A livestock product epidemic prevention and control system based on cloud block chain

Abstract

The present application relates to the technical field of livestock health management, in particular to a livestock product epidemic prevention and control system based on cloud block chain, which comprises an on-chain health analysis module, an epidemic prevention strategy scheduling module, a contract early warning blocking module, a prevention and control model evolution module and an epidemic prevention configuration synchronization module. In the present application, the introduction of blockchain technology and smart contract ensures the accuracy and traceability of the health status, the non-tamperability of the blockchain ensures the safety and transparency of the data, the smart contract automatically triggers the epidemic prevention measures according to the health data, realizes accurate disease isolation and resource scheduling, improves the efficiency and response speed of epidemic prevention and control, intelligently adjusts the prevention and control strategy, adjusts the measures according to the epidemic transmission characteristics, realizes the standardized monitoring of health, enhances the adaptability and flexibility of the epidemic prevention system, effectively solves the problems of data lag, information asymmetry and inaccurate traceability in the prior art, and optimizes the health management and epidemic prevention and control of the livestock industry.

Description

Technical Field

[0001] This invention relates to the field of livestock health management technology, and in particular to a livestock product disease prevention and control system based on cloud blockchain. Background Technology

[0002] The field of livestock health management technology involves the monitoring, management, and protection of animal health. Its core task is to prevent and control various diseases occurring in livestock farming through scientific and technological means, ensuring the quality and safety of livestock products. This field encompasses multiple aspects, including disease monitoring technology, disease prevention and control methods, collection and analysis of animal health data, and traceability and quality management of livestock products. With the advancement of technology, more and more high-tech technologies, such as the Internet of Things, big data, artificial intelligence, and blockchain technology, are being applied to livestock health management, aiming to improve the accuracy and efficiency of disease prevention and control, and ensure the sustainable development and safety of livestock farming.

[0003] Traditional cloud-based blockchain-based livestock product disease prevention and control systems utilize cloud computing and blockchain technologies, combined with end-to-end monitoring and traceability of livestock products, to establish an efficient, transparent, and secure disease prevention and control system. The system uses blockchain technology to ensure the traceability of livestock products and the immutability of data, while cloud computing provides powerful data storage and processing capabilities, enabling real-time monitoring of the health status of livestock products. Traditional disease prevention and control systems typically rely on centralized management platforms, using data acquisition devices and sensors for health monitoring, combined with manual inspections and disease diagnosis for prevention and control. While effective, traditional methods suffer from problems such as information asymmetry, delayed data processing, and inaccurate traceability.

[0004] The limitations of existing technologies are mainly reflected in untimely information transmission, lagging data processing, and inaccurate disease tracing. Traditional livestock product disease prevention and control systems rely on centralized management platforms. While data collection and the use of sensors can provide health monitoring, the reliance on manual inspections and disease diagnosis easily leads to information asymmetry, long system response times, and the inability to update health status in real time. This approach results in insufficient timeliness and accuracy in disease prevention and control, making it difficult to cope with sudden animal diseases and dynamic transmission patterns. Traditional traceability systems often suffer from data tampering and incompleteness, affecting the reliability and security of product traceability. Therefore, a more intelligent, efficient, and transparent solution is urgently needed to address these challenges. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud blockchain-based livestock product disease prevention and control system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a cloud blockchain-based livestock product disease prevention and control system includes:

[0007] The on-chain health analysis module uses physiological sensor data of livestock products, generates data fingerprints using blockchain hash algorithms, stores them in a distributed ledger through a consensus mechanism, and calls smart contracts to parse physiological data and generate a health status benchmark list.

[0008] The epidemic prevention strategy scheduling module calls the health status benchmark list, extracts the suspected transmission links and isolation priorities among livestock products, and automatically triggers epidemic prevention measures using on-chain contracts based on the distribution of epidemic prevention resources in farms and the principle of blocking transmission, and executes epidemic prevention and control operations to establish a dynamic disease prevention and control optimization model.

[0009] The contract early warning and blocking module calls the dynamic disease prevention and control optimization model, matches the pathological characteristics and health indicator requirements of livestock products, embeds the smart contract automatic triggering mechanism and disease isolation logic, and forms a standardized health monitoring set for livestock products.

[0010] The prevention and control model evolution module calls the standardized health monitoring set of livestock products, combines the health management records recorded in the original blockchain block, extracts key pathogenic parameters and high-frequency infection characteristics, and constructs an intelligent optimization architecture system for livestock product disease prevention and control.

[0011] As a further embodiment of the present invention, the health status benchmark list includes health classification items, status description items, and benchmark comparison items; the dynamic disease prevention and control optimization model includes risk assessment items, control strategy items, and resource allocation items; the standardized health monitoring set for livestock products includes monitoring indicator items, early warning level items, and isolation condition items; and the intelligent optimization architecture system for livestock product disease prevention and control includes parameter parsing items, model update items, and strategy generation items.

[0012] As a further aspect of the present invention, the on-chain health analysis module includes:

[0013] The data cleaning and on-chain sub-module is based on the physiological sensor data of livestock products. It removes environmental noise and invalid collection points, analyzes the logic of physiological indicator changes, and generates state baseline mapping values.

[0014] The pathological feature recognition submodule identifies potential pathological types based on the state benchmark mapping value and combines the original health data from the blockchain distributed ledger, and marks the upper and lower limits of normal fluctuations of physiological indicators to obtain a pathological feature dataset.

[0015] The contract state mapping submodule calls the pathological feature dataset to identify contact dependencies and infection risks between individuals, and establishes a health status benchmark list based on the smart contract structure mapped by the electronic ear tag number and pathological type.

