Milk industry data early warning analysis system and device and readable storage medium

Abstract

The invention provides a milk industry data early warning analysis system and device and a readable storage medium, and the system comprises an early warning rule configuration module which is used for building an early warning rule scene, and triggering an early warning operation when milk industry data obtained by Internet of Things equipment meets a scene preset condition; the DHI laboratory data early warning module is used for butting milk industry sample detection data of a DHI measurement center and carrying out early warning analysis on the detection progress and the measurement result; the forage grass planting data early warning module is used for identifying the risks of plant diseases and insect pests, abnormal growth and meteorological disasters by utilizing an Internet of Things management system based on the forage grass planting data; the pasture breeding data early warning module is used for recognizing cow physiological health information and pasture environment information by utilizing a rule engine based on the pasture breeding data; and the dairy product processing data early warning module is used for realizing product detection early warning, dairy product in-time early warning, raw milk quality early warning and sales order early warning through data analysis based on the dairy product processing data.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and more specifically to a dairy industry data early warning analysis system, device and readable storage medium. Background Art

[0002] In the field of dairy production, with the rapid development of scale and intensification, the complexity and uncertainty of the production process have increased significantly, and higher requirements have been put forward for the efficiency and accuracy of production management. Traditional management methods often rely on manual experience, lack of information technology to monitor production data, low production efficiency, and difficulty in achieving real-time monitoring and accurate early warning of the entire production chain, resulting in delayed risk response, high production costs and reduced market competitiveness. In this context, the introduction of advanced information technology means and the construction of an intelligent dairy data early warning analysis system have become key ways to improve dairy production efficiency, optimize resource allocation, and ensure product quality.

[0003] In related technologies, although some dairy companies have begun to use IoT technology to monitor production links, most systems have data islands and single early warning functions, making it difficult to achieve comprehensive analysis and early warning of the entire dairy production process. At the same time, the lack of a unified early warning rule configuration platform has led to insufficient flexibility and adaptability of early warning rules, making it difficult to meet the personalized and dynamic management needs of dairy companies. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a dairy data early warning analysis system, device and readable storage medium, which uses rule engines, Internet of Things and other advanced technologies to monitor, warn and analyze each link in the dairy production process. The present invention can not only help dairy companies respond to risks and optimize decisions in a timely manner, but also improve production efficiency, reduce costs and enhance market competitiveness.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention discloses a dairy industry data early warning analysis system, comprising:

[0007] The warning rule configuration module is used to configure custom rules, indicators, data types and query conditions through the rule engine combined with the IoT system to establish warning rule scenarios. When the dairy data obtained by the IoT device meets the preset conditions of the scenario, the warning operation is triggered;

[0008] The DHI laboratory data early warning module is used to connect to the dairy sample test data of the DHI test center, and conduct early warning analysis on the test progress, data quality and test results according to the preset thresholds, rules and historical data; the forage planting data early warning module is used to identify the risks of pests and diseases, abnormal growth and meteorological disasters based on the forage planting data collected by the IoT devices deployed in the forage planting area, and generate early warning information using the IoT management system;

[0009] The ranch breeding data early warning module is used to identify the physiological health information of dairy cows and ranch environment information based on the ranch breeding data collected by the IoT devices deployed in the dairy cow living area, and generate early warning information using the rule engine of the IoT management system;

[0010] The dairy processing data early warning module is used to monitor the dairy production, storage and sales links in real time based on the dairy processing data collected by IoT devices. Through data analysis, it can realize product detection early warning, dairy product expiration early warning, raw milk quality early warning and sales order early warning.

[0011] Further, the DHI laboratory data early warning module is specifically used for:

[0012] Obtaining dairy sample test data and its corresponding sample test progress and data integrity information from the database of the DHI test center through an API interface or database synchronization technology; the dairy sample test data includes a variety of key indicators, including milk fat rate, milk yield, protein content, somatic cell count, and urea nitrogen content;

[0013] Clean the dairy sample test data to remove duplicate, erroneous or invalid data records;

[0014] Calculate the difference between the sample detection progress and the corresponding node in the preset timetable. If the difference exceeds the preset time difference threshold, it is determined that the sample detection progress is lagging behind.

