Quail digital breeding data processing system based on end-cloud collaboration

Abstract

The invention discloses a quail digital breeding data processing system based on terminal cloud collaboration, and belongs to the technical field of quail breeding. Quail breeding is monitored in real time, and breeding collection data is obtained; processing the culture collection data according to a preset data processing mode, and sending the processed culture collection data to a cloud platform; identifying analysis requirements of the user, and analyzing the breeding collection data according to each analysis requirement to obtain an analysis result of each analysis requirement; setting a traceability demand table, and performing real-time traceability analysis on the database according to the traceability demand to obtain each sharing management demand; processing the stored data of the database according to each sharing management demand; real-time, continuous and accurate monitoring of quail breeding environment parameters is achieved, any abnormal changes of key environment indexes such as temperature, humidity and ammonia gas concentration can be captured in time, and farmers can rapidly take effective adjustment measures.

Description

Technical Field

[0001] This invention belongs to the field of quail farming technology, specifically a digital quail farming data processing system based on edge-cloud collaboration. Background Technology

[0002] In traditional quail farming, farmers primarily rely on experience and manual observation to manage the breeding process. Environmental parameters, such as temperature, humidity, and ammonia concentration, are often measured intermittently using simple thermometers and hygrometers, making real-time, continuous monitoring impossible. This hinders the timely detection of abnormal changes in environmental parameters and the implementation of effective adjustments, potentially leading to poor growing conditions for quails and impacting their health and productivity. Furthermore, traditional methods struggle to accurately obtain physiological and behavioral data. For instance, key information such as activity levels and feeding behavior can only be estimated through general observation, lacking precise quantification. This makes it difficult to accurately assess the quails' health and nutritional needs, hindering timely adjustments to feeding and management strategies. While farmers record some production performance data, such as egg production rate and egg quality, the recording methods are often rudimentary, lacking systematicity and accuracy, making it difficult to scientifically evaluate and optimize breeding efficiency. With the expansion of breeding scale and the increasing demands of modern quail farming, the shortcomings of traditional methods are becoming increasingly apparent, failing to meet the needs of modern quail farming. In recent years, although some farms have begun to introduce some simple monitoring equipment, most of these devices are single-function and can only monitor a certain type of data. Moreover, there is a lack of effective integration and data sharing between the devices, making it impossible to form a comprehensive farming data system and making it difficult to achieve precise management and intelligent decision-making in the quail farming process. Summary of the Invention

[0003] To address the problems of the aforementioned solutions, this invention provides a data processing system for quail digital farming based on edge-cloud collaboration, in order to solve existing problems in quail digital farming.

[0004] The objective of this invention can be achieved through the following technical solutions: A data processing system for digital quail farming based on edge-cloud collaboration, including a cloud platform and a user terminal; Furthermore, the cloud platform establishes a communication connection with the user terminal.

[0005] The user terminal includes a data acquisition module, an edge processing module, and a display module; The data acquisition module is used to monitor quail farming in real time and obtain corresponding farming data.

[0006] The edge processing module is used to process the aquaculture data collected according to a preset data processing method, and the processed aquaculture data collected is sent to the cloud platform.

[0007] The display module is used to display the received data to the user.

[0008] The cloud platform includes a database, a data analysis module, and a shared management module; The database is used for data storage, which includes aquaculture data collected by users and analysis results corresponding to various analysis needs.

[0009] The data analysis module is used to analyze the aquaculture data, identify the user's analysis needs, analyze the aquaculture data according to each analysis need, obtain the analysis results for each analysis need, and send the analysis results to the user's display module and database respectively.

[0010] Furthermore, the data collected from aquaculture will be mined.

[0011] Furthermore, methods for mining data collected from aquaculture include: The platform establishes a demand database, which is used to store potential demands, data analysis methods, and data requirements. Identify the user's various analytical needs, match and analyze the potential needs that are not analytical needs in the demand database with the aquaculture collection data, and obtain the matching results of the potential needs. The matching results include successful matching and failed matching. The potential needs that are successfully matched are marked as mining needs. The aquaculture collection data is mined and analyzed according to the mining needs and data analysis methods to obtain mining data. The mining data is then added to the aquaculture collection data. The mining requirements are marked as analysis requirements, and the data analysis methods for the analysis requirements are matched from the requirement library and loaded.