[0016] As a further aspect of the present invention, the epidemic prevention strategy scheduling module includes:

[0017] The epidemic prevention demand extraction submodule calls the health status benchmark list to extract the infection risk chain and isolation priority among livestock products, analyzes the virus transmission intensity and the allocation order of epidemic prevention materials, and obtains the epidemic prevention demand relationship matrix.

[0018] The blocking strategy calculation submodule calculates the urgency value of epidemic prevention and blocking of livestock products based on the epidemic prevention demand relationship matrix, combined with the distribution of isolation resources in farms and the principle of blocking transmission, and selects the optimal isolation and material allocation scheme.

[0019] The dynamic instruction generation submodule, based on the optimal isolation and material allocation scheme, identifies livestock product numbers and isolation path diagrams, recognizes disposal priorities and material fluctuation ranges, and generates a dynamic disease prevention and control optimization model.

[0020] As a further aspect of the present invention, the disease prevention and control urgency value of the livestock product is determined by the following formula:

[0021] ;

[0022] in, This indicates the urgency value for disease prevention and control in livestock products. Indicates the first The average infection risk index of each aquaculture area Indicates the first Virus transmission coefficient in each aquaculture area Indicates the first Current stock density in each breeding area This indicates the total amount of available medical and isolation resources. Indicates the number of breeding areas.

[0023] As a further aspect of the present invention, the contract warning and blocking module includes:

[0024] The real-time symptom matching submodule calls the dynamic disease prevention and control optimization model, retrieves the livestock product identity hash list, collects real-time data in the health indicator sampling window, identifies the indicator value range and pathogenic threshold, divides infection risk labels, records block timestamps and version numbers, and generates a real-time health status label table for livestock products.

[0025] The contract triggering embedded submodule reads smart contract terms based on the real-time health status tag table of the livestock products, sets disease type enumeration and trigger condition fields, embeds isolation access control trigger actions and harmless treatment processes, configures statutory alarm levels and reporting strategies, binds automated execution channels, marks contract rule numbers and effective scopes, and forms an on-chain abnormal early warning rule index.

[0026] The traceability file generation submodule calls the on-chain anomaly warning rule index, combines the epidemic prevention operation response sequence with the preset health indicator threshold, identifies the corresponding credible standardized health monitoring file of livestock products, and marks the transaction hash value to form a standardized health monitoring set of livestock products.

[0027] As a further aspect of the present invention, the prevention and control model evolution module includes:

[0028] The medical record data collection submodule calls the standardized health monitoring set of livestock products, extracts the health management records uploaded in the original blockchain block, analyzes the effectiveness and rehabilitation efficiency of the treatment plan, and classifies them to form a confirmed medical record database;

[0029] The pathogenic parameter extraction submodule, based on the confirmed medical record database, statistically analyzes high-frequency pathogenic features and key variant parameters, calculates pathogenic feature weight values, and obtains core pathogenic labels.

[0030] The defense model update submodule incrementally updates the basic disease feature database based on the core pathogenic tags, combined with the key pathological index and the epidemic prevention standard table, records the model iteration frequency, and constructs an intelligent optimization architecture system for livestock product disease prevention.

[0031] The basic disease feature database refers to the database that is constructed and updated by collecting original disease data, expert knowledge bases, and health management records uploaded to the blockchain, and combining data analysis and expert review.

[0032] As a further aspect of the present invention, the system also includes an epidemic prevention configuration synchronization module:

[0033] The disease prevention configuration synchronization module calls the intelligent optimization architecture system for disease prevention of livestock products, and automatically broadcasts the disease prevention contract and parameters of the corresponding livestock products based on the livestock product number and the disease prevention area identifier, marks the block height and contract version, and outputs the configuration distribution storage path association table.

[0034] The aforementioned disease prevention area identifier refers to a geographical area identifier associated with disease prevention activities for livestock products;

[0035] The configuration distribution storage path association table includes path record items, version identifier items, and region association items.

[0036] As a further aspect of the present invention, the epidemic prevention configuration synchronization module includes:

[0037] The contract version management submodule calls the intelligent optimization architecture system for livestock product disease prevention and control, identifies the version number and update time of the disease prevention and control smart contract, compares the current version of the disease prevention and control contract with the first released baseline version, determines the target contract version, and generates a contract version mapping table.

[0038] The synchronization path generation submodule, based on the contract version mapping table and combining the livestock product number and IoT device identifier, identifies the synchronization path of the corresponding livestock product, records the node identifier and consensus timestamp, and generates a configuration distribution storage path association table.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] This invention, by introducing blockchain technology and smart contracts, enables real-time recording and analysis of physiological data of livestock products, ensuring the accuracy and traceability of health status. The immutability of blockchain guarantees data security and transparency, while smart contracts can automatically trigger epidemic prevention measures based on health data, achieving precise disease isolation and resource allocation, greatly improving the efficiency and response speed of disease control. Through a dynamic optimization model, the system can intelligently adjust prevention and control strategies according to disease transmission characteristics, achieving standardized monitoring of livestock product health, enhancing the adaptability and flexibility of the epidemic prevention system, and effectively solving problems such as data lag, information asymmetry, and inaccurate traceability in existing technologies, thereby optimizing health management and disease control in animal husbandry. Attached Figure Description

[0041] Figure 1 This is a system flowchart of the present invention;

[0042] Figure 2 This is a flowchart of the on-chain health analysis module in this invention;

[0043] Figure 3 This is a flowchart of the epidemic prevention strategy scheduling module in this invention;

[0044] Figure 4 This is a flowchart of the contract early warning and blocking module in this invention;

[0045] Figure 5 This is a flowchart of the prevention and control model evolution module in this invention;

[0046] Figure 6 This is a flowchart of the epidemic prevention configuration synchronization module in this invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0049] Please see Figure 1 A cloud-based blockchain-based livestock product disease prevention and control system includes:

[0050] The on-chain health analysis module uses physiological sensor data of livestock products, generates data fingerprints using blockchain hash algorithms, stores them in a distributed ledger through a consensus mechanism, and calls smart contracts to parse physiological data and generate a health status benchmark list.

