An intelligent control method for heat storage in an assembled solar greenhouse

Abstract

The present invention relates to the field of solar greenhouse control, and particularly to an intelligent heat storage control method for prefabricated solar greenhouses. The method obtains a multi-dimensional environmental parameter vector sequence of crops and an initial PID parameter value; obtains an environmental parameter fluctuation factor, a crop growth stage factor, and a long-term environmental trend factor according to the multi-dimensional environmental parameter vector sequence, and obtains a target clustering number and clustering clusters; obtains a target clustering cluster according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the clustering cluster; calculates a PID parameter adjustment factor according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target clustering cluster and the internal and external temperature difference, obtains the final PID parameter according to the PID parameter adjustment factor and the initial PID parameter value, and performs real-time control on the heat storage amount of the greenhouse environment where the crops are located. The present invention optimizes the selection of the K value in clustering, enabling it to adaptively respond to changes in the environment and the growth requirements of crops.

Description

Technical Field

[0001] The invention relates to the field of solar greenhouse control, and in particular to a heat storage intelligent control method for an assembled solar greenhouse. Background Art

[0002] Prefabricated solar greenhouses are widely used in modern agricultural production, but their environmental control, especially the intelligent management of heat storage, still faces many challenges. Traditional greenhouse environmental control methods mainly rely on preset fixed control strategies, which are difficult to adapt to complex and changing environments and crop growth requirements. Therefore, in recent years, machine learning technology, especially K-means clustering algorithm, has been introduced into the field of greenhouse environmental control.

[0003] In the existing greenhouse environment control methods, clustering algorithms usually use a fixed number of clusters or select the optimal K value through the elbow rule. However, the fixed number of clusters cannot adapt to the dynamic changes of the greenhouse environment, resulting in insufficient classification when the environment changes rapidly and inability to flexibly respond to environmental state changes, which leads to inaccurate environmental state classification, thereby affecting the accuracy and efficiency of greenhouse environment PID control, resulting in poor crop growth conditions and energy waste.

[0004] Therefore, how to achieve dynamic selection of clustering number so that it can adaptively respond to changes in environment and crop growth requirements has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a heat storage intelligent control method for an assembled solar greenhouse to solve the problem of how to achieve dynamic selection of the number of clusters so that it can adaptively respond to changes in the environment and crop growth requirements.

[0006] An embodiment of the present invention provides a heat storage intelligent control method for an assembled solar greenhouse, the method comprising the following steps:

[0007] Obtain a multidimensional environmental parameter vector of the crop at each sampling moment, and obtain a multidimensional environmental parameter vector sequence within the current preset period, wherein each of the multidimensional environmental parameter vectors includes temperature, humidity, light intensity, concentration and internal and external temperature difference, obtaining the initial PID parameter value of each multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence;

[0008] According to the multidimensional historical environmental parameter vector sequence before the multidimensional environmental parameter vector sequence, the environmental parameter fluctuation factor, the crop growth stage factor and the long-term environmental trend factor within the current preset time period are obtained, and according to the environmental parameter fluctuation factor, the crop growth stage factor and the long-term environmental trend factor within the current preset time period, the target cluster quantity is obtained;

[0009] Cluster the multi-dimensional environmental parameter vector sequence to obtain K clustering clusters equal to the target number of clusters, obtain a new multi-dimensional environmental parameter vector of the crop, and obtain the target clustering cluster to which the new multi-dimensional environmental parameter vector belongs according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of each clustering cluster;

[0010] Calculate a PID parameter adjustment factor according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target clustering cluster, and the internal and external temperature difference in the new multi-dimensional environmental parameter vector. Obtain the final PID parameter of the new multi-dimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter value of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, and perform real-time control on the heat storage capacity of the greenhouse environment where the crop is located according to the final PID parameter.

[0011] Preferably, the method for obtaining the environmental parameter fluctuation factor includes:

[0012] According to the multi-dimensional environmental parameter vector sequence, obtain the temperature standard deviation, humidity standard deviation, light intensity standard deviation and concentration standard deviation respectively;

[0013] According to a preset number of multi-dimensional historical environmental parameter vector sequences and the temperature standard deviation, humidity standard deviation, light intensity standard deviation and concentration standard deviation respectively corresponding to the multi-dimensional environmental parameter vector sequence, obtain the average value of the temperature standard deviation, the average value of the humidity standard deviation, the average value of the light intensity standard deviation and the average value of the concentration standard deviation;

[0014] Calculate a first ratio between the temperature standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the temperature standard deviation, calculate a second ratio between the humidity standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the humidity standard deviation, calculate a third ratio between the light intensity standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the light intensity standard deviation, and calculate the multi-dimensional environmental parameter vector sequence's concentration standard deviation and the fourth ratio between the average value of the concentration standard deviation, and calculate the sum of the squares of the first ratio, the second ratio, the third ratio and the fourth ratio as the environmental parameter fluctuation factor of the crop.

[0015] Preferably, the method for obtaining the crop growth stage factor includes:

[0016] For any multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, calculate the first difference between the temperature of the multi-dimensional environmental parameter vector and a preset basic temperature, obtain the fourth ratio between the first difference and the number of multi-dimensional environmental parameter vectors in the multi-dimensional environmental parameter vector sequence, obtain the maximum value between the fourth ratio and a preset value, and accumulate the maximum values corresponding to each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence. The corresponding accumulated value is used as the temperature index of the multi-dimensional environmental parameter vector sequence;

[0017] Obtain the temperature index of each of the multi-dimensional historical environmental parameter vector sequences before the multi-dimensional environmental parameter vector sequence, obtain the maximum temperature index among the temperature indices of the multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the multi-dimensional environmental parameter vector sequence, and obtain the accumulated value of the temperature indices between the multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the multi-dimensional environmental parameter vector sequence;

[0018] Calculate the fifth ratio between the accumulated value of the temperature index and the maximum temperature index, perform inverse proportional normalization processing on the product of the fifth ratio and a preset first environmental sensitivity parameter, and the corresponding result is used as the first inverse proportional normalization value. Calculate the difference between the constant 1 and the first inverse proportional normalization value as the temperature growth trend index;

[0019] Obtain the sine value of the product of the fifth ratio and pi, calculate the first product of the sine value and a preset second environmental sensitivity parameter, and calculate the sum of the constant 1 and the first product as the temperature adjustment coefficient;

[0020] Calculate the multiplication result among a preset base value, the temperature growth trend index, and the temperature adjustment coefficient, and use the sum of the multiplication result and the preset base value as the crop growth stage factor.

