An intelligent quail breeding system and method fusing internet of things and symbiotic cycle

Abstract

The present application relates to the field of agricultural breeding, and specifically discloses an intelligent quail breeding system and method combining the Internet of Things and symbiotic circulation, comprising: S1, collecting multi-dimensional data of quail breeding, aquaculture, vegetable planting and other symbiotic units and preprocessing and verifying; S2, constructing a correlation model to calculate the cycle matching parameters; S3, realizing the graded degradation of fecal pollution and the accurate distribution of nutrients through the stage adaptive unit; S4, linkage control of breeding environment, symbiotic unit and disease prevention and control; S5, digital recording and encrypted traceability of the whole process data, forming a closed-loop management. The present application solves the defects of extensive quail breeding environment regulation, low efficiency of waste recycling, passive disease prevention and control, and insufficient digital management, effectively improves the breeding efficiency and resource utilization rate, and realizes efficient material and energy circulation and whole-process controllable management.

Description

Technical Field

[0001] This invention relates to the field of agricultural breeding technology, specifically to an intelligent quail breeding system and method that integrates the Internet of Things and symbiotic cycles. Background Technology

[0002] Quail farming, as a high-efficiency agricultural project with low investment, quick returns, and short cycles, has seen its scale expand continuously in recent years due to the sustained growth in market demand for quail eggs and meat products. However, in actual farming practices, the shortcomings of the existing technology system are becoming increasingly apparent: Regarding environmental control, most farms still rely on manual experience to adjust temperature, humidity, ventilation, and light parameters, lacking real-time monitoring and automatic response mechanisms. This leads to frequent problems such as large fluctuations in temperature and humidity and excessive concentrations of harmful gases like ammonia, easily triggering stress in quails and affecting their survival rate and egg production performance. In the area of ​​waste treatment and resource recycling, traditional models often involve direct discharge of manure or a simple linear "manure-fish-vegetable" process, failing to classify and treat manure according to the differences in nutrient composition at different growth stages of quails. This results in an imbalance between nutrient supply and demand, not only wasting resources but also easily causing... The current system faces a cascading problem of deteriorating water quality in fish ponds and poor vegetable growth. In disease control, existing technologies primarily rely on manual observation of quail feeding and mental state to diagnose illness, lacking early warning models based on physiological indicators and environmental parameters. Measures are often only taken after an outbreak, resulting in significant delays in control. Furthermore, the lack of a coordinated isolation mechanism between breeding and symbiotic units makes it easy for pathogens to spread across units. In terms of aquaculture management, data from various stages is stored in a fragmented manner, lacking a unified digital management platform. Data on feed input, environmental control, and disease prevention cannot be analyzed in a coordinated manner, and full-process traceability of products from breeding to market is difficult to implement.

[0003] In summary, existing quail farming technologies suffer from numerous shortcomings, including extensive environmental control, low resource recycling efficiency, passive and lagging disease prevention and control, and a lack of full-process digital management. These shortcomings fail to meet the development needs of modern agriculture for efficient, green, and controllable farming. There is an urgent need for an intelligent quail farming system and method that integrates the Internet of Things and symbiotic cycles to achieve precise control of the farming environment, graded degradation of waste and precise nutrient matching, early warning of diseases and cross-unit joint prevention and control, as well as digital recording and encrypted traceability of data throughout the entire farming process. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, this invention provides an intelligent quail farming system and method that integrates the Internet of Things and symbiotic cycles. This system solves the shortcomings of existing quail farming practices, such as extensive environmental control, inefficient waste recycling, passive disease prevention and control, and insufficient digital management, thereby improving farming efficiency and resource utilization.

