Tobacco growth period optimal configuration method, medium and system for improving quality

Abstract

The invention relates to the technical field of tobacco planting management, and particularly discloses a tobacco growth period optimal configuration method, medium and system for improving the quality, and the method comprises the steps: obtaining meteorological data containing time labels of a tobacco sample in the growth process according to a sensor installed in a planting environment, and obtaining a meteorological data matrix; obtaining a tobacco image containing a time label of the tobacco sample; registering the tobacco images based on the time labels, and comparing and clustering the registered tobacco images to obtain different types of tobacco images; and for similar tobacco images, intercepting corresponding meteorological data matrixes according to the time labels, comparing the meteorological data matrixes, and determining the importance of each parameter. According to the method, the historical environment data and the tobacco images of the tobacco samples are obtained, the tobacco images are clustered, then the historical environment data are clustered, the historical environment data of the same kind are compared, the importance degree of each parameter is determined, and reference is provided for the resource allocation process.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco planting management, and in particular to a method, medium and system for optimizing tobacco growth period configuration for improving quality. Background Art

[0002] The growth period of tobacco includes the sowing period (seed germination period), seedling period, growth period, flower bud differentiation period, flowering period, maturity period and harvest period. During the growth of tobacco, different environmental parameters may form different growth states. In the existing planting scenarios, the environmental parameters are adjustable. Adjusting the environmental parameters involves the problem of resource allocation. How to optimize the resource allocation process is the technical problem that the technical solution of the present invention wants to solve. Summary of the invention

[0003] The object of the present invention is to provide a method, medium and system for optimizing the configuration of tobacco growth period to improve the quality, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing tobacco growth period configuration to improve quality, the method comprising: Receiving the target growth period, obtaining tobacco samples within the target growth period; The meteorological data containing time tags of tobacco samples during the growth process are obtained according to sensors installed in the planting environment to obtain a meteorological data matrix; Acquire tobacco images containing time tags of tobacco samples; Register tobacco images based on time tags, compare and cluster the registered tobacco images to obtain tobacco images of different categories; For the same type of tobacco images, the corresponding meteorological data matrix is ​​intercepted according to the time tag, and the meteorological data matrix is ​​compared to determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the tobacco image of this type; When receiving the target image input by the user, the class corresponding to the image is determined based on the same image comparison scheme, and the importance of each parameter corresponding to the tobacco image of this class is queried; Environmental control resources are configured based on the importance.

[0005] As a further solution of the present invention: the step of obtaining the meteorological data containing time tags of tobacco samples during the growth process according to the sensors installed in the planting environment to obtain the meteorological data matrix includes: Acquiring meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; Arrange various meteorological data with the same time tag according to the preset index order to obtain row data; Arrange the row data in chronological order according to the time labels to obtain the meteorological data matrix.

[0006] As a further solution of the present invention, the steps of registering tobacco images based on time tags, comparing and clustering the registered tobacco images, and obtaining tobacco images of different categories include: Register tobacco images based on time tags; For tobacco images containing the same time label, feature points are marked in the tobacco images; Compare the labeling results of different tobacco images and calculate the image distance; The tobacco images are clustered according to the image distance to obtain tobacco images of different categories.

[0007] As a further solution of the present invention: for tobacco images containing the same time tag, the step of marking feature points in the tobacco images includes: Extract the layers of the tobacco image under different channels, traverse the pixels in each layer, record the values ​​of the pixels, and construct an array; the traversal order of the pixels in all layers is the same; For each array, the independent value of each value is calculated in sequence; the independent value is used to indicate the degree of differentiation of each value from other values ​​in the array; Accumulate independent values. When the independent value reaches the preset value, query the position corresponding to the value and mark it as a feature point. The calculation process of the independent value is: ; In the formula, is an independent value, is the preset parameter, is the larger value of the distance between the current element and the two ends of the array, The distance from the current element in the array is The difference between the value of the element that is being retrieved and the value of the current element.

