The mapping model building module is used to collect all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and build a relationship mapping model between the key influencing factors and refrigeration parameters;

[0058] The regulation rate calculation module is used to calculate the refrigeration parameter regulation rate corresponding to each level of key influencing factors using the influence values ​​of key influencing factors of different levels;

[0059] The early warning module is used to calculate the change threshold of each key influencing factor and make real-time judgment and early warning;

[0060] The regulating module is used to select a regulating rate to regulate the refrigeration parameters according to the level of the warning key influencing factor after the refrigeration unit issues an early warning.

[0061] The regulation rate calculation module includes a level weight calculation unit and a regulation rate calculation unit;

[0062] The level weight calculation unit is used to judge and count the key influencing factors in the records to obtain the total number of abnormal records in each level, and calculate the level weight of the key influencing factors of each level;

[0063] The regulation rate calculation unit is used to calculate the corresponding refrigeration parameter regulation rate of each level of key influencing factors using the influence values ​​and level weights of key influencing factors of different levels.

[0064] The early warning module includes a threshold calculation unit and a judgment and early warning unit;

[0065] The threshold calculation unit is used to respectively calculate the average value and standard deviation of the key influencing factor data values ​​when the deep-sea fish refrigeration preservation fails in history, and calculate the change threshold value using the average value and standard deviation;

[0066] The judgment and early warning unit is used to make real-time judgment and early warning on each key influencing factor using a change threshold.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. This invention calculates the impact of environmental data on refrigeration efficiency, accurately identifies key influencing factors, and calculates corresponding refrigeration parameter adjustment rates based on the impact values ​​of key influencing factors at different levels, achieving precise adjustment of refrigeration parameters. This helps minimize energy consumption, improve energy utilization efficiency, and achieve energy conservation goals while meeting refrigeration needs.

[0069] 2. The present invention constructs a mapping model between key influencing factors and refrigeration parameters. This model can adjust refrigeration parameters in real time based on changes in key influencing factors, keeping the refrigeration system in optimal operating condition and improving refrigeration efficiency. During the deep-sea fish preservation process, by real-time monitoring of key influencing factor data values ​​and using change thresholds for early warning, issues that may affect refrigeration preservation can be promptly identified and resolved, helping to ensure deep-sea fish quality and reduce product losses caused by improper refrigeration. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a module distribution diagram of a high-efficiency refrigeration unit control system for deep-sea fish according to the present invention;

[0071] Figure 2 The present invention is a schematic diagram of the steps of a high-efficiency refrigeration unit control method for deep-sea fish. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0073] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0074] A method for controlling an efficient refrigeration unit for deep-sea fish, the method comprising the following steps:

[0075] S100, collecting all environmental data and refrigeration efficiency of the historical refrigeration unit during operation, calculating the impact of all environmental data on the refrigeration efficiency, and extracting the environmental data that affects the refrigeration efficiency as key influencing factors;

[0076] The specific steps for extracting environmental data that affect refrigeration efficiency as key influencing factors are as follows:

[0077] S101. Collect all historical environmental data and refrigeration efficiency of the refrigeration unit during operation, use refrigeration efficiency as the dependent variable and environmental data as the variable, and construct a multiple linear regression model of environmental data and refrigeration efficiency. The model formula is:

[0078] y=β0+β1×x1+β2×x2+...+β n ×x n ;

[0079] In the formula, y represents the cooling efficiency, x1, x2, ...x n represents the 1st, 2nd, ..., nth environmental data, β0 represents the constant term in the model, β1, β2, ..., β n The coefficients representing the 1st, 2nd, ..., nth environmental data;

[0080] S102, collect all the environmental data and refrigeration efficiency of the refrigeration unit during operation for m times as observation values, and set the observation value of the i-th environmental data as {x i1 、x i2 、x i3 ,...x im}, where x i1 、x i2 、x i3 ,...x imRepresents the value of the i-th environmental data collected for the 1st, 2nd, 3rd, ...mth time. Use all the observation values ​​of the n types of environmental data to construct the independent variable observation matrix. Set the first column of the observation matrix to 1, corresponding to the constant term in the multivariate linear regression model. The number of elements in the independent variable observation matrix is ​​m × (n + 1). Use the m-times collected cooling efficiency to construct the variable observation matrix. The number of elements in the variable observation matrix is ​​m. Use the variable observation matrix and the independent variable observation matrix to calculate the coefficient matrix. The formula is:

