A cloud-edge-terminal collaborative processing method and system for smart orchard image information

Abstract

The application provides a cloud edge terminal cooperative processing method and system for image information of a smart orchard, and relates to the technical field of smart orchards, wherein the method comprises the following steps: obtaining user management requirements of a smart orchard, analyzing the user management requirements, obtaining corresponding data processing targets, and determining corresponding smart orchard image processing targets according to the data processing targets; obtaining image information of the smart orchard in a preset time period, and preprocessing the image information; and sending the preprocessed image information to the cloud server for processing; the application makes full use of the geographical location advantage and real-time processing capability of edge computing, improves the accuracy and efficiency of image processing, and reduces the resource consumption of the cloud server.

Description

Technical Field

[0001] This invention relates to the field of smart orchard technology, and more specifically to a cloud-edge-device collaborative processing method and system for smart orchard image information. Background Technology

[0002] Currently, smart orchards represent a deep integration of traditional orchards with next-generation information technology. They utilize IoT, cloud computing, big data, and artificial intelligence technologies to monitor and process orchard data (such as image data) in real time, ultimately making intelligent decisions that make orchards more intelligent, refined, and efficient.

[0003] However, existing technologies for image data processing in smart orchards do not integrate cloud-edge-device technologies and typically employ a single data processing method. Limited by their own computing power, storage, communication, and intelligence, they are unable to adapt to complex and ever-changing environments and task requirements.

[0004] Therefore, how to provide a cloud-edge-device collaborative processing method for smart orchard image information that can solve the above problems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a cloud-edge-device collaborative processing method and system for smart orchard image information, which makes full use of the geographical location advantages and real-time processing capabilities of edge computing, improves the accuracy and efficiency of image processing, and reduces the resource consumption of cloud servers.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A cloud-edge-device collaborative processing method for smart orchard image information includes the following steps: The user management requirements of the smart orchard are obtained, and the user management requirements are parsed to obtain the corresponding data processing targets. Based on the data processing targets, the corresponding smart orchard image processing targets are determined. Acquire image information of the smart orchard during a preset time period, and preprocess the image information; The preprocessed image information is sent to the cloud server for further processing.

[0007] Preferably, the specific process for determining whether the image information needs to be preprocessed includes: Determine whether the space occupied by the image information reaches a preset threshold; If the value is less than or equal to the threshold, then the edge computing node is invoked to preprocess the image information.

[0008] Preferably, the method further includes: determining whether the image information needs to be preprocessed, the specific process of which includes: If the image information is greater than or equal to a preset threshold; The image information is segmented by calling multiple edge computing nodes and then sent to multiple edge computing nodes for preprocessing.

[0009] Preferably, the specific process of sending the data to the cloud server for processing includes: Cloud servers have various types of processing models pre-configured. The processing model selection result is determined based on the memory size occupied by the preprocessed image information, the target of the smart orchard image processing, and the cloud space size of the cloud server.

[0010] Preferably, the specific process for determining the processing model selection result includes: When the memory usage of the preprocessed image information is less than or equal to a first threshold, a dynamic selection model is constructed. The preprocessed image information is then input into the dynamic selection model for further processing. Based on the processing results of the dynamic selection model, a corresponding processing model is selected, and the processing model is used for simulation processing. Determine whether the time of the simulation processing result matches the target of the smart orchard image processing; if they match, select the processing model.

[0011] Preferably, the specific process for determining the processing model selection result also includes: When the memory occupied by the preprocessed image information is greater than the first threshold and less than or equal to the second threshold, the historical commonly used image processing methods of the smart orchard are obtained, and the historical commonly used image processing methods are sorted in descending order of the number of times they are used. The preprocessed image information is input into the dynamic selection model for processing, and the processing result of the dynamic selection model is matched with the ranking result to select the processing model that ranks before the smart orchard image processing target for processing. The preprocessed image information is segmented based on the matching results and then sequentially input into the corresponding processing models for further processing.

[0012] Preferably, the specific process for determining the processing model selection result also includes: When the memory occupied by the preprocessed image information exceeds the second threshold, the association rule mining model is used to determine the processing model of interest to the smart orchard. Image processing is performed using the processing model of interest.

