Intelligent planning method and system for whole-cycle planting of phalaenopsis based on digital twinning

Abstract

The application is suitable for the phalaenopsis planting technical field, and particularly relates to a phalaenopsis whole-cycle planting intelligent planning method and system based on digital twinning, which comprises the following steps: selecting a to-be-monitored area requiring phalaenopsis planting management, collecting growth monitoring data by using a sensing device pre-deployed in the to-be-monitored area, constructing a digital twinning model, and mapping the growth monitoring data into the digital twinning model; finding out all the planting areas of phalaenopsis, creating a shooting task, and delivering the shooting task to a terminal of a management personnel corresponding to the planting area. The application can realize targeted improvement of the to-be-monitored area, quickly optimize the growth state, reduce the trial-and-error cost, form quantifiable management rules, provide data support for planting decisions, enable excellent experience to be reused and popularized, and thus continuously improve the yield and quality of the to-be-monitored area while ensuring the growth quality.

Description

Technical Field

[0001] This invention relates to the field of Phalaenopsis orchid cultivation technology, and in particular to a method and system for intelligent planning of Phalaenopsis orchid cultivation throughout its entire life cycle based on digital twins. Background Technology

[0002] Phalaenopsis orchids are ornamental orchids with high environmental requirements. Their cultivation typically takes place in controlled environments such as greenhouses or polytunnels, requiring precise control of key factors like temperature, humidity, light, and ventilation to achieve stable growth. Digital twins refer to the creation of a virtual counterpart that corresponds one-to-one with a real-world object and can be simulated and predicted.

[0003] In current technologies, Phalaenopsis orchid cultivation typically relies on manual experience or single environmental parameters for management. Even in greenhouses that have introduced temperature and humidity sensors and automated control equipment, control is mostly based on fixed values, lacking a comprehensive analysis of the differences between different cultivation areas. Especially during the same batch of cultivation, uneven light distribution, variations in ventilation conditions, or inconsistent human operation often lead to significant differences in growth status. Therefore, Phalaenopsis orchid cultivation requires differentiated settings for environmental parameters and cultivation operations based on their different growth stages, thus demanding a high level of experience from the growers.

[0004] Therefore, "how to combine digital twin technology to realize the transfer of excellent planting experience" is the technical problem that this invention needs to solve. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent planning of Phalaenopsis orchid cultivation throughout its entire life cycle based on digital twins, in order to solve the problem raised in the background art of "how to combine digital twin technology to realize the transfer of excellent cultivation experience".

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A digital twin-based intelligent planning method for the entire lifecycle of Phalaenopsis orchid cultivation, the method comprising:

[0008] Select the area to be monitored for Phalaenopsis orchid cultivation and management, use the sensing devices pre-deployed in the area to collect growth monitoring data, construct a digital twin model, and map the growth monitoring data into the digital twin model;

[0009] All Phalaenopsis orchid planting areas are located, a shooting task is created and sent to the corresponding management personnel terminal of the planting area. Image data is received from the management personnel terminal, and key features are extracted. The key features include at least: leaf area, disease ratio and number of flower buds. Based on the key features, high-quality areas are selected from the planting areas, video monitoring data in the high-quality areas is collected, snapshots are captured, planting behavior management data is identified, the corresponding time is recorded, cultivation records are generated, and the user terminals of the area to be monitored are given access to view the cultivation records.

[0010] Establish a correspondence between production monitoring data and planting behavior management data, construct a reference table, and, based on the key features and image data, identify the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area, and define it as the control area. Define the reference table corresponding to the control area as the standard value, and use the digital twin model to verify and correct the standard value. Obtain control authority for the adjustment equipment in the area to be monitored, and adjust the adjustment equipment in conjunction with the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting, and circulating fans.

[0011] Furthermore, the step of selecting the area to be monitored for Phalaenopsis orchid cultivation and management, and collecting growth monitoring data using sensors pre-deployed in the area to be monitored, includes:

[0012] The growth monitoring data is divided into several individual items, wherein the individual items include at least: temperature, humidity and light intensity.

[0013] Establish the correlation between each individual item and the key features.

