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

Abstract

The invention provides a batch progress monitoring method and system for animal epidemic disease detection samples, and relates to the technical field of intelligent monitoring, and the method comprises the steps: introducing an online mirror image system for an epidemic detection project period, receiving an epidemic detection task, carrying out lightweight deduction, and determining an epidemic detection chain; performing rechecking and correction according to the personnel state and the equipment state, receiving the detection sample and performing batch radio frequency code identification; through cooperation of radio frequency tracking and visual monitoring, with an epidemic detection chain as a guide, batch epidemic detection driving management and data uplink storage are performed on a detection sample, an epidemic detection data chain is determined, and a knowledge association graph of epidemic detection is determined through batch mining. The technical problem that in the prior art, the sample batch progress is difficult to accurately monitor and track in real time due to information isolation and data incoherence of all links of the epidemic detection process, and the epidemic detection efficiency is affected is solved, the sample whole-process tracking is carried out by introducing the online mirror image system and combining the radio frequency code identification, and the efficiency of epidemic detection is improved. And the epidemic disease detection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring technology, and in particular to a batch progress monitoring method and system for animal disease detection samples. Background Art

[0002] Animal disease detection is a crucial component of safeguarding livestock health and public health safety. Existing animal disease detection methods often rely on manual registration, paper records, or decentralized information management systems. Tracking and summarizing the progress of multiple steps, such as sample collection, transportation, and laboratory testing, has achieved, to a certain extent, the management of sample batches, but many deficiencies remain. Because the information at each step of the quarantine inspection process in existing methods is often independent of one another and lacks an effective data integration and sharing mechanism, the relevant information on sample batches is scattered and isolated, making it difficult to achieve real-time synchronous updates and comprehensive management across steps. Secondly, existing sample information entry and updates are often manual or semi-automated, which is susceptible to human factors and carries the risk of data loss, tampering, and duplicate records, impacting the accuracy and credibility of quarantine inspection results. Furthermore, the lack of a unified, intelligent progress monitoring platform makes it difficult to accurately track the progress of sample batches in real time, resulting in inefficient quarantine inspection task scheduling and resource allocation, which restricts the improvement of overall quarantine inspection efficiency.

[0003] In summary, the existing technology has technical problems such as isolated information and discontinuous data in various links of the quarantine inspection process, which makes it difficult to accurately monitor and track the progress of sample batches in real time, thereby affecting the efficiency of the quarantine inspection process. Summary of the Invention

[0004] The purpose of this application is to provide a batch progress monitoring method and system for animal disease detection samples, so as to solve the technical problem in the existing technology that due to the isolation of information and incoherent data in each link of the quarantine inspection process, the progress of sample batches is difficult to be accurately monitored and tracked in real time, thereby affecting the efficiency of the quarantine inspection process.

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

[0006] In the first aspect, the present application provides a batch progress monitoring method for animal disease detection samples, which is implemented by a batch progress monitoring system for animal disease detection samples, wherein the batch progress monitoring method for animal disease detection samples includes: for the quarantine project cycle, introducing an online mirror system, receiving quarantine tasks and performing lightweight deduction of online progress, and determining the quarantine chain; for the quarantine chain, reviewing and correcting according to personnel status and equipment status, receiving test samples and performing batch radio frequency coding identification; through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, performing batch quarantine drive management and data chain storage on the test samples, and determining the quarantine data chain, wherein, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the quarantine execution is managed, and the knowledge association graph of the quarantine is determined through batch mining.

[0007] Optionally, the equipment deployment information and basic personnel information of the quarantine project are retrieved, and the simulated engineering space is determined by performing low-element simulation, wherein the information on the physical end is simplified to a low-element standard; historical quarantine records are obtained, and simulation training and lightweight adjustments are performed on the simulated engineering space to determine the online mirror system, wherein a preset simulation convergence degree is used as a lightweight standard.

[0008] Optionally, based on the deduced quarantine data, key quarantine nodes are located, wherein the key quarantine nodes trigger automatic check-in of the progress; for each quarantine node, skill-driven task allocation is performed from the personnel dimension and the equipment dimension, the task direction and quarantine progress are evaluated for resilience, and the physical fence conditions are determined; based on the deduced quarantine data, unstable quarantine nodes are located, and emergency expansion channels are introduced, wherein the emergency expansion channels are triggered in standby; according to the key quarantine nodes, physical fence conditions and the emergency expansion channels, the quarantine chain is marked, and a node imprint is generated.

[0009] Optionally, a coding mode is introduced, wherein the coding mode at least includes a batch code element, a sample code element, and a task code element; according to the coding mode, the detection samples are encoded one by one to determine the batch radio frequency code.

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

[0011] Optionally, based on the visual monitoring data, determine whether the node imprint of the first quarantine node is triggered; if not triggered, generate a first identifier of the standard progress; if triggered, generate a second identifier of the delayed progress, wherein the second identifier is associated with the 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, where the insertion method is new node insertion or original chain node insertion.

