Fishery greenhouse environment control method

Abstract

The invention relates to the technical field of intelligent agriculture, in particular to a fishery greenhouse environment control method, which comprises the following steps: performing data transmission and preprocessing on a collected sensing signal to obtain a preprocessed sensor assembly signal to provide basic information for subsequent analysis; the method comprises the steps of performing initial detection on a preprocessed sensor assembly signal, calculating a fishery greenhouse environment influence coefficient, further analyzing a fishery greenhouse environment influence level, obtaining an influence judgment result of the fishery greenhouse environment, performing secondary detection on fish growth condition data, generating an environment detection subset, and performing early warning according to an early warning rule. Different early warning measures are distributed to cope with different environment abnormal conditions so as to maintain a good breeding environment. The method is used for solving the technical problem that the fishery breeding benefit is not high due to the fact that the fishery greenhouse environment is not precisely controlled in the existing scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of smart agriculture, and particularly to a method for controlling the environment of a fishery greenhouse. Background Art

[0002] Smart agriculture refers to the technology of using modern information technology, communication technology, automation technology and other means to intelligently manage and control the agricultural production process. These technologies include the Internet of Things, big data analysis, artificial intelligence, unmanned aerial vehicles, sensor technology, etc., aiming to improve the efficiency, quality and sustainability of agricultural production.

[0003] In fishery farming, the environment of the fishery greenhouse directly affects the growth, health and farming benefits of the cultured species. For example, the large temperature fluctuation inside the fishery greenhouse may lead to a decrease in the physiological adaptability of the cultured species, affecting its growth rate and immunity; the light may be insufficient or uneven, affecting the growth and development of the cultured species, etc. Therefore, effective environmental control and management are crucial for the success of fishery greenhouse farming. However, in the environmental management of fishery greenhouses, various parameters in the fishery greenhouse environment may interact with each other, but the current environmental control methods may not be able to comprehensively consider these influence relationships well. Therefore, abnormal problems in the fishpond farming state are not easily detected, and there is a lack of effective environmental monitoring methods and control measures, thus affecting the farming benefits. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems in the background art, and a method for controlling the environment of a fishery greenhouse is proposed.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for controlling the environment of a fishery greenhouse includes:

[0007] Step 1: Complete environmental data collection through a sensor assembly installed at the monitoring points in the fishery greenhouse; among them, the sensor assembly is composed of multiple sensors, including a temperature sensor, a humidity sensor, a light intensity sensor, and a carbon dioxide sensor;

[0008] Step 2: Transmit and preprocess the collected sensing signals wirelessly to obtain the signals of the sensor assembly after preprocessing;

[0009] Step 3: Divide the monitoring period, perform an initial detection on the signals of the sensor assembly after preprocessing, and then obtain the environmental impact coefficient of the fishery greenhouse;

[0010] Step 4: Analyze the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse, and obtain the environmental impact determination result of the fishery greenhouse;

[0011] Step 5: Obtain the corresponding fish growth status data from the management background for the fishery greenhouse environmental impact determination result for secondary detection, and process and analyze to obtain an environmental detection subset.

[0012] Step 6: Combine the environmental detection subset to conduct a warning assessment on the fishery greenhouse environment, formulate warning rules, and allocate warning measures.

[0013] It should be noted that the application object of a fishery greenhouse environment control method proposed by the present invention can be the monitoring of the internal environment of the fishery greenhouse, and can be used to monitor whether the breeding state of the internal environment parameters of the fishery greenhouse is abnormal. Specifically, it can comprehensively monitor, accurately analyze, and optimize the temperature, humidity, light intensity, and carbon dioxide concentration of the internal environment of the fishery greenhouse during the fishery breeding process through intelligent agricultural technology, judge whether the fish growth status inside the fishery greenhouse is normal, promptly discover abnormal situations, and take corresponding measures for adjustment to maintain a good breeding environment. Among them, the environmental parameters inside the fishery greenhouse are monitored in real time through sensors, and through data preprocessing and analysis, the change trend of the internal environment of the fishery greenhouse can be observed more intuitively, realizing the refined management of the fishery greenhouse environment, effectively saving energy and resources, reducing energy consumption during the breeding process, and achieving the purpose of environmental protection and energy conservation.

[0014] Furthermore, the process of transmitting and preprocessing the collected sensing signals in a wireless manner to obtain the preprocessed sensor component signals includes:

[0015] Collect the above-mentioned sensor component signals according to a certain sampling period.

[0016] Perform normalization processing on the data collected by different sensors, and scale the collected periodic data to a unified range.

[0017] Use PCA for data dimensionality reduction, analyze the temperature, humidity, light intensity, and carbon dioxide concentration data of the sensor components, screen out the main components that can best represent the changes in the fishery greenhouse environment data, and then obtain the dimensionality-reduced data set.

