Fish school behavior critical phenomenon identification method based on reforming group

Abstract

The invention discloses a fish school behavior critical phenomenon identification method based on a reforming group, and the method comprises the steps: determining the Hamilton amount, statistical distribution and order parameters of a large-scale aquaculture production system based on a reforming group theory and a statistical physics method; the activity property of the fish school is identified by designing a simulation algorithm; by applying external or internal disturbance factors, observing changes of fish shoal behaviors, and confirming a phase change point and a critical point of the system and a response mechanism and an adaptive process of the fish shoal behaviors to the changes; and analyzing the activity state corresponding to the collective behavior of the cultured fish shoal by combining the phase change critical point of the system, establishing a behavior-state mapping relation matrix, reflecting the corresponding relation between the behavior of the fish shoal and the activity state, and providing an accurate judgment basis for culturists. The invention aims at ascertaining Hamiltonian, statistical distribution and sequence parameters of the aquaculture system, realizing accurate characterization of the critical state of the system, and providing a scientific basis for quality and efficiency improvement of aquaculture.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and more specifically to a method for identifying critical phenomena of fish school behavior based on a renormalization group. Background Art

[0002] Traditionally, statistical physics studies the collective behavior of a large number of individual systems based on the Hamiltonian and statistical distribution of the system. Phase change and critical phenomenon are the most significant collective behaviors of the system, and their theoretical research takes order parameters as the starting point. The aquaculture process is a complex system involving multiple factors such as climate, life, ecology, environment, and society. The collective behavior of fish schools and the critical phenomenon of phase change are closely related to various factors. Analyzing whether the critical factors of the system are close to or at the critical point, and whether there is a correlation between different critical factors, are major scientific issues in the study of complex systems. At present, there is a lack of knowledge about the Hamiltonian, statistical distribution, and order parameters of aquaculture systems. Therefore, the present invention develops an algorithm for identifying critical phenomena of fish school behavior for aquaculture systems. Through the collective behavior of farmed fish schools, various states corresponding to their growth process are studied and judged, and then the growth laws and disease manifestation characteristics of farmed objects are analyzed to promote the quality and efficiency of aquaculture. This method has certain applications in the critical identification of group behaviors such as bird flocks, people, and bee colonies, and no related research has been found in the analysis and identification of critical behaviors of fish schools. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for identifying critical phenomena of fish school behavior based on renormalization groups, to ascertain the Hamiltonian, statistical distribution and order parameters of an aquaculture system, to achieve accurate characterization of the critical state of the system, and then to analyze the growth laws and disease manifestation characteristics of the cultured objects, to provide a scientific basis for improving the quality and efficiency of aquaculture.

[0004] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A method for identifying critical phenomena of fish school behavior based on renormalization groups includes the following steps: A. Determine the Hamiltonian, statistical distribution and order parameters of large-scale aquaculture production systems based on renormalization group theory and statistical physics methods; B. Identify the activity nature of fish schools through simulation calculations; C. By applying external or internal disturbance factors, observe the changes in fish school behavior, confirm the phase transition point and critical point of the system, as well as the response mechanism and adaptation process of fish school behavior to these changes; D. Combined with the critical point of system phase transition, the activity state corresponding to the collective behavior of farmed fish is analyzed, and a behavior-state mapping relationship matrix is ​​established to reflect the correspondence between the behavior of fish and the activity state, providing accurate judgment basis for breeders.