[0051] The epidemic prevention strategy scheduling module calls the health status baseline list, extracts the suspected transmission links and isolation priorities among livestock products, and automatically triggers epidemic prevention measures using on-chain contracts based on the distribution of epidemic prevention resources in farms and the principle of blocking transmission, and executes epidemic prevention and control operations to establish a dynamic disease prevention and control optimization model.

[0052] The contract early warning and blocking module calls the dynamic disease prevention and control optimization model, matches the pathological characteristics and health indicator requirements of livestock products, embeds the smart contract automatic triggering mechanism and disease isolation logic, and forms a standardized health monitoring set for livestock products.

[0053] The prevention and control model evolution module calls the standardized health monitoring set of livestock products, combines the health management records recorded in the original blockchain block, extracts key pathogenic parameters and high-frequency infection characteristics, and constructs an intelligent optimization architecture system for livestock product disease prevention and control.

[0054] The disease prevention configuration synchronization module calls the intelligent optimization architecture system for disease prevention of livestock products. Based on the livestock product number and the disease prevention area identifier, it automatically broadcasts the corresponding disease prevention contract and parameters of the livestock product, marks the block height and contract version, and outputs the configuration distribution storage path association table.

[0055] The term "epidemic prevention area identifier" refers to a geographical area identifier associated with epidemic prevention activities for livestock products.

[0056] The health status benchmark list includes health classification items, status description items, and benchmark comparison items; the dynamic disease prevention and control optimization model includes risk assessment items, control strategy items, and resource allocation items; the standardized health monitoring set for livestock products includes monitoring indicator items, early warning level items, and isolation condition items; the intelligent optimization architecture system for livestock product disease prevention and control includes parameter parsing items, model update items, and strategy generation items; and the configuration distribution storage path association table includes path record items, version identifier items, and regional association items.

[0057] Please see Figure 2 The on-chain health analysis module includes:

[0058] The data cleaning and on-chain sub-module is based on the physiological sensor data of livestock products. It removes environmental noise and invalid collection points, analyzes the logic of physiological indicator changes, and generates state baseline mapping values.

[0059] Based on physiological sensor data of body temperature, heart rate, and activity level collected every minute over 24 hours from the electronic ear tag SN-0755, environmental noise removal is first performed. The noise judgment logic of the body temperature sensor is set so that when the absolute value of the difference between the single minute collection value and the moving average of the five minutes before and after it is higher than 2.5℃, the collection value is judged as an environmental noise point and discarded. Then, invalid collection points are removed. All data points in the period when the electronic ear tag battery is lower than 5% or the communication signal strength is lower than -95dBm are identified and discarded. Then, the physiological index change logic is analyzed on the processed dataset. The 24-hour data is divided into four 6-hour windows. In each window, the linear regression slope of body temperature and the sum of activity level are calculated. The logical combination of body temperature rise, activity level decrease, and heart rate stabilization is encoded into a logical vector [1, -1, 0]. Finally, this vector is concatenated with the ear tag number SN-0755 and the current timestamp, and then the state baseline mapping value is generated by SHA-256 algorithm.

[0060] The pathological feature recognition submodule identifies potential pathological types based on state baseline mapping values ​​and combines them with the original health data from the blockchain distributed ledger. It also marks the upper and lower limits of normal fluctuations in physiological indicators to obtain a pathological feature dataset.

[0061] Based on the state baseline mapping value, the state logic vector [1, -1, 0] is restored through decryption. The original health data of the livestock product (number SN-0755) for the past 30 days is retrieved from the blockchain distributed ledger. This logic vector is matched with a pathological feature knowledge base. The feature vector for "early stage of viral fever" stored in this knowledge base is [1, -1, -1]. By calculating the Euclidean distance between the two vectors, when the distance value of 1.0 is less than the preset threshold of 1.5, "early stage of viral fever" is identified as the pathology of SN-0755. Next, the upper and lower limits of the normal fluctuation of the physiological indicators of the livestock product are marked. These limits are set by statistically analyzing the physiological data of 1,000 healthy samples of the same breed over a continuous period of 6 months and calculating the 95% confidence interval. The normal body temperature fluctuation range is 38.1℃ to 39.2℃. Since the current body temperature of SN-0755 is 39.5℃, which exceeds the upper limit, this indicator is marked as abnormal. Finally, the livestock product number, pathological type, abnormal indicator, normal fluctuation range and original data hash are integrated to obtain the pathological feature dataset.

[0062] The contract state mapping submodule calls the pathological feature dataset to identify the contact dependence and infection risk between individuals, and maps the smart contract structure according to the electronic ear tag number and pathological type to establish a health status benchmark list.