[0015] Conduct data integrity checks on dairy sample test data to ensure the data integrity of sample numbers, test dates, and test results;

[0016] Use statistical analysis methods to compare each key indicator with the corresponding historical data to identify abnormal key indicators;

[0017] When key indicators of sample testing progress lag or abnormality are identified, corresponding warning information is automatically triggered.

[0018] Furthermore, the IoT devices deployed in the forage planting area include: pest and disease monitoring cameras, soil moisture sensors, temperature sensors, and weather stations;

[0019] The forage planting data include: pest and disease data, grass condition data and meteorological data; the grass condition data include growth rate and leaf color; the meteorological data include: temperature, humidity, rainfall, wind speed.

[0020] Furthermore, the forage planting data early warning module is specifically used for:

[0021] The collected forage planting data is transmitted to the Internet of Things management system via wireless network;

[0022] The forage planting data is cleaned and processed through the Internet of Things management system to remove duplicate, erroneous or invalid data records;

[0023] The forage planting data is sorted in a unified format through the Internet of Things management system to generate formatted forage planting data;

[0024] Applying preset rules and statistical models to analyze the formatted forage planting data to generate analysis results; the analysis results include pest and disease feature identification results, growth status assessment results, and meteorological disaster risk prediction results;

[0025] Based on the analysis results, determine whether there is a risk of pests and diseases, abnormal growth or meteorological disasters; if there is a risk, the early warning mechanism is triggered and a warning message is issued.

[0026] Furthermore, the IoT devices deployed in the cows’ living area include: activity monitoring sensors, body temperature sensors, heart rate monitors, and environmental sensors;

[0027] The ranch farming data includes: cow activity pattern data, body temperature, heart rate, and ambient temperature and humidity.

[0028] Furthermore, the pasture breeding data early warning module is specifically used for:

[0029] Clean and format ranch breeding data;

[0030] Based on the processed pasture breeding data, the activity pattern characteristics, body temperature fluctuation characteristics, heart rate fluctuation characteristics and the average value of environmental temperature and humidity of the dairy cows were extracted;

[0031] Combined with the typical behavioral characteristics of the estrus period, the estrus signs of dairy cows are identified according to the characteristics of the cow's activity pattern, body temperature fluctuation characteristics, and heart rate fluctuation characteristics, and estrus warning information is generated; the estrus warning information includes the cow number, estrus time, and expected duration;

[0032] Identify cow health problems based on the characteristics of cow activity patterns, body temperature fluctuations, and heart rate fluctuations, and generate health warning information; health warning information includes identified abnormal data, possible causes, and recommended treatment measures;

[0033] Compare the average ambient temperature and humidity with the corresponding ambient threshold. When the ambient threshold is exceeded, an environmental warning message is automatically generated. The environmental warning message includes abnormal temperature and humidity data and related monitoring ranges.

[0034] Integrate estrus warning information, health warning information and environmental warning information to form a warning record; push the warning record to the system platform of the ranch manager or relevant person in charge.

[0035] Furthermore, the dairy processing data collected by the IoT device includes: product testing data, raw milk index data, inventory data and order data;

[0036] The product testing data includes nutritional information, microbial content information, and additive usage information collected by testing equipment;

[0037] The raw milk index data include physical and chemical indexes and microbiological indexes of raw milk;

[0038] The inventory data includes product type, quantity, production date, and shelf life;

[0039] The order data includes order quantity, product type, customer demand, and delivery time.

[0040] Furthermore, the dairy product processing data early warning module is specifically used for:

[0041] Clean and format dairy processing data;

[0042] Based on the processed dairy processing data, product testing data, raw milk index data, inventory data and order data are extracted respectively, and corresponding index thresholds are set for each type of data;

[0043] Use machine learning methods to perform statistical analysis on extracted product testing data, raw milk index data, inventory data, and order data to establish an early warning model;

[0044] Real-time monitoring of dairy processing data through early warning models;

[0045] When abnormal product detection information is detected, product detection warning information is automatically generated and sent;

[0046] Automatically calculate and predict the expiration date of dairy products based on the production date and shelf life information in the inventory data; automatically generate and send expiration warning information when the product reaches the expiration date;

[0047] When the physical and chemical index or microbiological index of raw milk in the raw milk index data exceeds the corresponding index threshold, the raw milk quality warning information is automatically generated and sent;

[0048] The early warning model is used to analyze the order data, generate analysis results and input them into the preset order anomaly analysis system; if a supply and demand imbalance or an order anomaly is identified, sales order early warning information is automatically generated and sent.

