Vegetable and fruit greenhouse planting environment control method based on intelligent regulation

Abstract

The present invention relates to the field of greenhouse equipment adjustment technology, and more specifically to a method for controlling the greenhouse planting environment for fruits and vegetables based on intelligent control. The method comprises: obtaining the data deviation of each type of sensor in each control device based on the range of the data variation of each type of sensor at all change moments; presetting a number of adjustment parameter combinations; obtaining the adjusted PID output value of each control device under each adjustment parameter combination; obtaining the control preference of each adjustment parameter combination based on the adjusted PID output value, the data deviation, and the improved environmental impact coefficient; and controlling the control devices in the greenhouse based on the control preference. The present invention ensures the stability of various environmental parameters in the greenhouse.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse equipment adjustment, and in particular to a method for controlling the planting environment of fruit and vegetable greenhouses based on intelligent regulation. Background Art

[0002] In modern agriculture, monitoring and controlling the greenhouse vegetable cultivation environment is crucial for ensuring healthy crop growth. Traditional PID (Proportional-Integral-Derivative) control methods are widely used in greenhouse environmental control, primarily for controlling environmental parameters such as temperature, humidity, and light. However, in practice, multiple control devices within a greenhouse (such as heaters, humidity controllers, and fans) often interact with each other, resulting in suboptimal control. For example, a heater's operation may affect humidity sensor data, while a fan's operation may affect temperature sensor data. This multi-dimensional interaction makes it difficult for traditional PID control methods to achieve optimal results when multiple control devices are operating simultaneously, and it is impossible to guarantee the stability of various environmental parameters within the greenhouse. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation, the method comprising:

[0004] Obtain the output value of the PID parameters at each change moment during the operation of each control device in the historical time period, as well as the data change of each sensor in each type of sensor;

[0005] According to the data change amount of each type of sensor at different change moments, the data environment volatility of each type of sensor in each control device is obtained; according to the output value of the PID parameter at each change moment and the data change amount of each type of sensor, the environmental impact coefficient of each type of sensor in each control device is obtained; the environmental impact coefficient is corrected by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device;

[0006] Obtain the data deviation of each type of sensor in each control device based on the range of the data change of each type of sensor at all change moments; preset several adjustment parameter combinations; obtain the adjusted PID output value of each control device under each adjustment parameter combination; obtain the control preference degree of each adjustment parameter combination based on the adjusted PID output value, data deviation, and improved environmental impact coefficient;

[0007] The control equipment in the greenhouse is regulated based on the degree of control preference.

[0008] Preferably, the method of obtaining the data environment volatility of each type of sensor in each control device according to the data change amount of each type of sensor at different change moments includes:

[0009] The first During the operation of the control equipment The next moment of change The standard deviation of the data variation of all sensors in the same type of sensor is recorded as The next moment of change The data environment fluctuation factor of the sensor type; During the operation of a control device, at all changing moments The normalized value of the mean value of the data environment fluctuation factor of the sensor type is used as the The first control device The data environment volatility of different types of sensors.

[0010] Preferably, the environmental impact coefficient of each type of sensor in each control device is obtained according to the output value of the PID parameter at each change moment and the data change amount of each type of sensor, including the specific method of:

[0011] According to the output value of the PID parameter at each change moment during the operation of each control device and the data change of each sensor in each type of sensor, the influence factor of each sensor at each change moment is obtained;

[0012] The first During the operation of a control device, at all changing moments The average value of the influencing factors of the sensors is recorded as The environmental impact factor of each sensor; The first control device The average value of the environmental impact factors of all sensors in the sensor type is recorded as The first control device Environmental impact coefficients of different sensor types.

[0013] Preferably, the method of obtaining the influence factor of each sensor at each change moment according to the output value of the PID parameter at each change moment during the operation of each control device and the data change amount of each sensor of each type of sensor includes the following specific methods:

[0014] The first During the operation of the control equipment The next moment of change Type of sensor The data change of the first sensor is During the operation of the control equipment The ratio between the output values ​​of the PID parameters at the time of change is recorded as The next moment of change The influencing factor of the sensor.

[0015] Preferably, the environmental impact coefficient is corrected by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device, including the specific method of:

[0016] Combine 1 with The first control device The sum of the data environment fluctuations of the three types of sensors is recorded as the correction factor; the correction factor is added to the The first control device The product of the environmental impact coefficients of the two types of sensors is used as the The first control device Improved environmental impact coefficients of various sensor types.