[0012] Furthermore, the potential non-analytical demands in the demand database are matched with the aquaculture data collected, including: Establish a matching analysis model. The expression for the matching analysis model is: ; In the formula: (s, YQ) i ) represents the input data, s represents the aquaculture data collected, and YQ i This represents the data requirements of potential non-analytical requirements in the demand library, where i represents the potential non-analytical requirements in the demand library, i = 1, 2, ..., n, and n is the number of potential non-analytical requirements in the demand library; s→YQ i This indicates that the data collected from aquaculture meets the data requirements of the corresponding potential demand; the output data is the matching analysis value PV(s, YQ). i The matching analysis value is 1 or 0; The data collected from aquaculture is integrated with the data requirements of potential non-analytical needs in the demand database as input data and then input into the matching analysis model for analysis to obtain the matching analysis value of the corresponding potential needs. When the matching analysis value is 1, the matching result is a successful match; When the matching analysis value is 0, the matching result is a failure.

[0013] Furthermore, the aquaculture data collected includes temperature, humidity, ammonia concentration, and video monitoring data. The corresponding analysis requirements for the aquaculture data collected are temperature warning, humidity warning, ammonia concentration warning, and aquaculture safety protection. The aquaculture data collected is mined to obtain data for each quail, including egg production, feed intake, feather images, and status and behavior data. The mined data is added to the aquaculture data collected, and additional analysis requirements are determined based on the mined data.

[0014] The shared management module is used for data sharing management, setting up a traceability requirement table, which compiles various traceability requirements; performing real-time traceability analysis on the database based on the traceability requirements to obtain various shared management requirements; and processing the stored data in the database based on the various shared management requirements.

[0015] Furthermore, real-time traceability analysis is performed on the database based on traceability requirements, including: Establish a traceability assessment model, identify the stored data information in the database, integrate the stored data information with each traceability requirement as input data and input it into the traceability assessment model for analysis, obtain the traceability assessment value of the corresponding traceability requirement, the traceability assessment value is 1 or 0; the traceability requirement with a traceability assessment value of 1 is marked as a shared management requirement.

[0016] Furthermore, the expression for the source tracing assessment model is: ; In the formula: (q, p) are the input data, q and p represent the stored data information and traceability requirements respectively, q→p means that the stored data information meets the traceability requirements; the output data is the traceability evaluation value SY(q, p).

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention, applied to quail farming, overcomes the limitations of traditional farming methods, enabling real-time, continuous, and precise monitoring of environmental parameters. Any abnormal changes in key environmental indicators such as temperature, humidity, and ammonia concentration can be promptly detected, allowing farmers to quickly implement effective adjustments to create a stable and suitable growth environment for the quails, ensuring their health and improving production performance. Regarding the monitoring of quail physiological and behavioral data, it can accurately quantify key information such as activity levels and feeding behavior, enabling farmers to accurately assess the health status and nutritional needs of the quails and adjust feeding and management strategies accordingly. Production performance data can be recorded systematically and accurately, facilitating scientific evaluation and optimization of farming efficiency. Furthermore, this invention overcomes the shortcomings of some farms that rely on simple monitoring equipment with limited functionality, lack of integration, and insufficient data sharing, forming a comprehensive farming data system. This system supports precise management and intelligent decision-making in the quail farming process, fully meeting the demands of modern quail farming's expanded scale and increased requirements. 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, a quail digital farming data processing system based on edge-cloud collaboration includes a cloud platform and a user terminal; The communication connection between the cloud platform and the user terminal can also be achieved through other methods, as long as the corresponding functions can be realized.

[0022] The user terminal includes a data acquisition module, an edge processing module, and a display module. The data acquisition module is used to monitor quail farming in real time and obtain corresponding farming data.

[0023] In one embodiment, various data points related to aquaculture are determined by pre-installed data acquisition devices and sensors, and corresponding data are collected, such as temperature and humidity, ammonia concentration, and monitoring videos.

