Intelligent control method and system for LED plant growth lamp

Abstract

The invention discloses an intelligent control method and system for an LED plant growth lamp, and the method comprises the steps: arranging environment data according to the sequence of collection time, and obtaining an environment sequence; splitting the environment sequence to obtain a window, and arranging according to the quantity of the environment data in the window to obtain a quantity sequence; performing feature extraction on the environment sequence and the number sequence to obtain a number sequence feature and an environment sequence feature, performing feature value calculation to obtain an environment feature value, and combining the environment feature value with the plant feature vector to obtain a fusion feature vector; performing spectrum combination adjustment according to the position index of the corresponding optical wavelength in the fusion feature vector; and obtaining the growth stage of the plant according to the plant type, carrying out matching based on the plant type, the growth stage and the adjusted spectrum distribution to obtain a spectrum proportion and a light period, and generating a control instruction to control the LED plant growth lamp. The method can meet the growth requirements of different plants in different periods.

Description

Technical Field

[0001] The present invention relates to the field of LED lamp control, and in particular to an intelligent control method and system for an LED plant growth lamp. Background Art

[0002] Currently, LED plant growth lights are lamps that use light-emitting diode technology to provide the light needed for plant growth. This type of lamp is often used in the agricultural field to simulate sunlight and promote plant growth, flowering and fruiting. Traditional plant growth lights use visible light, while LED plant growth lights use more advanced LED technology, which enables them to more accurately simulate natural sunlight and provide the red and blue light required for plant growth. Compared with traditional plant growth lights, LED plant growth lights have many advantages.

[0003] In one existing technology, LED plant growth lights are usually controlled by relying on manual experience or preset light cycles, using simple rules or a single control strategy. This often lacks consideration of specific plant needs and environmental data, and cannot meet the growth needs of different plants at different times, which may lead to insufficient or excessive light, resulting in unstable plant growth conditions and affecting plant growth and yield.

[0004] In summary, the existing control methods of LED plant growth lamps cannot meet the growth needs of different plants at different times, and are likely to lead to insufficient or excessive light, affecting the growth and yield of plants. Summary of the invention

[0005] The present invention provides an intelligent control method and system for an LED plant growth lamp to meet the growth requirements of different plants at different stages.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent control method of an LED plant growth lamp, comprising: Acquire plant species data, environmental data, and a collection time corresponding to the environmental data, wherein the plant species data includes a plant type and a plant feature vector; Arranging the environmental data in chronological order of collection time to obtain an environmental sequence; Splitting the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence; Extracting features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; Calculating characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values; Combining the environmental feature value with the plant feature vector to obtain a fused feature vector; According to the position index of the corresponding light wavelength in the fused feature vector, the spectrum combination is adjusted to obtain an adjusted spectrum distribution; Acquiring a growth stage of the plant according to the plant type, and matching the plant type, the growth stage, and the adjusted spectral distribution to obtain a spectral ratio and a photoperiod; A control instruction is generated according to the spectrum ratio and the light cycle to control the LED plant growth lamp.

[0007] In an optional implementation manner, the acquiring of plant species data, environmental data, and a collection time corresponding to the environmental data includes: Collecting plant pictures placed in the plant planting device, and classifying the plants according to the characteristics of the plants in the plant pictures to obtain the plant types; By analyzing the plant picture, shape features, texture features and color features are extracted to construct a plant feature vector; The environmental data including temperature, humidity, carbon dioxide concentration and light intensity are collected by an environmental information monitoring device, and the corresponding collection time is recorded.

[0008] In an optional implementation, splitting the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence includes: Acquire a period length from the environment sequence, and split the environment sequence according to the period length to obtain a plurality of subsequences; Calculate the switching coefficient of each environmental data according to the adjacent elements in the subsequence, and split the subsequence to obtain a window; The data in each window is counted to obtain the quantity of environmental data, and all windows are arranged from large to small according to the quantity of environmental data to obtain a quantity sequence.

[0009] In an optional implementation, splitting the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence includes: Acquire a period length from the environment sequence, and split the environment sequence according to the period length to obtain a plurality of subsequences; Calculate the switching coefficient of each environmental data according to the adjacent elements in the subsequence, and split the subsequence to obtain a window; The data in each window is counted to obtain the quantity of environmental data, and all windows are arranged from large to small according to the quantity of environmental data to obtain a quantity sequence.

