Intelligent laying hen drinking water purification method and monitoring system

Abstract

The application relates to the technical field of laying hen breeding water purification, in particular to an intelligent laying hen water purification method and a monitoring system, which comprises receiving an intelligent purification instruction to confirm a target breeding farm and an intelligent monitoring system, using target breeding information to screen a historical water quality data set to generate a target water quality data set, combining a first purification parameter set and the target water quality data set to obtain target purification parameters, and judging whether the purification scheme needs to be updated through a preset monitoring frequency. The application can accurately predict water purification parameters according to specific information of the target breeding farm, guarantee information safety through encryption technology, and dynamically adjust the purification scheme in actual application, so that the accuracy, stability and safety of laying hen water purification are improved.

Description

Technical Field

[0001] This invention belongs to the field of drinking water purification technology for laying hen farming, specifically an intelligent drinking water purification method and monitoring system for laying hens. Background Technology

[0002] With the continuous development of intelligent farming technologies, the optimization of drinking water purification and monitoring systems for laying hens has become a crucial aspect of improving farming efficiency and ensuring animal health. In laying hen farming, drinking water quality directly affects the growth and development, egg production rate, and overall health of the hens; therefore, efficient purification and real-time monitoring of drinking water are of paramount importance. However, existing drinking water purification equipment and technologies still have significant shortcomings in their application to laying hen farming scenarios, particularly in terms of intelligent monitoring, precise purification, and long-term operational stability.

[0003] A search revealed that patent CN115025547B relates to a drinking water purification device for animal husbandry. This device, through the design of a filter cartridge, filter element, and cold-resistant components, can adapt to low-temperature environments and ensure uniform water flow distribution. However, this technical solution primarily focuses on improvements to the physical structure and lacks support for real-time water quality monitoring. It cannot dynamically provide feedback on purification effects or provide early warnings of potential problems, making it difficult to meet the real-time monitoring needs of intelligent aquaculture. Furthermore, the device relies on the addition of chemical flocculants but does not provide an automated dosing and dosage control mechanism, which may lead to uneven application of the flocculant, affecting not only the purification effect but also increasing aquaculture costs.

[0004] Another patent, CN117945600B, proposes a direct drinking water purification and control system. This system achieves real-time detection and alarm of terminal water quality through multi-module collaborative operation, and can quickly locate contaminated pipelines, thereby improving maintenance efficiency. However, this technical solution is mainly aimed at human drinking water applications and does not fully consider the special needs of egg-laying hen farming, such as the precise control of microbial and mineral content in the water. Furthermore, the system lacks data collection and analysis functions based on the drinking behavior characteristics of egg-laying hens, making it difficult to support intelligent management decisions. In addition, its high dependence on hardware equipment may increase system complexity and operating costs, hindering its promotion in large-scale farms.

[0005] The aforementioned problems indicate that existing drinking water purification devices and control systems still have significant shortcomings in terms of intelligent monitoring, precise purification, and adaptability to layer hen farming scenarios. Specifically, current technologies lack the ability to monitor and dynamically regulate the quality of drinking water for layer hens in real time, fail to fully consider the drinking behavior characteristics of layer hens and the special needs of the farming environment, and have certain limitations in terms of system integration, operational stability, and economy. Therefore, there is an urgent need for an intelligent drinking water purification method and monitoring system for layer hens that integrates real-time monitoring, intelligent regulation, and efficient purification functions to solve the problems existing in current technologies, thereby improving the quality of drinking water for layer hens, reducing farming risks, and promoting the sustainable development of the poultry industry. Summary of the Invention

[0006] This invention provides an intelligent method and monitoring system for purifying drinking water for laying hens, the main purpose of which is to improve the real-time monitoring capability, precise purification efficiency, and long-term operational stability of drinking water quality for laying hens.