[0018] Extract the sensor component signals corresponding to the data set according to the obtained data set.

[0019] Furthermore, the process of using PCA for data dimensionality reduction includes:

[0020] Collect the original data of temperature, humidity, light intensity, and carbon dioxide concentration.

[0021] Arbitrarily obtain two original variables, and use the covariance matrix to measure the linear relationship between the two random original variables; among them, for two original variables x and y, their original covariance is obtained through the following formula:

[0022]

[0023] Wherein, xi and yi are the observed values corresponding to x and y respectively; are the means of x and y respectively; n is the total number of data; the value range of the covariance is any real number. When the covariance is positive, it indicates that x and y are positively correlated; when the covariance is negative, it indicates that x and y are negatively correlated; when the covariance is zero, it indicates that there is no linear correlation between x and y;

[0024] Fill the covariances showing linear correlation between all random original variables into the environmental covariance matrix to obtain the environmental covariance matrix C:

[0025]

[0026] Perform original eigenvalue decomposition on the environmental covariance matrix C to obtain environmental eigenvalues and corresponding environmental eigenvectors; wherein, the decomposition process is as follows: solve the environmental eigenvalue equation (C - xI)y = 0 to obtain the environmental eigenvalues as x1, x2,..., xn, and the corresponding environmental eigenvectors as y1, y2,..., yn; wherein, I is the identity matrix;

[0027] Sort the environmental eigenvectors according to the magnitudes of the environmental eigenvalues, and select the environmental eigenvectors corresponding to the largest k environmental eigenvalues as the principal components; wherein, k is the number of dimensions desired to be retained;

[0028] Project the original data onto the selected principal components to obtain the reduced - dimensional dataset.

[0029] Further, the process of initially detecting the pre - processed sensor component signals and then obtaining the environmental impact coefficient of the fishery greenhouse includes:

[0030] Use the following detection formula to obtain the environmental impact coefficient η(s) of the fishery greenhouse:

[0031]

[0032] Wherein, j = 1, 2, 3, respectively representing temperature, humidity, and carbon dioxide concentration; represents the air quality index in the s - th period of the initial detection of the fishery greenhouse environment; respectively represent the temperature impact factor, humidity impact factor, and carbon dioxide concentration impact factor corresponding to the preset air quality index; Δhj respectively represent the pre - determined temperature reference difference, humidity reference difference, and carbon dioxide concentration reference difference; respectively represent the temperature value, humidity value, and carbon dioxide concentration value in the s - th period of the initial detection of the fishery greenhouse environment; They represent the mean temperature, humidity and carbon dioxide concentration of the sth period in the initial detection of the fishery greenhouse environment; dj represents the air quality index weight factor corresponding to the preset temperature, humidity and carbon dioxide concentration; b' s represents the mean light intensity of the sth period in the initial detection of the fishery greenhouse environment; b s It represents the light intensity value in the sth period in the initial detection of the fishery greenhouse environment; Lif represents the light intensity influencing factor.

[0033] Furthermore, the process of analyzing the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse and obtaining the environmental impact determination result of the fishery greenhouse includes:

[0034] The values ​​of the environmental impact coefficients of the fishery greenhouses during all monitoring periods were arranged in descending order, and the maximum value Nmax and the minimum value Nmin were eliminated and the average was taken to obtain the environmental impact benchmark value N0;

[0035] Combine the maximum value to obtain the fluctuation range of environmental impact In the formula, N1 and N2 are the medians of Nmin and N0, Nmax and N0 respectively;

[0036] When the environmental impact coefficient of the fishery greenhouse is graded and matched according to the environmental impact fluctuation range, the labels of no impact, slight impact, general impact and severe impact are obtained;

[0037] Among them, if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (1), it is judged that there is no impact on the environment of the fishery greenhouse and it is in normal operation state, and it is associated with a no impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (2), it is judged that the environmental impact of the fishery greenhouse is small, and it is associated with a slight impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (3), it is judged that the environmental impact of the fishery greenhouse is general, and it is associated with a general impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (4), it is judged that the environmental impact of the fishery greenhouse is extremely obvious, and it is associated with a serious impact label;

[0038] The environmental impact assessment results of fishery greenhouses are composed of slight impact labels, general impact labels and severe impact labels, and a secondary detection signal is generated.