[0008] As a further solution of the present invention: for the same type of tobacco images, the steps of intercepting the corresponding meteorological data matrix according to the time tag, comparing the meteorological data matrix, and determining the importance of each parameter include: For any tobacco image of the same type, locate row data in the meteorological data matrix according to the time label; Obtain all row data before the located row data as the intercepted meteorological data matrix; Compare each intercepted meteorological data matrix to determine the importance of each parameter; The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the positive ratio of the mean; the relationship between the importance and the mean is a linear function with a positive coefficient.

[0009] As a further solution of the present invention: the method further comprises: Receive the tobacco to be optimized selected by the user, record the meteorological data in real time, and obtain the real-time meteorological matrix; Obtain the meteorological data matrix with the time label as the end point as the reference matrix; The real-time meteorological matrix is ​​compared with the reference matrix in real time, the similarity is calculated, and the reference matrix whose similarity reaches a preset threshold is selected to obtain its corresponding tobacco image as the prediction image.

[0010] The technical solution of the present invention also provides a tobacco growth period optimization configuration system for improving quality, the system comprising: A tobacco sample acquisition module is used to receive a target growth period and acquire tobacco samples within the target growth period; A meteorological data statistics module is used to obtain meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment to obtain a meteorological data matrix; A tobacco image acquisition module, used for acquiring tobacco images containing time tags of tobacco samples; A tobacco image clustering module is used to register tobacco images based on time tags, compare and cluster the registered tobacco images, and obtain tobacco images of different categories; The parameter analysis module is used to intercept the corresponding meteorological data matrix according to the time tag for the same type of tobacco image, compare the meteorological data matrix, and determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the type of tobacco image; The importance query module is used to determine the class corresponding to the image based on the same image comparison scheme when receiving the target image input by the user, and query the importance of each parameter corresponding to the tobacco image of this class; A resource configuration module is used to configure the environment control resources based on the importance.

[0011] As a further solution of the present invention: the meteorological data statistics module includes: A sensor monitoring unit, used to obtain meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; A row data generating unit is used to arrange various meteorological data with the same time tag according to a preset index sequence to obtain row data; The column data generating unit is used to arrange the row data according to the time sequence of the time labels to obtain the meteorological data matrix.

[0012] As a further solution of the present invention: the tobacco image clustering module includes: An image registration unit, for registering tobacco images based on time tags; A feature extraction unit, for marking feature points in tobacco images containing the same time tag; A distance calculation unit, used for comparing the marking results of different tobacco images and calculating the image distance; The clustering execution unit is used to cluster the tobacco images according to the image distance to obtain tobacco images of different categories.

[0013] As a further solution of the present invention: the parameter analysis module includes: A row data positioning unit, used for positioning row data in the meteorological data matrix according to a time tag for any tobacco image of the same type; A data interception unit is used to obtain all row data before the located row data as an intercepted meteorological data matrix; A matrix comparison unit is used to compare each intercepted meteorological data matrix and determine the importance of each parameter; The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the positive ratio of the mean; the relationship between the importance and the mean is a linear function with a positive coefficient.

[0014] Compared with the prior art, the beneficial effects of the present invention are: the present invention obtains historical environmental data and tobacco images of tobacco samples, clusters the tobacco images, and then clusters the historical environmental data, compares similar historical environmental data, determines the importance of each parameter, and provides a reference for the resource allocation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0016] Figure 1Flow chart of the method for optimizing the configuration of tobacco growth period to improve quality.

[0017] Figure 2 Block diagram of a system for optimizing the configuration of tobacco growth periods to improve quality. DETAILED DESCRIPTION

[0018] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Figure 1 The flowchart of the method for optimizing the configuration of tobacco growth period to improve the quality is provided. In an embodiment of the present invention, a method for optimizing the configuration of tobacco growth period to improve the quality comprises: Step S100: receiving a target growth period and obtaining a tobacco sample within the target growth period; The growth period of tobacco includes the sowing period (seed germination period), seedling period, growth period, flower bud differentiation period, flowering period, maturity period and harvest period. Each growth period needs to be analyzed separately. The technical solution of the present invention only analyzes tobacco in the same growth period. Accordingly, for four growth periods, four identical steps can be applied.