[0007] To achieve the above objectives, the present invention provides an intelligent drinking water purification method for laying hens, comprising: The system receives intelligent purification instructions and identifies the intelligent monitoring system and the target farm based on these instructions. The intelligent monitoring system includes a water quality data acquisition unit, a purification control unit, and a data processing center. Based on the target farm, target aquaculture information is obtained, and data acquisition instructions from the water quality data acquisition unit are confirmed. The historical water quality dataset is obtained using the data acquisition instructions. The historical water quality dataset includes multiple historical water quality records. Based on the data processing center, target aquaculture information, and historical water quality dataset, target water quality dataset is obtained. The target water quality dataset includes multiple target water quality records. The target aquaculture information includes aquaculture scale, laying hen breed, environmental temperature and humidity, and water source type. Confirm receipt of purification sharing instruction from purification control unit, obtain first purification parameter set based on purification sharing instruction and target aquaculture information, wherein the first purification parameter set includes multiple first purification parameters, and obtain target purification parameters based on the first purification parameter set and target water quality dataset; Based on the target purification parameters, a target purification scheme is obtained. After confirming that the target purification scheme is applied to the target aquaculture farm, the water quality of the target aquaculture farm is monitored based on a preset monitoring frequency to obtain a set of monitoring index ranges and monitoring stages. Determine whether the monitoring phase is the target aquaculture phase; If the monitoring stage differs from the target breeding stage, an updated purification plan is obtained using the monitoring stage and the range of monitoring indicators, and intelligent purification of the target breeding farm is achieved based on the updated purification plan.

[0008] Furthermore, the step of obtaining the target water quality dataset based on the data processing center, target aquaculture information, and historical water quality dataset includes: Perform the following operations on all historical water quality records in the historical water quality dataset: Historical water quality information is obtained based on the historical water quality records. The historical water quality information includes reference breeding scale, reference laying hen breed, reference environmental temperature and humidity, reference water source type, purification measures and water quality index range set. The water quality index range set includes pH value range, dissolved oxygen range, mineral content range and microbial concentration range. The historical water quality information is used to perform a labeling operation on the historical water quality records to obtain labeled water quality information. The labeled water quality information is then summarized to obtain a labeled water quality information set. The target aquaculture information is used to filter the identified water quality information set to obtain an initial water quality information set. The initial water quality information set includes multiple initial water quality information sets, and the reference aquaculture scale, reference laying hen breed, and reference environmental temperature and humidity corresponding to the initial water quality information are the same as the aquaculture scale, laying hen breed, and environmental temperature and humidity of the target aquaculture information. The target water quality dataset is obtained by utilizing the data processing center and the initial water quality information set.

[0009] Furthermore, the step of obtaining the target water quality dataset using the data processing center and the initial water quality information set includes: The following operations are performed on all initial water quality information in the initial water quality information set: The reference evaluation value is calculated based on the reference aquaculture scale and reference environmental temperature and humidity corresponding to the initial water quality information. The target evaluation value is calculated using the aquaculture scale and environmental temperature and humidity corresponding to the target aquaculture information. The reference evaluation values ​​are then summarized to obtain a reference evaluation value set. Screening conditions are constructed using the target evaluation values. An updated water quality information set is obtained based on the screening conditions and the reference evaluation value set. The target water quality dataset is obtained from the updated water quality information set and data processing center.

[0010] Further, the step of obtaining an updated water quality information set based on the screening conditions and the reference evaluation value set includes: The following operations are performed on all reference evaluation values ​​in the reference evaluation value set: If the reference evaluation value meets the screening criteria, the initial water quality information corresponding to the reference evaluation value is retained; otherwise, the initial water quality information corresponding to the reference evaluation value is discarded. The initial water quality information that is retained is summarized to obtain an updated water quality information set.

[0011] Further, the step of obtaining the target water quality dataset based on the updated water quality information set and data processing center includes: Confirm receipt of data optimization instructions from the data processing center, and obtain a set of reference water quality index ranges based on the data optimization instructions. The set of reference water quality index ranges includes reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range. Perform the following operations on the updated water quality information in the updated water quality information set: If it is confirmed that the pH range, dissolved oxygen range, mineral content range, and microbial concentration range corresponding to the updated water quality information are subsets of the reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range in the reference water quality indicator range set, then the updated water quality information is retained; otherwise, the updated water quality information is removed. The retained updated water quality information is then summarized to obtain the target water quality dataset.