[0081] β′=(X T X) -1 X T Y;

[0082] In the formula, β' represents the coefficient matrix, X represents the independent variable observation matrix, and X T represents the transposed matrix of the independent variable observation matrix, Y represents the variable observation matrix; β'=[β0, β1, β2, ..., β n ] T ;

[0083] S103, extract β1, β2, ..., β n As the impact value of the corresponding type of environmental data, the impact value threshold is manually set to β threshold , when |β i |>β threshold When , the corresponding environmental data is judged as the key influencing factor G, and the key influencing factor set is obtained after judging the impact values ​​of all environmental data.

[0084] By collecting all environmental data and cooling efficiency data from historical refrigeration unit operations, we can fully understand the various factors that may affect cooling efficiency and avoid missing important information. Calculating the impact of all environmental data on cooling efficiency and extracting key influencing factors helps identify key areas of focus and provides a basis for subsequent cooling system optimization. These key factors can be controlled and adjusted in a targeted manner to improve cooling efficiency.

[0085] S200, analyzing the impact values ​​of all key influencing factors, extracting two classification nodes, and using the two classification nodes to classify the key influencing factors into primary factors, secondary factors, and tertiary factors;

[0086] The specific steps for using two classification nodes to classify key influencing factors into primary factors, secondary factors, and tertiary factors are as follows:

[0087] S201, the absolute value of all impact values ​​in the key impact factor set |β i| Sort from small to large, calculate the absolute value difference of adjacent influence values, compare and judge all the differences, extract the maximum difference and the second largest difference, and set the absolute value of the two influence values ​​corresponding to the maximum difference to be [β before , β after ], β before A classification node J1 is extracted from the second largest difference value, and a classification node J2 is extracted from the second largest difference value;

[0088] S202. Filter the two extracted classification nodes to obtain first-level nodes and second-level nodes. The formula is:

[0089] J frist =max{J1, J2}

[0090] J second =min{J1, J2};

[0091] In the formula, J first represents the first-level node, J second Represents the secondary node; use the two nodes to judge all key influencing factors. In the key influencing factor set, when |β i | <J second When J second ≤|β i |≤J first When |β i |>J first When , it is judged as a first-level factor; after judging all key influencing factors in the key influencing factor set, three levels of factor sets are obtained.

[0092] Analyzing the impact values ​​of key influencing factors and extracting classification nodes, classifying key influencing factors into different levels, facilitates more detailed management and research of factors. Factors at different levels may require different management strategies and control measures, facilitating more targeted optimization.

[0093] S300, extracting the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collecting all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and constructing a relationship mapping model between the key influencing factors and refrigeration parameters;

[0094] The specific steps for constructing the relationship mapping model between key influencing factors and refrigeration parameters are as follows:

[0095] S301. Extract the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collect all key influencing factors G and refrigeration parameters P of the refrigeration unit during historical operation; divide the collected key influencing factors and refrigeration parameters into a training set, a validation set, and a test set; and standardize all collected data;

[0096] S302, constructing the structure of the mapping model, specifically: setting an input layer, the number of input layer nodes is the number of key influencing factor change values ​​collected, setting an output layer, the number of output layer nodes is the number of refrigeration parameters collected, setting a hidden layer, and manually setting the number of hidden layer nodes; using ReLU in the hidden layer and a linear activation function in the output layer; using machine learning to train the mapping model to obtain a specific relationship mapping model, the formula is:

[0097] P j =f(G1, G2, ..., G k ; W);

[0098] In the formula, P j represents the jth cooling parameter, G1, G1, ..., G1 represents the 1st, 2nd, ..., kth key influencing factors, and W represents the model weight parameter; the model weight parameter is obtained by machine training fitting.