[0014] Furthermore, the step of receiving image data fed back from the management personnel terminal and extracting key features includes:

[0015] Image samples of the planting area were collected and manually labeled to generate a training set;

[0016] Create an image recognition model, train it using a training set, input image data into the trained image recognition model, and output key features.

[0017] Furthermore, the steps of extracting planting behavior management data, recording the corresponding time, generating cultivation records, and granting user terminals in the monitored area access to view the cultivation records include:

[0018] Create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform;

[0019] An identity verification mechanism is established and integrated into the planting guidance platform. Technical personnel are selected, and the planting guidance information submitted by the technical personnel is received and forwarded to the user terminals in the area to be monitored.

[0020] Furthermore, the step of establishing the correspondence between production monitoring data and planting behavior management data, and constructing a lookup table, includes:

[0021] Record the start time of Phalaenopsis orchid planting, draw a timeline chart, and map production monitoring data and planting behavior management data onto the timeline chart;

[0022] From the planting areas, select the abnormal management areas, and establish a communication link between the user terminals corresponding to the abnormal management areas and the high-quality areas through the planting guidance platform.

[0023] Furthermore, the step of obtaining control permissions for the regulating devices in the area to be monitored, and adjusting the regulating devices in conjunction with standard values, includes:

[0024] Using the aforementioned control permissions, several environmental management modes are created and deployed to the regulating equipment;

[0025] Establish risk assessment rules, and activate a pre-edited reminder mechanism when the planting behavior management data meets the risk assessment rules.

[0026] Furthermore, the system includes:

[0027] The mapping module is used to select the area to be monitored for Phalaenopsis orchid cultivation and management, collect growth monitoring data using sensors pre-deployed in the area to be monitored, construct a digital twin model, and map the growth monitoring data into the digital twin model.

[0028] An open module is used to locate all Phalaenopsis orchid planting areas, create shooting tasks, and send them to the management terminals of the corresponding planting areas. It receives image data from the management terminals, extracts key features, including at least leaf area, disease ratio, and number of flower buds. Based on the key features, it selects high-quality areas from the planting areas, collects video monitoring data from the high-quality areas, captures snapshots, identifies planting behavior management data, records the corresponding time, generates cultivation records, and grants access to the cultivation records to user terminals in the monitored areas.

[0029] The linkage adjustment module is used to establish the correspondence between production monitoring data and planting behavior management data, construct a reference table, and, through the key features and image data, find the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area and define it as the control area. The reference table corresponding to the control area is defined as the standard value. The standard value is verified and corrected using the digital twin model, and the control authority of the adjustment equipment in the area to be monitored is obtained. The adjustment equipment is then linked and adjusted according to the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting, and circulating fans.

[0030] Furthermore, the mapping module includes:

[0031] A segmentation unit is used to segment growth monitoring data into several individual items, wherein the individual items include at least: temperature, humidity and light intensity.

[0032] Establish units to establish the correlation between each individual item and key features.

[0033] Furthermore, the open module includes:

[0034] The acquisition unit is used to acquire image samples of the planting area, perform manual annotation, and generate a training set;

[0035] The output unit is used to create an image recognition model. It is trained using a training set, and the image data is input into the trained image recognition model to output key features.

[0036] The publishing unit is used to create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform.

[0037] The forwarding unit is used to build an identity verification mechanism, which is integrated into the planting guidance platform. It selects technical personnel, receives planting guidance information submitted by the technical personnel, and forwards it to the user terminals in the area to be monitored.

[0038] Furthermore, the linkage adjustment module includes:

[0039] The recording unit is used to record the start time of planting Phalaenopsis orchids, draw a timeline chart, and map production monitoring data and planting behavior management data onto the timeline chart;

[0040] The communication unit is used to select abnormal management areas from the planting area and establish a communication link between the abnormal management areas and the user terminals corresponding to the high-quality areas through the planting guidance platform.