[0063] By accessing the pathological feature dataset, the location data from the farm's RFID reader network was first analyzed to identify contact dependencies and infection risks among individuals. Since SN-0755, SN-0756, and SN-0812 spent more than 30 minutes in the same water trough area, and had 15 minutes of close contact with SN-0758 in the rest area, a contact dependency graph was established: SN-0755 → {SN-0756, SN-0812, SN-0758}. Subsequently, the infection risk was calculated using a pre-set two-dimensional matrix, classifying the risk of "early stage of viral febrile disease" and contact in the same area for more than 30 minutes as the associated risk. The value is set to 0.6, and the risk value for close contact for 15 minutes is set to 0.4. Therefore, the transmission risk of SN-0756 and SN-0812 is marked as 0.6, and SN-0758 is marked as 0.4. Next, based on the electronic ear tag number and pathology type, a smart contract structure containing AnimalID, HealthStatus, PathologyType, ContactGraph and Timestamp fields is dynamically mapped to generate or update a contract status instance for each evaluated livestock product. Finally, all instances are aggregated to establish a health status baseline list.

[0064] Please see Figure 3 The epidemic prevention strategy scheduling module includes:

[0065] The epidemic prevention demand extraction submodule calls the health status baseline list, extracts the infection risk chain and isolation priority among livestock products, analyzes the intensity of virus transmission and the order of epidemic prevention material allocation, and obtains the epidemic prevention demand relationship matrix.

[0066] The health status baseline list of all livestock products in the farm was retrieved. Based on the ContactGraph field of SN-0755 in the list, the transmission risk chain SN-0755→SN-0756 (risk 0.6), SN-0812 (risk 0.6), and SN-0758 (risk 0.4) were identified. According to the preset risk value classification standard ([0.6, 1.0] is level 1, [0.3, 0.6) is level 2), SN-0755, SN-0756, and SN-0812 were assigned level 1 isolation priority, and SN-0758 was assigned level 2 isolation priority. Subsequently, the virus transmission intensity and the order of distribution of epidemic prevention materials were analyzed. The virus transmission intensity was quantified as 2.0 based on the number of contacts with level 1 priority. The order of distribution of epidemic prevention materials strictly followed the isolation priority, satisfying the needs of level 1 priority first and then the needs of level 2. Finally, the analysis results were structured to obtain an epidemic prevention demand relationship matrix with livestock product number as the row and "isolation priority" and "testing reagent demand" as the columns.

[0067] The blocking strategy calculation submodule, based on the epidemic prevention demand matrix and considering the distribution of isolation resources in farms and the principle of blocking transmission, uses the following formula:

[0068] ;

[0069] Calculate the urgency value of disease prevention and control for livestock products, and screen out the optimal isolation and material distribution plan;

[0070] in, This indicates the urgency value for disease prevention and control in livestock products. Indicates the first The average infection risk index of each aquaculture area Indicates the first Virus transmission coefficient in each aquaculture area Indicates the first Current stock density in each breeding area This indicates the total amount of available medical and isolation resources. Indicates the number of aquaculture areas;

[0071] Based on the relationship matrix of epidemic prevention needs, the formula is used. The urgency value for epidemic prevention and control is calculated to select the optimal solution. In the formula, This represents the total urgency of disease prevention and control for the entire farm. It is a dimensionless indicator; the higher the value, the more urgent the need for comprehensive containment measures. (Summarization symbol) Used to transport all within the farm The urgency of each region is accumulated to achieve an overall risk assessment and avoid local optimization from affecting the overall situation;

[0072] Internal multiplication and division structures A risk and stress model was constructed, in which, Based on the regional average infection risk index With the virus transmission coefficient The ratio represents the relative severity of the risk at the current rate of transmission; while Then use the current stock density relative to the total amount of available medical and isolation resources The ratio reflects the degree of resource scarcity. Multiplying the two together aims to obtain the comprehensive risk pressure borne by a unit of resource at a unit transmission rate, and thus deduce the urgency of epidemic prevention in the region.

[0073] The advantage of this formula lies in its use of risk index. speed of transmission Stocking density Total resources The four key elements are uniformly quantified, enabling epidemic prevention decisions to be based on comparable, data-driven urgency indicators, rather than relying solely on experience-based judgments. In particular, by correlating the dual ratios of "risk / transmission speed" and "density / resources," dangerous areas characterized by "high risk, high density, rapid transmission, and low resources" can be accurately identified, thereby achieving optimal resource allocation and improving the efficiency of early intervention. The following describes three areas within the farm ( Demonstrate with a calculation example:

[0074] First, determine the values ​​of each parameter: These are designated as Area A, Area B, and Area C, respectively.

[0075] This represents the total available medical and isolation resources. This value is obtained by quantifying and weighting existing resources. The calculation method is: H = (number of isolation pens × 15) + (number of rapid test kits × 0.5) + (effective veterinary working hours × 5). Assuming there are currently 20 isolation pens, 500 test kits, and the veterinary team can provide 80 effective working hours per day, then H = (20 × 15) + (500 × 0.5) + (80 × 5) = 300 + 250 + 400 = 950. This total resource amount provides a benchmark for subsequent calculations.

[0076] For area A:

[0077] (Average infection risk index in area A) is calculated by retrieving the health status baseline list of all individuals (100 in total) in the area and calculating the arithmetic mean of their infection risk values. Assuming that area A includes SN-0755 and its main contacts, the average risk index is calculated to be 0.55.

[0078] The (virus transmission coefficient in area A) is preset based on the environment (closed cattle shed) and virus type (airborne transmission) of the area. Its setting refers to the epidemiological survey data of the same type of virus in similar environments. By comparing 20 sets of historical animal disease data, the average transmission rate is taken and normalized, and set to 1.8.