[0049] In a second aspect, the present invention further discloses a dairy industry data early warning analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the functions of the dairy industry data early warning analysis system as described in any one of the above items when executing the computer program.

[0050] In a third aspect, the present invention further discloses a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the function of the dairy data early warning analysis system as described in any one of the above items is realized.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention realizes customized real-time monitoring and data collection of dairy production equipment and environment through rule engine and Internet of Things technology. It can monitor dairy production data and market conditions in real time, detect abnormal situations and issue early warnings in time, help enterprises to quickly respond, and improve the automation level of the production process.

[0053] 2. The present invention can analyze and mine a large amount of dairy data, integrate the data resource monitoring indicators of the production process of four types of dairy enterprises, use data analysis technology to mine the potential correlation and rules of the data, and provide decision support and optimization suggestions for enterprises.

[0054] 3. The present invention realizes closed-loop management of the dairy industry chain by analyzing product quality data, integrating data resources and monitoring indicators, which can help enterprises discover the root causes of product quality problems, guide enterprises to improve production processes and quality management, and improve product quality.

[0055] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0057] Figure 1It is a system structure diagram of a specific implementation mode of the present invention.

[0058] In the figure, 1. Warning rule configuration module; 2. DHI laboratory data warning module; 3. Forage planting data warning module; 4. Pasture breeding data warning module; 5. Dairy processing data warning module. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 As shown, this embodiment provides a dairy data early warning analysis system, including: an early warning rule configuration module 1, a DHI laboratory data early warning module 2, a forage planting data early warning module 3, a pasture breeding data early warning module 4 and a dairy product processing data early warning module 5.

[0061] The early warning rule configuration module 1 is used to configure custom rules, indicators, data types and query conditions through the rule engine combined with the Internet of Things system to establish an early warning rule scenario, and trigger an early warning operation when the dairy data obtained by the Internet of Things device meets the preset conditions of the scenario.

[0062] In a specific implementation, the warning rule configuration module 1 utilizes a rule engine and is configured in conjunction with an Internet of Things system. It recommends rule warning scenarios through custom rules, indicators, data types, and query conditions. When the data of an Internet of Things device meets the preset condition values ​​of the scenario, a warning operation can be triggered.

[0063] The warning rule configuration module 1 uses advanced rule engine technology to provide a set of flexible and powerful warning configuration tools for this system. Through this module, users can define various complex warning logics according to actual needs, including but not limited to data thresholds, data change trends, data associations, etc. These custom rules will serve as the basis for the system to determine whether to trigger a warning.

[0064] As an example, the warning rule configuration module 1 specifically includes the following functions:

[0065] Customized rules: Users can customize warning rules according to the needs of business scenarios. These rules can be based on the threshold judgment of a single data point (such as temperature, humidity, etc.), or based on the correlation between multiple data points or data change trends.

[0066] Indicator and data type configuration: The module supports the configuration of multiple data types and indicators, including but not limited to numeric type, text type, enumeration type, etc. Users can select appropriate indicators for configuration according to the data type actually collected by the IoT device.

[0067] Query condition settings: In addition to basic rule configuration, users can also set query conditions to further filter the data to be monitored. These query conditions can be set based on multiple dimensions such as time range, device ID, geographic location, etc.

[0068] Warning scenario construction: Various complex warning scenarios can be constructed by combining custom rules, indicators, data types, and query conditions. These scenarios will serve as the basis for the system to determine whether a warning needs to be triggered.

[0069] Automatically trigger warnings: When the data collected by IoT devices meets the preset condition values ​​of a warning scenario, the system will automatically trigger the warning operation. This includes sending warning notifications (such as emails, SMS, push notifications, etc.) to relevant personnel, recording warning logs, and executing preset emergency response procedures.

[0070] DHI laboratory data early warning module 2 is used to connect to the dairy sample test data of the DHI testing center, and conduct early warning analysis on the test progress, data quality and test results based on preset thresholds, rules and historical data.