[0020] Specifically, the execution body of the method first receives a growth period selected by the user, called a target growth period, and the target growth period is one of the sowing period (seed germination period), seedling period, growth period, flower bud differentiation period, flowering period, maturity period and harvest period; after receiving the target growth period, a tobacco sample within the target growth period is selected, and the selection process is very simple, because tobacco is planted in different regions and stages during planting, and one plot of land corresponds to one growth period. The tobacco planted in the plot itself is registered data and can be directly read.

[0021] Step S200: obtaining meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment to obtain a meteorological data matrix; In the existing tobacco planting environment, environmental detectors are pre-installed, that is, collection sensors, which obtain meteorological data and obtain a meteorological data matrix; wherein, each column of the meteorological data matrix corresponds to a type of meteorological data, and each row corresponds to meteorological data at a collection time; it should be noted that the collection cycle of all sensors must be the same, so that different types of data collected can be used as data in the same row; in addition, in actual scenarios, although the collection cycle is the same, there will still be some discrepancies in the time tags of the acquired data. For example, if the collection cycle is half an hour, the acquired data may be affected by the transmission process, and there will be errors of several seconds. These errors need to be ignored. Due to the difference in magnitude between the errors and cycles, it is not difficult to identify them.

[0022] Step S300: obtaining a tobacco image containing a time tag of a tobacco sample; Each plant grown is a tobacco sample. With the help of a camera installed in the planting environment, tobacco images of the tobacco samples can be obtained. While obtaining the tobacco images, the acquisition time needs to be recorded, which is called a time tag.

[0023] Step S400: registering tobacco images based on time tags, and comparing and clustering the registered tobacco images to obtain tobacco images of different categories; For each tobacco sample, a batch of tobacco images with time labels can be obtained, indicating the growth status of the tobacco sample at different times. All tobacco images at the same time are counted and analyzed, and the analysis results obtained are also the analysis results of that time. Its practical significance lies in that, for example, if the time label is half of the development cycle, all tobacco images within the time label are obtained. All the tobacco images obtained represent the status of all tobacco samples after half of the development cycle. The statistically obtained tobacco images are compared and then clustered, so that tobacco samples with similar status after half of the development cycle are classified into one category.

[0024] Step S500: for the same type of tobacco images, intercept the corresponding meteorological data matrix according to the time tag, compare the meteorological data matrix, and determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the tobacco image of this type; For similar tobacco images, clustering is performed based on time labels, which means that after the same growth time, similar growth states are obtained, which is the output in the mapping relationship. Furthermore, the independent variable of the growth state is meteorological data, and the meteorological data matrix before the time label is intercepted as the input in the mapping relationship. The importance of each parameter can be evaluated for the intercepted meteorological data matrix. The importance indicates the degree of influence of each parameter in the process of tobacco growing into the growth state corresponding to this type of tobacco image.

[0025] Step S600: upon receiving a target image input by a user, determining the class to which the image corresponds based on the same image comparison scheme, and querying the importance of each parameter corresponding to the tobacco image of that class; The target image input by the user indicates how the user wants the tobacco to grow. When the target image input by the user is received, the target image is classified based on the same scheme as step S400 to obtain the class corresponding to the target image, and then the importance of each parameter corresponding to the tobacco image of this class is queried.

[0026] It should be noted that in the technical solution of the present invention, the target image input by the user may also contain a time tag (if not entered, the default is the final moment), and then the aligned tobacco image is first queried according to the time tag, and then the class is determined; it is worth mentioning that in the process of first querying the aligned tobacco image according to the time tag, the time range is generally expanded. For example, when the time tag is half a growth cycle, the time range can be floated by one-tenth of the growth cycle based on the time tag.