[0099] By collecting key influencing factors and refrigeration parameters and constructing a relationship mapping model, we can clearly reveal the inherent connections between these factors. This helps us gain a deeper understanding of the refrigeration system's operating mechanisms and provides theoretical support for subsequent parameter adjustment and optimization. This model allows us to predict the changing trends of refrigeration parameters based on changes in key influencing factors, enabling us to make parameter adjustments in advance, achieve optimal control of the refrigeration system, and improve cooling performance and efficiency.

[0100] S400, calculating the cooling parameter adjustment rate corresponding to each level of key influencing factor using the influence values ​​of the key influencing factors at different levels;

[0101] The specific steps for calculating the corresponding refrigeration parameter adjustment rate of each level of key influencing factors using the influence values ​​of different levels of key influencing factors are as follows:

[0102] S401. Collect historical records of abnormalities of deep-sea fish during refrigeration storage, extract the key influencing factors with the largest changes in each record, mark them as abnormal records caused by the corresponding key influencing factors, judge and count the key influencing factors in the records to obtain the total number of abnormal records at each level, and calculate the level weight of the key influencing factors at each level. The formula is:

[0103]

[0104] In the formula, Le v Indicates the level weight of the key impact factor of the vth level, C v Indicates the total number of abnormal records in the vth level, C z Indicates the total number of all abnormal records collected; calculate the level weights of the three levels of key influencing factors in sequence;

[0105] S402. Calculate the adjustment rate of the cooling parameters for the key influencing factors at each level using the formula:

[0106]

[0107] In the formula, V v represents the refrigeration parameter adjustment rate of the key influencing factor of the vth level, |βp| v represents the mean impact value of all key impact factors in the vth level, It represents the sum of the impact values ​​of all key influencing factors in the vth level; the same calculation results in the adjustment rates of the refrigeration parameters of the three levels of key influencing factors are V1, V2 and V3 respectively.

[0108] By calculating the cooling parameter adjustment rate corresponding to each level of key influencing factors, the system can precisely adjust cooling parameters based on the degree of influence of factors at different levels. This avoids problems such as energy waste and poor cooling performance caused by blindly adjusting parameters, achieving precise and intelligent cooling parameter adjustment. A reasonable adjustment rate helps maintain the stability of the cooling system, prevents system impacts caused by overly fast or slow parameter adjustments, and extends the service life of the refrigeration equipment.

[0109] S500, collecting data values ​​of key influencing factors when deep-sea fish refrigeration preservation fails in history, and calculating change thresholds of each key influencing factor; detecting data values ​​of key influencing factors in real time when the deep-sea fish is preserved and using the change thresholds to make judgments and early warnings;

[0110] The specific steps for real-time detection of the data values ​​of key influencing factors during deep-sea fish storage and use of change thresholds for judgment and early warning are as follows:

[0111] S501. Collect historical data values ​​of key influencing factors when deep-sea fish fail to be refrigerated and stored, calculate the average value and standard deviation of the key influencing factors when deep-sea fish fail to be refrigerated and stored, and calculate the change threshold value using the average value and standard deviation. The formula is:

[0112] Th=x_p±x_b;

[0113] In the formula, Th represents the change threshold of the key influencing factor, x_p represents the mean value of the key influencing factor data, and x_b represents the standard deviation of the key influencing factor data. When the impact value of the key influencing factor is negative, the threshold formula sign is +, and when the impact value of the key influencing factor is positive, the threshold formula sign is -.

[0114] S502. When the data values of all key influencing factors are detected in real time during the preservation of deep-sea fish as xs, and judgment is made using the change threshold. When the influence value of the key influencing factor is negative, the judgment rule is: when xs≥Th, it is judged that there is an abnormal risk in refrigeration preservation and an early warning is given; when xs < Th, it is judged that the refrigeration preservation is normal;

[0115] When the influence value of the key influencing factor is positive, the judgment rule is opposite.