[0039] Furthermore, the corresponding fish growth status data of the management background is obtained for secondary detection based on the environmental impact assessment results of the fishery greenhouse, and the process of processing and analyzing to obtain the environmental detection subset includes:

[0040] The fish growth index GR is calculated using the detection formula:

[0041]

[0042] In the formula, GR represents the fish growth index; Wg and Wo respectively represent the final growth weight and the initial weight of the fish; Lg and Lo respectively represent the final growth body length and the initial body length of the fish; t represents the growth period; ε represents the random error value of fish growth; q represents the fish growth rate parameter;

[0043] Integrate the fish growth index data, compare it with the parameter standard range table corresponding to the fish growth period, mark the fish growth index belonging to the corresponding parameter standard range as the first attribute class, mark the fish growth index smaller than the corresponding parameter standard range as the second attribute class, and mark the fish growth index larger than the corresponding parameter standard range as the third attribute class;

[0044] It can be understood that the parameter standard range table of the fish growth period is a reference table describing the growth parameter ranges of different fish at specific growth stages; the parameter standard range is obtained through a large amount of experimental data, project observations, and scientific research, and is used to evaluate and monitor the growth status of fish; researchers will monitor and measure different species of fish, record data such as their weights and body lengths at different growth stages, and perform statistical analysis and modeling on these data to determine the average growth parameter ranges of each fish at specific growth stages; the above standard range table is of great significance for understanding the growth characteristics of fish, evaluating the feeding effect, formulating aquaculture strategies, etc.

[0045] For the marked second attribute class, integrate the fish growth index and the dataset of the corresponding fishery greenhouse environmental impact coefficient in this category into an environmental detection subset.

[0046] Furthermore, the process of combining the environmental detection subset to conduct early warning assessment on the fishery greenhouse environment, formulating early warning rules, and allocating early warning measures includes:

[0047] Use the following formula to obtain the environmental early warning assessment coefficient D(GR2,η) of the fishery greenhouse under the second attribute class:

[0048]

[0049] In the formula, D(GR2,η) represents the environmental early warning assessment coefficient of the fishery greenhouse under the second attribute class; z represents the number of early warning assessments in the second attribute class; m represents the total number of corresponding environmental impact coefficients in the second attribute class; Huv represents the value of the vth fishery greenhouse environmental impact coefficient in the uth early warning assessment; Tv represents the weight value of the vth fishery greenhouse environmental impact coefficient; λu represents the threshold parameter of the fish growth index corresponding to the second attribute class; αu represents the adjustment parameter of the fish growth index corresponding to the second attribute class; Ru represents the correction coefficient of the uth early warning assessment; σ represents the preset constant parameter;

[0050] Define the critical threshold of the environmental warning evaluation coefficient;

[0051] Perform a difference operation on the environmental warning evaluation coefficient and its critical threshold, and formulate warning rules through comparison and analysis: If the result of the difference operation is less than the lower limit value of the alarm of the fishery greenhouse environmental detector, generate a first warning signal; If the result of the difference operation is not less than the lower limit value of the alarm of the fishery greenhouse environmental detector and not greater than the upper limit value of the alarm of the fishery greenhouse environmental detector, generate a second warning signal; If the result of the difference operation is greater than the upper limit value of the alarm of the fishery greenhouse environmental detector, generate a third warning signal;

[0052] Allocate different warning measures according to the generated different warning signals to complete the real-time control task of the fishery greenhouse environment;

[0053] It can be understood that according to the first warning signal, send an alarm to notify relevant management personnel, start the preliminary investigation to record the data situation, and upload the data; According to the second warning signal, increase the monitoring frequency, deeply investigate the cause of the anomaly, start the emergency response plan, and take further control and optimization measures; According to the third warning signal, immediately start the emergency response plan and take emergency control and repair measures; Determine that the abnormal types include humidity abnormality, temperature abnormality, carbon dioxide concentration abnormality, and light intensity abnormality, and perform corresponding optimization operations for different types of abnormalities; The specific optimization operations include: when there is a humidity abnormality, increase or decrease the irrigation amount and adjust the ventilation equipment; when there is a temperature abnormality, adjust the heating or cooling equipment and change the shading measures; when there is a carbon dioxide concentration abnormality, increase the ventilation volume and adjust the carbon dioxide supply; when there is a light intensity abnormality, adjust the shading equipment or perform artificial supplementary lighting.

[0054] Compared with the existing technology, the advantages of the fishery greenhouse environment control method provided by the present invention are as follows:

[0055] 1. The present invention obtains the environmental parameters inside the fishery greenhouse in real time through sensors, and preprocesses the collected data using data dimensionality reduction, which is beneficial to obtaining the change trend of the environment inside the fishery greenhouse for subsequent analysis and application; Perform an initial detection on the signal of the preprocessed sensor component, calculate the environmental impact coefficient of the fishery greenhouse, and provide a scientific basis for decision-making;

[0056] 2. The present invention analyzes the environmental impact level according to the environmental impact coefficient of the fishery greenhouse to obtain the impact determination result of the fishery greenhouse environment, which is more conducive to analyzing the impact degree of the environment inside the greenhouse on the growth of fish;

[0057] 3. According to the determination result of environmental impact, the present invention obtains the fish growth status data of the management background for secondary detection, generates an environmental detection subset for further environmental assessment and the formulation of early warning rules, and allocates different early warning measures according to the early warning rules to cope with different environmental anomalies, which helps to maintain a good breeding environment.

