Live pig breeding equipment full life cycle monitoring management system, medium and terminal

Abstract

The invention discloses a live pig breeding equipment full life cycle monitoring management system, a medium and a terminal. The system comprises a user login module used for user authority management and data query; the data acquisition module is used for acquiring full-life-cycle data of intelligent equipment used for pig breeding; the data preprocessing module is used for preprocessing operation data of various intelligent devices monitored in real time by using the Internet of Things technology; the data storage module is used for storing various data information; the data analysis module is used for predicting and evaluating the state of the intelligent equipment by adopting a deep neural network model; the equipment state early warning module is used for setting an early warning rule and establishing an early warning mechanism according to the characteristics and business requirements of the equipment, and sending out early warning and corresponding measure notifications; the intelligent decision-making module is used for generating an equipment state evaluation report and generating a multi-dimensional data analysis report; and the display module is used for displaying the generated multi-dimensional data analysis report. The intelligent equipment is supervised, and the breeding efficiency and the management level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent breeding equipment detection and management, and in particular to a full life cycle monitoring and management system, medium and terminal for pig breeding equipment. Background Art

[0002] In the modern large-scale pig farming industry, it is very important to monitor the growth and health status of pigs.

[0003] Under the current trend, in order to accurately monitor and optimize the growth environment of pigs, farms generally use a variety of IoT devices to form a highly integrated intelligent monitoring system. Through the intelligent monitoring system, a data deep learning model is built for statistical analysis. The system needs to deploy a large number of data acquisition and monitoring devices, but these devices may fail due to various reasons within a certain period of operation, such as aging, environmental interference (such as dust, humidity), unstable power supply or software failure. If these failures are not discovered and handled in time, it will directly weaken the monitoring ability of the pig growth environment, thereby affecting the timely understanding of the growth rate and health status of the pigs. Summary of the invention

[0004] The purpose of the present invention is to provide a full life cycle monitoring and management system, medium and terminal for pig breeding equipment, which monitor various functions of the intelligent management system of the full life cycle of pig breeding intelligent equipment and improve breeding efficiency and management level.

[0005] In order to achieve one of the above purposes, the present invention adopts the following technical solution:

[0006] A monitoring and management system for the whole life cycle of pig breeding equipment, including: a user login module, a data acquisition module, a data preprocessing module, a data storage module, a data analysis module, an equipment status early warning module, an intelligent decision-making module and a display module.

[0007] The user login module is used for user authority management and data query;

[0008] The data collection module is used to collect the full life cycle data of smart devices used in pig farming, including: basic information of smart devices, real-time data of smart device operation, historical data and environmental data;

[0009] The data preprocessing module is used to preprocess the real-time monitoring operation data of various smart devices using the Internet of Things technology;

[0010] The data storage module is used to store various data information preprocessed by the data preprocessing module;

[0011] The data analysis module is used to predict and evaluate the status of smart devices using a deep neural network model for various pre-processed data information;

[0012] The device status warning module is used to set warning rules and establish a warning mechanism according to the characteristics of the device and business requirements. If the data analyzed by the data analysis module reaches the preset warning conditions and is determined to be abnormal, a warning and a corresponding measure notification are issued;

[0013] The intelligent decision-making module is used to generate an equipment status assessment report based on the assessment of various equipment statuses in the data analysis module;

[0014] The display module is used to display the generated multi-dimensional data analysis report.

[0015] Furthermore, it also includes the data verification module, which is used to verify the data processed by the data preprocessing module, including: verifying whether the data format is correct, whether the data range is within a reasonable range, whether the timestamp is continuous, and whether the data fields are consistent.

[0016] Furthermore, it also includes an expandable module, which is used to adapt to subsequent controllable business needs and uncontrollable potential needs in an expandable manner.

