A Data Analysis System and Method for Optimizing Light Arrays for Plant Lighting

Abstract

This invention discloses a data analysis system and method for optimizing light arrays in plant lighting, relating to the field of light array optimization technology. The analysis method includes the following steps: pre-deploying monitoring equipment in the plant growth area to collect lighting data; dividing the plant growth area into several lighting sub-regions; evaluating the light intensity value of each lighting sub-region; comparing the differences in light intensity values ​​between different lighting sub-regions to identify several deviation areas; capturing deviation features of lighting data in any deviation area; analyzing the expected adjustment state of each deviation feature to identify the optimization needs of the deviation area; generating corresponding optimization requirement instructions for any deviation area and extracting target light arrays that meet the optimization requirements; formulating and adjusting the light array layout scheme for the target light arrays in each deviation area; thus achieving early detection and precise intervention of lighting problems.

Description

Technical Field

[0001] This invention relates to the field of light array optimization technology, specifically to a light array optimization data analysis system and method for plant lighting. Background Technology

[0002] With the rapid development of facility agriculture and plant factories, the role of artificial light sources in the plant growth process is becoming increasingly prominent. Especially in scenarios where light conditions are limited or where precise control of the light environment is required, light array lighting systems have become key equipment to ensure efficient photosynthesis in plants.

[0003] Currently, plant lighting mostly adopts uniform lamp distribution or simple arrangement based on experience. It lacks the ability to dynamically adapt to the actual light field distribution and the light requirements of plants, and lacks a refined identification and targeted optimization mechanism for regional light intensity differences. It is difficult to achieve precise control of light distribution, resulting in problems such as low light energy utilization efficiency, insufficient or excessive local light intensity, which limits the further improvement of light efficiency and plant growth quality. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis system and method for optimizing light arrays for plant lighting, in order to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing light array data for plant lighting, the method comprising the following steps:

[0006] Step S100: Pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; divide the plant growth area into several lighting sub-areas according to the plant distribution;

[0007] Step S200: Evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions to identify several deviation regions that cause light intensity deviation;

[0008] Step S300: Capture the deviation features of the lighting data in any deviation area; analyze the expected adjustment state of each deviation feature and identify the optimization needs of the deviation area;

[0009] Step S400: Generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0010] Furthermore, step S100 includes the following steps:

[0011] Step S101: Establish a two-dimensional Cartesian coordinate system in the plant growth area, determine the position of the light array installed above the plant growth area, and obtain the position coordinates of each light bead in the light array; capture the shadow range of each plant under the illumination of the light array in the plant growth area, and obtain the area range of each plant in the coordinate system; arbitrarily select the area range of a plant, summarize all the light beads whose position coordinates belong to the area range, and set it as the set of light beads of the selected plant;

[0012] Step S102: Arrange the LEDs in the light array according to the preset arrangement rules and number each LED; pre-deploy several monitoring devices, pre-set several monitoring indicators for each monitoring device, collect the lighting data of each LED in the light array under each monitoring indicator at each unit time point, and summarize the lighting data collected by each monitoring device at each unit cycle to obtain the lighting data set of each LED in any unit cycle; the preset monitoring indicators for the monitoring devices include light intensity, illumination time, illumination angle, color temperature, etc.

[0013] Step S103: Obtain the area range of each plant in the plant growth area. Randomly select two plants. If there is a common area between the area ranges of the two plants or no area range of other plants, and the two plants are of the same species, then set the two selected plants as plants in the same area. Summarize all plants in the same area and perform a union operation on the area ranges of the plants in the same area to obtain a whole area of ​​plants in the same area and set it as a lighting sub-area of ​​the plant growth area. Summarize the LED sets of each plant in any lighting sub-area to obtain the sub-light array of the lighting sub-area.

[0014] Furthermore, step S200 includes the following steps:

[0015] Step S201: Arbitrarily select a lighting sub-region to obtain a sub-lamp array for the selected lighting sub-region, and arbitrarily select a unit period to extract the lighting data set of each lamp in the sub-lamp array during the selected unit period; a lighting database is pre-constructed, which stores lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule. The monitoring indicators in the lighting data set are compared with the lighting indicators, and the monitoring indicators that are the same as the lighting indicators are set as target indicators to obtain the light intensity evaluation rules for each target indicator; the lighting indicators stored in the lighting database include illuminance, illuminance uniformity, illuminance directionality, etc., and the corresponding light intensity evaluation rules, such as illuminance, can be directly used to obtain the light intensity value, and illuminance uniformity can be obtained by the ratio of minimum light intensity to average light intensity.

