Method and system for batch progress monitoring of animal disease detection samples

Abstract

This application provides a method and system for batch progress monitoring of animal disease testing samples, belonging to the field of intelligent monitoring technology. The method includes: introducing an online mirroring system to receive testing tasks and perform lightweight simulations to determine the testing chain, based on the testing process cycle; verifying and correcting based on personnel and equipment status; receiving test samples and assigning batch radio frequency coding (RFID) identification; and implementing batch-driven testing management and data storage on the testing chain through collaborative RFID tracking and visual monitoring to determine the testing data chain and establish a knowledge graph of testing through batch mining. This application solves the technical problem in existing technologies where isolated information and discontinuous data at each stage of the testing process make it difficult to monitor and track sample batch progress in real time, thus affecting testing efficiency. By introducing an online mirroring system combined with RFID identification for full-process sample tracking, the efficiency of disease testing is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology, and in particular to a method and system for batch progress monitoring of animal disease testing samples. Background Technology

[0002] Animal disease testing is a crucial link in ensuring livestock health and public health safety. Existing animal disease testing methods largely rely on manual registration, paper records, or decentralized information management systems. While these systems track and summarize the progress of sample collection, transportation, and laboratory testing to some extent, they still have significant shortcomings. Because information at each stage of the testing process is often independent, the lack of effective data integration and sharing mechanisms results in fragmented and isolated information for each sample batch, making real-time synchronous updates and comprehensive management across stages difficult. Secondly, current sample information entry and updates are mostly done manually or semi-automatically, making them susceptible to human error and posing risks of data loss, tampering, and duplicate recording, thus affecting the accuracy and reliability of testing results. Furthermore, the lack of a unified, intelligent progress monitoring platform makes it difficult to track sample batch progress accurately and in real time, leading to inefficient task scheduling and resource allocation, and hindering overall testing efficiency.

[0003] In summary, existing technologies suffer from technical problems such as isolated information and inconsistent data at each stage of the quarantine inspection process, making it difficult to monitor and track the progress of sample batches in real time and thus affecting the efficiency of the quarantine inspection process. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for monitoring the batch progress of animal disease testing samples, in order to solve the technical problem in the prior art that the batch progress of samples is difficult to monitor and track in real time due to the isolation of information and the lack of data continuity in each link of the disease testing process, thereby affecting the efficiency of the disease testing process.

[0005] In view of the above problems, this application provides a method and system for batch progress monitoring of animal disease testing samples.

[0006] Firstly, this application provides a method for monitoring the batch progress of animal disease testing samples. This method is implemented through a batch progress monitoring system for animal disease testing samples. The method includes: introducing an online mirroring system to receive testing tasks and perform lightweight online progress simulations to determine the testing chain; verifying and correcting the testing chain based on personnel and equipment status; receiving testing samples and performing batch radio frequency coding; and, through collaborative radio frequency tracking and visual monitoring, using the testing chain as a guide, performing batch testing-driven management and data storage on the testing samples to determine the testing data chain. This involves using node imprints as constraints to implement automatic imprint triggering and feedback response management for testing execution, and determining the knowledge association graph of the testing through batch mining.

[0007] Optionally, the equipment deployment information and personnel basic information of the epidemic prevention and control project are retrieved, and the simulated project space is determined by performing low-factor simulation, wherein the physical end information is simplified as the low-factor standard; historical epidemic prevention and control records are obtained, and the simulated project space is simulated and lightly adjusted to determine the online mirror system, wherein the preset simulation convergence degree is used as the light-weight standard.

[0008] Optionally, based on the simulated quarantine data, key quarantine nodes are identified, wherein the key quarantine nodes trigger automatic progress tracking; for each quarantine node, skill-driven task allocation is performed from both personnel and equipment perspectives, resilience assessment is conducted on task direction and quarantine progress, and physical fence conditions are determined; based on the simulated quarantine data, unstable quarantine nodes are identified, and an emergency expansion channel is introduced, wherein the emergency expansion channel is set to trigger on standby; based on the key quarantine nodes, physical fence conditions, and the emergency expansion channel, the quarantine chain is marked, and node imprints are generated.

[0009] Optionally, an encoding pattern is introduced, wherein the encoding pattern includes at least batch code elements, sample code elements, and task code elements; according to the encoding pattern, the test samples are encoded one by one to determine the batch radio frequency code.

[0010] Optionally, the first quarantine node is triggered, node task is issued, and a first task queue is determined; a first tracking node is generated through radio frequency scanning, a vision device is activated synchronously, the first task queue is executed, and the first task data is determined, wherein the first task data includes visual monitoring data and node quarantine data; the first task data is stored on the blockchain at the first tracking node, wherein structured key data is used as the data storage constraint.

[0011] Optionally, for the visual monitoring data, it is determined whether the node imprint of the first quarantine node is triggered; if not triggered, a first identifier of standard progress is generated; if triggered, a second identifier of delayed progress is generated, wherein the second identifier is associated with progress impact data based on delay tracing.

[0012] Optionally, the node quarantine data is verified to determine whether a new quarantine task is generated; if generated, the new quarantine task is inserted into the quarantine chain, wherein the insertion method is either inserting a new node or inserting an existing chain node.

[0013] Optionally, guided by the epidemic inspection chain, progressive triggering and task queue distribution based on epidemic inspection nodes are executed, and node tracking and monitoring and data storage on the chain are performed.

[0014] Optionally, the quarantine data chain is identified, and batch positive determination is performed, wherein each test sample corresponds to one quarantine data chain; if the determination result is positive, the same node mapping of each quarantine data chain is performed, abnormal quarantine data is located, and a knowledge association graph of quarantine results is constructed; wherein, by identifying the abnormal quarantine features of each node and calculating the proportion, the abnormal quarantine features of each node are associated and the feature proportion is marked as the knowledge association graph.