[0017] Furthermore, the basic information of the smart device includes: device name, model, manufacturer, installation location, timestamp, device ID information and service life;

[0018] The real-time operation data of the intelligent device includes: temperature, humidity, pressure, flow, ammonia concentration, speed sensor readings, and the working status of the device (such as start, stop, fault) information;

[0019] The historical data information includes: past operation records, maintenance records, and fault records of the equipment;

[0020] The environmental data information includes: temperature, humidity, and lighting environment conditions of the farm.

[0021] Furthermore, the data preprocessing module is used to clean, denoise and format the data collected by the data acquisition module. The cleaning process includes removing duplicate data, filling missing data and correcting erroneous data to ensure the integrity and accuracy of the data; the formatting process includes converting each data into a unified format, splitting time series data into single data points and encoding the data.

[0022] Furthermore, the data analysis module is used to predict and evaluate the status of the smart device using a deep neural network model for various pre-processed data information, specifically including:

[0023] It is used to use the Internet of Things technology to monitor the operating data of various smart devices in real time, and combine the basic information data of smart devices, historical data information of smart devices, and environmental data information. Through the feature fusion of these four types of multimodal data, the long short-term memory network model built based on the PyTorch deep learning framework is used to analyze the time series data and predict the device status.

[0024] Furthermore, the intelligent equipment includes various sensors (temperature and humidity sensors, ammonia concentration sensors, light intensity sensors and network cameras installed in various corners of the farm), intelligent feeders, intelligent environmental controllers, intelligent inspection robots and 3D weight estimators.

[0025] Furthermore, the data storage module is provided with a basic database, a model storage database, an evaluation management library, an equipment weekly inspection / rotation library and a data analysis library. The basic database is used to store the basic information of the smart device, the real-time data of the smart device operation, historical data and environmental data; wherein the real-time data and environmental data of the smart device operation belong to real-time data information, and database views are adopted; the basic information and historical data of the smart device belong to non-real-time data information, and an intermediate library plus a scheduled task is established to transfer the interactive data to the intermediate library table in an asynchronous non-real-time manner, and the whole table or the difference data is synchronously updated at certain intervals.

[0026] In order to achieve the second of the above objectives, the present invention adopts the following technical solution:

[0027] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the full life cycle monitoring and management system for pig breeding equipment as described in any one of the above items.

[0028] In order to achieve the third of the above objectives, the present invention adopts the following technical solutions:

[0029] An information data processing terminal is used to provide a user input interface to implement the full life cycle monitoring and management system of pig breeding equipment as described in any of the above items when the information data processing terminal is implemented on an electronic device.

[0030] Beneficial effects of the present invention:

[0031] The present invention is an intelligent management system for the pig farming industry, covering the full life cycle management of pig farming equipment, focusing on comprehensive recording and analysis of the basic information of the equipment, real-time operation data and historical data information, and environmental data. The system integrates real-time data and non-real-time data. Real-time data includes the operating status of the equipment, equipment readings (such as temperature and humidity), while non-real-time data covers the basic information and historical records of the equipment (such as maintenance and fault records). By comprehensively analyzing these data, the system can provide accurate monitoring and decision support, improve farming efficiency, reduce management costs, and promote the sustainable development of the pig farming industry.

[0032] The operation data collected from smart devices in real time through the Internet of Things technology is time series data, and combined with the basic information data of smart devices, historical data information of smart devices, and environmental data information, the four types of multimodal data are analyzed by the long short-term memory network model through the feature fusion of these four types of multimodal data to predict the equipment status. The long short-term memory network model is constructed based on the PyTorch deep learning framework. The model includes an input layer, three long short-term memory network layers, and an output layer. The input layer receives the normalized time series data, and the long short-term memory network layer is responsible for extracting the features in the time series data and predicting the status results, and outputting the predicted status of the equipment. The model improves the accuracy and robustness of the evaluation and analysis of the status of pig breeding equipment. The long short-term memory network model combined with multi-source heterogeneous detection data can help reveal the changing laws of equipment status and provide more accurate status evaluation and prediction. This combination effectively copes with the challenges of data time series and integrity, making the monitoring system more generalized and improving the accuracy of the prediction of internal laws, thereby realizing accurate evaluation of the status of pig breeding equipment and online early warning of early faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a principle block diagram of specific embodiment 1 of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0035] Embodiment 1:

