Greenhouse all-weather environment monitoring and control system

Abstract

The invention relates to the technical field of temperature control, in particular to an all-weather environment monitoring and control system for a greenhouse. According to the invention, the temperature change degree of each monitoring point is obtained according to the monitored temperature data distribution of each monitoring point at different moments; obtaining a plurality of approximate segments of a curve formed by the ambient temperature data outside the greenhouse at all moments, and combining the relative distance between the monitored temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all moments and the fluctuation degree of the monitored temperature data in the time range corresponding to different approximate segments; obtaining the environment temperature influence degree and the position influence degree of each monitoring point; according to the position influence degree distribution of the monitoring points in different areas, the temperature influence degree of each monitoring point is obtained, and the temperature in the greenhouse is controlled. According to the invention, the temperature influence degree of each monitoring point is accurately obtained, and the temperature of the greenhouse is accurately regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and specifically relates to an all-weather environment monitoring and control system for a greenhouse. Background Art

[0002] With the acceleration of the process of agricultural modernization, smart agriculture has become a key way to improve agricultural production efficiency, ensure the quality and yield of agricultural products; through the multi-layer structure design, the greenhouse effectively utilizes space, increases the planting area, realizes all-weather environment monitoring and control in the greenhouse, can accurately adjust environmental parameters such as temperature, humidity, light, and carbon dioxide concentration in the greenhouse, creates ideal conditions for the growth of crops in the greenhouse, thereby improving the quality and yield of crops, reducing labor costs and resource waste.

[0003] In the prior art, the overall temperature of the greenhouse is analyzed by the average value of the temperature data at each monitoring point. However, due to the different positions of different monitoring points, the influence of the external temperature change is different, and the plant species and growth states corresponding to different monitoring points are different, that is, the heat changes generated by plants through various physiological processes are different, resulting in certain differences in the temperature changes at each monitoring point. If the regulation is directly carried out according to the average value of the monitored temperature data at each monitoring point, the control result deviates from the actual temperature situation. Summary of the Invention

[0004] In order to solve the technical problem that there are certain differences in the temperature changes at each monitoring point and the control effect of direct average value regulation is poor, the purpose of the present invention is to provide an all-weather environment monitoring and control system for a greenhouse, and the specific technical solution adopted is as follows: The present invention provides an all-weather environment monitoring and control system for a greenhouse, and the system includes: Greenhouse environment data acquisition module: Obtain the monitored temperature data of each monitoring point in different areas inside the greenhouse and the environmental temperature data outside the greenhouse according to the time sequence; Temperature data analysis module: Obtain the degree of temperature change of each monitoring point according to the distribution of the monitored temperature data of each monitoring point at different times; Obtain multiple approximate segments of the curve formed by the environmental temperature data outside the greenhouse at all times, and according to the relative distance between the monitored temperature data of each monitoring point inside the greenhouse and the environmental temperature data outside the greenhouse at all times, the degree of temperature change of the corresponding monitoring point and the degree of fluctuation of the monitored temperature data within the time range corresponding to different approximate segments, obtain the degree of influence of the environmental temperature on each monitoring point; Temperature influence analysis module: According to the location distribution of different monitoring points and the distribution of environmental temperature influence degrees, obtain the location influence degree of each monitoring point; According to the location influence degree distribution of monitoring points within different regions, obtain the location influence update degree of each monitoring point; According to the location distribution and location influence update degree of different monitoring points, obtain the temperature influence degree of each monitoring point; Temperature control module: Control the temperature inside the shed according to the temperature influence degree of each monitoring point.

[0005] Furthermore, the method for obtaining the temperature change degree includes: For any monitoring point, obtain the temperature curve composed of the monitoring temperature data at all times, and obtain the average value of the absolute values of the slopes of the data points between all adjacent times on the temperature curve as the first temperature change coefficient corresponding to the monitoring point; Obtain the average value of the differences in monitoring temperature data between each extreme point on the temperature curve and the adjacent extreme points before and after, and normalize it as the temperature change amplitude of each extreme point; Count the number of extreme points in all extreme points where the temperature change amplitude is greater than or equal to the preset amplitude threshold as the second temperature change coefficient; Obtain the difference between each monitoring temperature data on the temperature curve and the maximum or minimum value, and normalize it as the first difference; Count the number of monitoring temperature data corresponding to the first difference less than the preset difference threshold among all monitoring temperature data, and normalize it as the third temperature change coefficient; Obtain the range of the monitoring temperature data on the temperature curve, and calculate the product of the first temperature change coefficient, the second temperature change coefficient, the third temperature change coefficient, and the range as the temperature change degree of each monitoring point.

