A data management method and system for monitoring plant growth stages

Abstract

The present invention relates to the field of data processing technology, and in particular to a data management method and system for monitoring plant growth links, including: obtaining the carbon dioxide concentration, light intensity and growth rate at each moment of all days, and constructing a growth rate data model according to the growth rate; obtaining the ideal growth rate sequence of each day, and calculating the similarity with the growth rate sequence of the day, combining the similarity between the weather conditions of historical days and the day, obtaining the fitting index of the growth rate at each moment of the day, and further obtaining the difference evaluation of the growth rate at each moment of the day; reconstructing the growth rate data model according to the difference evaluation of the growth rate at all moments of the day to obtain a new growth rate data model, and using the new growth rate data model to realize data management of the plant growth link. The present invention aims to optimize the management of monitoring data in the plant growth link.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data management method and system for monitoring plant growth stages. Background Art

[0002] During the plant growth process, the plant growth rate and environmental factors including light intensity and carbon dioxide concentration are monitored. Different environmental factors have different effects on the growth rate of plants. A data model is constructed for the growth rate monitored during the plant growth process. Subsequent monitoring data that conforms to the model can be represented by the model, and the model can be modified for monitoring data that does not conform to the model, thereby realizing data management for monitoring the plant growth process.

[0003] When using data models to predict growth rates, each growth rate in the data used to build the model has the same impact on the accuracy of the model, so that abnormal data also performs better in the prediction, which affects the data management of plant growth monitoring. Summary of the invention

[0004] The present invention provides a data management method and system for monitoring plant growth stages to solve the existing problems.

[0005] A data management method and system for monitoring plant growth stages of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a data management method for monitoring plant growth stages, the method comprising the following steps:

[0007] Obtain the carbon dioxide concentration, light intensity and growth rate at every moment of all days, and build a growth rate data model based on the growth rate;

[0008] According to the difference in light intensity between day and night, the growth rate of diurnal plants is quantified in combination with the increment of carbon dioxide concentration to obtain the ideal growth rate sequence for each day.

[0009] According to the difference between the actual growth rate in the growth rate sequence of the day and the ideal growth rate at the same moment in the ideal growth rate sequence of each day, the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day is obtained;

[0010] According to the degree of deviation of the light intensity at each moment in the historical day and the corresponding moment of the current day, the similarity between the weather conditions of the historical day and the current day is obtained; according to the similarity between the weather conditions of the historical day and the current day, and the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the current day, the fitting index of the growth rate at each moment of the current day is obtained; according to the difference between the fitting index of the growth rate at all moments of the current day and the actual growth rate in the growth rate sequence, the difference evaluation of the growth rate at each moment of the current day is obtained;

[0011] The growth rate data model is reconstructed according to the difference evaluation of the growth rate at all times of the day to obtain a new growth rate data model, and the new growth rate data model is used to realize data management of the plant growth link.

[0012] Furthermore, the step of obtaining the ideal growth rate sequence for each day includes:

[0013] Obtaining the diurnal state of plant growth at each moment of the day, wherein the diurnal state is divided into day and night;

[0014] The plant growth time at the i-th moment of the day is during the day, and the product of the inversely proportional normalized value of the carbon dioxide concentration increment at the i-th moment of the day and the normalized value of the light intensity increment at the i-th moment of the day, and the sum of the inversely proportional normalized value of the carbon dioxide concentration increment at the i-th moment of the day, is recorded as the ideal growth rate at the i-th moment of the day;

[0015] The plant growth time at the i-th moment of the day is at night, and the inverse proportional normalized value of the carbon dioxide concentration increment at the i-th moment of the day is recorded as the ideal growth rate at the i-th moment of the day.

[0016] Furthermore, obtaining the diurnal state of plant growth at each moment of the day includes:

[0017] When the normalized value of the light intensity increment at the i-th moment of the day is greater than the preset judgment threshold, the day and night state of the plant growth at the i-th moment of the day is recorded as daytime;

[0018] When the normalized value of the light intensity increment at the i-th moment of the day is less than or equal to the preset judgment threshold, the day and night state of the plant growth at the i-th moment of the day is recorded as night.