[0005] To achieve the above objectives, this invention proposes an intelligent quail farming system and method integrating the Internet of Things and symbiotic cycles, comprising: S1. Collect multi-dimensional data from quail breeding units and aquaponics units through IoT sensing modules to complete data preprocessing and data verification; S2. Based on the preprocessed data, a correlation model of quail physiological state, breeding environment and symbiotic unit is constructed through the intelligent management and control platform to calculate the matching parameters of material and energy cycle. The calculation formula for the correlation model of quail physiological state-breeding environment-symbiotic unit is as follows: ; In the formula, The nutrient requirement matching degree of the symbiotic unit, with a value ranging from 0 to 1. The quail's overall physiological condition is scored, with a value ranging from 0 to 1. These are standardized values ​​for aquaculture environmental parameters, ranging from 0 to 1. The weighting of the influence of quail physiological state on the symbiotic cycle is defined as follows: , The weighting of the impact of the aquaculture environment on the symbiotic cycle is defined as follows: And satisfy ; S3. Based on the output results of the correlation model, the adaptive symbiotic cycle unit in the control stage completes the graded degradation and precise nutrient allocation of quail manure. The specific steps of the graded degradation are as follows: S31. Determine the growth stage based on the age of the quails. Use high-protein degrading bacteria during the brooding period and high-calcium conversion bacteria during the egg-laying period. S32. By monitoring the temperature and pH value within the fermentation unit through the Internet of Things, the fermentation temperature during the brooding period is maintained at 55~60℃ and the pH value at 7.0~7.2, while the fermentation temperature during the egg-laying period is maintained at 58~62℃ and the pH value at 7.2~7.5. S33, the nutrient buffer distribution module dynamically adjusts the degradation product delivery ratio based on the symbiotic unit data. The delivery ratio is calculated using the following formula: ; In the formula, The percentage of degradation products delivered to fish farming ponds, ranging from 0% to 100%. The standard value for ammonia nitrogen concentration in fish farming ponds. This represents the actual ammonia nitrogen concentration in the fish farming pond. This represents the actual rate of nutrient absorption by vegetable roots. This represents the standard value for the nutrient absorption rate of vegetable roots. S4. Through the full-chain linkage execution module, the coordinated operation of aquaculture environment regulation, symbiotic unit linkage, and disease prevention and control is realized; The coordinated operation of disease prevention and control includes disease early warning and joint isolation steps, specifically: S421. Construct an epidemic early warning index based on multi-dimensional data. The calculation formula is as follows: ; In the formula, This is a disease early warning index, with a value range of 0 to 1. This standardizes the ammonia concentration, mapping the actual ammonia concentration to the 0-1 range. The change rate of quail egg production rate, To standardize the bacterial count in eggshells, the actual bacterial count is mapped to the 0-1 range. , , The warning weights are all within the range of [0.2, 0.4], and satisfy the following conditions: ; The rate of change in quail egg production was: ; In the formula, This represents the egg production rate of the previous day. This represents the current egg production rate; S422, Set warning threshold ,when When a Level 1 warning is triggered, the system automatically shuts down the material conveying channels between the breeding unit and the symbiotic unit, and activates the ultraviolet disinfection module to disinfect the conveying pipeline. The disinfection duration t is positively correlated with the warning index. min; S423. Add targeted health supplements to the integrated feeding system, with the dosage calculated based on the quail's weight: ; In the formula, This refers to the amount added per unit of body weight, with a range of values. , The average weight of quails Dosage for adding health supplement to a single quail; S5. Digitally record and encrypt data throughout the entire breeding process to form a closed-loop management system.

[0006] Preferably, in S1, the multi-dimensional data includes quail physiological data, breeding environment data, and symbiotic unit data; wherein, the quail physiological data includes the degree of feather damage, feeding frequency, weight change, and egg production rate, collected through a polarized camera and wearable physiological sensors; the breeding environment data includes temperature, humidity, ammonia concentration, hydrogen sulfide concentration, and light intensity; the symbiotic unit data includes ammonia nitrogen concentration in fish ponds, pH value, nutrient absorption rate of vegetable roots, and photosynthetic rate; the data preprocessing includes outlier removal, the removal formula being: ; In the formula, The data to be verified. The mean of the data. Let be the standard deviation, when satisfying Values ​​identified as outliers are removed. The preset threshold value ranges from 2.5 to 3.0. The wearable physiological sensor includes a weight sensor and a heart rate sensor, with a data acquisition frequency of once every 5 minutes; the polarization camera acquires images once every 10 minutes, and calculates the degree of feather damage using an image recognition algorithm. The recognition steps are as follows: The acquired quail images were converted to grayscale and edge extracted; the feather region and the damaged region were segmented, and the area of ​​the damaged region was calculated. Total area of ​​feathers To obtain the degree of feather damage The calculation results are accurate to 0.1%.

[0007] Preferably, in S1, the data verification includes sensor data consistency verification, which is achieved through comparison of data from multiple sensors originating from the same source. The specific steps are as follows: S11. Obtain the same monitoring indicator Data collected by different sensors , ... ; S12. Calculate the relative deviation of each group of data from the mean. The calculation formula is as follows: ; In the formula, For the first The relative deviation of the group data For the first Group data collection, for The mean of the group of data; S13, Set deviation threshold When all The data is deemed valid when it is valid; if there is a problem... At that time, activate backup sensor data and mark the faulty sensor. The value range is 2.0% to 3.0%.