[0058] In summary, the present invention can monitor the state of the internal environment of the fishery greenhouse according to the actual situation, timely grasp the change trend of the internal environment of the fishery greenhouse, regulate environmental parameters such as temperature, humidity, and light in the fishery greenhouse, optimize the fish growth environment, improve the breeding efficiency, shorten the growth cycle, increase the yield through comprehensive data analysis and precise control, and timely respond to environmental anomalies to reduce losses caused by environmental problems, realizing refined monitoring and control of the breeding environment and ensuring the normal implementation of a method for controlling the environment of a fishery greenhouse. Brief Description of the Drawings

[0059] Figure 1 It is a flowchart of a method for controlling the environment of a fishery greenhouse proposed by the present invention. Detailed Embodiments

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Refer to Figure 1 , a method for controlling the environment of a fishery greenhouse, including:

[0062] Step 1: Complete environmental data collection through the sensor assembly installed at the monitoring points of the fishery greenhouse; among them, the sensor assembly is composed of a variety of sensors, including a temperature sensor, a humidity sensor, a light intensity sensor, and a carbon dioxide sensor;

[0063] Step 2: Transmit and preprocess the collected sensing signals wirelessly to obtain the preprocessed sensor assembly signals;

[0064] Step 3: Divide the monitoring time period, conduct an initial detection on the preprocessed sensor assembly signals, and then obtain the environmental impact coefficient of the fishery greenhouse;

[0065] Step 4: Analyze the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse and obtain the determination result of the environmental impact of the fishery greenhouse;

[0066] Step 5: Obtain the corresponding fish growth status data from the management background for secondary detection based on the determination result of the fishery greenhouse environment impact, and process and analyze to obtain an environmental detection subset;

[0067] Step 6: Combine the environmental detection subset to conduct a warning assessment on the fishery greenhouse environment, formulate warning rules, and allocate warning measures.

[0068] It should be noted that the application object of a fishery greenhouse environment control method proposed by the present invention can be the monitoring of the internal environment of the fishery greenhouse, and can be used to monitor whether the breeding status of the internal environment parameters of the fishery greenhouse is abnormal. Specifically, it can comprehensively monitor, accurately analyze, and optimize the temperature, humidity, light intensity, and carbon dioxide concentration of the internal environment of the fishery greenhouse during the fishery breeding process through intelligent agriculture technology, judge whether the fish growth status inside the fishery greenhouse is normal, timely discover abnormal situations, and take corresponding measures for adjustment to maintain a good breeding environment. Among them, the environmental parameters inside the fishery greenhouse are monitored in real time through sensors, and through data preprocessing and analysis, the change trend of the internal environment of the fishery greenhouse can be observed more intuitively, realizing the refined management of the fishery greenhouse environment, effectively saving energy and resources, reducing energy consumption during the breeding process, and achieving the purpose of environmental protection and energy conservation.

[0069] In the second step, the steps of wirelessly transmitting and preprocessing the collected sensing signals to obtain the preprocessed sensor component signals include:

[0070] S101: Collect the above-mentioned sensor component signals according to a certain sampling period;

[0071] S102: Perform normalization processing on the data collected by different sensors to scale the collected periodic data to a unified range;

[0072] S103: Use PCA for data dimensionality reduction, analyze the temperature, humidity, light intensity, and carbon dioxide concentration data of the sensor components, screen out the main components that can best represent the changes in the fishery greenhouse environment data, and then obtain the dimensionality-reduced data set;

[0073] In step S103, the steps of using PCA for data dimensionality reduction include:

[0074] A1: Collect the original data of temperature, humidity, light intensity, and carbon dioxide concentration;

[0075] A2: Arbitrarily obtain two original variables, and use the covariance matrix to measure the linear relationship between the two random original variables; among them, for two original variables x and y, their original covariance is obtained through the following formula:

[0076]

[0077] Wherein, xi and yi are the observed values corresponding to x and y respectively; are the mean values of x and y respectively; n is the total number of data; the value range of the covariance is any real number. When the covariance is positive, it indicates that x and y are positively correlated; when the covariance is negative, it indicates that x and y are negatively correlated; when the covariance is zero, it indicates that there is no linear correlation between x and y;

[0078] A3. Fill the covariances showing linear correlation between all random original variables into the environmental covariance matrix to obtain the environmental covariance matrix C:

[0079]

[0080] A4. Perform original eigenvalue decomposition on the environmental covariance matrix C to obtain environmental eigenvalues and corresponding environmental eigenvectors; among them, the decomposition process is as follows: solve the environmental eigenvalue equation (C - xI)y = 0 to obtain environmental eigenvalues as x1, x2,..., xn, and the corresponding environmental eigenvectors as y1, y2,..., yn; where I is the identity matrix;

[0081] A5. Sort the environmental eigenvectors according to the magnitudes of the environmental eigenvalues, and select the environmental eigenvectors corresponding to the largest k environmental eigenvalues as the principal components; where k is the number of dimensions to be retained;

[0082] A6. Project the original data onto the selected principal components to obtain the reduced - dimensional dataset;

[0083] S104. Extract the sensor component signals corresponding to the obtained dataset.

[0084] In the third step, the steps of performing initial detection on the pre - processed sensor component signals and then obtaining the environmental impact coefficient of the fishery greenhouse include:

[0085] S201. Obtain the environmental impact coefficient η(s) of the fishery greenhouse using the following detection formula:

[0086]

[0087] Wherein, j = 1, 2, 3, respectively representing temperature, humidity, and carbon dioxide concentration; represents the air quality index in the s - th period of the initial detection of the fishery greenhouse environment; respectively represent the temperature impact factor, humidity impact factor, and carbon dioxide concentration impact factor corresponding to the preset air quality index; Δhj respectively represent the pre - determined temperature reference difference, humidity reference difference, and carbon dioxide concentration reference difference; They represent the temperature, humidity and carbon dioxide concentration values ​​in the sth period of the initial detection of the fishery greenhouse environment; They represent the mean temperature, humidity and carbon dioxide concentration of the sth period in the initial detection of the fishery greenhouse environment; dj represents the air quality index weight factor corresponding to the preset temperature, humidity and carbon dioxide concentration; b' s represents the mean light intensity of the sth period in the initial detection of the fishery greenhouse environment; b s It represents the light intensity value in the sth period in the initial detection of the fishery greenhouse environment; Lif represents the light intensity influencing factor.

[0088] In the step 4, the step of analyzing the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse and obtaining the environmental impact determination result of the fishery greenhouse includes:

[0089] S301, arranging the values ​​of the environmental impact coefficients of the fishery greenhouses in all monitoring periods in descending order, removing the maximum value Nmax and the minimum value Nmin and taking the average, to obtain the environmental impact reference value N0;

[0090] S302. Obtain environmental impact fluctuation range by combining the maximum value In the formula, N1 and N2 are the medians of Nmin and N0, Nmax and N0 respectively;

[0091] S303, when the environmental impact coefficient of the fishery greenhouse is graded and matched according to the environmental impact fluctuation range, a no-impact label, a slight-impact label, a general-impact label, and a severe-impact label are obtained;

[0092] In step S303, if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (1), it is determined that there is no impact on the environment of the fishery greenhouse and it is in a normal operating state, and a no-impact label is associated; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (2), it is determined that the environmental impact of the fishery greenhouse is small, and a slight impact label is associated; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (3), it is determined that the environmental impact of the fishery greenhouse is general, and a general impact label is associated; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (4), it is determined that the environmental impact of the fishery greenhouse is extremely obvious, and a serious impact label is associated;

[0093] S304, the slight impact label, the general impact label and the serious impact label constitute the fishery greenhouse environmental impact determination result, and generate a secondary detection signal.

[0094] In step 5, the steps of obtaining the corresponding fish growth status data of the management background for secondary detection based on the environmental impact determination result of the fishery greenhouse and processing and analyzing to obtain the environmental detection subset include:

[0095] S401. Calculate the fish growth index GR using the detection formula:

[0096]

[0097] In the formula, GR represents the fish growth index; Wg and Wo represent the final growth weight and initial weight of the fish respectively; Lg and Lo represent the final growth body length and initial body length of the fish respectively; t represents the growth period; ε represents the random error value of fish growth; q represents the fish growth rate parameter.

[0098] S402. Integrate the fish growth index data, compare it with the parameter standard range table corresponding to the fish growth period, mark the fish growth index belonging to the corresponding parameter standard range as the first attribute class, mark the fish growth index less than the corresponding parameter standard range as the second attribute class, and mark the fish growth index greater than the corresponding parameter standard range as the third attribute class.

