Information generation method and device and computer readable storage medium

Abstract

The invention provides an information generation method and device and a computer readable storage medium, relates to the technical field of agriculture, and can determine PPFD of plants in a three-dimensional space. The method comprises the following steps: acquiring three-dimensional point cloud data of a target plant and multispectral data of the target plant; determining a first photosynthetic photon flux density PPFD according to the three-dimensional point cloud data, and determining a second PPFD according to the multispectral data; and determining a target PPFD of the target plant in the three-dimensional space according to the first PPFD, the second PPFD and the target weight.

Description

Technical Field

[0001] The present application relates to the field of agriculture, and in particular to an information generation method, device, and computer-readable storage medium. Background Art

[0002] In the agricultural field, regulating the light environment of plants is crucial to their growth. To ensure plant growth, it is necessary to determine the PPFD of the plants so that the PPFD of the plants can be adjusted subsequently through lighting tools.

[0003] Therefore, how to determine the PPFD of plants becomes an urgent problem to be solved. Summary of the Invention

[0004] The present application provides an information generation method, apparatus, and computer-readable storage medium capable of determining the PPFD of plants in three-dimensional space.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, an information generation method is provided, characterized in that the method includes: obtaining three-dimensional point cloud data of a target plant and multispectral data of the target plant; determining a first PPFD based on the three-dimensional point cloud data, and determining a second PPFD based on the multispectral data; and determining a target PPFD of the target plant in three-dimensional space based on the first PPFD, the second PPFD, and a target weight.

[0007] The system acquires 3D point cloud data indicating the target plant's 3D structural layer and multispectral data of the target plant. A first photosynthetic photon flux density (PPFD) is then determined based on the 3D point cloud data, and a second PPFD is determined based on the multispectral data. A target PPFD for the target plant in 3D space is then determined based on the first PPFD, the second PPFD, and a target weight. The 3D point cloud data can reflect the impact of the target plant's structure on light distribution. Furthermore, the multispectral data can account for the spectral characteristics of different light sources and the spectral composition of ambient light. This resulting target PPFD more accurately reflects the plant's actual light exposure in complex lighting environments, enabling the determination of the target PPFD for the target plant in 3D space.

[0008] In combination with the first aspect, in certain embodiments of the first aspect, the target PPFD of the target plant in the three-dimensional space is determined based on the first PPFD, the second PPFD and the target weight, including: taking the sum of the first product and the second product as the target PPFD of the target plant in the three-dimensional space; the first product is the product of the first PPFD and the target weight, the second product is the product of the second PPFD and the first difference, and the first difference is the difference between 1 and the target weight.

[0009] In combination with the first aspect, in certain embodiments of the first aspect, the method further includes: obtaining multiple first data sets for each plant in at least one plant; the first data set includes a third PPFD, a fourth PPFD and a fifth PPFD of the plant under a preset light intensity, the third PPFD is a PPFD determined based on the three-dimensional point cloud data of the plant, the fourth PPFD is a PPFD determined based on the multispectral data of the plant, and the fifth PPFD is the actual PPFD of the plant, and the preset light intensity corresponding to each first data set is different; for each original weight in the multiple original weights, determining the sixth PPFD corresponding to each first data set; the sixth PPFD is the sum of the third product and the fourth product, the third product is the product of the third PPFD and the original weight in the first data set, the fourth product is the product of the fourth PPFD in the first data set and the second difference, the second difference being the difference between 1 and the original weight; determining the target weight based on multiple second data sets for each original weight in the multiple original weights; the second data set includes the sixth PPFD and the fifth PPFD corresponding to the sixth PPFD.

[0010] A first data set is obtained for each of at least one plant, including a third PPFD, a fourth PPFD, and a fifth PPFD of the plant at a preset light intensity. Subsequently, for each of a plurality of original weights, a sixth PPFD corresponding to each of the first data sets is determined. The sixth PPFD is the sum of a third product and a fourth product. The third product is the product of the third PPFD in the first data set and the original weight. The fourth product is the product of the fourth PPFD in the first data set and a second difference, where the second difference is the difference between 1 and the original weight. Subsequently, a target weight is determined based on a plurality of second data sets for each of the plurality of original weights. Since the second data set includes the sixth PPFD and a fifth PPFD corresponding to the sixth PPFD, the third PPFD is a PPFD determined based on the three-dimensional point cloud data of the plant, the fourth PPFD is a PPFD determined based on the multispectral data of the plant, and the fifth PPFD is the actual PPFD of the plant. Since each of the second data sets corresponds to a different preset light intensity, the target weight can be determined based on different light intensities and different plants, thereby improving the accuracy of the determined target weight.

[0011] In combination with the first aspect, in certain embodiments of the first aspect, a target weight is determined based on multiple second data sets for each original weight among multiple original weights, including: for each second data set of each original weight, taking the difference between the sixth PPFD and the corresponding fifth PPFD as the third difference; for each original weight among the multiple original weights, determining the mean square error of the multiple third differences of the original weights; iteratively calculating the target weight based on the mean square errors corresponding to the multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the target weight is less than the mean square error corresponding to any original weight.