[0041] The deployment unit is used to create several environmental management modes via the control permissions and deploy them to the regulating device;

[0042] The activation unit is used to construct risk assessment rules. When the planting behavior management data is detected to meet the risk assessment rules, the pre-edited reminder mechanism is activated.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By creating a digital twin model, the impact of different growth monitoring data on Phalaenopsis orchid growth can be simulated in a virtual environment, enabling visualized monitoring of Phalaenopsis orchid growth status, optimizing planting plans, reducing failure costs, and objectively quantifying the growth status of Phalaenopsis orchids by collecting key features. This provides a data foundation for scientific planting and also enables early warning and rapid intervention for Phalaenopsis orchids. By generating cultivation records, reusable excellent planting experience can be obtained, greatly improving the yield and quality of Phalaenopsis orchids in the monitored area. It provides a sample comparison for remote automated management of Phalaenopsis orchid planting. By identifying control areas, targeted improvements can be made to the monitored area, rapidly optimizing growth status, reducing trial and error costs, forming quantifiable management rules, providing data support for planting decisions, and enabling the reuse and promotion of excellent experience. Thus, while ensuring growth quality, the yield and quality of the monitored area can be continuously improved. Attached Figure Description

[0045] Figure 1 A flowchart illustrating the intelligent planning method for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins, provided in an embodiment of the present invention.

[0046] Figure 2 This is a first sub-flowchart of the intelligent planning method for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins provided in an embodiment of the present invention.

[0047] Figure 3 This is a second sub-flowchart of the intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins provided in an embodiment of the present invention.

[0048] Figure 4 The third sub-flowchart of the intelligent planning method for the whole-cycle planting of Phalaenopsis orchids based on digital twins provided in the embodiments of the present invention;

[0049] Figure 5 This is a block diagram of the intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins provided in an embodiment of the present invention;

[0050] Figure 6 A block diagram of the mapping module in the intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins provided in an embodiment of the present invention;

[0051] Figure 7 A block diagram of the open modules in the intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins provided in this embodiment of the invention;

[0052] Figure 8 This is a block diagram of the linkage adjustment module in the intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins, provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] In Example 1, Figure 1 The implementation flow of the intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins provided in this embodiment of the invention is illustrated below in detail:

[0055] S100: Select the area to be monitored for Phalaenopsis orchid cultivation and management, use the sensing devices pre-deployed in the area to collect growth monitoring data, construct a digital twin model, and map the growth monitoring data into the digital twin model.

[0056] The monitoring area is determined, referring to the planting area requiring focused attention. In this embodiment, a greenhouse is used as an example; the planting area refers to a greenhouse with growth problems or inadequate management, which can be simply understood as a greenhouse where Phalaenopsis orchids are growing poorly. However, the monitoring area can also be other planting areas of Phalaenopsis orchids. If the monitoring area is a greenhouse with good growth, continuous monitoring may increase unnecessary management and data collection burdens. Growth monitoring data is collected in the monitoring area using temperature and humidity sensors, light sensors, soil moisture sensors, and environmental monitoring cameras installed in the monitoring area. This growth monitoring data includes temperature, humidity, and light intensity within the greenhouse.

[0057] A digital twin model is created to recreate the growth state and environmental conditions of Phalaenopsis orchids in a virtual space. Collected data such as temperature, humidity, light intensity, and image features are integrated and corresponding environmental parameter layers and growth state layers are constructed in the digital twin model. Acquired growth monitoring data is input into the digital twin model, and the corresponding parameters in the digital twin model are dynamically updated to ensure that the digital twin model is consistent with the actual planting area and to map the Phalaenopsis orchid growth process in real time.

[0058] S200: Locate all Phalaenopsis orchid planting areas, create a shooting task, and send it to the corresponding management personnel terminal of the planting area. Receive image data from the management personnel terminal, extract key features, including at least: leaf area, disease ratio, and number of flower buds. Based on the key features, select high-quality areas from the planting areas, collect video monitoring data from the high-quality areas, capture snapshots, identify planting behavior management data, record the corresponding time, generate cultivation records, and grant viewing access to cultivation records to user terminals in the monitored areas.