[0010] In an optional implementation, the extracting features of the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features includes: Arrange the quantity sequence in descending order of elements, and obtain the quantity sequence feature by rounding the difference between each element and the window value on the left side of each element or the quotient obtained by the ratio of adjacent elements; In the environmental sequence, the time period length of each environmental data is measured according to the acquisition time, the sequence length of adjacent environmental data within the period is extracted, and the variance value of the environmental data is calculated; Determine whether there is a change in the environmental data category within each time period, If there is a change in the category of environmental data, the environmental sequence is directly used as the data sequence; If there is no change in the category of the environmental data, the sequence length of each time period in the environmental sequence is obtained according to the variance value of the environmental data, each time period is divided according to the sequence length to obtain a subsequence, the switching coefficient of adjacent environmental data in the subsequence is obtained, each subsequence is split according to the switching coefficient, and the split environmental sequence is used as the data sequence; In an optional implementation, the calculating of characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values ​​includes: Calculate the mean value, standard deviation and variance according to the environmental sequence characteristics and the quantity sequence characteristics, and use the mean value, standard deviation and variance as environmental characteristic values; The mean value, standard deviation and variance are calculated by the following formula:

[0011]

[0012]

[0013] in, , , denote the mean value, the standard deviation and the variance respectively, For the The environmental sequence characteristics, For the The quantity sequence characteristics, is the total number of feature items.

[0014] In an optional implementation, performing spectrum combination adjustment according to the position index of the corresponding light wavelength in the fused feature vector to obtain the adjusted spectrum distribution includes: The adjusted spectral distribution is obtained by the following formula:

[0015] in, represents the adjusted spectral distribution, is the number of light wavelengths, For the The value of wavelength, is the duration of darkness in the photoperiod, is the photoperiod, The wavelength of light The corresponding eigenvalues ​​are The first The length of the wavelength of light, The wavelength of light The position index of the corresponding element in the plant feature vector.

[0016] In an optional implementation, the merging of the environmental feature value and the plant feature vector to obtain a fused feature vector includes: The environmental characteristic value and the plant characteristic vector are fused by using a weighted average method to obtain fused data of each environmental data; A sequence feature is obtained according to the environment sequence and the fused data, a feature is extracted from the fused data according to the sequence feature to obtain a fused feature, and the fused feature and the sequence feature are combined to obtain a fused feature vector.

[0017] In a second aspect, the present invention provides an intelligent control system for an LED plant growth lamp, comprising: A data acquisition module, used to acquire plant species data, environmental data and a collection time corresponding to the environmental data, wherein the plant species data includes plant type and plant feature vector; A sorting module, used to arrange the environmental data in the order of collection time to obtain an environmental sequence; A splitting and sorting module, used for performing a splitting operation on the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence; A feature extraction module is used to extract features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; A calculation module, used to calculate characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values; A fusion module, used for merging the environmental feature value with the plant feature vector to obtain a fused feature vector; A spectrum combination adjustment algorithm module, used to adjust the spectrum combination according to the position index of the corresponding light wavelength in the fusion feature vector to obtain an adjusted spectrum distribution; The result output module is used to obtain the growth stage of the plant according to the plant type, and based on the plant type, the growth stage and the adjusted spectral distribution, match the spectral ratio and the light cycle, and then generate a control instruction to control the LED plant growth lamp.

[0018] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent control method of an LED plant growth lamp described in any one of the above is implemented.

