A greenhouse all-weather environmental monitoring and control system

Abstract

The present invention relates to the field of temperature control technology, and specifically to an all-weather environmental monitoring and control system for a greenhouse. The present invention obtains the degree of temperature change of each monitoring point according to the distribution of monitored temperature data of each monitoring point at different times; obtains multiple approximate segments of a curve composed of the ambient temperature data outside the greenhouse at all times, and combines 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, as well as the degree of fluctuation of the monitored temperature data within the corresponding time range of different approximate segments to obtain the degree of ambient temperature influence and the degree of position influence of each monitoring point; obtains the degree of temperature influence of each monitoring point according to the distribution of the degree of position influence of monitoring points in different areas, and controls the temperature in the greenhouse. The present invention accurately controls the temperature of the greenhouse by accurately obtaining the degree of temperature influence of each monitoring point.

Description

Technical Field

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

[0002] With the acceleration of agricultural modernization, smart agriculture has become a key way to improve agricultural production efficiency and ensure the quality and output of agricultural products. Greenhouses use multi-layer structural design to effectively utilize space, increase planting area, and achieve all-weather environmental monitoring and control in greenhouses. They can accurately adjust environmental parameters such as temperature, humidity, light, and carbon dioxide concentration in the greenhouse, creating ideal conditions for crop growth in the greenhouse, thereby improving crop quality and output, reducing labor costs and minimizing resource waste.

[0003] In the existing technology, the overall temperature of the greenhouse is analyzed by the mean of the temperature data of each monitoring point. However, due to the different locations of different monitoring points, they are affected differently by external temperature changes, and different monitoring points correspond to different plant species and growth states. That is, the heat changes generated by plants through various physiological processes are different, resulting in certain differences in the temperature changes of each monitoring point. If the control is directly based on the mean of the temperature data monitored at each monitoring point, the control result will deviate from the actual temperature conditions. Summary of the Invention

[0004] In order to solve the technical problem that the temperature changes at each monitoring point vary to a certain extent and the control effect of direct mean control is poor, the purpose of the present invention is to provide an all-weather environmental monitoring and control system for a greenhouse. The technical solutions adopted are as follows:

[0005] The present invention proposes a greenhouse all-weather environmental monitoring and control system, the system comprising:

[0006] Greenhouse environment data acquisition module: acquires the monitoring temperature data of each monitoring point in different areas of the greenhouse, as well as the ambient temperature data outside the greenhouse in time sequence;

[0007] Temperature data analysis module: Based on the distribution of monitored temperature data at each monitoring point at different times, the temperature change degree of each monitoring point is obtained; multiple approximate segments of the curve formed by the ambient temperature data outside the greenhouse at all times are obtained, and 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 temperature change degree of the corresponding monitoring point, and the degree of fluctuation of the monitored temperature data within the corresponding time range of different approximate segments are obtained to obtain the degree of ambient temperature influence of each monitoring point;

[0008] Temperature impact analysis module: According to the location distribution of different monitoring points and the distribution of ambient temperature impact, the location impact of each monitoring point is obtained; according to the location impact distribution of monitoring points in different areas, the location impact update degree of each monitoring point is obtained; according to the location distribution and location impact update degree of different monitoring points, the temperature impact degree of each monitoring point is obtained; Temperature control module: According to the temperature impact degree of each monitoring point, the temperature in the greenhouse is controlled.

[0009] Furthermore, the method for obtaining the degree of temperature change includes:

[0010] For any monitoring point, a temperature curve consisting of the monitored temperature data at all times is obtained, and the average of the absolute values ​​of the slopes of the data points between all adjacent moments on the temperature curve is obtained as the first temperature variation coefficient of the corresponding monitoring point;

[0011] Obtaining the mean difference in monitored temperature data between each extreme point in the temperature curve and the adjacent extreme points before and after it, and normalizing it to obtain the temperature variation amplitude of each extreme point; counting the number of extreme points whose temperature variation amplitude is greater than or equal to a preset amplitude threshold among all extreme points, and obtaining the second temperature variation coefficient;

[0012] Obtaining the difference between each monitored temperature data and the maximum value in the temperature curve and normalizing it as the first difference; counting the number of monitored temperature data corresponding to the first difference being less than the preset difference threshold among all monitored temperature data, and normalizing it as the third temperature variation coefficient;

[0013] The range of the monitored temperature data in the temperature curve is obtained, and the product of the first temperature variation coefficient, the second temperature variation coefficient, the third temperature variation coefficient and the range is calculated as the temperature variation degree of each monitoring point.