[0021] Preferably, the method for obtaining the long-term environmental trend factor includes:

[0022] According to the humidity, average light intensity, and concentration corresponding to a preset number of multi-dimensional historical environmental parameter vector sequences and the multi-dimensional environmental parameter vector sequence respectively, obtain the average humidity value, average light intensity value, and concentration average value, calculate the absolute value of the first difference between the average humidity value and a preset optimal humidity, calculate the absolute value of the second difference between the average light value and a preset optimal light, calculate the concentration average value and the preset optimal The absolute value of the third difference of the concentration, obtain the mean value of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference, perform hyperbolic tangent processing on the product of the mean value and the preset third environmental sensitivity parameter to obtain the corresponding hyperbolic tangent processing result, and use the sum of the hyperbolic tangent processing result and the constant 1 as the long-term environmental trend factor.

[0023] Preferably, the obtaining of the target number of clusters according to the environmental parameter fluctuation factor, the crop growth stage factor, and the long-term environmental trend factor within the current preset time period includes:

[0024] Obtain the initial number of clusters according to the environmental parameter fluctuation factor, and optimize the initial number of clusters according to the crop growth stage factor and the long-term environmental trend factor to obtain the target number of clusters.

[0025] Preferably, the obtaining of the initial number of clusters according to the environmental parameter fluctuation factor includes:

[0026] Calculate the difference between the preset maximum number of clusters and the preset minimum number of clusters as the second difference, perform inverse proportional normalization processing on the product of the environmental parameter fluctuation factor and the preset fourth environmental sensitivity parameter to obtain the second inverse proportional normalization value, calculate the third difference between the constant 1 and the second inverse proportional normalization value, obtain the product value of the second difference and the third difference, and perform rounding processing on the sum of the preset minimum number of clusters and the product value to obtain the initial number of clusters of the crop.

[0027] Preferably, the optimizing the initial number of clusters according to the crop growth stage factor and the long-term environmental trend factor to obtain the target number of clusters includes:

[0028] Obtain the second product of the crop growth stage factor and the preset growth stage factor weight coefficient, calculate the addition result of the second product and the constant 1, perform rounding processing on the addition result, the product of the initial number of clusters and the long-term environmental trend factor, and the corresponding result obtained is used as the target number of clusters.

[0029] Preferably, the calculating of the PID parameter adjustment factor according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster, and the internal and external temperature difference in the new multi-dimensional environmental parameter vector includes:

[0030] Obtain the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster as the target distance, perform inverse proportional normalization on the product of the preset first parameter and the target distance to obtain the third inverse proportional normalization value, obtain the first subtraction result of the constant 1 and the third inverse proportional normalization value, calculate the product of the first subtraction result and the preset second parameter as the third product, and calculate the first addition result of the third product and the constant 1;

[0031] Perform inverse proportional normalization on the product of the preset third parameter and the absolute value of the internal and external temperature difference of the new multi-dimensional environmental parameter vector to obtain the fourth inverse proportional normalization value, obtain the second subtraction result of the constant 1 and the fourth inverse proportional normalization value, calculate the product of the second subtraction result and the preset fourth parameter as the fourth product, and calculate the second addition result of the fourth product and the constant 1;

[0032] Obtain the product of the first addition result and the second addition result as the PID parameter adjustment factor.

[0033] Preferably, obtaining the final PID parameter of the new multi-dimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter value of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence includes:

[0034] According to the initial PID parameter values of each multi-dimensional environmental parameter vector in the target cluster to which the new multi-dimensional environmental parameter vector belongs, obtain the average value of the initial PID parameter values; calculate the product of the average value of the initial PID parameter values and the PID parameter adjustment factor as the final PID parameter of the new multi-dimensional environmental parameter vector.

[0035] Preferably, obtaining the target cluster to which the new multi-dimensional environmental parameter vector belongs according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of each cluster includes:

[0036] Select the minimum distance between the new multi-dimensional environmental parameter vector and the cluster center of each cluster, and the cluster corresponding to the minimum distance is used as the target cluster to which the new multi-dimensional environmental parameter vector belongs.

[0037] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0038] The present invention obtains the multi-dimensional environmental parameter vector at each sampling moment of the crop, and obtains the multi-dimensional environmental parameter vector sequence within the current preset time period, wherein each of the multi-dimensional environmental parameter vectors includes temperature, humidity, light intensity, Based on the concentration and the internal and external temperature difference, obtain the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence; according to the multi-dimensional historical environmental parameter vector sequence before the multi-dimensional environmental parameter vector sequence, obtain the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset period, and based on the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset period, obtain the target number of clusters; cluster the multi-dimensional environmental parameter vector sequence to obtain K cluster clusters equal to the target number of clusters, obtain the new multi-dimensional environmental parameter vector of the crop, and based on the distance between the new multi-dimensional environmental parameter vector and the cluster center of each cluster cluster, obtain the target cluster cluster to which the new multi-dimensional environmental parameter vector belongs; calculate the PID parameter adjustment factor based on the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster cluster and the internal and external temperature difference in the new multi-dimensional environmental parameter vector, and based on the PID parameter adjustment factor and the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, obtain the final PID parameter of the new multi-dimensional environmental parameter vector, and perform real-time control on the heat storage amount of the greenhouse environment where the crop is located according to the final PID parameter. By introducing the growth stage factor and the long-term environmental trend factor to optimize the selection of the K value in clustering, the present invention can automatically increase the number of clusters during the critical growth stage of the crop, provide a more refined classification of the environmental state, and this refined classification helps to finely adjust the PID parameters to achieve faster and more accurate control of environmental parameters, meeting the specific requirements of the crop at different growth stages. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a flowchart of a method for intelligent control of heat storage amount in a prefabricated solar greenhouse provided in Embodiment 1 of the present invention. Detailed Embodiments

[0041] The following will describe in detail the embodiments of the present disclosure, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure and should not be construed as a limitation of the present disclosure.