[0016] Step S202: Randomly select the i-th LED from the sub-lamp array, and simultaneously extract the j-th unit time point from the selected unit period to obtain the illumination data of each target indicator for the i-th LED at the j-th unit time point. Randomly select the illumination data of the k-th target indicator, and obtain the light intensity value G of the k-th target indicator according to the light intensity evaluation rule. k (i,j), the light intensity values ​​of all target indicators are summed to obtain the light intensity value G(i,j) of the i-th LED at the j-th unit time point, according to the formula:

[0017] ;

[0018] Where a is the number of LEDs in the sub-lamp array, and b is the number of time points per unit period; the comprehensive light intensity value L of the selected lighting sub-region under the selected unit period is calculated;

[0019] Step S203: Set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same plant species as the target species, and set them as target sub-regions; arbitrarily select a target sub-region, acquire the light intensity value of the selected target sub-region in each unit cycle, and calculate the average light intensity value of the selected target sub-region; set the average light intensity value of the p-th target sub-region as L. (ave,p) According to the formula:

[0020] ;

[0021] Where M is the number of target sub-regions; the mean μ and standard deviation σ of the average light intensity values ​​among all target sub-regions are calculated;

[0022] Step S204: Preset a light intensity deviation amplitude θ, and obtain the expected deviation difference value ΔL. ex =θ×σ, if |L (ave,p) -μ|≥ΔL ex Then, the p-th target sub-region is set as the deviation region; the lighting sub-regions corresponding to various types of plants are identified, and several deviation regions of the plant growth area are generated.

[0023] Furthermore, step S300 includes the following steps:

[0024] Step S301: Randomly select a deviation area, extract the sub-lamp array of the selected deviation area, and obtain the comprehensive light intensity value L of the selected deviation area under a certain unit period. ’ The expected deviation difference of the selected deviation area is set to ΔL. ’ The mean of the relevant illumination sub-region is μ ’ If |L ’ -μ ’ |≥ΔL’ Then, the aforementioned unit period is set as the deviation period, if |L ’ -μ ’ |<ΔL ’ Then, the aforementioned unit period will be set as the normal period;

[0025] Step S302: Extract any normal period from the selected deviation area to obtain the lighting data of each monitoring indicator at each unit time point. Select any monitoring indicator to obtain the lighting data range corresponding to the selected monitoring indicator in the normal period.

[0026] Step S303: Extract a deviation period from the selected deviation area. Randomly select a unit time point from the deviation period and acquire the lighting data of the selected monitoring indicator at that unit time point. If the lighting data of the selected monitoring indicator is not within the corresponding lighting data range at the selected unit time point, then set the selected monitoring indicator as an abnormal indicator of the deviation period. Count N the number of abnormal indicators in the deviation period. exp Set the number of monitoring indicators to N. total The abnormal proportion η=N of a certain deviation period is calculated. exp / N total Preset an abnormality percentage threshold η th If η≥η th Then, the aforementioned deviation period is set as the target deviation period;

[0027] Step S304: Randomly select a target deviation period from the selected deviation area, and randomly select a lamp from the selected sub-lamp array. Extract the lighting data of each monitoring indicator of the selected lamp in the selected target deviation period. If the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, then set the certain monitoring indicator as a deviation feature to obtain several deviation features of the selected deviation area.

[0028] Step S305: Set the lighting data of a certain monitoring indicator as D1, and the corresponding lighting data range is (D ’ D ’’ The abnormal difference value of a certain monitoring indicator is obtained as ΔD = Max(D1 - D). ’’ D ’ -D1); Randomly select a deviation feature from the selected deviation region, and extract the abnormal difference value of the selected deviation feature in each target deviation period to obtain the maximum abnormal difference value ΔD of the selected deviation feature. max And according to the corresponding light intensity evaluation rules, the maximum light intensity difference ΔG corresponding to the maximum abnormal difference is obtained. maxThe maximum light intensity difference of each deviation feature is accumulated to obtain the maximum demand adjustment value ΔG for the selected deviation area. need If |L ’ -μ ’ -ΔG need |≥ΔL ’ Then, set the first optimization requirement marker for the selected deviation area, |L ’ -μ ’ -ΔG need |<ΔL ’ Then, a second optimization requirement flag is set for the selected deviation area; the first optimization requirement flag indicates that the current deviation area cannot meet the requirements through its own adjustment and needs to retrieve a new light array from other areas, while the second optimization requirement flag indicates that the deviation area can meet the requirements through the angle or brightness adjustment of its own light array.