[0006] To further optimize the technical solution, in step A, the Hamiltonian is constructed by analyzing the motion trajectory, speed, and acceleration dynamics parameters of the fish school, combined with the water quality, temperature, and light environment factors of the breeding environment; Considering the interaction between the two nearest neighboring fish bodies, the Hamiltonian of the system is expressed as follows: (1) Where N is the number of fish, σ n and σ n’ are the rotation parameters of two adjacent fish bodies, J is the exchange integral constant, H is the external environmental field, n and n' are the serial numbers of two adjacent grid points; The first term simulates the interaction energy between two fish bodies, and the second term simulates the effect of the external environment on the fish body. When the fish school is under external predation or prey pressure, the pressure effect will interact with the fish school activity. Therefore, a pressure equivalent field is added to the external environment field H, and the pressure equivalent field H P It can be expressed as (2) Where P is the external predation pressure parameter, M is the external environmental field parameter, λ is the influence coefficient of the external environmental field on the action state of the fish school, μ0 represents the transmission rate of the action state of the fish school, λ and μ0 are both obtained by fitting the experimental observation data, and (3) In the formula, λ S is the maximum value of the influence coefficient of the external environment field on the action state of the fish school, M S is the maximum value of the external environment field parameter, λ S and M S are all measured experimentally; thus, the Hamiltonian of the system becomes (4).

[0007] To further optimize the technical solution, in step A, the statistical distribution is obtained by statistically analyzing the fish school behavior data to obtain the probability distribution function of each state, the fish school behavior data is obtained through experimental observation and computational simulation, and then the kernel density estimation method is used to process and analyze the data to calculate the probability distribution of each state.

[0008] To further optimize the technical solution, the statistical distribution method is as follows: let any fish in the fish school occupy a grid point n, and its three coordinates are i, j, k, and n=(i, j, k), and (5) Under this constraint, the periodic boundary condition is defined as: (6) Where A(i, j, k) is any observation quantity on the grid point n=(i, j, k) occupied by the fish body, and there is, (7) σ n is the rotation direction parameter of the nth fish, σ （i，j，z） is the rotation direction parameter of the fish occupying the grid point n=(i, j, k); In the model, if the distance between two grid points occupied by fish bodies is one spacing unit, then these two grid points are nearest neighbor interaction pairs, and the Hamiltonian of the aquaculture system is expressed as the sum of all nearest neighbor interaction pairs, that is: (8) h represents the external environmental field parameters; If we want to solve the ensemble average of the state quantity and its average value over time, we must obey the following distribution probability function: (9).

[0009] Further optimizing the technical solution, in step A, the order parameter is a parameter that can reflect the overall behavior and dynamic changes of the fish school, including the total energy of the system, the average speed, the density and the degree of clustering; the expression of the critical exponential order parameter energy E that characterizes the phase change of the aquaculture system is as follows: (10) In the formula, s i and j are the grid point rotation states of the fish body at the i-th and j-th grid points, respectively, and s i and j It can only take the value of +1 or -1, corresponding to rotation or non-rotation respectively; J is the exchange integral constant, which is obtained by fitting the experiment; after defining the system Hamiltonian and confirming the order parameter, consider a three-dimensional cubic cluster system composed of N fish body grid points, and the spin of each grid point is s i , its two values ​​are ±1; s i Take 1, indicating spin upward, s i Take -1, indicating spin down; therefore, a state of this system is described by the following sequence: (11) Based on the Hamiltonian formula corresponding to this sequence, step B is used to perform iterative calculation to obtain the final total energy value of the system.

[0010] Further optimizing the technical solution, in step B, when simulating the activity properties of the fish school, the initial value of the system state sequence is defined by formula (11), the temperature point is taken as the inversion temperature J / kT, k is the Boltzmann constant, T is the temperature, and the fish school simulation calculation includes the following steps: B1. Given the size of the three-dimensional fish school grid system L×L×L, the initial value of temperature J / kT and the size of the external pressure field H; choose a fish school lattice arrangement as the initial state of the system, and calculate the transition probability from the spin of each grid point and its nearest neighbor grid point. When the grid point is a boundary grid point, use the periodic boundary condition to determine its nearest neighbor grid point; B2. Generate a random number between [0, 1], compare the random number with the transition probability, and determine whether the lattice spin state accepts flipping; B3. Determine the state distribution of all lattice points in the lattice system, that is, complete a single calculation step; B4. Repeat steps B2 and B3, i.e., cycle enough calculation steps, to make the system reach equilibrium state, then extract a state as a sample every several calculation steps, calculate the energy of the sample, extract enough samples, and obtain the statistical average value of the state quantity; B5. Add an increment to the temperature or external pressure field value as the new value, repeat the above steps, and finally obtain the relationship between the total energy value of the system and the change of the external environmental field.