[0013] This invention also provides a cloud-edge-device collaborative processing system for smart orchard image information, comprising: The acquisition module is used to acquire user management requirements of the smart orchard, parse the user management requirements to obtain the corresponding data processing targets, and determine the corresponding smart orchard image processing targets based on the data processing targets. The preprocessing module is used to acquire image information of the smart orchard during a preset time period and to preprocess the image information. The processing module is used to send the preprocessed image information to the cloud server for further processing.

[0014] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a cloud-edge-device collaborative processing method and system for smart orchard image information, which has the following beneficial effects: 1. This invention, by accurately analyzing user management needs, ensures the targeted and effective nature of subsequent image processing. This improves the intelligence level of orchard management and reduces unnecessary investment of manpower and resources. 2. This invention provides timely data support for orchard management by acquiring orchard image information in real time.

[0015] Preprocessing reduces the space occupied by image information and improves processing efficiency. Calling edge computing nodes for preprocessing fully utilizes the geographical advantages and real-time processing capabilities of edge computing.

[0016] 3. The cloud server configured in this invention has multiple types of processing models pre-set, which can be flexibly selected according to actual needs.

[0017] 4. This invention can automatically select the most suitable processing model based on factors such as the memory size of the image information and the processing target. This improves the accuracy and efficiency of image processing, reduces the resource consumption of cloud servers, and ensures the effectiveness and relevance of the processing. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This invention provides an overall flowchart of a cloud-edge-device collaborative processing method for smart orchard image information. Figure 2 The present invention provides a structural principle block diagram of a cloud-edge-device collaborative processing system for smart orchard image information. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] See Figure 1 As shown in the figure, this invention discloses a cloud-edge-device collaborative processing method for smart orchard image information, including the following steps: The user management requirements of the smart orchard are obtained, and the user management requirements are analyzed to obtain the corresponding data processing objectives. Based on the data processing objectives, the corresponding smart orchard image processing objectives are determined. The image information of the smart orchard during a preset time period is acquired, and the image information is preprocessed. The preprocessing process may include filtering, resizing, and binarization. The pre-processed image information is sent to the cloud server for further processing.

[0022] In one specific embodiment, the method further includes: determining whether the image information needs to be preprocessed, the specific process of which includes: Determine whether the space occupied by the image information reaches a preset threshold; If the value is less than or equal to the threshold, then the edge computing node is invoked to preprocess the image information.

[0023] In one specific embodiment, the process of determining whether image information needs to be preprocessed further includes: If the image information is greater than or equal to the preset threshold; Multiple edge computing nodes are invoked to segment the image information, and the segmented information is then sent to multiple edge computing nodes for preprocessing.

[0024] The embodiments of the present invention can reduce the space occupied by image information and improve processing efficiency by calling edge computing nodes for preprocessing through the above steps, thus making full use of the geographical location advantages and real-time processing capabilities of edge computing.

[0025] In one specific embodiment, the process of sending the data to the cloud server for processing includes: The cloud server is pre-configured with various types of processing models, which may include various types of neural network models, semantic segmentation models, convolutional neural network models and other data processing models. The processing model selection result is determined based on the memory size of the preprocessed image information, the image processing target of the smart orchard, and the cloud space size of the cloud server.

[0026] In one specific embodiment, the process of determining the specific steps for processing the model selection result includes: When the memory occupied by the preprocessed image information is less than or equal to the first threshold, a dynamic selection model is constructed, and the preprocessed image information is input into the dynamic selection model for processing. The dynamic selection model can construct a resource selection algorithm based on data models such as cluster node matrix, CPU load matrix, disk I / O load matrix, and network load matrix. Based on the processing results of the dynamic selection model, the corresponding processing model is selected, and the processing model is used for simulation processing; Determine whether the time of the simulation processing result matches the target of the smart orchard image processing. If they match, select the processing model. Through the above process, the optimal processing model can be obtained, further improving data processing efficiency.

[0027] In one specific embodiment, the process of determining the specific steps for processing the model selection result further includes: When the memory occupied by the preprocessed image information is greater than the first threshold and less than or equal to the second threshold, the historical commonly used image processing methods of the smart orchard are obtained, and the historical commonly used image processing methods are sorted in descending order of the number of times they are used. The preprocessed image information is input into the dynamic selection model for processing, and the processing result of the dynamic selection model is matched with the ranking result. The processing model that ranks before the target image processing in the smart orchard is selected for processing. At this time, the dynamic selection model is the same as the dynamic selection model mentioned above. The preprocessed image information is segmented based on the matching results and then sequentially input into the corresponding processing models for further processing.