[0019] Furthermore, the obtaining of the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day includes:

[0020] According to the DTW algorithm, the DTW distance between the fth growth rate in the growth rate sequence of the day and the fth ideal growth rate in the ideal growth rate sequence of the day is obtained;

[0021] The calculation formula for the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day is:

[0022]

[0023] Where Z represents the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day; d f represents the DTW distance between the fth growth rate in the growth rate sequence of the day and the fth ideal growth rate in the ideal growth rate sequence of the day; F represents the number of growth rates in the growth rate sequence of the day and the ideal growth rate sequence; norm() represents the linear normalization function.

[0024] Furthermore, obtaining the difference evaluation of the growth rate at each moment of the day includes:

[0025] The absolute value of the difference between the fitting index of the growth rate at the nth moment of the day and the growth rate at the nth moment of the day is normalized, and the normalized result is recorded as the difference evaluation of the growth rate at the nth moment of the day.

[0026] Furthermore, the specific steps for obtaining the fitting index of the growth rate are:

[0027] The calculation formula for the fitting index of the growth rate at the nth moment of the day is:

[0028]

[0029] Among them, K n represents the fitting index of the growth rate at the nth moment of the day; B h,n represents the growth rate at the nth moment on the hth day in the ideal growth rate sequence; C h Indicates the similarity between the weather conditions on the hth day and today; Z h It indicates the similarity between the ideal growth rate sequence on the hth day and the growth rate sequence on the current day; H represents the number of days collected.

[0030] Furthermore, the specific steps for obtaining the similarity between the weather conditions on the hth day and the current day are:

[0031] The calculation formula for the similarity between the weather conditions on the hth day and today is:

[0032]

[0033] Among them, C h Indicates the similarity between the weather conditions on the hth day and today; N h represents the number of collection moments in the hth day; G h,n represents the light intensity at the nth moment of the hth day; G nRepresents the light intensity at the nth moment of the day; exp[] represents an exponential function with a natural constant as the base.

[0034] Further, the obtaining of a new growth rate data model includes:

[0035] If the growth rate of the day is a normal growth rate, the existing growth rate data model is used as a new growth rate data model; if the growth rate of the day is an abnormal growth rate, the growth data of all days including the day and before the day are used as the basic data for constructing the data model to construct a new growth rate data model.

[0036] Furthermore, the specific method of obtaining the normal growth rate and the abnormal growth rate is:

[0037] When the difference evaluation of the growth rate at all times of the day is greater than or equal to the preset abnormal threshold, the growth rate of the day is recorded as the abnormal growth rate;

[0038] When the difference evaluation of the growth rate at all times of the day is less than the preset abnormal threshold, the growth rate of the day is recorded as the normal growth rate.

[0039] The present invention also proposes a data management system for monitoring plant growth stages, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned data management method for monitoring plant growth stages.

[0040] The beneficial effects of the technical solution of the present invention are: obtaining the carbon dioxide concentration, light intensity and growth rate at each moment of all days, and constructing a growth rate data model according to the growth rate; quantifying the growth rate of diurnal plants according to the difference in light intensity during the day and night in combination with the increment of carbon dioxide concentration, and obtaining an ideal growth rate sequence for each day, the purpose of which is to further understand the intensity of the influence of carbon dioxide concentration on the growth rate and provide a basis for plant growth optimization; obtaining the degree of similarity between the ideal growth rate sequence for each day and the growth rate sequence for each day according to the difference between the actual growth rate in the growth rate sequence for that day and the ideal growth rate at the same moment in the ideal growth rate sequence for that day, the purpose of which is to evaluate the relationship between the actual growth rate and the quantified ideal growth rate and to judge whether the plant growth rate is abnormal; and The degree of deviation of the light intensity at each moment in the historical day from the corresponding moment of the current day is used to obtain the similarity between the weather conditions of the historical day and the current day; the fitting index of the growth rate at each moment of the current day is obtained based on the similarity between the weather conditions of the historical day and the current day, and the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the current day; the difference evaluation of the growth rate at each moment of the current day is obtained based on the difference between the fitting index of the growth rate at all moments of the current day and the actual growth rate in the growth rate sequence, in order to evaluate the abnormality of the growth rate of the current day; the growth rate data model is reconstructed based on the difference evaluation of the growth rate at all moments of the current day to obtain a new growth rate data model, and the new growth rate data model is used to realize data management in the plant growth link, so as to optimize the management of monitoring data in the plant growth link. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0042] Figure 1 The present invention is a flowchart of the steps of a data management method for monitoring plant growth stages. DETAILED DESCRIPTION