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

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

[0015] In the second aspect, the present application also provides a batch progress monitoring system for animal disease detection samples, which is used to execute the batch progress monitoring method for animal disease detection samples as described in the first aspect, wherein the batch progress monitoring system for animal disease detection samples includes: an inspection chain determination module, which is used to introduce an online mirror system for the inspection project cycle, receive inspection tasks and perform lightweight deduction of online progress, and determine the inspection chain; a sample identification module, which is used to review and correct the inspection chain according to personnel status and equipment status, receive inspection samples and perform batch radio frequency coding identification; a knowledge association graph determination module, which is used to perform batch inspection drive management and data chain storage on the inspection samples through collaborative radio frequency tracking and visual monitoring, guided by the inspection chain, to determine the inspection data chain, wherein, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the inspection execution is carried out, and the knowledge association graph of the inspection is determined 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 mirror system for the quarantine project cycle, quarantine tasks are received and lightweight online progress deduction is performed to determine the quarantine chain. The quarantine chain is then reviewed and corrected based on personnel and equipment status, and test samples are received and batch radio frequency coding is performed. Through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine drive management and data on-chain storage are performed on the test samples to determine the quarantine data chain. Using node imprints as constraints, automatic imprint triggering and feedback response management for quarantine execution are performed, and batch mining is used to determine the knowledge association graph for quarantine. In other words, by introducing an online mirror system to achieve real-time synchronization and lightweight deduction of quarantine chain data, combined with personnel and equipment status review and batch radio frequency coding, accurate tracking of the entire disease detection process is achieved. Security and reliability are ensured through on-chain data storage, and real-time feedback efficiency is improved based on the node automatic triggering mechanism, thereby improving the efficiency of the entire disease detection process.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0020] Figure 1 Schematic diagram of the process of batch progress monitoring of animal disease testing samples in this application.

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

[0022] Description of the accompanying drawings: quarantine chain determination module 11, sample identification module 12, knowledge association graph determination module 13. DETAILED DESCRIPTION

[0023] This application provides a batch progress monitoring method and system for animal disease detection samples, addressing the existing technical issues of difficulty in accurately monitoring and tracking sample batch progress in real time due to information isolation and data discontinuity in each link of the disease detection process, thereby affecting the efficiency of the disease detection process. By introducing an online mirroring system to achieve real-time synchronization and lightweight deduction of disease detection chain data, combined with personnel and equipment status review and batch radio frequency coding, accurate tracking of disease detection samples throughout the entire process is achieved. Data storage on the chain ensures security and reliability, and the node automatic triggering mechanism improves real-time feedback efficiency, thereby improving the efficiency of the entire disease detection process.

[0024] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0025] For example 1, please refer to the attached Figure 1 The present application provides a method for monitoring the batch progress of animal disease detection samples, wherein the method is performed by a batch progress monitoring system for animal disease detection samples, and the method specifically includes the following steps:

[0026] S100: In view of the quarantine project cycle, an online mirror system is introduced to receive quarantine tasks and conduct lightweight online progress deduction to determine the quarantine chain.

[0027] Furthermore, the present application S100 includes:

[0028] The equipment deployment information and basic personnel information of the quarantine project are retrieved, and the simulation project space is determined by performing low-element simulation, wherein the information on the physical end is simplified to the low-element standard; historical quarantine records are obtained, and simulation training and lightweight adjustments are performed on the simulation project space to determine the online mirror system, wherein the preset simulation convergence is used as the lightweight standard.

[0029] Specifically, the equipment deployment information and basic personnel information of the quarantine inspection project are retrieved. The equipment deployment information is used to understand the distribution, performance and working status of the equipment, and the basic personnel information is used to evaluate the capabilities, working status and task allocation of the personnel. The quarantine inspection project is the entire inspection project used to detect animal diseases, including the entire process from sample collection, transportation, testing to result summary. Equipment deployment information is information about the equipment configuration, operation status, location distribution, etc. in the disease detection process, including the model, working mode (such as automatic or manual), working hours, etc. of each detection instrument, which is used to analyze whether the equipment meets the quarantine inspection requirements and whether it can support efficient disease detection. Basic personnel information is the basic information of staff related to disease detection, including name, position, qualifications, work experience, work arrangements, etc., which is used to evaluate whether personnel can perform various tasks efficiently.

[0030] By refining and simplifying equipment and personnel information, a low-element simulation engineering space is constructed. Low-element simulation refers to omitting unnecessary complex details when building a model, converting it into a more basic or standardized form, and retaining only the key information that influences decision-making and results, thereby improving simulation efficiency and responsiveness. By simplifying the operating mechanisms of equipment and the steps of personnel, only the most basic operating parameters are focused. For example, equipment details (such as circuits and drives) are ignored, retaining only key functional descriptions; only each person's work hours, task priorities, skill levels, etc. are recorded, without involving specific operational procedures.

[0031] Simplifying physical information to low-level standards means that the details of actual physical equipment or operational processes are reduced to abstract, essential parameters and standards to meet simulation and deduction requirements. This means further simplifying physical equipment information, abstracting complex equipment operating principles into parameters such as time, task, and status. For example, in a quarantine inspection project, equipment deployment information includes: Device A: PCR testing equipment with a maximum processing speed of 100 samples / hour and a failure rate of 1%; Device B: Nucleic acid extraction equipment with a maximum processing speed of 80 samples / hour and a failure rate of 2%. Basic personnel information includes: Personnel C: with five years of experience, an advanced skill level, and an eight-hour workday; Personnel D: with two years of experience, an intermediate skill level, and an eight-hour workday. Devices A and B are simplified into functional abstractions for PCR testing and nucleic acid extraction, respectively, recording only data such as their processing speed, failure rate, and test cycle, without simulating the internal operational details of the devices. For Personnel C and D, only their work hours and skill levels need to be recorded. When assigning tasks, higher-difficulty tasks are prioritized based on their skill levels.