[0071] In a specific implementation, the DHI laboratory data early warning module 2 is specifically used for:

[0072] Obtaining dairy sample test data and its corresponding sample test progress and data integrity information from the database of the DHI test center through an API interface or database synchronization technology; the dairy sample test data includes a variety of key indicators, including milk fat rate, milk yield, protein content, somatic cell count, and urea nitrogen content;

[0073] Clean the dairy sample test data to remove duplicate, erroneous or invalid data records;

[0074] Calculate the difference between the sample detection progress and the corresponding node in the preset timetable. If the difference exceeds the preset time difference threshold, it is determined that the sample detection progress is lagging behind.

[0075] Conduct data integrity checks on dairy sample test data to ensure the data integrity of sample numbers, test dates, and test results;

[0076] Use statistical analysis methods to compare each key indicator with the corresponding historical data to identify abnormal key indicators;

[0077] When key indicators of sample testing progress lag or abnormality are identified, corresponding warning information is automatically triggered.

[0078] As an example, the DHI laboratory data warning module 2 needs to connect to the sample test data of the DHI measurement center. After reading the data, it filters the test data that generates warnings according to the system's preset thresholds and rules, industry standards, historical data and other conditions. It mainly includes test progress warnings, data quality warnings and abnormal test results warnings. The corresponding data processing flow is as follows:

[0079] (1) Data collection: The system automatically obtains data such as sample testing progress, data integrity, and milk fat percentage from the DHI laboratory database.

[0080] (2) Data processing and analysis: Perform preliminary processing on the collected data, including data cleaning and verification. Monitor the progress of sample testing and calculate the deviation from the preset time plan. Check the data integrity to ensure the integrity of key fields. Calculate the milk fat percentage and compare and analyze it with historical data or industry standards.

[0081] (3) Early warning trigger: When the detection progress lags behind or the data is abnormal, the early warning trigger module automatically issues an early warning. When the milk fat percentage is abnormal, the early warning trigger module issues an early warning and provides cause analysis and treatment suggestions.

[0082] The forage planting data early warning module 3 is used to identify the risks of pests and diseases, abnormal growth and meteorological disasters based on the forage planting data collected by the Internet of Things devices deployed in the forage planting area, and generate early warning information using the Internet of Things management system.

[0083] Among them, the IoT devices deployed in the forage planting area include: pest and disease monitoring cameras, soil moisture sensors, temperature sensors, and weather stations; forage planting data include: pest and disease data, grass condition data, and meteorological data; grass condition data include growth rate and leaf color; meteorological data include: temperature, humidity, rainfall, and wind speed.

[0084] In a specific implementation, the forage planting data early warning module 3 is specifically used for:

[0085] The collected forage planting data is transmitted to the Internet of Things management system via wireless network;

[0086] The forage planting data is cleaned and processed through the Internet of Things management system to remove duplicate, erroneous or invalid data records;

[0087] The forage planting data is sorted in a unified format through the Internet of Things management system to generate formatted forage planting data;

[0088] Applying preset rules and statistical models to analyze the formatted forage planting data to generate analysis results; the analysis results include pest and disease feature identification results, growth status assessment results, and meteorological disaster risk prediction results;

[0089] Based on the analysis results, it is determined whether there is a risk of pests and diseases, abnormal growth or meteorological disasters. If there is a risk, the early warning mechanism is triggered and an early warning message is issued.

[0090] As an example, based on the data collected by the IoT devices, the IoT management system generates warning information, and the forage planting data warning module 3 assembles the final warning information according to the device information to form a warning record. The functions mainly include pest and disease warning, abnormal growth warning and meteorological disaster warning. The corresponding data processing flow is as follows:

[0091] (1) Data collection: IoT monitoring equipment collects pest, weed and weather data in real time. The data is transmitted to the IoT management system.

[0092] (2) Data processing and analysis: After receiving the data, the IoT management system performs preliminary processing, such as data cleaning and formatting. It applies preset rules and models to conduct in-depth analysis of the data to identify the risks of pests and diseases, growth abnormalities, and meteorological disasters.

[0093] (3) Generation of early warning information: Based on the analysis results, the system automatically generates early warning information and corresponding prevention and control recommendations.

[0094] The pasture breeding data early warning module 4 is used to identify the physiological health information of dairy cows and pasture environment information based on the pasture breeding data collected by the Internet of Things devices deployed in the living area of ​​dairy cows, and generate early warning information using the rule engine of the Internet of Things management system.

[0095] Among them, the IoT devices deployed in the cows’ living area include: activity monitoring sensors, body temperature sensors, heart rate monitors, and environmental sensors; the ranch breeding data includes: cows’ activity pattern data, body temperature, heart rate, and ambient temperature and humidity.