[0012] By taking the difference between the sixth PPFD and the corresponding fifth PPFD as the third difference for each second data set of each original weight; determining the mean square error of the multiple third differences of the original weight for each original weight; and iteratively calculating the target weight based on the mean square error corresponding to the multiple original weights and the Nelder-Mead algorithm, since the mean square error corresponding to the target weight is smaller than the mean square error corresponding to any original weight, the Nelder-Mead algorithm can perform iterative calculations to search for the optimal target weight within a wider range of weight values. During each iteration, the weight will be adjusted according to certain rules based on the current weight value and the mean square error, and an attempt will be made to find a smaller mean square error. This can gradually approach the global optimal solution, rather than being limited to multiple original weights, thereby obtaining a more accurate target weight and improving the accuracy of determining the PPFD of the target plant.

[0013] In combination with the first aspect, in certain embodiments of the first aspect, an iterative calculation is performed based on the mean square errors corresponding to multiple original weights and the Nelder-Mead algorithm to determine the target weight, including: an iterative calculation is performed based on the mean square errors corresponding to multiple original weights and the Nelder-Mead algorithm to determine the initial weight; the mean square error corresponding to the initial weight is less than the mean square error corresponding to any original weight; a two-tailed t-test is performed based on the initial weight to obtain a test result; and when the test result indicates that the test passed, the initial weight is used as the target weight.

[0014] The initial weights are determined by iteratively calculating the mean square errors corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the initial weights is less than the mean square error corresponding to any of the original weights; a two-tailed t-test is performed based on the initial weights to obtain the test results; when the test results indicate that the test has passed, it means that the results are reliable and the target weights obtained by optimization are valid. Using the initial weights as the target weights can improve the accuracy of the determined target weights.

[0015] In combination with the first aspect, in certain embodiments of the first aspect, the method further includes: obtaining an actual growth curve of the environmental data and target growth data of the target plant within a target time period; determining that the growth of the target plant is abnormal when the similarity between the actual growth curve and the standard growth curve is less than a preset similarity threshold; and determining growth recommendation information for the target plant based on the environmental data.

[0016] Obtain the actual growth curve of the environmental data and target growth data of the target plant within the target time period; determine that the growth of the target plant is abnormal when the similarity between the actual growth curve and the standard growth curve is less than a preset similarity threshold; determine the growth recommendation information of the target plant based on the environmental data, and the growth recommendation information of the target plant can be determined based on the environmental data and growth data of the target plant.

[0017] In combination with the first aspect, in certain embodiments of the first aspect, the method further includes: visualizing the target PPFD of the target plant to obtain a PPFD cloud map of the target plant in a three-dimensional space.

[0018] By visualizing the target PPFD, growers can intuitively see the light distribution within the plant's three-dimensional space. Based on the PPFD cloud map, growers can accurately determine which parts are insufficiently illuminated and adjust the position and intensity of the fill light fixtures to achieve precise fill light.

[0019] In a second aspect, an information generation device is provided for implementing the information generation method of the first aspect. The information generation device includes modules, units, or means corresponding to the above-mentioned method. The modules, units, or means can be implemented through hardware, software, or hardware executing corresponding software implementations. The hardware or software includes one or more modules or units corresponding to the above-mentioned functions.

[0020] In combination with the second aspect, in certain embodiments of the second aspect, the information generating device includes: the device includes: an acquisition module and a processing module; the acquisition module is used to acquire three-dimensional point cloud data of the target plant and multispectral data of the target plant; the three-dimensional point cloud data is used to indicate the three-dimensional structure of the target plant; the processing module is used to determine a first PPFD based on the three-dimensional point cloud data, and to determine a second PPFD based on the multispectral data; the processing module is also used to determine the target PPFD of the target plant in the three-dimensional space based on the first PPFD, the second PPFD and the target weight.

[0021] In a third aspect, an information generating device is provided, comprising: at least one processor and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement the method provided in the first aspect and any possible implementation manner thereof.

[0022] In a fourth aspect, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by the processor of the information generating device, the information generating device is enabled to execute the method provided in the first aspect and any possible implementation method thereof.

[0023] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the method provided in the first aspect and any possible implementation manner thereof.

[0024] Among them, the technical effects brought about by any implementation of the second to fifth aspects can refer to the technical effects brought about by different implementations of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the architecture of an information generation system provided in this application;

[0026] Figure 2 A schematic diagram of the working principle of a laser radar provided in this application;

[0027] Figure 3 A schematic diagram of the range of a multi-spectrum provided for this application;

[0028] Figure 4 A flowchart of an information generation method provided in this application;

[0029] Figure 5 A flowchart of another information generation method provided by this application;

[0030] Figure 6 A flowchart of another information generation method provided by this application;

[0031] Figure 7 A flowchart of another information generation method provided by this application;

[0032] Figure 8 A flowchart of another information generation method provided by this application;

[0033] Figure 9 A schematic structural diagram of an information generating device provided in this application;

[0034] Figure 10 This is a structural diagram of another information generating device provided by this application. DETAILED DESCRIPTION

[0035] In the description of this application, unless otherwise specified, "plurality" means two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0036] In addition, to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0037] At the same time, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0038] It will be understood that the “embodiment” mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the various embodiments in the entire specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It will be understood that in the various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0039] It can be understood that in this application, "when", "if" and "if" all mean that corresponding processing will be taken under certain objective circumstances, and do not limit the time, nor do they require judgment actions when implementing them, nor do they mean that there are other limitations.

[0040] It is understood that some optional features in the embodiments of the present application may, in certain scenarios, be implemented independently of other features, such as the solution on which they are currently based, to solve corresponding technical problems and achieve corresponding effects. They may also be combined with other features in certain scenarios as needed. Accordingly, the devices provided in the embodiments of the present application may also implement these features or functions accordingly, which will not be described in detail here.