[0014] Furthermore, the method for obtaining the approximate segmentation includes:

[0015] The APCA segmentation algorithm is used to segment the curve composed of the ambient temperature data outside the greenhouse at all times to obtain multiple approximate segments.

[0016] Furthermore, the method for obtaining the degree of influence of the ambient temperature includes:

[0017] Obtain the DTW distance of the curve formed by the monitored temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all times as the relative distance;

[0018] The degree of influence of the ambient temperature at each monitoring point is obtained based on the temperature change degree, relative distance, and the fluctuation degree of the monitored temperature data within the corresponding time range of different approximate segments. The degree of temperature change is positively correlated with the degree of influence of the ambient temperature, while the relative distance and the fluctuation degree of the monitored temperature data are negatively correlated with the degree of influence of the ambient temperature.

[0019] Furthermore, the method for obtaining the position influence degree includes:

[0020] For any target location at the shed boundary, ventilation outlet, or temperature control equipment outlet, 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 independence of each monitoring point;

[0021] According to the location independence and distribution of the influence of ambient temperature at different monitoring points, the updated influence of ambient temperature at each monitoring point and the high-impact monitoring points are obtained;

[0022] The location independence of each monitoring point is negatively mapped, and the product of the negative correlation mapping result, the updated degree of environmental temperature influence of each monitoring point, and the number of high-impact monitoring points in the neighborhood is obtained as the location influence degree of each monitoring point.

[0023] Furthermore, the method for obtaining the degree to which the ambient temperature affects the update includes:

[0024] According to the degree of influence of the ambient temperature at each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar environments;

[0025] The mean difference in position independence between each monitoring point and other monitoring points in the corresponding environmentally similar monitoring cluster is obtained as the adjustment weight of each monitoring point; the ratio of the position independence of each monitoring point to the adjustment weight is obtained as the degree of update of the ambient temperature influence of each monitoring point.

[0026] Furthermore, the method for obtaining the high-impact monitoring points includes:

[0027] The position independence of all monitoring points is normalized, and the monitoring points corresponding to the normalized results smaller than the preset independence threshold are marked as high-impact monitoring points.

[0028] Furthermore, the method for obtaining the location impact update degree includes:

[0029] The average location influence level of all monitoring points in each area was obtained as the overall location influence level of each area;

[0030] Based on the location influence degree of each monitoring point, the difference and fluctuation degree of the overall location influence level between the corresponding area and other areas, and the overall location influence level, the location influence update degree of each monitoring point is obtained. The location influence degree, the difference and fluctuation degree of the overall location influence level, and the overall location influence level are all positively correlated with the location influence update degree.

[0031] Furthermore, the method for obtaining the degree of temperature influence includes:

[0032] According to the location independence of each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar locations;

[0033] Obtain the cumulative difference in the degree of location influence update between each monitoring point and other monitoring points in the corresponding similar monitoring cluster, and calculate the product of the cumulative result and the degree of location influence update of each monitoring point as the degree of temperature influence of each monitoring point.

[0034] Furthermore, the preset irrelevant threshold is 0.3.

[0035] The present invention has the following beneficial effects:

[0036] The present invention obtains the temperature change degree of each monitoring point according to the distribution of monitoring temperature data of each monitoring point at different times, and evaluates the temperature fluctuation of the monitoring point; obtains multiple approximate segments of the curve composed of the ambient temperature data outside the greenhouse at all times, decomposes the complex ambient temperature change into multiple approximately linear or stable stages, and obtains the ambient temperature influence degree of each monitoring point according to the relative distance between the monitoring temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all times, the temperature change degree of the corresponding monitoring point, and the fluctuation degree of the monitoring temperature data within the corresponding time range of different approximate segments, and quantifies the temperature influence of the external environment on each monitoring point; obtains the position influence degree of each monitoring point according to the position distribution of different monitoring points and the distribution of the ambient temperature influence degree, and evaluates the contribution of the monitoring point position to the temperature change; obtains the temperature influence degree of each monitoring point according to the position influence degree distribution of monitoring points in different regions, and controls the temperature in the greenhouse. The present invention accurately controls the temperature of the greenhouse by accurately obtaining the temperature influence degree of each monitoring point. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flowchart of an implementation method of an all-weather environmental monitoring and control system for a greenhouse provided by one embodiment of the present invention;