[0032] Historical quarantine inspection records—data from past animal disease inspections, covering information such as test samples, testing procedures, personnel configuration, and equipment status—are acquired. These records are imported into the simulation project space for simulation training and lightweight adjustments. Based on the data from these records, the inspection process is simulated under different scenarios. The operating status, timing parameters, and task allocation of each link are adjusted based on the data from these records to ensure that the simulation results are as close to reality as possible. Repeated adjustments may be required during training until the behavior of the simulation project space closely matches the actual historical records. Based on the simulation training, lightweight adjustments are used to further optimize the simulation results. Adjustments focus on key parameters that significantly impact inspection progress (such as equipment processing speed and personnel task efficiency), rather than complex equipment details or operational procedures. These lightweight adjustments aim to improve system responsiveness and computational efficiency while ensuring the accuracy of simulation results. Furthermore, the entire simulation project space no longer requires a detailed description of each device's structure. Instead, each device is represented by a component graphic with only key indicator labels.

[0033] The preset simulation convergence level is a standard or threshold set during the simulation process to measure the accuracy and consistency of simulation results. When simulation results converge to the preset convergence level, the simulation is considered to have achieved the expected accuracy and further adjustments can be discontinued. This includes the error range and the fluctuation range of the results. For example, if the deviation between the predicted cycle of the simulated engineering space and the historical actual cycle is less than 3%, the accuracy of the simulation results is measured according to the preset convergence level. When the error or fluctuation of the simulation results is reduced to the preset standard, the accuracy is considered to have reached an acceptable level and further simulation training and adjustments can be discontinued. For example, by simulating the load and personnel configuration of different batches of tasks, the overall cycle error of <3% can be used as the preset convergence level to control the training termination condition. If, after 10 rounds of training, the predicted value deviates from the historical average task cycle (e.g., 6 hours) by less than 10 minutes (i.e., <3%), the simulation model is considered stable and can be used for actual simulation. The resulting system is then called an online mirroring system. The online mirroring system not only reflects various indicators of the quarantine inspection process in real time but also responds to changes in quarantine inspection progress under different conditions. Through low-element simulation and lightweight adjustment, a simplified quarantine engineering simulation space is constructed, which can reflect the main processes and key elements of the actual quarantine engineering.

[0034] The quarantine project cycle refers to the time required for a complete animal disease testing project, from task assignment to result delivery, encompassing stages such as sample collection, transportation, laboratory testing, data entry, and review and reporting. An online mirroring system is introduced to enable lightweight simulation of the entire quarantine inspection cycle, enabling precise control of the quarantine inspection chain. This lightweight simulation of online progress involves real-time inference, prediction, and scheduling of the quarantine inspection process by simplifying parameters, models, and processes, using only coarse-grained data such as key tasks, equipment status, and personnel load. For example, scheduling decisions can be made solely based on whether equipment is currently idle and whether personnel are available for execution.

[0035] After the quarantine inspection task is issued, the quarantine inspection task is received, including the sample batch number, test type (such as avian influenza nucleic acid test), deadline, etc. Real-time status data is read, such as which inspectors are idle, which equipment is running, and the estimated completion time. According to the task type and the test process template, the standard quarantine inspection path for the task is automatically generated. For example, the quarantine inspection path determined according to a certain received quarantine inspection task is as follows: sample reception → sample sorting → nucleic acid extraction → PCR amplification → data review → report issuance. Not all tasks have the same quarantine inspection chain. The online mirror system does not deeply simulate the execution details of each device, but only tracks key elements in a lightweight deduction manner, such as the quarantine inspection path, which part of the task the equipment or personnel need to perform, the progress status and related identification trigger information. For example, which equipment and personnel are assigned to which subtasks; the progress status of each stage; whether there are task delays or blockages at each node; which nodes will trigger the start of subsequent tasks after completion, etc.

[0036] Based on coarse-grained parameters, such as whether the equipment is idle (only 0 / 1), whether the personnel are qualified, and whether the previous node has been completed, the approximate cycle and progress bottlenecks of the entire process can be quickly deduced. By analyzing progress deviations (such as processing timeouts at a certain node, equipment failures, etc.), timely warnings are issued or suggestions for adjusting the task flow are made to determine the quarantine chain. The quarantine chain is the task path chain from receiving samples to issuing reports for quarantine tasks, including different task nodes (such as sample transportation, nucleic acid extraction, PCR testing, review and issuance, etc.), and the overall task progress is reflected through the status changes of each node. By introducing an online mirror system and combining simplified task parameters with a progress node identification mechanism, a task quarantine chain is constructed in real time, connecting the entire process nodes from sample reception to result reporting, helping to optimize workflows and reduce human errors and waste of resources.

[0037] Furthermore, the present application further comprises the following steps:

[0038] Based on the deduced quarantine data, key quarantine nodes are located, wherein the key quarantine nodes trigger automatic check-in of progress; for each quarantine node, skill-driven task allocation is performed from the personnel dimension and the equipment dimension, the task direction and quarantine progress are evaluated for resilience, and the physical fence conditions are determined; based on the deduced quarantine data, unstable quarantine nodes are located, and emergency expansion channels are introduced, wherein the emergency expansion channels are triggered in standby; according to the key quarantine nodes, physical fence conditions and the emergency expansion channels, the quarantine chain is marked, and a node imprint is generated.