[0042] It should be noted that the terms "first", "second", etc. in the description of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure.

[0043] In order to illustrate the technical solution of the present invention, the following will be described through specific embodiments.

[0044] See Figure 1 , which is a method flow chart of an intelligent heat storage control method for an assembled solar greenhouse provided in the first embodiment of the present invention. As Figure 1 shown, the method may include:

[0045] Step S101, obtaining a multi-dimensional environmental parameter vector at each sampling moment of the crop to obtain a multi-dimensional environmental parameter vector sequence within a current preset period. Among them, each of the multi-dimensional environmental parameter vectors includes temperature, humidity, light intensity, concentration, and internal and external temperature difference, and obtaining an initial PID parameter value of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence.

[0046] Collect the environmental parameter information of the assembled solar greenhouse. At intervals of 5 minutes, record the average values of the temperature, humidity, light intensity, concentration, and internal and external temperature difference of the crop within 5 minutes to obtain a multi-dimensional environmental parameter vector X, , and collect 288 multi-dimensional environmental parameter vectors every 24 hours to obtain a multi-dimensional environmental parameter vector sequence within 24 hours. Among them, i represents the serial number of the multi-dimensional environmental parameter vector sequence of the crop, and j represents the serial number of the multi-dimensional environmental parameter vector. is the temperature value of the jth multi-dimensional environmental parameter vector in the ith multi-dimensional environmental parameter vector sequence (that is, the average temperature within 5 minutes), is the humidity value of the jth multi-dimensional environmental parameter vector in the ith multi-dimensional environmental parameter vector sequence (that is, the average humidity within 5 minutes), is the light intensity value of the jth multi-dimensional environmental parameter vector in the ith multi-dimensional environmental parameter vector sequence (that is, the average light intensity within 5 minutes), is the concentration value of the jth multi-dimensional environmental parameter vector in the ith multi-dimensional environmental parameter vector sequence (that is, the average concentration within 5 minutes), is the internal-external temperature difference of the j-th multi-dimensional environmental parameter vector in the i-th multi-dimensional environmental parameter vector sequence (that is, the difference between the average internal and external temperatures within 5 minutes). In this embodiment, 5 minutes is set as each sampling moment, and 24 hours is set as the preset time period. There is no limitation here and it can be set according to specific implementation scenarios.

[0047] Collect the initial PID parameter values of each multi-dimensional environmental parameter vector in each multi-dimensional environmental parameter vector sequence according to the records of the PID controller. The PID control parameters (initial PID parameter values) are represented as Y, , where P, I, and D are the proportional, integral, and derivative parameters of the PID controller respectively, , , are the proportional, integral, and derivative parameters corresponding to the j-th multi-dimensional environmental parameter vector in the i-th multi-dimensional environmental parameter vector sequence respectively. The PID control parameters are used as the regulation data for calculating the heat storage capacity intelligent control.

[0048] So far, a multi-dimensional environmental parameter vector sequence within the current 24 hours and the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence can be obtained.

[0049] Step S102: Obtain the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset time period according to the multi-dimensional historical environmental parameter vector sequence before the multi-dimensional environmental parameter vector sequence. Obtain the target clustering number according to the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset time period.

[0050] The challenge in the intelligent control of the heat storage capacity of the assembled solar greenhouse is how to accurately capture the short-term dynamic changes of the greenhouse environment. The greenhouse environment is affected by various factors such as sunlight intensity, external air temperature, humidity changes, etc. These factors will change significantly in a short time as the environment changes. For example, on a sunny summer day, the temperature and light intensity in the greenhouse may rise sharply within an hour at sunrise; while on a cloudy day, this change may be relatively slow. The traditional fixed clustering number method cannot flexibly cope with this dynamic change, resulting in insufficient classification when the environment changes rapidly, unable to capture the environmental state transition, or overly detailed classification when the environment is relatively stable, causing waste of computing resources.

[0051] To solve this problem, obtain a preliminary value selection method based on environmental parameter fluctuations. The core idea of this method is to dynamically determine the initial clustering number by analyzing the short-term fluctuations of the key environmental parameters in the greenhouse, and select temperature, humidity, light intensity, and Concentration and the internal and external temperature difference are used as key parameters. These parameters directly affect plant growth and can better reflect the overall state of the greenhouse environment. Specifically, first, according to the method for obtaining the multi-dimensional environmental parameter vector sequence within the current 24 hours, the multi-dimensional historical environmental parameter vector sequence before the current 24 hours is obtained respectively. Then, based on the multi-dimensional historical environmental parameter vector sequence before the multi-dimensional environmental parameter vector sequence within the current 24 hours, the environmental parameter fluctuation factor W within the current 24 hours is obtained. The change ranges of temperature, humidity, light intensity, and concentration within the current 24 hours are comprehensively considered, and these change ranges are compared with the data of the previous few days to obtain a standardized and comparable environmental parameter fluctuation factor. The initial clustering number within the current 24 hours is calculated by combining the obtained environmental parameter fluctuation factor with the preset maximum and minimum clustering numbers.

[0052] Among them, the method for obtaining the environmental parameter fluctuation factor W of the multi-dimensional environmental parameter vector sequence corresponding to the current 24 hours is as follows:

[0053] According to the multi-dimensional environmental parameter vector sequence, the temperature standard deviation, humidity standard deviation, light intensity standard deviation, and concentration standard deviation are obtained respectively; according to a preset number of multi-dimensional historical environmental parameter vector sequences and the temperature standard deviation, humidity standard deviation, light intensity standard deviation, and concentration standard deviation corresponding to the multi-dimensional environmental parameter vector sequence respectively, the average value of the temperature standard deviation, the average value of the humidity standard deviation, the average value of the light intensity standard deviation, and the average value of the concentration standard deviation are obtained.

[0054] Calculate the first ratio between the temperature standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the temperature standard deviation, calculate the second ratio between the humidity standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the humidity standard deviation, calculate the third ratio between the light intensity standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the light intensity standard deviation, calculate the concentration standard deviation of the multi-dimensional environmental parameter vector sequence and the average value of the concentration standard deviation to obtain the fourth ratio, and calculate the sum of the squares of the first ratio, the second ratio, the third ratio, and the fourth ratio as the environmental parameter fluctuation factor of the crop.