[0039] Figure 2 A flow chart of a method for obtaining the degree of temperature change provided by one embodiment of the present invention;

[0040] Figure 3 A flow chart of a method for obtaining the degree of influence of ambient temperature provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] To further illustrate the technical means and effects employed by the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effects of a greenhouse all-weather environmental monitoring and control system proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] The following describes in detail a greenhouse all-weather environmental monitoring and control system provided by the present invention with reference to the accompanying drawings.

[0044] See also 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.

[0045] Greenhouse environment data acquisition module 101: acquires the monitoring temperature data of each monitoring point in different areas of the greenhouse and the ambient temperature data outside the greenhouse in a time sequence.

[0046] In an embodiment of the present invention, the greenhouse realizes all-weather environmental monitoring and control, avoids insufficient regulation or excessive regulation, obtains and analyzes data in the greenhouse, and optimizes the temperature in the greenhouse; first, the greenhouse is divided into different areas according to different planting varieties, and then each area is divided into equal parts to obtain multiple equal blocks, and the center points of the equal blocks are taken as monitoring points, and temperature sensors are installed at each monitoring point. For short crops, the temperature sensor is installed at 0.5 to 1 meter from the ground. For tall crops, the temperature sensor is installed near the crop canopy, and the temperature of each monitoring point is collected.

[0047] It should be noted that, in one embodiment of the present invention, temperature data is collected every 20 minutes between time series; in other embodiments of the present invention, the time series interval can be set by the implementer according to the specific situation, which will not be elaborated here.

[0048] Temperature data analysis module 102: obtain the degree of temperature change of each monitoring point according to the distribution of monitored temperature data of each monitoring point at different times; obtain multiple approximate segments of the curve composed of the ambient temperature data outside the shed at all times, and obtain the degree of ambient temperature influence of each monitoring point according to the relative distance between the monitored temperature data of each monitoring point in the shed at all times and the ambient temperature data outside the shed, the degree of temperature change of the corresponding monitoring point and the degree of fluctuation of the monitored temperature data within the corresponding time range of different approximate segments.

[0049] In a smart greenhouse, crops at different locations will be affected by factors such as differences in light, ventilation conditions, or crop growth stages, resulting in different temperature changes at different locations. The degree of temperature change at each monitoring point is obtained based on the distribution of monitored temperature data at different times.

[0050] Preferably, in one embodiment of the present invention, the method for obtaining the degree of temperature change is as follows: Figure 2 , which shows a temperature change including:

[0051] Step S201: For any monitoring point, a temperature curve consisting of the monitored temperature data at all times is obtained, and the average of the absolute values ​​of the slopes of the data points between all adjacent moments on the temperature curve is obtained as the first temperature variation coefficient of the corresponding monitoring point.

[0052] A temperature curve is constructed with time as the horizontal axis and monitored temperature data as the vertical axis to more intuitively understand the changing trend of monitored temperature data at different times; the absolute value of the slope of data points between adjacent moments can reflect the speed of temperature change between moments. The larger the absolute value of the slope, the faster the temperature changes, and the larger the first temperature change coefficient.

[0053] It should be noted that the absolute value of the slope is calculated by calculating the ratio of the difference between the vertical coordinates and the horizontal coordinates of the data points between adjacent moments as the absolute value of the slope. The specific means are well known to those skilled in the art and will not be elaborated here.

[0054] Step S202: Obtain the mean difference in the monitored temperature data between each extreme point in the temperature curve and the adjacent extreme points before and after it, and normalize it as the temperature change amplitude of each extreme point; count the number of extreme points among all extreme points whose temperature change amplitude is greater than or equal to the preset amplitude threshold as the second temperature change coefficient.