[0039] Specifically, for the deduced quarantine data, key quarantine nodes are located, that is, nodes that play a key role in the disease detection process (such as those that connect the past and the future, have high task density or high risk sensitivity), usually including bottleneck points, result judgment points or task switching points, etc. The completion of key quarantine nodes has a significant impact on the overall progress. When the task execution advances to the key quarantine node, the progress punch-in is automatically executed to record the current task number, whether the sample flow arrives as expected, whether the node starts execution on time as planned, and whether the node resource configuration is complete. Automatic progress punch-in means that when a key node task is achieved, the time, status, executor and other information are automatically recorded for process progress confirmation.

[0040] For each quarantine node, tasks are allocated from two major dimensions, including personnel dimension and equipment dimension, which means that the execution of tasks is considered from the perspectives of personnel skills, scheduling, and available status, as well as equipment capabilities, occupancy, and maintenance status. That is, tasks are dynamically allocated based on the personnel's skill profile (such as whether they have PCR operation capabilities) and equipment status to avoid task delays caused by low matching. For example, PCR amplification must be completed by personnel with PCR operation certificates on a thermal cycler with temperature control function. First, the node task corresponding to the current batch of samples is retrieved, and the current list of on-duty personnel and the equipment status table are obtained, including: personnel qualification level (such as PCR qualification is A, B level); current task load (such as employee A has completed 4 batches in this period); equipment operating status (such as the current temperature control of equipment A is stable, and equipment B requires maintenance). Combined with approximate matching rules, skill-driven task allocation is carried out, and tasks are assigned first to personnel whose skills fully match the tasks; and equipment with redundant capabilities and moderate load.

[0041] A resilience assessment is conducted on the mission direction and quarantine progress to determine whether the mission can be maintained in the face of disturbances (such as equipment failures and sudden manpower shortages), allowing for a certain tolerance. Each node task is scored, and its ability to recover in the face of emergencies is analyzed, including the availability of alternative equipment, replacement personnel, and allowable delay time. This results in a resilience rating, which can be divided into strong, medium, and weak resilience.

[0042] Based on the resilience assessment, physical fence conditions—the control boundary conditions—are determined. Once a task's status deviates from the normal range (e.g., due to excessive delays, personnel shortages, or equipment overload), automatic control mechanisms such as early warnings and rescheduling are triggered. These include time fences (automatically alerting if a node's execution exceeds 25 minutes), resource fences (triggering resource scheduling priority policies if a node has no alternative equipment), and process deviation fences (initiating manual review if the task's execution process is skipped or retried).

[0043] Based on the simulated quarantine data, we identify unstable quarantine nodes, which are steps that are unstable and prone to becoming bottlenecks or sources of failure, such as frequent equipment failures, high staff vacancy rates, and large sample backlogs. For example, a node with an average latency rate exceeding 25% is considered an unstable candidate node, a device with a historical failure frequency exceeding three is considered unstable, a current task backlog exceeding 110% is considered task overload, and a manpower shortage exceeding 30% is considered an instability risk. If a node has a latency rate greater than 20 minutes in the last 10 rounds of tasks, 35%, and the equipment used has failed five times within a month, it will be marked as an unstable quarantine node.

[0044] Once an unstable quarantine inspection node is identified, an emergency expansion channel is automatically activated based on the current available resources. This is a redundant mechanism used to temporarily deploy additional resources (manpower, equipment, venues, networks, etc.). It can be triggered when a node becomes unstable to ensure uninterrupted processes. For example, a backup extraction device can be activated (such as from another workshop); the number of parallel tasks for the existing equipment can be increased from 48 samples to 64 samples (adjusting the time interval and extraction load); backup shift personnel can be dispatched for support, and the task relay mechanism within the platform can be activated, with tasks pushed directly to the expansion node after completion from the previous node; and quarantine inspection tasks can be split and rearranged, with high-priority batches being transferred to the expansion node first, and low-priority batches being started one hour later.

[0045] The emergency capacity expansion channel serves as a backup trigger mechanism, activating only when certain trigger conditions (such as progress deviation thresholds, resource crashes, or node downtime) are met. By combining key quarantine nodes, physical fencing conditions, and the emergency capacity 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. Departures from the preset path automatically trigger the fencing conditions. Resilience can be understood as a tolerance for deviation. By locating key quarantine nodes and establishing an automatic progress check-in mechanism, quarantine progress can be monitored in real time, allowing problems to be identified and resolved promptly. Through skill-driven task allocation and the establishment of physical fencing conditions, workflows are optimized and quarantine efficiency is improved. The introduction of the emergency capacity expansion channel ensures the smooth progress of the quarantine process, improving its reliability and resilience. By generating node imprints, the status and progress information of key nodes in the quarantine process are recorded, facilitating tracking and auditing.

[0046] S200: For the quarantine inspection chain, review and calibrate according to the personnel status and equipment status, receive the test samples and perform batch radio frequency coding and identification.

[0047] Furthermore, the present application S200 includes:

[0048] A coding mode is introduced, wherein the coding mode at least includes a batch code element, a sample code element, and a task code element; according to the coding mode, the detection samples are encoded one by one to determine the batch radio frequency code.

[0049] Specifically, the system automatically checks the current status of quarantine personnel and equipment within a defined quarantine chain, rechecking and correcting the chain to ensure that personnel and equipment 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. If any deviation is found (e.g., equipment undergoing maintenance or personnel temporarily off-duty), dynamic adjustments are made.