[0116] S600. When the refrigeration unit gives an early warning, collect the warning key influencing factors and input them into the relationship mapping model to calculate the adjustment target value of the refrigeration parameters; select the corresponding refrigeration parameter adjustment rate according to the level of the warning key influencing factor and adjust the refrigeration parameters to the target value.

[0117] The specific steps of selecting the corresponding refrigeration parameter adjustment rate according to the level of the warning key influencing factor and adjusting the refrigeration parameters to the target value are as follows: <00OO338>

[0118] S601. When the refrigeration unit gives an early warning, collect the warning key influencing factors and input them into the relationship mapping model to calculate the adjustment target value Pm of the refrigeration parameters. Search for the real-time warning key influencing factors in the three-level factor set to obtain the level of the real-time warning key influencing factor;

[0119] Use the level of the real-time warning key influencing factor to select the corresponding refrigeration parameter adjustment rate to adjust the refrigeration parameters in the refrigeration unit until reaching the adjustment target value Pm and then stop. <00003)42>

[0120] When the refrigeration unit gives an early warning, calculate the adjustment target value of the refrigeration parameters using the relationship mapping model, and select the corresponding adjustment rate according to the level of the warning key influencing factor for adjustment. It can quickly and accurately respond to problems in the refrigeration system. Adjust the refrigeration parameters in a timely manner to restore the system to a normal state and ensure the preservation conditions of deep-sea fish. Selecting the adjustment rate according to the factor level further optimizes the adjustment strategy, making the adjustment process more scientific and reasonable. It not only considers the different influences of different factors on the refrigeration efficiency but also minimizes the interference to the system while ensuring the adjustment effect.

[0121] An efficient refrigeration unit control system for deep-sea fish, the efficient refrigeration unit control system includes a data acquisition module, a key influencing factor search module, an influencing factor classification module, a mapping model construction module, an adjustment rate calculation module, an early warning module, and an adjustment module;

[0122] The data acquisition module is used to collect all environmental data and refrigeration efficiency during the operation of the historical refrigeration unit, and collect the records of abnormalities that occurred during the refrigeration preservation of deep-sea fish in history;

[0123] The key influencing factor search module is used to calculate the impact value of all environmental data on the refrigeration efficiency, and extract the environmental data that affects the refrigeration efficiency as the key influencing factors;

[0124] The impact factor classification module is used to analyze the impact values ​​of all key impact factors, extract two classification nodes, and use the two classification nodes to classify the key impact factors into primary factors, secondary factors, and tertiary factors;

[0125] The mapping model building module is used to collect all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and build a relationship mapping model between the key influencing factors and refrigeration parameters;

[0126] The regulation rate calculation module is used to calculate the refrigeration parameter regulation rate corresponding to each level of key influencing factors using the influence values ​​of key influencing factors of different levels;

[0127] The early warning module is used to calculate the change threshold of each key influencing factor and make real-time judgment and early warning;

[0128] The regulating module is used to select a regulating rate to regulate the refrigeration parameters according to the level of the warning key influencing factor after the refrigeration unit issues an early warning.

[0129] The regulation rate calculation module includes a level weight calculation unit and a regulation rate calculation unit;

[0130] The level weight calculation unit is used to judge and count the key influencing factors in the records to obtain the total number of abnormal records in each level, and calculate the level weight of the key influencing factors of each level;

[0131] The regulation rate calculation unit is used to calculate the corresponding refrigeration parameter regulation rate of each level of key influencing factors using the influence values ​​and level weights of key influencing factors of different levels.

[0132] The early warning module includes a threshold calculation unit and a judgment and early warning unit;

[0133] The threshold calculation unit is used to respectively calculate the average value and standard deviation of the key influencing factor data values ​​when the deep-sea fish refrigeration preservation fails in history, and calculate the change threshold value using the average value and standard deviation;

[0134] The judgment and early warning unit is used to make real-time judgment and early warning on each key influencing factor using a change threshold.