[0012] Furthermore, the step of obtaining the reference water quality index range set based on the data optimization instructions includes: Based on the target aquaculture information and data optimization instructions, multiple initial water quality index range sets were identified, including the initial pH range, initial dissolved oxygen range, initial mineral content range, and initial microbial concentration range. Perform the following operation on the initial water quality index range set of multiple initial water quality index range sets: The initial pH range corresponding to the initial water quality index range set is mapped to the pre-constructed index distribution map to obtain the mapping interval. Based on the number of initial water quality index range sets in the multiple initial water quality index range sets corresponding to the mapping interval, the mapping interval is divided into one or more initial partitions. The reference extracted values ​​are calculated based on the preset stability coefficient; Using the reference extracted values, one or more first partitions are extracted from one or more initial partitions, wherein the number of initial water quality index range sets corresponding to the first partitions is greater than or equal to the reference extracted values; One or more second partitions are obtained based on the one or more first partitions, wherein the second partitions are composed of one or more consecutive first partitions; Use the one or more second partitions to obtain the third interval value, and calculate one or more interval ratios based on the one or more second partitions and the third interval value; Sort the interval proportions in one or more interval proportions in descending order to obtain an initial interval proportion sequence. Then, use a preset interval proportion threshold to identify the target interval proportion sequence from the initial interval proportion sequence. Extract the reference interval proportions sequentially from the target interval proportion sequence, and perform the following operations on the extracted reference interval proportions: Based on the reference interval ratio, the lag interval ratio is identified in the target interval ratio sequence, wherein the lag interval ratio is the interval ratio that is adjacent to the reference interval ratio in the target interval ratio sequence and lags behind the reference interval ratio. The interval is obtained by using the reference interval ratio and the lag interval ratio. If the interval is less than or equal to the preset interval threshold, the fitted interval range is obtained based on the reference interval ratio and the lag interval ratio. Based on the fitted interval range, a reference pH range is obtained, and reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range are obtained respectively. The reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range are summarized to obtain a set of reference water quality index ranges.

[0013] Further, the step of obtaining the first purification parameter set based on the purification sharing instruction and target aquaculture information includes: Multiple data transmission groups are identified using the purification sharing command. Each data transmission group includes a data transmission end and a data receiving end. Encryption parameters are calculated based on the target aquaculture information. Calculate the decryption parameters using the encryption parameters; Obtain the public and private keys based on the encryption and decryption parameters, and perform encryption operations on the target aquaculture information based on the public key to obtain encrypted information; Perform the following operation for each of the multiple data transmission groups: The encrypted information and private key are sent to the data receiving end using the data transmission end. The first purification instruction returned from the data receiving end is confirmed. The first purification instruction is parsed to obtain reference parameters, which include reference purification strength and reference purification number. The reference parameters are summarized to obtain a reference parameter set. The first purification parameter set is calculated based on the reference parameter set.

[0014] Furthermore, the target purification parameters are obtained based on the first purification parameter set and the target water quality dataset, and the final target purification parameters are determined by comprehensively analyzing the first purification parameter set and the target water quality dataset and combining them with the actual needs of the target aquaculture farm.

[0015] To achieve the above objectives, the present invention also provides an intelligent drinking water purification and monitoring system for laying hens, comprising: The intelligent purification environment confirmation module is used to receive intelligent purification instructions and confirm the intelligent monitoring system and the target farm based on the intelligent purification instructions. The intelligent monitoring system includes a water quality data acquisition unit, a purification control unit, and a data processing center. The historical water quality screening module is used to obtain target aquaculture information based on the target farm, confirm receipt of data acquisition instructions from the water quality data acquisition unit, and use the data acquisition instructions to obtain a historical water quality dataset, wherein the historical water quality dataset includes multiple historical water quality records. The module also obtains a target water quality dataset based on the data processing center, the target aquaculture information, and the historical water quality dataset, wherein the target water quality dataset includes multiple target water quality records. The target aquaculture information includes the aquaculture scale, laying hen breed, environmental temperature and humidity, and water source type. The target purification parameter acquisition module is used to confirm receipt of the purification sharing instruction from the purification control unit, acquire a first purification parameter set based on the purification sharing instruction and the target aquaculture information, wherein the first purification parameter set includes multiple first purification parameters, and acquire the target purification parameter based on the first purification parameter set and the target water quality dataset. The water quality monitoring module is used to obtain the target purification plan based on the target purification parameters, and after confirming that the target purification plan is applied to the target aquaculture farm, to perform monitoring operations on the water quality of the target aquaculture farm based on a preset monitoring frequency, thereby obtaining a set of monitoring index ranges and monitoring stages. Determine whether the monitoring phase is the target aquaculture phase; If the monitoring stage differs from the target breeding stage, an updated purification plan is obtained using the monitoring stage and the range of monitoring indicators, and intelligent purification of the target breeding farm is achieved based on the updated purification plan.

[0016] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the above-described intelligent drinking water purification method for laying hens.

[0017] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned intelligent hen drinking water purification method.