[0050] A coding pattern is introduced to uniquely identify quarantine samples and their contextual information. It is a combined coding structure that includes batch code elements, sample code elements, and task code elements. The batch code element is used to indicate the batch to which the sample belongs and to identify all samples belonging to the same quarantine batch; the sample code element is used to distinguish each specific sample; and the task code element is used to identify which test task the current sample is in. According to the coding pattern, the test samples are encoded one by one to determine the batch radio frequency code. For example, after encoding a test sample, 0614A-0321-RCRN1 is obtained, where 0614A is the batch code element, indicating the test date + number; 0321 is the sample code element, indicating the 321st sample in the batch; and PCRN1 is the task code element, indicating that the task is PCR extraction, path N1. After the test sample arrives at the receiving area, the encoding operation is completed by the RFID writer, and a unique radio frequency code is written for each test sample, and the label is bound and affixed to the sample carrier.

[0051] Verify and correct personnel and equipment status to ensure smooth quarantine inspections and avoid delays caused by personnel and equipment issues. Introduce a coding model to uniquely identify and track each sample, improving the accuracy and efficiency of sample management. Utilize radio frequency (RF) technology for batch coding, enabling rapid access and automated management of sample information, further improving the efficiency of quarantine inspections.

[0052] S300: Through coordinated radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine drive management and data chain storage are performed on the test samples to determine the quarantine data chain, wherein, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the quarantine execution is managed, and the knowledge association graph of the quarantine is determined through batch mining.

[0053] Furthermore, the present application S300 includes:

[0054] Trigger the first quarantine node, execute the node task distribution, and determine the first task queue; generate the first tracking node through radio frequency scanning, synchronously activate the visual device, execute the first task queue and determine the first task data, wherein the first task data includes visual monitoring data and node quarantine data; store the first task data on the chain at the first tracking node, wherein structured key data is used as the data storage constraint.

[0055] Specifically, the first quarantine node is triggered, initiating the quarantine process and issuing the first task queue. The first quarantine node is the first specific operational step in the quarantine process, such as initial sample screening or information registration, depending on the quarantine task. As the starting point of the quarantine chain, the first quarantine node marks the beginning of the process. Node task issuance involves assigning specific operational tasks to a quarantine node according to the task plan, including assigned operators, task parameters, and execution equipment. The first task queue refers to the sequence of pending tasks generated within the first quarantine node.

[0056] By using video scanning to read the animal disease detection sample labels, the time and location of the animal disease detection sample entering the first quarantine node are automatically recorded, and the first tracking node information is generated. The first tracking node represents the first traceable processing point of the radio frequency tag after the sample enters the quarantine process, that is, the starting point of the sample processing process. Radio frequency scanning usually uses RFID reading equipment to scan and identify samples with electronic tags (RFID tags), thereby generating sample information records for the first tracking node. When the samples arrive at the first quarantine node, the operator uses an RFID reader to perform batch scanning, automatically identifying the sample number, batch code, and task category, and marking the sample as having entered the quarantine chain.

[0057] Activate visual equipment, including cameras, image acquisition devices, etc., to capture images of the sample surface (such as whether the blood sample tube is contaminated, whether the label has fallen off, whether there is abnormal color precipitation, etc.). Radio frequency scanning can obtain the radio frequency tag information on the sample, and visual equipment can be used to monitor and record the task execution process. Record the time, location, operator and other information when the sample is first confirmed, and generate the first tracking record of the sample. Execute the first task queue, which includes a series of tasks to be executed, such as sample reception, information entry, preliminary testing, etc. During the execution of the task, collect the generated first task data, including visual monitoring data and node quarantine data.

[0058] Based on RF scanning and visual devices, the first task queue, consisting of a series of pending tasks, is executed. During task execution, first task data is collected, including visual monitoring data and node quarantine data. Visual monitoring data can be analyzed and processed using image recognition technology to extract key information from the task execution process. Node quarantine data includes task execution results and status information for related devices.

[0059] Extract key fields from the first task data according to a preset structured template, and store the data on-chain in the first tracking node to form an unchangeable data record. Data storage on-chain refers to writing the first task data into an unalterable distributed ledger system (such as a blockchain) to achieve traceability, verification, and tamper-proof functions. Structured key data is data content with a standard field format, such as sample number, test time, image analysis score, etc. Through the synchronous activation of radio frequency scanning and visual equipment, the task execution process is monitored and recorded in real time to ensure the smooth progress of the task. By storing data on-chain, the immutability and traceability of the data are ensured, and the security and reliability of the data are improved. By structuring key data, query and analysis are facilitated, further improving the efficiency of the quarantine project.

[0060] Furthermore, the present application further comprises the following steps:

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

[0062] Specifically, the collected visual monitoring data is analyzed in real time to determine the task execution status of the first quarantine node. The visual monitoring data is compared with the aforementioned node imprint rules to determine whether key nodes, physical fence conditions, emergency expansion channels, and other imprints have been triggered. If the node imprint is not triggered, the sample is considered normal and the task is progressing as scheduled, and a first identifier is generated. If the node imprint is triggered, a progress delay is determined, and a second identifier is generated, along with progress impact data for tracking and tracing. The second identifier indicates that the sample has a progress delay at this node and is associated with a set of progress impact data, including the abnormal image number, problem type, equipment number, and delay time estimate. For example, the number of samples that triggered the node imprint is 38 batches (19%); the number of standard progress samples that did not trigger the imprint is 162 batches (81%); the average delay time is estimated to range from 120 to 240 seconds, with tag recognition failure and placement deviation being the most common causes of delay. By determining whether the visual monitoring data triggers the node imprint of the first quarantine node, the task execution status is monitored in real time, and delays are promptly identified and addressed. The first identifier for the standard progress is generated to indicate that the task was completed on time, and the quarantine process can proceed smoothly to the next node. The second identifier for the delayed progress is generated and associated with the progress impact data based on the delay traceability, which is used to improve the quarantine process and increase efficiency.