[0096] In a specific implementation, the ranch breeding data early warning module 4 is specifically used for:

[0097] Clean and format ranch breeding data;

[0098] Based on the processed pasture breeding data, the activity pattern characteristics, body temperature fluctuation characteristics, heart rate fluctuation characteristics and the average value of environmental temperature and humidity of the dairy cows were extracted;

[0099] Combined with the typical behavioral characteristics of the estrus period, the estrus signs of dairy cows are identified according to the characteristics of the cow's activity pattern, body temperature fluctuation characteristics, and heart rate fluctuation characteristics, and estrus warning information is generated; the estrus warning information includes the cow number, estrus time, and expected duration;

[0100] Identify cow health problems based on the characteristics of cow activity patterns, body temperature fluctuations, and heart rate fluctuations, and generate health warning information; health warning information includes identified abnormal data, possible causes, and recommended treatment measures;

[0101] Compare the average ambient temperature and humidity with the corresponding ambient threshold. When the ambient threshold is exceeded, an environmental warning message is automatically generated. The environmental warning message includes abnormal temperature and humidity data and related monitoring ranges.

[0102] Integrate estrus warning information, health warning information and environmental warning information to form warning records;

[0103] Push the warning records to the system platform of the ranch manager or relevant person in charge.

[0104] As an example, based on the data collected by the IoT devices, the rule engine of the IoT management system is used to form device-based warning information, and then the ranch breeding data warning module 4 assembles the final warning information according to the device information to form a warning record. The functions of this module mainly include estrus system warning, dairy cow health warning and ranch environment warning. The corresponding data processing flow is as follows:

[0105] (1) Data collection: Install necessary sensor equipment in the cows’ living area to ensure that sensor data can be synchronized to the system in real time.

[0106] (2) Data analysis: First, the collected data is cleaned and formatted, and then key data features are extracted to provide input for the early warning model.

[0107] (3) Warning generation: When the cow activity data shows signs of estrus, the system automatically generates an estrus warning. When abnormal cow activity is detected, the system automatically generates a health warning. When environmental monitoring data exceeds the preset threshold, the system automatically generates an environmental warning. The warning information is integrated, including the cow number, time, abnormal details, etc., and then pushed to the system platform through the preset communication channel.

[0108] The dairy product processing data early warning module 5 is used to monitor the dairy product production, storage and sales links in real time based on the dairy product processing data collected by the Internet of Things devices, and realize product detection early warning, dairy product expiration early warning, raw milk quality early warning and sales order early warning through data analysis.

[0109] Among them, the dairy processing data collected by IoT devices include: product testing data, raw milk index data, inventory data and order data. Specifically, product testing data includes nutritional information, microbial content information, and additive usage information collected by testing equipment; raw milk index data includes physical and chemical indicators and microbial indicators of raw milk; inventory data includes product type, quantity, production date, and shelf life; order data includes order volume, product type, customer demand, and delivery time.

[0110] In a specific implementation, the dairy product processing data early warning module 5 is specifically used for:

[0111] Clean and format dairy processing data;

[0112] Based on the processed dairy processing data, product testing data, raw milk index data, inventory data and order data are extracted respectively, and corresponding index thresholds are set for each type of data;

[0113] Use machine learning methods to perform statistical analysis on extracted product testing data, raw milk index data, inventory data, and order data to establish an early warning model;

[0114] Real-time monitoring of dairy processing data through early warning models;

[0115] When abnormal product detection information is detected, product detection warning information is automatically generated and sent;

[0116] Automatically calculate and predict the expiration date of dairy products based on the production date and shelf life information in the inventory data;

[0117] When the product reaches the temporary time, the expiration warning information is automatically generated and sent;

[0118] When the physical and chemical index or microbiological index of raw milk in the raw milk index data exceeds the corresponding index threshold, the raw milk quality warning information is automatically generated and sent;

[0119] The early warning model is used to analyze the order data, generate analysis results and input them into the preset order anomaly analysis system; if a supply and demand imbalance or an order anomaly is identified, sales order early warning information is automatically generated and sent.