[0015] Secondly, this application also provides a batch progress monitoring system for animal disease testing samples, used to execute the batch progress monitoring method for animal disease testing samples as described in the first aspect. The batch progress monitoring system for animal disease testing samples includes: a disease testing chain determination module, used to introduce an online mirroring system for the disease testing project cycle, receive disease testing tasks, and perform lightweight online progress simulations to determine the disease testing chain; a sample identification module, used to verify and correct the disease testing chain based on personnel and equipment status, receive test samples, and perform batch radio frequency coding identification; and a knowledge association graph determination module, used to perform batch disease testing-driven management and data on-chain storage for the test samples through collaborative radio frequency tracking and visual monitoring, guided by the disease testing chain, to determine the disease testing data chain. Specifically, it uses node imprints as constraints to perform automatic imprint triggering and feedback response management for disease testing execution, and determines the disease testing knowledge association graph through batch mining.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects:

[0017] By introducing an online mirroring system to target the entire disease inspection process cycle, the system receives inspection tasks and performs lightweight online progress simulations to determine the inspection chain. For this chain, verification and correction are performed based on personnel and equipment status. Test samples are received and batch-specifically RFID-coded. Through collaborative RFID tracking and visual monitoring, guided by the inspection chain, batch-driven management and on-chain data storage are implemented for the test samples, determining the inspection data chain. This chain uses node imprints as constraints to implement automatic imprint triggering and feedback response management for inspection execution. Batch mining is used to determine the knowledge graph of the inspection process. In other words, by introducing an online mirroring system, real-time synchronization and lightweight simulation of the inspection chain data are achieved. Combined with personnel and equipment status verification and batch RFID coding, accurate tracking of disease testing samples throughout the entire process is realized. On-chain data storage ensures security and reliability, and the automatic node triggering mechanism improves real-time feedback efficiency, thus enhancing the overall efficiency of the disease inspection process.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the batch progress monitoring method for animal disease testing samples in this application.

[0021] Figure 2 This is a schematic diagram of the batch progress monitoring system for animal disease testing samples in this application.

[0022] Explanation of the attached diagram labels: Epidemic inspection chain determination module 11, sample identification module 12, knowledge association graph determination module 13. Detailed Implementation

[0023] This application provides a method and system for monitoring the batch progress of animal disease testing samples. It addresses the technical problem in existing technologies where isolated information and discontinuous data at each stage of the disease testing process make it difficult to monitor and track sample batch progress in real time, thus affecting the efficiency of the testing process. By introducing an online mirroring system to achieve real-time synchronization and lightweight simulation of the disease testing chain data, combined with personnel and equipment status verification and batch RFID coding, it achieves accurate tracking of disease testing samples throughout the entire process. On-chain data storage ensures security and reliability, and an automatic node triggering mechanism improves real-time feedback efficiency, thereby enhancing the overall efficiency of the disease testing process.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for monitoring the batch progress of animal disease testing samples. The method is executed through an animal disease testing sample batch progress monitoring system, and specifically includes the following steps:

[0026] S100: To address the quarantine project cycle, an online mirroring system is introduced to receive quarantine tasks and perform lightweight online progress simulations to determine the quarantine chain.

[0027] Furthermore, this application S100 includes:

[0028] The equipment deployment information and personnel basic information of the epidemic prevention and control project are retrieved, and the simulated project space is determined by performing low-factor simulation, wherein the physical end information is simplified as the low-factor standard; historical epidemic prevention and control records are obtained, and the simulated project space is simulated and lightly adjusted to determine the online mirror system, wherein the preset simulation convergence degree is used as the light-weight standard.

[0029] Specifically, the process involves retrieving equipment deployment information and personnel basic information for the disease control and prevention (DIV) project. Equipment deployment information is used to understand the distribution, performance, and operational status of the equipment, while personnel basic information is used to assess personnel capabilities, work status, and task allocation. The DIV project encompasses the entire process of detecting animal diseases, including sample collection, transportation, testing, and result aggregation. Equipment deployment information details the configuration, operation, and location of equipment used in the disease detection process, including the model of each testing instrument, its operating mode (e.g., automated or manual), and operating hours. This information is used to analyze whether the equipment meets DIV requirements and can support efficient disease detection. Personnel basic information includes the basic data of personnel involved in disease detection, such as name, position, qualifications, work experience, and work assignments. This information is used to assess whether personnel can efficiently perform their tasks.

[0030] By refining and simplifying equipment and personnel information, a low-feature simulation engineering space is constructed. Low-feature simulation refers to omitting unnecessary complex details when building the model, transforming it into a more basic or standardized form, and retaining only the key information affecting decisions and outcomes to improve simulation efficiency and response speed. This is achieved by simplifying the operating mechanisms of equipment and the actions of personnel, focusing only on the most basic operating parameters. For example, equipment details (such as circuits and drives) are ignored, retaining only key functional descriptions; only the working time, task priority, and skill level of each person are recorded, without needing to involve specific operational procedures.

[0031] Simplifying physical information to low-level standards means reducing the details of actual physical equipment or operational processes to abstract, essential parameters and standards to meet simulation and deduction needs. In other words, it involves further simplifying physical equipment information, abstracting complex equipment operating principles into parameters such as time, task, and status. For example, suppose in a certain epidemic prevention and control project, equipment deployment information includes: Equipment A: PCR testing equipment, maximum processing speed of 100 samples / hour, failure rate of 1%; Equipment B: Nucleic acid extraction equipment, maximum processing speed of 80 samples / hour, failure rate of 2%. Personnel basic information includes: Personnel C: 5 years of experience, advanced skill level, 8-hour workday; Personnel D: 2 years of experience, intermediate skill level, 8-hour workday. Equipment A and Equipment B are simplified to functional abstractions of PCR testing and nucleic acid extraction, respectively. Only their processing speed, failure rate, and testing cycle data are recorded, without simulating the internal operating details of the equipment. For Personnel C and Personnel D, only their working hours and skill level need to be recorded. During task allocation, higher-difficulty tasks are prioritized based on skill level.