[0011] To further optimize the technical solution, in step C, the disturbance factors include changes in predation status, feed changes and temperature changes.

[0012] To further optimize the technical solution, in step C, the method for determining the phase change point and the critical point is to change the disturbance factor and obtain the changing relationship between the system energy value and the external ambient temperature through step B. The energy suddenly drops to the system phase change point, and the feeding activity is equivalent to adding a predation positive feedback energy field to the fish system. As the amount of feeding increases, the critical point of the system also increases, indicating that the tolerance of the aquaculture system to the upper temperature limit is also improved.

[0013] To further optimize the technical solution, in step D, the relationship between the total energy of the system and various aquaculture-related parameters is calculated through a model, and energy mutation is used as a critical point for distinction. The higher the energy, the more active the fish school is, and an energy value of zero indicates the death of the fish school. Dissolved oxygen, nutrients, nitrogen and phosphorus, and pH variables are selected for calculation to determine the value at which the critical point appears. This value represents the maximum value of the parameter that the fish school system can bear. If this value is exceeded, the fish school cannot continue to survive.

[0014] Due to the adoption of the above technical scheme, the technical progress achieved by the present invention is as follows.

[0015] The present invention provides a method for identifying critical phenomena of fish school behavior based on renormalization groups, which responds to external factors and undergoes random changes by exchanging information and protecting individuals from harm. When the fish school operates at a critical point between two states with different structures and functions, some of these characteristics may appear, resulting in maximum response to external stimuli and effective dissemination of information. How to study the fish school behavior, phase transition and critical phenomena of complex breeding systems based on data, ascertain the Hamiltonian and order parameters of complex aquaculture systems, and realize the critical state characterization of the system are key points in studying aquaculture issues from a systems theory perspective.

[0016] The present invention is used to study the growth characteristics of cultured objects under different growth environments, aiming to explore the Hamiltonian, statistical distribution and order parameters of the aquaculture system, to achieve accurate characterization of the critical state of the system, and then to analyze the growth laws and disease manifestation characteristics of the cultured objects, so as to provide a scientific basis for improving the quality and efficiency of aquaculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the present invention; Figure 2 A graph showing the relationship between the system energy value and the external environment temperature of grass carp farming according to the present invention; Figure 3 This is a graph showing the relationship between the system energy value and the external environment temperature when the disturbance factor of the present invention changes. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] A method for identifying critical phenomena of fish school behavior based on renormalization groups, combined with Figure 1 As shown, it includes the following steps: A. Determination of the system Hamiltonian, statistical distribution and order parameters.

[0020] Based on the theory of complex systems and statistical physics methods, the Hamiltonian, statistical distribution and order parameters of large-scale aquaculture production systems are determined. Hamiltonian is used to describe the total energy of the system, statistical distribution is used to describe the probability distribution of each state in the system, and order parameters are used to describe the macroscopic state and phase change characteristics of the system. Hamiltonian is constructed by analyzing the dynamic parameters such as the movement trajectory, speed, acceleration, etc. of the fish school, combined with environmental factors such as water quality, temperature, and light in the aquaculture environment.