[0041] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, and the various implementation methods in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various implementation methods in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various implementation methods in each embodiment can be combined to form new embodiments, implementation methods, implementation methods, or implementation methods according to their inherent logical relationships. The following implementation methods of this application do not constitute a limitation on the scope of protection of this application.

[0042] Figure 1 This is a schematic diagram of the architecture of an information generation system provided by this application. The technical solution of the embodiment of this application can be applied to Figure 1 The information generation system shown,the information generation system can be deployed in a greenhouse, where plants are grown, such as Figure 1 As shown, the information generation system 10 includes an information generation device 11, a laser radar 12, a multispectral camera 13, a display device 14, and a lamp controller 15.

[0043] Among them, the information generating device 11 is respectively connected to the laser radar 12, the multispectral camera 13, the display device 14, and the lamp controller 15, and the information generating device 11 is used to execute the information generating method provided in this application.

[0044] The laser radar 12 is used to generate three-dimensional point cloud data of plants. Figure 2 A schematic diagram of the working principle of a laser radar provided in this application, such as Figure 2 As shown, the laser radar 12 emits laser to illuminate the plants, and the plants reflect the laser. The laser radar 12 can receive the laser reflected by the plants and then generate three-dimensional point cloud data of the plants.

[0045] For example, taking a rectangular greenhouse with an area of 500 square meters as an example, the laser radar 12 is installed at the center of the top of the greenhouse so that its scanning range can cover the plant area of the entire greenhouse. A full calibration is performed once a week, including angle calibration and distance calibration. In daily operation, the laser radar is calibrated at a frequency of 15Hz and at a rate of 0.1mm. 3 The plants in the greenhouse are scanned with a resolution of 1000 nm to obtain high-precision three-dimensional point cloud data.

[0046] Furthermore, during the sowing season, UHF RFID tags compliant with the EPC Gen2 standard and storing 24-bit genetic code can be implanted near each plant's seed. When the plants reach a certain stage of growth, industrial touch terminals, such as Advantech's TPC-1581H industrial tablet, can be installed at the edge of the planting area. Their high resolution and high refresh rate clearly capture plant growth data, such as leaf expansion and color changes. The tablet transmits the collected data in real time to the information generation device 11 via an internal network.

[0047] The multispectral camera 13 is used to generate multispectral data of plants. Figure 3 A schematic diagram of a multi-spectral range provided for this application, such as Figure 3 As shown, light in the 380nm-780nm band can be called visible light, and light in the 780nm-1700nm band can be called near-infrared light. The multispectral camera 13 can determine the distribution of stomata on plant leaves by emitting light in the 450nm band, the chlorophyll concentration in plant leaves by emitting light in the 680nm band, and the moisture content of plants by emitting light in the 950nm band.

[0048] For example, multispectral cameras are strategically installed within the greenhouse based on the height and distribution of the plants. For example, in the case of a tomato plant, multiple cameras are positioned one meter above the plant at regular intervals to ensure comprehensive spectral capture of different parts of the plant. A hardware trigger mechanism synchronizes the multispectral camera and lidar, with a time deviation of less than 10μs. The multispectral camera covers the 380-1700nm band with a spectral resolution of 3nm, enabling it to capture the reflection and absorption characteristics of tomato plants under different light spectra.

[0049] The number of display devices 14 can be one or more. For example, the display device 14 can be a mobile terminal with a display screen, AR glasses, etc.

[0050] A dedicated mobile app is deployed on mobile terminals. This app allows users to track data from the entire plant growth cycle by scanning UHF RFID tags. By scanning the tag or entering the plant's identification information, users can obtain detailed data from the plant's entire life cycle, from sowing to harvest, including environmental data, growth data, and agricultural operation records. This data is intuitively presented on the terminal's display screen in the form of charts and graphs, facilitating data analysis and decision-making for growers.

[0051] AR glasses, such as Microsoft HoloLens 3, achieve millimeter-level spatial matching between virtual information and actual physical plants. Before using AR glasses, they must be rigorously calibrated and configured. Through data interaction with other devices, virtual information such as the plant's real-time growth status, supplemental lighting recommendations based on light field analysis, and pest and disease warnings can be accurately superimposed on the actual plant. This provides growers with an intuitive and convenient information display, helping them to more accurately understand the plant's condition and make decisions. For example, when a grower inspects a greenhouse with strawberry plants, wearing AR glasses, they can see the real-time growth indicators of each strawberry plant and adjust the lighting in a timely manner based on the supplemental lighting recommendations.

[0052] Furthermore, AR glasses integrate pest and disease identification models, which can identify signs of pests and diseases by photographing plants. If pests and diseases are found, corresponding prevention and control measures are provided based on the type of pest, such as the use of biological control or chemical control.

[0053] The number of the lamp controller 15 can be one or more, and the lamp controller 15 can receive instructions to control the lamp.

[0054] Furthermore, the information generating system 10 may also include a light sensor and a temperature sensor. The light sensor can sense the light data in the greenhouse, and the temperature sensor can sense the temperature in the greenhouse.

[0055] The temperature sensor integrates an encryption module supporting the AES-256 algorithm. Temperature is crucial to plant growth, so data security in the temperature sensor is particularly important. If unauthorized removal of the temperature sensor is detected, the anti-tampering self-destruct mechanism immediately activates, destroying sensitive data stored within the sensor. The encryption module is tested and updated quarterly to ensure its security.