[0055] In an embodiment, the multi-dimensional environmental parameter vector sequence within the current 24 hours and the multi-dimensional historical environmental parameter vector sequence of 2 days before the current 24 hours are obtained. There is no limitation here and it can be set according to the specific implementation scenario. Calculate the environmental parameter fluctuation factor W of the multi-dimensional environmental parameter vector sequence within the current 24 hours:

[0056]

[0057] Where: W is the environmental parameter fluctuation factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; , , , respectively represent the standard deviation of temperature, the standard deviation of humidity, the standard deviation of light intensity, and the standard deviation of concentration in the multi-dimensional environmental parameter vector sequence within the current 24 hours; , , , respectively represent the average value of the standard deviation of temperature, the average value of the standard deviation of humidity, the average value of the standard deviation of light intensity, and the average value of the standard deviation of concentration between the multi-dimensional environmental parameter vector sequence within the current 24 hours and the multi-dimensional historical environmental parameter vector sequence 2 days before the current 24 hours;

[0058] Further, the method for calculating the initial clustering number within the current hours by combining the obtained environmental parameter fluctuation factor W with the preset maximum and minimum clustering numbers is as follows:

[0059] Calculate the difference between the preset maximum clustering number and the preset minimum clustering number as the second difference, perform inverse proportional normalization on the product of the environmental parameter fluctuation factor and the preset fourth environmental sensitivity parameter to obtain the second inverse proportional normalization value, calculate the third difference between the constant 1 and the second inverse proportional normalization value, obtain the product value of the second difference and the third difference, and perform rounding on the sum of the preset minimum clustering number and the product value to obtain the initial clustering number of the crop.

[0060] The calculation formula for the initial clustering number:

[0061]

[0062] Where, is the initial clustering number, is the preset minimum clustering number; is the preset maximum clustering number; is the preset fourth environmental sensitivity parameter; is the rounding function; W is the environmental parameter fluctuation factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; is the inverse proportional normalization function.

[0063] It should be noted that the preset minimum clustering number and the preset maximum clustering number are set according to the characteristics of external environmental changes. In this embodiment, , , which is not restricted here and can be set according to specific implementation scenarios; preset the fourth environmental sensitivity parameter is an adjustable sensitivity parameter used to control the influence degree of the environmental parameter fluctuation factor on the initial clustering number; The function is used to round the result to the nearest integer; the environmental parameter fluctuation factor W of the current 24-hour multi-dimensional environmental parameter vector sequence reflects the severity of short-term environmental changes. The greater the severity of short-term environmental changes, the larger the corresponding initial clustering number.

[0064] For example: Suppose that on a summer day, the standard deviations of temperature, humidity, light intensity, and concentration within the past hours are relatively large compared to the average standard deviation of the most recent days. The calculated environmental parameter fluctuation factor W is . Set , , , which is not restricted here and can be set according to specific implementation scenarios. The calculated is approximately , which means that clustering clusters are used to describe the environmental state in the case of large environmental changes in summer. In contrast, during a period of relatively stable environment, the hour standard deviation of each environmental parameter is close to the average standard deviation of the most recent days, and the calculated W is only . At this time, may be only or . This dynamic adjustment ensures that the environmental state can be classified more carefully when the environment changes violently, while fewer clusters are used when the environment is relatively stable, which not only ensures the accuracy of classification but also avoids unnecessary waste of computing resources.

[0065] Obtaining the environmental parameter fluctuation factor according to the short-term dynamic changes of the greenhouse environment can get the initial clustering number. However, there are limitations in determining the target clustering number only relying on short-term environmental fluctuations, because the greenhouse environment control is highly affected by external environmental changes, and there are significant differences in the environmental requirements of crops at different growth stages. For example, tomatoes have stricter requirements for temperature and humidity during the flowering and fruiting periods than during the vegetative growth stage; as the seasons change, the overall environmental trend outside the greenhouse will also change, with larger temperature differences between morning and evening, slower warming in the morning, and more rainy and cloudy days from summer to early autumn. Therefore, the initial clustering number obtained only through short-term environmental fluctuations ignores these long-term change factors of external environment and plant growth requirements, affecting the accuracy of environmental control and the growth quality of crops. To solve the above problems, it is necessary to obtain the crop growth stage factor and the long-term environmental trend factor within the current 24 hours based on the multi-dimensional historical environmental parameter vector sequence before the multi-dimensional environmental parameter vector sequence within the current 24 hours, and use them to optimize and adjust the initial clustering number to obtain a more accurate clustering number, that is, the target clustering number.

[0066] First, obtain the crop growth stage factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours:

[0067] (1) For any multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, calculate the first difference between the temperature of the multi-dimensional environmental parameter vector and the preset basic temperature, obtain the fourth ratio between the first difference and the number of multi-dimensional environmental parameter vectors in the multi-dimensional environmental parameter vector sequence, obtain the maximum value between the fourth ratio and the preset value, and accumulate the maximum values corresponding to each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence. The corresponding accumulated value is used as the temperature index of the multi-dimensional environmental parameter vector sequence.

[0068] In an embodiment, taking the i-th multi-dimensional environmental parameter vector sequence as an example, calculate the temperature index of the i-th multi-dimensional environmental parameter vector sequence:

[0069]

[0070] Among them, is the temperature index of the i-th multi-dimensional environmental parameter vector sequence, is the temperature value of the j-th multi-dimensional environmental parameter vector in the i-th multi-dimensional environmental parameter vector sequence; is the basic temperature of the crop.

[0071] It should be noted that ensure that the temperature index only considers positive temperature differences. If is lower than take , because when the temperature is lower than the base temperature, the growth of plants almost stagnates and is not included in the effective accumulated temperature.

[0072] (2) Obtain the temperature index of each of the multi-dimensional historical environmental parameter vector sequences before obtaining the multi-dimensional environmental parameter vector sequence. Obtain the maximum temperature index from among the temperature indices of the multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the multi-dimensional environmental parameter vector sequence. Obtain the cumulative temperature index value between the multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the multi-dimensional environmental parameter vector sequence.