[0043] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a data management method and system for monitoring plant growth stages proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0044] 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.

[0045] The following is a detailed description of a data management method and system for monitoring plant growth stages provided by the present invention in conjunction with the accompanying drawings.

[0046] See also Figure 1 , which shows a flow chart of the steps of a data management method for plant growth monitoring provided by an embodiment of the present invention, the method comprising the following steps:

[0047] Step S001: Obtain the carbon dioxide concentration, light intensity and growth rate at every moment of all days, and construct a growth rate data model according to the growth rate.

[0048] Using a height measuring instrument, the plant height is collected once every hour. The growth rate of the plant at each moment is obtained based on the difference between the plant height at each moment and the plant height at the previous moment. The growth rate sequence composed of the growth rates at all moments in a day is collected, and the growth rate sequence of all days in the most recent month is collected.

[0049] By collecting the growth rate in the time series, a growth rate data model is established for the growth rate in the time series using linear fitting to describe the relationship between the growth rate and time. The growth rate data model can predict the future growth trend of the plant and analyze the trend of the growth rate and time change; since the change in growth rate is linearly related, this embodiment uses the ARIMA prediction model to construct the growth rate data model.

[0050] While collecting the plant height, use the carbon dioxide concentration sensor and light intensity sensor to collect the carbon dioxide concentration and light intensity during the plant growth process once every hour. Collect the carbon dioxide concentration and light intensity at all times of the day. The carbon dioxide concentration at all times of the day constitutes the daily carbon dioxide concentration data sequence. Collect the carbon dioxide concentration data sequence of all days in the past month and the light intensity at each moment of each day.

[0051] Step S002: According to the difference in light intensity between day and night, the growth rate of the diurnal plant is quantified in combination with the increment of carbon dioxide concentration to obtain the ideal growth rate sequence for each day.

[0052] It should be noted that during the plant growth process, plants will perform aerobic respiration and photosynthesis. Plants will absorb oxygen and release carbon dioxide while performing aerobic respiration, and absorb carbon dioxide when performing photosynthesis. Therefore, the growth rate of plants can be observed based on the increase in carbon dioxide concentration. In order to further understand the intensity of the impact of carbon dioxide concentration on growth rate and provide a basis for plant growth optimization, it is necessary to quantify the relationship between carbon dioxide concentration and growth rate. If the quantification results show that carbon dioxide concentration has a greater impact on growth rate, changing the supply of carbon dioxide can maximize the growth rate of plants.

[0053] It should be further explained that during the sunrise stage, the light intensity begins to increase, the plants begin to photosynthesize, and photosynthesis begins to absorb carbon dioxide. The carbon dioxide concentration increases more slowly than at night, and after gradually reaching a steady state, the photosynthesis rate is greater than the respiration rate, the carbon dioxide in the air gradually decreases, and the oxygen content gradually increases. Oxygen provides oxygen molecules for the plants to breathe, so the plant growth rate increases relatively slowly. During the day to sunset period, the light intensity gradually reaches the highest period, the photosynthesis rate reaches the highest point, photosynthesis is greater than respiration, the carbon dioxide concentration gradually decreases, the oxygen content increases, and the generated oxygen is first supplied to the aerobic respiration of the plant, rather than being discharged, so aerobic respiration reaches the maximum value and the growth rate is the fastest, which has a significant gain effect on the plant growth rate, that is, the plant growth rate increases. After sunset, the plant is in the dark and cannot perform photosynthesis. It mainly relies on aerobic respiration to maintain basic life activities, releasing a large amount of carbon dioxide, the carbon dioxide concentration increases, and the plant growth rate is further reduced or even stagnant. Therefore, the carbon dioxide concentration and growth rate show a negative correlation within a day.