[0056] Furthermore, the information generation system 10 may also include a storage device (not shown in the figure), which can store data from various devices. The storage device can use the InfluxDB spatiotemporal database to store data. In the database, the timestamp accuracy is set to ±1ms, and the spatial coordinate accuracy is set to ±0.1mm. For example, when recording the light data of a plant at a certain growth stage, not only the accurate time is recorded, but also the specific location of the light sensor in the greenhouse. By writing scripts, an index table related to light environment-growth traits is established, such as recording the growth rate of plants under different light intensities, changes in the number of leaves, etc. Back up the database once a week, and regularly clean up invalid data to optimize database performance.

[0057] The database can be asymmetrically encrypted using the national SM9 algorithm. A blockchain-based evidence storage system with an operation log records all database operations, such as data insertion, modification, and deletion. If data is tampered with, the blockchain's timestamp and chain structure can quickly detect the tampering. Regular maintenance and upgrades of the blockchain-based evidence storage system ensure its reliability and the security and integrity of plant data.

[0058] Furthermore, in order to enable the data in the information generation system 10 to be transmitted between different devices, a time-sensitive network (TSN) protocol can be used to build a data transmission network for the information generation system 10. In terms of network architecture design, the network topology is planned according to the layout of the greenhouse and the distribution of equipment to ensure smooth data transmission. At the same time, a CRC32 checksum is added to each data packet, and the checksum is calculated at the data sending end and attached to the data packet. After the receiving end receives the data packet, the checksum is recalculated and compared with the received checksum. If the two are inconsistent, the receiving end immediately asks the sending end to resend the data to ensure the accuracy of data transmission and strictly control the bit error rate to less than 10^-12. In addition, the network is monitored in real time, and network failures, delays and other problems are discovered and handled in a timely manner through special network monitoring software or equipment to ensure that the end-to-end delay is always less than 10ms.

[0059] During data transmission within the data transmission network, MQTT over TLS 1.3 can be used. Configure two-way certificate authentication on both the server and client to ensure the authenticity of both communicating parties. Timestamp each data packet to prevent replay attacks. Use network traffic analysis tools to monitor data transmission traffic in real time. If an abnormal increase in data traffic is detected within a specific time period, immediate analysis is performed to determine whether a network attack is occurring, and timely preventative measures are taken.

[0060] In practical applications, the information generating method provided in the embodiment of the present application can be applied to the information generating device 11, and can also be applied to the devices included in the information generating device 11.

[0061] The information generation method provided in the embodiment of the present application is described below with reference to the accompanying drawings, taking the application of the information generation method to the information generation device 11 as an example.

[0062] Figure 4 A flow chart of an information generation method provided in this application, such as Figure 4 As shown, the method includes the following steps:

[0063] S401 : The information generating device obtains three-dimensional point cloud data and multispectral data of the target plant.

[0064] It should be noted that the three-dimensional point cloud data is used to reflect the three-dimensional structure of the target plant.

[0065] The target plants may be strawberries, lettuce, cotton, and of course, the target plants may also be other types of plants, which is not specifically limited in this application.

[0066] As a possible implementation, combining Figure 1 The information generation device sends a command to the laser radar. After receiving the command, the laser radar starts working and obtains 3D point cloud data of the target plant. The laser radar sends feedback information including the 3D point cloud data of the target plant to the information generation device. In response, the information generation device receives the feedback information from the laser radar and obtains the 3D point cloud data of the target plant.

[0067] The information generation device sends a command to the multispectral camera, which then begins operating to obtain multispectral data of the target plant. The multispectral camera then sends feedback information containing the target plant's multispectral data to the information generation device. The information generation device then receives the feedback from the multispectral camera and obtains the target plant's multispectral data.

[0068] It should be noted that after acquiring the three-dimensional point cloud data and the multispectral data of the target plant, the information generation device can time-align the time corresponding to the three-dimensional point cloud data with the time corresponding to the multispectral data. The time alignment deviation is less than or equal to 10 μs.

[0069] It should be noted that the specific solution for time alignment can refer to the existing solution, which will not be described in this application.

[0070] S402: The information generating device determines a first photosynthetic photon flux density (PPFD) according to the three-dimensional point cloud data, and determines a second PPFD according to the multispectral data.

[0071] As a possible implementation method, the information generating device performs the steps of photon flux distribution simulation, photon flux to PPFD conversion, and spatial aggregation based on the three-dimensional point cloud data to obtain the first PPFD.

[0072] The information generation device performs radiation correction, reflectance calculation, photosynthetic active radiation estimation, canopy light interception rate calculation, PPFD distribution modeling, pixel-level PPFD mapping, time dynamic analysis and other steps based on multispectral data to obtain the second PPFD.

[0073] It should be noted that the specific description of this possible implementation method can refer to the existing solution, and this application will not explain it again.

[0074] S403 : The information generating device determines a target PPFD of the target plant in the three-dimensional space according to the first PPFD, the second PPFD and the target weight.

[0075] As a possible implementation manner, the information generating device uses the sum of the first product and the second product as the target PPFD of the target plant in the three-dimensional space.

[0076] The first product is the product of the first PPFD and the target weight, the second product is the product of the second PPFD and the first difference, and the first difference is the difference between 1 and the target weight.