[0032] Historical disease control records are acquired, encompassing data from past animal disease testing, including information on samples tested, testing procedures, personnel configuration, and equipment status. These records are then imported into a simulation engineering space for training and minor adjustments. Based on the data in these records, the disease control process under different scenarios is simulated. The operational status, time parameters, and task allocation methods for each step are adjusted according to the historical data to ensure the simulation results closely approximate reality. Repeated adjustments may be necessary during training until the behavior of the simulation engineering space closely matches historical records. Building upon the training, minor adjustments further optimize the simulation results. At this stage, the focus is on key parameters that significantly impact the progress of disease control (such as equipment processing speed and personnel efficiency), without addressing complex equipment details or operational procedures. These minor adjustments aim to improve system response speed and computational efficiency while ensuring the accuracy of the simulation results. Furthermore, the entire simulation engineering space no longer requires detailed descriptions of the structure of each piece of equipment; instead, it uses a component diagram to represent the equipment, with only key indicator labels attached.

[0033] The preset simulation convergence is a standard or threshold set during the simulation process to measure the accuracy and consistency of the simulation results. When the simulation results converge to the preset simulation convergence, the simulation is considered to have reached the expected accuracy, and further adjustments can be stopped. This includes adjusting the error range and the magnitude of result fluctuations, for example, the deviation between the predicted cycle of the simulated engineering space and the historical actual cycle should be less than 3%. The accuracy of the simulation results is measured according to the set preset simulation convergence standard. When the error or fluctuation of the simulation results decreases to the preset standard, it is considered to have reached an acceptable level of accuracy, and further simulation training and adjustments can be stopped. For example, by simulating the load and personnel configuration under different batches of tasks, the preset simulation convergence standard is set with an overall cycle error of <3%, controlling the end condition of training. If, after 10 rounds of training, the deviation between the predicted value and the historical average task cycle (e.g., 6 hours) is less than 10 minutes (i.e., <3%), the simulation model is considered stable and can be used for actual simulation. The result at this point is the online mirror system. The online mirror system can not only reflect various indicators in the epidemic inspection process in real time, but also respond in real time to changes in the progress of epidemic inspection under different conditions. By using low-factor simulation and lightweight adjustments, a simplified simulation space for epidemic prevention and control engineering is constructed, which can reflect the main processes and key elements of actual epidemic prevention and control engineering.

[0034] The quarantine project cycle refers to the entire time required for a complete animal disease testing project, from task assignment to result delivery, including stages such as sample collection, transportation, laboratory testing, data entry, and review and reporting. Introducing an online mirroring system allows for lightweight simulation of the entire quarantine cycle, thereby achieving precise control over the quarantine chain. Lightweight online progress simulation refers to using simplified parameters, models, and processes, employing only coarse-grained data such as key tasks, equipment status, and personnel workload to perform real-time reasoning, prediction, or scheduling of the quarantine process. For example, scheduling decisions can be made simply by considering whether equipment is currently idle and whether personnel are available to perform the task.

[0035] After a quarantine inspection task is issued, the system receives the task, including sample batch number, test type (e.g., avian influenza nucleic acid test), and deadline. It reads real-time status data, such as which inspectors are available, which equipment is running, and estimated completion time. Based on the task type and testing process template, it automatically generates a standard quarantine inspection path for that task. For example, the quarantine inspection path determined for a specific received task might be: Sample Receiving → Sample Sorting → Nucleic Acid Extraction → PCR Amplification → Data Review → Report Issuance. Not all tasks will have the same quarantine inspection chain. The online mirroring system does not deeply simulate the execution details of every device; instead, it uses a lightweight simulation approach to track only key elements, such as the quarantine inspection path, which tasks the equipment or personnel need to perform, progress status, and related trigger information. For example, it tracks which equipment and personnel are assigned to which sub-tasks; the progress status of each stage; whether there are task delays or blockages at each node; and which nodes, upon completion, will trigger the start of subsequent tasks.

[0036] Based on coarse-grained parameters, such as equipment idleness (0 / 1 only), personnel qualification, and completion of the previous node, the approximate cycle and progress bottlenecks of the entire process can be quickly deduced. By analyzing progress deviations (such as timeouts at certain nodes or equipment malfunctions), timely warnings or suggestions for adjusting the task flow are issued to determine the quarantine chain. The quarantine chain is the task path from receiving samples to issuing reports, including different task nodes (such as sample transportation, nucleic acid extraction, PCR testing, review and issuance, etc.), and the status changes of each node reflect the overall task progress. By introducing an online mirroring system and combining it with simplified task parameters and progress node identification mechanisms, the task quarantine chain is constructed in real time, connecting all nodes from sample reception to result reporting, helping to optimize workflows and reduce human error and resource waste.

[0037] Furthermore, this application also includes the following steps:

[0038] Based on the simulated quarantine data, key quarantine nodes are identified, and these key nodes trigger automatic progress tracking. For each quarantine node, skill-driven task allocation is performed from both personnel and equipment perspectives. Resilience assessments are conducted on task direction and quarantine progress to determine physical fencing conditions. Based on the simulated quarantine data, unstable quarantine nodes are identified, and an emergency expansion channel is introduced, which has a backup trigger. According to the key quarantine nodes, physical fencing conditions, and the emergency expansion channel, the quarantine chain is marked, and node imprints are generated.

[0039] Specifically, based on the simulated epidemic prevention and control data, key epidemic prevention and control nodes are identified. These are nodes that play a crucial role in the disease detection process (e.g., those that connect preceding and following steps, have high task density, or are highly risk-sensitive). These typically include bottlenecks, result determination points, or task switching points. The completion status of these key nodes significantly impacts the overall progress. When the task execution progresses to a key epidemic prevention and control node, automatic progress tracking is implemented, recording the current task number, whether sample transfer has arrived as expected, whether the node started execution on time as planned, and whether node resource configuration is complete. Automatic progress tracking means that when a key node task is completed, information such as time, status, and personnel involved is automatically recorded for process progress confirmation.