[0059] The system identifies all greenhouses cultivating Phalaenopsis orchids and generates shooting tasks according to a preset cycle. These tasks include shooting time, angle, range, and key areas of focus (e.g., leaves, flower buds, or diseased parts). These tasks are sent to the terminals of the corresponding management personnel in each greenhouse. The personnel then take photos of the Phalaenopsis orchids on-site according to the task requirements and upload the photos after completion. Upon receiving the image data uploaded by the management personnel, the system inputs the image data into an image recognition model, outputting key features including: leaf area, disease ratio, and number of flower buds. Leaf area is obtained through leaf contour recognition and pixel calculation, thus assessing the plant's growth vitality. The disease ratio reflects the degree of impact of pests and diseases on the leaves or flowers; by identifying the proportion of diseased areas within the overall leaf or flower area, a quantitative assessment of disease development trends is achieved. The number of flower buds, the total number of flower buds in the image data, characterizes the growth status of the Phalaenopsis orchid.

[0060] Professionals select high-quality areas from all planting regions based on key characteristics. High-quality areas are defined as those with excellent growth, low disease prevalence, and good flowering. Cameras installed in these high-quality areas collect video monitoring data, capturing snapshots at preset time intervals. By comparing adjacent snapshots, planting behavior management data is identified. This data describes human interventions and environmental controls during Phalaenopsis orchid cultivation, such as detecting personnel entering and watering, activating drip irrigation, or opening shade curtains or windows for ventilation. Simply put, planting behavior management data is a record of various operations and environmental controls during Phalaenopsis orchid cultivation. The corresponding time periods for each planting behavior management data point are determined. The identified data is then organized and correlated chronologically to generate a complete cultivation record. This record includes the time, duration, and corresponding area for actions such as watering, ventilation, shading, and fertilization. This cultivation record serves as a record of the cultivation process. The cultivation records are published to a pre-set platform, and viewing access is granted to user terminals corresponding to the monitored areas. This allows growers in the monitored areas to view the cultivation process of high-performing areas at any time, compare and analyze potential shortcomings in their own cultivation practices, and provide a reference for planting decisions. It should be noted that there are multiple high-quality areas mentioned above, therefore multiple sets of cultivation records are available for reference.

[0061] S300: Establish the correspondence between production monitoring data and planting behavior management data, construct a reference table, and from the key features and image data, find the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area and define it as the control area. Define the reference table corresponding to the control area as the standard value. Use the digital twin model to verify and correct the standard value, obtain the control authority of the adjustment equipment in the area to be monitored, and adjust the adjustment equipment in linkage according to the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting and circulating fan.

[0062] Using time as a benchmark, the collected production monitoring data is aligned with the identified planting behavior management data to establish a correspondence between the two, and a comparison table is constructed. The comparison table consists of production monitoring data items and planting behavior management data items. For example, Table 1 below is a comparison table for a greenhouse on December 1st.

[0063] time Production monitoring data Planting behavior management data December 1st 08:00 Leaf area: 12.5 cm²; Disease rate: 0%; Number of flower buds: 3 Start watering using the drip irrigation system. December 1, 12:00 Leaf area: 12.8 cm²; Disease rate: 0%; Number of flower buds: 3 Sunshade curtain open …… …… ……

[0064] Table 1

[0065] Using deep learning algorithms, an image feature matching model is constructed. Images of the monitored area at the current moment are compared sequentially with images of various planting areas, and similarity is calculated. The planting area with the highest similarity is defined as the control area, which is the area whose growth progress is closest to that of the monitored area (e.g., both are in the bud development stage, and their plant height and number of buds are similar). Production monitoring data and planting behavior management data from the control area are defined as standard values. These standard values ​​are input into a digital twin model for verification and correction in a virtual environment. By simulating the impact of environmental parameters and management operations corresponding to the monitored area, the feasibility of the standard values ​​under actual planting conditions is determined, and the standard values ​​are fine-tuned based on feedback from the digital twin model. The advantages of this method are that it ensures the scientific rationality of the standard values ​​and can be applied to optimize the management plan of the monitored area, achieving precise and low-risk growth control.

[0066] Obtain control permissions for various regulating devices in the area to be monitored. These devices include temperature and humidity control equipment (drip irrigation system), supplemental lighting, and circulating fans. The regulating devices are used to control the environmental conditions inside the greenhouse and adjust growth monitoring data. Using the control area as a sample, the regulating devices in the area to be monitored are adjusted in a coordinated manner.