[0017] Preferably, the method of obtaining the data deviation of each type of sensor in each control device according to the range of the data change amount of each type of sensor at all change moments includes:

[0018] The first During the operation of a control device, at all changing moments The average value of the data changes of all sensors in the sensor type is recorded as The first control device The mean value of data variation of different types of sensors;

[0019] According to the range of the sensor data change, obtain the median value of the data change of each type of sensor in each control device;

[0020] The first The first control device The difference between the mean value of the data variation and the median value of the data variation of the sensor type is recorded as The first control device The data deviation of different sensor types.

[0021] Preferably, the method of obtaining the median value of the data variation of each type of sensor in each control device according to the range of the data variation of the sensor includes:

[0022] The first During the operation of a control device, at all changing moments The average value between the maximum and minimum values ​​of the data changes of all sensors in the sensor type is recorded as The first control device The median value of the data change of the sensor type.

[0023] Preferably, the specific method of obtaining the control preference degree of each adjustment parameter combination according to the adjusted PID output value, the data deviation and the improved environmental impact coefficient is as follows:

[0024] According to the adjusted PID output value, data deviation and improved environmental impact coefficient, the data synchronization approach factor of each type of sensor in each control device under each adjustment parameter combination is obtained;

[0025] The first All control devices under the adjustment parameter combination The cumulative sum of the data synchronization convergence factors of the various types of sensors is recorded as Adjust the parameter combination The data control factor of the sensor type; The inverse proportional normalized value of the mean of the data control factors of all types of sensors under the adjustment parameter combination is used as the The degree of control preference of a combination of adjustment parameters.

[0026] Preferably, the method of obtaining the data synchronization approach factor of each type of sensor in each control device under each adjustment parameter combination according to the adjusted PID output value, the data deviation and the improved environmental impact coefficient includes the following specific methods:

[0027] The first Adjust the parameter combination The adjusted PID output value of the first control device is The first control device The product of the improved environmental impact coefficients of the two types of sensors is recorded as the first product; the first product is multiplied by the second The first control device The ratio of the data deviation between the two types of sensors is recorded as Adjust the parameter combination The first control device Data synchronization convergence factor of different types of sensors.

[0028] Preferably, the specific method of regulating the regulating equipment in the greenhouse based on the regulation preference degree is as follows:

[0029] The three elements in the adjustment parameter combination corresponding to the maximum value of the control preference degree are used as the improved PID control parameters corresponding to the control equipment in the greenhouse; according to the improved PID control parameters, the PID output values ​​corresponding to each control equipment are corrected.

[0030] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the data environment volatility of each type of sensor in each control device according to the data change of each type of sensor at different change moments; obtains the environmental impact coefficient of each type of sensor in each control device according to the output value of the PID parameter at each change moment and the data change of each type of sensor; corrects the environmental impact coefficient by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device; obtains the data deviation of each type of sensor in each control device according to the range of the data change of each type of sensor at all change moments; presets several adjustment parameter combinations; obtains the adjusted PID output value of each control device under each adjustment parameter combination; obtains the control preference degree of each adjustment parameter combination according to the adjusted PID output value, data deviation and improved environmental impact coefficient; controls the control equipment in the greenhouse based on the control preference degree; thereby, adaptive adjustment of the PID parameters is achieved by real-time analysis of the data volatility of each type of sensor and the influence coefficient of each control device, which can effectively cope with the mutual influence between multiple control devices and ensure the stability of various environmental parameters in the greenhouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is a flowchart of the steps of the vegetable and fruit greenhouse planting environment control method based on intelligent regulation of the present invention;

[0033] Figure 2 This is a flow chart showing the characteristic relationships of the vegetable and fruit greenhouse planting environment control method based on intelligent regulation according to the present invention. DETAILED DESCRIPTION

[0034] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of the vegetable and fruit greenhouse planting environment control method based on intelligent regulation proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0036] The specific scheme of the vegetable and fruit greenhouse planting environment control method based on intelligent regulation provided by the present invention is described in detail below with reference to the accompanying drawings.

[0037] See also Figure 1 , which shows a flowchart of a method for controlling a greenhouse planting environment for fruits and vegetables based on intelligent regulation according to an embodiment of the present invention. The method includes the following steps:

[0038] Step S001: Obtain the output value of the PID parameter at each change moment during the operation of each control device in a historical time period, as well as the data change amount of each sensor in each type of sensor.