[0036] See also Figure 1As shown, a full life cycle monitoring and management system for pig breeding equipment includes a user login module 1, a data acquisition module 2, a data preprocessing module 3, a data verification module 4, a data storage module 5, a data analysis module 6, an equipment status early warning module 7, an intelligent decision-making module 8 and a display module 9, wherein the user login module is connected to the data acquisition module respectively, and the data acquisition module is connected to the data preprocessing module, the data verification module, the data storage module, the data analysis module, the equipment status early warning module, the intelligent decision-making module and the display module in sequence, and the display module is connected to the user login module.

[0037] The user login module is used for user authority management, data query and remote control. The user terminal can be a tablet, computer, notebook or other terminal.

[0038] The data acquisition module is used to collect the full life cycle data of smart devices used in pig farming, including: basic information of smart devices, real-time data of smart device operation, historical data and environmental data; the smart devices include various sensors, smart feeders, smart environmental controllers, smart inspection robots and 3D weight estimators. Various sensors include temperature and humidity sensors, ammonia concentration sensors, light intensity sensors and network cameras installed in every corner of the farm.

[0039] The basic information of the smart device includes: device name, model, manufacturer, timestamp, device ID information, installation location and service life;

[0040] The real-time operation data of the intelligent device includes: temperature, humidity, pressure, flow, ammonia concentration, speed sensor readings, and the working status of the device (such as start, stop, fault) information;

[0041] The historical data information includes: past operation records, maintenance records, and fault records of the equipment;

[0042] The environmental data information includes: temperature, humidity, and lighting environment conditions of the farm.

[0043] The data preprocessing module is used to preprocess the real-time monitoring operation data of various smart devices using the Internet of Things technology; specifically includes:

[0044] It is used to clean, denoise and format the data collected by the data collection module. The cleaning process includes removing duplicate data, filling missing data and correcting erroneous data to ensure the integrity and accuracy of the data. The formatting process includes converting each data into a unified format, splitting time series data into single data points to facilitate time series analysis, and encoding the data to ensure the readability and interpretability of the data. This data preprocessing module facilitates data storage, subsequent analysis, and data exchange and understanding between different systems.

[0045] Possibly, after the data preprocessing module, the data verification module is also included, which is used to verify the data processed by the data preprocessing module, including whether the data format is correct, whether the data range is within a reasonable range, and whether the timestamp is continuous. 、 Are the data fields consistent?

[0046] The data storage module is used to store various data information preprocessed by the data preprocessing module; specifically, the data storage module is provided with a basic database, a model storage database, an evaluation management library, an equipment weekly inspection / rotation library and a data analysis library. The basic database is used to store the basic information of the intelligent device, the real-time data of the intelligent device operation, historical data and environmental data, wherein the real-time data and environmental data of the intelligent device operation belong to real-time data information, and a database view is adopted; the basic information and historical data of the intelligent device belong to non-real-time data information, and an intermediate library plus a scheduled task is established to transfer the interactive data to the intermediate library table in an asynchronous non-real-time manner, and the whole table or difference data is synchronously updated at a certain interval. The equipment weekly inspection / rotation library is used to store the inspection and replacement data of the equipment.

[0047] The data analysis module is used to predict and evaluate the status of smart devices using a deep neural network model for various pre-processed data information; specifically, it includes:

[0048] It is used to use the Internet of Things technology to monitor the operation data of various smart devices in real time, and combine the basic information data of smart devices, historical data information of smart devices, and environmental data information. Through the feature fusion of these four types of multimodal data, the long short-term memory network model built based on the PyTorch deep learning framework is used to analyze the time series data and predict the device status. The long short-term memory network model. The model includes an input layer, three long short-term memory network layers, and an output layer. The input layer receives normalized time series data. The long short-term memory network layer is responsible for extracting features from the time series data and predicting the status results, and outputting the predicted device status.