[0067] In summary, in this embodiment, high-quality areas with excellent Phalaenopsis orchid growth are first selected from all planting areas. Then, control areas that are closest to the growth progress of the area to be monitored are selected from the high-quality areas. The usage parameters of the adjustment equipment in the control areas (such as the setting of the drip irrigation system and the intensity of the supplemental lighting) are obtained. These usage parameters are then fine-tuned using a digital twin model. Finally, the adjustment equipment in the area to be monitored is adjusted synchronously according to these usage parameters.

[0068] In Example 2, Figure 2 The first sub-flowchart of the intelligent planning method for Phalaenopsis orchid full-cycle planting based on digital twins provided by an embodiment of the present invention is shown. The following details the steps of selecting the monitoring area to be managed for Phalaenopsis orchid planting and collecting growth monitoring data using sensors pre-deployed in the monitoring area:

[0069] S101: Divide the growth monitoring data into several individual items, wherein the individual items include at least: temperature, humidity and light intensity.

[0070] Growth monitoring data is divided into several individual items, such as temperature, humidity, and light intensity.

[0071] S102: Establish the correlation between each individual item and the key features.

[0072] Establish correlations between individual items and key features, including both positive and negative correlations. For example, when the temperature within the suitable growing area of ​​a Phalaenopsis orchid is higher, the leaf growth rate is relatively faster. In other words, there is a positive correlation between temperature and leaf area among the key features; within the suitable range, higher temperatures result in larger leaf areas.

[0073] In Example 3, Figure 3 The second sub-flowchart of the intelligent planning method for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins provided in this embodiment of the invention is shown. The following details the step of receiving image data fed back by the management personnel terminal and extracting key features:

[0074] S201: Collect image samples of the planting area, manually label them, and generate a training set.

[0075] During the image recognition model building phase, several image datasets were randomly selected from all planting areas as image samples. These samples were then labeled by professionals, who identified leaf area, disease prevalence, and number of flower buds, among other things, to generate a training set. These professionals could be orchid cultivation experts or experienced orchid growers.

[0076] S202: Create an image recognition model, train it using a training set, input image data into the trained image recognition model, and output key features.

[0077] Using deep learning algorithms, an image recognition model is created that can automatically identify the leaves, flower buds, and diseased areas of Phalaenopsis orchids and calculate key features, including leaf area, number of flower buds, and disease ratio.

[0078] In Example 4, Figure 3 The second sub-flowchart of the intelligent planning method for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins provided in this embodiment of the invention is shown. The following details the steps of extracting cultivation behavior management data, recording the corresponding time, generating cultivation records, and granting access to the cultivation records to user terminals in the area to be monitored:

[0079] S203: Create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform.

[0080] A planting guidance platform was created to extract monitoring segments of planting activities such as watering, ventilation, fertilization, and supplemental lighting from high-quality video surveillance data in the area, and publish the corresponding monitoring segments to the planting guidance platform. The planting guidance platform is mainly used to provide growers with visual operation references and cultivation experience, so as to facilitate experience sharing and communication among growers.

[0081] S204: Construct an identity verification mechanism, integrate it into the planting guidance platform, select technical personnel, receive planting guidance information submitted by technical personnel, and forward it to user terminals in the area to be monitored.

[0082] An identity verification mechanism is established, defining agricultural experts and planting personnel in planting areas who have passed the identity verification as technical personnel. Technical personnel can publish planting guidance information about the areas to be monitored on the planting guidance platform. The planting guidance information includes specific operating methods, time arrangements, and precautions for watering, fertilizing, ventilation, and supplemental lighting. The planting guidance information is then forwarded to the user terminals of the planting personnel in the areas to be monitored.

[0083] In Example 5, Figure 4 The third sub-flow diagram of the intelligent planning method for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins provided in this embodiment of the invention is shown. The following details the steps of establishing the correspondence between production monitoring data and cultivation behavior management data and constructing the lookup table:

[0084] S301: Record the start time of Phalaenopsis orchid planting, draw a timeline chart, and map production monitoring data and planting behavior management data onto the timeline chart.