[0039] Specifically, we first need to collect the output values ​​of the PID parameters at each change moment during the operation of each control device in the historical time period, as well as the data change of each sensor in each type of sensor. The specific process is as follows:

[0040] Record the past week as a historical period, and record the moment when the power of each control device changes within the historical period as the change moment of each control device;

[0041] For the A control device is set according to the initial PID parameters, and the traditional parameter tuning calculation table is used to obtain the first The output value of the PID parameter at each changing moment during the operation of the control device; obtain the The amount of data change of each type of sensor at each change moment during the operation of a control device.

[0042] The PID parameters include a proportional coefficient, an integral coefficient, and a differential coefficient. This embodiment is described by taking any one PID parameter as an example. The data change refers to the change in the sensor data before and after the current change moment.

[0043] At this point, the above method is used to obtain the output value of the PID parameter at each change moment during the operation of each control device in the historical time period, as well as the data change of each sensor in each type of sensor.

[0044] The data collected by each type of sensor is normalized to a value between 0 and 1, unifying the dimensions of data of different dimensions. The normalization method is the maximum and minimum standardization method, which is a well-known technology.

[0045] Step S002: According to the data change amount of each type of sensor at different change moments, the data environment volatility of each type of sensor in each control device is obtained; according to the output value of the PID parameter at each change moment and the data change amount of each type of sensor, the environmental impact coefficient of each type of sensor in each control device is obtained; the environmental impact coefficient is corrected by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device.

[0046] It should be noted that when considering PID control, there are many sensors of different types in the greenhouse. When the data changes between multiple sensors are very different, the stability of the corresponding control results is poor; the PID control results for environmental temperature and humidity and other data will have a large lag; when the control equipment performs PID adjustment, the oscillation phenomenon in the control process will be enhanced due to the slow change of data from different types of sensors. When the difference between the corresponding sensor data is relatively small at the same change moment, the control result is relatively more accurate; therefore, the similarity of the changes in data obtained by multiple sensor devices of the same type at different change moments is used to quantify the environmental volatility of the data obtained by each type of sensor in the greenhouse.

[0047] Preferably, in some implementations of the embodiments of the present invention, a specific method for obtaining the data environment volatility of each type of sensor in each control device according to the data change amount of each type of sensor at different change moments during the operation of each control device is:

[0048] The first During the operation of the control equipment The next moment of change The standard deviation of the data variation of all sensors in the same type of sensor is recorded as The next moment of change The data environment fluctuation factor of the sensor type; During the operation of a control device, at all changing moments The normalized value of the mean value of the data environment fluctuation factor of the sensor type is used as the The first control device The volatility of the data environment of different sensor types;

[0049] The specific formula is:

[0050]

[0051] Where, Indicates the The first control device The volatility of the data environment of different sensor types; Indicates the The number of all changing moments during the operation of a control device; Indicates the During the operation of the control equipment The next moment of change The standard deviation of the data variation of all sensors within a type of sensor; Represents the linear normalized value.

[0052] It should be noted that during the operation of a single control device, the control devices of other dimensions will also undergo certain changes. For example, the heater mainly controls the temperature changes in the greenhouse, but when the heater power is high, it will also affect the humidity and other data inside the greenhouse. That is, during the operation of the above-mentioned single control device, the data changes corresponding to other types of sensors will all change, but the data changes corresponding to each sensor are different. And since the data is only obtained when a single control device is running, the difference in the data changes of each type of sensor indicates the difference in the impact of a single control device on data of different dimensions.

[0053] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the environmental impact coefficient of each type of sensor in each control device according to the output value of the PID parameter at each change moment during the operation of each control device and the change amount of each type of sensor is:

[0054] The first During the operation of the control equipment The next moment of change Type of sensor The data change of the first sensor is During the operation of the control equipment The ratio between the output values ​​of the PID parameters at the time of change is recorded as The next moment of change The influencing factor of the sensor; During the operation of a control device, at all changing moments The average value of the influencing factors of the sensors is recorded as The environmental impact factor of each sensor; The first control device The average value of the environmental impact factors of all sensors in the sensor type is recorded as The first control device Environmental impact coefficients of various sensor types;

[0055] The specific formula is:

[0056]

[0057] Where, Indicates the The first control device Environmental impact coefficients of various sensor types; Indicates the The first control device The number of all sensors in a sensor type; Indicates the The number of all changing moments during the operation of a control device; Indicates the During the operation of the control equipment The next moment of change Type of sensor The amount of data change of each sensor; Indicates the During the operation of the control equipment The output value of the PID parameter at each changing moment.