[0028] Specifically, when the two do not match, a decision model is constructed to determine the segmentation method of image information and the specific type of the corresponding processing model.

[0029] In one specific embodiment, the process of determining the specific steps for processing the model selection result further includes: When the memory occupied by the preprocessed image information exceeds the second threshold, the association rule mining model is used to determine the processing model of interest to the smart orchard. The association rule mining model can be either the Apriori algorithm or the FP-Growth algorithm. Image processing is performed using the processing model of interest.

[0030] Specifically, the process of using the processing model of interest to complete image processing also includes: When the number of processing models of interest exceeds the threshold, the segmentation method of image information is determined by the decision model. Simultaneously, the processing models of interest are used to simulate image processing, and the image processing time is sorted. Using the image processing target of the smart orchard as the threshold, the processing models less than or equal to the image processing target of the smart orchard are selected to complete the image processing, thereby further improving the efficiency of data processing.

[0031] See Figure 2 As shown, this embodiment of the invention also provides a system for cloud-edge-device collaborative processing of smart orchard image information as described in any of the above embodiments, comprising: The acquisition module is used to acquire user management requirements of the smart orchard, parse the user management requirements to obtain the corresponding data processing targets, and determine the corresponding smart orchard image processing targets based on the data processing targets. The preprocessing module is used to acquire image information of the smart orchard during a preset time period and to preprocess the image information. The processing module is used to send the pre-processed image information to the cloud server for further processing.

[0032] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0033] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cloud-edge-device collaborative processing method for smart orchard image information, characterized in that, Includes the following steps: The user management requirements of the smart orchard are obtained, and the user management requirements are parsed to obtain the corresponding data processing targets. Based on the data processing targets, the corresponding smart orchard image processing targets are determined. Acquire image information of the smart orchard during a preset time period, and preprocess the image information; The preprocessed image information is sent to the cloud server for further processing.

2. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 1, characterized in that, Also includes: Determining whether the image information needs preprocessing includes the following steps: Determine whether the space occupied by the image information reaches a preset threshold; If the value is less than or equal to the threshold, then the edge computing node is invoked to preprocess the image information.

3. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 2, characterized in that, The specific process for determining whether the image information needs to be preprocessed also includes: If the image information is greater than or equal to a preset threshold; The image information is segmented by calling multiple edge computing nodes and then sent to multiple edge computing nodes for preprocessing.

4. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 1, characterized in that, The specific process of sending the data to the cloud server for processing includes: Cloud servers have various types of processing models pre-configured. The processing model selection result is determined based on the memory size occupied by the preprocessed image information, the target of the smart orchard image processing, and the cloud space size of the cloud server.

5. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 4, characterized in that, The specific process for determining the processing model selection results includes: When the memory usage of the preprocessed image information is less than or equal to a first threshold, a dynamic selection model is constructed. The preprocessed image information is then input into the dynamic selection model for further processing. Based on the processing results of the dynamic selection model, a corresponding processing model is selected, and the processing model is used for simulation processing. Determine whether the time of the simulation processing result matches the target of the smart orchard image processing; if they match, select the processing model.

6. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 5, characterized in that, The specific process for determining the results of the model selection also includes: When the memory occupied by the preprocessed image information is greater than the first threshold and less than or equal to the second threshold, the historical commonly used image processing methods of the smart orchard are obtained, and the historical commonly used image processing methods are sorted in descending order of the number of times they are used. The preprocessed image information is input into the dynamic selection model for processing, and the processing result of the dynamic selection model is matched with the ranking result to select the processing model that ranks before the smart orchard image processing target for processing. The preprocessed image information is segmented based on the matching results and then sequentially input into the corresponding processing models for further processing.

7. The cloud-edge-device collaborative processing method for smart orchard image information according to claim 5, characterized in that, The specific process for determining the results of the model selection also includes: When the memory occupied by the preprocessed image information exceeds the second threshold, the association rule mining model is used to determine the processing model of interest to the smart orchard. Image processing is performed using the processing model of interest.

8. A system for cloud-edge-device collaborative processing of smart orchard image information according to any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire user management requirements of the smart orchard, parse the user management requirements to obtain the corresponding data processing targets, and determine the corresponding smart orchard image processing targets based on the data processing targets. The preprocessing module is used to acquire image information of the smart orchard during a preset time period and to preprocess the image information. The processing module is used to send the preprocessed image information to the cloud server for further processing.

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