[0008] Preferably, in S33, the ammonia nitrogen concentration standard value is set according to the fish species, and the ammonia nitrogen concentration standard value for freshwater aquaculture fish does not exceed 0.5 mg / L; the nutrient absorption rate standard value is set according to the vegetable species, and the nutrient absorption rate standard value for leafy vegetables is [0.03, 0.05]; when the ammonia nitrogen concentration in the fish farming pond exceeds 0.5 mg / L, the proportion of degradation products transported to the fish farming pond is 0, and the products are preferentially transported to the vegetable planting area until the ammonia nitrogen concentration standard value for freshwater aquaculture fish is lower than 0.5 mg / L, at which point the proportion of degradation products in the fish farming pond is recalculated according to the formula and the transport is resumed.

[0009] Preferably, in S4, the aquaculture environment control includes dynamic adaptation and adjustment of temperature and light, with the specific adjustment rules being: S411. Temperature regulation during the brooding period: Maintain a temperature of 37-38℃ for the first 1-3 days, then decrease the temperature by 1℃ each day. The adjustment formula is as follows: ; In the formula, For the first Target temperature for each day of life, in °C. The brooding age is [number] days, and the range is [number] days. ,when hour, ,when When calculating, use the formula; S412. Lighting regulation during the egg-laying period: Maintain 14-16 hours of light per day at a light intensity of 15 lux. When the quail's egg production rate drops by more than 10%, increase the light duration by 0.5 hours and the light intensity by 2 lux. The adjusted light duration is as follows: ; The adjusted light intensity is: ; In the formula, Based on the benchmark egg production rate, Given the current egg production rate, It is a floor function, and after adjustment Hour, Lux.

[0010] Preferably, in S5, the digital record includes the following data synchronously stored according to timestamps: raw data collected by the Internet of Things, correlation model parameters, cyclic matching parameters, environmental control instructions, disease prevention and control records, and product harvesting information; the encrypted traceability adopts a blockchain encryption algorithm, and the encryption formula is: ; In the formula, For encryption algorithms, A unique identifier for each aquaculture unit. For quail breeding batch number, For timestamps, The hash value of the entire process data set. For string concatenation operators, This is the final blockchain traceability hash value generated.

[0011] A smart quail farming system integrating the Internet of Things (IoT) and symbiotic cycle includes an IoT sensing module, a stage-adaptive symbiotic cycle unit, an intelligent control platform, and a full-chain linkage execution module. The IoT sensing module includes a multimodal sensor group and a data acquisition terminal. The stage-adaptive symbiotic cycle unit includes a brooding period manure treatment module, an egg-laying period manure treatment module, and a nutrient buffer distribution module.

[0012] Therefore, this invention proposes an intelligent quail farming system and method that integrates the Internet of Things and symbiotic cycles, with the following beneficial effects: (1) Through multi-dimensional sensing and intelligent control of the Internet of Things, the parameters of the breeding environment are accurately matched. Combined with the stage-adaptive symbiotic cycle mode, the graded degradation of manure and the precise allocation of nutrients are completed, which greatly improves the resource utilization rate and reduces breeding pollution.

[0013] (2) Construct an early warning and joint prevention and control system for diseases, realize early identification and cross-unit blocking of diseases, cooperate with full-process digital traceability, ensure product quality and safety, and promote the development of quail farming towards high efficiency, green and controllable direction.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is an overall flowchart of an intelligent quail breeding system and method that integrates the Internet of Things and symbiotic cycle according to the present invention; Figure 2 This is an overall architecture diagram of an intelligent quail breeding system and method that integrates the Internet of Things and symbiotic cycle, according to the present invention. Figure 3 This is a flowchart illustrating the structure and data acquisition process of the IoT sensing module in an intelligent quail breeding system and method that integrates IoT and symbiotic cycle, as described in this invention. Figure 4 This is a schematic diagram of the stage-adaptive symbiotic cycle unit of an intelligent quail breeding system and method that integrates the Internet of Things and symbiotic cycle according to the present invention. Detailed Implementation

[0016] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0017] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0018] like Figures 1-4 As shown, this invention provides an intelligent quail breeding system and method that integrates the Internet of Things and symbiotic cycles.

[0019] A smart quail farming system integrating the Internet of Things (IoT) and symbiotic cycle includes: an IoT sensing module, a stage-adaptive symbiotic cycle unit, an intelligent control platform, and a full-chain linkage execution module; the IoT sensing module includes a multimodal sensor group and a data acquisition terminal, and the stage-adaptive symbiotic cycle unit includes a brooding period manure treatment module, an egg-laying period manure treatment module, and a nutrient buffer distribution module.