[0085] By identifying image data of each planting area, the start time of Phalaenopsis orchid planting in each planting area is determined. A timeline is drawn with the start time of planting as the starting point, and production monitoring data and planting behavior management data at each time point are marked on the timeline to intuitively present the correspondence between each operation and the growth status.

[0086] S302: Select the abnormal management area from the planting area, and establish a communication link between the user terminal corresponding to the abnormal management area and the high-quality area through the planting guidance platform.

[0087] Analyze all planting areas and select areas where abnormalities occur during management, i.e., abnormal management areas, such as areas with abnormal leaf growth, high disease rates, or delayed flowering. Through the planting guidance platform, establish a communication link between the user terminals corresponding to the abnormal management areas and the high-quality areas, so that the growers in the abnormal management areas can obtain the cultivation experience, operation references, and guidance information from the high-quality areas.

[0088] In Example 6, Figure 4 The diagram shows the third sub-flow of the intelligent planning method for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins provided in this embodiment of the invention. The following details the steps of obtaining control permissions for the adjustment devices in the area to be monitored and adjusting the adjustment devices in conjunction with standard values:

[0089] S303: Through the aforementioned control permissions, create several environmental management modes and deploy them to the regulating device.

[0090] Using the acquired control permissions of the regulating equipment, several environmental control modes are created based on different growth stages and environmental requirements. For example, the supplemental lighting mode refers to automatically turning on the supplemental lights from 6:00 AM to 10:00 AM and from 4:00 PM to 6:00 PM every day. All environmental control modes are then written into the regulating equipment.

[0091] S304: Construct risk assessment rules. When the planting behavior management data is detected to meet the risk assessment rules, activate the pre-edited reminder mechanism.

[0092] Create risk assessment rules, which refer to operational errors during the planting process. For example, one rule might be: during the bud development stage, if the soil moisture content exceeds 70%, the drip irrigation system is still detected to be activated. Implement an alert mechanism, which can send alarm messages to the user terminals of the corresponding growers. The advantage of this approach is that it enables growers to respond quickly and take timely intervention measures, thereby reducing the impact of operational errors on the growth of Phalaenopsis orchids and ensuring the safety and stability of the planting process.

[0093] Figure 5 The diagram illustrates the structural composition of the intelligent planning system for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins, as provided in an embodiment of the present invention. The intelligent planning system 1 for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins includes:

[0094] The mapping module 11 is used to select the area to be monitored for Phalaenopsis orchid cultivation management, collect growth monitoring data using the sensing devices pre-deployed in the area to be monitored, construct a digital twin model, and map the growth monitoring data into the digital twin model.

[0095] The open module 12 is used to find all planting areas of Phalaenopsis orchids, create shooting tasks, and send them to the management terminals of the corresponding planting areas. It receives image data fed back by the management terminals, extracts key features, including at least leaf area, disease ratio and number of flower buds. Based on the key features, it selects high-quality areas from the planting areas, collects video monitoring data in the high-quality areas, captures snapshots, identifies planting behavior management data, records the corresponding time, generates cultivation records, and grants access to the cultivation records to the user terminals of the areas to be monitored.

[0096] The linkage adjustment module 13 is used to establish the correspondence between production monitoring data and planting behavior management data, construct a reference table, and, through the key features and image data, find the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area and define it as the control area. The reference table corresponding to the control area is defined as the standard value. The standard value is verified and corrected using the digital twin model, the control authority of the adjustment equipment in the area to be monitored is obtained, and the adjustment equipment is linked and adjusted according to the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting and circulating fan.

[0097] Figure 6 This diagram illustrates the composition of the mapping module 11 in the intelligent planning system for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins provided in an embodiment of the present invention. The mapping module 11 includes:

[0098] The segmentation unit 111 is used to segment the growth monitoring data into several individual items, wherein the individual items include at least: temperature, humidity and light intensity.

[0099] Unit 112 is established to establish the correlation between each item and the key features.

[0100] Figure 7 This diagram illustrates the structural composition of open module 12 in the intelligent planning system for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins provided in an embodiment of the present invention. Open module 12 includes:

[0101] The acquisition unit 121 is used to acquire image samples of the planting area, perform manual annotation, and generate a training set;

[0102] The output unit 122 is used to create an image recognition model, train it using a training set, input image data into the trained image recognition model, and output key features.

