Greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate

Abstract

The invention discloses a greenhouse intelligent temperature control method based on fuzzy adaptive PID and a temperature change rate, which combines real-time temperature data of a greenhouse, determines a temperature deviation change rate, and dynamically adjusts PID parameters according to the temperature deviation change rate so as to dynamically output a corresponding rotation instruction. The number of turns and the direction of rotation of a motor of the ventilator are accurately controlled, and then the size of the ventilation opening of the greenhouse is adjusted. The conditions that the stability of the system is reduced, and equipment abrasion and energy consumption fluctuation are aggravated due to the fact that the air discharging machine acts too violently are avoided; high-precision, low-energy-consumption and long-service-life temperature control is achieved in a complex and changeable greenhouse environment.

Description

Technical Field

[0001] This invention relates to the field of greenhouse control technology, and in particular to a greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate. Background Technology

[0002] In the field of smart agriculture, greenhouse temperature control has a crucial impact on crop growth, development, yield, and quality. While traditional PID control methods are simple in structure and easy to implement, their parameters remain unchanged once set, making them ill-suited to the complex nonlinear characteristics and variable weather conditions of a greenhouse environment. Fixed-parameter fuzzy adaptive PID controllers often struggle to keep up with changes, easily exhibiting overshoot, oscillation, or response lag, thus significantly reducing temperature control accuracy.

[0003] Furthermore, traditional PID strategies rely solely on the static error signal of temperature deviation for adjustment, failing to adequately consider the crucial dynamic information of temperature change rate, which reflects temperature trends. In cases of rapid temperature increases or decreases, relying solely on temperature deviation makes it difficult to predict trends in advance. The controller can only activate after the temperature deviation becomes apparent, often leading to excessively abrupt fan movements. This not only reduces system stability but may also exacerbate equipment wear and energy consumption fluctuations.

[0004] On the other hand, when the current temperature approaches the set value, traditional PID control is prone to "integral saturation" due to the continuous accumulation of the integral term, resulting in slight oscillations. This affects the stability of temperature maintenance, shortens the lifespan of the ventilation fan, and increases operating costs. Therefore, there is an urgent need for an intelligent temperature control method that can sense temperature change trends in real time and dynamically adapt to environmental disturbances. By introducing temperature change rate information and a self-adjusting mechanism, and improving traditional PID control, high-precision, low-energy-consumption, and long-life temperature management can be achieved in complex and variable greenhouse environments. Summary of the Invention

[0005] Based on this, it is necessary to propose a method, device, computer equipment, and storage medium for intelligent greenhouse temperature control based on fuzzy adaptive PID and temperature change rate to address the above problems.

[0006] A greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate, the method comprising:

[0007] Real-time temperature data of the greenhouse is collected, and the rate of temperature change is determined based on the temperature data of the greenhouse at different times.

[0008] The temperature deviation is determined based on the target thermometer and the current temperature in the temperature data, and the rate of change of the temperature deviation is determined based on the temperature deviation.

[0009] determine a proportional link output, an integral link output and a differential link output according to the temperature deviation change rate and PID parameters;

[0010] adjust PID parameters dynamically based on a fuzzy self-adaptive PID controller and the temperature deviation and the temperature deviation change rate;

[0011] determine a PID output quantity based on the proportional link output, the integral link output and the differential link output; and generate a rotation instruction according to the PID output quantity to control the rotation number and direction of the fan motor, thereby adjusting the size of the greenhouse air outlet.

[0012] In one embodiment, the temperature change rate is obtained by the following expression:

[0013]

[0014] wherein, is the temperature change rate; T(k) is the current temperature, T(k-1) is the temperature at the previous time, and ΔT is the sampling time interval.

[0015] In one embodiment, the temperature deviation is determined according to the target temperature and the current temperature in the temperature data, and the temperature deviation change rate is obtained according to the temperature deviation by the following expression:

[0016]

[0017] wherein, T h represents the highest target temperature, and T l represents the lowest target temperature.

[0018] The temperature deviation change rate is calculated by:

[0019]

[0020] wherein, e(k-1) is the deviation between the temperature at the previous time and the target temperature.