[0020] A smart quail farming system and method integrating the Internet of Things and symbiotic cycles, comprising: S1. Collect multi-dimensional data from quail breeding units and aquaponics units through IoT sensing modules to complete data preprocessing and data verification; Multi-dimensional data includes quail physiological data, breeding environment data, and symbiotic unit data. Quail physiological data includes feather damage level, feeding frequency, weight change, and egg production rate, collected using a polarized camera and wearable physiological sensors. Breeding environment data includes temperature, humidity, ammonia concentration, hydrogen sulfide concentration, and light intensity. Symbiotic unit data includes ammonia nitrogen concentration in fish ponds, pH value, nutrient absorption rate of vegetable roots, and photosynthetic rate. Data preprocessing includes outlier removal, using the following formula: ; In the formula, The data to be verified. The mean of the data. Let be the standard deviation, when satisfying Values ​​identified as outliers are removed. The preset threshold value ranges from 2.5 to 3.0. Wearable physiological sensors include weight sensors and heart rate sensors, with data acquisition frequency of once every 5 minutes.

[0021] The polarization camera captures images every 10 minutes. An image recognition algorithm calculates the degree of feather damage. The recognition steps are as follows: The acquired quail images were converted to grayscale and edge extracted; the feather region and the damaged region were segmented, and the area of ​​the damaged region was calculated. Total area of ​​feathers To obtain the degree of feather damage The calculation results are accurate to 0.1%.

[0022] Data verification includes sensor data consistency verification, which is achieved by comparing data from multiple sensors that originate from the same source. The specific steps are as follows: S11. Obtain the same monitoring indicator Data collected by different sensors , ... ; S12. Calculate the relative deviation of each group of data from the mean. The calculation formula is as follows: ; In the formula, For the first The relative deviation of the group data For the first Group data collection, for The mean of the group of data; S13, Set deviation threshold When all The data is deemed valid when it is valid; if there is a problem... At that time, activate backup sensor data and mark the faulty sensor. The value range is 2.0% to 3.0%.

[0023] S2. Based on the preprocessed data, a correlation model of quail physiological state, breeding environment and symbiotic unit is constructed through the intelligent management and control platform to calculate the matching parameters of material and energy cycle. The formula for calculating the correlation model of quail physiological state, breeding environment, and symbiotic unit is as follows: ; In the formula, The nutrient requirement matching degree of the symbiotic unit, with a value ranging from 0 to 1. The quail's overall physiological condition is scored, with a value ranging from 0 to 1. These are standardized values ​​for aquaculture environmental parameters, ranging from 0 to 1. The weighting of the influence of quail physiological state on the symbiotic cycle is defined as follows: , The weighting of the impact of the aquaculture environment on the symbiotic cycle is defined as follows: And satisfy .

[0024] S3. Based on the output results of the correlation model, the adaptive symbiotic cycle unit in the control stage completes the graded degradation and precise nutrient allocation of quail manure. The specific steps of graded degradation are as follows: S31. Determine the growth stage based on the age of the quails. Use high-protein degrading bacteria during the brooding period and high-calcium conversion bacteria during the egg-laying period. S32. By monitoring the temperature and pH value within the fermentation unit through the Internet of Things, the fermentation temperature during the brooding period is maintained at 55~60℃ and the pH value at 7.0~7.2, while the fermentation temperature during the egg-laying period is maintained at 58~62℃ and the pH value at 7.2~7.5. S33, the nutrient buffer distribution module dynamically adjusts the degradation product delivery ratio based on the symbiotic unit data. The delivery ratio is calculated using the following formula: ; In the formula, The percentage of degradation products delivered to fish farming ponds, ranging from 0% to 100%. The standard value for ammonia nitrogen concentration in fish farming ponds. This represents the actual ammonia nitrogen concentration in the fish farming pond. This represents the actual rate of nutrient absorption by vegetable roots. This represents the standard value for the nutrient absorption rate of vegetable roots.

[0025] In S33, the standard value for ammonia nitrogen concentration is set according to the fish species, and the standard value for ammonia nitrogen concentration of freshwater farmed fish shall not exceed 0.5 mg / L; the standard value for nutrient absorption rate is set according to the vegetable species, and the standard value for nutrient absorption rate of leafy vegetables is [0.03, 0.05]; when the ammonia nitrogen concentration in the fish farming pond exceeds 0.5 mg / L, the proportion of degradation products transported to the fish farming pond is 0, and the products are preferentially transported to the vegetable planting area until the standard value for ammonia nitrogen concentration of freshwater farmed fish is lower than 0.5 mg / L, at which point the proportion of degradation products in the fish farming pond is recalculated according to the formula and the transport is resumed.