[0058] It should be noted that, considering that the data ranges corresponding to different sensors are different, when the same type of sensor is affected by the control equipment, there will be a certain deviation between the data received by multiple sensors. For example, when the heater adjusts the temperature, the temperature gradient will change in the greenhouse. It is obviously not accurate enough to quantify the degree of impact by simply using the average temperature change at each position. Therefore, during the operation of the above-mentioned control equipment, the environmental impact coefficient is adjusted according to the data environment volatility of each type of sensor to obtain an improved environmental impact coefficient.

[0059] Preferably, in some implementations of the embodiments of the present invention, the environmental impact coefficient of each type of sensor in each control device is corrected by the data environment volatility thereof, and the specific method for obtaining the improved environmental impact coefficient of each type of sensor in each control device is:

[0060] Combine 1 with The first control device The sum of the data environment fluctuations of the three types of sensors is recorded as the correction factor; the correction factor is added to the The first control device The product of the environmental impact coefficients of the two types of sensors is used as the The first control device Improved environmental impact coefficients of various sensor types;

[0061] The specific formula is:

[0062]

[0063] Where, Indicates the The first control device Improved environmental impact coefficients of various sensor types; Indicates the The first control device The volatility of the data environment of different sensor types; Indicates the The first control device Environmental impact coefficients of different sensor types.

[0064] At this point, the improved environmental impact coefficient of each type of sensor in each control device is obtained through the above method.

[0065] Step S003: Obtain the data deviation of each type of sensor in each control device based on the range of the data change of each type of sensor at all change moments; preset several adjustment parameter combinations; obtain the adjusted PID output value of each control device under each adjustment parameter combination; obtain the control preference degree of each adjustment parameter combination based on the adjusted PID output value, data deviation and improved environmental impact coefficient.

[0066] It should be noted that when data from multiple types of sensors deviate from the standard values, it is necessary to change the power of two or more control devices. For example, when the temperature and humidity data in the greenhouse deviate from the standard temperature and humidity data at the same time, and both are small, it is necessary to increase the water pressure of the water pump while turning on the heater to adjust the temperature and humidity data; since traditional PID control only considers the changes in single-dimensional data, its control process will be affected by the difference between the two, resulting in large fluctuations in temperature and humidity during the control process, which in turn affects the growth of vegetables in the greenhouse.

[0067] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the data deviation of each type of sensor in each control device is as follows:

[0068] The first During the operation of a control device, at all changing moments The average value of the data changes of all sensors in the sensor type is recorded as The first control device The average value of the data variation of the sensor type; During the operation of a control device, at all changing moments The average value between the maximum and minimum values ​​of the data changes of all sensors in the sensor type is recorded as The first control device The median value of the data change of the sensor type; The first control device The difference between the mean value of the data variation and the median value of the data variation of the sensor type is recorded as The first control device The data deviation of different types of sensors;

[0069] The specific formula is:

[0070]

[0071] Where, Indicates the The first control device The data deviation of different types of sensors; Indicates the During the operation of a control device, at all changing moments The average value of the data changes of all sensors in the same type of sensor; Indicates the During the operation of a control device, at all changing moments The minimum value of the data change of all sensors in the same type of sensor; Indicates the During the operation of a control device, at all changing moments The maximum value of the data change of all sensors in the same type of sensor.

[0072] Preferably, in some implementations of the embodiments of the present invention, it is considered to adjust each PID parameter to a certain extent based on the initial PID parameters so that the fluctuation of the sensor data during the change process is small. The specific method of presetting several adjustment parameter combinations is as follows:

[0073] The sequence constructed by (5.6, 5.7, 5.8, …, 8.3, 8.4) is recorded as the first sequence; the sequence constructed by (0.008, 0.009, 0.01, …, 0.013, 0.014) is recorded as the second sequence; the sequence constructed by (20, 21, 22, …, 29, 30) is recorded as the third sequence; the combinations consisting of one element randomly selected from the first sequence, the second sequence, and the third sequence are all recorded as adjustment parameter combinations.