[0055] The greater the difference in the monitored temperature data between adjacent extreme points, the greater the amplitude of the temperature change. The more extreme points with large amplitudes of change there are, the greater the change in the monitored temperature data.

[0056] 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.

[0057] 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 may be set according to specific circumstances, which is not limited or elaborated herein.

[0058] 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 corresponding to the first difference less than the preset difference threshold in all monitored temperature data, and normalize it as the third temperature change coefficient.

[0059] The difference between the monitored temperature data and the maximum value can reflect the temperature fluctuation. 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, the greater the temperature variation coefficient.

[0060] It should be noted that the method for normalizing the number of monitored temperature data is: the ratio of the calculated number of monitored temperature data to the total number of monitored temperature data is calculated. The larger the calculated number of monitored temperature data is, the greater the temperature change is.

[0061] It should be noted that, in one embodiment of the present invention, the preset difference threshold is 0.3; in other embodiments of the present invention, the preset difference threshold may be set according to specific circumstances, which is not limited or elaborated herein.

[0062] 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.

[0063] It should be noted that the range represents 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.

[0064] In other embodiments of the present invention, the positive correlation is expressed by calculating the product of the first temperature change coefficient, the second temperature change coefficient, the third temperature change coefficient and the range, or by calculating the cumulative sum, so that the larger each parameter is, the larger the cumulative sum is, and the greater the degree of temperature change is; the specific means are technical means well known to those skilled in the art and are not limited or elaborated here.

[0065] The monitoring points in the greenhouse are affected differently by the ambient temperature outside the greenhouse. In order to more accurately analyze the correlation between the monitoring points in the greenhouse and the temperature outside the greenhouse, the ambient temperature is approximately divided into segments, which helps to analyze the temperature state under similar ambient temperature conditions more specifically in the subsequent analysis; multiple approximate segments of the curve consisting of the ambient temperature data outside the greenhouse at all times are obtained; it should be noted that, in an embodiment of the present invention, the APCA algorithm can divide data with similar change characteristics into multiple curve segments with relatively similar ambient temperatures according to the changes between the data, which helps to process the data subsequently; the specific APCA algorithm is a technical means well known to those skilled in the art and will not be elaborated here.

[0066] Since the temperature collection frequency of each monitoring point in the greenhouse is the same as the collection frequency of the ambient temperature data outside the greenhouse, the number of monitoring temperature data and ambient temperature data at each monitoring point is consistent. By analyzing the relative distance between the data, the correlation of the monitoring temperature data can be reflected. The larger the relative distance, the smaller the correlation, which indicates that the temperature of the monitoring point does not change with the environment. Taking into account that the monitoring points in the greenhouse are affected differently by the ambient temperature outside the greenhouse within different time ranges, the monitoring temperature data within the corresponding time range of the approximate segmentation are analyzed to reflect the changes in the monitoring temperature data within the time range with similar ambient temperature. The smaller the change in the monitoring temperature data, the greater the impact of the ambient temperature. According to the relative distance between the monitoring temperature data of each monitoring point in the greenhouse and the outside of the greenhouse at different times, the degree of temperature change of the corresponding monitoring point and the degree of fluctuation of the monitoring temperature data within the corresponding time range of different approximate segments, the degree of influence of the ambient temperature of each monitoring point is obtained.

[0067] Preferably, in one embodiment of the present invention, the method for obtaining the degree of influence of the ambient temperature includes:

[0068] Obtain the DTW distance of the curve formed by the monitored temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all times as the relative distance;

[0069] The degree of influence of the ambient temperature at each monitoring point is obtained based on the temperature change degree, relative distance, and the fluctuation degree of the monitored temperature data within the corresponding time range of different approximate segments. The degree of temperature change is positively correlated with the degree of influence of the ambient temperature, while the relative distance and the fluctuation degree of the monitored temperature data are negatively correlated with the degree of influence of the ambient temperature.