[0054] At different stages of the day, the increment of carbon dioxide concentration is different from the increment of light intensity. At sunrise, the increment of carbon dioxide concentration gradually decreases, while the increment of light intensity gradually increases, and the values ​​are positively correlated; from day to sunset, the increment of carbon dioxide concentration gradually decreases, while the increment of light intensity gradually increases, and the values ​​are positively correlated; after sunset, the increment of carbon dioxide concentration gradually increases, while the increment of light intensity remains unchanged, and the growth rate only refers to the carbon dioxide concentration. It is necessary to determine whether the plant growth is during the day or at night based on the increment of light intensity, and then quantify the growth rate based on the different increments of carbon dioxide concentration during the day and at night.

[0055] Specifically, the judgment threshold a=0.5 preset in this embodiment is used as an example for description. Other implementations may be set to other values, which are not limited in this embodiment. The day and night state of plant growth at each moment of the day is judged according to the light intensity increment: when the normalized value of the light intensity increment at the i-th moment of the day is greater than the preset judgment threshold a, the day and night state of plant growth at the i-th moment is recorded as daytime; when the normalized value of the light intensity increment at the i-th moment of the day is less than or equal to the preset judgment threshold a, the day and night state of plant growth at the i-th moment is recorded as nighttime.

[0056] The growth rate of plants during the day and at night is quantified by the increase in carbon dioxide concentration. The ideal growth rate at the i-th moment of the day is calculated as:

[0057]

[0058] Among them, A i represents the ideal growth rate at the i-th moment of the day; d i represents the normalized value of the light intensity increment at the i-th moment of the day; a represents the preset judgment threshold; b i It represents the normalized value of the incremental carbon dioxide concentration at the i-th moment of the day; exp() represents an exponential function with a natural constant as the base; || represents the absolute value function.

[0059] Similarly, the ideal growth rate at each moment of the day is obtained, and the ideal growth rates at all moments of the day constitute an ideal growth rate sequence, which reflects the expected growth rates under different carbon dioxide concentrations.

[0060] Step S003: According to the difference between the actual growth rate in the growth rate sequence of the day and the ideal growth rate at the same moment in the ideal growth rate sequence of each day, the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day is obtained.

[0061] It should be noted that the growth rate sequence is a data sequence obtained based on the actual growth height of the plant, while the ideal growth rate sequence is a sequence of ideal growth rates obtained by quantifying the actual growth rate with carbon dioxide based on the relationship between carbon dioxide concentration and growth rate. In addition to carbon dioxide and light, there are other possible environmental factors that affect plant growth. In order to evaluate the relationship between the actual growth rate and the quantified ideal growth rate and determine whether the plant growth rate is abnormal, it is necessary to calculate the similarity between the growth rate sequence and the ideal growth rate sequence. If the two sequences are not similar, it indicates that the plant is growing poorly in the current environment and the growth rate may be abnormal.

[0062] Specifically, the DTW distance between the fth growth rate in the growth rate sequence of the day and the fth ideal growth rate in the ideal growth rate sequence of the day is obtained according to the DTW algorithm, wherein the DTW algorithm is a well-known technology and the specific method is not introduced here. The calculation formula for the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day is:

[0063]

[0064] Where Z represents the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day; d f represents the DTW distance between the fth growth rate in the growth rate sequence of the day and the fth ideal growth rate in the ideal growth rate sequence of the day; F represents the number of growth rates in the growth rate sequence of the day and the ideal growth rate sequence; norm() represents the linear normalization function, which normalizes the data values ​​to the interval [0,1].

