Synchronous measurement and stable regulation and control system for electrical quantity and non-electrical quantity

Abstract

The invention discloses a synchronous measurement and stable regulation and control system for electrical quantity and non-electrical quantity. The system comprises a data acquisition and processing unit, a control algorithm unit, a central control module and a system self-regulation and control unit, the data collecting and processing unit is used for collecting electrical quantity and non-electrical quantity data and transmitting the electrical quantity and non-electrical quantity data to the central processing unit module, the control algorithm unit is used for conducting complex operation on the basis of the received data through a specific algorithm, the optimal voltage value and the optimal parameters of the humidity and the temperature are calculated, the voltage, the humidity and the temperature are accurately adjusted, and the control precision is improved. And the optimal state is achieved. According to the invention, through real-time accurate monitoring and stable regulation and control of the electrical quantity, potential electrical safety hazards can be found and solved in time, and the safety of the production environment is ensured; accurate electrical quantity and non-electrical quantity data are measured, analysis results based on the data are obtained, a comprehensive and accurate decision basis is provided for an enterprise management layer, and an enterprise is helped to make a long-term development strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to a synchronous measurement and stable control system for electrical and non-electrical quantities. Background Art

[0002] In today's industrial production and manufacturing, precise monitoring and effective control of electrical quantities (such as voltage, current, and power) and non-electrical quantities (such as temperature, pressure, humidity, and flow) are key factors in ensuring the safe and efficient operation of production equipment, stable product quality, and reducing energy consumption and environmental pollution. However, existing technical solutions have many shortcomings.

[0003] On the one hand, traditional electrical quantity monitoring methods often fail to meet the requirements of high precision, real-time performance, and comprehensiveness. Accurate measurement of key electrical parameters such as voltage and current is crucial for assessing the operating status of power systems and preventing failures. However, in actual operation, the complexity of the grid structure, dynamic load fluctuations, and the presence of interference factors make obtaining accurate and stable electrical quantity data difficult.

[0004] On the other hand, non-electrical quantities also have a significant impact on electrical systems. Humidity and temperature, as critical environmental parameters, are directly related to the insulation performance, service life, and operational stability of electrical equipment. Excessive humidity can cause condensation on the surface of electrical equipment, degrading insulation performance and even causing short circuits. Excessive temperature, on the other hand, accelerates the aging of internal components, reducing performance and reliability. In previous electrical system operation and management, the monitoring and control of these non-electrical quantities often lagged behind, making it difficult to accurately control the overall operating status of the electrical system.

[0005] In addition, with the large-scale access of new energy sources and the advancement of smart grid construction, modern electrical systems have placed higher demands on the synchronous measurement and stable regulation of electrical and non-electrical quantities. Renewable energy generation is characterized by intermittency and volatility, which brings new challenges to the voltage stability and frequency regulation of the power grid. More accurate electrical quantity monitoring and regulation methods are needed to maintain the stable operation of the system. At the same time, in the smart grid environment, it is required to achieve all-round and intelligent management of electrical equipment, which requires comprehensive consideration of electrical and non-electrical quantities, and through efficient measurement and regulation methods, improve the operating efficiency, reliability and safety of the entire electrical system. Therefore, there is an urgent need to develop a method that can achieve synchronous measurement and stable regulation of electrical and non-electrical quantities. Summary of the Invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a synchronous measurement and stable control system for electrical and non-electrical quantities.

[0007] Technical solution: The synchronous measurement and stable control system of electrical and non-electrical quantities described in the present invention includes a data acquisition and processing unit, a control algorithm unit, a central control module and a system self-control unit; the data acquisition and processing unit includes a voltage sensor, a humidity sensor, a temperature sensor, an analog / digital converter, a data collector and a microprocessor; the control algorithm unit includes electrical quantity data analysis, non-electrical quantity data analysis, L index and optimized Osprey algorithm; the system self-control unit includes a transformer, a potentiometer, a humidifier, a dehumidifier, a heater and a refrigerator; the data acquisition and processing unit is used to collect electrical and non-electrical quantity data and transmit it to the central processing unit module, and the control algorithm unit is used to perform complex calculations based on the received data using a specific algorithm to calculate the optimal voltage value and the optimal parameters of humidity and temperature, and accurately adjust the voltage, humidity and temperature to achieve the optimal state.