[0021] In one embodiment, the determination of the proportional link output, the integral link output and the differential link output according to the temperature deviation change rate and PID parameters comprises:

[0022]

[0023]

[0024]

[0025] wherein, is the proportional link output; is the integral link output; for the differential element output; , and constitute PID parameters; for the proportional gain; for the integral gain; for the differential gain; for the temperature deviation; for the temperature deviation accumulation; for the sampling time interval; for the temperature deviation of the previous time temperature and the target temperature;

[0026] when T l <T(k)<T h , e(k)=0, and none of the three elements outputs;

[0027] when T(k)>T h , ;

[0028] when T(k)<T l , ;

[0029] wherein, T h represents the highest target temperature; T l represents the lowest target temperature; T(k) is the current temperature; T(k-1) is the temperature at the previous time; for the temperature change rate;

[0030] Therefore, for the differential element output, , is directly associated with the temperature change rate.

[0031] In one embodiment, the dynamic adjustment of the PID parameters based on the fuzzy adaptive PID controller and in combination with the temperature deviation and the temperature deviation change rate comprises:

[0032] setting an initial proportional gain, an initial integral gain and an initial differential gain; fuzzifying the temperature deviation and the temperature deviation change rate into 7-level language variables; matching the 7-level language variables to corresponding output variables according to a fuzzy rule table; processing the output variables by centroid method to obtain a proportional gain adjustment amount, an integral gain adjustment amount and a differential gain adjustment amount, the initial proportional gain, the initial integral gain and the initial differential gain and the proportional gain adjustment amount, the integral gain adjustment amount and the differential gain adjustment amount constituting the PID parameters, and the expression is as follows:

[0033]

[0034]

[0035]

[0036] wherein, Kp is a proportional gain; Ki is an integral gain; Kd is a derivative gain; T is a temperature deviation; Kp0 is an initial proportional gain; Ki0 is an initial integral gain; Kd0 is an initial derivative gain; Kp is a proportional gain adjustment; Ki is an integral gain adjustment; Kd is a derivative gain adjustment.

[0037] In one embodiment, the PID output quantity is determined based on the proportional link output, the integral link output and the derivative link output, and the expression is as follows:

[0038]

[0039] wherein, Kp is a proportional gain; Kp is a proportional link output; Ki is an integral link output; Kd is a derivative link output.

[0040] In one embodiment, the greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature rate of change further comprises:

[0041] When the current temperature is higher than the preset maximum temperature and the temperature rate of change is positive, according to the temperature deviation and the size of the temperature rate of change, the fuzzy adaptive PID controller will increase the output in stages, increase the positive rotation number of the exhaust fan motor, and increase the exhaust port.

[0042] When the current temperature is higher than the preset maximum temperature but the temperature rate of change is negative, according to the temperature deviation and the size of the temperature rate of change, the fuzzy adaptive PID controller will decrease the output in stages, increase the reverse rotation number of the exhaust fan motor, and reduce the exhaust port.

[0043] When the current temperature is lower than the preset minimum temperature and the temperature rate of change is negative, according to the temperature deviation and the size of the temperature rate of change, the fuzzy adaptive PID controller will increase the output in stages, significantly or appropriately increase the reverse rotation number of the exhaust fan motor, and reduce the exhaust port.

[0044] When the current temperature is lower than the preset minimum temperature but the temperature change rate is positive, according to the temperature deviation and the size of the temperature change rate, the fuzzy adaptive PID controller will reduce the output in stages, greatly or gradually increase the positive rotation number of the ventilator motor, and increase the ventilation opening.

[0045] In one embodiment,

[0046] The integration item accumulation range of the integral element in the fuzzy adaptive PID controller is limited to ±200 circles, when the ventilator motor and the ventilation opening of the greenhouse are connected through a rope, the ventilator motor rotates to adjust the opening degree of the ventilation opening to reach the limit position of 0% or 80%, and the integration accumulation is stopped.

[0047] The fuzzy adaptive PID controller freezes the PID parameter adjustment and makes the PID output 0 when the current temperature is between the preset minimum temperature and the preset maximum temperature.