[0065] It represents the average value of the DTW distance between the growth rate and the ideal growth rate. The larger the value, the greater the deviation between the growth rate of the day and the ideal growth rate, that is, the lower the overall similarity between the growth rate sequence of the day and the ideal growth rate sequence.

[0066] Similarly, according to the above steps, the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day can be obtained.

[0067] Step S004: According to the degree of deviation between the light intensity at each moment in the historical day and the corresponding moment of the current day, obtain the similarity between the weather conditions of the historical day and the current day; according to the similarity between the weather conditions of the historical day and the current day, and the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the current day, obtain the fitting index of the growth rate at each moment of the current day; according to the difference between the fitting index of the growth rate at all moments of the current day and the actual growth rate in the growth rate sequence, obtain the difference evaluation of the growth rate at each moment of the current day.

[0068] It should be noted that in order to better manage the data monitored during the plant growth process and determine whether the constructed data model needs to be updated, it is necessary to determine the abnormality of the current growth rate. Before determining the abnormality of the current growth rate, it is necessary to obtain the fitting index of the growth rate at all times of the day. Due to the different weather conditions every day, the photosynthesis and growth performance of plants are affected. On sunny days, there is sufficient light, and plants can absorb a large amount of light energy for photosynthesis, which increases the growth rate of plants; on cloudy days, there is insufficient light, and the photosynthesis capacity of plants is limited, which affects the growth rate of plants. It is not possible to determine whether the current growth rate is abnormal. Therefore, it is necessary to select days with similar weather conditions to the current day in the historical weather conditions as one of the factors to determine whether the current growth rate is abnormal.

[0069] Furthermore, among the historical weather conditions, historical days similar to the weather conditions of the current day are selected, and the degree of similarity is obtained based on the degree of deviation between the light intensity at each moment in the historical day and the light intensity at the corresponding moment of the current day. The smaller the degree of deviation, the more similar the light intensity distribution of the historical day is to that of the current day, that is, the weather conditions of the historical day are relatively consistent with the weather conditions of the current day.

[0070] Specifically, the calculation formula for the similarity between the weather conditions on the hth day and the current day is:

[0071]

[0072] Among them, C h Indicates the similarity between the weather conditions on the hth day and today; N h represents the number of collection moments in the hth day; G h,n represents the light intensity at the nth moment of the hth day; G n Represents the light intensity at the nth moment of the day; exp[] represents an exponential function with a natural constant as the base.

[0073] It represents the average value of the deviation between the light intensity at each moment in the hth day and the overall light intensity of the day. The larger the value, the greater the deviation between the light intensity on the hth day and the light intensity on the day, and the smaller the similarity. h The larger the value of , the greater the similarity between the weather conditions on the hth day and today.

[0074] It should be further explained that the similarity between the growth rate sequence and the ideal growth rate sequence is used to measure the closeness between the actual growth rate and the expected growth rate, reflecting whether the growth situation meets expectations. The closer the growth situation is, the lower the degree of abnormality may be. The similarity between the day with the most similar weather conditions in history and the current day measures the similarity between the current weather conditions and the past historical weather, reflecting whether the environmental conditions are conducive to normal growth. The more similar the weather is, the more favorable it may be for growth and the lower the degree of abnormality may be.

[0075] Taking the similarity between the weather conditions on the hth day and the current day, and the similarity between the ideal growth rate sequence on the hth day and the growth rate sequence on the current day as the reference weights for the growth rate at the nth moment on the hth day in the ideal growth rate sequence can illustrate the comprehensive effect of the growth rate being affected by the growth conditions and environmental conditions.

[0076] The calculation formula for the fitting index of the growth rate at the nth moment of the day is:

[0077]

[0078] Among them, K n represents the fitting index of the growth rate at the nth moment of the day; B h,n represents the growth rate at the nth moment on the hth day in the ideal growth rate sequence; C h Indicates the similarity between the weather conditions on the hth day and today; Z h It indicates the similarity between the ideal growth rate sequence on the hth day and the growth rate sequence on the current day; H represents the number of days collected.