[0073] In one embodiment, taking the i-th multi-dimensional environmental parameter vector sequence as an example, calculate the cumulative temperature index value between the i-th multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the i-th multi-dimensional environmental parameter vector sequence. Then, the calculation expression for the cumulative temperature index value is:

[0074]

[0075] Wherein, is the cumulative temperature index value between the i-th multi-dimensional environmental parameter vector sequence and each of the multi-dimensional historical environmental parameter vector sequences before the i-th multi-dimensional environmental parameter vector sequence, is the temperature index of the i-th multi-dimensional environmental parameter vector sequence; n is the total number of sequences up to the i-th multi-dimensional environmental parameter vector sequence; i represents the sequence number of the crop multi-dimensional environmental parameter vector sequence.

[0076] (3) Calculate the fifth ratio of the cumulative temperature index value to the maximum temperature index. Perform inverse normalization processing on the product of the fifth ratio and the preset first environmental sensitivity parameter, and the corresponding result is used as the first inverse normalization value. Calculate the difference between the constant 1 and the first inverse normalization value as the temperature growth trend index; obtain the sine value of the product of the fifth ratio and pi. Calculate the first product of the sine value and the preset second environmental sensitivity parameter. Calculate the sum of the constant 1 and the first product as the temperature adjustment coefficient; calculate the multiplication result among the preset base value, the temperature growth trend index, and the temperature adjustment coefficient, and use the sum of the multiplication result and the preset base value as the crop growth stage factor.

[0077] In one embodiment, calculate the crop growth stage factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours:

[0078]

[0079] Among them, G is the crop growth stage factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; 0.5 is the base value of the crop growth cycle; n is the total number of sequences up to the i-th multi-dimensional environmental parameter vector sequence; is the cumulative temperature index value between the i-th multi-dimensional environmental parameter vector sequence and each multi-dimensional historical environmental parameter vector sequence before the i-th multi-dimensional environmental parameter vector sequence; is the maximum temperature index between each multi-dimensional historical environmental parameter vector sequence before the current 24 hours and the multi-dimensional environmental parameter vector sequence within the current 24 hours; a is a preset first environmental sensitivity parameter; b is a preset second environmental sensitivity parameter; is the inverse proportional normalization function.

[0080] It should be noted that the base value of 0.5 ensures the minimum value of the crop growth stage factor G, which can be set according to specific crops. For example, the base value of tomatoes with a growth cycle of 100 days is set to 0.5. Even in the initial growth stage, a certain degree of environmental control accuracy is required. The reason for not using a higher value is that the plant is protected by the soil in the initial growth stage, and minor changes in the external environment or small adjustments to the temperature in the solar greenhouse have little impact on it. There is no limitation here and it can be set according to specific implementation scenarios; the temperature growth trend index describes the overall growth trend of the value of the crop growth stage factor G. As the plant starts to grow, the value of G gradually increases. The preset first environmental sensitivity parameter a controls the growth rate. A larger value of a will make G grow faster in the initial growth stage, reflecting the process that the requirements for environmental control gradually increase as the plant grows; the temperature adjustment coefficient controls to reach the peak in the middle growth stage. Taking tomatoes as an example, their flowering and fruiting period is in the middle growth stage, which is a relatively important stage in cultivation. This coefficient makes the environmental control accuracy reach the highest in the key growth stage.

[0081] Then, obtain the long-term environmental trend factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours, including:

[0082] According to the humidity, average light intensity, and concentration corresponding to a preset number of multi-dimensional historical environmental parameter vector sequences and the multi-dimensional environmental parameter vector sequence respectively, obtain the average humidity value, average light intensity value, and concentration average value, calculate the absolute value of the first difference between the average humidity value and the preset optimal humidity, calculate the absolute value of the second difference between the average light value and the preset optimal light, calculate the concentration average value and the preset optimal The absolute value of the third difference in concentration, obtain the mean value of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference, perform a hyperbolic tangent process on the product of the mean value and the preset third environmental sensitivity parameter to obtain the corresponding hyperbolic tangent process result, and use the sum of the hyperbolic tangent process result and the constant 1 as the long-term environmental trend factor.

[0083] In one embodiment, calculate the long-term environmental trend factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours:

[0084]

[0085] where L is the long-term environmental trend factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; 、 、 are respectively the humidity average value, the light intensity average value, and the concentration average value of the 6 multi-dimensional historical environmental parameter vector sequences before the current 24 hours and the multi-dimensional environmental parameter vector sequence within the current 24 hours; 、 、 are respectively the optimal humidity, the optimal light intensity, and the optimal concentration at the crop growth stage; is the preset third environmental sensitivity parameter; is the hyperbolic tangent function, and | | represents the absolute value symbol.

[0086] It should be noted that the preset third environmental sensitivity parameter is an adjustable sensitivity parameter, with an initial value of 0.1. Larger values can be adjusted according to actual needs, and it will be more sensitive to environmental changes; the hyperbolic tangent function ensures that when the difference between the humidity average value, the light intensity average value, and the concentration average value of the multi-dimensional environmental parameter vector of the 6 multi-dimensional historical environmental parameter vector sequences before the current 24 hours and the multi-dimensional environmental parameter vector sequence within the current 24 hours and the optimal humidity, the optimal light intensity, and the optimal concentration at the crop growth stage is large, the value of L increases significantly, and L remains relatively stable when approaching the optimal state.

[0087] For example: The optimal humidity, the optimal light intensity, and the optimal concentration at the tomato growth stage 、 、 Referenced the results of multiple scientific studies, including but not limited to Peet & Welles (2005), Shamshiri et al. (2018), and Kubota et al. (2018), etc. These studies provided the range of optimal environmental parameters for tomatoes at different growth stages, including but not limited to humidity, light intensity, and concentration, and gave the optimal parameter values for each growth stage of tomatoes:

[0088] Seedling stage :

[0089]

[0090] Vegetative growth stage :

[0091]

[0092] Flowering and fruiting stage :

[0093]

[0094] Maturity stage :

[0095]

[0096] Finally, the initial clustering number is optimized according to the crop growth stage factor and the long-term environmental trend factor to obtain the target clustering number, including:

[0097] Obtain the second product of the crop growth stage factor and the preset growth stage factor weight coefficient, calculate the sum of the second product and the constant 1, and round off the product of the sum result, the initial clustering number, and the long-term environmental trend factor. The corresponding result is used as the target clustering number.