[0099] In step S402, the parameter standard range table of the fish growth period is a reference table describing the growth parameter ranges of different fish at specific growth stages; the parameter standard ranges are obtained through a large amount of experimental data, project observations, and scientific research, and are used to evaluate and monitor the growth status of fish; researchers will monitor and measure different species of fish, record data such as their weights and body lengths at different growth stages, and perform statistical analysis and modeling on these data to determine the average growth parameter ranges of each fish at specific growth stages; the above standard range table is of great significance for understanding the growth characteristics of fish, evaluating the feeding effect, formulating aquaculture strategies, etc.

[0100] S403. For the marked second attribute class, integrate the data set of the fish growth index and the corresponding environmental impact coefficient of the fishery greenhouse in this category into an environmental detection subset.

[0101] In step six, the steps of combining the environmental detection subset to conduct a warning assessment of the fishery greenhouse environment, formulating warning rules, and allocating warning measures include:

[0102] S501. Use the following formula to obtain the environmental warning assessment coefficient D(GR2,η) of the fishery greenhouse under the second attribute class:

[0103]

[0104] In the formula, D(GR2,η) represents the environmental warning evaluation coefficient of the fishery greenhouse under the second attribute class; z represents the number of warning evaluations in the second attribute class; m represents the total number of environmental impact coefficients corresponding to the second attribute class; Huv represents the value of the environmental impact coefficient of the v-th fishery greenhouse in the u-th warning evaluation; Tv represents the weight value of the environmental impact coefficient of the v-th fishery greenhouse; λu represents the threshold parameter of the fish growth index corresponding to the second attribute class; αu represents the adjustment parameter of the fish growth index corresponding to the second attribute class; Ru represents the correction coefficient of the u-th warning evaluation; σ represents the preset constant parameter;

[0105] S502. Define the critical threshold of the environmental warning evaluation coefficient;

[0106] S503. Perform a difference operation on the environmental warning evaluation coefficient and its critical threshold, and formulate a warning rule through comparison and analysis: If the result of the difference operation is less than the lower limit value of the alarm of the fishery greenhouse environmental detector, generate a first warning signal; if the result of the difference operation is not less than the lower limit value of the alarm of the fishery greenhouse environmental detector and not greater than the upper limit value of the alarm of the fishery greenhouse environmental detector, generate a second warning signal; if the result of the difference operation is greater than the upper limit value of the alarm of the fishery greenhouse environmental detector, generate a third warning signal;

[0107] S504. Allocate different warning measures according to the generated different warning signals to complete the real-time control task of the fishery greenhouse environment;

[0108] In step S504, send an alarm notification to relevant management personnel according to the first warning signal, start the preliminary investigation to record the data situation, and upload the data; according to the second warning signal, increase the monitoring frequency, deeply investigate the cause of the abnormality, start the emergency response plan, and take further control and optimization measures; according to the third warning signal, immediately start the emergency response plan and take emergency control and repair measures; determine that the abnormal types include humidity abnormality, temperature abnormality, carbon dioxide concentration abnormality, and light intensity abnormality, and perform corresponding optimization operations for different types of abnormalities; the specific optimization operations include: when there is a humidity abnormality, increase or decrease the irrigation amount and adjust the ventilation equipment; when there is a temperature abnormality, adjust the heating or cooling equipment and change the shading measures; when there is a carbon dioxide concentration abnormality, increase the ventilation volume and adjust the carbon dioxide supply; when there is a light intensity abnormality, adjust the shading equipment or perform artificial supplementary lighting.

[0109] In the embodiments of the present invention, environmental data is collected through the sensor components installed at the monitoring points of the fishery greenhouse, so as to obtain the environmental parameters inside the greenhouse in real time. Through data preprocessing of the collected sensing signals, subsequent analysis and application can be carried out. Through initial detection of the signals of the preprocessed sensor components, the environmental impact coefficient of the fishery greenhouse is calculated to reflect the impact degree of the current environment on the fishery greenhouse. Through the environmental impact coefficient of the fishery greenhouse, the analysis of the environmental impact level can be carried out, and the impact determination result of the fishery greenhouse environment can be obtained, that is, whether the environment is normal or there are abnormal situations. By obtaining the fish growth status data of the management background according to the environmental impact determination result for secondary detection, and generating an environmental detection subset for further environmental assessment and the formulation of early warning rules. By allocating different early warning measures according to the early warning rules to cope with different environmental abnormal situations, the early warning measures include adjusting parameters such as temperature, humidity, and light to ensure the stability of the fishery greenhouse environment and the balance of fish growth. To sum up, the embodiments of the present invention involve data processing, precise analysis, and decision-making for optimization operations, solving the technical problem that the existing solutions do not accurately control the environment of the fishery greenhouse, resulting in low fishery breeding efficiency. In actual situations, more data and context information may be required to make specific decisions and optimization plans.