[0070] 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 inside the greenhouse and the ambient temperature outside the greenhouse. The larger the distance, the smaller the correlation coefficient, and the smaller the distance, the larger the correlation coefficient. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0071] It should be noted that, in one embodiment of the present invention, the degree of fluctuation of the monitored temperature data can be expressed by variance. The larger the variance, the greater the degree of fluctuation, and the smaller the variance, the smaller the degree of fluctuation. In other embodiments of the present invention, the degree of fluctuation can also be expressed by standard deviation, range, etc. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0072] In one embodiment of the present invention, the formula for the degree of influence of ambient temperature is expressed as:

[0073] ;

[0074] in, Indicates the The degree of influence of ambient temperature at each monitoring point; Indicates the The degree of temperature change at each monitoring point; Indicates the number of The DTW distance of the curve formed by the monitoring temperature data of each monitoring point and the ambient temperature data outside the greenhouse, that is, the relative distance; Indicates the The variance of the monitored temperature data within the time range of the approximate segment, that is, the degree of fluctuation; Indicates the number of approximate segments; Represents an exponential function with a natural constant as its base.

[0075] In the formula of the influence of ambient temperature, the exponential function with the natural constant as the base is used to convert Perform negative correlation mapping, It represents the mean variance of the monitored temperature data within the corresponding time range of all approximate segments. The larger the variance, the greater the 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 inside the shed and the ambient temperature outside the shed, and the smaller the influence of the ambient temperature. Therefore, the degree of temperature change is adjusted based on the influence of the ambient temperature. The smaller the influence of the ambient temperature, the smaller the temperature change degree is adjusted, and the smaller the influence of the ambient temperature of the monitoring point is.

[0076] Temperature impact analysis module 103: obtain the location impact of each monitoring point based on the location distribution of different monitoring points and the distribution of the degree of environmental temperature impact; obtain the updated degree of location impact of each monitoring point based on the distribution of the degree of location impact of monitoring points in different areas; obtain the temperature impact degree of each monitoring point based on the location distribution and the updated degree of location impact of each monitoring point.

[0077] The extent to which different monitoring points are affected by the ambient temperature outside the greenhouse depends on the location of the monitoring point in the greenhouse. The closer the monitoring point is to the greenhouse boundary, vents or air outlets of the temperature control equipment, the more easily the monitoring temperature data of the monitoring point is affected by the ambient temperature. In theory, the more similar the ambient temperature is, the more consistent the temperature of the monitoring point is affected by the location. Considering that crops of the same type planted at the same time will have certain differences in growth during the growth process, the impact of the position of each monitoring point on the temperature is evaluated. According to the location distribution of different monitoring points and the distribution of the degree of influence of the ambient temperature, the degree of position influence of each monitoring point is obtained.

[0078] Preferably, in one embodiment of the present invention, the method for obtaining the position influence degree is as follows: Figure 3 , which shows a flow chart of a method for obtaining the degree of location influence, including:

[0079] Step S301: For any target location in the shed boundary, ventilation outlet, and temperature control equipment outlet, 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 position independence of each monitoring point.

[0080] The smaller the distance between the monitoring point and the target location, the greater the degree of environmental influence, and the less independent the temperature of the monitoring point is from its location.

[0081] Step S302: According to the location independence and the distribution of the influence degree of the ambient temperature of different monitoring points, the updated influence degree of the ambient temperature of each monitoring point and the high-influence monitoring points are obtained.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the degree to which the ambient temperature affects the update includes:

[0083] According to the degree of influence of the ambient temperature at each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar environments;

[0084] The mean difference in position independence between each monitoring point and other monitoring points in the corresponding environmentally similar monitoring cluster is obtained as the adjustment weight of each monitoring point; the ratio of the position independence of each monitoring point to the adjustment weight is obtained as the degree of update of the ambient temperature influence of each monitoring point.

[0085] In one embodiment of the present invention, the formula for the influence of ambient temperature on the update degree is expressed as:

[0086] ;

[0087] in, Indicates the The ambient temperature of each monitoring point affects the update degree; Indicates the The degree of influence of ambient temperature at each monitoring point; Indicates the The mean difference in location independence between a monitoring point and other monitoring points in the corresponding environmentally similar monitoring cluster is the adjustment weight of each monitoring point.