[0074] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the adjusted PID output value of each control device under each adjustment parameter combination is:

[0075] Input the three element values ​​in each adjustment parameter combination into each control device as PID parameters, and perform the existing PID control process to obtain the adjusted PID output value of each control device under each adjustment parameter combination;

[0076] Preferably, in some implementations of the embodiments of the present invention, since there are multiple control devices that need to be controlled, the control results of the multiple control devices should cause the data of each type of sensor to be further corrected according to the proportion of the data deviation. The specific method for obtaining the control preference degree of each adjustment parameter combination based on the adjusted PID output value, the data deviation and the improved environmental impact coefficient is as follows:

[0077] The first Adjust the parameter combination The adjusted PID output value of the first control device is The first control device The product of the improved environmental impact coefficients of the two types of sensors is recorded as the first product; the first product is multiplied by the second The first control device The ratio of the data deviation between the two types of sensors is recorded as Adjust the parameter combination The first control device The data synchronization convergence factor of the type of sensor; All control devices under the adjustment parameter combination The cumulative sum of the data synchronization convergence factors of the various types of sensors is recorded as Adjust the parameter combination The data control factor of the sensor type; The inverse proportional normalized value of the mean of the data control factors of all types of sensors under the adjustment parameter combination is used as the The degree of control optimization of the adjustment parameter combination;

[0078] The specific formula is:

[0079]

[0080] Where, Indicates the The degree of control optimization of the adjustment parameter combination; Indicates the number of all types of sensors in the control device; Indicates the number of all control devices; Indicates the Adjust the parameter combination The adjusted PID output value of each control device; Indicates the The first control device The data deviation of different types of sensors; Indicates the The first control device Improved environmental impact coefficients of various sensor types; Represents an exponential function with a natural constant as the base, and the embodiment adopts Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can choose the inverse proportional function and normalization function according to the actual situation.

[0081] The product of the improved environmental impact coefficient and the adjusted PID output value (the first product) represents the deviation under environmental influences, while the data deviation measures the difference between the actual sensor value and the ideal value. Therefore, the closer the first product is to the data deviation, the smaller the data synchronization convergence factor, indicating that the corresponding control result will make the sensor data synchronization in multiple dimensions approach the optimal standard. In other words, the higher the synchronization between the device and the sensor, the closer the device control effect is to the target state. Therefore, the inversely proportional normalized value of the mean of the data control factors of all sensor types is taken as the control optimization degree. The greater the control optimization degree, the more consistent the device feedback effect and sensor performance are, the closer the system control effect is to the ideal target, the better the device adjustment effect is, and the more accurate the system performance is. The control optimization degree for each adjustment parameter combination is thus obtained through the above method.

[0082] Step S004: regulating the control equipment in the greenhouse based on the control preference degree.

[0083] Preferably, in some implementations of the embodiments of the present invention, when the control preference of the adjustment parameter combination reaches the maximum, the corresponding control result will make the sensor data of multiple dimensions synchronously approach the optimal standard; the specific method of controlling the control equipment in the greenhouse based on the control preference is:

[0084] The three elements in the adjustment parameter combination corresponding to the maximum value of the control preference degree are used as the improved PID control parameters corresponding to the control equipment in the greenhouse; according to the improved PID control parameters, the PID output values ​​corresponding to each control equipment are corrected, so that the control results further eliminate the mutual influence between multiple control devices.

[0085] See also Figure 2 , which shows a characteristic relationship flow chart of a vegetable and fruit greenhouse planting environment control method based on intelligent regulation;