[0008] Furthermore, the pressure sensor, humidity sensor and temperature sensor in the data acquisition and processing unit acquire data via an analog / digital converter, and then transmit the information to the central processing unit module via a microprocessor.

[0009] Furthermore, the control algorithm unit obtains information from the central processing unit module, analyzes the electrical quantity data and non-electrical quantity data, uses the L index and the optimized Osprey algorithm to perform calculations, and feeds back the results to the central processing unit module.

[0010] Furthermore, the system self-adjusting unit uses a transformer and a potentiometer to adjust the voltage, uses a humidifier and a dehumidifier to adjust the humidity, and uses a heater and a refrigerator to adjust the temperature.

[0011] Furthermore, the central control module receives data from voltage, current, humidity, and temperature sensors in real time and accurately collects and removes noise and abnormal values, and compares and analyzes electrical and non-electrical quantity data in different time periods, different areas, or different equipment to identify local abnormalities or trend problems in the system.

[0012] Furthermore, the implementation process of the L indicator in the control algorithm unit is as follows:

[0013] (1.1) Establish node network equation;

[0014] (1.2) After optimizing the established equations and eliminating the contact nodes in the network, the nodes in the power network can be divided into two groups: one group is the node set α of all generators G , the other group is the node set α of all loads L ;

[0015] (1.3) Substitute the optimized equation into the L index for evaluation;

[0016] (1.4) Adjust the voltage to keep it in a stable state. If the measured L index is less than 1.0, do not perform voltage processing; if the measured L index is ≥1.0, use a transformer and potentiometer to reduce the voltage; determine whether the conditions are met and terminate the adjustment; after the adjustment, calculate the L index again to determine whether the condition of L<1.0 is met. If so, terminate the adjustment; if not, continue the adjustment.

[0017] Furthermore, the equation established in step (1.1) based on Kirchhoff's current law is:

[0018]

[0019] Among them, V G and I G are the voltage and current vectors of the generator nodes respectively; V L and I L are the voltage and current vectors of the load node respectively; V κ is the voltage vector of the system connection node; Y′ GG , Y′ GL , Y′ GK , Y′ LG , Y′ LL , Y′ LK , Y′ KG , Y′ KL and Y′ κκ is a submatrix of the node admittance matrix.

[0020] Furthermore, the equation established in step (1.2) is transformed into:

[0021]

[0022] Then by The above formula is transformed into:

[0023]

[0024] Furthermore, the local voltage stability index L of the load node j in step (1.3) is j for:

[0025]

[0026] in, and are the voltage phasors of nodes i and j respectively; V j is the voltage amplitude of load node j; is the equivalent load of the system on node i; is the mutual impedance conjugate between load nodes j and i; is the self-impedance conjugate of load node j; Yij is the magnitude of the load node self-admittance, Y jj ∈Y LL0 The local voltage stability index of all load nodes in the network constitutes the system stability index vector L = [L1, L2, ..., L n ], where n∈α L , the voltage stability index of the entire system is defined as:

[0027] L=||L|| ∞

[0028] The relationship between the local voltage stability index L and the system voltage stability is: ①L<1.0, the system voltage is stable; ②L=1.0, the system voltage is critically stable; ③L>1.0, the system voltage is unstable.

[0029] Furthermore, the control algorithm unit optimizes the Osprey algorithm to control the temperature and humidity stability, and the implementation process includes:

[0030] (2.1) The improved Logistic-Tent chaotic mapping strategy is used to establish and initialize the osprey population, and the randomly distributed osprey positions are generated as follows:

[0031]

[0032] Among them, i refers to the current iteration number; X i+1 , X i Refers to the state value of the Osprey at the i-th iteration; α and r refer to control factors, α is a random number between (0, 4), and r is a random number between (0, 1);

[0033] (2.2) The randomly generated osprey positions are substituted into the objective function for evaluation. Each osprey represents a possible solution for a set of temperature and humidity control parameters. The objective function formula is as follows:

[0034]

[0035] Where O is the objective function value, T t and H t are the actual temperature and humidity at the moment, T set and H set are the set temperature and humidity values, T is the total number of time steps of observation,

[0036] The relationship between the objective function and the temperature and humidity stability is: ①O<1.0, the system temperature and humidity are stable; ②O=1.0, the system temperature and humidity are critically stable; ③O>1.0, the system temperature and humidity are unstable;

[0037] (2.3) is mapped to the search space to locate the most promising area. In the optimized osprey algorithm, the osprey with a better objective function value is considered to have a school of fish underwater. The formula for the location of the school of fish for each osprey is as follows:

[0038] P n，d =lb d +(ub d -kb d )x d，n

[0039] Among them, p n，d Refers to the corresponding population individual in this dimension; ub d lb d are the upper and lower bounds of the variable respectively; x d，n It refers to the nth chaotic sequence value for the dth dimension;

[0040] (2.4) The osprey randomly finds the position of one of the fish and attacks it. Based on the simulation of the osprey moving towards the fish, the new position of the corresponding osprey is calculated using the following formula:

[0041]

[0042] If this new position improves the value of the objective function, i.e., finding the appropriate temperature T and humidity H, then replace the previous position of the Osprey with:

[0043]

[0044] in, is the new position of the i-th osprey in the first stage; is its j-th dimension value; F i P1 is the objective function value; SF i is the new position of the i-th osprey in the first phase; SF i，j is its j-th dimension value; I i，j is a random number uniformly distributed between [1, 2];

[0045] (2.5) Update the osprey's position in the search space. After catching a fish, the osprey takes it to a suitable location. A new random position is calculated as the "suitable location for eating fish" according to the following formula:

[0046]

[0047] If the value of the objective function is improved at this new position, that is, a more suitable temperature T and humidity H are found, then the previous position of the corresponding osprey is replaced according to the following formula:

[0048]

[0049] in, is the new position of the i-th osprey in the second stage; is its j-th dimension value; F i P2 is the objective function value; r is a random number uniformly distributed between [0, 1]; t is the current number of iterations; T is the maximum number of iterations of the algorithm;

[0050] (2.6) Determine whether the termination condition is met. If the termination condition O≤1.0 is not met, return to step (2.3); if the constraint condition is met, go to step (2.7);

[0051] (2.7) Adjust the humidity and temperature to maintain a stable state. If the measured humidity and temperature are too high, use a dehumidifier or refrigerator to reduce the humidity and temperature; if the measured humidity and temperature are too low, use a humidifier or heater to increase the humidity and temperature.

[0052] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0053] (1) Through real-time and accurate monitoring and stable regulation of electrical quantities, potential electrical safety hazards can be discovered and resolved in a timely manner, ensuring the safety of the production environment;

[0054] (2) Achieved efficient energy utilization and pollutant emission reduction, and reasonable resource allocation and equipment management extended the service life of equipment and reduced resource waste;

[0055] (3) Measuring precise electrical and non-electrical quantity data and obtaining analysis results based on these data provide comprehensive and accurate decision-making basis for enterprise management, which helps enterprises formulate long-term development strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the system structure of the present invention;

[0057] Figure 2 Flowchart of the control voltage for L indicator;

[0058] Figure 3 Flowchart of the optimized Osprey algorithm for temperature and humidity stability control;

[0059] Figure 4 This is a flow chart for the synchronous measurement and stable regulation of electrical and non-electrical quantities. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0061] like Figure 1As shown, a method for synchronous measurement and stable control of electrical and non-electrical quantities includes a data acquisition and processing unit, a control algorithm unit, a central control module, and a system self-control unit. The data acquisition and processing unit includes a voltage sensor, a humidity sensor, a temperature sensor, an analog-to-digital converter (ADC), a data collector, and a microprocessor (MCU); the control algorithm unit includes electrical quantity data analysis, non-electrical quantity data analysis, the L index, and an optimized Osprey algorithm; and the system self-control unit includes a transformer, a potentiometer, a humidifier, a dehumidifier, a heater, and a cooler.

[0062] In the data acquisition and processing unit, the voltage sensor, humidity sensor, and temperature sensor collect data via an analog-to-digital converter (ADC), and then transmit the information to the central processing unit (CPU) via a microprocessor (MCU). The control algorithm unit obtains information from the CPU module, analyzes the electrical and non-electrical data, and performs calculations using the L indicator and an optimized Osprey algorithm, then feeds the results back to the CPU module. The system self-adjustment unit uses a transformer and potentiometer to adjust the voltage, a humidifier and dehumidifier to adjust the humidity, and a heater and cooler to adjust the temperature. This method uses the data acquisition and processing unit to collect electrical and non-electrical data and transmit it to the CPU module. The control algorithm unit then uses a specific algorithm to perform complex calculations based on the received data to calculate the optimal voltage value, as well as the optimal humidity and temperature parameters, and accurately adjusts the voltage, humidity, and temperature to achieve the optimal state.