[0077] Exemplarily, the information generating device determines the target PPFD of the target plant based on the following relationship:

[0078] PPFD final =α·PPFD lidar+ (1-α)·PPFD spectral

[0079] Among them, PPFD lidar Indicates the first PPFD, PPFD spectral Indicates the second PPFD, PPFD final represents the target PPFD, α represents the target weight, and the first product is α·PPFD lidar , the second product is (1-α)·PPFD spectral .

[0080] Based on S401-S403, 3D point cloud data indicating the target plant's 3D structural layer and multispectral data of the target plant are acquired. A first photosynthetic photon flux density (PPFD) is then determined based on the 3D point cloud data, and a second PPFD is determined based on the multispectral data. A target PPFD for the target plant in 3D space is then determined based on the first PPFD, the second PPFD, and a target weight. The 3D point cloud data can reflect the impact of the target plant's structure on light distribution. Furthermore, the multispectral data can account for the spectral characteristics of different light sources and the spectral composition of ambient light. This resulting target PPFD more realistically reflects the plant's actual light exposure in complex lighting environments, enabling the determination of the target PPFD for the target plant in 3D space.

[0081] S404 , the information generating device performs visualization processing on the target PPFD of the target plant to obtain a PPFD cloud map in the three-dimensional space of the target plant.

[0082] As a possible implementation method, the information generation device converts the target PPFD into a three-dimensional PPFD cloud map using graphics processing software, such as a related image processing toolbox in MATLAB. Through color coding, areas with high PPFD intensity are displayed in red, and areas with low intensity are displayed in blue.

[0083] Based on S404, by visualizing the target PPFD, growers can intuitively see the light distribution within the plant's three-dimensional space. Based on the PPFD cloud map, growers can accurately determine which parts of the plant are under-illuminated and adjust the position and intensity of the fill light fixtures to achieve precise fill light.

[0084] The above is a general description of the information generation method provided by this application. The information generation method provided by this application will be further described below in conjunction with the accompanying drawings.

[0085] In one design, Figure 5 A flow chart of another information generation method provided in this application is as follows: Figure 5 As shown, the information generation method provided by this application may also include the following steps:

[0086] S501: An information generating device obtains multiple first data sets of each of at least one plant.

[0087] Among them, the first data set includes the third PPFD, fourth PPFD and fifth PPFD of the plant under the preset light intensity, the third PPFD is the PPFD determined based on the three-dimensional point cloud data of the plant, the fourth PPFD is the PPFD determined based on the multispectral data of the plant, and the fifth PPFD is the actual PPFD of the plant. The preset light intensity corresponding to each first data set is different.

[0088] It should be noted that the at least one plant may be of the same variety or of different varieties. For example, taking the number of at least one plant as 20, the 20 plants may include plants of 20 varieties, 19 varieties, 1 variety, or 2 varieties, and this application does not impose any specific restrictions on this.

[0089] As a possible implementation method, 20 common and representative economic crops are provided, and 5 different preset light intensities are set. The preset light intensities range from 200 to 1000 μmol / m 2 / s. 400 sets of experimental conditions were assigned according to the L25(5^6) orthogonal table.

[0090] The information generation device sends instructions to the lidar and multispectral camera every 5 minutes for each plant under each light intensity to obtain the three-dimensional point cloud data and multispectral data of the plant, determine the third PPFD based on the three-dimensional point cloud data, and determine the fourth PPFD based on the multispectral data.

[0091] At the same time, the information generation device sends instructions to the PPFD sensor every 5 minutes for each plant under each light intensity to obtain the fifth PPFD of the plant. The PPFD sensor can measure the actual PPFD of the plant.

[0092] It should be noted that the specific description of determining the third PPFD based on three-dimensional point cloud data, determining the fourth PPFD based on multispectral data, and the PPFD sensor can refer to the existing solutions and will not be further described in this application.

[0093] S502: The information generating device determines, for each original weight among the multiple original weights, a sixth PPFD corresponding to each first data set.

[0094] Among them, the sixth PPFD is the sum of the third product and the fourth product, the third product is the product of the third PPFD in the first data set and the original weight, the fourth product is the product of the fourth PPFD in the first data set and the second difference, and the second difference is the difference between 1 and the original weight.

[0095] As a possible implementation, taking the implementation provided in S501 as an example, the information generating device sets a plurality of original weights, and each original weight ranges from 0 to 1.

[0096] For each of the multiple original weights, and for each of the multiple first data sets in S501, the information generating device multiplies the third PPFD in the first data set by the original weight to obtain a third product. The information generating device multiplies the fourth PPFD in the first data set by the second difference (i.e., the difference between 1 and the original weight) to obtain a fourth product. The information generating device adds the third product and the fourth product to obtain a sixth PPFD corresponding to each first data set.

[0097] S503: The information generating device determines a target weight based on multiple second data sets of each original weight in the multiple original weights.

[0098] The second data set includes the sixth PPFD and the fifth PPFD corresponding to the sixth PPFD.

[0099] It should be noted that after the information generating device determines a sixth PPFD corresponding to a first data set for each of multiple original weights, the sixth PPFD and the fifth PPFD in the first data set corresponding to the sixth PPFD can be used as a second data set of the original weights.

[0100] As a possible implementation method, the information generating device takes the difference between the sixth PPFD and the corresponding fifth PPFD as the third difference for each second data set of each original weight, and then determines the mean square error of the multiple third differences of the original weight for each original weight. Then, iterative calculation is performed based on the mean square errors corresponding to the multiple original weights and the Nelder-Mead algorithm to determine the target weight; the mean square error corresponding to the target weight is smaller than the mean square error corresponding to any original weight.