[0029] Furthermore, step S400 includes the following steps:

[0030] Step S401: Randomly select a deviation region and obtain the optimization requirement marker for the selected deviation region; if the selected deviation region is the first optimization requirement marker, then extract the adjacent sub-regions adjacent to the selected deviation region, and extract several lamp beads from the sub-lamp arrays in the adjacent sub-regions, and accumulate the light intensity values ​​of the several lamp beads to obtain the total light intensity value G. need Set the maximum demand adjustment value ΔG for the selected deviation area. need If |G need +L ’ -μ ’ -ΔG need |<ΔL ’ If the selected area is the second optimization requirement marker, then the selected sub-array of the selected area is set as the target array.

[0031] Step S402: Generate an optimization instruction for the target light array, wherein the optimization instruction includes adjusting at least one parameter, either the brightness or the illumination direction of the lamp beads in the target light array; according to the optimization instruction, re-collect the illumination data of each lamp bead in the target light array to obtain the expected light intensity value of the target light array; if the difference between the expected light intensity value and the average value of the illumination sub-region of the same plant species is less than the expected deviation difference, then confirm the light array layout scheme and perform the adjustment; otherwise, reselect the target light array or adjust the parameters until the conditions are met.

[0032] To better implement the above methods, a data analysis system for optimizing light arrays is also proposed. The analysis system includes a regional lighting acquisition module, a light intensity difference analysis module, a deviation demand analysis module, and a light array layout optimization module.

[0033] The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; and to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0034] The light intensity difference analysis module is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions; and identify several deviation regions that cause light intensity deviation.

[0035] The deviation requirement analysis module is used to capture deviation features of lighting data in any deviation area; analyze the expected adjustment state of each deviation feature, and identify the optimization requirements of the deviation area.

[0036] The light array layout optimization module is used to generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0037] Furthermore, the area lighting analysis module includes a lighting data acquisition unit and a lighting area division unit;

[0038] The lighting data acquisition unit is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; the lighting area division unit is used to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0039] Furthermore, the light intensity difference analysis module includes a regional light intensity analysis unit and a deviation region identification unit;

[0040] The regional light intensity analysis unit is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; the deviation region identification unit is used to compare the differences in light intensity values ​​between different lighting sub-regions and identify several deviation regions that cause light intensity deviation.

[0041] Furthermore, the deviation requirement analysis module includes a deviation feature capture unit and an optimization requirement identification unit;

[0042] The deviation feature capture unit is used to capture the deviation features of lighting data in any deviation area; the optimization requirement identification unit is used to analyze the expected adjustment state of each deviation feature and identify the optimization requirements of the deviation area.

[0043] Furthermore, the light array layout optimization module includes a target light array extraction unit and a light array layout adjustment unit;

[0044] The target light array extraction unit is used to generate corresponding optimization requirement instructions for any deviation area and extract the target light array that meets the optimization requirements; the light array layout adjustment unit is used to adjust the light array layout scheme of the target light array in each deviation area.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention achieves precise perception and dynamic monitoring of light conditions in plant growth areas by finely dividing the lighting sub-regions and evaluating light intensity based on plant species and distribution characteristics. It effectively solves the problem of uneven local light intensity caused by traditional uniform lighting methods and significantly improves the pertinence and scientific nature of light environment regulation.

[0047] 2. This invention can automatically detect and locate areas of abnormal light intensity, and combine multi-dimensional lighting indicators for in-depth diagnosis, thereby accurately identifying optimization needs. It overcomes the limitations of traditional methods that rely on human experience and have a slow response, and realizes early detection and precise intervention of lighting problems.