[0135] Example: During operation, a deep-sea fish refrigeration unit needs to dynamically adjust refrigeration parameters based on environmental factors to maintain preservation. Through historical data analysis, the following key influencing factors and their influence coefficients on refrigeration efficiency have been identified:

[0136] Ambient temperature: 0.9, seawater salinity: 0.8, compressor load: 0.6, humidity: 0.4, air pressure: 0.3; sorted from smallest to largest: 0.1, 0.3, 0.5, 0.7, 0.9; the calculated first-level node is 0.6, and the second-level node is 0.4; the first-level factors are: ambient temperature, seawater salinity; the second-level factors are: compressor load, humidity; and the third-level factor is: air pressure;

[0137] The comprehensive impact values ​​of the three levels are calculated to be 1.7, 1 and 0.3 respectively; the level weights of the three levels are set to be 0.5, 0.4 and 0.1 respectively; the cooling parameter adjustment rates of the three levels are calculated to be 0.25, 0.2 and 0.03.

[0138] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A high-efficiency refrigeration unit control method for deep-sea fish, characterized by: The method comprises the following steps: S100, collecting all environmental data and refrigeration efficiency of the historical refrigeration unit during operation, calculating the impact of all environmental data on the refrigeration efficiency, and extracting the environmental data that affects the refrigeration efficiency as key influencing factors; The specific steps for extracting environmental data that affect refrigeration efficiency as key influencing factors are as follows: S101. Collect all historical environmental data and refrigeration efficiency of the refrigeration unit during operation, use refrigeration efficiency as the dependent variable and environmental data as the variable, and construct a multiple linear regression model of environmental data and refrigeration efficiency. The model formula is: ； In the formula, y represents the cooling efficiency, x1, x2, ...x n represents the 1st, 2nd, ..., nth environmental data, β0 represents the constant term in the model, β1, β2, ..., β n The coefficients representing the 1st, 2nd, ..., nth environmental data; S102, collect all the environmental data and refrigeration efficiency of the refrigeration unit during operation for m times as observation values, and set the observation value of the i-th environmental data as {x i1 、x i2 、x i3 ,...x im }, where x i1 、x i2 、x i3 ,...x im Represents the value of the i-th environmental data collected for the 1st, 2nd, 3rd, ...mth time. Use all the observation values ​​of n types of environmental data to construct the independent variable observation matrix. Set the first column of the observation matrix to 1, corresponding to the constant term in the multivariate linear regression model. The number of elements in the independent variable observation matrix is ​​m × (n + 1). Use the m-times collected cooling efficiency to construct the variable observation matrix. The number of elements in the variable observation matrix is ​​m. Use the variable observation matrix and the independent variable observation matrix to calculate the coefficient matrix. The formula is: ； In the formula, β' represents the coefficient matrix, X represents the independent variable observation matrix, and X T represents the transposed matrix of the independent variable observation matrix, Y represents the variable observation matrix; β'=[β0, β1, β2, ..., β n ] T ; S103, extract β1, β2, ..., β n As the impact value of the corresponding type of environmental data, the impact value threshold is manually set to β threshold , when |β i |>β threshold When , the corresponding environmental data is judged as the key influencing factor G, and the key influencing factor set is obtained after judging the impact values ​​of all environmental data; S200, analyzing the impact values ​​of all key influencing factors, extracting two classification nodes, and using the two classification nodes to classify the key influencing factors into primary factors, secondary factors, and tertiary factors; S300, extracting the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collecting all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and constructing a relationship mapping model between the key influencing factors and refrigeration parameters; S400, calculating the cooling parameter adjustment rate corresponding to each level of key influencing factor using the influence values ​​of the key influencing factors at different levels; S500, collecting data values ​​of key influencing factors when deep-sea fish refrigeration preservation fails in history, and calculating change thresholds of each key influencing factor; detecting data values ​​of key influencing factors in real time when the deep-sea fish is preserved and using the change thresholds to make judgments and early warnings; S600: When the refrigeration unit issues an early warning, the early warning key influencing factors are collected and input into the relationship mapping model to calculate the adjustment target value of the refrigeration parameter; according to the level of the early warning key influencing factors, the corresponding refrigeration parameter adjustment rate is selected to adjust the refrigeration parameter to the target value.