[0019] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent control methods for LED plant growth lamps.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The invention discloses an intelligent control method for an LED plant growth lamp, comprising the steps of acquiring plant species data, environmental data and a collection time corresponding to the environmental data, wherein the plant species data comprises a plant type and a plant feature vector; arranging the environmental data in the order of the collection time to obtain an environmental sequence; performing a split operation on the environmental sequence to obtain a window, and arranging the environmental data in the window according to the number of environmental data to obtain a quantity sequence; performing feature extraction on the environmental sequence and the quantity sequence to obtain a quantity sequence feature and an environmental sequence feature; performing feature value calculation according to the environmental sequence feature and the quantity sequence feature to obtain an environmental feature value; merging the environmental feature value with the plant feature vector to obtain a fused feature vector; performing spectrum combination adjustment according to a position index corresponding to a light wavelength in the fused feature vector to obtain an adjusted spectrum distribution; acquiring a plant growth stage according to the plant type, and matching the plant type, the growth stage and the adjusted spectrum distribution to obtain a spectrum ratio and a light cycle; and generating a control instruction according to the spectrum ratio and the light cycle to control the LED plant growth lamp. The invention disclosed in the invention discloses an intelligent control method for LED plant growth lamps, which can automatically generate control instructions according to preset plant species and environmental data, thereby controlling the LED plant growth lamps, and can meet the growth needs of different plants at different times. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a schematic flow chart of an intelligent control method for an LED plant growth lamp provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an intelligent control system of an LED plant growth lamp provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 The first embodiment of the present invention provides an intelligent control method for an LED plant growth lamp, comprising the following steps: S11, acquiring plant species data, environmental data, and a collection time corresponding to the environmental data, wherein the plant species data includes a plant type and a plant feature vector; S12, arranging the environmental data in the order of collection time to obtain an environmental sequence; S13, splitting the environment sequence to obtain a window, and arranging the windows according to the number of environmental data in the window to obtain a quantity sequence; S14, performing feature extraction on the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; S15, performing characteristic value calculation according to the environmental sequence characteristics and the quantity sequence characteristics to obtain an environmental characteristic value; S16, merging the environmental feature value with the plant feature vector to obtain a fused feature vector; S17, adjusting the spectrum combination according to the position index of the corresponding light wavelength in the fused feature vector to obtain an adjusted spectrum distribution; S18, obtaining a growth stage of the plant according to the plant type, and matching the plant type, the growth stage and the adjusted spectral distribution to obtain a spectral ratio and a photoperiod; S19, generating a control instruction according to the spectrum ratio and the light cycle to control the LED plant growth lamp.

[0023] In step S11, plant species data, environmental data, and a collection time corresponding to the environmental data are obtained, wherein the plant species data includes plant types and plant feature vectors, including: The plant images placed in the plant planting device are collected by the plant growth image acquisition device, and the collected plant images are denoised, cropped, grayed or normalized. The plant type is obtained by classifying the plant features in the plant images, including leaf shape, vein structure, leaf edge characteristics, flower morphology, number and arrangement of petals, fruit shape and size, bark texture, overall plant morphology (such as plant height, branch form) and color characteristics (color of flowers, leaves, and fruits).

[0024] The plant feature vector is constructed by extracting shape features, texture features and color features from plant pictures.

[0025] Shape features such as the circumference, area, aspect ratio, etc. of leaves, flowers, and fruits are extracted using edge detection or contour detection; texture features are extracted through gray-level co-occurrence matrix or LBP; color characteristics such as the color of leaves, flowers, and fruits are analyzed through color histogram or HSV space.

[0026] The environmental data include temperature, humidity, carbon dioxide concentration and light intensity, which are collected by an environmental information monitoring device, and the collection time corresponding to the environmental data is recorded.

[0027] In step S12, the environmental data are arranged in chronological order of collection time to obtain an environmental sequence.

[0028] By sorting the collection time corresponding to the environmental data, the environmental data can be arranged into a set of time series data to obtain the environmental sequence. For example, assuming that the collected data is: (10:00, 25°C), (10:05, 26°C), (10:10, 27°C), then after arranging in time order, the environmental sequence is: {(10:00, 25°C), (10:05, 26°C), (10:10, 27°C)}.

[0029] In step S13, the environment sequence is split to obtain windows, and the environment data in the windows are arranged according to the quantity to obtain a quantity sequence, including: Acquire a period length from the environment sequence, and split the environment sequence according to the period length to obtain a plurality of subsequences; Calculate the switching coefficient of each environmental data according to the adjacent elements in the subsequence, and split the subsequence to obtain a window; The data in each window is counted to obtain the quantity of environmental data, and all windows are arranged from large to small according to the quantity of environmental data to obtain a quantity sequence.

[0030] The calculation formula for the switching coefficient of environmental data is as follows:

[0031] Where a represents the switching coefficient, Indicates The environmental data value at a time point, R is the count sum of all environmental data values.

[0032] In step S14, feature extraction is performed on the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features, including: Arrange the quantity sequence in descending order of elements, and obtain the quantity sequence feature by rounding the difference between each element and the window value on the left side of each element or the quotient obtained by the ratio of adjacent elements; In the environmental sequence, the time period length of each environmental data is measured according to the acquisition time, the sequence length of adjacent environmental data within the period is extracted, and the variance value of the environmental data is calculated; Determine whether there is a change in the category of environmental data within each time period. If there is a change in the category of environmental data, directly use the environmental sequence as a data sequence. If there is no change in the category of environmental data, obtain the sequence length of each time period in the environmental sequence according to the variance value of the environmental data, divide each time period according to the sequence length to obtain a subsequence, obtain the switching coefficient of adjacent environmental data in the subsequence, split each subsequence according to the switching coefficient, and use the split environmental sequence as a data sequence.