[0098] In one embodiment, the calculation formula for the target clustering number:

[0099]

[0100] Wherein, is the target clustering number; is the initial clustering number; G is the crop growth stage factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; L is the long-term environmental trend factor of the multi-dimensional environmental parameter vector sequence within the current 24 hours; is the preset growth stage factor weight coefficient; is the rounding function.

[0101] It should be noted that the larger the crop growth stage factor G and the long-term environmental trend factor L of the multi-dimensional environmental parameter vector sequence within the current 24 hours, the larger the target clustering number, and the preset weight coefficient of the growth stage factor used to adjust the influence degree of the growth stage factor is initially set to 0.5 and can be adjusted according to specific requirements.

[0102] For example: A greenhouse growing tomatoes is in the flowering stage on the 40th day of tomato growth. Suppose the initial , the calculated growth stage factor is about 0.87, indicating that it is currently in a stage that requires a higher control precision; the average environmental parameters in the past days have a certain deviation from the optimal parameters: the average humidity is lower than the optimal humidity by , the average light intensity is lower than the optimal light intensity by , while the average concentration is close to the optimal concentration; these deviations result in the calculated long-term environmental trend factor being about 1.24, indicating that it is necessary to further increase the number of clusters to better control these deviations; setting , then the finally calculated target clustering number is about 11; this means that it is recommended to increase the number of clusters from the initial to 11 to cope with the current critical growth stage and the unsatisfactory long-term environmental trend.

[0103] So far, the target clustering number of the crop is obtained.

[0104] Step S103: Cluster the multi-dimensional environmental parameter vector sequence to obtain K clustering clusters equal to the target clustering number, obtain a new multi-dimensional environmental parameter vector of the crop, and obtain the target clustering cluster to which the new multi-dimensional environmental parameter vector belongs according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of each clustering cluster.

[0105] In an embodiment, after obtaining the target clustering number , use the classical K-means algorithm to perform clustering analysis on the multi-dimensional environmental parameter vector sequence: Use the environmental parameter vector including temperature, humidity, light intensity, concentration, and the internal and external temperature difference, and use the target clustering number as the clustering number K to perform clustering analysis on the multi-dimensional environmental parameter vector sequence within the past 24 hours to obtain K clustering clusters. Since the K-means algorithm is an existing method, the brief steps are described as follows: (1) Randomly select Use these points as the initial cluster centers; (2) Assign each data point to the nearest cluster center; (3) Recalculate the center point of each cluster; (4) Repeat steps (2) and (3) until the cluster centers no longer change significantly or reach the preset number of iterations. After clustering, each cluster center represents a specific environmental state.

[0106] Obtain a new multi-dimensional environmental parameter vector of the crop , for the k-th cluster among the K clusters, calculate the distance between the new multi-dimensional environmental parameter vector and the cluster center of the k-th cluster:

[0107]

[0108] where is the distance between the new multi-dimensional environmental parameter vector and the cluster center of the k-th cluster; , , , , are respectively the temperature value, humidity value, light intensity value of the new multi-dimensional environmental parameter vector, concentration and internal-external temperature difference value; , , , , are respectively the temperature value, humidity value, light intensity value of the cluster center of the k-th cluster, concentration value and internal-external temperature difference; k represents the serial number of the cluster; a is the serial number of the new multi-dimensional environmental parameter vector.

[0109] Furthermore, based on the distances between the new multi-dimensional environmental parameter vector and the cluster centers of each cluster, obtain the target cluster to which the new multi-dimensional environmental parameter vector belongs, including:

[0110] Select the minimum distance between the new multi-dimensional environmental parameter vector and the cluster centers of each of the clusters, and the cluster corresponding to the minimum distance is used as the target cluster to which the new multi-dimensional environmental parameter vector belongs.

[0111] Thus, the target cluster to which the new multi-dimensional environmental parameter vector belongs is obtained.

[0112] Step S104: Calculate the PID parameter adjustment factor based on the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster, and the internal and external temperature difference in the new multi-dimensional environmental parameter vector. Obtain the final PID parameters of the new multi-dimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, and perform real-time control on the heat storage amount of the greenhouse environment where the crops are located.

[0113] After obtaining the target cluster to which the new multi-dimensional environmental parameter vector belongs, it is necessary to dynamically adjust the parameters of the PID parameter controller according to the distance between the new multi-dimensional environmental parameter vector and the target cluster to which it belongs, in combination with the internal and external temperature difference.

[0114] First, calculate the PID parameter adjustment factor according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster to which it belongs, and the internal and external temperature difference in the new multi-dimensional environmental parameter vector. The specific method includes:

[0115] Obtain the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster as the target distance, perform inverse proportional normalization on the product of the preset first parameter and the target distance to obtain the third inverse proportional normalization value, obtain the first subtraction result of the constant 1 and the third inverse proportional normalization value, calculate the product of the first subtraction result and the preset second parameter as the third product, and calculate the first addition result of the third product and the constant 1.

[0116] Perform inverse proportional normalization on the product of the preset third parameter and the absolute value of the internal and external temperature difference of the new multi-dimensional environmental parameter vector to obtain the fourth inverse proportional normalization value, obtain the second subtraction result of the constant 1 and the fourth inverse proportional normalization value, calculate the product of the second subtraction result and the preset fourth parameter as the fourth product, and calculate the second addition result of the fourth product and the constant 1.

[0117] Obtain the product of the first addition result and the second addition result as the PID parameter adjustment factor.

[0118] In an embodiment, the calculation formula of the PID parameter adjustment factor:

[0119]

[0120] Wherein, is the PID parameter adjustment factor; is the target distance between the new multi-dimensional environmental parameter vector and the cluster center of the target cluster; is the preset first parameter; is the preset second parameter; is the internal and external temperature difference of the new multi-dimensional environmental parameter vector; is to preset the third parameter; is to preset the fourth parameter; is an inverse proportional normalization function.