[0049] The device status warning module is used to set warning rules and establish a warning mechanism according to the characteristics of the device and business requirements. If the data analyzed by the data analysis module reaches the preset warning conditions and is determined to be abnormal, a warning and a corresponding measure notification will be issued; the warning conditions can be formulated based on the actual usage experience of the equipment.

[0050] The intelligent decision-making module is used to generate an equipment status assessment report based on the assessment of various equipment statuses in the data analysis module, so as to help managers quickly understand the operation status of the equipment throughout its life cycle and make scientific and reasonable equipment management decisions;

[0051] The display module is used to display the generated multi-dimensional data analysis report and present it in the form of charts and tables, so that managers can intuitively understand the status of the entire life cycle of smart devices.

[0052] Possibly, it also includes an expandable module, which is used to adapt to controllable business needs and uncontrollable potential needs in an expandable manner.

[0053] This system is an application system built on a three-tier architecture platform based on middleware. It adopts the J2EE multi-layer technology architecture that combines technological advancement and maturity to improve the system's flexibility, scalability, security and concurrent processing capabilities. It has the advantages of cross-platform, high performance, standard structure, and easy expansion.

[0054] Based on the design concept of SOA, SOA (service-oriented architecture) is a service-oriented architecture that connects different functional units of an application (called services) through well-defined interfaces and contracts between these services. The interface is defined in a neutral way, which is independent of the hardware platform, operating system and programming language that implements the service. This allows services built in various such systems to interact in a unified and universal way. This feature of having a neutral interface definition (without being forced to bind to a specific implementation) is called loose coupling between services. There are two benefits of loose coupling. One is its flexibility, and the other is that it can continue to exist when the internal structure and implementation of each service that makes up the entire application gradually change.

[0055] The design uses a component-based development concept and consists of a series of independently deployed components. The component design should meet the following requirements:

[0056] Component multi-instance operation: The requirement for each component to run multiple component instances simultaneously should be met as much as possible to ensure high reliability and scalability of the system.

[0057] Reasonable determination of component granularity: The system performance, scalability and other factors should be comprehensively considered, while taking into account the system's requirements in deployment, maintenance and management, and reasonably determining the component granularity.

[0058] Stable component interface definition: The stability of the interface between components should be fully considered, and XML or similar structures should be used to ensure the scalability of interface transmission parameters and content.

[0059] This system must have sufficient compatibility and be cross-platform, cross-operating system, and cross-database. The application system uses JAVA technology to ensure the system's compatibility with hardware platforms and operating systems; the system uses data access middleware to ensure the system's compatibility with various databases; the unified data exchange standard based on XML technology ensures that the system platform and other application systems can be easily connected. The system provides a complete set of security measures to ensure the reliable operation of the system. The system provides monitoring and fault recovery strategies for network, server, and database performance to ensure the high reliability of the system's physical layer. To ensure the normal operation of the network, the network management system can monitor the operation of each network node to ensure the smooth and secure operation of each node in the network.

[0060] In order to facilitate the use of system maintenance and management personnel, this system adopts a simple and intuitive graphical interface and multiple input methods. Through the graphical operation and management interface, maintenance personnel can easily complete the configuration management of the entire system. The application software sets three levels of security control, namely: network security level, processing security level and user security level. The application system will use a combination of three verification methods: network login verification, database login verification, and application system use verification to verify the user. In order to ensure the security of data transmission and prevent the transmitted data from being stolen or changed, the important original data collected by the application system can be transmitted by network encryption, database encryption or application system data encryption. Use logs to record the operations of system login users, and conduct post-analysis based on the logs to find the cause of the accident, the person responsible or the illegal user.

[0061] The system architecture is mainly divided into front-end service layer, back-end service layer, data service layer and hardware service layer.