[0040] For each quarantine checkpoint, tasks are allocated from two main dimensions: personnel and equipment. This means considering personnel skills, scheduling, and availability, as well as equipment capabilities, occupancy, and maintenance status. Specifically, tasks are dynamically allocated based on personnel skill profiles (e.g., PCR operation skills) and equipment status to avoid task delays caused by low matching. For example, PCR amplification must be performed by personnel with PCR operation certificates on a temperature-controlled thermal cycler. First, the checkpoint task corresponding to the current batch of samples is retrieved, along with a list of currently on-duty personnel and equipment status, including: personnel qualification level (e.g., PCR qualification A, B); current workload (e.g., employee A has completed 4 batches in this period); and equipment operating status (e.g., equipment A is currently temperature-controlled and stable, equipment B requires maintenance). Using approximate matching rules, skill-driven task allocation is performed, prioritizing tasks assigned to personnel whose skills perfectly match the task, and to equipment with redundant capacity and appropriate workload.

[0041] A resilience assessment is conducted on the mission direction and quarantine progress to determine whether the mission can maintain its progress in the face of disturbances (such as equipment failure or sudden manpower shortages), allowing for a certain degree of tolerance for deviation. Each task node is scored to analyze its recoverability in the event of emergencies, including the availability of alternative equipment, substitute personnel, and permissible delay time, resulting in a resilience level, categorized as strong, moderate, or weak.

[0042] Based on resilience assessment, physical fence conditions are determined, which are the set control boundary conditions. Once the task status deviates from the normal range (such as excessive delays, personnel shortages, equipment overload, etc.), automatic control mechanisms such as early warnings and rescheduling will be triggered. Physical fence conditions include time fences (such as automatic alarms when a node executes for more than 25 minutes), resource fences (if a node has no alternative equipment, a resource scheduling priority strategy is triggered), and process deviation fences (if the task execution process is skipped or retried, it enters the manual review channel), etc.

[0043] Based on the simulated quarantine data, unstable quarantine nodes are identified, which are steps that are unstable in operation and prone to becoming bottlenecks or sources of failure, such as frequent equipment failures, high staff vacancy rates, and large backlogs of samples. For example, a node with an average delay rate higher than 25% is considered a candidate node for instability; equipment with a failure history frequency exceeding 3 times is considered unstable; a current task backlog exceeding 110% is considered task overload; and a staff shortage exceeding 30% is considered a risk of instability. If a node has a delay greater than 20 minutes in 35% of the last 10 rounds of tasks, or if the equipment used has failed 5 times within the month, it is marked as an unstable quarantine node.

[0044] Once an unstable quarantine node is identified, an emergency expansion channel is automatically activated based on the current available resources. This is a redundancy mechanism used to temporarily allocate additional resources (personnel, equipment, facilities, network, etc.) and can be triggered when a node becomes unstable to ensure uninterrupted processes. For example, a backup extraction device is activated (e.g., transferred from another workshop); the number of parallel tasks on existing equipment is increased from 48 to 64 samples (adjusting the time interval and extraction load); backup shifts are scheduled to provide support; the task relay mechanism within the platform is activated, with tasks pushed directly to the expansion node after completion by the previous node; quarantine tasks are split and rearranged, with high-priority batches prioritized for transfer to the expansion node, and low-priority batches delayed by one hour.

[0045] The emergency expansion channel serves as a backup trigger mechanism, activated only when certain conditions are met (such as progress deviation from thresholds, resource collapse, or node downtime). By combining key quarantine nodes, physical fence conditions, and the emergency expansion channel, the quarantine chain is marked and node imprints are generated. For example, during the actual quarantine process, reaching a key node automatically triggers the key node imprint; deviating from the preset path automatically triggers the fence condition. Resilience can be understood as the tolerance for deviation. By locating key quarantine nodes and setting up an automatic progress tracking mechanism, quarantine progress is monitored in real time, allowing for timely identification and resolution of problems. Skill-driven task allocation and physical fence condition settings optimize workflows and improve quarantine efficiency. The introduction of the emergency expansion channel ensures the smooth operation of the quarantine process, enhancing its reliability and resilience. The generation of node imprints records the status and progress information of key nodes during the quarantine process, facilitating tracking and auditing.

[0046] S200: For the aforementioned quarantine chain, verify and correct the status of personnel and equipment, receive test samples, and perform batch radio frequency coding identification.

[0047] Furthermore, this application S200 includes:

[0048] An encoding pattern is introduced, wherein the encoding pattern includes at least batch code elements, sample code elements, and task code elements; the test samples are encoded one by one according to the encoding pattern to determine the batch radio frequency code.

[0049] Specifically, for a defined quarantine chain, the system automatically checks the current status of quarantine personnel and equipment, and verifies and corrects the quarantine chain to ensure that personnel and equipment can meet the requirements of the quarantine task. In other words, before executing the quarantine chain, the planned personnel / equipment configuration is compared with its current status, and if any discrepancies are found (such as equipment being under maintenance or personnel temporarily leaving their posts), dynamic adjustments are made.

[0050] A coding scheme is introduced to uniquely identify quarantine samples and their contextual information. This scheme includes batch codes, sample codes, and task codes. The batch code indicates the batch to which the sample belongs, identifying all samples belonging to the same quarantine batch. The sample code distinguishes each specific sample. The task code identifies the current testing task of the sample. Based on the coding scheme, each sample is individually coded to determine its batch RFID code. For example, a sample might be coded as 0614A-0321-RCRN1, where 0614A is the batch code, representing the testing date and number; 0321 is the sample code, indicating the 321st sample in the batch; and PCRN1 is the task code, indicating PCR extraction and path N1. Upon arrival at the receiving area, the RFID writer completes the coding operation, writing a unique RFID code to each sample, binding the tag, and attaching it to the sample carrier.