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

[0064] 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, where the insertion method is new node insertion or original chain node insertion.

[0065] Specifically, the node's quarantine data is verified to automatically identify any defects or anomalies. If the verification determines that the patient is qualified, no new tasks are required and the process proceeds normally. If the verification determines that additional testing is required, additional quarantine tasks are automatically generated, including re-inspections, special tests, or quality reviews. Based on the detection data from the first node, it is determined whether additional tests are necessary. For example, if a detected lesion may be associated with complications, additional tests may be required.

[0066] New quarantine tasks are inserted into the quarantine chain. Depending on the specific situation, the insertion strategy can be categorized into two types: new node insertion and original chain node insertion. New node insertion involves adding a complete node to the current quarantine chain, such as adding a re-verification test, and assigning the new task to this node. This involves adding a new node to the quarantine chain to execute the new quarantine task. Original chain node insertion involves adding a subtask within an existing node, such as performing secondary or supplemental testing on samples at that node, without changing the chain structure. This involves inserting the new quarantine task at the original node in the quarantine chain.

[0067] If the test data anomaly is severe or affects the accuracy of the overall test results, and an independent testing process is required to verify or supplement the information, choose to insert a new node. If the anomaly is a minor deviation or a local indicator anomaly, and only repeated testing or supplementary testing is required within the original test node, choose to insert the original chain node. Inserting a new node means adding steps to the testing process, which may require additional resource allocation and time costs. If resources are tight or the anomaly is not serious, inserting the original chain node is preferred. Update the status of the quarantine chain to ensure that the new tasks can be executed and the quarantine process can proceed smoothly. By verifying the node quarantine data, anomalies are promptly identified and new quarantine tasks are generated for processing. Inserting the new tasks into the quarantine chain ensures that the anomaly is handled promptly and does not affect the smooth progress of the quarantine process. By updating the status of the quarantine chain, the new tasks can be executed, improving the flexibility and adaptability of the quarantine process.

[0068] Furthermore, the present application further comprises the following steps:

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

[0070] Specifically, using the quarantine chain as a guide, tasks at each quarantine node are progressively triggered based on the order of tasks in the chain. Before each node executes a task, a task queue is issued to clarify task requirements and assign the task to the corresponding executor or device. Each quarantine node is tracked and monitored, recording key data and status information during task execution. This tracked and monitored data, including task status and execution results, is uploaded to the blockchain to ensure data immutability and traceability. Key node task data (such as test results, timestamps, and operation records) is structured and written to the blockchain to ensure data immutability. This process is repeated until all tasks in the quarantine chain are completed. For example, suppose the quarantine chain contains three quarantine nodes: sample reception, laboratory testing, and result reporting. First, the sample reception node is triggered to execute a task queue, which is issued to the sample reception personnel. Simultaneously, node status tracking and monitoring begins. After sample reception is completed, the data is uploaded to the blockchain, triggering the next node, laboratory testing, to execute the task. After the laboratory testing task is completed, the data is also uploaded to the blockchain, triggering the final node, result reporting, to execute the task. Throughout the entire process, the quarantine chain guides the progressive triggering of tasks and the storage of data on the chain, ensuring the orderly progress of the quarantine process. Through progressive task triggering, the quarantine process is automated, the process flows smoothly, human delays are reduced, and task queues are dynamically dispatched, improving resource utilization and optimizing inspection efficiency.

[0071] Furthermore, the present application further comprises the following steps:

[0072] Identify the quarantine data chain and perform batch positive judgment, wherein each test sample corresponds to a quarantine data chain; if the judgment result is yes, perform same-node mapping of each quarantine data chain, locate abnormal quarantine data, and construct a knowledge association graph of the quarantine results; wherein, by identifying the abnormal quarantine features of each node and performing a proportion calculation, the abnormal quarantine features of each node are associated and the feature proportion is identified as the knowledge association graph.

[0073] Specifically, by performing batch quarantine drive management and data chain storage on the test samples, a quarantine data chain is obtained, that is, a series of data sets generated in the quarantine process for each test sample, including a complete data record chain from sample collection and testing to results. A batch positive judgment is made on the quarantine data chain of all test samples in a batch, that is, a unified judgment is made on the test results of a batch of samples, and which samples are tested positive (with disease markers). In other words, a centralized judgment is made on the test results of a batch of test samples, and which samples are tested positive, that is, samples confirmed to have disease markers or pathogens, are identified.

[0074] Batch data analysis technology (such as statistical analysis of batch processing) is used to analyze the quarantine data chains of all samples to determine whether the samples are positive, usually based on thresholds. If the judgment result indicates that it is positive, the quarantine data chains corresponding to all positive samples are mapped node to node, that is, the detection data of the same detection node are compared. The data of each node are matched and compared from different quarantine data chains to locate abnormal quarantine data. In other words, the same detection stages or detection links (i.e. nodes) corresponding to the quarantine data chains of different samples are compared and mapped to find out the performance of these nodes in multiple sample data. It refers to identifying abnormal data on the same node through comparison and analysis, such as abnormal test values, abnormal equipment, abnormal processes, etc. For example, the virus concentration in a certain detection link is abnormally high, the detection time is abnormally extended, the equipment is abnormally shut down, etc.