[0018] To address the problems described in the background art, this invention provides an embodiment that obtains target aquaculture information based on the target farm, confirms receipt of a data acquisition instruction from a water quality data acquisition unit, and uses the data acquisition instruction to obtain a historical water quality dataset. This historical water quality dataset includes multiple historical water quality records. A target water quality dataset is then obtained based on the data processing center, the target aquaculture information, and the historical water quality dataset. This target water quality dataset includes multiple target water quality records. The target aquaculture information includes the aquaculture scale, laying hen breed, environmental temperature and humidity, and water source type. It is evident that this invention considers the specific information of the target farm and filters the historical water quality records in the historical water quality dataset using the target aquaculture information to obtain the target water quality dataset. This allows the obtained target water quality dataset to more accurately represent the water purification requirements of a typical farm, thereby improving the accuracy of predicting the water purification parameters required by the target farm. This embodiment of the invention confirms receipt of a purification sharing instruction from a purification control unit. Based on the purification sharing instruction and target aquaculture information, a first purification parameter set is obtained. The first purification parameter set includes multiple first purification parameters. Target purification parameters are obtained based on the first purification parameter set and a target water quality dataset. Therefore, this embodiment of the invention not only considers predicting the water quality purification parameters required by the target aquaculture farm based on the target water quality dataset, but also combines the first purification parameter set to jointly predict the water quality purification parameters required by the target aquaculture farm. Furthermore, the target aquaculture information is encrypted before obtaining the first purification parameter set to ensure that the target aquaculture information is not tampered with or leaked. This improves the accuracy and security of predicting the water quality purification parameters required by the target aquaculture farm. This embodiment of the invention, based on the... The invention obtains a target purification plan based on the target purification parameters. After confirming the application of the target purification plan to the target farm, it performs water quality monitoring operations on the target farm based on a preset monitoring frequency, obtaining a set of monitoring index ranges and a monitoring stage. It then determines whether the monitoring stage matches the target farming stage. If the monitoring stage differs from the target farming stage, an updated purification plan is obtained using the monitoring stage and the set of monitoring index ranges. Based on this updated plan, intelligent purification of the target farm is achieved. Therefore, this invention, after applying the target purification plan to the target farm, monitors the water quality index ranges and monitoring stages of the target farm, updating the required water quality purification parameters for the target farm through these parameters. This ensures the accuracy of the predicted water quality purification parameters for the target farm. Thus, this invention can improve the accuracy and stability of drinking water purification for laying hens. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of an intelligent drinking water purification method for laying hens according to an embodiment of the present invention. Figure 2This is a functional block diagram of an intelligent drinking water purification and monitoring system for laying hens provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the intelligent drinking water purification method for laying hens, as provided in an embodiment of the present invention. Detailed Implementation

[0020] This invention provides an intelligent method and monitoring system for purifying drinking water for laying hens. Its main objective is to improve the real-time monitoring capability, precise purification efficiency, and long-term operational stability of drinking water quality for laying hens. The specific embodiments of this invention will be described in detail below with reference to the accompanying drawings. Figure 1 The diagram shown is a flowchart illustrating an intelligent drinking water purification method for laying hens according to an embodiment of the present invention; Figure 2 The diagram shown is a functional block diagram of an intelligent drinking water purification and monitoring system for laying hens provided in an embodiment of the present invention; as shown... Figure 3 The diagram shown is a structural schematic of an electronic device for implementing the intelligent hen drinking water purification method according to an embodiment of the present invention.

[0021] In this embodiment, the intelligent purification environment confirmation module first receives an intelligent purification command from an external user and, based on the command, identifies the target farm and the matching intelligent monitoring system. The intelligent monitoring system includes a water quality data acquisition unit, a purification control unit, and a data processing center. The water quality data acquisition unit collects real-time water quality information from the farm; the purification control unit generates a purification plan based on the analysis results and executes the purification operation; and the data processing center is responsible for data storage, analysis, and optimization. In practical applications, users can send intelligent purification commands to the system via mobile terminals or computers. Upon receiving the command, the system automatically identifies the location of the target farm and its relevant information, such as the farm's scale, laying hen breed, environmental temperature and humidity, and water source type.

[0022] Next, the historical water quality screening module acquires target aquaculture information based on the target farm and receives data acquisition instructions from the water quality data acquisition unit to obtain a historical water quality dataset. This historical water quality dataset contains multiple historical water quality records, covering water quality indicators such as pH, dissolved oxygen, mineral concentration, and microbial concentration over different time periods. To ensure that the extracted data accurately reflects the needs of the target farm, the system filters the historical water quality dataset using the target aquaculture information to obtain the target water quality dataset. Specifically, the system first performs the following operations on each historical water quality record in the dataset: It acquires historical water quality information based on the record, including reference aquaculture scale, reference laying hen breed, reference environmental temperature and humidity, reference water source type, purification measures, and a set of water quality indicator ranges. Then, it uses this historical water quality information to perform an identification operation on the historical water quality records, forming an identified water quality information set. Based on this, the system further filters the identified water quality information set using the target aquaculture information to obtain the initial water quality information set. The initial water quality information set retains only the initial water quality information that matches the target aquaculture information, that is, those records where the reference aquaculture scale, reference laying hen breed, and reference environmental temperature and humidity are consistent with the target aquaculture information.