[0021] In large-scale aquaculture systems, Hamiltonian is an important physical quantity that describes the total energy of the system. It includes the kinetic energy, potential energy of individual fish and the interaction energy between them. Considering the interaction between the two nearest neighboring fish, the Hamiltonian of the system is expressed as follows: (1) Where N is the number of fish, σ n and σ n’ are the rotation parameters of two adjacent fish bodies, J is the exchange integral constant, H is the external environmental field, n and n' are the serial numbers of two adjacent grid points; The first term simulates the interaction energy between two fish bodies, and the second term simulates the effect of the external environment on the fish body. When the fish school is under external predation or prey pressure, the pressure effect will interact with the fish school activity. Therefore, a pressure equivalent field is added to the external environment field H, and the pressure equivalent field H P It can be expressed as (2) Where P is the external predation pressure parameter, M is the external environmental field parameter, λ is the influence coefficient of the external environmental field on the action state of the fish school, μ0 represents the transmission rate of the action state of the fish school, λ and μ0 are both obtained by fitting the experimental observation data, and (3) In the formula, λ S is the maximum value of the influence coefficient of the external environment field on the action state of the fish school, M S is the maximum value of the external environment field parameter, λ S and M S are all measured experimentally; thus, the Hamiltonian of the system becomes (4).

[0022] Statistical distribution can be obtained by statistical analysis of fish school behavior data to obtain the probability distribution function of each state.

[0023] In large-scale aquaculture production systems, the behavior and state of fish schools can be regarded as a random process, and its statistical distribution can be described by probability density functions or distribution functions. The behavioral data of fish schools, such as speed, position, acceleration, etc., are obtained through experimental observation and computational simulation. Then, the kernel density estimation method is used to process and analyze the data and calculate the probability distribution of each state. By analyzing the changes in the speed distribution of fish schools, it is determined whether the fish schools are in a clustered state or a dispersed state. By analyzing the changes in the position distribution of fish schools, it is determined whether the fish schools are foraging, escaping, etc.

[0024] The statistical distribution method is: Suppose any fish in the school occupies a grid point n, and its three coordinates are i, j, k, and n=(i, j, k), and (5) Under this constraint, the periodic boundary condition is defined as: (6) Where A(i, j, k) is any observation quantity on the grid point n=(i, j, k) occupied by the fish body, and there is, (7) σ n is the rotation direction parameter of the nth fish, σ （i，j，z） is the rotation direction parameter of the fish occupying the grid point n=(i, j, k); In the model, if the distance between two grid points occupied by fish bodies is one spacing unit, then these two grid points are nearest neighbor interaction pairs, and the Hamiltonian of the aquaculture system is expressed as the sum of all nearest neighbor interaction pairs, that is, (8) h is the external environmental field parameter; If we want to solve the ensemble average of the state quantity and its average value over time, we must obey the following distribution probability function (9).

[0025] Order parameters can be selected to reflect the overall behavior and dynamic changes of the fish school, such as total system energy, average speed, density, degree of clustering, etc.

[0026] The order parameter is a physical quantity that describes the macroscopic state of the system, reflecting the degree of interaction and coordination between the various components within the system. In large-scale aquaculture production systems, the total energy of the system is selected as the parameter that reflects the overall behavior and dynamic changes of the fish school. The behavioral data of the fish school is obtained through experimental observation and computational simulation, and then the value of the order parameter is calculated. Through the analysis of the order parameter, the overall behavior and dynamic changes of the fish school can be understood, providing an important basis for the identification of critical phenomena in the behavior of the fish school. When the total energy of the system mutates, it may mean that the fish school is undergoing a phase change or critical phenomenon, such as a transition from a free-swimming state to a clustered foraging state. The expression of the critical exponential order parameter energy E that characterizes the phase change of the aquaculture system is as follows: (10) In the formula, s i and j are the grid point rotation states of the fish body at the i-th and j-th grid points, respectively, and s i and j It can only take the value of +1 or -1, corresponding to rotation or non-rotation respectively; J is the exchange integral constant, which is obtained by fitting the experiment; after defining the system Hamiltonian and confirming the order parameter, consider a three-dimensional cubic cluster system composed of N fish body grid points, and the spin of each grid point is s i , its two values ​​are ±1; s i Take 1, indicating spin upward, s iTake -1, indicating spin down; therefore, a state of this system can be described by the following sequence (11) Based on the Hamiltonian formula corresponding to this sequence, the final total energy value of the system can be obtained by iterative calculation using step B.