[0088] In the formula of the influence of ambient temperature on the degree of renewal, Add 0.01 in the middle to avoid the denominator of the formula being 0, which would make the formula meaningless; the larger the adjustment weight, the greater the difference in position independence between monitoring points, indicating that the influence of location at monitoring points with similar environments is more inconsistent, the more likely there is an impact on the growth of the crop itself, and the smaller the adjustment on the degree of influence of ambient temperature.

[0089] Preferably, in one embodiment of the present invention, the method for acquiring high-impact monitoring points includes:

[0090] The position independence of all monitoring points is normalized, and the monitoring points corresponding to the normalized results smaller than the preset independence threshold are marked as high-impact monitoring points.

[0091] It should be noted that, in one embodiment of the present invention, the size of the preset irrelevant threshold is 0.3; in one embodiment of the present invention, the size of the preset irrelevant threshold can be specifically set according to specific circumstances, which is not limited or elaborated herein.

[0092] Step S303: Perform negative correlation mapping on the location independence of each monitoring point, and obtain the product of the negative correlation mapping result, the ambient temperature impact update degree of each monitoring point, and the number of high-impact monitoring points in the neighborhood as the location influence degree of each monitoring point.

[0093] In one embodiment of the present invention, the formula for the position influence degree is expressed as:

[0094] ;

[0095] in, Indicates the The influence degree of the location of each monitoring point; Indicates the The ambient temperature of each monitoring point affects the update degree; Indicates the The location independence of each monitoring point; Indicates the The number of high-impact monitoring points within the neighborhood of a monitoring point.

[0096] In the formula of the degree of location influence, the greater the degree of update influence of ambient temperature, the smaller the location independence, the greater the number of high-impact monitoring points in the neighborhood, and the greater the degree of location influence.

[0097] It should be noted that, in one 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 set according to specific circumstances, and is not limited or elaborated here.

[0098] Different crops are affected differently by temperature changes outside the greenhouse due to their different planting varieties and their varying resistance to interference. The impact of temperature changes can be assessed by analyzing the location impact distribution of monitoring points. Based on the location impact distribution of monitoring points within different regions, the updated location impact of each monitoring point can be obtained.

[0099] Preferably, in one embodiment of the present invention, the method for obtaining the location impact update degree includes:

[0100] The average location influence level of all monitoring points in each area was obtained as the overall location influence level of each area;

[0101] Based on the location influence degree of each monitoring point, the difference and fluctuation degree of the overall location influence level between the corresponding area and other areas, and the overall location influence level, the location influence update degree of each monitoring point is obtained. The location influence degree, the difference and fluctuation degree of the overall location influence level, and the overall location influence level are all positively correlated with the location influence update degree.

[0102] In one embodiment of the present invention, the formula for the degree of location impact update is expressed as:

[0103] ;

[0104] in, Indicates the The location of each monitoring point affects the degree of update; Indicates the The influence degree of the location of each monitoring point; Indicates the The average location influence level of all monitoring points in the region, i.e. the overall location influence level; Indicates the the degree of fluctuation in the level of overall locational influence between a region and different other regions; Represents the normalization function.

[0105] In the formula of temperature influence, the greater the overall position influence level of the monitoring point, the greater the difference in the overall position influence level between regions. The more dissimilar the affected situation of a region is to that of other regions, the greater the anti-interference ability is, the greater the adjustment of the position impact degree is, and the greater the position impact update degree is.

[0106] Multiple crops are planted in a greenhouse at the same time. The planting density of different crops is different, and the temperature in different areas will also be different. The greater the planting density, the denser the crop leaves are, which will restrict air circulation and cause the temperature to rise faster. When the same crop is planted in a 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, the degree of temperature impact at each monitoring point is obtained based on the location distribution of different monitoring points and the degree of location impact update.

[0107] Preferably, in one embodiment of the present invention, the method for obtaining the degree of temperature influence is:

[0108] According to the location independence of each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar locations;

[0109] Obtain the cumulative difference in the degree of location influence update between each monitoring point and other monitoring points in the corresponding similar monitoring cluster, and calculate the product of the cumulative result and the degree of location influence update of each monitoring point as the degree of temperature influence of each monitoring point.