[0079] C h ×Z h represents the reference weight of the growth rate on day h, B h,n ×C h ×Z h The growth rate at the nth moment on the hth day in the ideal growth rate sequence is fitted based on the reference weight of the growth rate on the hth day to obtain the fitting index of the growth rate at the nth moment on the same day.

[0080] Similarly, the fitting index of the growth rate at all times of the day is obtained according to the above method.

[0081] By comparing the difference between the fitted index of the growth rate at all times of the day and the actual growth rate, the abnormal degree of the current growth rate is determined.

[0082] M n =norm|K n -L n |

[0083] Among them, M nK represents the difference evaluation of the growth rate at the nth moment of the day; n represents the fitting index of the growth rate at the nth moment of the day; L n represents the growth rate at the nth moment of the day; norm|| represents the linear normalization function, which normalizes the data values ​​to the range [0,1].

[0084] M n The larger the value of M is, the greater the possibility that the growth rate at the nth moment of the day is abnormal. n The smaller the value of , the smaller the possibility that the growth rate at the nth moment of the day is abnormal.

[0085] Step S005: Reconstruct the growth rate data model according to the difference evaluation of the growth rates at all times of the day to obtain a new growth rate data model, and use the new growth rate data model to implement data management of the plant growth link.

[0086] According to the above steps, the difference evaluation of the growth rate at the current moment is obtained, and a threshold judgment is performed on the difference evaluation of the growth rate at the current moment to obtain the abnormal condition of the current growth rate.

[0087] The abnormal threshold value b=0.4 preset in this embodiment is used as an example for description. Other implementations may be set to other values, which are not limited in this embodiment. If the difference evaluation of the growth rate at all times of the day is greater than or equal to the preset abnormal threshold value b, the growth rate of the day is recorded as an abnormal growth rate; if the difference evaluation of the growth rate at all times of the day is less than the preset abnormal threshold value b, the growth rate of the day is recorded as a normal growth rate.

[0088] If the growth rate of the day is normal, the existing growth rate data model is used as a new growth rate data model; if the growth rate of the day is abnormal, the growth data of all days including the day and before the day are used as the basic data for building the data model to build a new growth rate data model. The new growth rate data model is stored, and the stored data is replaced by the stored model to achieve the purpose of optimizing the monitoring data management in the plant growth link.

[0089] So far, the present invention is completed.

[0090] To sum up, in an embodiment of the present invention, the present invention also provides a data management system for monitoring plant growth stages, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned data management method for monitoring plant growth stages.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data management method for monitoring plant growth stages, characterized in that: The method comprises the following steps: Obtain the carbon dioxide concentration, light intensity and growth rate at every moment of all days, and build a growth rate data model based on the growth rate; According to the difference in light intensity between day and night, the growth rate of diurnal plants is quantified in combination with the increment of carbon dioxide concentration to obtain the ideal growth rate sequence for each day. According to the difference between the actual growth rate in the growth rate sequence of the day and the ideal growth rate at the same moment in the ideal growth rate sequence of each day, the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day is obtained; According to the degree of deviation of the light intensity at each moment in the historical day and the corresponding moment of the current day, the similarity between the weather conditions of the historical day and the current day is obtained; according to the similarity between the weather conditions of the historical day and the current day, and the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the current day, the fitting index of the growth rate at each moment of the current day is obtained; according to the difference between the fitting index of the growth rate at all moments of the current day and the actual growth rate in the growth rate sequence, the difference evaluation of the growth rate at each moment of the current day is obtained; The growth rate data model is reconstructed according to the difference evaluation of the growth rate at all times of the day to obtain a new growth rate data model, and the new growth rate data model is used to realize the data management of the plant growth link; The method of obtaining the ideal growth rate sequence for each day includes: Obtaining the diurnal state of plant growth at each moment of the day, wherein the diurnal state is divided into day and night; The day When the plant growth time is during the day, The inverse normalized value of the carbon dioxide concentration increment at the time and the day The product of the normalized value of the light intensity increment at the time and the The sum of the inversely proportional normalized values ​​of the carbon dioxide concentration increment at the time is recorded as the day's The ideal growth rate at a given moment; The day When the plant growth time is at night, The inverse proportional normalized value of the carbon dioxide concentration increment at the time is recorded as the day's The ideal growth rate at any given moment.