[0026] S4. Through the full-chain linkage execution module, the coordinated operation of aquaculture environment regulation, symbiotic unit linkage, and disease prevention and control is realized; The control of the aquaculture environment includes the dynamic adaptation and adjustment of temperature and light, and the specific adjustment rules are as follows: S411. Temperature regulation during the brooding period: Maintain a temperature of 37-38℃ for the first 1-3 days, then decrease the temperature by 1℃ each day. The adjustment formula is as follows: ; In the formula, For the first Target temperature for each day of life, in °C. The brooding age is [number] days, and the range is [number] days. ,when hour, ,when When calculating, use the formula; S412. Lighting regulation during the egg-laying period: Maintain 14-16 hours of light per day at a light intensity of 15 lux. When the quail's egg production rate drops by more than 10%, increase the light duration by 0.5 hours and the light intensity by 2 lux. The adjusted light duration is as follows: ; The adjusted light intensity is: ; In the formula, Based on the benchmark egg production rate, Given the current egg production rate, It is a floor function, and after adjustment Hour, Lux.

[0027] The coordinated operation of epidemic prevention and control includes epidemic early warning and joint isolation steps, specifically: S421. Construct an epidemic early warning index based on multi-dimensional data. The calculation formula is as follows: ; In the formula, This is a disease early warning index, with a value range of 0 to 1. This standardizes the ammonia concentration, mapping the actual ammonia concentration to the 0-1 range. The change rate of quail egg production rate, To standardize the bacterial count in eggshells, the actual bacterial count is mapped to the 0-1 range. , , The warning weights are all within the range of [0.2, 0.4], and satisfy the following conditions: ; The rate of change in quail egg production was: ; In the formula, This represents the egg production rate of the previous day. This represents the current egg production rate; S422, Set warning threshold ,when When a Level 1 warning is triggered, the system automatically shuts down the material conveying channels between the breeding unit and the symbiotic unit, and activates the ultraviolet disinfection module to disinfect the conveying pipeline. The disinfection duration t is positively correlated with the warning index. min; S423. Add targeted health supplements to the integrated feeding system, with the dosage calculated based on the quail's weight: ; In the formula, This refers to the amount added per unit of body weight, with a range of values. , The average weight of quails This refers to the dosage of health supplement added to a single quail.

[0028] S5. Digitally record and encrypt data throughout the entire breeding process to form a closed-loop management system.

[0029] Digital records include the following data stored synchronously with timestamps: raw data collected by the Internet of Things, correlation model parameters, cyclic matching parameters, environmental control instructions, disease prevention and control records, and product harvesting information; encrypted traceability uses a blockchain encryption algorithm, and the encryption formula is: ; In the formula, For encryption algorithms, A unique identifier for each aquaculture unit. For quail breeding batch number, For timestamps, The hash value of the entire process data set. For string concatenation operators, This is the final blockchain traceability hash value generated.

[0030] This embodiment focuses on the breeding of 1000 North Korean Yongseong egg-laying quails, equipped with a 10m... 2 Freshwater grass carp breeding pond and 20m 2 In lettuce growing areas, the system and method of this invention are applied to aquaculture, and the specific implementation steps are as follows: S1. Collect multi-dimensional data from quail breeding units and aquaponics units through IoT sensing modules to complete data preprocessing and data verification; In the IoT sensing module, wearable physiological sensors (weight sensor, heart rate sensor) collect quail physiological data every 5 minutes, and polarized cameras collect quail images every 10 minutes; environmental sensors such as temperature and humidity collect aquaculture environment data in real time, and symbiotic unit data such as ammonia nitrogen concentration in fish ponds and nutrient absorption rate of lettuce roots are collected synchronously.

[0031] In the collected quail weight data, one set of data was 120g, and the mean of this set of data was... 135g, standard deviation 5g, according to the outlier removal formula , Taking 2.8 as the value, |120-135|=15, 2.8×5=14, 15>14, so this data is determined to be an outlier and is removed; Consistency verification is performed on the data collected by 3 sets of sensors for the same monitoring indicator (temperature: 22.3°C, 22.5°C, 22.4°C), and the mean value is 22.4°C. Calculated according to the relative deviation formula , the relative deviations of the 3 sets of data are 0.44%, 0.44% and 0% respectively, all of which are less than the deviation threshold =2.5%, and the data verification is determined to be qualified; The image collected by the polarization camera is processed through an image recognition algorithm, and after grayscaling, edge extraction and region segmentation, the area of a certain quail feather damage region is obtained as 0.8cm 2 , and the total feather region area is 80cm 2 . Calculated according to the feather damage degree formula , the feather damage degree is 1.0%.