[0101] It should be noted that the specific description of this possible implementation method can be referred to the relevant description in the subsequent part of the specific implementation method of this application, and this application will not explain it here.

[0102] Based on S502-S503, a first data set is obtained for each of at least one plant, including a third PPFD, a fourth PPFD, and a fifth PPFD of the plant at a preset light intensity. Subsequently, for each of the plurality of original weights, a sixth PPFD corresponding to each of the first data sets is determined. The sixth PPFD is the sum of a third product and a fourth product. The third product is the product of the third PPFD in the first data set and the original weight. The fourth product is the product of the fourth PPFD in the first data set and a second difference, where the second difference is the difference between 1 and the original weight. Subsequently, a target weight is determined based on the plurality of second data sets for each of the plurality of original weights. Since the second data set includes the sixth PPFD and a fifth PPFD corresponding to the sixth PPFD, the third PPFD is the PPFD determined based on the three-dimensional point cloud data of the plant, the fourth PPFD is the PPFD determined based on the multispectral data of the plant, and the fifth PPFD is the actual PPFD of the plant. Since each of the second data sets corresponds to a different preset light intensity, target weights can be determined based on different light intensities and different plants, thereby improving the accuracy of the determined target weights.

[0103] In one design, Figure 6 A flow chart of an information generation method provided in this application, such as Figure 6 As shown, S503 provided in this application specifically includes the following steps:

[0104] S601 : For each second data set of each original weight, the information generating device uses the difference between the sixth PPFD and the corresponding fifth PPFD as the third difference.

[0105] As a possible implementation manner, the information generating device subtracts the sixth PPFD from the corresponding fifth PPFD for each second data set of each original weight to obtain a third difference value, and further obtains multiple third difference values for each original weight.

[0106] S602: The information generating device determines, for each of the multiple original weights, a mean square error of multiple third differences of the original weights.

[0107] As a possible implementation manner, the information generating device determines the mean square error of the multiple third differences of each original weight according to the following relationship:

[0108] MSE=Σa 2 / n

[0109] Wherein, MSE represents the mean square error of an original weight, a represents the third difference of the original weight, and n identifies the number of the third differences of the original weight.

[0110] S603: The information generating device performs iterative calculation based on the mean square error corresponding to the multiple original weights and the Nelder-Mead algorithm to determine the target weight.

[0111] Among them, the mean square error corresponding to the target weight is smaller than the mean square error corresponding to any original weight.

[0112] As a possible implementation method, the information generating device determines the initial weight by iterative calculation based on the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the initial weight is less than the mean square error corresponding to any original weight; a two-tailed t-test is performed on the initial weight to obtain a test result; when the test result indicates that the test is passed, the initial weight is used as the target weight.

[0113] It should be noted that the specific description of this possible implementation method can be referred to the relevant description in the subsequent part of the specific implementation method of this application, and this application will not explain it here.

[0114] As another possible implementation method, the information generating device determines the target weight by iterative calculation based on the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the target weight is smaller than the mean square error corresponding to any original weight.

[0115] It should be noted that the specific description of this possible implementation method can refer to the existing solution, and this application will not explain it again here.

[0116] Based on S601-S603, for each second data set of each original weight, the difference between the sixth PPFD and the corresponding fifth PPFD is used as the third difference; for each original weight in the multiple original weights, the mean square error of the multiple third differences of the original weight is determined; based on the mean square errors corresponding to the multiple original weights and the Nelder-Mead algorithm, iterative calculation is performed to determine the target weight. Since the mean square error corresponding to the target weight is smaller than the mean square error corresponding to any original weight, the Nelder-Mead algorithm can perform iterative calculation to search for the optimal target weight within a wider range of weight values. During each iteration, the weight will be adjusted according to certain rules based on the current weight value and the mean square error, and an attempt will be made to find a smaller mean square error. This can gradually approach the global optimal solution, rather than being limited to multiple original weights, thereby obtaining a more accurate target weight and improving the accuracy of determining the PPFD of the target plant.

[0117] In one design, Figure 7 A flow chart of another information generation method provided in this application is as follows: Figure 7 As shown, S603 provided in the specific implementation of this application may specifically include the following steps:

[0118] S701: The information generating device performs iterative calculation based on the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm to determine the initial weights.

[0119] Among them, the mean square error corresponding to the initial weight is smaller than the mean square error corresponding to any original weight.

[0120] It should be noted that the specific description of this possible implementation method can refer to the existing solution, and this application will not explain it again here.

[0121] S702: The information generating device performs a two-tailed t-test based on the initial weights to obtain a test result.

[0122] As a possible implementation method, the information generating device performs a two-tailed t-test to calculate the p-value for each set of iteration results. If the p-value is less than 0.01 and the iteration result is within the 95% confidence interval, the test result is determined to be passed; otherwise, the test result is determined to be failed.

[0123] S703: When the detection result indicates that the detection is passed, the information generating device uses the initial weight as the target weight.

[0124] Furthermore, when the inspection result indicates that the inspection has failed, the information generating device sends a message indicating that the inspection has failed to the display device, so that the display device displays the inspection result.

[0125] Based on S701-S703, the initial weight is determined by iteratively calculating the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the initial weight is less than the mean square error corresponding to any original weight; a two-tailed t-test is performed based on the initial weight to obtain the test result; when the test result indicates that the test passes, it means that the result is reliable and the target weight obtained by optimization is valid. Using the initial weight as the target weight can improve the accuracy of the determined target weight.