[0027] B. Computational simulation of fish school model.

[0028] The activity properties of fish schools are identified through simulation calculations, and the time series of individual states of the aquaculture system are extracted through experimental observations and computational simulations. Experimental observations can be carried out through sensors and monitoring equipment installed in the aquaculture system to obtain data such as the movement trajectory, speed, acceleration, etc. of the fish schools. Computational simulations can use models based on individual behavior, such as self-organization models and agent models, to simulate the behavior and dynamic changes of fish schools. In experimental observations, reasonable observation schemes and sampling frequencies are designed to ensure the accuracy and completeness of the data. In computational simulations, models and parameters suitable for specific fish species are selected to simulate the behavior of fish schools in real aquaculture systems. At the same time, the simulation results need to be verified and calibrated to ensure their reliability and accuracy.

[0029] When simulating the activity properties of fish schools, the initial value of the system state sequence is defined by formula (11), the temperature point is taken as the inversion temperature J / kT, k is the Boltzmann constant, T is the temperature, and the fish school simulation calculation includes the following steps: B1. Given the size of the three-dimensional fish school grid system L×L×L, the initial value of temperature J / kT and the size of the external pressure field H; choose any fish school lattice arrangement as the initial state of the system, and calculate the transition probability from the spin of each grid point and its nearest neighbor grid point. When the grid point is a boundary grid point, its nearest neighbor grid point can be determined using the periodic boundary condition; B2. Generate a random number between [0, 1], compare the random number with the transition probability, and determine whether the lattice spin state accepts flipping; B3. Determine the state distribution of all lattice points in the lattice system, that is, complete a single calculation step; B4. Repeat steps B2 and B3, i.e., cycle enough calculation steps, to make the system reach equilibrium state, then extract a state as a sample every several calculation steps, calculate the energy of the sample, extract enough samples, and obtain the statistical average value of the state quantity; B5. Add an increment to the temperature or external pressure field value as the new value, repeat the above steps, and finally get the relationship between the total energy value of the system and the change of the external environmental field.

[0030] Taking grass carp farming as an example, a grass carp farming system with 20×20×20 grid points (i.e., the number of fish grid points N is 8000) is simulated and calculated. The number of calculation steps is 5000000. The final calculated relationship between the system energy value and the external environment temperature is shown in the figure below. Figure 2 As shown, the energy drops suddenly, which is the phase transition point of the system.

[0031] C. Application of disturbance factors and confirmation of critical points.

[0032] By applying external or internal disturbance factors, observing the changes in fish behavior, and confirming the phase transition and critical point of the system. The disturbance factors can be the amount of bait or fishing intensity, etc. These changes will affect the behavior and growth status of the fish. By observing the changes in the behavior of the fish, it is possible to determine whether the system is in a critical state, and then take corresponding management measures. Apply different disturbance factors and monitor the changes in the behavior of the fish in real time. By analyzing the experimental data, the phase transition point or critical point of the system, as well as the response mechanism and adaptation process of the fish behavior to these changes, can be determined.

[0033] Taking the change of disturbance factor P as an example, repeating the calculation process of step B can obtain the relationship between the energy value of the system and the external environment temperature. The sudden drop in energy is the phase transition point of the system. Feeding activity is equivalent to adding a predation positive feedback energy field to the fish system. As the amount of feeding increases, the critical point of the system also increases, which means that the tolerance of the aquaculture system to the upper temperature limit has also increased. Figure 3 This is a graph showing the relationship between the system energy value and the external environment temperature when the disturbance factor changes.

[0034] D. Establishment of behavior-state mapping relationship matrix.