[0006] Furthermore, the method for obtaining the approximate segments includes: Use the APCA segmentation algorithm to segment the curve composed of the environmental temperature data outside the shed at all times to obtain multiple approximate segments.

[0007] Furthermore, the method for obtaining the environmental temperature influence degree includes: Obtain the DTW distance of the curve formed between the monitoring temperature data of each monitoring point inside the shed and the environmental monitoring temperature data outside the shed at all times as the relative distance; According to the temperature change degree, relative distance of each monitoring point, and the fluctuation degree of the monitoring temperature data within the time range corresponding to different approximate segments, obtain the environmental temperature influence degree of each monitoring point. The temperature change degree is positively correlated with the environmental temperature influence degree, and both the relative distance and the fluctuation degree of the monitoring temperature data are negatively correlated with the environmental temperature influence degree.

[0008] Furthermore, the method for obtaining the location influence degree includes: For any target position among the shed boundary, ventilation openings, and air outlets of temperature control equipment, obtain the Euclidean distance between each monitoring point and the target position, and select the minimum Euclidean distance between each monitoring point and all target positions as the position independence of each monitoring point; According to the position independence and the distribution of the degree of environmental temperature influence of different monitoring points, obtain the updated degree of environmental temperature influence for each monitoring point and the high-influence monitoring points; Perform a negative correlation mapping on the position independence of each monitoring point, and obtain the product of the negative correlation mapping result, the updated degree of environmental temperature influence of each monitoring point, and the number of high-influence monitoring points within the neighborhood range as the position influence degree of each monitoring point.

[0009] Further, the method for obtaining the updated degree of environmental temperature influence includes: Cluster all monitoring points according to the degree of environmental temperature influence of each monitoring point to obtain multiple environmental similarity monitoring clusters; Obtain the mean difference in position independence between each monitoring point and other monitoring points within the corresponding environmental similarity monitoring cluster as the adjustment weight for each monitoring point; obtain the ratio of the position independence and the adjustment weight of each monitoring point as the updated degree of environmental temperature influence of each monitoring point.

[0010] Further, the method for obtaining the high-influence monitoring points includes: Normalize the position independence of all monitoring points, and mark the monitoring points corresponding to the normalized result less than the preset independence threshold as high-influence monitoring points.

[0011] Further, the method for obtaining the updated degree of position influence includes: Obtain the mean value of the position influence degrees of all monitoring points within each region as the overall position influence level of each region; According to the position influence degree of each monitoring point, the degree of difference fluctuation between the overall position influence levels of the corresponding region and different other regions, and the overall position influence level, obtain the updated degree of position influence of each monitoring point. The position influence degree, the degree of difference fluctuation of the overall position influence level, and the overall position influence level are all positively correlated with the updated degree of position influence.

[0012] Further, the method for obtaining the degree of temperature influence includes: Cluster all monitoring points according to the position independence of each monitoring point to obtain multiple position similarity monitoring clusters; Obtain the cumulative difference in position influence update degree between each monitoring point and other monitoring points within the corresponding position similarity monitoring cluster, and calculate the product of the cumulative result and the position influence update degree of each monitoring point as the degree of temperature influence of each monitoring point.

[0013] Further, the preset irrelevant threshold is 0.3.

[0014] The present invention has the following beneficial effects: According to the distribution of the monitored temperature data of each monitoring point at different times, the present invention obtains the degree of temperature change of each monitoring point, and evaluates the temperature fluctuation of the monitoring point; obtains multiple approximate segments of the curve formed by the environmental temperature data outside the greenhouse at all times, decomposes the complex environmental temperature change into multiple approximate linear or stable stages, and according to the relative distance between the monitored temperature data of each monitoring point in the greenhouse and the environmental temperature data outside the greenhouse at all times, the degree of temperature change of the corresponding monitoring point and the degree of fluctuation of the monitored temperature data within the time range corresponding to different approximate segments, obtains the degree of influence of the environmental temperature on each monitoring point, and quantifies the influence of the external environment on the temperature of each monitoring point; according to the position distribution of different monitoring points and the distribution of the degree of influence of the environmental temperature, obtains the degree of influence of the position of each monitoring point, and evaluates the contribution of the position of the monitoring point to the temperature change; according to the distribution of the degree of influence of the position of the monitoring points in different regions, obtains the degree of influence of the temperature on each monitoring point, and controls the temperature in the greenhouse. The present invention accurately obtains the degree of influence of the temperature on each monitoring point, and precisely regulates the temperature of the greenhouse. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of an implementation method of an all-weather environment monitoring and control system for a greenhouse provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for obtaining the degree of temperature change provided by an embodiment of the present invention; Figure 3 It is a flowchart of a method for obtaining the degree of influence of the environmental temperature provided by an embodiment of the present invention. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a greenhouse all-weather environment monitoring and control system proposed according to the present invention, including its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0019] The following specifically describes, in conjunction with the accompanying drawings, the specific solution of a greenhouse all-weather environment monitoring and control system provided by the present invention.