2. A data management method for monitoring plant growth according to claim 1, characterized in that: The obtaining of the diurnal state of plant growth at each moment of the day comprises: The day When the normalized value of the light intensity increment at the moment is greater than the preset judgment threshold, the day’s The diurnal state of plant growth at that moment was recorded as daytime; The day When the normalized value of the light intensity increment at the moment is less than or equal to the preset judgment threshold, the day’s The diurnal state of plant growth at that moment is recorded as night.

3. A data management method for monitoring plant growth according to claim 1, characterized in that: The obtaining of the similarity between the ideal growth rate sequence of each day and the growth rate sequence of the day comprises: According to the DTW algorithm, the growth rate sequence of the day is obtained. The growth rate is the same as the ideal growth rate sequence for the day. The DTW distance of an ideal growth rate; The calculation formula for the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day is: ； in, Indicates the similarity between the ideal growth rate sequence of the day and the growth rate sequence of the day; Indicates the number of the growth rate sequence for the day The growth rate is the same as the ideal growth rate sequence for the day. The DTW distance of an ideal growth rate; Indicates the number of growth rates in the growth rate sequence of the day and the ideal growth rate sequence; represents the linear normalization function.

4. The data management method for plant growth monitoring according to claim 1, characterized in that: The differential evaluation of the growth rate at each moment of the day comprises: The day's The fitting index of the growth rate at the moment is consistent with the The absolute value of the difference in growth rate at the time is normalized, and the normalized result is recorded as the first Evaluation of differences in growth rate at different moments.

5. A data management method for monitoring plant growth stages according to claim 4, characterized in that: The specific steps for obtaining the fitting index of the growth rate are: The day The calculation formula of the fitting index of the growth rate at the moment is: ； in, Indicates the day The fitting index of the growth rate at the moment; represents the ideal growth rate sequence Tiandi The growth rate at that moment; Indicates the similarity of the weather conditions of the day to the current day; Indicates The degree of similarity between the ideal growth rate sequence for the day and the growth rate sequence for the day; Indicates the number of days of collection.

6. A data management method for monitoring plant growth stages according to claim 5, characterized in that: The said The specific steps for obtaining the similarity between the weather conditions of the day and the current day are: No. The formula for calculating the similarity between the weather conditions of the day and the day is: ； in, Indicates the similarity of the weather conditions of the day to the current day; Indicates The number of collection moments per day; Indicates Day's Light intensity at the time; Indicates the day's Light intensity at the time; Represents an exponential function with a natural constant as its base.

7. A data management method for monitoring plant growth stages according to claim 1, characterized in that: The obtaining of a new growth rate data model comprises: If the growth rate of the day is a normal growth rate, the existing growth rate data model is used as a new growth rate data model; if the growth rate of the day is an abnormal growth rate, the growth data of all days including the day and before the day are used as the basic data for constructing the data model to construct a new growth rate data model.

8. A data management method for monitoring plant growth stages according to claim 7, characterized in that: The specific method for obtaining the normal growth rate and the abnormal growth rate is: When the difference evaluation of the growth rate at all times of the day is greater than or equal to the preset abnormal threshold, the growth rate of the day is recorded as the abnormal growth rate; When the difference evaluation of the growth rate at all times of the day is less than the preset abnormal threshold, the growth rate of the day is recorded as the normal growth rate.

9. A data management system for monitoring plant growth stages, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a data management method for monitoring plant growth stages as described in any one of claims 1 to 8 are implemented.

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