[0051] Verification and correction based on personnel and equipment status ensures the smooth progress of quarantine inspection tasks and avoids delays caused by personnel and equipment issues. An encoding system is introduced to uniquely identify and track each sample, improving the accuracy and efficiency of sample management. Batch RFID encoding using radio frequency technology enables rapid reading and automated management of sample information, further enhancing the efficiency of the quarantine inspection process.

[0052] S300: Through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine-driven management and data storage on the chain are performed on the test samples to determine the quarantine data chain. Among them, with node imprints as constraints, automatic triggering of imprints and feedback response management of quarantine execution are performed. The knowledge association graph of quarantine is determined through batch mining.

[0053] Furthermore, this application S300 includes:

[0054] The first quarantine node is triggered, the node task is issued, and the first task queue is determined; the first tracking node is generated through radio frequency scanning, the vision device is activated synchronously, the first task queue is executed and the first task data is determined, wherein the first task data includes visual monitoring data and node quarantine data; the first task data is stored on the blockchain at the first tracking node, wherein structured key data is used as the data storage constraint.

[0055] Specifically, triggering the first quarantine checkpoint initiates the quarantine process and issues the first task queue. The first quarantine checkpoint is the first concrete operational step in the quarantine process, such as initial sample screening or information registration, depending on the specific quarantine task. As the starting point of the quarantine chain, the first quarantine checkpoint marks the beginning of the process. Task issuance refers to assigning specific operational tasks to a particular quarantine checkpoint according to the task plan, including the assigned operators, task parameters, and execution equipment. The first task queue refers to the sequence of tasks to be executed generated at the first quarantine checkpoint.

[0056] By scanning animal disease testing samples via video, the system automatically records the time and location of each sample entering the first quarantine checkpoint, generating information for the first tracking node. The first tracking node represents the first traceable processing point of the RFID tag after the sample enters the quarantine process, marking the beginning of the sample processing. RFID scanning typically uses RFID readers to scan and identify samples with electronic tags (RFID tags), thus generating the sample information record for the first tracking node. When a sample arrives at the first quarantine checkpoint, operators use an RFID reader for batch scanning, automatically identifying the sample number, batch code, and task category, marking the sample as having entered the quarantine chain.

[0057] Activate vision devices, including cameras and image acquisition units, to acquire images of the sample surface (e.g., whether blood sample tubes are contaminated, whether tags have fallen off, or whether there is abnormal color precipitation). Radio frequency scanning can obtain RFID tag information on the sample, while vision devices can be used to monitor and record the task execution process. Record the time, location, and operator information of the sample's first confirmation, generating the sample's first tracking record. Execute the first task queue, which includes a series of tasks to be performed, such as sample reception, information entry, and preliminary testing. During task execution, collect the generated first task data, including visual monitoring data and node quarantine data.

[0058] Based on radio frequency scanning and vision equipment, a first task queue is executed, comprising a series of tasks to be performed. During task execution, the generated first task data is collected, including visual monitoring data and node quarantine data. The visual monitoring data can be analyzed and processed using image recognition technology to extract key information from the task execution process. The node quarantine data includes the results of task execution and the status information of relevant equipment.

[0059] The first task data is processed by extracting key fields according to a preset structured template and stored on the blockchain at the first tracking node, forming an immutable data record. On-chain data storage refers to writing the first task data into an immutable distributed ledger system (such as blockchain) to achieve functions such as traceability, verification, and tamper-proofing. Structured key data consists of data content with standard field formats, such as sample number, testing time, and image analysis score. Through the synchronous activation of RF scanning and vision equipment, the task execution process is monitored and recorded in real time, ensuring the smooth progress of the task. On-chain data storage ensures the immutability and traceability of the data, improving data security and reliability. Structured key data facilitates querying and analysis, further improving the efficiency of the epidemic prevention and control project.

[0060] Furthermore, this application also includes the following steps:

[0061] For the visual monitoring data, determine whether the node imprint of the first quarantine node is triggered; if not triggered, generate a first identifier for standard progress; if triggered, generate a second identifier for delayed progress, wherein the second identifier is associated with progress impact data based on delay tracing.

[0062] Specifically, the visually monitored data collected is analyzed in real time to determine the task execution status of the first quarantine inspection node. The visually monitored data is compared with the aforementioned node imprint rules to determine whether to trigger imprints such as critical nodes, physical fence conditions, and emergency expansion channels. If the node imprint is not triggered, it is considered that the sample status is normal, and the task progresses as scheduled, generating a first identifier. If the node imprint is triggered, it is determined that there is a delay in progress, generating a second identifier, accompanied by progress impact data for traceability. The second identifier indicates that there is a progress delay of the sample at this node and is associated with a set of progress impact data, including abnormal image numbers, problem types, equipment numbers, delay time estimates, etc. For example, the number of samples triggering the node imprint: 38 batches (accounting for 19%); the number of standard progress samples not triggered: 162 batches (accounting for 81%); the average estimated delay time ranges from 120 to 240 seconds, and the main reasons for the delay are mostly label recognition failures and placement deviations. By determining whether the visually monitored data triggers the node imprint of the first quarantine inspection node, the task execution status is monitored in real time, and delays are promptly discovered and processed. Generating the first identifier for the standard progress indicates that the task is completed on time, and the quarantine inspection process can proceed smoothly to the next node. Generating the second identifier for the delayed progress and associating it with the progress impact data based on delay traceability is used to improve the quarantine inspection process and increase efficiency.

[0063] Furthermore, the present application further includes the following steps:

[0064] Verify the node quarantine inspection data to determine whether a new quarantine inspection task is generated; if so, insert the new quarantine inspection task into the quarantine inspection chain, where the insertion method is either inserting a new node or inserting into an existing node in the chain.

[0065] Specifically, verify the node quarantine inspection data to automatically identify whether there are detection defects or abnormalities. If the verification determines that it is qualified, there is no need to add a new task, and the process progresses normally. If the verification determines that supplementary detection is required, a new quarantine inspection task is automatically generated, including re-inspection, special inspection, or quality review. Based on the detection data of the first node, determine whether a certain inspection needs to be added. For example, a certain detected lesion may be accompanied by complications, so a new inspection is required.