[0033] Extracting features from the data sequence to obtain features of the environment sequence; Wherein, the feature extraction operation includes: Extract data distribution characteristics and obtain mean, variance and peak value; Extract data change characteristics to obtain increments, decrements and trends; Extract data switching patterns and obtain switching frequency and switching intensity.

[0034] The calculation formula of the variance value of the environmental data is:

[0035] in, Indicates The variance value of the environmental data is Indicates The environmental data are listed in The value at a time point, Indicates The average value of the environmental data is Indicates the number of time points in a time period; It should be noted that: it is determined whether there is a change in the category of environmental data within each time period. If there is a change in the category of environmental data, the environmental sequence is directly used as the data sequence. For example, assuming that the environmental sequence is: {(10:00, 25°C), (10:05, 26°C), (10:10, 27°C)}, if there is a change in the category of environmental data within the time period, the data sequence is: {(10:00, 25°C), (10:05, 26°C), (10:10, 27°C)}; If there is no change in the environmental data category, the sequence length of each time period in the environmental sequence is obtained according to the variance value of the environmental data, each time period is divided according to the sequence length to obtain a subsequence, the switching coefficient of adjacent environmental data in the subsequence is obtained, each subsequence is split according to the switching coefficient, and the split environmental sequence is used as the data sequence. Exemplarily, assuming that the environmental sequence is: {(10:00, 25°C), (10:05, 26°C), (10:10, 27°C)}, there is no change in the environmental data category within the time period, and the variance value is calculated to obtain the sequence length of each time period, for example, divided into subsequences: {(10:00, 25°C), (10:05, 26°C)} and {(10:10, 27°C)}, and then split according to the switching coefficient a, for example, when a>0.5a, the split data sequence is: {(10:00, 25°C)}, {(10:05, 26°C)}, {(10:10, 27°C)}; if a≤0.5a, the retained subsequences are: {(10:00, 25°C), (10:05, 26°C)} and {(10:10, 27°C)}.

[0036] In step S15, characteristic value calculation is performed according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values, including: Calculate the mean value, standard deviation and variance according to the environmental sequence characteristics and the quantity sequence characteristics, and use the mean value, standard deviation and variance as environmental characteristic values; The mean value, standard deviation and variance are calculated by the following formula:

[0037]

[0038]

[0039] in, , , denote the mean value, the standard deviation and the variance respectively, For the The environmental sequence characteristics, For the The quantity sequence characteristics, is the total number of feature items.

[0040] It is worth noting that the average It reflects the overall level of environmental data and quantitative data, such as the average value of temperature, humidity, carbon dioxide concentration and light intensity, which can describe the overall trend of environmental conditions and provide basic support for further analysis; standard deviation It reflects the volatility of environmental and quantitative data, such as the variation of parameters such as temperature and humidity during the measurement period. It can help determine the stability of environmental conditions and identify possible abnormal fluctuations or unstable states. The range of the data, such as the difference between the maximum and minimum values ​​of temperature or humidity, is calculated to reflect the range of variation of environmental conditions during the measurement period and help evaluate the overall fluctuation of environmental conditions.

[0041] In step S16, the environmental feature value and the plant feature vector are combined to obtain a fused feature vector, including: The environmental characteristic value and the plant characteristic vector are merged by using the weighted average method, and the fusion strategy is adjusted according to the weight distribution of different characteristics, and different weight ratios are given to the environmental characteristic value and the plant characteristic vector to reflect their different contributions to spectral adjustment.

[0042] Exemplarily, assuming that the environmental characteristic values ​​are temperature, humidity, etc., and the plant feature vector contains leaf shape, photosynthesis efficiency, etc., the environmental characteristic values ​​and the plant feature vector are merged according to the weight ratio of 40% for the environmental characteristic values ​​and 60% for the plant feature vector. The fused feature vector can be expressed as {0.4 times the environmental characteristic value, 0.6 times the plant feature vector}.

[0043] Through this flexibly adjusted merging strategy, the system can dynamically generate more accurate fusion feature vectors according to different growth stages and specific needs, provide a scientific basis for subsequent spectral adjustment and photoperiod optimization, and achieve refined control of the plant growth environment.