2. The method for controlling a high-efficiency refrigeration unit for deep-sea fish according to claim 1, characterized in that: The specific steps of using two classification nodes in S200 to classify key influencing factors into primary factors, secondary factors, and tertiary factors are as follows: S201, the absolute value of all impact values ​​in the key impact factor set |β i | Sort from small to large, calculate the absolute value difference of adjacent influence values, compare and judge all the differences, extract the maximum difference and the second largest difference, and set the absolute value of the two influence values ​​corresponding to the maximum difference to be [β before , β after ], β before A classification node J1 is extracted from the second largest difference value, and a classification node J2 is extracted from the second largest difference value; S202. Filter the two extracted classification nodes to obtain first-level nodes and second-level nodes. The formula is: ； In the formula, J first represents the first-level node, J second Represents the secondary node; use the two nodes to judge all key influencing factors. In the key influencing factor set, when |β i | <J second When J second ≤|β i |≤J first When |β i |>J first When , it is judged as a first-level factor; after judging all key influencing factors in the key influencing factor set, three levels of factor sets are obtained.

3. The method for controlling a high-efficiency refrigeration unit for deep-sea fish according to claim 2, characterized in that: The specific steps of constructing the relationship mapping model between key influencing factors and refrigeration parameters in S300 are: S301. Extract the equipment parameters of the refrigeration unit during cooling as refrigeration parameters, collect all key influencing factors G and refrigeration parameters P of the refrigeration unit during historical operation; divide the collected key influencing factors and refrigeration parameters into a training set, a validation set, and a test set; and standardize all collected data; S302, constructing the structure of the mapping model, specifically: setting an input layer, the number of input layer nodes is the number of key influencing factor change values ​​collected, setting an output layer, the number of output layer nodes is the number of refrigeration parameters collected, setting a hidden layer, and manually setting the number of hidden layer nodes; using ReLU in the hidden layer and a linear activation function in the output layer; using machine learning to train the mapping model to obtain a specific relationship mapping model, the formula is: ； In the formula, P j represents the jth cooling parameter, G1, G1, ..., G1 represents the 1st, 2nd, ..., kth key influencing factors, and W represents the model weight parameter; the model weight parameter is obtained by machine training fitting.

4. The method for controlling a high-efficiency refrigeration unit for deep-sea fish according to claim 3, characterized in that: The specific steps of calculating the cooling parameter adjustment rate corresponding to each level of key influencing factor using the influence values ​​of key influencing factors of different levels in S400 are: S401. Collect the records of abnormalities that occurred during the refrigerated storage of deep-sea fish in history, extract the key influencing factors with the largest changes in each record, mark them as abnormal records caused by the corresponding key influencing factors, judge and count the key influencing factors in the records to obtain the total number of abnormal records within each level, and calculate the level weights of the key influencing factors at each level. The formula is as follows: ； In the formula, Le v Indicates the level weight of the key impact factor of the vth level, C v Indicates the total number of abnormal records in the vth level, C z Indicates the total number of all abnormal records collected; calculate the level weights of the three levels of key influencing factors in sequence; S402. Calculate the adjustment rate of the refrigeration parameters for each level of key influencing factors. The formula is as follows: ； In the formula, V v represents the refrigeration parameter adjustment rate of the key influencing factor of the vth level, |βp| v represents the mean impact value of all key impact factors in the vth level, It represents the sum of the impact values ​​of all key influencing factors in the vth level; the same calculation results in the adjustment rates of the refrigeration parameters of the three levels of key influencing factors are V1, V2 and V3 respectively.