[0066] Insert the new quarantine inspection task into the quarantine inspection chain. The insertion strategy is divided into two categories according to the specific situation, namely inserting a new node or inserting into an existing node in the chain. Inserting a new node means adding a complete node to the current quarantine inspection chain, such as adding a review test, and arranging the new task at this node, that is, adding a node to the quarantine inspection chain to execute the new quarantine inspection task. Inserting into an existing node in the chain means adding a subtask inside an existing node, such as performing a secondary test or supplementary test on the sample of this node, without changing the chain structure, that is, inserting the new quarantine inspection task at the existing node in the quarantine inspection chain. [[ID=!4]]

[0067] If the abnormal test data is severe or affects the accuracy of the overall test results, and a separate testing process is required to verify or supplement the information, then a new node should be added. If the abnormality is a minor deviation or a localized indicator anomaly, and only requires repeat testing or supplementary testing within the original testing node, then the original chain node should be added. Adding a new node means adding a step to the testing process, which may bring additional resource allocation and time costs; if resources are tight or the abnormality is not severe, the original chain node should be prioritized. Update the status of the quarantine chain to ensure that new tasks can be executed and the quarantine process can proceed smoothly. By verifying the quarantine data of the nodes, abnormalities can be detected in a timely manner, and new quarantine tasks can be generated for processing. Inserting new tasks into the quarantine chain ensures that abnormalities are handled in a timely manner and do not affect the smooth progress of the quarantine process. By updating the status of the quarantine chain, it is ensured that new tasks can be executed, improving the flexibility and adaptability of the quarantine process.

[0068] Furthermore, this application also includes the following steps:

[0069] Guided by the aforementioned quarantine chain, progressive triggering and task queue distribution based on quarantine nodes are executed, along with node tracking and monitoring and data storage on the chain.

[0070] Specifically, the quarantine inspection chain serves as a guide, triggering the execution of tasks at each quarantine inspection node progressively according to the task sequence within the chain. Before each node's task execution, a task queue is issued, clarifying task requirements and assigning tasks to the corresponding executors or equipment. Each quarantine inspection node is tracked and monitored, recording key data and status information during task execution. The tracked and monitored data, including task status and execution results, is stored on the blockchain to ensure data immutability and traceability. Key node task data (such as test results, timestamps, and operation records) is structured and then written into the blockchain storage to guarantee data immutability. The above steps are repeated until all tasks in the quarantine inspection chain are completed. For example, suppose the quarantine inspection chain contains three quarantine inspection nodes: sample receiving, laboratory testing, and result reporting. First, the sample receiving node's task execution is triggered, issuing a task queue to the sample receiving personnel and simultaneously starting node status tracking and monitoring. After sample receiving is completed, the data is stored on the blockchain, triggering the next node, laboratory testing. After laboratory testing is completed, the data is also stored on the blockchain, triggering the final node, result reporting. Throughout the process, the quarantine inspection chain guides the progressive triggering of tasks and the on-chain storage of data, ensuring the orderly progress of the quarantine inspection process. Through progressive task triggering, the quarantine inspection process is automated, the process is seamlessly connected, human error is reduced, and the dynamic scheduling of task queues improves resource utilization and optimizes testing efficiency.

[0071] Furthermore, this application also includes the following steps:

[0072] The quarantine data chain is identified, and batch positive determination is performed, wherein each test sample corresponds to one quarantine data chain; if the determination result is positive, the same node mapping of each quarantine data chain is performed, abnormal quarantine data is located, and a knowledge association graph of quarantine results is constructed; wherein, by identifying the abnormal quarantine features of each node and calculating the proportion, the abnormal quarantine features of each node are associated and the feature proportion is marked as the knowledge association graph.

[0073] Specifically, by implementing batch-driven quarantine management and on-chain data storage for tested samples, a quarantine data chain is obtained. This chain represents a series of data sets generated during the quarantine process for each tested sample, encompassing the complete data record chain from sample collection and testing to the results. A batch positive determination is then performed on the quarantine data chains of all tested samples in a batch. This involves a unified assessment of the test results for a batch of samples to identify which samples tested positive (exhibiting disease markers). In other words, a centralized assessment of the test results for a batch of samples identifies which samples tested positive, confirming that they carry disease markers or pathogens.

[0074] Batch data analysis techniques (such as batch statistical analysis) are used to analyze the quarantine data chains of all samples to determine whether a sample is positive, usually based on a threshold. If the result indicates a positive result, the quarantine data chains corresponding to all positive samples are mapped node-to-node, that is, the test data of the same testing node are compared. The data of each node is matched and compared from different quarantine data chains to locate abnormal quarantine data. In other words, the same testing stage or testing link (i.e., node) in the quarantine data chains of different samples is compared and mapped to find out the performance of these nodes in multiple sample data. This refers to identifying abnormal data at the same node through comparison and analysis, such as abnormal test values, equipment abnormalities, process abnormalities, etc. For example, abnormally high virus concentration, abnormally long testing time, abnormal equipment downtime, etc. in a certain testing link.

[0075] The proportion of anomalous quarantine features at each node is calculated, i.e., the frequency or percentage of their occurrence in the entire sample set (e.g., the anomalous feature accounts for 15% of all samples), to reflect their prevalence. Proportion calculation involves statistically analyzing the proportion of a particular anomalous feature in all samples or nodes, used to quantify the prevalence and impact of the anomalous feature. The anomalous features and their proportions at different nodes are then organized chronologically or causally to construct a knowledge graph, illustrating the relationships and influence paths between the anomalous features. Finally, the anomalous features at different nodes and their interrelationships are represented graphically, forming a knowledge network that reveals the inherent connections and potential patterns in the anomalous data.