[0048] The present application combines the real-time temperature data of the greenhouse, determines the temperature deviation change rate, dynamically adjusts the PID parameters according to the temperature deviation change rate, dynamically outputs the corresponding rotation instruction, accurately controls the rotation number and direction of the ventilator motor, and then adjusts the size of the ventilation opening of the greenhouse. Avoid the ventilator action too violent, reduce the stability of the system, aggravate the equipment wear and tear and energy consumption fluctuation; In the complex and changeable greenhouse environment, high precision, low energy consumption and long service life temperature control are realized. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0050] Among them:

[0051] Figure 1 It is an application environment diagram of the greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate in one embodiment;

[0052] Figure 2 It is a flow chart of the greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate in one embodiment;

[0053] Figure 3 It is a structural block diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0054] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0055] In the field of smart agriculture, greenhouse temperature control has a crucial influence on the growth and development, yield and quality of crops. Although the traditional PID control method has the advantages of simple structure and easy implementation, its parameters remain unchanged once set, which is difficult to adapt to the complex nonlinear characteristics and variable weather conditions in the greenhouse environment. The fixed-parameter fuzzy adaptive PID controller often fails to keep up in time and is prone to overshoot, oscillation or response lag, thereby greatly reducing the temperature control accuracy. In addition, the traditional PID strategy only relies on the temperature deviation, a static error signal, for adjustment, and does not fully consider the temperature change rate, a key dynamic information that can reflect the temperature trend. In the case of rapid temperature rise or sudden drop, it is difficult to predict the trend in advance based on the temperature deviation alone, and the controller can only start to act after the temperature deviation appears, often leading to overaction of the exhaust fan, reducing the stability of the system, and possibly exacerbating equipment wear and energy consumption fluctuations. On the other hand, when the current temperature is close to the set value, the traditional PID control is prone to "integral saturation" phenomenon due to continuous accumulation of the integral term, resulting in small oscillations, which not only affects the stability of temperature maintenance, but also shortens the service life of the exhaust fan and increases the operating cost. In view of this, there is an urgent need for an intelligent temperature control method that can real-time perceive the temperature change trend and dynamically adapt to environmental disturbances. By introducing temperature change rate information and self-adjusting mechanism, the traditional PID can be improved to achieve high-precision, low-energy-consumption and long-life temperature control in complex and variable greenhouse environments.

[0056] To solve the above technical problems, the present application provides a greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate.

[0057] Figure 1 An application environment diagram for a greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate in an embodiment. Refer to Figure 1The greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature rate of change is applied to a greenhouse intelligent temperature control system based on fuzzy adaptive PID and temperature rate of change. The greenhouse intelligent temperature control system based on fuzzy adaptive PID and temperature rate of change includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network, and the terminal 110 can be a desktop terminal or a mobile terminal, and the mobile terminal can be at least one of a mobile phone, a tablet computer, a notebook computer, etc. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers. The terminal 110 is used to collect temperature data of a greenhouse in real time and determine a temperature rate of change based on temperature data of the greenhouse at different times; the server 120 is used to determine a temperature deviation according to a target temperature meter and a current temperature in the temperature data, and determine a temperature deviation rate according to the temperature deviation; determine a proportional link output, an integral link output and a differential link output according to the temperature deviation rate and PID parameters; dynamically adjust PID parameters based on a fuzzy adaptive PID controller and in combination with the temperature deviation and the temperature deviation rate; determine a PID output quantity based on the proportional link output, the integral link output and the differential link output; and generate a rotation instruction according to the PID output quantity to control the rotation number and direction of a fan motor, thereby adjusting the size of a ventilation opening of the greenhouse.

[0058] As shown in Figure 2 In one embodiment, a greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature rate of change is provided. The method can be applied to a terminal or a server, and the embodiment is illustrated by application to a terminal. The greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature rate of change specifically includes the following steps:

[0059] S10: Collect temperature data of a greenhouse in real time and determine a temperature rate of change based on temperature data of the greenhouse at different times;

[0060] S20: Determine a temperature deviation according to a target temperature meter and a current temperature in the temperature data, and determine a temperature deviation rate according to the temperature deviation;

[0061] S30: Determine a proportional link output, an integral link output and a differential link output according to the temperature deviation rate and PID parameters;

[0062] S40: Dynamically adjust PID parameters based on a fuzzy adaptive PID controller and in combination with the temperature deviation and the temperature deviation rate;

[0063] S50: determining a PID output based on the proportional link output, the integral link output and the differential link output; and generating a rotation instruction according to the PID output to control the rotation number and direction of the fan motor, and then adjust the size of the greenhouse vent.

[0064] In one embodiment, the temperature change rate is obtained by the following expression:

[0065]

[0066] Wherein, is the temperature change rate; T(k) is the current temperature, T(k-1) is the temperature at the previous time, and ΔT is the sampling time interval.