[0126] In one design, Figure 8 The present application provides a flow chart of another information generation method, such as Figure 8 The information generation method provided in this application may further include the following steps:

[0127] S801: The information generating device obtains the actual growth curve of the environmental data and target growth data of the target plant within a target time period.

[0128] It should be noted that environmental data may include data such as temperature, humidity, and light, and target growth data may include data such as plant height and leaf status. This application does not impose specific restrictions on this.

[0129] As a possible implementation method, the information generating device receives data from various environmental sensors, such as temperature sensors, humidity sensors, and light sensors to obtain environmental data, receives data from equipment that monitors the growth of target plants, obtains target growth data, and further generates an actual growth curve.

[0130] It should be noted that the specific solution for generating the actual growth curve can refer to the existing solution, which will not be described in this application.

[0131] S802: When the similarity between the actual growth curve and the standard growth curve is less than a preset similarity threshold, the information generating device determines that the target plant has abnormal growth.

[0132] It should be noted that the similarity threshold may be 80% or 90%. Of course, the similarity threshold may also be other values, and this application does not impose any specific limitation on this.

[0133] As a possible implementation method, the information generating device compares the actual growth curve with the preset standard growth curve to obtain the similarity between the two. When the similarity is less than a preset similarity threshold, it is determined that the growth of the target plant is abnormal.

[0134] S803: The information generating device determines growth recommendation information of the target plant according to the environmental data.

[0135] As one possible implementation, the information generation device uses an established light environment-growth trait correlation index table to analyze the correlation between light environment data and growth data. If light intensity falls below the standard and the target plant grows slowly, it may be that insufficient light is affecting photosynthesis. In this case, it is recommended to adjust the position of the supplemental lighting fixture, increase the duration of the lighting, or increase the light intensity. By analyzing multispectral data, it is determined whether the target plant is deficient in specific nutrients. If the spectrum shows low chlorophyll content, it may be a nitrogen deficiency, and the solution is to apply nitrogen fertilizer appropriately.

[0136] Based on S801-S803, the actual growth curve of the environmental data and target growth data of the target plant within the target time period is obtained; when the similarity between the actual growth curve and the standard growth curve is less than the preset similarity threshold, the target plant growth is determined to be abnormal; the growth recommendation information of the target plant is determined based on the environmental data, and the growth recommendation information of the target plant can be determined based on the environmental data and growth data of the target plant.

[0137] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the information generating device executing the information generating method. In order to realize the above functions, the information generating device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] The embodiment of the present application can divide the functional modules of the information generating device according to the above method example. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, the "module" here can refer to a specific application-specific integrated circuit (ASIC), a circuit, a processor and memory that executes one or more software or firmware programs, an integrated logic circuit, and / or other devices that can provide the above functions.

[0139] In the case of functional module division, Figure 9 FIG. 1 shows a schematic diagram of the structure of an information generating device. Figure 9 As shown, the information generating device 90 includes an acquisition module 901 and a processing module 902 .

[0140] In some embodiments, the information generating device 90 may further include a storage module ( Figure 9 ), for storing program instructions and data.

[0141] Among them, the acquisition module 901 is used to obtain three-dimensional point cloud data and multispectral data of the target plant; the three-dimensional point cloud data is used to indicate the three-dimensional structure of the target plant; the processing module 902 is used to determine a first PPFD based on the three-dimensional point cloud data, and to determine a second PPFD based on the multispectral data; the processing module 902 is also used to determine a target PPFD of the target plant in three-dimensional space based on the first PPFD, the second PPFD and the target weight.

[0142] All relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0143] When the functions of the above functional modules are implemented in the form of hardware, Figure 10 FIG. 1 shows a schematic diagram of the structure of another information generating device. Figure 10 As shown, the information generating device 100 includes a processor 1001 , a memory 1002 and a bus 1003 . The processor 1001 and the memory 1002 may be connected via the bus 1003 .

[0144] Processor 1001 is the control center of information generating device 100 and can be a single processor or a collective term for multiple processing elements. For example, processor 1001 can be a general-purpose central processing unit (CPU) or other general-purpose processor. The general-purpose processor can be a microprocessor or any conventional processor.

[0145] As an embodiment, the processor 1001 may include one or more CPUs, such as Figure 10 CPU0 and CPU1 are shown in the figure.

[0146] The memory 1002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0147] As a possible implementation, memory 1002 can exist independently of processor 1001. Memory 1002 can be connected to processor 1001 via bus 1003 to store instructions or program codes. When processor 1001 calls and executes the instructions or program codes stored in memory 1002, the information generation method provided in the embodiments of the present application can be implemented.

[0148] In another possible implementation, the memory 1002 may also be integrated with the processor 1001 .

[0149] The bus 1003 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0150] It should be pointed out that Figure 10 The structure shown does not constitute a limitation on the information generating device 100. Figure 10 In addition to the components shown, the information generating device 100 may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0151] As an example, combining Figure 9 The functions implemented by the acquisition module 901 and the processing module 902 in the information generating device 90 are the same as those implemented by the Figure 10 The functions of the processor 1001 are the same as those of the processor 1001 in FIG.

[0152] Optional, such as Figure 10 As shown, the information generating device 100 provided in the embodiment of the present application may further include a communication interface 1004 .