5. The method for controlling a high-efficiency refrigeration unit for deep-sea fish according to claim 4, characterized in that: The specific steps for real-time detecting the data values of the key influencing factors and using the change threshold for judgment and warning during the storage of deep-sea fish in S500 are as follows: S501. Collect the data values of the key influencing factors when the refrigerated storage of historical deep-sea fish fails. Calculate the average value and standard deviation of the data values of the key influencing factors when the refrigerated storage of historical deep-sea fish fails, and calculate the change threshold using the average value and standard deviation. The formula is as follows: ； In the formula, Th represents the change threshold of the key influencing factor, x_p represents the average value of the data values of the key influencing factor, and x_b represents the standard deviation of the data values of the key influencing factor; when the influencing value of the key influencing factor is negative, the + sign is selected for the threshold formula symbol, and when the influencing value of the key influencing factor is positive, the - sign is selected for the threshold formula symbol. S502. Real-time detect the data values of all key influencing factors as xs during the storage of deep-sea fish, and use the change threshold for judgment. When the influencing value of the key influencing factor is negative, the judgment rule is: when xs ≥ Th, it is judged that there is an abnormal risk in the refrigerated storage and a warning is issued; when xs < Th, it is judged that the refrigerated storage is normal. When the influencing value of the key influencing factor is positive, the judgment rule is opposite.

6. The method for controlling a high-efficiency refrigeration unit for deep-sea fish according to claim 5, characterized in that: The specific steps for adjusting the refrigeration parameters to the target value according to the level of the warning key influencing factor in S600 are as follows: S601. When the refrigeration unit issues a warning, collect the warning key influencing factor and input it into the relationship mapping model to calculate the adjustment target value Pm of the refrigeration parameters. Search for the real-time warning key influencing factor in the three-level factor set to obtain the level of the real-time warning key influencing factor. Use the level of the real-time warning key influencing factor to select the corresponding refrigeration parameter adjustment rate to adjust the refrigeration parameters in the refrigeration unit until the adjustment target value Pm is reached and then stop.

7. A high-efficiency refrigeration unit control system for deep-sea fish using the high-efficiency refrigeration unit control method for deep-sea fish according to any one of claims 1 to 6, characterized in that: The high-efficiency refrigeration unit control system includes a data acquisition module, a key influencing factor search module, an influencing factor classification module, a mapping model construction module, an adjustment rate calculation module, a warning module, and an adjustment module. The data acquisition module is used to collect all environmental data and refrigeration efficiency during the operation of the historical refrigeration unit, and collect the records of abnormalities that occurred during the refrigerated storage of deep-sea fish in history. The key influencing factor search module is used to calculate the influencing values of all environmental data on the refrigeration efficiency, and extract the environmental data that affects the refrigeration efficiency as the key influencing factors. The influencing factor classification module is used to analyze the influencing values of all key influencing factors, extract two classification nodes, and use the two classification nodes to divide the key influencing factors into primary factors, secondary factors, and tertiary factors. The mapping model building module is used to collect all key influencing factors and refrigeration parameters of the historical refrigeration unit during operation, and build a relationship mapping model between the key influencing factors and refrigeration parameters; The regulation rate calculation module is used to calculate the refrigeration parameter regulation rate corresponding to each level of key influencing factors using the influence values ​​of key influencing factors of different levels; The early warning module is used to calculate the change threshold of each key influencing factor and make real-time judgment and early warning; The regulating module is used to select a regulating rate to regulate the refrigeration parameters according to the level of the warning key influencing factor after the refrigeration unit issues an early warning.

8. The high-efficiency refrigeration unit control system for deep-sea fish according to claim 7, characterized in that: The adjustment rate calculation module includes a level weight calculation unit and an adjustment rate calculation unit; The level weight calculation unit is used to judge and count the key influencing factors in the records to obtain the total number of abnormal records in each level, and calculate the level weight of the key influencing factors of each level; The regulation rate calculation unit is used to calculate the corresponding refrigeration parameter regulation rate of each level of key influencing factors using the influence values ​​and level weights of key influencing factors of different levels.

9. The high-efficiency refrigeration unit control system for deep-sea fish according to claim 7, characterized in that: The early warning module includes a threshold calculation unit and a judgment and early warning unit; The threshold calculation unit is used to respectively calculate the average value and standard deviation of the key influencing factor data values ​​when the deep-sea fish refrigeration preservation fails in history, and calculate the change threshold value using the average value and standard deviation; The judgment and early warning unit is used to make real-time judgment and early warning on each key influencing factor using a change threshold.

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