[0103] The publishing unit 123 is used to create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform;

[0104] The forwarding unit 124 is used to build an identity verification mechanism, which is integrated into the planting guidance platform, selects technical personnel, receives planting guidance information submitted by technical personnel, and forwards it to user terminals in the area to be monitored.

[0105] Figure 8 This diagram illustrates the structural composition of the linkage adjustment module 13 in the intelligent planning system for the entire lifecycle cultivation of Phalaenopsis orchids based on digital twins provided in an embodiment of the present invention. The linkage adjustment module 13 includes:

[0106] Recording unit 131 is used to record the start time of planting Phalaenopsis orchids, draw a timeline graph, and map production monitoring data and planting behavior management data onto the timeline graph;

[0107] The communication unit 132 is used to select abnormal management areas from the planting areas and establish a communication link between the abnormal management areas and the user terminals corresponding to the high-quality areas through the planting guidance platform.

[0108] Deployment unit 133 is used to create several environmental management modes via the control permissions and deploy them to the regulating device;

[0109] Activation unit 134 is used to construct risk assessment rules. When the planting behavior management data is detected to meet the risk assessment rules, the pre-edited reminder mechanism is activated.

[0110] The mapping module 11 is mainly used to complete step S100, the opening module 12 is mainly used to complete step S200, and the linkage adjustment module 13 is mainly used to complete step S300.

[0111] The segmentation unit 111 is mainly used to complete step S101, and the establishment unit 112 is mainly used to complete step S102.

[0112] The acquisition unit 121 is mainly used to complete step S201, the output unit 122 is mainly used to complete step S202, the publishing unit 123 is mainly used to complete step S203, and the forwarding unit 124 is mainly used to complete step S204.

[0113] The recording unit 131 is mainly used to complete step S301, the communication unit 132 is mainly used to complete step S302, the deployment unit 133 is mainly used to complete step S303, and the activation unit 134 is mainly used to complete step S304.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital twin-based intelligent planning method for the entire lifecycle cultivation of Phalaenopsis orchids, characterized in that, The method includes: Select the area to be monitored for Phalaenopsis orchid cultivation and management, use the sensing devices pre-deployed in the area to collect growth monitoring data, construct a digital twin model, and map the growth monitoring data into the digital twin model; All Phalaenopsis orchid planting areas are located, a shooting task is created and sent to the corresponding management personnel terminal of the planting area. Image data is received from the management personnel terminal, and key features are extracted. The key features include at least: leaf area, disease ratio and number of flower buds. Based on the key features, high-quality areas are selected from the planting areas, video monitoring data in the high-quality areas is collected, snapshots are captured, planting behavior management data is identified, the corresponding time is recorded, cultivation records are generated, and the user terminals of the area to be monitored are given access to view the cultivation records. Establish a correspondence between production monitoring data and planting behavior management data, construct a reference table, and, based on the key features and image data, identify the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area, and define it as the control area. Define the reference table corresponding to the control area as the standard value, and use the digital twin model to verify and correct the standard value. Obtain control authority for the adjustment equipment in the area to be monitored, and adjust the adjustment equipment in conjunction with the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting, and circulating fans.

2. The intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins according to claim 1, characterized in that, The steps of selecting the area to be monitored for Phalaenopsis orchid cultivation and management, and collecting growth monitoring data using sensors pre-deployed in the area include: The growth monitoring data is divided into several individual items, wherein the individual items include at least: temperature, humidity and light intensity. Establish the correlation between each individual item and the key features.

3. The intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins according to claim 2, characterized in that, The step of extracting key features from the image data fed back by the management personnel terminal includes: Image samples of the planting area were collected and manually labeled to generate a training set; Create an image recognition model, train it using a training set, input image data into the trained image recognition model, and output key features.

4. The intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins according to claim 1, characterized in that, The steps of extracting planting behavior management data, recording the corresponding time, generating cultivation records, and granting access to the cultivation records to user terminals in the area to be monitored include: Create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform; An identity verification mechanism is established and integrated into the planting guidance platform. Technical personnel are selected, and the planting guidance information submitted by the technical personnel is received and forwarded to the user terminals in the area to be monitored.