[0044] In step S17, the spectrum combination is adjusted according to the position index of the corresponding light wavelength in the fused feature vector to obtain the adjusted spectrum distribution. It is worth noting that: The adjusted spectral distribution is obtained by the following formula:

[0045] in, represents the adjusted spectral distribution, is the number of light wavelengths, For the The value of wavelength, is the duration of darkness in the photoperiod, is the photoperiod, The wavelength of light The corresponding eigenvalues ​​are The first The length of the wavelength of light, The wavelength of light The position index of the corresponding element in the plant feature vector.

[0046] The spectral distribution adjustment method directly combines spectral adjustment with the physiological needs of plants through the correlation between light wavelength and plant characteristics, and simultaneously incorporates the darkness duration and total duration of the photoperiod to dynamically reflect the changes in plant response to the spectrum.

[0047] By introducing the combined effect of wavelength characteristic values ​​and duration, the contribution of each wavelength to the overall spectrum is dynamically adjusted, so that the adjusted spectral distribution is more in line with the growth needs of plants, the lighting conditions are optimized, and the light utilization efficiency of plants is improved, thereby achieving scientific management and precise control of the plant growth environment.

[0048] In step S18, the growth stage of the plant is obtained according to the plant type, and based on the plant type, the growth stage and the adjusted spectral distribution, a spectral ratio and a photoperiod are matched, including: The growth stage of the plant (such as germination stage, growth stage or flowering stage) is determined according to the plant type and the current time.

[0049] Each growth stage corresponds to a specific spectral ratio. Combined with the adjusted spectral distribution, each light wavelength is proportionally matched to ensure that the output spectrum meets the needs of the plant's current growth stage. For example, in the budding stage, the plant may need more blue light to promote leaf growth, while in the flowering stage, more red light is needed to promote flowering and fruiting.

[0050] According to the plant type and the growth stage, the ratio of daily light time to dark time is determined to obtain an optimized spectrum ratio and light cycle, for example, a longer light time and a shorter dark time are required during the growth period, while more dark time may be required during the dormant period.

[0051] In this way, the spectrum ratio and photoperiod are optimized to ensure that plants receive the best light conditions at different stages.

[0052] In step S19, a control instruction is generated according to the spectrum ratio and the light cycle to control the LED plant growth lamp, including: The light output power of each wavelength is calculated according to the spectrum ratio, and converted into a PWM signal to generate a spectrum control signal to control the LED light intensity of each wavelength.

[0053] According to the light cycle length, the on and off time of the LED lamp is set, and combined with the spectrum control signal to form a complete lighting control strategy.

[0054] The spectrum control signal is combined with the light cycle signal to generate a final control instruction, which is sent to the driving system of the LED plant growth lamp to achieve automatic control of the light.

[0055] For example, during the flowering period of strawberries, the system calculates the light output power of red light, blue light and other wavelengths according to the spectrum ratio, for example, setting red light to 60%, blue light to 30%, and other wavelengths to 10%. By converting into PWM signals, spectral control signals of red light, blue light and other wavelengths are generated to control the light intensity of each wavelength LED lamp; with 16 hours of light and 8 hours of darkness per day, the system sets the LED lamp to turn on at 6 am and turn off at 10 pm according to the length of the light cycle; the generated spectral control signal is combined with the light cycle signal to form a complete control instruction: the red light PWM signal maintains high power output during the light cycle, the blue light PWM signal is slightly lower, and the signals of all wavelengths are turned off during the dark cycle.

[0056] Finally, the system sends the control command to the driving system of the LED plant growth lamp to realize automatic control of the light and accurately meet the lighting needs of strawberries during the flowering period.

[0057] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0058] In a modern agricultural greenhouse, the LED plant growth lamp intelligent control system of the present invention optimizes the growth conditions of strawberry plants to improve the yield and quality of strawberries. Advanced plant image acquisition equipment and environmental monitoring equipment are installed in the greenhouse. The system obtains plant and environmental data in real time through these devices, and dynamically adjusts the spectral distribution and photoperiod of the light according to the method of the present invention to provide accurate lighting support for different growth stages of strawberries.