[0062] The front-end service layer is provided with a user login module to provide users with a simple and convenient interactive interface, and supports the following functions: Mobile APP and web platform: providing a management interface for mobile phones and computers, through which users can view data, receive information, remotely control, and report data in real time through the user login module interface; using the user login module for account management: supporting account management functions, enabling one account to manage all equipment in all farms and one device to be managed by multiple accounts; historical data recording and tracing: automatically recording historical data, including environmental parameters, equipment status changes, alarm events, etc., supporting query of records for any time period, and facilitating analysis and tracing of data changes over a period of time.

[0063] The communication service layer uses various communication protocols to ensure that the data of the front-end service and the back-end service can be correctly transmitted and synchronized. The interface service obtains or updates the simulation system data by defining a series of API interfaces (usually in JSON format) for the intelligent simulation agent to call. The RPC interface / RESTful API provides remote procedure calls (RPC) and REST-compliant APIs for clients to use. WebSocket provides full-duplex communication, allowing the server to actively send real-time data to the client.

[0064] The backend service layer is responsible for processing frontend requests, calling backend services, and executing business logic. The business logic includes: data acquisition module, which is used to collect real-time operation data of various intelligent devices; data preprocessing module, which is used to preprocess the real-time monitoring operation data of various intelligent devices using Internet of Things technology; data storage module, which is used to store various data information; data analysis module, which is used to predict and evaluate the status of intelligent devices using deep neural network models; equipment status early warning module, which is used to establish an early warning mechanism and take corresponding measures immediately once an abnormality is found; intelligent decision-making module, which is used to provide multi-dimensional data analysis reports based on rich production data, to help managers quickly understand the production status and make scientific and reasonable business decisions.

[0065] In this intelligent equipment life cycle management system, the data that needs to be processed mainly includes basic equipment information: including equipment name, model, manufacturer, installation location, and service life. Real-time operation data: such as sensor readings of temperature, humidity, pressure, flow, speed, and the working status of the equipment (such as start, stop, and failure), historical data: including past operation records, maintenance records, and failure records of the equipment. Environmental data: such as the temperature, humidity, and light environment conditions of the farm. Conditional warning is an important function in the equipment life cycle management system. Warning rules are set according to the characteristics of the equipment and business needs. When the temperature of the equipment exceeds the set threshold, the warning mechanism is triggered. When the temperature, humidity, and light environment conditions of the farm exceed or fall below the set threshold, the warning mechanism is triggered. Once the warning rule is triggered, the system will promptly notify the relevant personnel to handle it through warning.

[0066] Intelligent decision support is an advanced function in the equipment life cycle management system. It provides multi-dimensional data analysis reports based on rich production data to help managers quickly understand production conditions and make scientific and reasonable business decisions. Various types of data are collected from the equipment life cycle management system, including basic equipment information, real-time operation data, historical data, etc., and integrated to generate multi-dimensional data analysis reports, which are displayed in the form of charts, tables, etc., so that managers can intuitively understand the status of the equipment throughout its life cycle.

[0067] The data service layer is equipped with a data storage module and a data analysis module, which are mainly responsible for data collection and processing, encapsulating database access logic, and realizing data addition, deletion, modification and query operations. The main business logic includes: data collection and processing, receiving data from the hardware device layer, and performing preliminary processing and formatting to ensure that the data is accurately transmitted to the breeding cloud platform for analysis. Data storage uses the relational database MySQL to store structured data, and the NoSQL database MongoDB to store unstructured data (such as pictures, videos, etc.).