[0020] Please refer to Figure 1 , which shows a structural block diagram of a greenhouse all-weather environment monitoring and control system provided by an embodiment of the present invention. The system specifically includes: a greenhouse environment data acquisition module 101, a temperature data analysis module 102, a temperature impact analysis module 103, and a temperature control module 104.

[0021] The greenhouse environment data acquisition module 101: acquires the monitored temperature data of each monitoring point in different areas inside the greenhouse and the environmental temperature data outside the greenhouse according to time sequence.

[0022] In an embodiment of the present invention, the greenhouse realizes all-weather environment monitoring and control, avoids the situation of insufficient or excessive regulation, acquires and analyzes the data inside the greenhouse, and optimizes the temperature inside the greenhouse. First, the inside of the greenhouse is divided into different areas according to different planting varieties, and then each area is equally divided to obtain a plurality of equally divided blocks. The center points of the equally divided blocks are taken as monitoring points, and temperature sensors are installed at each monitoring point. For low-growing crops, the temperature sensors are installed at a height of 0.5 to 1 meter from the ground. For tall-stemmed crops, the temperature sensors are installed near the crop canopy, and the temperature of each monitoring point is collected.

[0023] It should be noted that in an embodiment of the present invention, the temperature data is collected once every 20 minutes between time sequences; in other embodiments of the present invention, the time sequence interval can be specifically set by the implementer according to specific circumstances and will not be elaborated here.

[0024] Temperature data analysis module 102: According to the distribution of the monitored temperature data of each monitoring point at different times, obtain the degree of temperature change of each monitoring point; obtain multiple approximate segments of the curve formed by the ambient temperature data outside the greenhouse at all times, and according to the relative distance between the monitored temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all times, the degree of temperature change of the corresponding monitoring point, and the degree of fluctuation of the monitored temperature data within the time range corresponding to different approximate segments, obtain the degree of influence of the ambient temperature on each monitoring point.

[0025] In an intelligent greenhouse, crops at different positions in the greenhouse are affected by factors such as light differences, ventilation conditions, or crop growth stages, resulting in different temperature changes at different positions. According to the distribution of the monitored temperature data of each monitoring point at different times, obtain the degree of temperature change of each monitoring point.

[0026] Preferably, in an embodiment of the present invention, for the method of obtaining the degree of temperature change, please refer to Figure 2 , which shows a kind of temperature change including: Step S201: For any monitoring point, obtain the temperature curve formed by the monitored temperature data at all times, and obtain the mean value of the absolute values of the slopes of the data points between all adjacent times on the temperature curve as the first temperature change coefficient corresponding to the monitoring point.

[0027] Taking time as the horizontal axis and the monitored temperature data as the vertical axis to construct a temperature curve can more intuitively understand the change trend of the monitored temperature data at different times; the absolute value of the slope between adjacent times can reflect the speed of temperature change between times. The larger the absolute value of the slope, the faster the temperature changes, and the larger the first temperature change coefficient.

[0028] It should be noted that the calculation method of the absolute value of the slope is: by calculating the ratio of the difference in the ordinates and the difference in the abscissas of the data points between adjacent times as the absolute value of the slope; the specific means are well-known technical means to those skilled in the art and will not be elaborated here.

[0029] Step S202: Obtain the mean value of the differences in the monitored temperature data between each extreme point on the temperature curve and the adjacent extreme points before and after, and perform normalization as the temperature change amplitude of each extreme point; count the number of extreme points with a temperature change amplitude greater than or equal to the preset amplitude threshold among all extreme points as the second temperature change coefficient.

[0030] The greater the difference in the monitored temperature data between adjacent extreme points, the greater the amplitude of temperature change is reflected. The more extreme points with a larger change amplitude exist, indicating that the change in the monitored temperature data is greater.