[0023] To further optimize the accuracy of the target water quality dataset, the system utilizes the data processing center to perform in-depth analysis of the initial water quality information set. The specific process is as follows: For each piece of initial water quality information in the initial set, the system first calculates a reference assessment value based on its corresponding reference aquaculture scale and reference environmental temperature and humidity. Simultaneously, it calculates the target assessment value using the aquaculture scale and environmental temperature and humidity from the target aquaculture information. Subsequently, the system aggregates all reference assessment values ​​to form a reference assessment value set and constructs filtering conditions based on the target assessment values. By filtering the reference assessment value set using these conditions, the system finally obtains the updated water quality information set. During this process, if the reference assessment value corresponding to a piece of initial water quality information meets the filtering conditions, the information is retained; otherwise, it is discarded. Finally, the system performs further verification on each piece of updated water quality information in the updated set. Only when the pH range, dissolved oxygen range, mineral content range, and microbial concentration range of the updated water quality indicator range set are all subsets of the reference water quality indicator range set will the updated water quality information be retained. Through the above steps, the system ultimately generates the target water quality dataset, providing accurate data support for water purification in target aquaculture farms.

[0024] Regarding the generation process of the reference water quality index range set, this embodiment employs a more refined algorithm model. First, the system identifies multiple initial water quality index range sets based on target aquaculture information and data optimization instructions. Each initial water quality index range set includes an initial pH range, an initial dissolved oxygen range, an initial mineral content range, and an initial microbial concentration range. Next, the system maps the initial pH ranges corresponding to the initial water quality index range sets to a pre-constructed index distribution map, obtaining mapping intervals. Based on the number of initial water quality index range sets corresponding to the mapping intervals, the system divides them into one or more initial partitions. Subsequently, the system calculates a reference extraction value based on a preset stability coefficient and uses this value to extract one or more first partitions from one or more initial partitions. The number of initial water quality index range sets corresponding to each first partition is greater than or equal to the reference extraction value. Based on the first partitions, the system further obtains second partitions, where the second partitions consist of one or more consecutive first partitions. Subsequently, the system uses the second partitions to obtain third interval values ​​and calculates one or more interval ratios based on the second partitions and the third interval values. The system sorts the interval ratios in descending order to obtain an initial interval ratio sequence and uses a preset interval ratio threshold to identify the target interval ratio sequence. Within the target interval ratio sequence, the system sequentially extracts the reference interval ratio and determines the lag interval ratio based on the reference interval ratio. If the interval interval between the reference interval ratio and the lag interval ratio is less than or equal to a preset interval threshold, the system obtains a fitted interval range based on both. Finally, the system obtains the reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range based on the fitted interval range, and summarizes them into a set of reference water quality index ranges.

[0025] Based on the acquired target water quality dataset, the system further confirms receipt of a purification sharing instruction from the purification control unit and generates a first purification parameter set based on this instruction and the target aquaculture information. Specifically, the system first uses the purification sharing instruction to identify multiple data transmission groups, each including a data transmission end and a data receiving end. Subsequently, the system calculates encryption parameters based on the target aquaculture information and uses these parameters to calculate decryption parameters. Based on the encryption and decryption parameters, the system generates a public key and a private key, and uses the public key to encrypt the target aquaculture information, obtaining encrypted information. During data transmission, the system uses the data transmission end to send the encrypted information and the private key to the data receiving end and confirms receipt of the first purification instruction returned from the data receiving end. After parsing the first purification instruction, the system obtains reference parameters, including a reference purification intensity and a reference purification count. The system aggregates all reference parameters to form a reference parameter set and calculates the first purification parameter set based on this set. The first purification parameter set contains multiple first purification parameters. By comprehensively analyzing the first purification parameter set and the target water quality dataset, combined with the actual needs of the target aquaculture farm, the system ultimately determines the target purification parameters.