[0027] Step S700: configuring environmental control resources based on the importance; After obtaining the importance of each parameter, the limited environmental control resources can be configured based on the importance. The specific configuration method is very flexible and is not the focus of the technical solution of the present invention. It is mainly determined by the growers themselves. The actual function of the technical solution of the present invention is that the user inputs a target image, the executive body of the present invention determines the importance of each parameter, and feeds back to the user, and the user configures the resources according to the importance. The technical solution of the present invention will not generate a definite configuration plan. In fact, in the mature tobacco planting field, the best planting ratio has long been determined and is common knowledge. However, even under the best planting ratio conditions, the growth of different plants is still unpredictable. The "best planting ratio" is actually just a "better" seed value ratio. In the technical solution of the present invention, the importance of various parameters is determined only based on the most recent planting samples, thereby assisting users in resource allocation. It only assists users in planting in a general direction. The actual growth process still retains extremely high diversity, which can easily produce better planting effects.

[0028] It should be noted that the importance of various parameters is determined based on the most recent planting samples, which changes over time, which also ensures the timeliness of the importance assessment results.

[0029] Regarding step S200, the step of obtaining the meteorological data containing time tags of the tobacco samples during the growth process according to the sensors installed in the planting environment to obtain the meteorological data matrix includes: Acquiring meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; Arrange various meteorological data with the same time tag according to the preset index order to obtain row data; Arrange the row data in chronological order according to the time labels to obtain the meteorological data matrix.

[0030] The process of obtaining meteorological data is very simple. The meteorological data with time tags of tobacco samples during the growth process will be obtained based on the sensors installed in the planting environment. Rows will be created in chronological order, and columns will be created according to the data type of the meteorological data to obtain the meteorological data matrix.

[0031] Regarding step S400, the steps of registering tobacco images based on time tags, comparing and clustering the registered tobacco images, and obtaining tobacco images of different categories include: Register tobacco images based on time tags; For tobacco images containing the same time label, feature points are marked in the tobacco images; Compare the labeling results of different tobacco images and calculate the image distance; The tobacco images are clustered according to the image distance to obtain tobacco images of different categories.

[0032] The above content explains the clustering process. First, tobacco images of the same moment (time tags are close enough) are obtained. This process is called registration. Then, the features of the tobacco images (the positions of the marked feature points) are extracted, and the differences in features are used as image distances to reflect the differences in images. Finally, after the image distance is determined, the tobacco images can be clustered using a distance-based clustering algorithm.

[0033] The above process actually involves two classifications: registering tobacco images based on time tags is the first classification, and clustering images based on image distance is the second classification.

[0034] Specifically, for tobacco images containing the same time tag, the step of marking feature points in the tobacco images includes: Extract the layers of the tobacco image under different channels, traverse the pixels in each layer, record the values ​​of the pixels, and construct an array; the traversal order of the pixels in all layers is the same; For each array, the independent value of each value is calculated in sequence; the independent value is used to indicate the degree of differentiation of each value from other values ​​in the array; Accumulate independent values. When the independent value reaches the preset value, query the position corresponding to the value and mark it as a feature point. The calculation process of the independent value is: ; In the formula, is an independent value, is the preset parameter, is the larger value of the distance between the current element and the two ends of the array, The distance from the current element in the array is The difference between the value of the element that is being retrieved and the value of the current element.

[0035] The above calculation process is explained as follows: According to the preset order, the pixels in the layer are converted into an array. For example, the value of each pixel is read from left to right and from top to bottom. This process is a dimensionality reduction process. The information in the layer will be converted into an array. For each array, the difference between each value and other values ​​is calculated in turn. If the difference is small, it can be considered that it forms a region with the surrounding pixels. At this time, the corresponding position is marked as a feature point. It is essentially a region recognition scheme used to extract tobacco areas in images.

[0036] In addition, regarding the calculation process of the independent value, for each element, the difference between its value and the value of other elements is calculated, and then accumulated. Taking into account the distance factor, this application introduces a weight coefficient so that the elements closer to the current element have a greater impact on the current element. In general, the larger the difference, the greater the difference between the current element and other elements, the higher the independence, and the larger the independent value. Therefore, in the above calculation process, the independent value is inversely proportional to the sum of the differences.