[0031] It should be noted that in an embodiment of the present invention, if the monitored temperature data at a certain moment are greater than or less than the monitored temperature data at the adjacent moments before and after it, the data point at the corresponding moment will be taken as the extreme value point; the extreme value can also be obtained by Newton's method; the specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0032] It should be noted that, in one embodiment of the present invention, the preset amplitude threshold is set to 0.6; in other embodiments of the present invention, the size of the preset amplitude threshold can be set according to specific circumstances, which is not limited or elaborated here.

[0033] Step S203: Obtain the minimum difference between each monitored temperature data and the maximum value in the temperature curve, and normalize it as the first difference; count the number of monitored temperature data whose first difference in all monitored temperature data is less than the preset difference threshold, and normalize it as the third temperature change coefficient.

[0034] The difference between the monitored temperature data and the maximum value can reflect the volatility of temperature. The smaller the minimum difference, the closer it is to the maximum or minimum value, the more drastic the temperature change. The more monitored temperature data corresponding to the maximum or minimum value is, the greater the temperature variation coefficient is.

[0035] It should be noted that the method for normalizing the number of monitored temperature data is: the number of monitored temperature data calculated and counted is divided by the number of all monitored temperature data. The larger the number of monitored temperature data calculated and counted is, the greater the temperature change is.

[0036] It should be noted that, in one embodiment of the present invention, the size of the preset difference threshold is 0.3; in other embodiments of the present invention, the size of the preset difference threshold can be set according to specific circumstances, which is not limited or elaborated here.

[0037] Step S204: obtaining the range of the monitored temperature data in the temperature curve, and calculating the product of the first temperature variation coefficient, the second temperature variation coefficient, the third temperature variation coefficient and the range as the temperature variation degree of each monitoring point.

[0038] It should be noted that the range indicates the difference between the maximum and minimum values of the monitored temperature data in the temperature curve, reflecting the temperature fluctuation range. The larger the range, the greater the temperature fluctuation and the greater the degree of temperature change. Therefore, the larger the first temperature change coefficient, the larger the second temperature change coefficient, the larger the third temperature change coefficient, and the larger the range, the greater the temperature change degree, which is positively correlated.

[0039] In other embodiments of the present invention, the product of the first temperature change coefficient, the second temperature change coefficient, the third temperature change coefficient, and the range is calculated to represent a positive correlation, or it can also be represented by calculating the cumulative sum, such that the larger each parameter is, the larger the cumulative sum is, and the greater the temperature change degree is; the specific means are well-known technical means to those skilled in the art and will not be limited or elaborated herein.

[0040] The monitoring points inside the greenhouse are affected differently by the external environmental temperature. To more accurately analyze the correlation between the monitoring points inside the greenhouse and the external temperature, the environmental temperature is approximately segmented, which helps to more specifically analyze the temperature state under similar environmental temperature conditions in the follow-up; multiple approximate segments of the curve formed by the external environmental temperature data at all times are obtained; it should be noted that in the embodiments of the present invention, the APCA algorithm can divide the data with similar change characteristics into multiple curve segments with relatively similar environmental temperatures according to the changes between the data, which helps to process the data in the follow-up; the specific APCA algorithm is a well-known technical means to those skilled in the art and will not be elaborated herein.

[0041] Since the temperature collection frequency of each monitoring point inside the greenhouse is the same as that of the external environmental temperature data, the number of monitoring temperature data and environmental temperature data of each monitoring point is the same. By analyzing the relative distance between the data, the correlation of the monitoring temperature data can be reflected. The larger the relative distance is, the smaller the correlation is, indicating that the temperature of the monitoring point does not change with the environment; considering that the monitoring points inside the greenhouse are affected differently by the external environmental temperature within different time ranges, by analyzing the monitoring temperature data within the time range corresponding to the approximate segment, the change of the monitoring temperature data under the time range with similar environmental temperature is reflected. The smaller the change of the monitoring temperature data is, the greater the influence of the environmental temperature is; according to the relative distance between the monitoring temperature data of each monitoring point inside the greenhouse and the outside at different times, the temperature change degree of the corresponding monitoring point, and the fluctuation degree of the monitoring temperature data within the time range corresponding to different approximate segments, the influence degree of the environmental temperature on each monitoring point is obtained.

[0042] Preferably, in an embodiment of the present invention, the method for obtaining the influence degree of the environmental temperature includes: Obtaining the DTW distance between the curve formed by the monitoring temperature data of each monitoring point inside the greenhouse and the environmental monitoring temperature data outside the greenhouse at all times as the relative distance; According to the temperature change degree of each monitoring point, the relative distance, and the fluctuation degree of the monitoring temperature data within the time range corresponding to different approximate segments, the influence degree of the environmental temperature on each monitoring point is obtained. The temperature change degree is positively correlated with the influence degree of the environmental temperature, and both the relative distance and the fluctuation degree of the monitoring temperature data are negatively correlated with the influence degree of the environmental temperature.