[0120] As an example, based on the data collected by the IoT devices, the dairy processing data warning module 5 uses the rule engine of the IoT management system to achieve comprehensive quality management of dairy production, storage, sales and other links through real-time monitoring and data analysis. It mainly includes product detection warning, dairy product expiration warning, raw milk quality warning and sales order warning. The corresponding data processing flow is as follows:

[0121] (1) Data collection: Use the IoT system to collect product testing data, raw milk index data, inventory data, and order data.

[0122] (2) Data analysis: First, the collected data is cleaned and formatted, and then key data features are extracted to provide input for the early warning model.

[0123] (3) Warning generation: The system automatically generates warnings based on preset rules and thresholds. When abnormal product detection indicators are detected, the system automatically generates product detection warnings. Automatically calculate and predict the expiration date of dairy products and generate dairy product expiration warnings. When the physical and chemical indicators and microbiological indicators of raw milk are abnormal, the warning is immediately triggered and the supply chain information is tracked. Monitor order data, predict supply and demand imbalances or order anomalies, and provide response measures.

[0124] The present invention provides a dairy data early warning analysis system, which can monitor the production data of dairy enterprises in real time, including milk output, quality indicators, production efficiency, etc., to help enterprises understand the production situation in a timely manner. By analyzing historical data and trends, it can provide enterprises with optimization suggestions for production efficiency, helping enterprises to improve production efficiency and reduce costs. This system can monitor data in real time and find abnormal situations, such as production decline, quality problems, etc., according to the set rules and indicators, and issue early warnings in time to help enterprises take measures to avoid losses. This system can monitor data in real time and find abnormal situations, such as production decline, quality problems, etc., according to the set rules and indicators, and issue early warnings in time to help enterprises take measures to avoid losses.

[0125] The present invention also provides a dairy industry data early warning analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the function of the dairy industry data early warning analysis system as described in any one of the above items is realized.

[0126] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the function of the dairy data early warning analysis system as described in any one of the above items is realized.

[0127] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0128] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the dairy data early warning analysis system in the invention by running or executing the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0129] If the dairy data early warning analysis system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be stored in a computer-readable storage medium through a computer program. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, point carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0130] The present invention has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0131] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0132] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0133] The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0134] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0135] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0136] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0137] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A dairy industry data early warning analysis system, characterized in that: include: The warning rule configuration module is used to configure custom rules, indicators, data types and query conditions through the rule engine combined with the IoT system to establish warning rule scenarios. When the dairy data obtained by the IoT device meets the preset conditions of the scenario, the warning operation is triggered; The DHI laboratory data early warning module is used to connect to the dairy sample test data of the DHI test center, and conduct early warning analysis on the test progress, data quality and test results according to the preset thresholds, rules and historical data; the forage planting data early warning module is used to identify the risks of pests and diseases, abnormal growth and meteorological disasters based on the forage planting data collected by the IoT devices deployed in the forage planting area, and generate early warning information using the IoT management system; The ranch breeding data early warning module is used to identify the physiological health information of dairy cows and ranch environment information based on the ranch breeding data collected by the IoT devices deployed in the dairy cow living area, and generate early warning information using the rule engine of the IoT management system; The dairy processing data early warning module is used to monitor the dairy production, storage and sales links in real time based on the dairy processing data collected by IoT devices. Through data analysis, it can realize product detection early warning, dairy product expiration early warning, raw milk quality early warning and sales order early warning.

2. The dairy industry data early warning analysis system according to claim 1, characterized in that: The DHI laboratory data early warning module is specifically used for: Obtaining dairy sample test data and its corresponding sample test progress and data integrity information from the database of the DHI test center through an API interface or database synchronization technology; the dairy sample test data includes a variety of key indicators, including milk fat rate, milk yield, protein content, somatic cell count, and urea nitrogen content; Clean the dairy sample test data to remove duplicate, erroneous or invalid data records; Calculate the difference between the sample detection progress and the corresponding node in the preset timetable. If the difference exceeds the preset time difference threshold, it is determined that the sample detection progress is lagging behind. Conduct data integrity checks on dairy sample test data to ensure the data integrity of sample numbers, test dates, and test results; Use statistical analysis methods to compare each key indicator with the corresponding historical data to identify abnormal key indicators; When key indicators of sample testing progress lag or abnormality are identified, corresponding warning information is automatically triggered.