[0024] For example, various types of sensors are installed in the quail breeding shed, including temperature sensors, humidity sensors, ammonia sensors, light sensors, and wind speed sensors. The temperature sensors are high-precision digital temperature sensors that can measure the temperature inside the breeding shed in real time and accurately, transmitting the data to a central processing unit. The humidity sensors accurately measure air humidity, the ammonia sensors quickly detect ammonia concentration, the light sensors monitor light intensity, and the wind speed sensors assess ventilation. These sensors are installed in different locations within the breeding shed according to a specific layout to ensure a comprehensive and accurate reflection of the environmental conditions inside.

[0025] The edge processing module is used to process the aquaculture data collected according to a preset data processing method, and the processed aquaculture data collected is sent to the cloud platform.

[0026] In one embodiment, the preset data processing method is set according to user needs. It can be as simple as data preprocessing, such as data cleaning, data merging, and data transformation. Other analysis methods can also be preset according to edge processing needs, such as issuing an early warning when the temperature and humidity exceed preset values. Lightweight intelligent models can be deployed for analysis. At the same time, the data can be pre-processed to reduce the amount of data transmitted and improve the transmission efficiency. Part of the data processing process is handled by the edge processing module.

[0027] The display module is used to display the received data to the user.

[0028] For example, the received analysis results can be displayed to the user, and aquaculture data can also be displayed.

[0029] The cloud platform includes a database, a data analysis module, and a shared management module; The database is used for data storage, which includes aquaculture data collected by users and analysis results corresponding to various analysis needs.

[0030] The data analysis module is used to analyze the aquaculture data and identify the user's analysis needs, such as aquaculture environment analysis, quail behavior analysis, environmental optimization analysis, aquaculture optimization analysis, health diagnosis, yield prediction, and other analysis needs. The specific needs are determined according to the user's actual needs and conditions. The aquaculture data is analyzed according to each analysis need to obtain the analysis results for each need, and the analysis results are sent to the user's display module and database respectively.

[0031] In one embodiment, the aquaculture data collected is analyzed according to various analytical needs. The platform presets corresponding data analysis methods according to each analytical need, and then the analysis is carried out according to the data analysis methods. For example, the analysis can be carried out by building corresponding intelligent models based on machine learning, deep learning algorithms, etc., or by various mathematical analysis methods.

[0032] In one embodiment, some users currently only perform preset target analysis based on the data collected from aquaculture. However, the uses and functions of the collected data may not have been fully explored. For example, only thermometers, hygrometers, ammonia concentration detectors, and video surveillance equipment are installed. The thermometers and hygrometers only have detection functions and cannot perform data transmission or analysis. The video surveillance is used for security protection and anti-theft functions. In this case, the monitoring range of the monitoring equipment can be adjusted to acquire image data from the thermometers and hygrometers and ammonia concentration detectors. The temperature, humidity, and ammonia concentration can be identified based on the image data. Furthermore, the distinctive features of each quail can be mined from the image data. Subsequently, the egg production, feeding behavior, feather condition, and mental state of each quail can be determined in real time based on the image data, achieving in-depth data mining without increasing equipment costs or with only a small increase in equipment costs, thus fulfilling more analytical needs.

[0033] In one embodiment, a method for mining data collected from aquaculture includes: The platform summarizes various analytical needs based on a large amount of quail farming data, marks them as potential needs, and sets corresponding data analysis methods and data requirements for each potential need. The data requirements are used to indicate the types and quality requirements of the farming data collected by the data analysis method. The specific requirements are determined according to the data analysis method. A corresponding demand library is established based on each potential need, data analysis method, and data requirements. Identify the user's various analytical needs, match the potential needs (excluding analytical needs) in the needs database with the aquaculture data collection, determine the appropriate data analysis method, and verify whether the aquaculture data collection meets the analytical requirements of the data analysis method, such as whether the video data range and clarity meet the usage requirements; obtain the matching results for the corresponding potential needs, including successful and unsuccessful matching results; The potential needs after a successful match are marked as mining needs. Based on the mining needs and data analysis methods, the aquaculture data is mined and analyzed to obtain the corresponding mining data. For example, data such as egg production, feeding data, feather images, and status behavior of quails are identified through monitoring videos. Existing technologies such as video recognition are used to determine the required data based on the mining needs and data analysis methods, and then the aquaculture data is mined and supplemented into the aquaculture data. The data mining needs are marked as analysis needs, and the corresponding data analysis methods are loaded, so that the aquaculture data can be analyzed and processed in the future based on these data analysis methods.