[0043] It should be noted that the DTW algorithm is selected to calculate the DTW distance between the sequences composed of temperature data, which represents the correlation coefficient between the monitored temperature in the shed and the ambient temperature outside the shed. The larger the distance, the smaller the correlation coefficient; the smaller the distance, the larger the correlation coefficient. The specific means are well-known technical means to those skilled in the art and will not be elaborated here.

[0044] It should be noted that in an embodiment of the present invention, the degree of fluctuation of the monitored temperature data can be represented by variance. The larger the variance, the greater the degree of fluctuation; the smaller the variance, the smaller the degree of fluctuation. In other embodiments of the present invention, the degree of fluctuation can also be represented by standard deviation, range, etc. The specific means are well-known technical means to those skilled in the art and will not be elaborated here.

[0045] In an embodiment of the present invention, the formula for the influence degree of ambient temperature is expressed as: ; Wherein, represents the influence degree of ambient temperature at the th monitoring point; represents the degree of temperature change at the th monitoring point; represents the DTW distance, i.e., the relative distance, between the monitored temperature data of the th monitoring point in the shed and the ambient temperature data outside the shed at all times, which constitutes a curve; represents the variance of the monitored temperature data within the time range corresponding to the th approximate segmented corresponding time, i.e., the degree of fluctuation; represents the number of approximate segments; represents the exponential function with the natural constant as the base.

[0046] In the formula for the influence degree of ambient temperature, through the exponential function with the natural constant as the base, is negatively correlated and mapped. represents the mean value of the variances of the monitored temperature data within the time ranges corresponding to all approximate segments. The larger the variance, the greater the degree of fluctuation of the monitored temperature data within a certain range of ambient temperature, and the smaller the influence of the ambient temperature; the larger the relative distance, the more inconsistent the changes in the temperature of the monitoring point in the shed and the ambient temperature outside the shed, and the smaller the influence of the ambient temperature. Therefore, based on the influence of the ambient temperature, the degree of temperature change is adjusted. The smaller the influence of the ambient temperature, the smaller the degree of temperature change is adjusted, and the smaller the influence degree of the ambient temperature on the monitoring point.

[0047] Temperature influence analysis module 103: Obtain the location influence degree of each monitoring point according to the location distribution of different monitoring points and the distribution of environmental temperature influence degree; obtain the location influence update degree of each monitoring point according to the location influence degree distribution of monitoring points in different regions; obtain the temperature influence degree of each monitoring point according to the location distribution and location influence update degree of each monitoring point.

[0048] The degree of influence of different monitoring points by the external environmental temperature of the greenhouse depends on the location of the monitoring points inside the greenhouse. When the monitoring point is closer to the greenhouse boundary, ventilation opening or the air outlet of the temperature control equipment, the monitored temperature data of the monitoring point is more likely to be affected by the environmental temperature. In theory, the more similar the influence of the environmental temperature, the more consistent the influence of the temperature of the monitoring point by its location; and considering the same type of crops planted simultaneously, there will be certain differences in the growth trend during the growth process, and evaluate the influence of the location of each monitoring point on the temperature; obtain the location influence degree of each monitoring point according to the location distribution of different monitoring points and the distribution of environmental temperature influence degree.

[0049] Preferably, in an embodiment of the present invention, for the method of obtaining the location influence degree, please refer to Figure 3 , which shows a flowchart of a method for obtaining the location influence degree, including: Step S301: For any target location among the greenhouse boundary, ventilation opening, and the air outlet of the temperature control equipment, obtain the Euclidean distance between each monitoring point and the target location, and select the minimum Euclidean distance between each monitoring point and all target locations as the location irrelevance of each monitoring point.

[0050] When the distance between the monitoring point and the target location is smaller, the degree of influence by the environment is greater, and the irrelevance of the temperature of the monitoring point to its location is smaller.

[0051] Step S302: Obtain the environmental temperature influence update degree of each monitoring point and the high-influence monitoring points according to the location irrelevance of different monitoring points and the distribution of environmental temperature influence degree.