[0067] In one embodiment, the temperature deviation is determined according to the target temperature and the current temperature in the temperature data, and the temperature deviation change rate is determined according to the temperature deviation and obtained by the following expression:

[0068]

[0069] Wherein, T h represents the highest target temperature, and T l represents the lowest target temperature.

[0070] The temperature deviation change rate is calculated as follows:

[0071]

[0072] Wherein, e(k-1) is the deviation between the temperature at the previous time and the target temperature.

[0073] In one embodiment, the proportional link output, the integral link output and the differential link output are determined according to the temperature deviation change rate and the PID parameters, which includes:

[0074]

[0075]

[0076]

[0077] Wherein, is the proportional link output, which adjusts the control output according to the deviation between the current temperature and the set temperature, and the proportional gain K p determines the response speed of the controller to the current temperature deviation, and the larger K p , the more sensitive the controller is to the deviation, but it may cause system oscillation; is the integral link output, which is used to eliminate the steady-state error of the system, accumulates the historical temperature deviation, and the integral gain K iThe larger the value, the stronger the integral effect, but this may lead to a slower or unstable system response. Therefore, the accumulation range of the integral term is limited in this invention. The output of the differential element is used to predict future error trends and suppress temperature fluctuations; the differential gain K d Adjusting the control output based on the rate of temperature change allows for early prediction of temperature trends, improving system response speed and stability; K d The larger the value, the more sensitive it is to the rate of temperature change. , and PID parameters are constituted; For proportional gain; For integral gain; This is the differential gain; Temperature deviation; Cumulative temperature deviation; The sampling time interval; This represents the temperature deviation between the previous temperature and the target temperature.

[0078] When T l <T(k)<T h When e(k) = 0, none of the three stages output anything;

[0079] When T(k) > T h hour, ;

[0080] When T(k) <T l hour, ;

[0081] Among them, T h Indicates the highest target temperature; T l This represents the minimum target temperature; T(k) is the current temperature; T(k-1) is the temperature at the previous moment. The rate of temperature change;

[0082] Therefore, for the output of the differential element, It is directly related to the rate of temperature change.

[0083] In one embodiment, the dynamic adjustment of PID parameters based on a fuzzy adaptive PID controller, combined with the temperature deviation and the rate of change of the temperature deviation, includes:

[0084] An initial proportional gain, an initial integral gain and an initial differential gain are set; the temperature deviation and the temperature deviation change rate are fuzzified into 7-level language variables; 7-level language variables are matched according to a fuzzy rule table to obtain output variables; the output variables are processed by a centroid method to obtain a proportional gain adjustment, an integral gain adjustment and a differential gain adjustment, and the initial proportional gain, the initial integral gain and the initial differential gain and the proportional gain adjustment, the integral gain adjustment and the differential gain adjustment constitute PID parameters, and the expression is as follows:

[0085]

[0086]

[0087]

[0088] wherein, is the proportional gain; is the integral gain; is the differential gain; is the temperature deviation; is the initial proportional gain; is the initial integral gain; is the initial differential gain; is the proportional gain adjustment; is the integral gain adjustment; is the differential gain adjustment.

[0089] In one embodiment, the PID output quantity is determined based on the proportional link output, the integral link output and the differential link output, and the expression is as follows:

[0090]

[0091] wherein, is the PID output quantity; is the proportional link output; is the integral link output; is the differential link output.

[0092] In one embodiment, the greenhouse intelligent temperature control method based on the fuzzy adaptive PID and the temperature change rate further comprises:

[0093] When the current temperature is higher than the preset maximum temperature and the temperature change rate is positive, according to the temperature deviation and the size of the temperature change rate, the fuzzy adaptive PID controller will increase the output in stages to increase the positive rotation number of the air exhaust fan motor and increase the air exhaust port.

[0094] When the current temperature is higher than the preset maximum temperature but the temperature change rate is negative, according to the temperature deviation and the size of the temperature change rate, the fuzzy adaptive PID controller will reduce the output in stages, increase the reverse rotation number of the ventilator motor, and reduce the air vent.

[0095] When the current temperature is lower than the preset minimum temperature and the temperature change rate is negative, according to the temperature deviation and the size of the temperature change rate, the fuzzy adaptive PID controller will increase the output in stages, greatly or appropriately increase the forward rotation number of the ventilator motor, and increase the air vent.

[0096] When the current temperature is lower than the preset minimum temperature but the temperature change rate is positive, according to the temperature deviation and the size of the temperature change rate, the fuzzy adaptive PID controller will reduce the output in stages, greatly or gradually increase the forward rotation number of the ventilator motor, and increase the air vent.