[0059] At the beginning of the experiment, the basic environmental conditions of the greenhouse were set, including parameters such as temperature, humidity and ventilation, to simulate the optimal growth environment for strawberries. Subsequently, the system started the plant image acquisition device to regularly photograph the strawberry plants in the greenhouse. Through the image processing algorithm, the system extracted the plant characteristics of strawberries, including the shape, texture and color characteristics of the leaves. The leaf shape characteristics mainly include the length, width and edge morphology of the leaves; the texture characteristics reflect the roughness and texture density of the leaf surface; the color characteristics are related to the chlorophyll content and are an important indicator of the health of strawberry growth. The above features are integrated into a plant feature vector by the system to describe the growth status of strawberries.

[0060] At the same time, the environmental monitoring equipment records the environmental parameters in the greenhouse in real time, including temperature, humidity, light intensity and carbon dioxide concentration. All collected data are time-stamped, and the system sorts the data according to the collection time to generate an environmental sequence, which provides a basis for subsequent analysis.

[0061] Next, the system splits the generated environmental sequence. According to the periodic change law of the strawberry growth environment data, the environmental sequence is divided into 12-hour cycles to obtain multiple subsequences. Each subsequence represents the distribution of environmental data within a time period. In order to further analyze the changing trend of environmental data within a time period, the system calculates the switching coefficient of adjacent data. The switching coefficient is used to quantify the degree of fluctuation of environmental data over time. For example, when the temperature or humidity in the greenhouse changes frequently, the corresponding switching coefficient will be higher. By calculating the switching coefficient, the system can identify the environmental stability within each time period.

[0062] After completing the subsequence division, the system further divides the subsequence into smaller windows. The number of data points in each window is counted and a quantity sequence is generated. After sorting, the quantity sequence can reflect the intensity of changes in environmental data in different time periods. The system pays special attention to windows with large fluctuations because the environmental conditions in these windows may have an important impact on the growth of strawberries.

[0063] The system then extracts features from the environmental sequence and the quantity sequence to quantify the correlation between the greenhouse environment and the growth status of the strawberries. For the environmental sequence, the system analyzes the amplitude, mean, and variance of the data changes within each cycle. For example, in the light intensity data, the system calculates the average value and fluctuation range of daily light, and uses these data to judge the stability of the light conditions in the greenhouse. For the quantity sequence, the system extracts the data change rate or difference between adjacent windows to generate characteristic values ​​that reflect environmental fluctuations. These features are further statistically processed to generate environmental characteristic values ​​to fully characterize the current environmental conditions of the greenhouse.

[0064] The system fuses the generated environmental feature values ​​with the plant feature vectors of strawberries to comprehensively reflect the growth requirements of strawberries under current environmental conditions. The fusion process uses a weighted average method, and the system adjusts the weight distribution according to the needs of strawberries at different growth stages. For example, during the rapid growth period of strawberries, the weight of the plant feature vector is higher because the needs of leaf expansion and photosynthesis need to be met first. During the flowering and fruiting periods, the weight of the environmental feature value is increased to ensure that the optimized lighting conditions can support fruit development and sweetness accumulation. The fused feature vector is used as the basis for subsequent spectral adjustments.

[0065] The spectrum adjustment stage is one of the core functions of the system. During the flowering period of strawberries, red light plays a key role in flower development and fruit formation, while blue light helps improve the photosynthetic efficiency of plants. The system dynamically adjusts the spectrum ratio of LED lights based on the weight distribution in the fused feature vector. At this stage, the system adjusts the spectrum to 60% red light, 30% blue light, and 10% other wavelengths. This adjustment scheme ensures that the number of strawberry flowers increases and the fruit develops well. During the fruiting period, the sugar accumulation demand of strawberries increases significantly, and the system further increases the proportion of red light to 70%, reduces the proportion of blue light to 20%, and maintains 10% for other wavelengths. This spectral distribution can maximize photosynthesis and increase the sweetness of strawberry fruits.

[0066] In addition to spectrum optimization, photoperiod adjustment is also crucial. Strawberries have different requirements for photoperiods at different growth stages. During the rapid growth and flowering periods, the system sets the photoperiod to 16 hours of light and 8 hours of darkness per day to simulate natural light conditions. After entering the fruiting period, the system extends the daily light time to 18 hours and dynamically compensates for the light intensity. For example, when the intensity of natural light outside is insufficient, the system will automatically increase the output power of the LED lamp to maintain a stable photosynthesis efficiency.