[0068] The data analysis module needs to meet the requirements of basic data integrity. The data service layer receives data from the hardware device layer. These data include sensor readings (such as temperature, humidity, ammonia concentration), device status information (such as working status, fault alarm), and possible additional information (such as timestamp, device ID). Before analyzing the data, it needs to be cleaned through the data preprocessing module, including removing duplicate data, filling missing data, and correcting erroneous data operations to facilitate data storage and subsequent analysis. The data service layer will also format the data. This includes converting the data into a unified format to facilitate data exchange and understanding between different systems; splitting time series data into single data points to facilitate time series analysis; and encoding the data to ensure data readability and interpretability. To ensure the integrity and accuracy of the data, the data needs to be verified to ensure the integrity and accuracy of the data, which includes checking whether the data format is correct, whether the data range is within a reasonable range, and whether the timestamp is continuous.

[0069] The hardware service layer integrates various sensors and intelligent devices to realize data collection and equipment control. The main business logic includes: Sensor equipment: Install temperature and humidity sensors, ammonia concentration sensors, light intensity sensors, and network cameras in every corner of the farm to collect various parameters in the breeding environment 24 hours a day. Intelligent equipment: including intelligent feeders, intelligent environmental controllers, intelligent inspection robots, 3D weight estimators, etc., to achieve the functions of precise feeding, environmental control, and data collection.

[0070] The pig farming intelligent equipment full life management system adopts a security architecture. The security protection of the pig farming intelligent equipment full life management system includes: application security, data security, host security, network security, terminal security, physical security, boundary security, etc. Through the above security precautions, we can prevent the paralysis of the information network, prevent the destruction of the application system, prevent the loss of business data, prevent tampering of network data, ensure the confidentiality of data transmission, ensure the security of data storage, prevent corporate information leakage, prevent terminal virus infection, prevent the spread of harmful information, and prevent malicious penetration attacks, so as to ensure the safe and stable operation of this management system and the security of business data.

[0071] 1. Complete security mechanism

[0072] The system security is ensured through isolation devices, firewalls, intrusion detection, password authentication, IP authentication, MAC address authentication and other security measures, and the system functional security and data security are achieved through system authority management.

[0073] 2. Data security protection

[0074] Access control: JWT (JSON Web Tokens), a concise, URL-safe token standard for securely transmitting information between two parties. It can be verified and the security of information can be achieved through digital signatures.

[0075] Data encryption: SSL / TLS is a security protocol used to provide data encryption, data integrity verification, identity authentication and other services during network communication to ensure the security of data transmission.

[0076] 3. Network security protection

[0077] Network environment security protection is aimed at the overall supporting network and the network environment facilities that provide a network support platform for each security domain. The network environment specifically includes the routing and switching equipment that provide connections in the network, as well as the security equipment and network basic service facilities introduced for the construction of the security protection system.

[0078] 4. Host system security protection

[0079] Host system security protection includes security protection for servers and desktop terminals. Servers include business application servers, network servers, WEB servers, files and communications, etc. Desktop terminals are desktop computers and laptops that serve as end-user workstations.

[0080] 5. Application system security protection

[0081] Application security protection includes protection of the main site application system itself, user interface security protection, inter-system data interface security protection, and intra-system data interface security protection.

[0082] Possibly, it also includes an expandable module, and the expandable module is used to adapt to controllable business needs and uncontrollable potential needs in an expandable manner.

[0083] This system is also designed to take into account the scalability of the existing system in the future, including the connection and expansion with the later system, and to design a set of standard systems to adapt to controllable business needs and uncontrollable potential needs in an extensible manner for different stages and periods. In addition, in data structure design and software development, the existing service-oriented standard design ideas should be adopted as much as possible to facilitate future expansion and expansion. That is, the system or subsystem can be customized, and the functional structure and distribution structure of the application system can be flexibly and conveniently adjusted according to the needs of users or changes in business structure, and various application subsystems or components can be conveniently combined.