[0086] At this point, this embodiment is completed.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation, characterized in that: The method comprises the following steps: Obtain the output value of the PID parameters at each change moment during the operation of each control device in the historical time period, as well as the data change of each sensor in each type of sensor; According to the data change amount of each type of sensor at different change moments, the data environment volatility of each type of sensor in each control device is obtained; according to the output value of the PID parameter at each change moment and the data change amount of each type of sensor, the environmental impact coefficient of each type of sensor in each control device is obtained; the environmental impact coefficient is corrected by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device; Obtain the data deviation of each type of sensor in each control device based on the range of the data change of each type of sensor at all change moments; preset several adjustment parameter combinations; obtain the adjusted PID output value of each control device under each adjustment parameter combination; obtain the control preference degree of each adjustment parameter combination based on the adjusted PID output value, data deviation, and improved environmental impact coefficient; Regulate the control equipment in the greenhouse based on the degree of control optimization; The method of obtaining the data environment volatility of each type of sensor in each control device according to the data change amount of each type of sensor at different change moments includes the following specific methods: The first During the operation of the control equipment The next moment of change The standard deviation of the data variation of all sensors in the same type of sensor is recorded as The next moment of change The data environment fluctuation factor of the sensor type; During the operation of a control device, at all changing moments The normalized value of the mean value of the data environment fluctuation factor of the sensor type is used as the The first control device The volatility of the data environment of different sensor types; The specific method for obtaining the environmental impact coefficient of each type of sensor in each control device based on the output value of the PID parameter at each change moment and the data change amount of each type of sensor is as follows: According to the output value of the PID parameter at each change moment during the operation of each control device and the data change of each sensor in each type of sensor, the influence factor of each sensor at each change moment is obtained; The first During the operation of a control device, at all changing moments The average value of the influencing factors of the sensors is recorded as The environmental impact factor of each sensor; The first control device The average value of the environmental impact factors of all sensors in the sensor type is recorded as The first control device Environmental impact coefficients of various sensor types; The step of obtaining the data deviation of each type of sensor in each control device includes: The first During the operation of a control device, at all changing moments The average value of the data changes of all sensors in the sensor type is recorded as The first control device The mean value of data variation of different types of sensors; According to the range of the sensor data change, obtain the median value of the data change of each type of sensor in each control device; The first The first control device The difference between the mean value of the data variation and the median value of the data variation of the sensor type is recorded as The first control device The data deviation of different types of sensors; The obtaining of the control preference degree of each adjustment parameter combination includes: According to the adjusted PID output value, data deviation and improved environmental impact coefficient, the data synchronization approach factor of each type of sensor in each control device under each adjustment parameter combination is obtained; The first All control devices under the adjustment parameter combination The cumulative sum of the data synchronization convergence factors of the various types of sensors is recorded as Adjust the parameter combination The data control factor of the sensor type; The inverse proportional normalized value of the mean of the data control factors of all types of sensors under the adjustment parameter combination is used as the The degree of control preference of a combination of adjustment parameters.

2. The method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation according to claim 1 is characterized in that: The method of obtaining the influence factor of each sensor at each change moment according to the output value of the PID parameter at each change moment during the operation of each control device and the data change amount of each sensor of each type of sensor at each change moment includes the following specific methods: The first During the operation of the control equipment The next moment of change Type of sensor The data change of the first sensor is During the operation of the control equipment The ratio between the output values ​​of the PID parameters at the time of change is recorded as The next moment of change The influencing factor of the sensor.

3. The method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation according to claim 1 is characterized in that: The environmental impact coefficient is corrected by the data environment volatility to obtain the improved environmental impact coefficient of each type of sensor in each control device, including the specific method: Combine 1 with The first control device The sum of the data environment fluctuations of the various types of sensors is recorded as the correction factor; The correction factor is combined with The first control device The product of the environmental impact coefficients of the two types of sensors is used as the The first control device Improved environmental impact coefficients of various sensor types.

4. The method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation according to claim 1 is characterized in that: The method of obtaining the median value of the data variation of each type of sensor in each control device according to the range of the data variation of the sensor includes: The first During the operation of a control device, at all changing moments The average value between the maximum and minimum values ​​of the data changes of all sensors in the sensor type is recorded as The first control device The median value of the data change of the sensor type.

5. The method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation according to claim 1 is characterized in that: The method of obtaining the data synchronization approach factor of each type of sensor in each control device under each adjustment parameter combination based on the adjusted PID output value, data deviation and improved environmental impact coefficient includes the following specific methods: The first Adjust the parameter combination The adjusted PID output value of the first control device is The first control device The product of the improved environmental impact coefficients of the two types of sensors is recorded as the first product; the first product is multiplied by the second The first control device The ratio of the data deviation between the two types of sensors is recorded as Adjust the parameter combination The first control device Data synchronization convergence factor of different types of sensors.

6. The method for controlling the vegetable and fruit greenhouse planting environment based on intelligent regulation according to claim 1, characterized in that: The specific method of regulating the control equipment in the greenhouse based on the control preference degree includes: The three elements in the adjustment parameter combination corresponding to the maximum value of the control preference degree are used as the improved PID control parameters corresponding to the control equipment in the greenhouse; according to the improved PID control parameters, the PID output values ​​corresponding to each control equipment are corrected.

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