[0026] Based on the target purification parameters, the system generates a target purification plan and applies it to the target aquaculture farm. After the target purification plan is implemented, the system monitors the water quality of the target aquaculture farm through a water quality monitoring module, obtaining a set of monitoring indicator ranges and monitoring stages. The monitoring indicator ranges include pH range, dissolved oxygen range, mineral content range, and microbial concentration range, while the monitoring stages reflect the temporal characteristics of water quality changes. The system determines whether the monitoring stage matches the target aquaculture stage. If the monitoring stage differs from the target aquaculture stage, the system generates an updated purification plan using the monitoring stage and the set of monitoring indicator ranges, and implements intelligent purification of the target aquaculture farm based on the updated purification plan. This dynamic adjustment mechanism ensures that the system can continuously optimize the purification effect based on real-time monitoring data, thereby improving the accuracy and stability of water quality purification.

[0027] In this embodiment, the system also provides support for electronic devices and computer-readable storage media. For example... Figure 3 As shown, the electronic device includes a memory and a processor. The memory stores at least one instruction, and the processor executes the instructions stored in the memory to implement the aforementioned intelligent drinking water purification method for laying hens. Furthermore, a computer-readable storage medium stores at least one instruction, which is executed by the processor in the electronic device to implement the aforementioned intelligent drinking water purification method for laying hens. Through the synergy of hardware and software, this invention can efficiently complete the collection, analysis, and purification of water quality data, providing a reliable intelligent solution for the laying hen farming industry.

[0028] In summary, this invention significantly improves the accuracy and stability of drinking water purification for laying hens through multi-dimensional data analysis and dynamic adjustment mechanisms. Both the generation process of the target water quality dataset and the calculation method of the first purification parameter set demonstrate the technological innovation and practicality of this invention. Furthermore, by encrypting and protecting the target breeding information, the system ensures data security and avoids the risk of information leakage. Therefore, this invention not only meets the needs of the laying hen farming industry for high-quality drinking water but also provides important technical support for the development of intelligent farming technology.

Claims

1. A method for purifying drinking water for laying hens based on intelligent technology, characterized in that, The method includes: The system receives intelligent purification instructions and identifies the intelligent monitoring system and the target farm based on these instructions. The intelligent monitoring system includes a water quality data acquisition unit, a purification control unit, and a data processing center. Based on the target farm, target aquaculture information is obtained, and data acquisition instructions from the water quality data acquisition unit are confirmed. The historical water quality dataset is obtained using the data acquisition instructions. The historical water quality dataset includes multiple historical water quality records. Based on the data processing center, target aquaculture information, and historical water quality dataset, target water quality dataset is obtained. The target water quality dataset includes multiple target water quality records. The target aquaculture information includes aquaculture scale, laying hen breed, environmental temperature and humidity, and water source type. Confirm receipt of purification sharing instruction from purification control unit, obtain first purification parameter set based on purification sharing instruction and target aquaculture information, wherein the first purification parameter set includes multiple first purification parameters, and obtain target purification parameters based on the first purification parameter set and target water quality dataset; Based on the target purification parameters, a target purification scheme is obtained. After confirming that the target purification scheme is applied to the target aquaculture farm, the water quality of the target aquaculture farm is monitored based on a preset monitoring frequency to obtain a set of monitoring index ranges and monitoring stages. Determine whether the monitoring phase is the target aquaculture phase; If the monitoring stage differs from the target breeding stage, an updated purification plan is obtained using the monitoring stage and the range of monitoring indicators, and intelligent purification of the target breeding farm is achieved based on the updated purification plan.

2. The intelligent drinking water purification method for laying hens as described in claim 1, characterized in that, The process of obtaining the target water quality dataset based on the data processing center, target aquaculture information, and historical water quality dataset includes: Perform the following operations on all historical water quality records in the historical water quality dataset: Historical water quality information is obtained based on the historical water quality records. The historical water quality information includes reference breeding scale, reference laying hen breed, reference environmental temperature and humidity, reference water source type, purification measures and water quality index range set. The water quality index range set includes pH value range, dissolved oxygen range, mineral content range and microbial concentration range. The historical water quality information is used to perform a labeling operation on the historical water quality records to obtain labeled water quality information. The labeled water quality information is then summarized to obtain a labeled water quality information set. The target aquaculture information is used to filter the identified water quality information set to obtain an initial water quality information set. The initial water quality information set includes multiple initial water quality information sets, and the reference aquaculture scale, reference laying hen breed, and reference environmental temperature and humidity corresponding to the initial water quality information are the same as the aquaculture scale, laying hen breed, and environmental temperature and humidity of the target aquaculture information. The target water quality dataset is obtained by utilizing the data processing center and the initial water quality information set.