[0084] Possibly, the performance requirements of this pig farming equipment full life cycle monitoring and management system are also divided according to different functional modules, mainly including transaction processing, query and statistical performance requirements. Dividing events into these three categories can effectively manage system performance and meet the needs of different operations, thereby improving the efficiency and reliability of the entire pig farming equipment management system. This classification method ensures that the system can still maintain stable operation under high load conditions, while providing users with flexible, timely and in-depth data support. See the table below:

[0085]

[0086] At the same time, this system also applies application software technology

[0087] 1. Reliability

[0088] The reliability of this management system refers to the ability of the system (or equipment) to complete the intended function under specified conditions and within a specified period of time, generally expressed in hours of "mean time between failures MTBF". System reliability MTBF is used to assess the reliability of repairable systems, which depends on the reliability of system equipment and software as well as the system structure.

[0089]

[0090] Where: T(t) is the system working time (the cumulative interval of normal operation of the system from the beginning to the end of the assessment), and r is the number of failures during the assessment time.

[0091] System reliability MTBF ≥ 3×104h.

[0092] 2. Availability

[0093] System availability A can be calculated by the following formula:

[0094]

[0095] Downtime includes time for troubleshooting and preventive maintenance.

[0096] System availability is calculated based on the statistical data provided by the operation and maintenance records. The period covered by the records should be no less than 6 months and should be calculated from the time when the first fault disappears and the system resumes operation. The annual availability rate is generally calculated and the annual availability rate of the main station and the annual availability rate of the terminal are calculated separately as follows:

[0097]

[0098] The annual availability of the system should be no less than 99.6%.

[0099] 3. Ease of use

[0100] The application system must provide a consistent graphical user interface style.

[0101] The application system's operating interface for ordinary users should be implemented in B / S mode.

[0102] The application system must use a paging mechanism to display query results and show the number of records returned, the current page, and the total number of pages.

[0103] If the application system finds that the user has submitted incorrect information, it must clearly prompt the user the reason for the error in the form of a pop-up window and place the interface control focus on the control object where the error occurred.

[0104] The operating interface of the application system must clearly identify the required input information.

[0105] Before executing an operation that causes changes to system data, the system should pop up a prompt window for user confirmation.

[0106] When the application system is executing a request submitted by a user and cannot return, it must be clearly marked that the system is in a busy phase.

[0107] The application system function menu must be organized according to the classification method of functional domains and functional items.

[0108] For menu functions that the operator does not have permission to use, the application system does not display the menu or sets it to an unavailable state.

[0109] The query and statistical results of the application system can be transferred to common format files such as EXECL.

[0110] The business processing interface for specific positions that are frequently faced at the grassroots level must be simple, practical, and intuitive, with data information displayed in layers, with frequently used information at the front and infrequently used information at the back or hidden.

[0111] The operation key sequence conforms to the work processing steps and can jump automatically to improve the efficiency of daily business processing.

[0112] The full life cycle management system uses Docker to implement containerized deployment and management of the system, uses Prometheus and Grafana to implement system monitoring and alarm functions, and uses GitLab CI / CD to realize automatic code construction, testing and deployment. Through the above technical solutions, the various functions of the intelligent management system for the full life cycle of pig farming intelligent equipment can be realized to improve farming efficiency and management level. This full life monitoring and management system for pig farming intelligent equipment starts with the environmental monitoring and control module, collects real-time data from various types of equipment, and completes the status evaluation of intelligent equipment after data analysis and decision support. Specific embodiment 2:

[0114] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the full life cycle monitoring and management system for pig breeding equipment described in specific embodiment 1, so this specific embodiment is omitted here. Specific embodiment 3:

[0116] An information data processing terminal is used to provide a user input interface to implement the full life cycle monitoring and management system of pig breeding equipment described in specific embodiment 1 when the information data processing terminal is implemented on an electronic device, so this specific embodiment is omitted here.