3. The dairy industry data early warning analysis system according to claim 1, characterized in that: The IoT devices deployed in the forage planting area include: pest and disease monitoring cameras, soil moisture sensors, temperature sensors, and weather stations; The forage planting data include: pest and disease data, grass condition data and meteorological data; the grass condition data include growth rate and leaf color; the meteorological data include: temperature, humidity, rainfall, wind speed.

4. The dairy industry data early warning analysis system according to claim 3, characterized in that: The forage planting data early warning module is specifically used for: The collected forage planting data is transmitted to the Internet of Things management system via wireless network; The forage planting data is cleaned and processed through the Internet of Things management system to remove duplicate, erroneous or invalid data records; The forage planting data is sorted in a unified format through the Internet of Things management system to generate formatted forage planting data; Applying preset rules and statistical models to analyze the formatted forage planting data to generate analysis results; the analysis results include pest and disease feature identification results, growth status assessment results, and meteorological disaster risk prediction results; Based on the analysis results, determine whether there is a risk of pests and diseases, abnormal growth or meteorological disasters; if there is a risk, the early warning mechanism is triggered and a warning message is issued.

5. The dairy industry data early warning analysis system according to claim 1, characterized in that: The IoT devices deployed in the cows’ living area include: activity monitoring sensors, body temperature sensors, heart rate monitors, and environmental sensors; The ranch farming data includes: cow activity pattern data, body temperature, heart rate, and ambient temperature and humidity.

6. The dairy industry data early warning analysis system according to claim 5, characterized in that: The ranch breeding data early warning module is specifically used for: Clean and format ranch breeding data; Based on the processed pasture breeding data, the activity pattern characteristics, body temperature fluctuation characteristics, heart rate fluctuation characteristics and the average value of environmental temperature and humidity of the dairy cows were extracted; Combined with the typical behavioral characteristics of the estrus period, the estrus signs of dairy cows are identified according to the characteristics of the cows' activity patterns, body temperature fluctuations, and heart rate fluctuations, and estrus warning information is generated; The estrus warning information includes the cow number, estrus time, and estimated duration; Identify cow health problems based on cow activity pattern characteristics, body temperature fluctuation characteristics, and heart rate fluctuation characteristics, and generate health warning information; Health warning information includes identified abnormal data, possible causes and recommended treatment measures; Compare the average value of ambient temperature and humidity with the corresponding ambient threshold. When the ambient threshold is exceeded, an environmental warning message is automatically generated. The environmental warning message includes abnormal temperature and humidity data and the relevant monitoring range. Integrate estrus warning information, health warning information and environmental warning information to form warning records; Push the warning records to the system platform of the ranch manager or relevant person in charge.

7. The dairy industry data early warning analysis system according to claim 1, characterized in that: The dairy processing data collected by the IoT device includes: product testing data, raw milk index data, inventory data and order data; The product testing data includes nutritional information, microbial content information, and additive usage information collected by testing equipment; The raw milk index data include physical and chemical indexes and microbiological indexes of raw milk; The inventory data includes product type, quantity, production date, and shelf life; The order data includes order quantity, product type, customer demand, and delivery time.

8. The dairy industry data early warning analysis system according to claim 7, characterized in that: The dairy product processing data early warning module is specifically used for: Clean and format dairy processing data; Based on the processed dairy processing data, product testing data, raw milk index data, inventory data and order data are extracted respectively, and corresponding index thresholds are set for each type of data; Use machine learning methods to perform statistical analysis on extracted product testing data, raw milk index data, inventory data, and order data to establish an early warning model; Real-time monitoring of dairy processing data through early warning models; When abnormal product detection information is detected, product detection warning information is automatically generated and sent; Automatically calculate and predict the expiration date of dairy products based on the production date and shelf life information in the inventory data; When the product reaches the temporary time, the expiration warning information is automatically generated and sent; When the physical and chemical index or microbiological index of raw milk in the raw milk index data exceeds the corresponding index threshold, the raw milk quality warning information is automatically generated and sent; Use the early warning model to analyze order data, generate analysis results and input them into the preset order anomaly analysis system; If a supply-demand imbalance or an abnormal order situation is identified, sales order warning information will be automatically generated and sent.

9. A dairy data early warning analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the functions of the dairy data early warning analysis system as described in any one of claims 1 to 8 are realized.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the function of the dairy data early warning analysis system as claimed in any one of claims 1 to 8 is realized.

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