[0067] Finally, the system generates complete control instructions based on the spectrum ratio and photoperiod. These instructions include the light output power of each wavelength, the duration of illumination, and the time of opening and closing, which are transmitted to the LED plant growth lamp through the driver module. The light responds to the adjustment of these instructions in real time to ensure that the strawberries are always under the best lighting conditions. The changes in spectrum adjustment and photoperiod settings can be viewed in real time through the system front-end interface.

[0068] Under the intelligent control of the system of the present invention, the quantity and quality of strawberry fruits are significantly improved. Compared with the traditional fixed light conditions, the weight of strawberry fruits increases and the sweetness improves. In addition, because the system can dynamically compensate for changes in ambient light, the overall energy consumption of the greenhouse is reduced, further verifying the advantages of the method of the present invention.

[0069] In summary, the present invention discloses an intelligent control method for an LED plant growth lamp, comprising acquiring plant species data, environmental data and a collection time corresponding to the environmental data, wherein the plant species data comprises a plant type and a plant feature vector; arranging the environmental data in chronological order of collection time to obtain an environmental sequence; performing a split operation on the environmental sequence to obtain a window, and arranging the environmental data in the window according to the number of environmental data to obtain a quantity sequence; performing feature extraction on the environmental sequence and the quantity sequence to obtain quantity sequence features and environmental sequence features; performing feature value calculation based on the environmental sequence features and the quantity sequence features to obtain an environmental feature value; merging the environmental feature value with the plant feature vector to obtain a fused feature vector; performing spectral combination adjustment based on a position index corresponding to a light wavelength in the fused feature vector to obtain an adjusted spectral distribution; acquiring the growth stage of the plant according to the plant type, and matching based on the plant type, the growth stage and the adjusted spectral distribution to obtain a spectral ratio and a light cycle; generating a control instruction based on the spectral ratio and the light cycle to control the LED plant growth lamp. The present invention discloses an intelligent control method for LED plant growth lamps, which can automatically generate control instructions according to preset plant species and environmental data, thereby controlling the LED plant growth lamps to meet the growth needs of different plants at different times. Reference Figure 2 The second embodiment of the present invention provides an intelligent control system for an LED plant growth lamp, comprising: A data acquisition module, used to acquire plant species data, environmental data and a collection time corresponding to the environmental data, wherein the plant species data includes plant type and plant feature vector; A sorting module, used to arrange the environmental data in the order of collection time to obtain an environmental sequence; A splitting and sorting module, used for performing a splitting operation on the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence; A feature extraction module is used to extract features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; A calculation module, used to calculate characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values; A fusion module, used for merging the environmental feature value with the plant feature vector to obtain a fused feature vector; A spectrum combination adjustment algorithm module, used to adjust the spectrum combination according to the position index of the corresponding light wavelength in the fusion feature vector to obtain an adjusted spectrum distribution; The result output module is used to obtain the growth stage of the plant according to the plant type, and based on the plant type, the growth stage and the adjusted spectral distribution, match the spectral ratio and the light cycle, and then generate a control instruction to control the LED plant growth lamp.

[0070] It should be noted that the intelligent control system of an LED plant growth lamp provided in an embodiment of the present invention is used to execute all the process steps of an intelligent control method of an LED plant growth lamp in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be repeated here.

[0071] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent control program for an LED plant growth lamp. When the processor executes the computer program, the steps in the above-mentioned intelligent control method embodiments of the LED plant growth lamp are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0072] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0073] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0074] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0075] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0076] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0077] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0078] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent control method for LED plant growth lamps, characterized in that: Executed by a computer, including: Acquire plant species data, environmental data, and a collection time corresponding to the environmental data, wherein the plant species data includes a plant type and a plant feature vector; Arranging the environmental data in chronological order of collection time to obtain an environmental sequence; Splitting the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence; Extracting features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; Calculating characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values; Combining the environmental feature value with the plant feature vector to obtain a fused feature vector; According to the position index of the corresponding light wavelength in the fused feature vector, the spectrum combination is adjusted to obtain an adjusted spectrum distribution; Acquiring a growth stage of the plant according to the plant type, and matching the plant type, the growth stage, and the adjusted spectral distribution to obtain a spectral ratio and a photoperiod; A control instruction is generated according to the spectrum ratio and the light cycle to control the LED plant growth lamp.

2. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The obtaining of plant species data, environmental data and a collection time corresponding to the environmental data includes: Collecting plant pictures placed in the plant planting device, and classifying the plants according to the characteristics of the plants in the plant pictures to obtain the plant types; By analyzing the plant picture, shape features, texture features and color features are extracted to construct a plant feature vector; The environmental data including temperature, humidity, carbon dioxide concentration and light intensity are collected by an environmental information monitoring device, and the corresponding collection time is recorded.

3. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The step of splitting the environment sequence to obtain windows, and arranging the windows according to the amount of environment data in the windows to obtain a quantity sequence includes: Acquire a period length from the environment sequence, and split the environment sequence according to the period length to obtain a plurality of subsequences; Calculate the switching coefficient of each environmental data according to the adjacent elements in the subsequence, and split the subsequence to obtain a window; The data in each window is counted to obtain the quantity of environmental data, and all windows are arranged from large to small according to the quantity of environmental data to obtain a quantity sequence.

4. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The step of extracting features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features includes: Arrange the quantity sequence in descending order of elements, and obtain the quantity sequence feature by rounding the difference between each element and the window value on the left side of each element or the quotient obtained by the ratio of adjacent elements; In the environmental sequence, the time period length of each environmental data is measured according to the acquisition time, the sequence length of adjacent environmental data within the period is extracted, and the variance value of the environmental data is calculated; Determine whether there is a change in the environmental data category within each time period, If there is a change in the category of environmental data, the environmental sequence is directly used as the data sequence; If there is no change in the environmental data category, the sequence length of each time period in the environmental sequence is obtained according to the variance value of the environmental data, each time period is divided according to the sequence length to obtain a subsequence, the switching coefficient of adjacent environmental data in the subsequence is obtained, each subsequence is split according to the switching coefficient, and the split environmental sequence is used as the data sequence.

5. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The step of calculating characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values ​​includes: Calculate the mean value, standard deviation and variance according to the environmental sequence characteristics and the quantity sequence characteristics, and use the mean value, standard deviation and variance as environmental characteristic values; The mean value, standard deviation and variance are calculated by the following formula: in, , , denote the mean value, the standard deviation and the variance respectively, For the The environmental sequence characteristics, For the The quantity sequence characteristics, is the total number of feature items.

6. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The step of adjusting the spectrum combination according to the position index of the corresponding light wavelength in the fused feature vector to obtain the adjusted spectrum distribution includes: The adjusted spectral distribution is obtained by the following formula: in, represents the adjusted spectral distribution, is the number of light wavelengths, For the The value of wavelength, is the duration of darkness in the photoperiod, is the photoperiod, The wavelength of light The corresponding eigenvalues ​​are The first The length of the wavelength of light, The wavelength of light The position index of the corresponding element in the plant feature vector.

7. The intelligent control method of LED plant growth lamp according to claim 1, characterized in that: The step of merging the environmental feature value with the plant feature vector to obtain a fused feature vector includes: The environmental characteristic value and the plant characteristic vector are fused by using a weighted average method to obtain fused data of each environmental data; A sequence feature is obtained according to the environment sequence and the fused data, a feature is extracted from the fused data according to the sequence feature to obtain a fused feature, and the fused feature and the sequence feature are combined to obtain a fused feature vector.

8. An intelligent control system for LED plant growth lamps, characterized in that: include: A data acquisition module, used to acquire plant species data, environmental data and a collection time corresponding to the environmental data, wherein the plant species data includes plant type and plant feature vector; A sorting module, used to arrange the environmental data in the order of collection time to obtain an environmental sequence; A splitting and sorting module, used for performing a splitting operation on the environment sequence to obtain windows, and arranging the windows according to the quantity of environment data in the windows to obtain a quantity sequence; A feature extraction module is used to extract features from the environment sequence and the quantity sequence to obtain quantity sequence features and environment sequence features; A calculation module, used to calculate characteristic values ​​according to the environmental sequence characteristics and the quantity sequence characteristics to obtain environmental characteristic values; A fusion module, used for merging the environmental feature value with the plant feature vector to obtain a fused feature vector; A spectrum combination adjustment algorithm module, used to adjust the spectrum combination according to the position index of the corresponding light wavelength in the fusion feature vector to obtain an adjusted spectrum distribution; The result output module is used to obtain the growth stage of the plant according to the plant type, and based on the plant type, the growth stage and the adjusted spectral distribution, match the spectral ratio and the light cycle, and then generate a control instruction to control the LED plant growth lamp.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the intelligent control method of the LED plant growth lamp according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent control method for the LED plant growth lamp according to any one of claims 1 to 7.

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