[0079] (Current stock density in Area A) is the number of animals in the area divided by the area. Area A has an area of ​​500 square meters and a stock density of 100 animals. =100 / 500=0.2 heads / square meter;

[0080] For region B (i=2):

[0081] The average infection risk index in Zone B is 0.15, as the general health status of individuals in this area is good.

[0082] The virus transmission coefficient in Zone B is set at 1.1 because it is a semi-open cattle shed with good air circulation.

[0083] (Current stock density in Zone B) is 150 heads / 1000 square meters = 0.15 heads / square meter;

[0084] For region C (i=3):

[0085] The average infection risk index in Zone C is 0.25, indicating a small number of individuals at secondary risk.

[0086] The virus transmission coefficient in Zone C is set at 1.5, and its ventilation conditions are between those in Zone A and Zone B.

[0087] (Current stock density in Zone C) is 120 heads / 600 square meters = 0.2 heads / square meter;

[0088] Substitute the above parameter values ​​into the formula to perform the calculation: ;

[0089] Calculate the urgency value components for each region: , , By comparing the values ​​of each component, The results indicate that the urgency of epidemic prevention and control is highest in Area A. Based on this, the system selects the optimal isolation and resource allocation plan, which prioritizes allocating more than 60% of isolation and testing resources to Area A, 30% to Area C, and the remaining 10% to preventive monitoring in Area B.

[0090] The dynamic instruction generation submodule generates a dynamic disease prevention and control optimization model based on the optimal isolation and material allocation scheme, identifies livestock product numbers and isolation path diagrams, identifies disposal priorities and material fluctuation ranges, and generates a dynamic disease prevention and control optimization model.

[0091] Based on the optimal isolation and resource allocation plan, the isolation path map for livestock products SN-0755, SN-0756, and SN-0812 with the first-level isolation priority in Area A was first determined. The farm map data and real-time access control status were queried to plan the path consisting of RFID sensing points G01→G04→G11→ISO-Gate03 and generate transfer instructions. Subsequently, the disposal priority and resource fluctuation range were identified. The detection priority of SN-0755 was marked as "urgent", and SN-0756 and SN-0812 were marked as "high". This fluctuation range allows on-site veterinarians to adjust the usage based on the preliminary diagnosis results. Finally, the individual numbers, movement paths, task priorities, and resource allocation details were integrated to generate a dynamic disease prevention and control optimization model.

[0092] Please see Figure 4 The contract warning and blocking module includes:

[0093] The real-time symptom matching submodule calls the dynamic disease prevention and control optimization model, retrieves the livestock product identity hash list, collects real-time data in the health indicator sampling window, identifies the indicator value range and pathogenic threshold, divides infection risk labels, records block timestamps and version numbers, and generates a real-time health status label table for livestock products.

[0094] The dynamic disease prevention and control optimization model is invoked. First, the identity hash list of livestock products that need to be monitored is retrieved from the model. Then, for individuals on the list, their body temperature and activity data are collected in real time at a frequency of once every 10 seconds within a 15-minute sampling window, forming a short time series containing 90 data points. Next, the preset health indicator value range and pathogenic threshold of the individuals are identified from the blockchain. The pathogenic threshold is defined as "the body temperature value of 5 consecutive data points is higher than the upper limit of the normal range by 0.3℃". The real-time collected data series is matched with this threshold. When it is found that the body temperature of SN-0756 is around 39.6℃ for 6 consecutive readings in the last minute, a new infection risk label "highly suspected" is assigned to it. At the same time, the block timestamp of the current operation and the model version number v1.2 are recorded. Finally, the latest status of all monitored individuals is integrated to generate a real-time health status label table for livestock products.

[0095] Table 1: Real-time Health Status Labels for Livestock Products

[0096] ;

[0097] As shown in Table 1, this table records the health status of key monitored objects, providing a basis for subsequent automated contract triggering. Among them, the status of SN-0756 has been upgraded to "highly suspected" based on real-time data.

[0098] The contract triggering embedded submodule is based on the real-time health status tag table of livestock products. It reads the smart contract terms, sets the disease type enumeration and trigger condition fields, embeds isolation access control trigger actions and harmless treatment processes, configures statutory alarm levels and reporting strategies, binds automated execution channels, marks the contract rule number and effective scope, and forms an on-chain abnormal early warning rule index.

[0099] Based on the real-time health status tag table for livestock products, the smart contract terms corresponding to SN-0756 are first read. According to the "highly suspected" tag in the tag table, the preset trigger condition field in the contract is set to HealthStatus "highly suspected". Next, the automated action is embedded in the contract logic. When the condition is met, the contract automatically calls the external function to send a command to the smart access control system in the area where SN-0756 is located, triggering the access control opening preparation action to the isolation area. It also automatically creates a harmless treatment plan with a status of "pending activation" in the veterinary workflow. Subsequently, the "highly suspected" status is matched as a level 2 alarm according to the internal rules. This alarm strategy is configured to send alarm information to the mobile terminals of the on-site veterinary supervisor and the A-zone breeder, and the event is bound to the automated execution channel. Finally, the newly added trigger rule is labeled with the rule number Rule-ID-201 and its effective scope is set to "Area A". It is written into the blockchain to form an on-chain abnormal warning rule index.