[0117] The technical solution provided by the present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A monitoring and management system for the entire life cycle of pig breeding equipment, characterized in that: include: User login module, data acquisition module, data preprocessing module, data storage module, data analysis module, equipment status warning module, intelligent decision-making module and display module, The user login module is used for user authority management and data query; The data collection module is used to collect the full life cycle data of smart devices used in pig farming, including: basic information of smart devices, real-time data of smart device operation, historical data and environmental data; The data preprocessing module is used to preprocess the real-time monitoring operation data of various smart devices using the Internet of Things technology; The data storage module is used to store various data information preprocessed by the data preprocessing module; The data analysis module is used to predict and evaluate the status of smart devices using a deep neural network model for various pre-processed data information; The device status warning module is used to set warning rules and establish a warning mechanism according to the characteristics of the device and business requirements. If the data analyzed by the data analysis module reaches the preset warning conditions and is determined to be abnormal, a warning and a corresponding measure notification are issued; The intelligent decision-making module is used to generate an equipment status assessment report based on the assessment of various equipment statuses in the data analysis module; The display module is used to display the generated multi-dimensional data analysis report.

2. The full life cycle monitoring and management system for pig breeding equipment according to claim 1 is characterized by: It also includes the data verification module, which is used to verify the data processed by the data preprocessing module, including: verifying whether the data format is correct, whether the data range is within a reasonable range, whether the timestamp is continuous, and whether the data fields are consistent.

3. The full life cycle monitoring and management system for pig breeding equipment according to claim 1 is characterized by: It also includes an expandable module, which is used to adapt to subsequent controllable business needs and uncontrollable potential needs in an expandable manner.

4. The full life cycle monitoring and management system for pig breeding equipment according to any one of claims 1 to 3, characterized in that: The basic information of the smart device includes: device name, model, manufacturer, installation location, timestamp, device ID information and service life; The real-time operation data of the intelligent device includes: temperature, humidity, pressure, flow, ammonia concentration, speed sensor readings, and working status information of the device; The historical data information includes: past operation records, maintenance records, and fault records of the equipment; The environmental data information includes: temperature, humidity, and lighting environment conditions of the farm.

5. The full life cycle monitoring and management system for pig breeding equipment according to any one of claims 1 to 3, characterized in that: The data preprocessing module is used to clean, denoise and format the data collected by the data acquisition module. The cleaning process includes removing duplicate data, filling missing data and correcting erroneous data; the formatting process includes converting each data into a unified format, splitting time series data into single data points and encoding the data.

6. The full life cycle monitoring and management system for pig breeding equipment according to any one of claims 1 to 3, characterized in that: The data analysis module is used to predict and evaluate the status of smart devices using a deep neural network model for various pre-processed data information, specifically including: It is used to use the Internet of Things technology to monitor the operating data of various smart devices in real time, and combine the basic information data of smart devices, historical data information of smart devices, and environmental data information. Through the feature fusion of these four types of multimodal data, the long short-term memory network model built based on the PyTorch deep learning framework is used to analyze the time series data and predict the device status.

7. The full life cycle monitoring and management system for pig breeding equipment according to any one of claims 1 to 3, characterized in that: The intelligent devices include various sensors, intelligent feeders, intelligent environmental controllers, intelligent inspection robots and 3D weight estimators.

8. The full life cycle monitoring and management system for pig breeding equipment according to any one of claims 1 to 3, characterized in that: The data storage module is provided with a basic database, a model storage database, an evaluation management library, an equipment weekly inspection / rotation library and a data analysis library. The basic database is used to store the basic information of the intelligent device, the real-time data of the intelligent device operation, historical data and environmental data; wherein, the real-time data and environmental data of the intelligent device operation belong to real-time data information, and database views are adopted; the basic information and historical data of the intelligent device belong to non-real-time data information, and an intermediate library plus a scheduled task is established to transfer the interactive data to the intermediate library table in an asynchronous non-real-time manner, and the whole table or the difference data is synchronously updated at a certain interval.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor executes the full life cycle monitoring and management system for pig breeding equipment as described in any one of claims 1 to 8.

10. An information data processing terminal, characterized in that: When the information data processing terminal is implemented and executed on an electronic device, it provides a user input interface to implement the full life cycle monitoring and management system of pig breeding equipment as described in any one of claims 1 to 8.

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