[0063] like Figure 2 As shown, the benchmark voltage L indicator proposed in the present invention is implemented as follows:

[0064] (1.1) Establish the node network equation. The equation established based on Kirchhoff's current law (KCL) is:

[0065]

[0066] Among them, V G and I G are the voltage and current vectors of the generator nodes respectively; V L and I L are the voltage and current vectors of the load node respectively; V κ is the voltage vector of the system connection node; Y′ GG , Y′ GL , Y′ GK , Y′ LG , Y′ LL , Y′ LK , Y′ KG , Y′ KL and Y′ κκis a submatrix of the node admittance matrix.

[0067] (1.2) Optimize the equation in step (1.1). After eliminating the contact nodes in the network, the nodes in the power network can be divided into two groups: one group is the node set of all generators (α G ), the other group is the node set of all loads (α L ). Therefore, formula (1) can be transformed into:

[0068]

[0069] Then by Transform formula (2) into:

[0070]

[0071] (1.3) Substitute equation (3) into the L index for evaluation. The local voltage stability index L of load node j is j for:

[0072]

[0073] in, and are the voltage phasors of nodes i and j respectively; V j is the voltage amplitude of load node j; is the equivalent load of the system on node i; is the mutual impedance conjugate between load nodes j and i; is the self-impedance conjugate of load node j; Y ij is the magnitude of the load node self-admittance, Y jj ∈Y LL0 The local voltage stability index of all load nodes in the network constitutes the system stability index vector L = [L1, L2, ..., L n ], where n∈α L The voltage stability index of the entire system is defined as:

[0074] L=||L|| ∞

[0075] The relationship between the local voltage stability index (L index) and the system voltage stability is: ①L<1.0, the system voltage is stable; ②L=1.0, the system voltage is critically stable; ③L>1.0, the system voltage is unstable.

[0076] (1.4) Adjust the voltage to a stable state. If the measured L index is less than 1.0, do not perform voltage processing. If the measured L index is ≥1.0, use a transformer and potentiometer to reduce the voltage. Determine whether the conditions are met and terminate the adjustment. After adjustment, calculate the L index again to determine whether the condition L < 1.0 is met. If so, terminate the adjustment; if not, continue adjustment.

[0077] like Figure 3 As shown, the present invention uses the optimized Osprey algorithm to control humidity and temperature, and the implementation process is as follows:

[0078] (2.1) The improved Logistic-Tent chaotic mapping strategy is used to establish and initialize the osprey population. The randomly distributed osprey positions are generated as follows:

[0079]

[0080] Among them, i refers to the current iteration number; X i+1 , X i Refers to the state value of the Osprey at the i-th iteration; α and r refer to control factors, α is a random number between (0, 4), and r is a random number between (0, 1).

[0081] (2.2) The randomly generated osprey positions are substituted into the objective function for evaluation. Each osprey represents a possible solution for a set of temperature and humidity control parameters. The objective function formula is as follows:

[0082]

[0083] Where O is the objective function value, T t and H t are the actual temperature and humidity at the moment, T set and H set are the set temperature and humidity values, respectively, and T is the total number of time steps of observation.

[0084] The relationship between the objective function and the temperature and humidity stability is: ①O<1.0, the system temperature and humidity are stable; ②O=1.0, the system temperature and humidity are critically stable; ③O>1.0, the system temperature and humidity are unstable.

[0085] (2.3) is mapped to the search space to locate the most promising area. In the optimized osprey algorithm, ospreys with better objective function values ​​are considered to have fish schools underwater. The formula for the fish school location of each osprey is as follows:

[0086] p n，d =lb d +(ub d -lb d )x d，n (7)

[0087] Among them, p n，d is the population individual corresponding to this dimension; ub d lb d are the upper and lower bounds of the variable respectively; x d，n It refers to the nth chaotic sequence value for the dth dimension.

[0088] (2.4) The osprey randomly finds the location of one of the fish and attacks it. Based on the simulation of the osprey moving towards the fish, the new position of the corresponding osprey is calculated using the following formula:

[0089]

[0090] If this new position improves the value of the objective function, i.e., finding the appropriate temperature T and humidity H, then replace the previous position of the Osprey with:

[0091]

[0092] in, is the new position of the i-th osprey in the first stage; is its j-th dimension value; F i P1 is the objective function value; SF i is the new position of the i-th osprey in the first phase; SF i，j is its j-th dimension value; I i，j is a random number uniformly distributed between [1, 2].