[0032] S2. Based on the preprocessed data, an association model of quail physiological state - breeding environment - symbiotic unit is constructed through an intelligent management and control platform, and the matching parameters of material and energy circulation are calculated; The intelligent management and control platform performs standardization processing on the preprocessed data, obtains that the comprehensive score of the quail physiological state is 0.92, and the standardized value of the breeding environment parameter is 0.88. According to the association model formula ( is taken as 0.4, is taken as 0.6, ), the nutrient demand matching degree M of the symbiotic unit is calculated as M=0.4×0.92+0.6×0.88=0.896, and the matching parameter of material and energy circulation is determined to be 0.896.

[0033] S3. According to the output result of the association model, the stage-adaptive symbiotic circulation unit is controlled to complete the graded degradation of quail manure and the precise distribution of nutrients; When the quail is in the brooding period (10 days old), the stage-adaptive symbiotic circulation unit enables high-protein degrading strains, and the temperature in the fermentation unit monitored by the Internet of Things is 58°C and the pH value is 7.1, which meets the requirements of fermentation parameters in the brooding period; The standard value of ammonia nitrogen concentration in the fish breeding pond is 0.5mg / L, the actual value is 0.3mg / L, the standard value of the nutrient absorption rate of lettuce roots is 0.04mg / (g·h), and the actual value is 0.05mg / (g·h). Calculated according to the conveying ratio formula , the conveying ratio is 39.2%, that is, 39.2% of the degradation products are conveyed to the fish breeding pond, and the remaining 60.8% are conveyed to the lettuce planting area; During the aquaculture process, the ammonia nitrogen concentration in the fish pond was monitored to be 0.6 mg / L, exceeding the standard value of 0.5 mg / L. At this time, the delivery ratio was adjusted to 0, and the delivery of degradation products to the fish pond was stopped. The products were then delivered to the lettuce growing area first. Three days later, the actual ammonia nitrogen concentration in the fish pond was monitored to be 0.45 mg / L. The delivery ratio was recalculated according to the formula and delivery was resumed.

[0034] S4. Through the full-chain linkage execution module, the coordinated operation of aquaculture environment regulation, symbiotic unit linkage, and disease prevention and control is realized; On the 5th day of the brooding period, calculate according to the temperature regulation formula. The temperature is adjusted to 36℃, and the entire chain linkage execution module controls the temperature control equipment to maintain the breeding environment temperature at 36℃; The baseline egg production rate during the laying period is 90%. At a certain time, the current egg production rate is monitored at 78%, a decrease of 13.3%, exceeding 10%. According to the adjusted light duration formula... Calculations show that the adjusted illumination duration is 14.5 hours, according to the formula for adjusted light intensity. The calculation shows that the adjusted light intensity is 17 lux. The lighting system is then adjusted to the corresponding parameters. In disease prevention and control, the standardized value of ammonia concentration is 0.7, and the change rate of quail egg production rate is... =(88%-78%) / 88%×100%≈11.4%, the standardized value of eggshell bacterial count is 0.6, and the warning weight is... , , The values ​​are 0.3, 0.4, and 0.3 respectively, according to the disease early warning index formula. Calculations show that 0.456 < 0.8, therefore a Level 1 warning was not triggered. When W is detected to be 0.82 ≥ 0.8, the system automatically closes the material conveying channels between the breeding unit and the symbiotic unit, according to the disinfection time formula. The calculated disinfection time is 49.2 minutes, and the ultraviolet disinfection module is activated for 49.2 minutes; simultaneously, the dosage of health supplements is calculated according to the formula. , Take 0.03g / kg, average weight of quail Based on a weight of 0.14 kg, the required amount of supplemental health medication is 0.0042 g per animal, and the supplemental health medication is added via the linked feeding system.

[0035] S5. Digitally record and encrypt data throughout the entire breeding process to form a closed-loop management system; Synchronously store IoT-collected raw data, associated model parameters, cyclic matching parameters, environmental control instructions, disease prevention and control records, and product harvesting information according to timestamps; When using encrypted traceability, a unique identification code for the breeding unit is used. =“QY-2024-001”, quail breeding batch number =“PC-20240501”, timestamp =“20240501143025”, the hash value of the entire process data set. =“a3f2d1e5b7c980123456789abcdef”, according to the blockchain encryption formula The final blockchain traceability hash value is calculated and generated as follows: .