[0121] It should be noted that the first addition result is a distance conversion based on an exponential function, and the second addition result is a temperature difference conversion based on an exponential function. When the distance or temperature difference increases, the values of these two parts will gradually transition from 0 to 1; the preset first parameter controls the influence rate of the distance on the adjustment factor; the preset second parameter is an adjustable parameter that controls the influence degree of the distance on the adjustment factor; the preset third parameter controls the influence rate of the temperature difference on the adjustment factor; the preset fourth parameter is an adjustable parameter that controls the influence degree of the distance and temperature difference on the adjustment factor; the value given in this embodiment is not limited here and can be set according to specific implementation scenarios.

[0122] Further, according to the PID parameter adjustment factor and the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, obtaining the final PID parameter of the new multi-dimensional environmental parameter vector includes:

[0123] According to the initial PID parameter values of each multi-dimensional environmental parameter vector in the target clustering cluster to which the new multi-dimensional environmental parameter vector belongs, obtaining the mean value of the initial PID parameter values; calculating the product of the mean value of the initial PID parameter values and the PID parameter adjustment factor as the final PID parameter of the new multi-dimensional environmental parameter vector.

[0124] In one implementation manner, obtaining the mean value of the initial PID parameter values in the target clustering cluster to which the new multi-dimensional environmental parameter vector belongs , calculating the final PID parameter of the new multi-dimensional environmental parameter vector:

[0125]

[0126] Among them, is the final PID parameter of the new multi-dimensional environmental parameter vector, is the mean value of the initial PID parameter values; is the PID parameter adjustment factor; k represents the serial number of the clustering cluster.

[0127] Finally, according to the final PID parameter, dynamically adjust the parameters of the PID parameter controller to perform real-time control on the heat storage amount of the greenhouse environment where the crops are located.

[0128] In summary, the embodiments of the present invention obtain the multi-dimensional environmental parameter vectors of the crop at each sampling moment, and obtain the multi-dimensional environmental parameter vector sequence within the current preset period. Each of the multi-dimensional environmental parameter vectors includes temperature, humidity, light intensity, concentration, and internal and external temperature difference, and obtain the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence; according to the multi-dimensional historical environmental parameter vector sequence before the multi-dimensional environmental parameter vector sequence, obtain the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset period, and obtain the target clustering number according to the environmental parameter fluctuation factor, crop growth stage factor, and long-term environmental trend factor within the current preset period; cluster the multi-dimensional environmental parameter vector sequence to obtain K clustering clusters equal to the target clustering number, obtain the new multi-dimensional environmental parameter vector of the crop, and obtain the target clustering cluster to which the new multi-dimensional environmental parameter vector belongs according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of each clustering cluster; calculate the PID parameter adjustment factor according to the distance between the new multi-dimensional environmental parameter vector and the cluster center of the target clustering cluster, and the internal and external temperature difference in the new multi-dimensional environmental parameter vector, and obtain the final PID parameter of the new multi-dimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter values of each multi-dimensional environmental parameter vector in the multi-dimensional environmental parameter vector sequence, and perform real-time control on the heat storage amount of the greenhouse environment where the crop is located. By introducing the growth stage factor and the long-term environmental trend factor to optimize the selection of the K value in clustering, the present invention can automatically increase the number of clusters during the critical growth stage of the crop, provide a more refined environmental state classification, and this refined classification helps to finely regulate parameters to achieve more rapid and accurate control of environmental parameters and meet the specific requirements of the crop at different growth stages.

[0129] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A heat storage intelligent control method for an assembled solar greenhouse, characterized in that: The method comprises: Obtain a multidimensional environmental parameter vector of the crop at each sampling moment, and obtain a multidimensional environmental parameter vector sequence within the current preset period, wherein each of the multidimensional environmental parameter vectors includes temperature, humidity, light intensity, concentration and internal and external temperature difference, obtaining the initial PID parameter value of each multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence; According to the multidimensional historical environmental parameter vector sequence before the multidimensional environmental parameter vector sequence, the environmental parameter fluctuation factor, the crop growth stage factor and the long-term environmental trend factor within the current preset time period are obtained, and according to the environmental parameter fluctuation factor, the crop growth stage factor and the long-term environmental trend factor within the current preset time period, the target cluster quantity is obtained; Clustering the multidimensional environmental parameter vector sequence to obtain K clusters equal to the number of target clusters, obtaining a new multidimensional environmental parameter vector of the crop, and obtaining the target cluster to which the new multidimensional environmental parameter vector belongs based on the distance between the new multidimensional environmental parameter vector and the cluster center of each cluster; Calculate a PID parameter adjustment factor according to the distance between the new multidimensional environmental parameter vector and the cluster center of the target cluster, and the internal and external temperature difference in the new multidimensional environmental parameter vector, obtain a final PID parameter of the new multidimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter value of each multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence, and perform real-time control of the heat storage amount of the greenhouse environment where the crop is located according to the final PID parameter; The method for obtaining the environmental parameter fluctuation factor comprises: According to the multidimensional environmental parameter vector sequence, the temperature standard deviation, humidity standard deviation, light intensity standard deviation and concentration standard deviation; The temperature standard deviation, humidity standard deviation, light intensity standard deviation and The concentration standard deviation, the average value of the temperature standard deviation, the average value of the humidity standard deviation, the average value of the light intensity standard deviation and mean of the standard deviations of the concentrations; Calculate a first ratio between the temperature standard deviation of the multidimensional environmental parameter vector sequence and the average value of the temperature standard deviation, calculate a second ratio between the humidity standard deviation of the multidimensional environmental parameter vector sequence and the average value of the humidity standard deviation, calculate a third ratio between the illumination intensity standard deviation of the multidimensional environmental parameter vector sequence and the average value of the illumination intensity standard deviation, calculate the illumination intensity of the multidimensional environmental parameter vector sequence. The standard deviation of concentration is a fourth ratio between the average value of the concentration and the standard deviation, and calculating the sum of squares of the first ratio, the second ratio, the third ratio and the fourth ratio as an environmental parameter fluctuation factor of the crop; The method for obtaining the crop growth stage factor comprises: For any multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence, calculate a first difference between the temperature of the multidimensional environmental parameter vector and a preset base temperature, obtain a fourth ratio between the first difference and the number of multidimensional environmental parameter vectors in the multidimensional environmental parameter vector sequence, obtain a maximum value between the fourth ratio and a preset value, accumulate the maximum values ​​corresponding to each multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence, and use the corresponding accumulated value as the temperature index of the multidimensional environmental parameter vector sequence; Obtaining the temperature index of each of the multidimensional historical environmental parameter vector sequences before the multidimensional environmental parameter vector sequence, obtaining the maximum temperature index among the temperature indexes of the multidimensional environmental parameter vector sequence and each of the multidimensional historical environmental parameter vector sequences before the multidimensional environmental parameter vector sequence, and obtaining the accumulated value of the temperature indexes between the multidimensional environmental parameter vector sequence and each of the multidimensional historical environmental parameter vector sequences before the multidimensional environmental parameter vector sequence; Calculate a fifth ratio of the accumulated value of the temperature index to the maximum temperature index, perform inverse normalization processing on the product of the fifth ratio and a preset first environmental sensitivity parameter, use the corresponding result as a first inverse normalized value, and calculate a difference between a constant 1 and the first inverse normalized value as a temperature growth trend indicator; Obtaining a sine value of the product of the fifth ratio and pi, calculating a first product of the sine value and a preset second environmental sensitivity parameter, and calculating a sum of a constant 1 and the first product as a temperature adjustment coefficient; Calculating the multiplication result of a preset basic value, the temperature growth trend index, and the temperature adjustment coefficient, and taking the sum of the multiplication result and the preset basic value as a crop growth stage factor; The method for obtaining the long-term environmental trend factor comprises: According to the preset number of multidimensional historical environmental parameter vector sequences and the humidity, light intensity and concentration, obtain the average humidity, average light intensity and concentration average, calculate the absolute value of the first difference between the humidity average and the preset optimal humidity, calculate the absolute value of the second difference between the light intensity average and the preset optimal light, calculate the Concentration average and preset optimum The third difference absolute value of the concentration is obtained, and the average of the first difference absolute value, the second difference absolute value, and the third difference absolute value is obtained. Hyperbolic tangent processing is performed on the product of the average and a preset third environmental sensitivity parameter to obtain a corresponding hyperbolic tangent processing result, and the sum of the hyperbolic tangent processing result and a constant 1 is used as the long-term environmental trend factor.