[0037] It is worth mentioning that the above summation process involves all elements in the array. In fact, the staff can set a range, such as 20% of the array elements, to determine the independent value of an element relative to a limited number of surrounding elements. Regarding step S600, the step of intercepting the corresponding meteorological data matrix according to the time tag for the same type of tobacco images, comparing the meteorological data matrix, and determining the importance of each parameter includes: For any tobacco image of the same type, locate row data in the meteorological data matrix according to the time label; Obtain all row data before the located row data as the intercepted meteorological data matrix; Compare each intercepted meteorological data matrix to determine the importance of each parameter.

[0038] For tobacco images of the same type, each tobacco image corresponds to a meteorological data matrix. The meteorological data matrix is ​​intercepted according to the time label of the tobacco image. At this time, the intercepted meteorological data matrix is ​​the independent variable and the tobacco image is the dependent variable. Finally, tobacco images of the same type can obtain a meteorological data matrix set. By comparing the meteorological data matrices in the meteorological data matrix set, the importance of each parameter can be determined.

[0039] The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the direct ratio of the mean.

[0040] The calculation principle of importance is explained as follows: For any parameter, it corresponds to a column of data in each intercepted meteorological data matrix. All columns of data are extracted to obtain multiple arrays. For arrays, cosine similarity is calculated between each other. The more similar, the closer the cosine similarity is to one. If the cosine similarities are relatively large (the mean is large), it means that the corresponding values ​​of the parameter in each matrix are similar. Accordingly, it can be used as the main factor to determine the importance according to its direct proportion. On the contrary, if the mean of the cosine similarity indicates that the same parameter has different corresponding values ​​in different matrices, but their growth states are still similar, it means that the influence of the parameter is not very important and its importance is low.

[0041] Furthermore, regarding the relationship between importance and mean, a proportional relationship can be achieved by using a linear function with a positive coefficient.

[0042] As a preferred embodiment of the technical solution of the present invention, the method further includes: Receive the tobacco to be optimized selected by the user, record the meteorological data in real time, and obtain the real-time meteorological matrix; Obtain the meteorological data matrix with the time label as the end point as the reference matrix; The real-time meteorological matrix is ​​compared with the reference matrix in real time, the similarity is calculated, and the reference matrix whose similarity reaches a preset threshold is selected to obtain its corresponding tobacco image as the prediction image.

[0043] In an example of the technical solution of the present invention, a prediction function is provided. The existing function of the present application is that no matter what stage of the target image the user uploads, the importance of each parameter can be obtained, thereby assisting in resource allocation; on this basis, the user can also select a tobacco, called the tobacco to be optimized, and obtain a real-time meteorological matrix based on the meteorological data recorded by the sensor (the number of columns continues to increase). The real-time meteorological matrix is ​​compared with the meteorological data matrix of the existing tobacco sample at the final moment (the number of columns reaches the maximum), and a sufficiently similar meteorological data matrix can be matched, which is called a reference matrix; the function of this process is to query tobacco samples with similar growth conditions to the tobacco to be optimized, obtain their tobacco images, and use them as prediction images, thereby providing a prediction function. The prediction image can also be used as a reference for resource allocation.

[0044] It is worth mentioning that during the prediction process, the more columns the real-time meteorological matrix has, the more difficult it is to match a sufficiently similar meteorological data matrix, and the fewer the number of predicted images. Therefore, during the prediction process, as the growth time goes by, the number of predicted images decreases.

[0045] Figure 2 The structural block diagram of the tobacco growth period optimization configuration system for improving the quality is shown in the embodiment of the present invention. The tobacco growth period optimization configuration system for improving the quality is shown in the embodiment of the present invention. The system 10 includes: The tobacco sample acquisition module 11 is used to receive a target growth period and acquire tobacco samples within the target growth period; The meteorological data statistics module 12 is used to obtain the meteorological data with time tags of the tobacco samples during the growth process according to the sensors installed in the planting environment to obtain the meteorological data matrix; A tobacco image acquisition module 13, used to acquire a tobacco image containing a time tag of a tobacco sample; The tobacco image clustering module 14 is used to register tobacco images based on time tags, compare and cluster the registered tobacco images, and obtain tobacco images of different categories; The parameter analysis module 15 is used to intercept the corresponding meteorological data matrix according to the time tag for the same type of tobacco image, compare the meteorological data matrix, and determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the type of tobacco image; The importance query module 16 is used to determine the class corresponding to the image based on the same image comparison scheme when receiving the target image input by the user, and query the importance of each parameter corresponding to the tobacco image of this class; The resource configuration module 17 is used to configure the environment control resources based on the importance.