[0048] 3. By establishing an optimization demand labeling and target lamp array extraction mechanism, the system can intelligently formulate adjustment strategies according to different deviations, support multi-parameter linkage control of lamp brightness, illumination direction, etc., and has strong adaptive optimization capabilities, effectively improving light energy utilization efficiency. Attached Figure Description

[0049] Figure 1 A schematic diagram illustrating the steps of data analysis for optimizing light arrays for plant lighting;

[0050] Figure 2 A schematic diagram of the structure of a data analysis system for optimizing light arrays for plant lighting. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example: Figures 1 to 2 As shown, this invention provides a data analysis method for optimizing light arrays for plant lighting. The analysis method includes the following steps:

[0053] Step S100: Pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; divide the plant growth area into several lighting sub-areas according to the plant distribution;

[0054] Step S100 includes the following steps:

[0055] Step S101: Establish a two-dimensional Cartesian coordinate system in the plant growth area, determine the position of the light array installed above the plant growth area, and obtain the position coordinates of each light bead in the light array; capture the shadow range of each plant under the illumination of the light array in the plant growth area, and obtain the area range of each plant in the coordinate system; arbitrarily select the area range of a plant, summarize all the light beads whose position coordinates belong to the area range, and set it as the set of light beads of the selected plant;

[0056] Example 1: A two-dimensional Cartesian coordinate system is established in the plant growth area, with the origin set at the lower left corner. The position coordinates of each LED bead are determined, for example: LED bead 1 is at (0.5, 0.5), LED bead 2 is at (0.5, 1.5), ..., LED bead 100 is at (9.5, 9.5). The area of ​​plant shadow is captured by a camera, and the area of ​​plant A is a rectangle from (1,1) to (2,2), and the area of ​​plant B is from (2,1) to (3,2). For plant A, the LED beads within the rectangle from (1,1) to (2,2) are grouped into the LED bead set of plant A.

[0057] Step S102: Arrange the LEDs in the light array according to the preset arrangement rules and number each LED; pre-deploy several monitoring devices, pre-set several monitoring indicators for each monitoring device, collect the lighting data of each LED in the light array under each monitoring indicator at each unit time point, and summarize the lighting data collected by each monitoring device at each unit cycle to obtain the lighting data set of each LED in any unit cycle.

[0058] Step S103: Obtain the area range of each plant in the plant growth area. Randomly select two plants. If there is a common area between the area ranges of the two plants or no area range of other plants, and the two plants are of the same species, then set the two selected plants as plants in the same area. Summarize all plants in the same area and perform a union operation on the area ranges of the plants in the same area to obtain a whole area of ​​plants in the same area and set it as a lighting sub-area of ​​the plant growth area. Summarize the LED sets of each plant in any lighting sub-area to obtain the sub-light array of the lighting sub-area.

[0059] Step S200: Evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions to identify several deviation regions that cause light intensity deviation;

[0060] Step S200 includes the following steps:

[0061] Step S201: Arbitrarily select a lighting sub-region to obtain a sub-lamp array of the selected lighting sub-region, and arbitrarily select a unit period to extract the lighting data set of each lamp in the sub-lamp array during the selected unit period; a lighting database is pre-constructed, which stores lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule. The monitoring indicators in the lighting data set are compared with the lighting indicators, and the monitoring indicators that are the same as the lighting indicators are set as target indicators to obtain the light intensity evaluation rules of each target indicator.

[0062] Step S202: Randomly select the i-th LED from the sub-lamp array, and simultaneously extract the j-th unit time point from the selected unit period to obtain the illumination data of each target indicator for the i-th LED at the j-th unit time point. Randomly select the illumination data of the k-th target indicator, and obtain the light intensity value G of the k-th target indicator according to the light intensity evaluation rule. k (i,j), the light intensity values ​​of all target indicators are summed to obtain the light intensity value G(i,j) of the i-th LED at the j-th unit time point, according to the formula:

[0063] ;

[0064] Where a is the number of LEDs in the sub-lamp array, and b is the number of time points per unit period; the comprehensive light intensity value L of the selected lighting sub-region under the selected unit period is calculated;

[0065] Example 2: Select lighting sub-region 1, and select the first day as the unit period, resulting in 6 LEDs in the sub-light array and 24 unit time points; the lighting database stores lighting indicators including light intensity, and the light intensity evaluation rule is to directly use the light intensity value. After comparing the monitoring indicators with the lighting indicators, the target indicator is light intensity; set the average light intensity values ​​of the 6 LEDs in the 24 unit time points to be 500, 400, 600, 480, 510, and 500, respectively, and then sum the 6 LEDs to obtain the comprehensive light intensity value L=2990;

[0066] Step S203: Set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same plant species as the target species, and set them as target sub-regions; arbitrarily select a target sub-region, acquire the light intensity value of the selected target sub-region in each unit cycle, and calculate the average light intensity value of the selected target sub-region; set the average light intensity value of the p-th target sub-region as L. (ave,p) According to the formula:

[0067] ;

[0068] Where M is the number of target sub-regions; the mean μ and standard deviation σ of the average light intensity values ​​among all target sub-regions are calculated;

[0069] Step S204: Preset a light intensity deviation amplitude θ, and obtain the expected deviation difference value ΔL. ex =θ×σ, if |L (ave,p) -μ|≥ΔL ex Then, the p-th target sub-region is set as the deviation region; the lighting sub-regions corresponding to various types of plants are identified, and several deviation regions of the plant growth area are generated;

[0070] Example 3: The plant species in illumination sub-region 1 is lettuce. Other lettuce illumination sub-regions, such as sub-region 2 and sub-region 3, are extracted as target sub-regions. Their average light intensity values ​​are L... (ave,1) =480, L (ave,2) =520, L (ave,3) =500, resulting in a mean μ=500 and a standard deviation σ=16.33; preset light intensity deviation amplitude θ=1.5, expected deviation difference ΔL ex =θ×σ=1.5×16.33=24.5; For each target sub-region, sub-region 1 satisfies |480-500|=20<24.5, so it is a non-deviation region; sub-region 2 satisfies |520-500|=20<24.5, so it is a non-deviation region; sub-region 3 satisfies |500-500|=0<24.5, so it is a non-deviation region; Assuming that the average light intensity of sub-region 4 is 450, then |450-500|=50>24.5, so it is identified as a deviation region.

[0071] Step S300: Capture the deviation features of the lighting data in any deviation area; analyze the expected adjustment state of each deviation feature and identify the optimization needs of the deviation area;

[0072] Step S300 includes the following steps:

[0073] Step S301: Randomly select a deviation area, extract the sub-lamp array of the selected deviation area, and obtain the comprehensive light intensity value L of the selected deviation area under a certain unit period. ’ The expected deviation difference of the selected deviation area is set to ΔL. ’ The mean of the relevant illumination sub-region is μ ’ If |L ’ -μ ’ |≥ΔL ’ Then, the aforementioned unit period is set as the deviation period, if |L ’ -μ ’ |<ΔL ’ Then, the aforementioned unit period will be set as the normal period;

[0074] Step S302: Extract any normal period from the selected deviation area to obtain the lighting data of each monitoring indicator at each unit time point. Select any monitoring indicator to obtain the lighting data range corresponding to the selected monitoring indicator in the normal period.

[0075] Step S303: Extract a deviation period from the selected deviation area. Randomly select a unit time point from the deviation period and acquire the lighting data of the selected monitoring indicator at that unit time point. If the lighting data of the selected monitoring indicator is not within the corresponding lighting data range at the selected unit time point, then set the selected monitoring indicator as an abnormal indicator of the deviation period. Count N the number of abnormal indicators in the deviation period. exp Set the number of monitoring indicators to N. total The abnormal proportion η=N of a certain deviation period is calculated. exp / N total Preset an abnormality percentage threshold η th If η≥η th Then, the aforementioned deviation period is set as the target deviation period;

[0076] Step S304: Randomly select a target deviation period from the selected deviation area, and randomly select a lamp from the selected sub-lamp array. Extract the lighting data of each monitoring indicator of the selected lamp in the selected target deviation period. If the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, then set the certain monitoring indicator as a deviation feature to obtain several deviation features of the selected deviation area.

[0077] Step S305: Set the lighting data of a certain monitoring indicator as D1, and the corresponding lighting data range is (D ’ D ’’ The abnormal difference value of a certain monitoring indicator is obtained as ΔD = Max(D1 - D). ’’ D ’ -D1); Randomly select a deviation feature from the selected deviation region, and extract the abnormal difference value of the selected deviation feature in each target deviation period to obtain the maximum abnormal difference value ΔD of the selected deviation feature. max And according to the corresponding light intensity evaluation rules, the maximum light intensity difference ΔG corresponding to the maximum abnormal difference is obtained. max The maximum light intensity difference of each deviation feature is accumulated to obtain the maximum demand adjustment value ΔG for the selected deviation area. need If |L ’ -μ ’ -ΔG need |≥ΔL ’Then, set the first optimization requirement marker for the selected deviation area, |L ’ -μ ’ -ΔG need |<ΔL ’ Then, a second optimization requirement marker is set for the selected deviation area.

[0078] Step S400: Generate corresponding optimization requirement instructions for any deviation area, extract target light arrays that meet the optimization requirements; formulate and adjust the light array layout scheme for the target light arrays in each deviation area.