[0075] Calculate the proportion of abnormal quarantine features of each node, that is, the frequency or proportion of occurrence in the entire sample set (for example, the abnormal feature accounts for 15% of all samples) to reflect its prevalence. Proportion calculation is to count the proportion of a certain abnormal feature in all samples or nodes, which is used to quantify the prevalence and influence of abnormal features. The abnormal features of different nodes and their proportion information are constructed into a knowledge association graph in chronological order or causal relationship, showing the association and influence path between the abnormal features. The abnormal features on different nodes and the association between them are expressed in a graphical structure to form a knowledge network, revealing the internal connection and potential laws of abnormal data.

[0076] By mapping the same nodes and locating abnormal data, we can accurately identify potential anomalies in the quarantine inspection process for positive samples, improving the transparency and controllability of the quarantine inspection process. The constructed knowledge association graph structures and visualizes complex anomaly features, facilitating rapid problem location and optimizing the quarantine inspection process, ultimately improving the efficiency and accuracy of quarantine inspections.

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

[0078] Beneficial effects:

[0079] By introducing an online mirror system for the quarantine project cycle, quarantine tasks are received and lightweight online progress deduction is performed to determine the quarantine chain. The quarantine chain is then reviewed and corrected based on personnel and equipment status, and test samples are received and batch radio frequency coding is performed. Through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine drive management and data on-chain storage are performed on the test samples to determine the quarantine data chain. Using node imprints as constraints, automatic imprint triggering and feedback response management for quarantine execution are performed, and batch mining is used to determine the knowledge association graph for quarantine. In other words, by introducing an online mirror system to achieve real-time synchronization and lightweight deduction of quarantine chain data, combined with personnel and equipment status review and batch radio frequency coding, accurate tracking of the entire disease detection process is achieved. Security and reliability are ensured through on-chain data storage, and real-time feedback efficiency is improved based on the node automatic triggering mechanism, thereby improving the efficiency of the entire disease detection process.

[0080] Example 2: Based on the same inventive concept as the batch progress monitoring method of animal disease detection samples in the aforementioned Example 1, this application also provides a batch progress monitoring system for animal disease detection samples, please refer to the attached Figure 2 The batch progress monitoring system for animal disease detection samples includes:

[0081] The quarantine chain determination module 11 is used to introduce an online mirror system for the quarantine project cycle, receive quarantine tasks and perform lightweight deduction of online progress to determine the quarantine chain; the sample identification module 12 is used to review and correct the quarantine chain according to the personnel status and equipment status, receive the test samples and perform batch radio frequency coding identification; the knowledge association graph determination module 13 is used to perform batch quarantine drive management and data chain storage on the test samples through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, to determine the quarantine data chain, wherein, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the quarantine execution is carried out, and the knowledge association graph of the quarantine is determined through batch mining.

[0082] Furthermore, the quarantine chain determination module 11 in the batch progress monitoring system for animal disease detection samples is further configured to:

[0083] The equipment deployment information and basic personnel information of the quarantine project are retrieved, and the simulation project space is determined by performing low-element simulation, wherein the information on the physical end is simplified to the low-element standard; historical quarantine records are obtained, and simulation training and lightweight adjustments are performed on the simulation project space to determine the online mirror system, wherein the preset simulation convergence is used as the lightweight standard.

[0084] Furthermore, the quarantine chain determination module 11 in the batch progress monitoring system for animal disease detection samples is further configured to:

[0085] Based on the deduced quarantine data, key quarantine nodes are located, wherein the key quarantine nodes trigger automatic check-in of progress; for each quarantine node, skill-driven task allocation is performed from the personnel dimension and the equipment dimension, the task direction and quarantine progress are evaluated for resilience, and the physical fence conditions are determined; based on the deduced quarantine data, unstable quarantine nodes are located, and emergency expansion channels are introduced, wherein the emergency expansion channels are triggered in standby; according to the key quarantine nodes, physical fence conditions and the emergency expansion channels, the quarantine chain is marked, and a node imprint is generated.

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

[0087] A coding mode is introduced, wherein the coding mode at least includes a batch code element, a sample code element, and a task code element; according to the coding mode, the detection samples are encoded one by one to determine the batch radio frequency code.

[0088] Furthermore, the knowledge association graph determining module 13 in the batch progress monitoring system for animal disease detection samples is further configured to:

[0089] Trigger the first quarantine node, execute the node task distribution, and determine the first task queue; generate the first tracking node through radio frequency scanning, synchronously activate the visual device, execute the first task queue and determine the first task data, wherein the first task data includes visual monitoring data and node quarantine data; store the first task data on the chain at the first tracking node, wherein structured key data is used as the data storage constraint.

[0090] Furthermore, the knowledge association graph determining module 13 in the batch progress monitoring system for animal disease detection samples is further configured to:

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

[0092] Furthermore, the knowledge association graph determining module 13 in the batch progress monitoring system for animal disease detection samples is further configured to:

[0093] 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, where the insertion method is new node insertion or original chain node insertion.