[0046] Furthermore, the meteorological data statistics module 12 includes: A sensor monitoring unit, used to obtain meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; A row data generating unit is used to arrange various meteorological data with the same time tag according to a preset index sequence to obtain row data; The column data generating unit is used to arrange the row data according to the time sequence of the time labels to obtain the meteorological data matrix.

[0047] Specifically, the tobacco image clustering module 14 includes: An image registration unit, for registering tobacco images based on time tags; A feature extraction unit, for marking feature points in tobacco images containing the same time tag; A distance calculation unit, used for comparing the marking results of different tobacco images and calculating the image distance; The clustering execution unit is used to cluster the tobacco images according to the image distance to obtain tobacco images of different categories.

[0048] Furthermore, the parameter analysis module 15 includes: A row data positioning unit, used for positioning row data in the meteorological data matrix according to a time tag for any tobacco image of the same type; A data interception unit is used to obtain all row data before the located row data as an intercepted meteorological data matrix; A matrix comparison unit is used to compare each intercepted meteorological data matrix and determine the importance of each parameter; The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the positive ratio of the mean; the relationship between the importance and the mean is a linear function with a positive coefficient.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of tobacco growth period to improve quality, It is characterized in that The method comprises: Receiving the target growth period, obtaining tobacco samples within the target growth period; The meteorological data containing time tags of tobacco samples during the growth process are obtained according to sensors installed in the planting environment to obtain a meteorological data matrix; Acquire tobacco images containing time tags of tobacco samples; Register tobacco images based on time tags, compare and cluster the registered tobacco images to obtain tobacco images of different categories; For the same type of tobacco images, the corresponding meteorological data matrix is ​​intercepted according to the time tag, and the meteorological data matrix is ​​compared to determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the tobacco image of this type; When receiving the target image input by the user, the class corresponding to the image is determined based on the same image comparison scheme, and the importance of each parameter corresponding to the tobacco image of this class is queried; Environmental control resources are configured based on the importance.

2. The method for optimizing the configuration of tobacco growth periods for improving quality according to claim 1, It is characterized in that The step of obtaining the meteorological data containing time tags of tobacco samples during the growth process according to the sensors installed in the planting environment to obtain the meteorological data matrix comprises: Acquiring meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; Arrange various meteorological data with the same time tag according to the preset index order to obtain row data; Arrange the row data in chronological order according to the time labels to obtain the meteorological data matrix.

3. The method for optimizing the configuration of tobacco growth periods for improving quality according to claim 1, It is characterized in that The steps of registering tobacco images based on time tags, comparing and clustering the registered tobacco images, and obtaining tobacco images of different categories include: Register tobacco images based on time tags; For tobacco images containing the same time label, feature points are marked in the tobacco images; Compare the labeling results of different tobacco images and calculate the image distance; The tobacco images are clustered according to the image distance to obtain tobacco images of different categories.

4. The method for optimizing the configuration of tobacco growth periods for improving quality according to claim 3, It is characterized in that For tobacco images containing the same time tag, the step of marking feature points in the tobacco images comprises: Extract the layers of the tobacco image under different channels, traverse the pixels in each layer, record the values ​​of the pixels, and construct an array; the traversal order of the pixels in all layers is the same; For each array, the independent value of each value is calculated in sequence; the independent value is used to indicate the degree of differentiation of each value from other values ​​in the array; Accumulate independent values. When the independent value reaches the preset value, query the position corresponding to the value and mark it as a feature point. The calculation process of the independent value is: ; In the formula, is an independent value, is the preset parameter, is the larger value of the distance between the current element and the two ends of the array, The distance from the current element in the array is The difference between the value of the element that is being retrieved and the value of the current element.