[0079] Step S400 includes the following steps:

[0080] Step S401: Randomly select a deviation region and obtain the optimization requirement marker for the selected deviation region; if the selected deviation region is the first optimization requirement marker, then extract the adjacent sub-regions adjacent to the selected deviation region, and extract several lamp beads from the sub-lamp arrays in the adjacent sub-regions, and accumulate the light intensity values ​​of the several lamp beads to obtain the total light intensity value G. need Set the maximum demand adjustment value ΔG for the selected deviation area. need If |G need +L ’ -μ ’ -ΔG need |<ΔL ’ If the selected area is the second optimization requirement marker, then the selected sub-array of the selected area is set as the target array.

[0081] Step S402: Generate an optimization instruction for the target light array, wherein the optimization instruction includes adjusting at least one parameter, either the brightness or the illumination direction of the lamp beads in the target light array; according to the optimization instruction, re-collect the illumination data of each lamp bead in the target light array to obtain the expected light intensity value of the target light array; if the difference between the expected light intensity value and the average value of the illumination sub-region of the same plant species is less than the expected deviation difference, then confirm the light array layout scheme and perform the adjustment; otherwise, reselect the target light array or adjust the parameters until the conditions are met.

[0082] A data analysis system for optimizing a light array includes a regional lighting acquisition module, a light intensity difference analysis module, a deviation demand analysis module, and a light array layout optimization module.

[0083] The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; and to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0084] The light intensity difference analysis module is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions; and identify several deviation regions that cause light intensity deviation.

[0085] The deviation requirement analysis module is used to capture deviation features of lighting data in any deviation area; analyze the expected adjustment state of each deviation feature, and identify the optimization requirements of the deviation area.

[0086] The light array layout optimization module is used to generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0087] The regional lighting analysis module includes a lighting data acquisition unit and a lighting area division unit;

[0088] The lighting data acquisition unit is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; the lighting area division unit is used to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0089] The light intensity difference analysis module includes a regional light intensity analysis unit and a deviation region identification unit.

[0090] The regional light intensity analysis unit is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; the deviation region identification unit is used to compare the differences in light intensity values ​​between different lighting sub-regions and identify several deviation regions that cause light intensity deviation.

[0091] The deviation requirement analysis module includes a deviation feature capture unit and an optimization requirement identification unit.

[0092] The deviation feature capture unit is used to capture the deviation features of lighting data in any deviation area; the optimization requirement identification unit is used to analyze the expected adjustment state of each deviation feature and identify the optimization requirements of the deviation area.

[0093] The light array layout optimization module includes a target light array extraction unit and a light array layout adjustment unit.

[0094] The target light array extraction unit is used to generate corresponding optimization requirement instructions for any deviation area and extract the target light array that meets the optimization requirements; the light array layout adjustment unit is used to adjust the light array layout scheme of the target light array in each deviation area.

[0095] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data analysis method for optimizing light arrays for plant lighting, characterized by: The analytical method includes the following steps: Step S100: Pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; divide the plant growth area into several lighting sub-areas according to the plant distribution; Step S200: Evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions to identify several deviation regions that cause light intensity deviation; Step S300: Capture the deviation features of the lighting data in any deviation area; analyze the expected adjustment state of each deviation feature and identify the optimization needs of the deviation area; Step S400: Generate corresponding optimization requirement instructions for any deviation area, extract target light arrays that meet the optimization requirements; formulate and adjust the light array layout scheme for the target light arrays in each deviation area. Step S400 includes the following steps: Step S401: Randomly select a deviation region and obtain the optimization requirement marker for the selected deviation region; if the selected deviation region is the first optimization requirement marker, then extract the adjacent sub-regions adjacent to the selected deviation region, and extract several lamp beads from the sub-lamp arrays in the adjacent sub-regions, and accumulate the light intensity values ​​of the several lamp beads to obtain the total light intensity value G. need Set the maximum demand adjustment value ΔG for the selected deviation area. need If |G need +L ’ -μ ’ -ΔG need |<ΔL ’ If the specified number of LEDs is selected as the target LED array, then the sub-LED array of the selected deviation area is set as the target LED array, where L ’ ΔL represents the combined light intensity value of the selected deviation area within a certain unit period. ’ This represents the expected deviation difference between the selected deviation regions, μ. ’ This represents the mean of the relevant lighting sub-region; Step S402: Generate an optimization instruction for the target light array, wherein the optimization instruction includes adjusting at least one parameter, either the brightness or the illumination direction of the lamp beads in the target light array; according to the optimization instruction, re-collect the illumination data of each lamp bead in the target light array to obtain the expected light intensity value of the target light array; if the difference between the expected light intensity value and the average value of the illumination sub-region of the same plant species is less than the expected deviation difference, then confirm the light array layout scheme and perform the adjustment; otherwise, reselect the target light array or adjust the parameters until the conditions are met.