[0094] Furthermore, the knowledge association graph determining module 13 in the batch progress monitoring system for animal disease detection samples is further configured to:

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

[0096] Furthermore, the knowledge association graph determining module 13 in the batch progress monitoring system for animal disease detection samples is further configured to:

[0097] Identify the quarantine data chain and perform batch positive judgment, wherein each test sample corresponds to a quarantine data chain; if the judgment result is yes, perform same-node mapping of each quarantine data chain, locate abnormal quarantine data, and construct a knowledge association graph of the quarantine results; wherein, by identifying the abnormal quarantine features of each node and performing a proportion calculation, the abnormal quarantine features of each node are associated and the feature proportion is identified as the knowledge association graph.

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

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

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

Claims

1. A method for monitoring the batch progress of animal disease detection samples, characterized in that: include: In view of the quarantine project cycle, an online mirror system is introduced to receive quarantine tasks and conduct lightweight online progress deduction to determine the quarantine chain; For the quarantine inspection chain, review and calibrate according to the status of personnel and equipment, receive test samples and perform batch radio frequency coding and identification; Through coordinated radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine drive management and data chain storage are performed on the test samples to determine the quarantine data chain. Among them, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the quarantine execution is carried out, and the knowledge association graph of the quarantine is determined through batch mining.

2. The method for monitoring the batch progress of animal disease detection samples according to claim 1, wherein: In view of the quarantine project cycle, an online mirror system is introduced. The construction of the online mirror system includes: Retrieve equipment deployment information and basic personnel information for the quarantine inspection project, and determine the simulated engineering space through low-element simulation. The physical end information is simplified to a low-element standard. Obtain historical quarantine records, perform simulation training and lightweight adjustments on the simulated engineering space, and determine the online mirror system, wherein a preset simulation convergence degree is used as a lightweight standard.

3. The method for monitoring the batch progress of animal disease detection samples according to claim 1, wherein: After the quarantine chain is determined, it includes: Based on the deduced quarantine data, key quarantine nodes are located, wherein the key quarantine nodes trigger automatic check-in of progress; For each quarantine node, skill-driven task allocation is implemented from the perspectives of personnel and equipment, resilience assessment is conducted on task direction and quarantine progress, and physical fence conditions are determined; Based on the deduced quarantine data, the unstable quarantine node is located and an emergency expansion channel is introduced, wherein the emergency expansion channel has a standby trigger; According to the key quarantine nodes, physical fence conditions and the emergency expansion channel, the quarantine chain is marked and a node imprint is generated.

4. The method for monitoring the batch progress of animal disease detection samples according to claim 1, wherein: Introducing a coding mode, wherein the coding mode at least includes a batch code element, a sample code element, and a task code element; According to the coding mode, the test samples are encoded one by one to determine the batch radio frequency code.

5. The method for monitoring the batch progress of animal disease detection samples according to claim 1, wherein: Through coordinated radio frequency tracking and visual monitoring, guided by the quarantine chain, batch quarantine drive management and data chain storage are performed on the test samples, including: Trigger the first quarantine node, execute node tasks, and determine the first task queue; Generate a first tracking node through radio frequency scanning, synchronously activate a visual device, execute the first task queue and determine first task data, wherein the first task data includes visual monitoring data and node quarantine data; The first task data is stored on the first tracking node, wherein structured key data is used as a data storage constraint.

6. The method for monitoring the batch progress of animal disease detection samples according to claim 5, wherein: After determining the first task data, the following steps are included: Determining, based on the visual monitoring data, whether a node imprint of the first quarantine inspection node is triggered; If not triggered, generate the first identifier of the standard progress; If triggered, a second identifier of the delayed progress is generated, wherein the second identifier is associated with progress impact data based on delay tracing.

7. The method for monitoring the batch progress of animal disease detection samples according to claim 6, wherein: After determining the first task data, the following steps are included: Verify the node quarantine data to determine whether to generate a new quarantine task; If generated, the newly added quarantine inspection task is inserted into the quarantine inspection chain, wherein the insertion method is to insert a new node or insert an original chain node.

8. The method for monitoring the batch progress of animal disease detection samples according to claim 7, wherein: Guided by the quarantine chain, progressive triggering and task queue distribution based on quarantine nodes are executed, and node tracking and monitoring and data chain storage are performed.

9. The method for monitoring the batch progress of animal disease detection samples according to claim 1, wherein: The knowledge association graph of quarantine inspection is determined through batch mining, including: Identify the quarantine data chain and perform batch positive determination, wherein each test sample corresponds to a quarantine data chain; If the result is yes, perform same-node mapping of each quarantine data chain, locate abnormal quarantine data, and construct a knowledge association graph of quarantine results; Among them, 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 proportions are marked to serve as the knowledge association graph.

10. A batch progress monitoring system for animal disease detection samples, characterized in that: The method for monitoring the batch progress of animal disease detection samples according to any one of claims 1 to 9 is used to implement the steps, wherein the batch progress monitoring system for animal disease detection samples comprises: The quarantine chain determination module is used to introduce an online mirror system to receive quarantine tasks and perform lightweight online progress deduction to determine the quarantine chain according to the quarantine project cycle; The sample identification module is used to review and calibrate the quarantine inspection chain according to the status of personnel and equipment, receive test samples and perform batch radio frequency coding identification; The knowledge association graph determination module is used to perform batch quarantine drive management and data chain storage on the test samples through collaborative radio frequency tracking and visual monitoring, guided by the quarantine chain, to determine the quarantine data chain, wherein, with the node imprint as a constraint, the execution imprint is automatically triggered and the feedback response management of the quarantine execution is carried out, and the knowledge association graph of the quarantine is determined through batch mining.

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