[0035] Combined with the critical point of system phase transition, the activity state corresponding to the collective behavior of farmed fish is analyzed, and a behavior-state mapping relationship matrix is ​​established. This matrix can reflect the correspondence between the behavior of fish and the activity state, providing accurate judgment basis for farmers.

[0036] This model can be used to calculate the relationship between the total energy of the system and various aquaculture-related parameters, and use energy mutation as the critical point for distinction. The higher the energy, the more active the fish school is, and an energy value of zero means the fish school is dead. The temperature selected in the example is used as a variable. In addition, variables such as dissolved oxygen, nutrients, nitrogen and phosphorus, and pH can also be selected for calculation to determine the value at the critical point. This value represents the maximum value of the parameter that the fish school system can bear. If this value is exceeded, the fish school cannot continue to survive.

[0037] Based on the above calculation results, subsequent work can extract the collective behavior characteristics of fish schools in different growth environments through analysis of experimental data, such as clustering degree, activity range, feeding intensity, etc. At the same time, it is also necessary to record the growth status indicators of fish schools, such as weight, body length, growth rate, etc. Then, using statistical analysis and machine learning technology, based on the established behavior-state mapping relationship matrix, the growth characteristics of farmed objects in different growth environments can be accurately judged.

Claims

1. A method for identifying critical phenomena of fish school behavior based on renormalization groups, characterized by: It includes the following steps: A. Determine the Hamiltonian, statistical distribution and order parameters of large-scale aquaculture production systems based on renormalization group theory and statistical physics methods; B. Identify the activity nature of fish schools through simulation calculations; C. By applying external or internal disturbance factors, observe the changes in fish school behavior, confirm the phase transition point and critical point of the system, as well as the response mechanism and adaptation process of fish school behavior to these changes; D. Combined with the critical point of system phase transition, the activity state corresponding to the collective behavior of farmed fish is analyzed, and a behavior-state mapping relationship matrix is ​​established to reflect the correspondence between the behavior of fish and the activity state, providing accurate judgment basis for breeders.

2. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step A, the Hamiltonian is constructed by analyzing the movement trajectory, speed, and acceleration dynamics parameters of the fish school, combined with the water quality, temperature, and light environment factors of the breeding environment; Considering the interaction between the two nearest neighboring fish bodies, the Hamiltonian of the system is expressed as follows: （1） Where N is the number of fish, σ n and σ n’ are the rotation parameters of two adjacent fish bodies, J is the exchange integral constant, H is the external environmental field, n and n' are the serial numbers of two adjacent grid points; The first term simulates the interaction energy between two fish bodies, and the second term simulates the effect of the external environment on the fish body. When the fish school is under external predation or prey pressure, the pressure effect will interact with the fish school activity. Therefore, a pressure equivalent field is added to the external environment field H, and the pressure equivalent field H P It can be expressed as （2） Where P is the external predation pressure parameter, M is the external environmental field parameter, λ is the influence coefficient of the external environmental field on the action state of the fish school, μ0 represents the transmission rate of the action state of the fish school, λ and μ0 are both obtained by fitting the experimental observation data, and （3） In the formula, λ S is the maximum value of the influence coefficient of the external environment field on the action state of the fish school, M S is the maximum value of the external environment field parameter, λ S and M S All are measured by experiments; In this way, the Hamiltonian of the system becomes （4）。 3. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step A, the statistical distribution is obtained by statistically analyzing the fish school behavior data to obtain the probability distribution function of each state, the fish school behavior data is obtained through experimental observation and computational simulation, and then the kernel density estimation method is used to process and analyze the data to calculate the probability distribution of each state.

4. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: The statistical distribution method is as follows: let n be the grid point occupied by any fish in the fish school, and its three coordinates be i, j, k, and let n=(i, j, k), and （5） Under this constraint, the periodic boundary condition is defined as: （6） Where A(i, j, k) is any observation quantity on the grid point n=(i, j, k) occupied by the fish body, and there is, （7） σ n is the rotation direction parameter of the nth fish, σ （i，j，z） is the rotation direction parameter of the fish occupying the grid point n=(i, j, k); In the model, if the distance between two grid points occupied by fish bodies is one spacing unit, then these two grid points are nearest neighbor interaction pairs, and the Hamiltonian of the aquaculture system is expressed as the sum of all nearest neighbor interaction pairs, that is: （8） h is the external environmental field parameter; If we want to solve the ensemble average of the state quantity and its average value over time, we must obey the following distribution probability function: （9）。 5. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step A, the order parameter is a parameter that can reflect the overall behavior and dynamic changes of the fish school, including the total energy of the system, average speed, density and clustering degree; the expression of the critical exponential order parameter energy E that characterizes the phase change of the aquaculture system is as follows: （10） In the formula, s i and j are the grid point rotation states of the fish body at the i-th and j-th grid points, respectively, and s i and j It can only take the value of +1 or -1, corresponding to rotation or non-rotation respectively; J is the exchange integral constant, which is obtained by fitting the experiment; after defining the system Hamiltonian and confirming the order parameter, consider a three-dimensional cubic cluster system composed of N fish body grid points, and the spin of each grid point is s i , its two values ​​are ±1; s i Take 1, indicating spin upward, s i Take -1, indicating spin down; therefore, a state of this system is described by the following sequence: （11） Based on the Hamiltonian formula corresponding to this sequence, step B is used to perform iterative calculation to obtain the final total energy value of the system.

6. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 5, characterized in that: In step B, when simulating the activity properties of the fish school, the initial value of the system state sequence is defined by formula (11), the temperature point is taken as the inversion temperature J / kT, k is the Boltzmann constant, T is the temperature, and the fish school simulation calculation includes the following steps: B1. Given the size of the three-dimensional fish school grid system L×L×L, the initial value of temperature J / kT and the size of the external pressure field H; choose a fish school lattice arrangement as the initial state of the system, and calculate the transition probability from the spin of each grid point and its nearest neighbor grid point. When the grid point is a boundary grid point, use the periodic boundary condition to determine its nearest neighbor grid point; B2. Generate a random number between [0, 1], compare the random number with the transition probability, and determine whether the lattice spin state accepts flipping; B3. Determine the state distribution of all lattice points in the lattice system, that is, complete a single calculation step; B4. Repeat steps B2 and B3, i.e., cycle enough calculation steps, to make the system reach equilibrium state, then extract a state as a sample every several calculation steps, calculate the energy of the sample, extract enough samples, and obtain the statistical average value of the state quantity; B5. Add an increment to the temperature or external pressure field value as the new value, repeat the above steps, and finally obtain the relationship between the total energy value of the system and the change of the external environmental field.

7. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step C, the disturbance factors include changes in predation status, feed and temperature.

8. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step C, the method for determining the phase change point and the critical point is to change the disturbance factor and obtain the changing relationship between the system energy value and the external ambient temperature through step B. The energy suddenly drops to the system phase change point. The feeding activity is equivalent to adding a predation positive feedback energy field to the fish system. As the amount of feeding increases, the critical point of the system also increases, indicating that the tolerance of the aquaculture system to the upper temperature limit is also improved.

9. The method for identifying critical phenomena of fish school behavior based on renormalization group according to claim 1, characterized in that: In step D, the relationship between the total energy of the system and various aquaculture-related parameters is calculated through a model, and energy mutation is used as a critical point for distinction. The higher the energy, the more active the fish school is, and an energy value of zero indicates the death of the fish school. Dissolved oxygen, nutrients, nitrogen and phosphorus, and pH variables are selected for calculation to determine the value at which the critical point appears. This value represents the maximum value of the parameter that the fish school system can bear. If this value is exceeded, the fish school cannot continue to survive.

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