2. The heat storage intelligent control method for an assembled solar greenhouse according to claim 1, characterized in that: The step of obtaining the target cluster quantity according to the environmental parameter fluctuation factor, the crop growth stage factor and the long-term environmental trend factor within the current preset time period includes: An initial number of clusters is obtained according to the environmental parameter fluctuation factor, and the initial number of clusters is optimized according to the crop growth stage factor and the long-term environmental trend factor to obtain a target number of clusters.

3. The heat storage intelligent control method for an assembled solar greenhouse according to claim 2 is characterized in that: The obtaining of the initial number of clusters according to the environmental parameter fluctuation factor comprises: The difference between the preset maximum number of clusters and the preset minimum number of clusters is calculated as the second difference, the product of the environmental parameter fluctuation factor and the preset fourth environmental sensitivity parameter is inversely normalized to obtain a second inversely normalized value, the third difference between the constant 1 and the second inversely normalized value is calculated, the product value of the second difference and the third difference is obtained, the sum of the preset minimum number of clusters and the product value is rounded off to obtain the initial number of clusters of the crop.

4. The heat storage intelligent control method for an assembled solar greenhouse according to claim 2 is characterized in that: The step of optimizing the initial number of clusters according to the crop growth stage factor and the long-term environmental trend factor to obtain the target number of clusters includes: Obtain a second product of the crop growth stage factor and a preset growth stage factor weight coefficient, calculate the addition result of the second product and a constant 1, round off the addition result, the product of the initial cluster number and the long-term environmental trend factor, and use the corresponding result as the target cluster number.

5. The heat storage intelligent control method for an assembled solar greenhouse according to claim 1, characterized in that: The calculating of the PID parameter adjustment factor according to the distance between the new multidimensional environmental parameter vector and the cluster center of the target cluster, and the internal and external temperature difference in the new multidimensional environmental parameter vector, comprises: Obtaining the distance between the new multidimensional environmental parameter vector and the cluster center of the target cluster as the target distance, performing inverse normalization processing on the product of a preset first parameter and the target distance to obtain a third inverse normalized value, obtaining a first subtraction result of a constant 1 and the third inverse normalized value, calculating the product of the first subtraction result and a preset second parameter as a third product, and calculating a first addition result of the third product and the constant 1; Performing inverse normalization processing on the product of the preset third parameter and the absolute value of the internal and external temperature difference of the new multidimensional environmental parameter vector to obtain a fourth inverse normalized value, obtaining a second subtraction result of a constant 1 and the fourth inverse normalized value, calculating the product of the second subtraction result and the preset fourth parameter as a fourth product, and calculating a second addition result of the fourth product and the constant 1; A product of the first addition result and the second addition result is obtained as a PID parameter adjustment factor.

6. The heat storage intelligent control method for an assembled solar greenhouse according to claim 1, characterized in that: The step of obtaining the final PID parameter of the new multidimensional environmental parameter vector according to the PID parameter adjustment factor and the initial PID parameter value of each multidimensional environmental parameter vector in the multidimensional environmental parameter vector sequence comprises: According to the initial PID parameter value of each multidimensional environmental parameter vector in the target cluster to which the new multidimensional environmental parameter vector belongs, the mean of the initial PID parameter values ​​is obtained; and the product of the mean of the initial PID parameter values ​​and the PID parameter adjustment factor is calculated as the final PID parameter of the new multidimensional environmental parameter vector.

7. The heat storage intelligent control method for an assembled solar greenhouse according to claim 1, characterized in that: The step of obtaining the target cluster to which the new multidimensional environmental parameter vector belongs according to the distance between the new multidimensional environmental parameter vector and the cluster center of each cluster, comprises: The minimum distance between the new multidimensional environmental parameter vector and the cluster center of each cluster is selected, and the cluster corresponding to the minimum distance is used as the target cluster to which the new multidimensional environmental parameter vector belongs.

Images