[0036] Therefore, this invention provides an intelligent quail farming system and method that integrates the Internet of Things (IoT) and symbiotic cycles. The system integrates an IoT sensing module, a stage-adaptive symbiotic cycle unit, an intelligent management and control platform, and a full-chain linkage execution module. The method covers five core steps: multi-dimensional data collection and verification, correlation model construction, graded degradation of manure and precise nutrient allocation, coordinated regulation of environment, symbiosis, and disease, and full-process digital encrypted traceability. Through a closed-loop operation mechanism of sensing-modeling-regulation-traceability, it accurately matches the material and energy needs of quail farming and aquaponics units, solving the pain points of existing technologies such as extensive environmental regulation, inefficient resource recycling, passive disease prevention and control, and lack of digital management, thereby achieving high efficiency, greening, and full controllability in quail farming.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. 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 still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart quail farming method integrating the Internet of Things and symbiotic cycles, characterized in that, include: S1. Collect multi-dimensional data from quail breeding units and fish-vegetable symbiotic units through IoT sensing modules, and complete data preprocessing and data verification. S2. Based on the preprocessed data, a correlation model of quail physiological state, breeding environment and symbiotic unit is constructed through the intelligent management and control platform to calculate the matching parameters of material and energy cycle. The calculation formula for the correlation model of quail physiological state-breeding environment-symbiotic unit is as follows: ； In the formula, The nutrient requirement matching degree of the symbiotic unit, with a value ranging from 0 to 1. The quail's overall physiological condition is scored, with a value ranging from 0 to 1. These are standardized values ​​for aquaculture environmental parameters, ranging from 0 to 1. The weighting of the influence of quail physiological state on the symbiotic cycle is defined as follows: , The weighting of the impact of the aquaculture environment on the symbiotic cycle is defined as follows: And satisfy ; S3. Based on the output results of the correlation model, the adaptive symbiotic cycle unit in the control stage completes the graded degradation and precise nutrient allocation of quail manure. The specific steps of the graded degradation are as follows: S31. Determine the growth stage based on the age of the quails. Use high-protein degrading bacteria during the brooding period and high-calcium conversion bacteria during the egg-laying period. S32. By monitoring the temperature and pH value within the fermentation unit through the Internet of Things, the fermentation temperature during the brooding period is maintained at 55~60℃ and the pH value at 7.0~7.2, while the fermentation temperature during the egg-laying period is maintained at 58~62℃ and the pH value at 7.2~7.

5. S33, the nutrient buffer distribution module dynamically adjusts the degradation product delivery ratio based on the symbiotic unit data. The delivery ratio is calculated using the following formula: ； In the formula, The percentage of degradation products delivered to fish farming ponds, ranging from 0% to 100%. The standard value for ammonia nitrogen concentration in fish farming ponds. This represents the actual ammonia nitrogen concentration in the fish farming pond. This represents the actual rate of nutrient absorption by vegetable roots. This represents the standard value for the nutrient absorption rate of vegetable roots. S4. Through the full-chain linkage execution module, the coordinated operation of aquaculture environment regulation, symbiotic unit linkage, and disease prevention and control is realized; The coordinated operation of disease prevention and control includes disease early warning and joint isolation steps, specifically: S421. Construct an epidemic early warning index based on multi-dimensional data. The calculation formula is as follows: ； In the formula, This is a disease early warning index, with a value range of 0 to 1. This standardizes the ammonia concentration, mapping the actual ammonia concentration to the 0-1 range. The change rate of quail egg production rate, To standardize the bacterial count in eggshells, the actual bacterial count is mapped to the 0-1 range. , , The warning weights are all within the range of [0.2, 0.4], and satisfy the following conditions: ; The rate of change in quail egg production was: ； In the formula, This represents the previous day's egg production rate. This represents the current egg production rate; S422, Set warning threshold ,when When a Level 1 warning is triggered, the system automatically shuts down the material conveying channels between the breeding unit and the symbiotic unit, and activates the ultraviolet disinfection module to disinfect the conveying pipeline. The disinfection duration t is positively correlated with the warning index. min; S423. Add targeted health supplements to the integrated feeding system, with the dosage calculated based on the quail's weight: ； In the formula, This refers to the amount added per unit of body weight, with a range of values. , The average weight of quails Dosage for adding health supplement to a single quail; S5. Digitally record and encrypt data throughout the entire breeding process to form a closed-loop management system.

2. The intelligent quail farming method integrating the Internet of Things and symbiotic cycle according to claim 1, characterized in that, In S1, the multi-dimensional data includes quail physiological data, breeding environment data, and symbiotic unit data. The quail physiological data includes the degree of feather damage, feeding frequency, weight change, and egg production rate, collected via a polarized camera and wearable physiological sensors. The breeding environment data includes temperature, humidity, ammonia concentration, hydrogen sulfide concentration, and light intensity. The symbiotic unit data includes ammonia nitrogen concentration in fish ponds, pH value, nutrient absorption rate of vegetable roots, and photosynthetic rate. The data preprocessing includes outlier removal, using the following formula: ； In the formula, The data to be verified. The mean of the data. Let be the standard deviation, when satisfying Values ​​identified as outliers are removed. The preset threshold value ranges from 2.5 to 3.