[0110] In one embodiment of the present invention, the formula for the degree of temperature influence is expressed as:

[0111] ;

[0112] in, Indicates the The degree of impact on the temperature of each monitoring point; Indicates the The location of each monitoring point affects the degree of update; Indicates the The monitoring points correspond to the monitoring clusters with similar locations. The location of other monitoring points affects the degree of update; Indicates the number of monitoring points in a monitoring cluster with similar locations.

[0113] In the formula for the degree of temperature influence, Indicates calculation of The cumulative difference between the position influence update degree between the monitoring point and the other monitoring points in the corresponding similar monitoring cluster is the cumulative difference value. The larger the cumulative difference value, the The more different the influence of location on the temperature of other monitoring points in the monitoring cluster is, the more different the influence of location is. The greater the influence of its own growth on a monitoring point, the greater the impact on temperature, and the greater the degree of temperature adjustment.

[0114] Temperature control module 104: controls the temperature in the greenhouse according to the degree of influence of the temperature at each monitoring point.

[0115] By adjusting the degree of temperature impact, the degree of temperature impact can be obtained, which can more accurately reflect the magnitude of temperature impact and effectively optimize the temperature in the greenhouse.

[0116] It should be noted that in another embodiment of the present invention, after obtaining the degree of temperature influence of each monitoring point, the temperature in the greenhouse is controlled, including: obtaining the average value of the temperature influence of all monitoring points, and normalizing it to obtain the temperature adjustment coefficient, which is used to input into the PID controller to adjust and optimize the temperature in the greenhouse, thereby contributing to the healthy growth of crops.

[0117] To sum up, the present invention obtains the temperature change degree of each monitoring point according to the distribution of monitored temperature data of each monitoring point at different times; obtains multiple approximate segments of the curve composed of the ambient temperature data outside the shed at all times, and obtains the ambient temperature influence degree of each monitoring point according to the relative distance between the monitored temperature data of each monitoring point in the shed at all times and the ambient temperature data outside the shed, the temperature change degree of the corresponding monitoring point and the fluctuation degree of the monitored temperature data within the corresponding time range of different approximate segments; obtains the position influence degree of each monitoring point according to the position distribution of different monitoring points and the distribution of ambient temperature influence degree; obtains the temperature influence degree of each monitoring point according to the distribution of position influence degree of monitoring points in different areas; obtains the temperature influence degree of each monitoring point according to the position distribution and temperature influence degree of different monitoring points, and controls the temperature in the shed; the present invention accurately controls the temperature of the greenhouse by accurately obtaining the temperature influence degree of each monitoring point.

[0118] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A greenhouse all-weather environmental monitoring and control system, characterized in that: The system comprises: Greenhouse environment data acquisition module: acquires the monitoring temperature data of each monitoring point in different areas of the greenhouse, as well as the ambient temperature data outside the greenhouse in time sequence; Temperature data analysis module: Based on the distribution of monitored temperature data at each monitoring point at different times, the temperature change degree of each monitoring point is obtained; multiple approximate segments of the curve formed by the ambient temperature data outside the greenhouse at all times are obtained, and 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 temperature change degree of the corresponding monitoring point, and the degree of fluctuation of the monitored temperature data within the corresponding time range of different approximate segments are obtained to obtain the degree of ambient temperature influence of each monitoring point; Temperature impact analysis module: Based on the location distribution of different monitoring points and the distribution of ambient temperature impact, the location impact of each monitoring point is obtained; based on the location impact distribution of monitoring points in different areas, the updated location impact of each monitoring point is obtained; based on the location distribution of different monitoring points and the updated location impact, the temperature impact of each monitoring point is obtained; Temperature control module: controls the temperature in the greenhouse according to the degree of temperature influence at each monitoring point; The method for obtaining the degree of influence of the ambient temperature includes: Obtain the DTW distance of the curve formed by the monitored temperature data of each monitoring point in the greenhouse and the ambient temperature data outside the greenhouse at all times as the relative distance; The degree of influence of the ambient temperature at each monitoring point is obtained based on the temperature change degree, relative distance, and the fluctuation degree of the monitored temperature data within the corresponding time range of different approximate segments. The degree of temperature change is positively correlated with the degree of influence of the ambient temperature, while the relative distance and the fluctuation degree of the monitored temperature data are negatively correlated with the degree of influence of the ambient temperature. The method for obtaining the position impact update degree includes: The average location influence level of all monitoring points in each area was obtained as the overall location influence level of each area; According to the location influence degree of each monitoring point, the difference and fluctuation degree of the overall location influence level between the corresponding area and other areas, and the overall location influence level, the location influence update degree of each monitoring point is obtained. The location influence degree, the difference and fluctuation degree of the overall location influence level, and the overall location influence level are all positively correlated with the location influence update degree. The method for obtaining the degree of temperature influence includes: According to the location independence of each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar locations; Obtain the cumulative difference in the degree of location influence update between each monitoring point and other monitoring points in the corresponding similar monitoring cluster, and calculate the product of the cumulative result and the degree of location influence update of each monitoring point as the degree of temperature influence of each monitoring point.