5. The intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins according to claim 4, characterized in that, The steps for establishing the correspondence between production monitoring data and planting behavior management data, and constructing a lookup table, include: Record the start time of Phalaenopsis orchid planting, draw a timeline chart, and map production monitoring data and planting behavior management data onto the timeline chart; From the planting areas, select the abnormal management areas, and establish a communication link between the user terminals corresponding to the abnormal management areas and the high-quality areas through the planting guidance platform.

6. The intelligent planning method for the entire life cycle planting of Phalaenopsis orchids based on digital twins according to claim 5, characterized in that, The steps of obtaining control permissions for the regulating devices in the area to be monitored and adjusting the regulating devices in conjunction with the standard values ​​include: Using the aforementioned control permissions, several environmental management modes are created and deployed to the regulating equipment; Establish risk assessment rules, and activate a pre-edited reminder mechanism when the planting behavior management data meets the risk assessment rules.

7. A digital twin-based intelligent planning system for the entire lifecycle of Phalaenopsis orchid cultivation, characterized in that: The system includes: The mapping module is used to select the area to be monitored for Phalaenopsis orchid cultivation and management, collect growth monitoring data using sensors pre-deployed in the area to be monitored, construct a digital twin model, and map the growth monitoring data into the digital twin model. An open module is used to locate all Phalaenopsis orchid planting areas, create shooting tasks, and send them to the management terminals of the corresponding planting areas. It receives image data from the management terminals, extracts key features, including at least leaf area, disease ratio, and number of flower buds. Based on the key features, it selects high-quality areas from the planting areas, collects video monitoring data from the high-quality areas, captures snapshots, identifies planting behavior management data, records the corresponding time, generates cultivation records, and grants access to the cultivation records to user terminals in the monitored areas. The linkage adjustment module is used to establish the correspondence between production monitoring data and planting behavior management data, construct a reference table, and, through the key features and image data, find the area with the highest similarity to the orchid planting progress in the area to be monitored from the high-quality area and define it as the control area. The reference table corresponding to the control area is defined as the standard value. The standard value is verified and corrected using the digital twin model, and the control authority of the adjustment equipment in the area to be monitored is obtained. The adjustment equipment is then linked and adjusted according to the standard value. The adjustment equipment includes at least: temperature and humidity equipment, supplemental lighting, and circulating fans.

8. The intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins according to claim 7, characterized in that, The mapping module includes: A segmentation unit is used to segment growth monitoring data into several individual items, wherein the individual items include at least: temperature, humidity and light intensity. Establish units to establish the correlation between each individual item and key features.

9. The intelligent planning system for the entire life cycle of Phalaenopsis orchid cultivation based on digital twins according to claim 8, characterized in that, The open module includes: The acquisition unit is used to acquire image samples of the planting area, perform manual annotation, and generate a training set; The output unit is used to create an image recognition model. It is trained using a training set, and the image data is input into the trained image recognition model to output key features. The publishing unit is used to create a planting guidance platform, obtain the monitoring segments corresponding to the cultivation records, and publish them to the planting guidance platform. The forwarding unit is used to build an identity verification mechanism, which is integrated into the planting guidance platform. It selects technical personnel, receives planting guidance information submitted by the technical personnel, and forwards it to the user terminals in the area to be monitored.

10. The intelligent planning system for the entire life cycle cultivation of Phalaenopsis orchids based on digital twins according to claim 9, characterized in that, The linkage adjustment module includes: The recording unit is used to record the start time of planting Phalaenopsis orchids, draw a timeline chart, and map production monitoring data and planting behavior management data onto the timeline chart; The communication unit is used to select abnormal management areas from the planting area and establish a communication link between the abnormal management areas and the user terminals corresponding to the high-quality areas through the planting guidance platform. The deployment unit is used to create several environmental management modes via the control permissions and deploy them to the regulating device; The activation unit is used to construct risk assessment rules. When the planting behavior management data is detected to meet the risk assessment rules, the pre-edited reminder mechanism is activated.

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