[0097] In one embodiment, the integration range of the integral term of the integral element in the fuzzy adaptive PID controller is limited to ±200 revolutions, and when the ventilator motor and the air vent of the greenhouse are connected by a rope, the ventilator motor rotates to adjust the opening degree of the air vent to reach the limit position of 0% or 80%, the integration accumulation is stopped; the fuzzy adaptive PID controller freezes the PID parameter adjustment when the current temperature is between the preset minimum temperature and the preset maximum temperature, and the PID output is 0.

[0098] The ventilator motor is driven to rotate forward or reverse to adjust the opening degree of the air vent until the temperature returns to the set interval.

[0099] Preferably, the execution module (single-chip microcomputer) is configured to drive the ventilator motor to rotate forward or reverse according to the positive or negative value of N(k) to adjust the size of the air vent, wherein the forward rotation increases the air vent and the reverse rotation reduces the air vent.

[0100] Preferably, in the execution module, one revolution of the ventilator motor corresponds to a fixed proportion of change in the opening degree of the air vent, and the maximum opening degree is limited to 80%.

[0101] Therefore, the greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate has the following beneficial effects:

[0102] (1) The fuzzy PID adaptive adjustment parameters are adopted, and manual repeated parameter adjustment is not required. The fuzzy logic unit outputs the parameter change amount in real time, realizes online dynamic adjustment of the PID parameters, adapts to environmental and load changes, eliminates the cumbersome process of traditional experience trial and error, and improves the control precision.

[0103] (2) By comprehensively utilizing the dual inputs of temperature deviation and temperature change rate, the temperature change trend is predicted, and the operation of the ventilation fan is controlled in advance to avoid excessively strong operation of the ventilation fan caused by the operation only after the temperature deviation has appeared. At the same time, by combining strategies such as dead zone freezing and integral limiting, the frequent start-up of the equipment is avoided, effectively resisting external disturbances and load fluctuations, reducing power consumption, and increasing system life and stability.

[0104] This application also provides a greenhouse intelligent temperature control device based on fuzzy adaptive PID and temperature change rate, the device comprising:

[0105] The data acquisition module is used to collect temperature data from the greenhouse in real time and determine the rate of temperature change based on the temperature data at different times in the greenhouse. ;

[0106] The PID control module is used to determine the temperature deviation based on the target thermometer and the current temperature in the temperature data. And according to the temperature deviation Determine the rate of change of temperature deviation ;

[0107] According to the temperature deviation change rate and PID parameters (K) p K i K d Determine the output of the proportional stage. Output of the integration stage and the output of the differential element ;

[0108] The fuzzy logic module is used to combine the temperature deviation with a fuzzy adaptive PID controller. and the rate of change of temperature deviation Dynamically adjust PID parameters (K) p K i K d );

[0109] The execution module is used to output based on the proportional element. Output of the integration stage and the output of the differential element Determine the PID output N(k); and generate a rotation command based on the PID output N(k) to control the number of rotations and direction of the ventilation fan motor, thereby adjusting the size of the ventilation opening in the greenhouse.

[0110] This invention combines real-time temperature data from the greenhouse and determines the rate of temperature deviation change. Based on this rate of change, PID parameters are dynamically adjusted to dynamically output corresponding rotation commands, precisely controlling the number of rotations and direction of the ventilation fan motor, thereby adjusting the size of the greenhouse ventilation openings. This avoids excessively abrupt ventilation fan movements, which could reduce system stability, exacerbate equipment wear, and cause energy consumption fluctuations. It achieves high-precision, low-energy-consumption, and long-life temperature control in complex and variable greenhouse environments.

[0111] Figure 3 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0113] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0114] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A greenhouse intelligent temperature control method based on fuzzy adaptive PID and temperature change rate, characterized in that, The method includes: Real-time temperature data of the greenhouse is collected, and the rate of temperature change is determined based on the temperature data of the greenhouse at different times. The temperature deviation is determined based on the target thermometer and the current temperature in the temperature data, and the rate of change of the temperature deviation is determined based on the temperature deviation. The proportional, integral, and derivative outputs are determined based on the temperature deviation change rate and PID parameters. The PID parameters are dynamically adjusted based on a fuzzy adaptive PID controller and in combination with the temperature deviation and the rate of change of the temperature deviation. The PID output is determined based on the proportional, integral, and derivative outputs; and a rotation command is generated based on the PID output to control the number of rotations and direction of the ventilation fan motor, thereby adjusting the size of the ventilation opening in the greenhouse.

2. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The rate of temperature change is obtained by the following expression: in, ΔT represents the rate of temperature change; T(k) represents the current temperature, T(k-1) represents the temperature at the previous moment, and ΔT represents the sampling time interval.

3. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The temperature deviation is determined based on the target thermometer and the current temperature in the temperature data, and the rate of change of the temperature deviation is obtained by the following expression: Among them, T h T represents the highest target temperature. l Indicates the minimum target temperature; The method for calculating the rate of change of temperature deviation is as follows: Where e(k-1) is the deviation between the temperature at the previous moment and the target temperature.

4. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The step of determining the proportional, integral, and derivative outputs based on the temperature deviation change rate and PID parameters includes: in, For proportional output; Output for the integration stage; For the output of the differential element; , and PID parameters are constituted; For proportional gain; This is the integral gain; This is the differential gain; Temperature deviation; Accumulated temperature deviation; The sampling time interval; This represents the temperature deviation between the previous temperature and the target temperature. When T l <T(k)<T h When e(k) = 0, none of the three stages output anything; When T(k) > T h hour, ; When T(k) <T l hour, ; Among them, T h Indicates the highest target temperature; T l This represents the minimum target temperature; T(k) is the current temperature; T(k-1) is the temperature at the previous moment. The rate of temperature change; Therefore, for the output of the differential element, It is directly related to the rate of temperature change.

5. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The dynamic adjustment of PID parameters based on the fuzzy adaptive PID controller, combined with the temperature deviation and the rate of change of the temperature deviation, includes: Set initial proportional gain, initial integral gain, and initial derivative gain; fuzzify the temperature deviation and the rate of change of temperature deviation into 7-level linguistic variables; match the corresponding output variables of the 7-level linguistic variables according to the fuzzy rule table; process the output variables using the centroid method to obtain the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment. The initial proportional gain, initial integral gain, and initial derivative gain, together with the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment, constitute the PID parameters, expressed as follows: in, For proportional gain; This is the integral gain; This is the differential gain; Temperature deviation; This is the initial proportional gain; This is the initial integral gain; This is the initial differential gain; This is the proportional gain adjustment amount; This is the integral gain adjustment amount; This is the differential gain adjustment amount.

6. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The expression for determining the PID output based on the proportional, integral, and derivative outputs is as follows: in, This is the PID output; For proportional output; Output for the integration stage; This is the output of the differential element.

7. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, Also includes: When the current temperature is higher than the preset maximum temperature and the temperature change rate is positive, the fuzzy adaptive PID controller will increase the output in stages according to the magnitude of the temperature deviation and the temperature change rate, increase the number of forward rotations of the ventilation fan motor, and increase the size of the ventilation opening; When the current temperature is higher than the preset maximum temperature but the temperature change rate is negative, the fuzzy adaptive PID controller will reduce the output in stages according to the magnitude of the temperature deviation and the temperature change rate, increase the number of reverse rotations of the ventilation fan motor, and reduce the number of ventilation outlets. When the current temperature is lower than the preset minimum temperature and the temperature change rate is negative, the fuzzy adaptive PID controller will increase the output in stages according to the magnitude of the temperature deviation and the temperature change rate, significantly or appropriately increase the number of reverse rotations of the ventilation fan motor, and reduce the ventilation opening. When the current temperature is lower than the preset minimum temperature but the temperature change rate is positive, the fuzzy adaptive PID controller will reduce the output in stages according to the magnitude of the temperature deviation and the temperature change rate, and increase the number of forward rotations of the ventilation fan motor significantly or gradually to increase the ventilation opening.

8. The intelligent greenhouse temperature control method based on fuzzy adaptive PID and temperature change rate according to claim 1, characterized in that, The integral term accumulation range of the integral term in the fuzzy adaptive PID controller is limited to ±200 revolutions. When the ventilation fan motor is connected to the ventilation opening of the greenhouse by a rope, and the ventilation fan motor rotates to adjust the opening of the ventilation opening to the limit position of 0% or 80%, the integral accumulation stops. When the current temperature is between a preset minimum temperature and a preset maximum temperature, the fuzzy adaptive PID controller synchronously freezes the PID parameter adjustment and sets the PID output to 0.

Images