[0110] In addition, the formulas involved above are all calculated by removing the dimension and taking their numerical values. It is a formula obtained by software simulation of a large amount of collected data to be closest to the real situation. The proportionality coefficient in the formula and each preset threshold in the analysis process are set by those skilled in the art according to the actual situation or obtained by simulation of a large amount of data; the size of the proportionality coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the size of the proportionality coefficient, it depends on the amount of sample data and the processing coefficients initially set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantified value is not affected.

[0111] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the device embodiments, since they are basically based on the method embodiments, they are described relatively simply, and the relevant parts can be referred to the partial description of the method embodiments.

[0112] For the convenience of description, when describing the above device, it is divided into various units according to functions for description. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0113] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0114] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0117] Secondly: In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0118] Finally: The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for controlling the environment of a fishery greenhouse, characterized in that: Step 1: complete environmental data collection through the sensor assembly installed at the monitoring point of the fishery greenhouse; wherein the sensor assembly is composed of multiple sensors, including temperature sensor, humidity sensor, light intensity sensor and carbon dioxide sensor; Step 2: wirelessly transmit and preprocess the collected sensor signals to obtain the preprocessed sensor component signals; Step 3: Divide the monitoring period, perform initial detection on the pre-processed sensor component signals, and then obtain the environmental impact coefficient of the fishery greenhouse; Step 4: Analyze the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse, and obtain the environmental impact assessment result of the fishery greenhouse; Step 5: Based on the results of the fishery greenhouse environmental impact assessment, the corresponding fish growth status data of the management background is obtained for secondary testing, and the environmental testing subset is obtained through processing and analysis; Step 6: Combine the environmental detection subset to conduct early warning assessment on the fishery greenhouse environment, formulate early warning rules, and assign early warning measures.

2. A method for controlling the environment of a fishery greenhouse according to claim 1, characterized in that: In the step 2, the collected sensor signals are transmitted and preprocessed in a wireless manner, and the process of obtaining the preprocessed sensor component signals includes: According to a certain sampling period, the above sensor component signals are collected; Normalize the data collected by different sensors and scale the collected periodic data to a uniform range; PCA was used to reduce the dimension of data, analyze the temperature, humidity, light intensity and carbon dioxide concentration data of sensor components, screen out the principal components that best represent the changes in the fishery greenhouse environment data, and then obtain the reduced-dimensional data set; According to the acquired data set, the sensor component signal corresponding to the data set is extracted.

3. A method for controlling the environment of a fishery greenhouse according to claim 2, characterized in that: The process of using PCA to reduce data dimension includes: Collect raw data on temperature, humidity, light intensity, and carbon dioxide concentration; Take any two original variables and use the covariance matrix to measure the linear relationship between the two random original variables; for the two original variables x and y, their original covariance is obtained by the following formula: In the formula, xi and yi are the observed values ​​corresponding to x and y respectively; are the means of x and y respectively; n is the total number of data; the value range of covariance is any real number. When the covariance is positive, it means that x and y are positively correlated; when the covariance is negative, it means that x and y are negatively correlated; when the covariance is zero, it means that there is no linear correlation between x and y; Fill the covariances of all random original variables that show linear correlation into the environmental covariance matrix to obtain the environmental covariance matrix C: The original eigenvalue decomposition is performed according to the environmental covariance matrix C to obtain the environmental eigenvalue and the corresponding environmental eigenvector; wherein the decomposition process is as follows: the environmental eigenvalues ​​are obtained by solving the environmental characteristic equation (C-xI) y=0 as x1, x2, ..., xn, and the corresponding environmental eigenvectors are y1, y2, ..., yn; wherein I is the unit matrix; Sort the environmental feature vectors according to the size of the environmental feature values, and select the environmental feature vectors corresponding to the largest k environmental feature values ​​as the principal components; where k is the number of dimensions that you want to retain; Project the original data onto the selected principal components to obtain the reduced-dimensionality dataset.

4. A method for controlling the environment of a fishery greenhouse according to claim 1, characterized in that: In the step 3, the process of performing initial detection on the pre-processed sensor component signal and then obtaining the environmental impact coefficient of the fishery greenhouse includes: The environmental impact coefficient η(s) of the fishery greenhouse is obtained using the following test formula: Wherein, j=1, 2, 3, respectively represent temperature, humidity and carbon dioxide concentration; θs represents the air quality index of the sth period in the initial detection of the fishery greenhouse environment; θj represents the temperature influence factor, humidity influence factor and carbon dioxide concentration influence factor corresponding to the preset air quality index; Δhj represents the pre-set temperature reference difference, humidity reference difference and carbon dioxide concentration reference difference; They represent the temperature, humidity and carbon dioxide concentration values ​​in the sth period of the initial detection of the fishery greenhouse environment; They represent the mean temperature, humidity and carbon dioxide concentration of the sth period in the initial detection of the fishery greenhouse environment; dj represents the air quality index weight factor corresponding to the preset temperature, humidity and carbon dioxide concentration; b' s represents the mean light intensity of the sth period in the initial detection of the fishery greenhouse environment; b s It represents the light intensity value in the sth period in the initial detection of the fishery greenhouse environment; Lif represents the light intensity influencing factor.