[0034] In one embodiment, potential non-analytical demands in the demand database are matched with aquaculture data. Matching analysis can be performed using existing methods, and the success of the match can be determined based on whether the aquaculture data and the mined data from the aquaculture data meet the data requirements.

[0035] In one embodiment, matching potential non-analytical demands in the demand database with aquaculture data collection includes: The platform uses a demand database and corresponding historical data to label the training set, identifying which aquaculture data collection meets the data requirements of the corresponding potential demand. A matching analysis model is then built based on the training set. The expression for the matching analysis model is: ; In the formula: (s, YQ) i ) represents the input data, s represents the aquaculture data collected, and YQ i This represents the data requirements of potential non-analytical requirements in the demand library, where i represents the potential non-analytical requirements in the demand library, i = 1, 2, ..., n, and n is the number of potential non-analytical requirements in the demand library; s→YQ i This indicates that the data collected from aquaculture meets the data requirements of the corresponding potential demand; the output data is the matching analysis value PV(s, YQ). i The matching analysis value is 1 or 0; The data collected from aquaculture is integrated with the data requirements of potential non-analytical needs in the demand database as input data and then input into the matching analysis model for analysis to obtain the matching analysis value of the corresponding potential needs. When the matching analysis value is 1, the matching result is a successful match; When the matching analysis value is 0, the matching result is a failure.

[0036] The shared management module is used for data sharing management, acquiring various traceability requirements for quail farming, and can also determine them by referring to other food traceability data. The specific settings are determined by the platform to form a traceability requirement table, which is used to statistically analyze various traceability requirements. Based on the traceability requirements, the database is analyzed in real time to determine which traceability requirements can be met by the stored data in the database, and the traceability requirements that can be met are marked as shared management requirements. The stored data in the database is processed according to various shared management needs.

[0037] In one embodiment, the database is subjected to real-time traceability analysis based on traceability requirements. The traceability analysis is performed based on the data required for the traceability requirements. When the stored data in the database can provide the corresponding traceability data, it is considered that the traceability requirements are met.

[0038] In one embodiment, real-time source tracing analysis of the database based on source tracing requirements also includes processing the stored data to determine if the requirements are met. Both direct and indirect fulfillment of the requirements after data processing are considered as fulfilling the source tracing requirements. Based on this, staff label the corresponding training set, i.e., they assess whether the requirements are met based on historical data. A source tracing evaluation model is then established based on the training set. The expression for the source tracing evaluation model is: ; In the formula: (q, p) are the input data, q and p represent the stored data information and traceability requirements respectively, q→p means that the stored data information meets the traceability requirements; the output data is the traceability evaluation value SY(q, p), and the traceability evaluation value is 1 or 0; Identify the stored data information in the database. This stored data information represents the various types and quantities of data stored in the database, avoiding analysis based on the entire database's stored data and improving analysis efficiency. Integrate the stored data information with various traceability requirements as input data and input it into the traceability evaluation model for analysis to obtain the corresponding traceability evaluation value. Traceability requirements with a traceability evaluation value of 1 are marked as shared management requirements.

[0039] In one embodiment, the aforementioned source tracing assessment model and matching analysis model can also be established based on machine learning, deep learning algorithms, etc.

[0040] In one embodiment, the stored data in the database is processed according to various shared management needs. When a user does not have a specific traceability service, the stored data is compressed and encrypted to improve storage efficiency and reduce the space occupied by the stored data. The stored data can also be preprocessed to retain the process data required for subsequent analysis and delete the corresponding original data and irrelevant data. When a user has a traceability service, the stored data can also be preprocessed and managed based on blockchain technology.