[0052] Preferably, in an embodiment of the present invention, the method for obtaining the environmental temperature influence update degree includes: Cluster all monitoring points according to the environmental temperature influence degree of each monitoring point to obtain multiple environmental similarity monitoring clusters; Obtain the difference mean value of the location irrelevance between each monitoring point and other monitoring points within the corresponding environmental similarity monitoring cluster as the adjustment weight of each monitoring point; obtain the ratio of the location irrelevance and the adjustment weight of each monitoring point as the environmental temperature influence update degree of each monitoring point.

[0053] In an embodiment of the present invention, the formula for the environmental temperature influence update degree is expressed as: ; Among them, represents the update degree of the environmental temperature influence of the th monitoring point; represents the influence degree of the environmental temperature of the th monitoring point; represents the average difference in position independence between the th monitoring point and other monitoring points within the monitoring cluster similar to the corresponding environment, that is, the adjustment weight of each monitoring point.

[0054] In the formula for the update degree of the environmental temperature influence, adding 0.01 in

[0055] is to avoid the denominator of the formula being 0 and the formula being meaningless; the greater the adjustment weight, the greater the difference in position independence between the monitoring points, indicating that the influence of the positions at the environmentally similar monitoring points is more inconsistent, and it is more likely that there is an influence on the growth of the crops themselves, and the adjustment of the environmental temperature influence degree is smaller. Preferably, in an embodiment of the present invention, the method for obtaining high-influence monitoring points includes:

[0056] Normalize the position independence of all monitoring points, and mark the monitoring points corresponding to the normalization result less than the preset independence threshold as high-influence monitoring points.

[0057] It should be noted that, in an embodiment of the present invention, the size of the preset independence threshold is 0.3; in an embodiment of the present invention, the size of the preset independence threshold can be specifically set according to specific circumstances, and will not be limited and elaborated here.

[0058] Step S303: Perform a negative correlation mapping on the position independence of each monitoring point, and obtain the product of the negative correlation mapping result, the update degree of the environmental temperature influence of each monitoring point, and the number of high-influence monitoring points within the neighborhood range as the position influence degree of each monitoring point. ; Among them, represents the position influence degree of the th monitoring point; represents the update degree of the environmental temperature influence of the th monitoring point; represents the position independence of the th monitoring point; represents the number of high-influence monitoring points within the neighborhood range of the th monitoring point.

[0059] In the formula for the degree of position influence, the greater the influence of the ambient temperature on the update degree, the smaller the position independence, the greater the number of high-influence monitoring points within the neighborhood range, and the greater the degree of influence by the position.

[0060] It should be noted that in an embodiment of the present invention, the neighborhood range is an 8-neighborhood range centered on the monitoring point and composed of 8 other adjacent monitoring points; in other embodiments of the present invention, the size of the neighborhood range can be specifically set according to specific circumstances, and no limitation and elaboration are made here.

[0061] The planting varieties in the area are different, and the anti-interference abilities of different crops are different, resulting in different influences by the temperature changes outside the shed. The degree of temperature influence is evaluated through the position influence distribution of the monitoring points. According to the position influence degree distribution of the monitoring points in different areas, the position influence update degree of each monitoring point is obtained.

[0062] Preferably, in an embodiment of the present invention, the method for obtaining the position influence update degree includes: Obtaining the average value of the position influence degrees of all monitoring points in each area as the overall position influence level of each area; According to the position influence degree of each monitoring point, the degree of difference fluctuation between the overall position influence levels of the corresponding area and different other areas, and the overall position influence level, the position influence update degree of each monitoring point is obtained. The position influence degree, the degree of difference fluctuation of the overall position influence level, and the overall position influence level are all positively correlated with the position influence update degree.

[0063] In an embodiment of the present invention, the formula for the position influence update degree is expressed as: ; Wherein, represents the position influence update degree of the th monitoring point; represents the position influence degree of the th monitoring point; represents the average value of the position influence degrees of all monitoring points in the th area, that is, the overall position influence level; represents the degree of difference fluctuation between the overall position influence levels of the th area and different other areas; represents the normalization function.

[0064] In the formula for the degree of temperature influence, the greater the overall position influence level of the monitoring point, the greater the degree of difference fluctuation between the overall position influence levels of the areas, the less similar the affected situations of the th area and other areas, the greater the anti-interference ability, the greater the adjustment of the position influence degree, and the greater the position influence update degree.

[0065] Multiple crops are planted in the greenhouse at the same time. The planting densities of different crops are different, and the temperatures in different areas also vary. The greater the planting density, the denser the crop leaves, which will limit air circulation and cause the temperature to rise faster. When the same crop is planted in the greenhouse, due to different planting times, there may be different growth stages in different areas. The transpiration and photosynthesis intensities of the same crop are different at different growth stages, and the amount of heat released is also different. Therefore, according to the position distribution and the degree of influence update of different monitoring points, the degree of temperature influence of each monitoring point is obtained.