2. The method for optimizing light arrays for plant lighting according to claim 1, characterized in that: Step S100 includes the following steps: Step S101: Establish a two-dimensional Cartesian coordinate system in the plant growth area, determine the position of the light array installed above the plant growth area, and obtain the position coordinates of each light bead in the light array; capture the shadow range of each plant under the illumination of the light array in the plant growth area, and obtain the area range of each plant in the coordinate system; arbitrarily select the area range of a plant, summarize all the light beads whose position coordinates belong to the area range, and set it as the set of light beads of the selected plant; Step S102: Arrange the LEDs in the light array according to the preset arrangement rules and number each LED; pre-deploy several monitoring devices, pre-set several monitoring indicators for each monitoring device, collect the lighting data of each LED in the light array under each monitoring indicator at each unit time point, and summarize the lighting data collected by each monitoring device at each unit cycle to obtain the lighting data set of each LED in any unit cycle. Step S103: Obtain the area range of each plant in the plant growth area. Randomly select two plants. If there is a common area between the area ranges of the two plants or no area range of other plants, and the two plants are of the same species, then set the two selected plants as plants in the same area. Summarize all plants in the same area and perform a union operation on the area ranges of the plants in the same area to obtain a whole area of ​​plants in the same area and set it as a lighting sub-area of ​​the plant growth area. Summarize the LED sets of each plant in any lighting sub-area to obtain the sub-light array of the lighting sub-area.

3. The method for optimizing light arrays for plant lighting according to claim 2, characterized in that: Step S200 includes the following steps: Step S201: Arbitrarily select a lighting sub-region to obtain a sub-lamp array of the selected lighting sub-region, and arbitrarily select a unit period to extract the lighting data set of each lamp in the sub-lamp array during the selected unit period; a lighting database is pre-constructed, which stores lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule. The monitoring indicators in the lighting data set are compared with the lighting indicators, and the monitoring indicators that are the same as the lighting indicators are set as target indicators to obtain the light intensity evaluation rules of each target indicator. Step S202: Randomly select the i-th LED from the sub-lamp array, and simultaneously extract the j-th unit time point from the selected unit period to obtain the illumination data of each target indicator for the i-th LED at the j-th unit time point. Randomly select the illumination data of the k-th target indicator, and obtain the light intensity value G of the k-th target indicator according to the light intensity evaluation rule. k (i,j), the light intensity values ​​of all target indicators are summed to obtain the light intensity value G(i,j) of the i-th LED at the j-th unit time point, according to the formula: ； Where a is the number of LEDs in the sub-lamp array, and b is the number of time points per unit period; the comprehensive light intensity value L of the selected lighting sub-region under the selected unit period is calculated; Step S203: Set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same plant species as the target species, and set them as target sub-regions; arbitrarily select a target sub-region, acquire the light intensity value of the selected target sub-region in each unit cycle, and calculate the average light intensity value of the selected target sub-region; set the average light intensity value of the p-th target sub-region as L. (ave,p) According to the formula: ； Where M is the number of target sub-regions; the mean μ and standard deviation σ of the average light intensity values ​​among all target sub-regions are calculated; Step S204: Preset a light intensity deviation amplitude θ, and obtain the expected deviation difference value ΔL. ex =θ×σ, if |L (ave,p) -μ|≥ΔL ex Then, the p-th target sub-region is set as the deviation region; the lighting sub-regions corresponding to various types of plants are identified, and several deviation regions of the plant growth area are generated.