2. A greenhouse all-weather environment monitoring and control system according to claim 1, characterized in that: The method for obtaining the degree of temperature change includes: For any monitoring point, a temperature curve consisting of the monitored temperature data at all times is obtained, and the average of the absolute values ​​of the slopes of the data points between all adjacent moments on the temperature curve is obtained as the first temperature variation coefficient of the corresponding monitoring point; Obtaining the mean difference in monitored temperature data between each extreme point in the temperature curve and the adjacent extreme points before and after it, and normalizing it to obtain the temperature variation amplitude of each extreme point; counting the number of extreme points whose temperature variation amplitude is greater than or equal to a preset amplitude threshold among all extreme points, and obtaining the second temperature variation coefficient; Obtaining the difference between each monitored temperature data and the maximum value in the temperature curve and normalizing it as the first difference; counting the number of monitored temperature data corresponding to the first difference being less than the preset difference threshold among all monitored temperature data, and normalizing it as the third temperature variation coefficient; The range of the monitored temperature data in the temperature curve is obtained, and the product of the first temperature variation coefficient, the second temperature variation coefficient, the third temperature variation coefficient and the range is calculated as the temperature variation degree of each monitoring point.

3. The greenhouse all-weather environment monitoring and control system according to claim 1 is characterized in that: The method for obtaining the approximate segmentation includes: The APCA segmentation algorithm is used to segment the curve composed of the ambient temperature data outside the greenhouse at all times to obtain multiple approximate segments.

4. The greenhouse all-weather environment monitoring and control system according to claim 1 is characterized in that: The method for obtaining the position influence degree includes: For any target location at the shed boundary, ventilation outlet, or temperature control equipment outlet, 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 independence of each monitoring point; According to the location independence and distribution of the influence of ambient temperature at different monitoring points, the updated influence of ambient temperature at each monitoring point and the high-impact monitoring points are obtained; The location independence of each monitoring point is negatively mapped, and the product of the negative correlation mapping result, the updated degree of environmental temperature influence of each monitoring point, and the number of high-impact monitoring points in the neighborhood is obtained as the location influence degree of each monitoring point.

5. The greenhouse all-weather environment monitoring and control system according to claim 4 is characterized in that: The method for obtaining the degree of update affected by the ambient temperature includes: According to the degree of influence of the ambient temperature at each monitoring point, all monitoring points are clustered to obtain multiple monitoring clusters with similar environments; The mean difference in position independence between each monitoring point and other monitoring points in the corresponding environmentally similar monitoring cluster is obtained as the adjustment weight of each monitoring point; the ratio of the position independence of each monitoring point to the adjustment weight is obtained as the degree of update of the ambient temperature influence of each monitoring point.

6. The greenhouse all-weather environment monitoring and control system according to claim 4 is characterized in that: The method for obtaining the high-impact monitoring points includes: The position independence of all monitoring points is normalized, and the monitoring points corresponding to the normalized results smaller than the preset independence threshold are marked as high-impact monitoring points.

7. The greenhouse all-weather environment monitoring and control system according to claim 6, characterized in that: The preset irrelevant threshold is 0.3.

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