3. The intelligent drinking water purification method for laying hens as described in claim 2, characterized in that, The process of obtaining the target water quality dataset using a data processing center and an initial water quality information set includes: The following operations are performed on all initial water quality information in the initial water quality information set: The reference evaluation value is calculated based on the reference aquaculture scale and reference environmental temperature and humidity corresponding to the initial water quality information. Reference assessment value This is used to quantify the fit between the reference aquaculture scenario corresponding to the initial water quality information and the optimal environment for egg-laying hen farming. The calculation formula is as follows: This is a reference aquaculture scale corresponding to the initial water quality information. For reference to the normalized value of aquaculture scale, the calculation method is as follows: ,in The maximum aquaculture scale in the historical water quality dataset, with a normalized value range of [0,1]. The reference ambient temperature corresponding to the initial water quality information. The calculation method is as follows, using the normalized value of ambient temperature as a reference: ,in The optimal temperature for laying hen breeding is 18-25℃, which is the industry standard. Trange is the suitable temperature range (25℃), and its normalized value range is [0,1]. The relative humidity of the reference environment corresponding to the initial water quality information is calculated as follows: ,in The optimal humidity for laying hen farming is 50%-70%, and Hrange is the suitable humidity range (45%), with normalized values ​​ranging from [0,1]. α and β are weighting coefficients, α + β = 1. Based on experience in egg-laying hen farming, α is set to 0.6 and β to 0.

4. The value ranges from [0,1]. The higher the value, the higher the reference value of the corresponding water quality data. The target evaluation value is calculated using the aquaculture scale and environmental temperature and humidity corresponding to the target aquaculture information. The reference evaluation values ​​are then summarized to obtain a reference evaluation value set. Screening conditions are constructed using the target evaluation values. An updated water quality information set is obtained based on the screening conditions and the reference evaluation value set. Target evaluation value The calculation formula is as follows: The actual breeding scale, ambient temperature, and relative humidity of the target farm; α and β are weighting coefficients, and α + β = 1; The target water quality dataset is obtained from the updated water quality information set and data processing center.

4. The intelligent drinking water purification method for laying hens as described in claim 3, characterized in that, The process of obtaining an updated water quality information set based on the screening criteria and reference evaluation value set includes: The following operations are performed on all reference evaluation values ​​in the reference evaluation value set: If the reference evaluation value meets the screening criteria, the initial water quality information corresponding to the reference evaluation value is retained; otherwise, the initial water quality information corresponding to the reference evaluation value is discarded. The initial water quality information that is retained is summarized to obtain an updated water quality information set.

5. The intelligent drinking water purification method for laying hens as described in claim 4, characterized in that, The step of obtaining the target water quality dataset based on the updated water quality information set and data processing center includes: Confirm receipt of data optimization instructions from the data processing center, and obtain a set of reference water quality index ranges based on the data optimization instructions. The set of reference water quality index ranges includes reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range. Perform the following operations on the updated water quality information in the updated water quality information set: If it is confirmed that the pH range, dissolved oxygen range, mineral content range, and microbial concentration range corresponding to the updated water quality information are subsets of the reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range in the reference water quality indicator range set, then the updated water quality information is retained; otherwise, the updated water quality information is removed. The retained updated water quality information is then summarized to obtain the target water quality dataset.

6. The intelligent drinking water purification method for laying hens as described in claim 5, characterized in that, The step of obtaining the reference water quality index range set based on the data optimization instructions includes: Based on the target aquaculture information and data optimization instructions, multiple initial water quality index range sets were identified, including the initial pH range, initial dissolved oxygen range, initial mineral content range, and initial microbial concentration range. Perform the following operation on the initial water quality index range set of multiple initial water quality index range sets: The initial pH range corresponding to the initial water quality index range set is mapped to the pre-constructed index distribution map to obtain the mapping interval. Based on the number of initial water quality index range sets in the multiple initial water quality index range sets corresponding to the mapping interval, the mapping interval is divided into one or more initial partitions. The reference extracted values ​​are calculated based on the preset stability coefficient; The preset stability coefficient K ranges from (0,1). A higher K value indicates a higher requirement for sample stability. For egg-laying hen farming scenarios, the default preset value is 0.6~0.