[0093] (2.5) Update the osprey's position in the search space. After catching a fish, the osprey takes it to a suitable location. A new random position is calculated as the "suitable location for eating fish" according to the following formula:

[0094]

[0095] If the value of the objective function is improved at this new position, that is, a more suitable temperature T and humidity H are found, then the previous position of the corresponding osprey is replaced according to the following formula:

[0096]

[0097] in, is the new position of the i-th osprey in the second stage; is its j-th dimension value; F i P2 is the objective function value; r is a random number uniformly distributed between [0, 1]; t is the current number of iterations; T is the maximum number of iterations of the algorithm.

[0098] (2.6) Determine whether the termination condition is met. If the termination condition o ≤ 1.0 is not met, return to step (2.3); if the constraint condition is met, go to step (2.7).

[0099] (2.7) Adjust the humidity and temperature to maintain a stable state. If the measured humidity and temperature are too high, use a dehumidifier or refrigerator to reduce the humidity and temperature; if the measured humidity and temperature are too low, use a humidifier or heater to increase the humidity and temperature.

[0100] like Figure 4 As shown in the figure, the system starts to enter the data collection stage, then passes the data to the central control module for processing, and then performs algorithm control to adjust the voltage, humidity and temperature. Finally, it is determined whether the termination adjustment is met. If the system is stable, the process ends; if it is unstable, the algorithm control is re-performed and the above process is re-circulated to ensure that the system reaches the expected operating state. The entire process forms a closed-loop control system to continuously optimize and adjust various parameters.

Claims

1. A synchronous measurement and stable control system for electrical and non-electrical quantities, characterized in that: It includes a data acquisition and processing unit, a control algorithm unit, a central control module and a system self-regulation unit; the data acquisition and processing unit includes a voltage sensor, a humidity sensor, a temperature sensor, an analog / digital converter, a data collector and a microprocessor; the control algorithm unit includes electrical quantity data analysis, non-electrical quantity data analysis, an L index and an optimized Osprey algorithm; the system self-regulation unit includes a transformer, a potentiometer, a humidifier, a dehumidifier, a heater and a refrigerator; the data acquisition and processing unit is used to collect electrical quantity and non-electrical quantity data and transmit them to the central processing unit module, and the control algorithm unit uses a specific algorithm to perform complex calculations based on the received data to calculate the optimal voltage value and the optimal parameters of humidity and temperature, and accurately adjust the voltage, humidity and temperature to achieve the optimal state.

2. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1 is characterized in that: The pressure sensor, humidity sensor and temperature sensor in the data acquisition and processing unit acquire data through an analog / digital converter, and then transmit the information to the central processing unit module through a microprocessor.

3. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1, characterized in that: The control algorithm unit obtains information from the central processing unit module, analyzes the electrical quantity data and non-electrical quantity data, uses the L index and the optimized Osprey algorithm to perform calculations, and feeds the results back to the central processing unit module.

4. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1, characterized in that: The system self-adjusting unit uses a transformer and a potentiometer to adjust the voltage, uses a humidifier and a dehumidifier to adjust the humidity, and uses a heater and a refrigerator to adjust the temperature.

5. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1, characterized in that: The central control module receives data from voltage, current, humidity, and temperature sensors in real time and accurately collects and removes noise and abnormal values, compares and analyzes electrical and non-electrical quantity data in different time periods, different areas, or different equipment, and finds local anomalies or trend problems in the system.

6. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1, characterized in that: The implementation process of the L indicator in the control algorithm unit is as follows: (1.1) Establish node network equation; (1.2) After optimizing the established equations and eliminating the contact nodes in the network, the nodes in the power network can be divided into two groups: one group is the node set α of all generators G , the other group is the node set α of all loads L ; (1.3) Substitute the optimized equation into the L index for evaluation; (1.4) Adjust the voltage to keep it stable. If the measured L index is less than 1.0, do not perform voltage processing. If the measured L index is ≥1.0, use the transformer and potentiometer to reduce the voltage; determine whether the adjustment is terminated; after the adjustment, calculate the L index again to determine whether the L<1.0 condition is met. If so, terminate the adjustment; if not, continue the adjustment.

7. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 6, characterized in that: The equation established in step (1.1) based on Kirchhoff's current law is: Among them, V G and I G are the voltage and current vectors of the generator nodes respectively; V L and I L are the voltage and current vectors of the load node respectively; V κ is the voltage vector of the system connection node; Y′ GG , Y′ GL , Y′ GK , Y′ LG , Y′ LL , Y′ LK , Y′ KG , Y′ KL and Y′ κκ is a submatrix of the node admittance matrix.

8. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 6, characterized in that: The equation established in step (1.2) is transformed into: Then by The above formula is transformed into:

9. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 6, characterized in that: The local voltage stability index L of the load node j in step (1.3) j for: in, and are the voltage phasors of nodes i and j respectively; V j is the voltage amplitude of load node j; is the equivalent load of the system on node i; is the mutual impedance conjugate between load nodes j and i; is the self-impedance conjugate of load node j; Y ij is the magnitude of the load node self-admittance, Y jj ∈Y LL0 The local voltage stability index of all load nodes in the network constitutes the system stability index vector L = [L1, L2, ..., L n ], where n∈α L , the voltage stability index of the entire system is defined as: L=||L|| ∞ The relationship between the local voltage stability index L and the system voltage stability is: ①L<1.0, the system voltage is stable; ②L=1.0, the system voltage is critically stable; ③L>1.0, the system voltage is unstable.

10. The synchronous measurement and stable control system of electrical and non-electrical quantities according to claim 1, characterized in that: The control algorithm unit optimizes the Osprey algorithm to control the temperature and humidity stability and realizes the process of implementing the control algorithm unit to control the temperature and humidity stability. (2.1) The improved Logistic-Tent chaotic mapping strategy is used to establish and initialize the osprey population, and the randomly distributed osprey positions are generated as follows: Among them, i refers to the current iteration number; X i+1 , X i Refers to the state value of the Osprey at the i-th iteration; α and r refer to control factors, α is a random number between (0, 4), and r is a random number between (0, 1); (2.2) The randomly generated osprey positions are substituted into the objective function for evaluation. Each osprey represents a possible solution for a set of temperature and humidity control parameters. The objective function formula is as follows: Where O is the objective function value, T t and H t are the actual temperature and humidity at the moment, T set and H set are the set temperature and humidity values, T is the total number of time steps of observation, The relationship between the objective function and the temperature and humidity stability is: ①O<1.0, the system temperature and humidity are stable; ②O=1.0, the system temperature and humidity are critically stable; ③O>1.0, the system temperature and humidity are unstable; (2.3) is mapped to the search space to locate the most promising area. In the optimized osprey algorithm, the osprey with a better objective function value is considered to have a school of fish underwater. The formula for the location of the school of fish for each osprey is as follows: p n,d =lb d +(ub d -lb d )x d,n Among them, p n，d Refers to the corresponding population individual in this dimension; ub d lb d are the upper and lower bounds of the variable respectively; x d,n It refers to the nth chaotic sequence value for the dth dimension; (2.4) The osprey randomly finds the position of one of the fish and attacks it. Based on the simulation of the osprey moving towards the fish, the new position of the corresponding osprey is calculated using the following formula: If this new position improves the value of the objective function, i.e., finding the appropriate temperature T and humidity H, then replace the previous position of the Osprey with: in, is the new position of the i-th osprey in the first stage; is its j-th dimension value; F i P1 is the objective function value; SF i is the new position of the i-th osprey in the first phase; SF i，j is its j-th dimension value; I i，j is a random number uniformly distributed between [1, 2]; (2.5) Update the osprey's position in the search space. After catching a fish, the osprey takes it to a suitable location. A new random position is calculated as the "suitable location for eating fish" according to the following formula: If the value of the objective function is improved at this new position, that is, a more suitable temperature T and humidity H are found, then the previous position of the corresponding osprey is replaced according to the following formula: in, is the new position of the i-th osprey in the second stage; is its j-th dimension value; F i P2 is the objective function value; r is a random number uniformly distributed between [0, 1]; t is the current number of iterations; T is the maximum number of iterations of the algorithm; (2.6) Determine whether the termination condition is met. If the termination condition O≤1.0 is not met, return to step (2.3); if the constraint condition is met, go to step (2.7); (2.7) Adjust the humidity and temperature to maintain a stable state. If the measured humidity and temperature are too high, use a dehumidifier or refrigerator to reduce the humidity and temperature; if the measured humidity and temperature are too low, use a humidifier or heater to increase the humidity and temperature.

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