[0100] The traceability file generation submodule calls the on-chain anomaly warning rule index, combines the epidemic prevention operation response sequence with the preset health indicator threshold, identifies the corresponding credible standardized health monitoring file of livestock products, and marks the transaction hash value to form a standardized health monitoring set of livestock products;

[0101] The on-chain anomaly warning rule index is invoked to first locate Rule-ID-201 related to SN-0756. This is then combined with the epidemic prevention operation response sequence generated after the rule is triggered. This sequence includes: veterinarian confirms the alarm at 15:10, on-site verification and rapid reagent testing at 15:25, positive test result at 15:40, and transfer to the isolation area at 15:50. Then, the operation sequence is correlated and verified with preset health indicator thresholds. Since the operations all comply with the plan and the indicators meet the diagnostic criteria, a standardized health monitoring file for SN-0756 is identified and generated. This file is in JSON format and contains the individual's historical physiological data, contact map, triggered warning rules, response operation sequence, and diagnostic results. Each key event in the file is marked with its corresponding transaction hash value on the blockchain. Finally, the monitoring file for SN-0756 is combined with other livestock product files processed concurrently to form a standardized health monitoring set for livestock products.

[0102] Please see Figure 5 The prevention and control model evolution module includes:

[0103] The medical record data collection submodule calls the standardized health monitoring set of livestock products, extracts the health management records uploaded in the original blockchain block, analyzes the effectiveness and rehabilitation efficiency of the treatment plan, and classifies them to form a confirmed medical record database;

[0104] The standardized health monitoring set for livestock products was invoked. First, key information of individuals such as SN-0755 and SN-0756, who had completed their treatment cycles, was extracted from the unarchived monitoring files. Health management records related to these two cases were parsed from the original blocks of the blockchain, including daily vital sign observation logs and medication instructions uploaded by veterinarians. Next, the effectiveness and recovery efficiency of the treatment plan were quantitatively analyzed. Recovery efficiency was defined as the total time from the first dose of medication to the body temperature returning to normal for 72 consecutive hours. Through statistical analysis of data from 50 similar cases, cases with clear diagnoses, complete treatment records, and clear treatment results were categorized and organized to form a confirmed case database.

[0105] The pathogenic parameter extraction submodule, based on the confirmed case database, statistically analyzes high-frequency pathogenic features and key variant parameters using the following formula:

[0106] ;

[0107] Calculate the pathogenic feature weight values ​​to obtain the core pathogenic label;

[0108] in, Represents the weight value of pathogenic characteristics. Representing the The frequency and intensity of the occurrence of key pathogenic parameters in the original diagnostic records. Representing the The fitting factors of key pathogenic parameters and current epidemiological models. Representing the Discrete distribution measurements of key pathogenic parameters in population samples. This represents the total number of pathogenic parameter categories extracted so far;

[0109] Based on 50 confirmed cases of "viral febrile diseases" accumulated in the confirmed medical record database, the pathogenic parameter extraction submodule uses the following formula to calculate the weight values ​​of different pathogenic characteristics. ,in, The summation is the overall mean of the biases of all feature weights, used to measure the overall deviation between the pathogenic features identified by the model and the actual pathogenic associations; (Summarization symbol) The algorithm iterates through and calculates all feature categories from 1 to N, and sums up the absolute values ​​of the weight biases. The purpose is to comprehensively evaluate the importance of all features. The internal absolute value sign || ensures that both overestimated and underestimated features will contribute positively to the total bias.

[0110] Core Calculation Items The constituent feature is the "observational significance score", in which the molecule It is the product of frequency of occurrence and model fit, representing importance in both theoretical and practical dimensions, while the denominator... As a measure of distribution dispersion, it serves to normalize and penalize universal features. If a feature frequently appears in the patient population ( High), in line with epidemiological predictions ( High) and concentrated distribution throughout the population ( A high score indicates rarity / specificity, resulting in a significant score. Subtracting 1 and taking the absolute value compares this "observational significance score" to a baseline of 1, determining the deviation. This deviation is the weight of a single feature. The advantage of this formula is that it doesn't simply rely on the frequency of a particular epidemiological feature, but innovatively introduces a fitting factor to existing epidemiological models. and the distribution dispersion in the entire sample This makes the weight assessment more comprehensive and scientific, effectively filtering out "pseudo-features" that are common but not disease-specific (such as mild loss of appetite), while uncovering key variant parameters that are infrequent but highly targeted, thereby improving the accuracy of the core pathogenic label. The following will illustrate the calculation examples for the four candidate pathogenic parameters (N=4).

[0111] First, determine the values ​​of each parameter:

[0112] N represents the total number of pathogenic parameter categories extracted so far, where N=4;

[0113] Body temperature consistently >40℃;

[0114] The joints are noticeably swollen;

[0115] Respiratory rate > 50 breaths / minute;

[0116] : XG-1 gene fragment positive;

[0117] For parameter x=1 (body temperature consistently above 40℃):

[0118] (Frequency and intensity of occurrence): This characteristic was observed in 45 out of 50 confirmed cases, therefore... ;

[0119] (Epidemiological model fit factor): The current epidemiological model predicts the frequency of this symptom to be 85%, while the actual frequency is 90%. The fit factor is calculated as follows: ;

[0120] (Discrete distribution measurement in the population sample): A sampling test of 1000 cattle across the entire farm revealed that this characteristic was highly concentrated in a small number of affected individuals, with a Gini coefficient of 0.8 calculated from the Lorenz curve. ;

[0121] For parameter x=2 (significant joint swelling):

[0122] ;

[0123] The model predicted 10%, but the actual figure was 30%. ;

[0124] This feature is highly specific, with a Gini coefficient of 0.6. ;

[0125] For parameter x=3 (respiratory rate exceeding 50 breaths / min):

[0126] ;

[0127] The model predicted 75%, but the actual result was 80%. ;

[0128] This characteristic also appears under stress and other conditions, and its distribution is relatively scattered, with a Gini coefficient of 0.5. ;

[0129] For parameter x=4 (gene fragment XG-1 positive):

[0130] ;

[0131] This is a newly discovered feature. There is no data in the model. An initial exploratory fitting factor is set, which is set to 1.5 based on its co-occurrence rate with the core symptoms.