[0066] Preferably, in an embodiment of the present invention, the method for obtaining the degree of temperature influence is as follows: Cluster all monitoring points according to the position independence of each monitoring point to obtain multiple monitoring clusters with similar positions; Obtain the cumulative difference in the degree of position influence update between each monitoring point and other monitoring points within the corresponding monitoring cluster with similar positions, and calculate the product of the cumulative result and the degree of position influence update of each monitoring point as the degree of temperature influence of each monitoring point.

[0067] In an embodiment of the present invention, the formula for the degree of temperature influence is expressed as: ; Wherein, represents the degree of temperature influence of the th monitoring point; represents the degree of position influence update of the th monitoring point; represents the degree of position influence update of the th other monitoring point in the monitoring cluster with similar positions corresponding to the th monitoring point; represents the number of monitoring points in the monitoring cluster with similar positions.

[0068] In the formula for the degree of temperature influence, represents the cumulative difference value of the degree of position influence update between the th monitoring point and different other monitoring points in the monitoring cluster with similar positions corresponding to it. The greater the cumulative difference value, the more different the influence of the th monitoring point and other monitoring points in the monitoring cluster with similar positions on the temperature due to the position, indicating that the th monitoring point is more affected by its own growth trend, affects the temperature, and the adjustment of the degree of temperature influence is greater.

[0069] Temperature control module 104: Control the temperature in the greenhouse according to the degree of temperature influence of each monitoring point.

[0070] By adjusting the degree of temperature influence, the degree of temperature being affected is obtained, which can more accurately reflect the magnitude of the temperature being affected, and effectively optimize the temperature in the greenhouse.

[0071] It should be noted that in another embodiment of the present invention, after obtaining the degree of temperature influence at each monitoring point, the temperature inside the greenhouse is controlled, including: obtaining the average value of the degree of temperature influence at all monitoring points and normalizing it to obtain a temperature adjustment coefficient, which is used to be input into the PID controller to adjust and optimize the temperature in the greenhouse, contributing to the healthy growth of crops.

[0072] In summary, the present invention obtains the degree of temperature change at each monitoring point according to the distribution of monitoring temperature data at each monitoring point at different times; obtains multiple approximate segments of the curve formed by the environmental temperature data outside the greenhouse at all times, and according to the relative distance between the monitoring temperature data at each monitoring point inside the greenhouse and the environmental temperature data outside the greenhouse at all times, the degree of temperature change at the corresponding monitoring point and the degree of fluctuation of the monitoring temperature data within the time range corresponding to different approximate segments, obtains the degree of environmental temperature influence at each monitoring point; according to the position distribution and the distribution of the degree of environmental temperature influence of different monitoring points, obtains the degree of position influence at each monitoring point; according to the distribution of the degree of position influence of the monitoring points in different regions, obtains the degree of temperature influence at each monitoring point; according to the position distribution and the degree of temperature influence of different monitoring points, obtains the degree of temperature being affected at each monitoring point, and controls the temperature inside the greenhouse; by accurately obtaining the degree of temperature being affected at each monitoring point, the present invention precisely regulates the temperature of the greenhouse.

[0073] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A greenhouse all-weather environment monitoring and control system, characterized in that, The system includes: A greenhouse environment data acquisition module: acquiring the monitored temperature data of each monitoring point in different areas inside the greenhouse and the environmental temperature data outside the greenhouse according to time sequence; A temperature data analysis module: obtaining the temperature change degree of each monitoring point according to the distribution of the monitored temperature data of each monitoring point at different times; obtaining multiple approximate segments of the curve formed by the environmental temperature data outside the greenhouse at all times, and according to the relative distance between the monitored temperature data of each monitoring point inside the greenhouse and the environmental temperature data outside the greenhouse at all times, the temperature change degree of the corresponding monitoring point and the fluctuation degree of the monitored temperature data within the time range corresponding to different approximate segments, obtaining the environmental temperature influence degree of each monitoring point; A temperature influence analysis module: obtaining the position influence degree of each monitoring point according to the position distribution and the environmental temperature influence degree distribution of different monitoring points; obtaining the position influence update degree of each monitoring point according to the position influence degree distribution of the monitoring points in different areas; obtaining the temperature influence degree of each monitoring point according to the position distribution and the position influence update degree of different monitoring points; A temperature control module: controlling the temperature inside the greenhouse according to the temperature influence degree of each monitoring point.