[0041] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0042] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A data processing system for digital quail farming based on edge-cloud collaboration, characterized in that, This includes cloud platforms and user terminals; The user terminal includes a data acquisition module, an edge processing module, and a display module; the cloud platform includes a database, a data analysis module, and a shared management module. The data acquisition module is used to monitor quail farming in real time and obtain corresponding farming data. The edge processing module is used to process the aquaculture data collected according to a preset data processing method, and the processed aquaculture data collected is sent to the cloud platform. The display module is used to display the received data to the user; The database is used for data storage, which includes aquaculture data collected by users and analysis results corresponding to various analysis needs. The data analysis module is used to analyze the aquaculture data, identify the user's analysis needs, analyze the aquaculture data according to each analysis need, obtain the analysis results for each analysis need, and send the analysis results to the user's display module and database respectively. The shared management module is used for data sharing management, setting up a traceability requirement table, which compiles various traceability requirements; performing real-time traceability analysis on the database based on the traceability requirements to obtain various shared management requirements; and processing the stored data in the database based on the various shared management requirements.

2. The quail digital farming data processing system based on edge-cloud collaboration according to claim 1, characterized in that, The cloud platform establishes a communication connection with the user terminal.

3. The quail digital farming data processing system based on edge-cloud collaboration according to claim 1, characterized in that, Mining data collected from aquaculture.

4. The quail digital farming data processing system based on edge-cloud collaboration according to claim 3, characterized in that, Methods for mining data collected from aquaculture include: The platform establishes a demand database, which is used to store potential demands, data analysis methods, and data requirements. Identify the user's various analytical needs, match and analyze the potential needs that are not analytical needs in the demand database with the aquaculture collection data, and obtain the matching results of the potential needs. The matching results include successful matching and failed matching. The potential needs that are successfully matched are marked as mining needs. The aquaculture collection data is mined and analyzed according to the mining needs and data analysis methods to obtain mining data. The mining data is then added to the aquaculture collection data. The mining requirements are marked as analysis requirements, and the data analysis methods for the analysis requirements are matched from the requirement library and loaded.

5. The quail digital farming data processing system based on edge-cloud collaboration according to claim 4, characterized in that, Matching potential non-analytical needs in the demand database with aquaculture data, including: Establish a matching analysis model. The expression for the matching analysis model is: ； In the formula: (s, YQ) i ) represents the input data, s represents the aquaculture data collected, and YQ i This represents the data requirements of potential non-analytical requirements in the demand library, where i represents the potential non-analytical requirements in the demand library, i = 1, 2, ..., n, and n is the number of potential non-analytical requirements in the demand library; s→YQ i This indicates that the data collected from aquaculture meets the data requirements of the corresponding potential demand; the output data is the matching analysis value PV(s, YQ). i The matching analysis value is 1 or 0; The data collected from aquaculture is integrated with the data requirements of potential non-analytical needs in the demand database as input data and then input into the matching analysis model for analysis to obtain the matching analysis value of the corresponding potential needs. When the matching analysis value is 1, the matching result is a successful match; When the matching analysis value is 0, the matching result is a failure.

6. The quail digital farming data processing system based on edge-cloud collaboration according to claim 3, characterized in that, The aquaculture data collected includes temperature, humidity, ammonia concentration, and video monitoring data. The corresponding analysis requirements for the aquaculture data are temperature warning, humidity warning, ammonia concentration warning, and aquaculture safety protection. The aquaculture data is mined to obtain individual data for each quail, including egg production, feed intake, feather images, and status and behavior data. The mined data is added to the aquaculture data, and additional analysis requirements are determined based on the mined data.

7. The quail digital farming data processing system based on edge-cloud collaboration according to claim 1, characterized in that, Real-time source tracing analysis is performed on the database based on source tracing requirements, including: Establish a traceability assessment model, identify the stored data information in the database, integrate the stored data information with each traceability requirement as input data and input it into the traceability assessment model for analysis, obtain the traceability assessment value of the corresponding traceability requirement, the traceability assessment value is 1 or 0; the traceability requirement with a traceability assessment value of 1 is marked as a shared management requirement.

8. The quail digital farming data processing system based on edge-cloud collaboration according to claim 7, characterized in that, The expression for the source tracing assessment model is: ； In the formula: (q, p) are the input data, q and p represent the stored data information and traceability requirements respectively, q→p means that the stored data information meets the traceability requirements; the output data is the traceability evaluation value SY(q, p).

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