[0132] This gene fragment was not found in healthy individuals, exhibiting extremely high specificity with a Gini coefficient of 0.95. ;

[0133] Substitute the parameters into the formula to calculate the weight components of each feature:

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] By comparing the weight components of each feature, The results showed that "positive XG-1 for a specific gene fragment" (weight 0.6842) and "respiratory rate exceeding 50 breaths / minute" (weight 0.68) were the two most indicative pathogenic features in the current data and were selected as the core pathogenic labels.

[0140] The defense model update submodule incrementally updates the basic disease feature database based on core pathogenic tags, combined with key pathological indexes and epidemic prevention standard tables, records the model iteration frequency, and constructs an intelligent optimization architecture system for livestock product disease prevention.

[0141] The basic disease feature database refers to the database constructed and updated by collecting original disease data, expert knowledge bases, and health management records uploaded to the blockchain, combined with data analysis and expert review.

[0142] Based on the core pathogenic labels "respiratory rate exceeding 50 breaths / minute" and "positive for specific gene fragment XG-1", these two labels were first compared with existing key pathological indexes and epidemic prevention standard tables. It was found that "respiratory rate exceeding 50 breaths / minute" already existed, but its weight value needed to be updated, while "positive for specific gene fragment XG-1" was a completely new feature. Subsequently, an incremental update operation was performed on the basic disease feature library. In the feature list of "viral febrile diseases", the weight parameter of the "respiratory rate" feature was updated to 0.68, and a new record was added, including the feature name "specific gene fragment XG-1", the feature type "gene marker", and the weight value of 0.6842. This record was then associated with the pathological identifier of "viral febrile diseases". At the same time, the operation time and operator ID of this model update were recorded, and the model version number was iterated from 2.1 to 2.2. Finally, an intelligent optimization architecture system for livestock product disease prevention was constructed.

[0143] Please see Figure 6 The epidemic prevention configuration synchronization module includes:

[0144] The contract version management submodule calls the intelligent optimization architecture system for livestock product disease prevention and control, identifies the version number and update time of the disease prevention and control smart contract, compares the current version of the disease prevention and control contract with the first released baseline version, determines the target contract version, and generates a contract version mapping table.

[0145] The intelligent optimization architecture system for livestock product disease prevention and control (version 2.2) was invoked. First, a full network scan was performed to identify the current version number and update time of the disease prevention smart contract deployed on each IoT device node. The results showed that the contract version of 950 nodes was 2.1 and the contract version of 50 nodes was 2.0. Next, the contract rules of the current version (2.1 and 2.0) of the disease prevention contract were compared with the target contract version (2.2) in the architecture system. The difference analysis report pointed out that version 2.2 added monitoring logic for "specific gene fragment XG-1". Based on this, it was determined that all contracts with versions lower than 2.2 need to be upgraded, and a contract version mapping table was generated.

[0146] Table 2: Contract Version Mapping Table

[0147] ;

[0148] As shown in Table 2, this mapping table lists the batches of equipment that need to be updated, the current version and the target version of the epidemic prevention contract, and provides a list of instructions for synchronization operations.

[0149] The synchronization path generation submodule uses the contract version mapping table, combined with the livestock product number and IoT device identifier, to identify the synchronization path of the corresponding livestock product, record the node identifier and consensus timestamp, and generate a configuration distribution storage path association table.

[0150] Based on the contract version mapping table, the devices from SN-0001 to SN-0950 in batch B001 are processed first. Combining the livestock product number and the IoT device identifier, a synchronization path is planned for the electronic ear tags that need to be updated. For ear tag SN-0001, which is located in area A and managed by the gateway numbered GW-A01, the synchronization path is: central cloud server → farm main server → area A edge computing node → GW-A01 gateway → SN-0001 ear tag device. While planning the path, a transaction is created for the batch update task, recording the identifiers of key nodes in the path. It is also preset that after the update package is successfully deployed, the consensus mechanism will record the consensus timestamp. Finally, the synchronization paths, node information, and timestamp fields to be recorded for all devices are integrated to generate a configuration distribution storage path association table.