[0153] The communication interface 1004 is used to connect to other devices via a communication network. The communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 1004 may include a receiving unit for receiving data and a sending unit for sending data.

[0154] In a possible implementation, in the information generating device 100 provided in the embodiment of the present application, the communication interface 1004 may also be integrated into the processor 1001, which is not specifically limited in the embodiment of the present application.

[0155] As a possible product form, the information generating device of the embodiment of the present application can also be implemented using the following: one or more field programmable gate arrays (FPGA), programmable logic devices (PLD), controllers, state machines, gate logic, discrete hardware components, any other suitable circuits, or any combination of circuits capable of performing the various functions described throughout this application.

[0156] Through the description of the above embodiments, those skilled in the art will clearly understand that for the sake of convenience and brevity, only the division of the above-mentioned functional units is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] An embodiment of the present application also provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed, the computer executes each step in the method flow shown in the above method embodiment.

[0158] An embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute each step of the method flow shown in the above method embodiment.

[0159] An embodiment of the present application provides a chip system, including: a processor and an interface circuit; the interface circuit is used to receive a computer program or instruction and transmit it to the processor; the processor is used to execute the computer program or instruction so that the chip system performs each step in the method flow shown in the above method embodiment.

[0160] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, and a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), registers, hard disks, optical fibers, portable Compact Disc Read-Only Memory (CD-ROM), optical storage devices, magnetic storage devices, or any other form of computer-readable storage medium known in the art, or any combination thereof. An exemplary storage medium is coupled to a processor so that the processor can read information from and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in a specific-purpose ASIC. In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0161] Since the information generating device, computer-readable storage medium, and computer program product provided in this embodiment can be applied to the information generating method provided by this embodiment, the technical effects that can be obtained can also refer to the above-mentioned method embodiments, and the embodiments of this application will not be repeated here.

[0162] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0163] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely illustrative of the present application as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the claims of the present application and their equivalents.

Claims

1. A method for generating information, characterized in that: The method comprises: Acquiring three-dimensional point cloud data of a target plant and multispectral data of the target plant; determining a first photosynthetic photon flux density (PPFD) based on the three-dimensional point cloud data, and determining a second PPFD based on the multispectral data; A target PPFD of the target plant in a three-dimensional space is determined according to the first PPFD, the second PPFD, and a target weight.

2. The method according to claim 1, characterized in that The determining a target PPFD of the target plant in the three-dimensional space according to the first PPFD, the second PPFD, and the target weight includes: The sum of the first product and the second product is used as the target PPFD of the target plant in the three-dimensional space; the first product is the product of the first PPFD and the target weight, and the second product is the product of the second PPFD and a first difference, and the first difference is the difference between 1 and the target weight.

3. The method according to claim 2, characterized in that The method further comprises: Acquiring multiple first data sets for each of at least one plant; the first data set includes a third PPFD, a fourth PPFD, and a fifth PPFD of the plant under a preset light intensity, the third PPFD being a PPFD determined based on three-dimensional point cloud data of the plant, the fourth PPFD being a PPFD determined based on multispectral data of the plant, and the fifth PPFD being an actual PPFD of the plant, wherein each first data set corresponds to a different preset light intensity; For each original weight in the plurality of original weights, determining a sixth PPFD corresponding to each first data set; the sixth PPFD is the sum of a third product and a fourth product, the third product being the product of a third PPFD in the first data set and the original weight, the fourth product being the product of a fourth PPFD in the first data set and a second difference, where the second difference is a difference between 1 and the original weight; The target weight is determined according to a plurality of second data sets of each original weight in a plurality of original weights; the second data set includes a sixth PPFD and a fifth PPFD corresponding to the sixth PPFD.

4. The method according to claim 3, characterized in that The determining the target weight according to the plurality of second data sets of each original weight in the plurality of original weights comprises: For each second data set of each original weight, taking the difference between the sixth PPFD and the corresponding fifth PPFD as the third difference; For each of the plurality of original weights, determining a mean square error of a plurality of third differences of the original weight; The target weight is determined by iteratively calculating the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the target weight is less than the mean square error corresponding to any original weight.

5. The method according to claim 4, characterized in that The iterative calculation based on the mean square error corresponding to the multiple original weights and the Nelder-Mead algorithm to determine the target weight includes: An initial weight is determined by iteratively calculating the mean square error corresponding to multiple original weights and the Nelder-Mead algorithm; the mean square error corresponding to the initial weight is less than the mean square error corresponding to any of the original weights; Perform a two-tailed t-test based on the initial weights to obtain a test result; In a case where the inspection result indicates that the inspection is passed, the initial weight is used as the target weight.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Obtaining an actual growth curve of environmental data and target growth data of the target plant within a target time period; When the similarity between the actual growth curve and the standard growth curve is less than a preset similarity threshold, determining that the target plant has abnormal growth; Growth recommendation information for the target plant is determined based on the environmental data.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: The target PPFD of the target plant is visualized to obtain a PPFD cloud map of the target plant in a three-dimensional space.

8. An information generating device, characterized in that: The information generating device includes: a processor, the processor is coupled to a memory, the memory is used to store programs or instructions, and when the program or instructions are executed by the processor, the device executes the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instructions are executed, the computer is caused to perform the method according to any one of claims 1 to 7.

10. A computer program product comprising instructions, which, when run on a computer, enables the computer to perform the method according to any one of claims 1 to 7.

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