5. The method for optimizing tobacco growth period configuration for improving quality according to claim 1, It is characterized in that The step of intercepting the corresponding meteorological data matrix according to the time tag for the same type of tobacco images, comparing the meteorological data matrix, and determining the importance of each parameter includes: For any tobacco image of the same type, locate row data in the meteorological data matrix according to the time label; Obtain all row data before the located row data as the intercepted meteorological data matrix; Compare each intercepted meteorological data matrix to determine the importance of each parameter; The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the positive ratio of the mean; the relationship between the importance and the mean is a linear function with a positive coefficient.

6. The method for optimizing tobacco growth period configuration for improving quality according to claim 1, It is characterized in that The method further comprises: Receive the tobacco to be optimized selected by the user, record the meteorological data in real time, and obtain the real-time meteorological matrix; Obtain the meteorological data matrix with the time label as the end point as the reference matrix; The real-time meteorological matrix is ​​compared with the reference matrix in real time, the similarity is calculated, and the reference matrix whose similarity reaches a preset threshold is selected to obtain its corresponding tobacco image as the prediction image.

7. A tobacco growth period optimization configuration system for improving quality, It is characterized in that The system comprises: A tobacco sample acquisition module is used to receive a target growth period and acquire tobacco samples within the target growth period; A meteorological data statistics module is used to obtain meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment to obtain a meteorological data matrix; A tobacco image acquisition module, used for acquiring tobacco images containing time tags of tobacco samples; A tobacco image clustering module is used to register tobacco images based on time tags, compare and cluster the registered tobacco images, and obtain tobacco images of different categories; The parameter analysis module is used to intercept the corresponding meteorological data matrix according to the time tag for the same type of tobacco image, compare the meteorological data matrix, and determine the importance of each parameter; the importance represents the influence of each parameter in the process of tobacco growing into the growth state corresponding to the type of tobacco image; The importance query module is used to determine the class corresponding to the image based on the same image comparison scheme when receiving the target image input by the user, and query the importance of each parameter corresponding to the tobacco image of this class; A resource configuration module is used to configure the environment control resources based on the importance.

8. The tobacco growth period optimization configuration system for improving quality according to claim 7, It is characterized in that The meteorological data statistics module includes: A sensor monitoring unit, used to obtain meteorological data containing time tags of tobacco samples during the growth process according to sensors installed in the planting environment; the meteorological data at least includes air temperature, light intensity, soil temperature and soil humidity; A row data generating unit is used to arrange various meteorological data with the same time tag according to a preset index sequence to obtain row data; The column data generating unit is used to arrange the row data according to the time sequence of the time labels to obtain the meteorological data matrix.

9. The tobacco growth period optimization configuration system for improving quality according to claim 7, It is characterized in that The tobacco image clustering module comprises: An image registration unit, for registering tobacco images based on time tags; A feature extraction unit, for marking feature points in tobacco images containing the same time tag; A distance calculation unit, used for comparing the marking results of different tobacco images and calculating the image distance; The clustering execution unit is used to cluster the tobacco images according to the image distance to obtain tobacco images of different categories.

10. The tobacco growth period optimization configuration system for improving quality according to claim 7, It is characterized in that The parameter analysis module includes: A row data positioning unit, used for positioning row data in the meteorological data matrix according to a time tag for any tobacco image of the same type; A data interception unit is used to obtain all row data before the located row data as an intercepted meteorological data matrix; A matrix comparison unit is used to compare each intercepted meteorological data matrix and determine the importance of each parameter; The calculation process of the importance of each parameter is as follows: For any parameter, extract its corresponding column in each intercepted meteorological data matrix; Calculate the cosine similarity between the extracted columns, and then calculate the mean of the cosine similarity; The importance is determined according to the positive ratio of the mean; the relationship between the importance and the mean is a linear function with a positive coefficient.

Images