[0151] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A livestock product epidemic prevention and control system based on a cloud blockchain, characterized in that, The system comprises: The on-chain health analysis module generates a data fingerprint based on the physiological sensing data of livestock products using a blockchain hash algorithm, stores it in a distributed ledger through a consensus mechanism, and calls a smart contract to analyze the physiological data to generate a health status benchmark list; The epidemic prevention strategy scheduling module calls the health status benchmark list, extracts suspected transmission links and isolation priorities between livestock products, and automatically triggers epidemic prevention measures based on the distribution of breeding farm epidemic prevention resources and the principle of blocking transmission to perform epidemic prevention control operations and establish a dynamic epidemic prevention and control optimization model; The epidemic prevention strategy scheduling module comprises: The epidemic prevention demand extraction submodule calls the health status benchmark list, extracts the transmission risk chain and isolation priority between livestock products, analyzes the virus transmission intensity and epidemic prevention material distribution sequence, and obtains an epidemic prevention demand relationship matrix; The blocking strategy calculation submodule calculates the epidemic prevention blocking urgency value of livestock products based on the epidemic prevention demand relationship matrix, combined with the distribution of breeding farm isolation resources and the principle of blocking transmission, to obtain the optimal isolation and material distribution scheme; The dynamic instruction generation submodule identifies livestock product numbers and isolation path maps based on the optimal isolation and material distribution scheme, identifies disposal priorities and material floating ranges, and generates a dynamic epidemic prevention and control optimization model; The contract early warning blocking module calls the dynamic epidemic prevention and control optimization model, matches the pathological characteristics of livestock products with health index requirements, embeds a smart contract automatic triggering mechanism and epidemic isolation logic, and forms a standardized livestock product health monitoring set; The control model evolution module calls the standardized livestock product health monitoring set, extracts key pathogenic parameters and high-frequency infection characteristics combined with the health management records recorded in the original block of the blockchain, and constructs an intelligent livestock product epidemic prevention and control optimization architecture system. 2.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 1, characterized in that, The health status benchmark list includes health classification items, state description items, and benchmark comparison items. The dynamic epidemic prevention and control optimization model includes risk assessment items, control strategy items, and resource allocation items. The standardized livestock product health monitoring set includes monitoring index items, early warning level items, and isolation condition items. The livestock product epidemic prevention and control intelligent optimization architecture system includes parameter analysis items, model update items, and strategy generation items. 3.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 1, characterized in that, The on-chain health analysis module comprises: The data cleaning and uploading module removes environmental noise and invalid collection points based on the physiological sensing data of livestock products, analyzes the physiological index change logic, and generates state benchmark mapping values; The pathological feature recognition submodule identifies potential pathological types based on the state benchmark mapping values and the original health data of the blockchain distributed ledger, marks the upper and lower limits of normal fluctuations of physiological indicators, and obtains a pathological feature dataset; The contract state mapping submodule calls the pathological feature dataset, identifies the contact dependence relationship and transmission risk between individuals, and establishes a health status benchmark list based on the electronic ear tag number and pathological type mapping smart contract structure. 4.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 1, characterized in that, The epidemic prevention blocking urgency value of the livestock product is calculated using the formula: ； wherein T represents an epidemic blocking urgency value of livestock products, represents an average infection risk index of the i-th breeding area, represents a virus transmission coefficient of the i-th breeding area, represents the current stocking density of the i-th breeding area, H represents the total amount of available medical and isolation resources, and n represents the number of breeding areas. 5.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 1, characterized in that, The contract early warning blocking module comprises: The real-time disease matching sub-module calls the dynamic epidemic prevention and control optimization model, calls the livestock product identity hash list, collects real-time data in the health index sampling window, identifies the index value range and the pathogenic threshold, divides the infection risk label, records the block timestamp and version number, and generates a livestock product real-time health status label table; The contract triggering embedded sub-module reads the smart contract clauses based on the livestock product real-time health status label table, sets the epidemic type enumeration and trigger condition field, embeds the isolation access control trigger action and harmless treatment process, configures the legal alarm level and reporting strategy, binds the automatic execution channel, marks the contract rule number and effective range, and forms an on-chain abnormal early warning rule index; The traceability archive generation sub-module calls the on-chain abnormal early warning rule index, combines the epidemic prevention operation response sequence and the preset health index threshold, identifies the corresponding livestock product's trusted standardized health monitoring file, and marks the transaction hash value, forming a livestock product standardized health monitoring set. 6.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 5, characterized in that, The prevention and control model evolution module includes: The medical record data collection sub-module calls the livestock product standardized health monitoring set, extracts the health management records uploaded in the blockchain original block, analyzes the effectiveness and recovery efficiency of the treatment scheme, and classifies to form a confirmed medical record database; The pathogenic parameter extraction sub-module, based on the confirmed medical record database, counts the high-frequency pathogenic characteristics and key variation parameters, calculates the pathogenic characteristic weight value, and obtains the core pathogenic label; The defense model update sub-module, according to the core pathogenic label, combines the key pathology index and the epidemic prevention standard table, incrementally updates the basic epidemic disease feature library, records the model iteration frequency, and constructs an intelligent optimization architecture system for livestock product epidemic prevention; The basic epidemic disease feature library is constructed and updated by collecting original epidemic data, expert knowledge base and health management records uploaded on the blockchain, and combining data analysis and expert review. 7.The cloud blockchain-based livestock epidemic prevention and control system according to claim 1, wherein, The system also includes an epidemic prevention configuration synchronization module: The epidemic prevention configuration synchronization module calls the livestock product epidemic prevention intelligent optimization architecture system, automatically broadcasts the epidemic prevention contract and parameters of the corresponding livestock product according to the livestock product number and the epidemic prevention area identifier, marks the block height and the contract version, and outputs the configuration distribution storage path association table; The epidemic prevention area identifier refers to a geographic area identifier associated with the epidemic prevention activities of the livestock product; The configuration distribution storage path association table includes a path record item, a version identifier item, and a region association item. 8.The cloud blockchain-based livestock product epidemic prevention and control system according to claim 7, characterized in that, The epidemic prevention configuration synchronization module includes: The contract version management sub-module calls the livestock product epidemic prevention intelligent optimization architecture system, identifies the version number and update time of the epidemic prevention smart contract, compares the current version of the epidemic prevention contract with the benchmark version of the first release, determines the target contract version, and generates a contract version mapping table; The synchronization path generation sub-module, based on the contract version mapping table, combines the livestock product number and the Internet of Things device identifier, identifies the synchronization path of the corresponding livestock product, records the node identifier and the consensus timestamp, and generates the configuration distribution storage path association table.

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