[0076] By mapping nodes together and locating anomalies, the system accurately identifies potential anomalies in the positive sample quarantine process, improving its transparency and controllability. The constructed knowledge graph structures and visualizes complex anomaly features, facilitating rapid problem localization and process optimization, ultimately enhancing quarantine efficiency and accuracy.

[0077] In summary, the batch progress monitoring method for animal disease testing samples provided in this application has the following characteristics:

[0078] Beneficial effects:

[0079] By introducing an online mirroring system to target the entire disease inspection process cycle, the system receives inspection tasks and performs lightweight online progress simulations to determine the inspection chain. For this chain, verification and correction are performed based on personnel and equipment status. Test samples are received and batch-specifically RFID-coded. Through collaborative RFID tracking and visual monitoring, guided by the inspection chain, batch-driven management and on-chain data storage are implemented for the test samples, determining the inspection data chain. This chain uses node imprints as constraints to implement automatic imprint triggering and feedback response management for inspection execution. Batch mining is used to determine the knowledge graph of the inspection process. In other words, by introducing an online mirroring system, real-time synchronization and lightweight simulation of the inspection chain data are achieved. Combined with personnel and equipment status verification and batch RFID coding, accurate tracking of disease testing samples throughout the entire process is realized. On-chain data storage ensures security and reliability, and the automatic node triggering mechanism improves real-time feedback efficiency, thus enhancing the overall efficiency of the disease inspection process.

[0080] Example 2: Based on the same inventive concept as the batch progress monitoring method for animal disease testing samples in Example 1, this application also provides a batch progress monitoring system for animal disease testing samples. Please refer to the appendix. Figure 2 The batch progress monitoring system for animal disease testing samples includes:

[0081] The quarantine chain determination module 11 is used to determine the quarantine chain by introducing an online mirroring system for the quarantine engineering cycle, receiving quarantine tasks and performing lightweight online progress simulations. The sample identification module 12 is used to verify and correct the quarantine chain based on personnel and equipment status, receive test samples, and perform batch RFID identification. The knowledge association graph determination module 13 is used to determine the quarantine data chain by performing batch quarantine-driven management and data on-chain storage for the test samples through collaborative RFID tracking and visual monitoring, guided by the quarantine chain. The module uses node imprints as constraints to perform automatic imprint triggering and feedback response management for quarantine execution, and determines the quarantine knowledge association graph through batch mining.

[0082] Furthermore, the disease detection chain determination module 11 in the batch progress monitoring system for animal disease testing samples is also used for:

[0083] The equipment deployment information and personnel basic information of the epidemic prevention and control project are retrieved, and the simulated project space is determined by performing low-factor simulation, wherein the physical end information is simplified as the low-factor standard; historical epidemic prevention and control records are obtained, and the simulated project space is simulated and lightly adjusted to determine the online mirror system, wherein the preset simulation convergence degree is used as the light-weight standard.

[0084] Furthermore, the disease detection chain determination module 11 in the batch progress monitoring system for animal disease testing samples is also used for:

[0085] Based on the simulated quarantine data, key quarantine nodes are identified, and these key nodes trigger automatic progress tracking. For each quarantine node, skill-driven task allocation is performed from both personnel and equipment perspectives. Resilience assessments are conducted on task direction and quarantine progress to determine physical fencing conditions. Based on the simulated quarantine data, unstable quarantine nodes are identified, and an emergency expansion channel is introduced, which has a backup trigger. According to the key quarantine nodes, physical fencing conditions, and the emergency expansion channel, the quarantine chain is marked, and node imprints are generated.

[0086] Furthermore, the sample identification module 12 in the batch progress monitoring system for animal disease testing samples is also used for:

[0087] An encoding pattern is introduced, wherein the encoding pattern includes at least batch code elements, sample code elements, and task code elements; the test samples are encoded one by one according to the encoding pattern to determine the batch radio frequency code.

[0088] Furthermore, the knowledge association graph determination module 13 in the batch progress monitoring system for animal disease testing samples is also used for:

[0089] The first quarantine node is triggered, the node task is issued, and the first task queue is determined; the first tracking node is generated through radio frequency scanning, the vision device is activated synchronously, the first task queue is executed and the first task data is determined, wherein the first task data includes visual monitoring data and node quarantine data; the first task data is stored on the blockchain at the first tracking node, wherein structured key data is used as the data storage constraint.

[0090] Furthermore, the knowledge association graph determination module 13 in the batch progress monitoring system for animal disease testing samples is also used for:

[0091] For the visual monitoring data, determine whether the node imprint of the first quarantine node is triggered; if not triggered, generate a first identifier for standard progress; if triggered, generate a second identifier for delayed progress, wherein the second identifier is associated with progress impact data based on delay tracing.

[0092] Furthermore, the knowledge association graph determination module 13 in the batch progress monitoring system for animal disease testing samples is also used for:

[0093] The node's epidemic inspection data is verified to determine whether a new epidemic inspection task should be generated. If generated, the new epidemic inspection task is inserted into the epidemic inspection chain, wherein the insertion method is either inserting a new node or inserting an existing chain node.

[0094] Furthermore, the knowledge association graph determination module 13 in the batch progress monitoring system for animal disease testing samples is also used for:

[0095] Guided by the aforementioned quarantine chain, progressive triggering and task queue distribution based on quarantine nodes are executed, along with node tracking and monitoring and data storage on the chain.

[0096] Furthermore, the knowledge association graph determination module 13 in the batch progress monitoring system for animal disease testing samples is also used for:

[0097] The quarantine data chain is identified, and batch positive determination is performed, wherein each test sample corresponds to one quarantine data chain; if the determination result is positive, the same node mapping of each quarantine data chain is performed, abnormal quarantine data is located, and a knowledge association graph of quarantine results is constructed; wherein, by identifying the abnormal quarantine features of each node and calculating the proportion, the abnormal quarantine features of each node are associated and the feature proportion is marked as the knowledge association graph.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The batch progress monitoring method and specific examples for animal disease testing samples in Example 1 are also applicable to the batch progress monitoring system for animal disease testing samples in this example. Through the foregoing detailed description of the batch progress monitoring method for animal disease testing samples, those skilled in the art can clearly understand the batch progress monitoring system for animal disease testing samples in this example. Therefore, for the sake of brevity, it will not be described in detail here.