8. (Refer to the extracted values.) To determine the minimum sample size threshold for the first partition, the theoretical average sample size for a single initial partition is calculated using the following formula: This refers to the total number of initial water quality index ranges included in the statistics. The total number of initial partitions obtained. The theoretical average number of samples for a single initial partition; Reference extracted values ​​were calculated based on the stability coefficient. in It is a rounding function. It is a positive integer; Using the reference extracted values, one or more first partitions are extracted from one or more initial partitions, wherein the number of initial water quality index range sets corresponding to the first partitions is greater than or equal to the reference extracted values; One or more second partitions are obtained based on the one or more first partitions, wherein the second partitions are composed of one or more consecutive first partitions; Use the one or more second partitions to obtain the third interval value, and calculate one or more interval ratios based on the one or more second partitions and the third interval value; Sort the interval proportions in one or more interval proportions in descending order to obtain an initial interval proportion sequence. Then, use a preset interval proportion threshold to identify the target interval proportion sequence from the initial interval proportion sequence. Extract reference interval proportions sequentially from the target interval proportion sequence, and perform the following operations on the extracted reference interval proportions: Based on the reference interval ratio, the lag interval ratio is identified in the target interval ratio sequence, wherein the lag interval ratio is the interval ratio that is adjacent to the reference interval ratio in the target interval ratio sequence and lags behind the reference interval ratio. The interval is obtained by using the reference interval ratio and the lag interval ratio. If the interval is less than or equal to the preset interval threshold, the fitted interval range is obtained based on the reference interval ratio and the lag interval ratio. Based on the fitted interval range, a reference pH range is obtained, and reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range are obtained respectively. The reference pH range, reference dissolved oxygen range, reference mineral content range, and reference microbial concentration range are summarized to obtain a set of reference water quality index ranges.

7. The intelligent drinking water purification method for laying hens as described in claim 6, characterized in that, The process of obtaining the first set of purification parameters based on the purification sharing instruction and target aquaculture information includes: Multiple data transmission groups are identified using the purification sharing command. Each data transmission group includes a data transmission end and a data receiving end. Encryption parameters are calculated based on the target aquaculture information. Calculate the decryption parameters using the encryption parameters; Obtain the public and private keys based on the encryption and decryption parameters, and perform encryption operations on the target aquaculture information based on the public key to obtain encrypted information; Perform the following operation for each of the multiple data transmission groups: The encrypted information and private key are sent to the data receiving end using the data transmission end. The first purification instruction returned from the data receiving end is confirmed. The first purification instruction is parsed to obtain reference parameters, which include reference purification strength and reference purification number. The reference parameters are summarized to obtain a reference parameter set. The first purification parameter set is calculated based on the reference parameter set.

8. The intelligent drinking water purification method for laying hens as described in claim 7, characterized in that, The target purification parameters are obtained based on the first purification parameter set and the target water quality dataset. The first purification parameter set and the target water quality dataset are then comprehensively analyzed, and the final target purification parameters are determined in combination with the actual needs of the target aquaculture farm.

9. A smart drinking water purification and monitoring system for laying hens, characterized in that, The system includes: The intelligent purification environment confirmation module is used to receive intelligent purification instructions and confirm the intelligent monitoring system and the target farm based on the intelligent purification instructions. The intelligent monitoring system includes a water quality data acquisition unit, a purification control unit, and a data processing center. The historical water quality screening module is used to obtain target aquaculture information based on the target farm, confirm receipt of data acquisition instructions from the water quality data acquisition unit, and obtain historical water quality datasets using the data acquisition instructions. The historical water quality datasets include multiple historical water quality records. The target water quality dataset is obtained based on the data processing center, target aquaculture information, and historical water quality datasets. The target water quality datasets include multiple target water quality records. The target aquaculture information includes aquaculture scale, laying hen breed, environmental temperature and humidity, and water source type. The target purification parameter acquisition module is used to confirm receipt of the purification sharing instruction from the purification control unit, acquire a first purification parameter set based on the purification sharing instruction and the target aquaculture information, wherein the first purification parameter set includes multiple first purification parameters, and acquire the target purification parameter based on the first purification parameter set and the target water quality dataset. The water quality monitoring module is used to obtain the target purification plan based on the target purification parameters, and after confirming that the target purification plan is applied to the target aquaculture farm, to perform monitoring operations on the water quality of the target aquaculture farm based on a preset monitoring frequency, thereby obtaining a set of monitoring index ranges and monitoring stages. Determine whether the monitoring phase is the target aquaculture phase; If the monitoring stage differs from the target breeding stage, an updated purification plan is obtained using the monitoring stage and the range of monitoring indicators, and intelligent purification of the target breeding farm is achieved based on the updated purification plan.

10. An electronic device, characterized in that, The electronic device includes: A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the above-described intelligent drinking water purification method for laying hens as described in any one of claims 1 to 8.

Images