4. The method for optimizing light arrays for plant lighting according to claim 3, characterized in that: Step S300 includes the following steps: Step S301: Randomly select a deviation area, extract the sub-lamp array of the selected deviation area, and obtain the comprehensive light intensity value L of the selected deviation area under a certain unit period. ’ The expected deviation difference of the selected deviation area is set to ΔL. ’ The mean of the relevant illumination sub-region is μ ’ If |L ’ -μ ’ |≥ΔL ’ Then, the aforementioned unit period is set as the deviation period, if |L ’ -μ ’ |<ΔL ’ Then, the aforementioned unit period will be set as the normal period; Step S302: Extract any normal period from the selected deviation area to obtain the lighting data of each monitoring indicator at each unit time point. Select any monitoring indicator to obtain the lighting data range corresponding to the selected monitoring indicator in the normal period. Step S303: Extract a deviation period from the selected deviation area. Randomly select a unit time point from the deviation period and acquire the lighting data of the selected monitoring indicator at that unit time point. If the lighting data of the selected monitoring indicator is not within the corresponding lighting data range at the selected unit time point, then set the selected monitoring indicator as an abnormal indicator of the deviation period. Count N the number of abnormal indicators in the deviation period. exp Set the number of monitoring indicators to N. total The abnormal proportion η=N of a certain deviation period is calculated. exp / N total Preset an abnormality percentage threshold η th If η≥η th Then, the aforementioned deviation period is set as the target deviation period; Step S304: Randomly select a target deviation period from the selected deviation area, and randomly select a lamp from the selected sub-lamp array. Extract the lighting data of each monitoring indicator of the selected lamp in the selected target deviation period. If the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, then set the certain monitoring indicator as a deviation feature to obtain several deviation features of the selected deviation area. Step S305: Set the lighting data of a certain monitoring indicator as D1, and the corresponding lighting data range is (D ’ D ’’ The abnormal difference value of a certain monitoring indicator is obtained as ΔD = Max(D1 - D). ’’ D ’ -D1); Randomly select a deviation feature from the selected deviation region, and extract the abnormal difference value of the selected deviation feature in each target deviation period to obtain the maximum abnormal difference value ΔD of the selected deviation feature. max And according to the corresponding light intensity evaluation rules, the maximum light intensity difference ΔG corresponding to the maximum abnormal difference is obtained. max The maximum light intensity difference of each deviation feature is accumulated to obtain the maximum demand adjustment value ΔG for the selected deviation area. need If |L ’ -μ ’ -ΔG need |≥ΔL ’ Then, set the first optimization requirement marker for the selected deviation area, |L ’ -μ ’ -ΔG need |<ΔL ’ Then, a second optimization requirement marker is set for the selected deviation area.

5. A light array optimization data analysis system, used to execute the light array optimization data analysis method for plant lighting as described in any one of claims 1-4, characterized in that: The analysis system includes a regional lighting acquisition module, a light intensity difference analysis module, a deviation demand analysis module, and a lamp array layout optimization module. The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; and to divide the plant growth area into several lighting sub-areas according to the plant distribution. The light intensity difference analysis module is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions; and identify several deviation regions that cause light intensity deviation. The deviation demand analysis module is used to capture deviation features of lighting data in any deviation area; Analyze the expected adjustment state of each deviation feature to identify the optimization needs of the deviation area; The light array layout optimization module is used to generate corresponding optimization requirement instructions for any deviation area, extract target light arrays that meet the optimization requirements, and formulate and adjust the light array layout scheme for the target light arrays in each deviation area.

6. The light array optimization data analysis system according to claim 5, characterized in that: The regional lighting analysis module includes a lighting data acquisition unit and a lighting area division unit; The lighting data acquisition unit is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; the lighting area division unit is used to divide the plant growth area into several lighting sub-areas according to the plant distribution.

7. The light array optimization data analysis system according to claim 6, characterized in that: The light intensity difference analysis module includes a regional light intensity analysis unit and a deviation region identification unit; The regional light intensity analysis unit is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; the deviation region identification unit is used to compare the differences in light intensity values ​​between different lighting sub-regions and identify several deviation regions that cause light intensity deviation.

8. The light array optimization data analysis system according to claim 7, characterized in that: The deviation requirement analysis module includes a deviation feature capture unit and an optimization requirement identification unit; The deviation feature capture unit is used to capture deviation features of lighting data in any deviation area; The optimization requirement identification unit is used to analyze the expected adjustment state of each deviation feature and identify the optimization requirements of the deviation area.

9. The light array optimization data analysis system according to claim 5, characterized in that: The light array layout optimization module includes a target light array extraction unit and a light array layout adjustment unit. The target light array extraction unit is used to generate corresponding optimization requirement instructions for any deviation area and extract the target light array that meets the optimization requirements; the light array arrangement adjustment unit is used to adjust the light array arrangement scheme of the target light array in each deviation area.

Images