[0099] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0100] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for batch progress monitoring of animal disease detection samples, characterized in that, Comprise: For the epidemic detection engineering cycle, an online mirror system is introduced to receive epidemic detection tasks and perform lightweight deduction of online progress, and determine the epidemic detection chain; For the epidemic detection chain, according to the personnel state and equipment state, the detection sample is received and batch radio frequency coding identification is performed; Through collaborative radio frequency tracking and visual monitoring, the batch epidemic detection driving management and data on-chain storage of the detection sample are performed for the epidemic detection chain, and the epidemic detection data chain is determined, wherein the node mark is constrained, the mark automatic triggering and feedback response management of epidemic detection execution are performed, and the knowledge association graph of epidemic detection is determined through batch mining; For the epidemic detection engineering cycle, an online mirror system is introduced, and the construction of the online mirror system comprises: Call the equipment deployment information and personnel basic information of the epidemic detection engineering, determine the simulation engineering space by performing low-element simulation, wherein the information simplification of the physical end is the low-element standard; Get the historical epidemic detection record, and perform simulation training and lightweight adjustment on the simulation engineering space to determine the online mirror system, wherein the preset simulation convergence degree is the lightweight standard; Lightweight deduction of online progress refers to using only key tasks, equipment state, and personnel load coarse-grained data to perform real-time reasoning, prediction or scheduling on the epidemic detection process by simplifying parameters, models and processes; After the epidemic detection task is issued, the epidemic detection task is received, including sample batch number, detection type and deadline; real-time state data is read, specifically which detection personnel is idle, which equipment is running and estimated completion time; according to the task type and detection process template, the standard epidemic detection path of the task is automatically generated; After determining the epidemic detection chain, it includes: For the deduced epidemic detection data, locate the key epidemic detection node, wherein the key epidemic detection node triggers progress automatic clock-in; For each epidemic detection node, perform skill-driven task allocation from the personnel dimension and equipment dimension, assess the task direction and epidemic detection progress for resilience, and determine the physical fence condition; For the deduced epidemic detection data, locate the unstable epidemic detection node, and introduce an emergency expansion channel, wherein the emergency expansion channel is triggered in standby mode; According to the key epidemic detection node, the physical fence condition and the emergency expansion channel, the epidemic detection chain is marked, and the node mark is generated.

2. The method of claim 1, wherein the animal disease detection sample batch progress monitoring method is characterized by, Introduce a coding mode, wherein the coding mode at least contains batch code elements, sample code elements and task code elements; According to the coding mode, the detection sample is encoded one by one to determine the batch radio frequency coding.

3. The method of claim 1, wherein the animal disease detection sample batch progress monitoring method is characterized by, Through collaborative radio frequency tracking and visual monitoring, the batch epidemic detection driving management and data on-chain storage of the detection sample are performed for the epidemic detection chain, including: Trigger the first epidemic detection node to perform node task issuance and determine the first task queue; Generate a first tracking node by radio frequency scanning, synchronously activate the visual equipment, execute the first task queue and determine the first task data, wherein the first task data includes visual monitoring data and node epidemic detection data; The first task data is stored on the first tracking node, wherein the structured key data is the data storage constraint condition.

4. The batch progress monitoring method for animal disease testing samples as described in claim 3, characterized in that, After determining the first task data, it includes: determining whether to trigger a node mark of the first epidemic detection node according to the visual monitoring data; generating a first identifier of a standard progress if the trigger is not triggered; generating a second identifier of a delayed progress if the trigger is triggered, wherein the second identifier is associated with progress influence data based on delay tracing.

5. The batch progress monitoring method for animal disease testing samples as described in claim 4, characterized in that, After determining the first task data, the method comprises: verifying the node epidemic detection data to determine whether to generate a new epidemic detection task; if the new epidemic detection task is generated, inserting the new epidemic detection task into the epidemic detection chain, wherein the insertion method is a new node insertion or an original chain node insertion.

6. The batch progress monitoring method for animal disease testing samples as described in claim 5, characterized in that, Based on the epidemic detection chain, progressive triggering based on the epidemic detection node and task queue issuing are performed, node tracking monitoring and data on-chain storage are performed.

7. The batch progress monitoring method for animal disease testing samples as described in claim 1, characterized in that, Through batch mining, a knowledge association graph of epidemic detection is determined, comprising: identifying the epidemic detection data chain and performing batch positive determination, wherein each detection sample corresponds to an epidemic detection data chain; if the determination result is yes, performing same node mapping of each epidemic detection data chain, locating abnormal epidemic detection data, and constructing a knowledge association graph of epidemic detection results; wherein, by identifying the abnormal epidemic detection characteristics of each node and performing proportion calculation, the abnormal epidemic detection characteristics of each node are associated and the characteristic proportion is identified as the knowledge association graph.

8. A batch progress monitoring system for animal disease detection samples, characterised in that, The steps for implementing the batch progress monitoring method of the animal epidemic detection sample according to any one of claims 1 to 7, the batch progress monitoring system of the animal epidemic detection sample comprises: an epidemic detection chain determination module for introducing an online mirror system for receiving epidemic detection tasks and performing lightweight deduction of online progress for an epidemic detection project cycle to determine an epidemic detection chain; a sample identification module for receiving detection samples and performing batch radio frequency coding identification according to personnel state and equipment state for the epidemic detection chain; a knowledge association graph determination module for performing batch epidemic detection driving management and data on-chain storage of the detection samples based on the epidemic detection chain through collaborative radio frequency tracking and visual monitoring, determining an epidemic detection data chain, wherein, under the constraint of a node mark, feedback response management of mark automatic triggering and epidemic detection execution is performed, and a knowledge association graph of epidemic detection is determined through batch mining.

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