0. The wearable physiological sensor includes a weight sensor and a heart rate sensor, with a data acquisition frequency of once every 5 minutes; the polarization camera acquires images once every 10 minutes, and calculates the degree of feather damage using an image recognition algorithm. The recognition steps are as follows: The acquired quail images were converted to grayscale and edge extracted; the feather region and the damaged region were segmented, and the area of ​​the damaged region was calculated. Total area of ​​feathers To obtain the degree of feather damage The calculation results are accurate to 0.1%.

3. The intelligent quail farming method integrating the Internet of Things and symbiotic cycle as described in claim 1, characterized in that, In S1, the data verification includes sensor data consistency verification, which is achieved by comparing data from multiple sensors that originate from the same source. The specific steps are as follows: S11. Obtain the same monitoring indicator Data collected by different sensors , ... ; S12. Calculate the relative deviation of each group of data from the mean. The calculation formula is as follows: ； In the formula, For the first The relative deviation of the group data for The first set of data collection, for The mean of the group of data; S13, Set deviation threshold When all The data is deemed valid when it is valid; when there is a... At that time, activate backup sensor data and mark the faulty sensor. The value range is 2.0% to 3.0%.

4. The intelligent quail farming method integrating the Internet of Things and symbiotic cycle according to claim 1, characterized in that, In S33, the ammonia nitrogen concentration standard value is set according to the fish species, and the ammonia nitrogen concentration standard value for freshwater aquaculture fish does not exceed 0.5 mg / L; the nutrient absorption rate standard value is set according to the vegetable species, and the nutrient absorption rate standard value for leafy vegetables is [0.03, 0.05]; when the ammonia nitrogen concentration in the fish pond exceeds 0.5 mg / L, the proportion of degradation products transported to the fish pond is 0, and the products are preferentially transported to the vegetable planting area until the ammonia nitrogen concentration standard value for freshwater aquaculture fish is lower than 0.5 mg / L, at which point the proportion of degradation products in the fish pond is recalculated according to the formula and the transport is resumed.

5. The intelligent quail farming method integrating the Internet of Things and symbiotic cycle according to claim 1, characterized in that, In S4, the aquaculture environment control includes dynamic adaptation and adjustment of temperature and light, and the specific adjustment rules are as follows: S411. Temperature regulation during the brooding period: Maintain a temperature of 37-38℃ for the first 1-3 days, then decrease the temperature by 1℃ each day. The adjustment formula is as follows: ； In the formula, For the first Target temperature for each day of life, in °C. The brooding age is [number] days, and the range is [number] days. ,when hour, ,when When calculating, use the formula; S412. Lighting regulation during the egg-laying period: Maintain 14-16 hours of light per day at a light intensity of 15 lux. When the quail's egg production rate drops by more than 10%, increase the light duration by 0.5 hours and the light intensity by 2 lux. The adjusted light duration is as follows: ； The adjusted light intensity is: ； In the formula, Based on the benchmark egg production rate, Given the current egg production rate, It is a floor function, and after adjustment Hour, Lux.

6. The intelligent quail farming method integrating the Internet of Things and symbiotic cycle according to claim 1, characterized in that, In S5, the digital record includes the following data stored synchronously by timestamp: raw data collected by the Internet of Things, correlation model parameters, cyclic matching parameters, environmental control instructions, disease prevention and control records, and product harvesting information; the encrypted traceability adopts a blockchain encryption algorithm, and the encryption formula is: ； In the formula, For encryption algorithms, A unique identifier for each aquaculture unit. For quail breeding batch number, For timestamps, The hash value of the entire process data set. For string concatenation operators, This is the final blockchain traceability hash value generated.

7. A smart quail farming system integrating the Internet of Things and symbiotic cycles, applied to the smart quail farming method integrating the Internet of Things and symbiotic cycles as described in any one of claims 1-6, characterized in that, It includes an IoT sensing module, a stage-adaptive symbiotic cycle unit, an intelligent management and control platform, and a full-chain linkage execution module; the IoT sensing module includes a multimodal sensor group and a data acquisition terminal, and the stage-adaptive symbiotic cycle unit includes a brooding period manure treatment module, an egg-laying period manure treatment module, and a nutrient buffer distribution module.

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