2. The all-weather environmental monitoring and control system for a greenhouse as claimed in claim 1, wherein, The method for obtaining the temperature change degree includes: For any monitoring point, obtaining the temperature curve formed by the monitored temperature data at all times, and obtaining the average value of the absolute values of the slopes of the data points between all adjacent times on the temperature curve as the first temperature change coefficient corresponding to the monitoring point; Obtaining the average value of the differences between the monitored temperature data between each extreme point and the adjacent extreme points before and after on the temperature curve, and normalizing it as the temperature change amplitude of each extreme point; counting the number of extreme points with a temperature change amplitude greater than or equal to a preset amplitude threshold among all extreme points as the second temperature change coefficient; Obtaining the difference between each monitored temperature data on the temperature curve and the maximum or minimum value, and normalizing it as the first difference; counting the number of monitored temperature data corresponding to the first difference less than the preset difference threshold among all monitored temperature data, and normalizing it as the third temperature change coefficient; Obtaining the range of the monitored temperature data on the temperature curve, and calculating the product of the first temperature change coefficient, the second temperature change coefficient, the third temperature change coefficient and the range as the temperature change degree of each monitoring point.

3. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 1, wherein The method for obtaining the approximate segments includes: Using the APCA segmentation algorithm to segment the curve formed by the environmental temperature data outside the greenhouse at all times to obtain multiple approximate segments.

4. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 1, wherein, The method for obtaining the environmental temperature influence degree includes: Obtaining the DTW distance of the curve formed between the monitored temperature data of each monitoring point inside the greenhouse and the environmental monitoring temperature data outside the greenhouse at all times as the relative distance; According to the temperature change degree, the relative distance of each monitoring point and the fluctuation degree of the monitored temperature data within the time range corresponding to different approximate segments, obtaining the environmental temperature influence degree of each monitoring point, the temperature change degree is positively correlated with the environmental temperature influence degree, and both the relative distance and the fluctuation degree of the monitored temperature data are negatively correlated with the environmental temperature influence degree.

5. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 1, wherein The method for obtaining the position influence degree includes: For any target position among the shed boundary, ventilation openings, and the air outlet of the temperature control device, obtain the Euclidean distance between each monitoring point and the target position, and select the minimum Euclidean distance among each monitoring point and all target positions as the position independence of each monitoring point; According to the position independence of different monitoring points and the distribution of the influence degree of environmental temperature, obtain the updated degree of environmental temperature influence for each monitoring point, as well as the high-influence monitoring points; Perform a negative correlation mapping on the position independence of each monitoring point, and obtain the product of the negative correlation mapping result, the updated degree of environmental temperature influence for each monitoring point, and the number of high-influence monitoring points within the neighborhood range as the position influence degree of each monitoring point.

6. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 5, wherein The method for obtaining the updated degree of environmental temperature influence includes: Cluster all monitoring points according to the influence degree of environmental temperature for each monitoring point to obtain multiple environmental similarity monitoring clusters; Obtain the mean difference in position independence between each monitoring point and other monitoring points within the corresponding environmental similarity monitoring cluster as the adjustment weight for each monitoring point; obtain the ratio of the position independence of each monitoring point to the adjustment weight as the updated degree of environmental temperature influence for each monitoring point.

7. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 5, wherein, The method for obtaining the high-influence monitoring points includes: Normalize the position independence of all monitoring points, and mark the monitoring points corresponding to the normalization result less than the preset independence threshold as high-influence monitoring points.

8. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 1, wherein, The method for obtaining the updated degree of position influence includes: Obtain the mean value of the position influence degrees of all monitoring points within each region as the overall position influence level of each region; According to the position influence degree of each monitoring point, the degree of difference fluctuation in the overall position influence level between the corresponding region and different other regions, and the overall position influence level, obtain the updated degree of position influence for each monitoring point. The position influence degree, the degree of difference fluctuation in the overall position influence level, and the overall position influence level are all positively correlated with the updated degree of position influence.

9. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 1, wherein, The method for obtaining the affected degree of temperature includes: Cluster all monitoring points according to the position independence of each monitoring point to obtain multiple position similarity monitoring clusters; Obtain the cumulative difference in the updated degree of position influence between each monitoring point and other monitoring points within the corresponding position similarity monitoring cluster, and calculate the product of the cumulative result and the updated degree of position influence of each monitoring point as the affected degree of temperature for each monitoring point.

10. The all-weather environment monitoring and control system for a greenhouse as claimed in claim 7, wherein The preset independence threshold is 0.3.

Images