5. A method for controlling the environment of a fishery greenhouse according to claim 1, characterized in that: In the step 4, the process of analyzing the environmental impact level of the fishery greenhouse based on the environmental impact coefficient of the fishery greenhouse and obtaining the environmental impact determination result of the fishery greenhouse includes: The values ​​of the environmental impact coefficients of the fishery greenhouses during all monitoring periods were arranged in descending order, and the maximum value Nmax and the minimum value Nmin were eliminated and the average was taken to obtain the environmental impact benchmark value N0; Combine the maximum value to obtain the fluctuation range of environmental impact In the formula, N1 and N2 are the medians of Nmin and N0, Nmax and N0 respectively; When the environmental impact coefficient of the fishery greenhouse is graded and matched according to the environmental impact fluctuation range, the labels of no impact, slight impact, general impact and severe impact are obtained; Among them, if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (1), it is judged that there is no impact on the environment of the fishery greenhouse and it is in normal operation state, and it is associated with a no impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (2), it is judged that the environmental impact of the fishery greenhouse is small, and it is associated with a slight impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (3), it is judged that the environmental impact of the fishery greenhouse is general, and it is associated with a general impact label; if the value of the environmental impact coefficient of the fishery greenhouse belongs to the environmental impact fluctuation range (4), it is judged that the environmental impact of the fishery greenhouse is extremely obvious, and it is associated with a serious impact label; The environmental impact assessment results of fishery greenhouses are composed of slight impact labels, general impact labels and severe impact labels, and a secondary detection signal is generated.

6. A method for controlling the environment of a fishery greenhouse according to claim 1, characterized in that: In step 5, the process of obtaining the corresponding fish growth status data of the management background for secondary detection based on the environmental impact determination result of the fishery greenhouse and processing and analyzing to obtain the environmental detection subset includes: The fish growth index GR is calculated using the detection formula: Wherein, GR represents the fish growth index; Wg and Wo represent the final growth weight and initial weight of the fish respectively; Lg and Lo represent the final growth length and initial length of the fish respectively; t represents the growth period; ε represents the random error value of fish growth; q represents the fish growth rate parameter; Integrate the fish growth index data, compare with the parameter standard range table of the corresponding growth period of fish, mark the fish growth index within the corresponding parameter standard range as the first attribute class, mark the fish growth index less than the corresponding parameter standard range as the second attribute class, and mark the fish growth index greater than the corresponding parameter standard range as the third attribute class; For the marked second attribute class, the data set of fish growth index and the corresponding fishery greenhouse environmental impact coefficient under this category is integrated into the environmental detection subset.

7. A method for controlling the environment of a fishery greenhouse according to claim 1, characterized in that: In step 6, the process of conducting early warning assessment on the fishery greenhouse environment in combination with the environmental detection subset, formulating early warning rules, and allocating early warning measures includes: The environmental warning assessment coefficient D(GR2,η) of the fishery greenhouse under the second attribute category is obtained using the following formula: Wherein, D(GR2,η) represents the environmental early warning assessment coefficient of the fishery greenhouse under the second attribute class; z represents the number of early warning assessments in the second attribute class; m represents the total number of environmental impact coefficients corresponding to the second attribute class; Huv represents the value of the environmental impact coefficient of the vth fishery greenhouse in the uth early warning assessment; Tv represents the weight value of the environmental impact coefficient of the vth fishery greenhouse; λu represents the threshold parameter of the fish growth index corresponding to the second attribute class; αu represents the adjustment parameter of the fish growth index corresponding to the second attribute class; Ru represents the correction coefficient of the uth early warning assessment; σ represents the preset constant parameter; Define critical thresholds for environmental warning assessment coefficients; The environmental early warning assessment coefficient is differentially calculated with its critical threshold, and the early warning rules are formulated through comparative analysis: if the differential calculation result is less than the lower limit value of the alarm of the fishery greenhouse environment detector, a first early warning signal is generated; if the differential calculation result is not less than the lower limit value of the alarm of the fishery greenhouse environment detector and not greater than the upper limit value of the alarm of the fishery greenhouse environment detector, a second early warning signal is generated; if the differential calculation result is greater than the upper limit value of the alarm of the fishery greenhouse environment detector, a third early warning signal is generated; Different early warning measures are assigned according to the different early warning signals generated to complete the real-time control task of the fishery greenhouse environment.

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