Multistage error report information display and feedback system and method with interactive traceability function for biological detection equipment

Abstract

The invention belongs to the technical field of biological detection, and relates to a multi-level error report information display and feedback system and method with an interactive traceability function for biological detection equipment. The system comprises a level error classification module, an interactive traceability module, an associated data visualization module and a processing process recording module. The method comprises the following steps: carrying out error coding on error reporting information according to'error level-error type-timestamp '; according to equipment operation data within 30 minutes before an error occurs, a traceability link is constructed, and the traceability link supports quick query of a time axis index and a node; for the multi-level errors with association, the association relationship is displayed in a directed graph form; for high-throughput detection, sample-error mapping is carried out; according to the error levels, different feedback is triggered, and standardized processing flow guidance is provided for different error levels to guide a user to complete each step of operation; and after error processing is completed, the system automatically generates a tracing report.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology and relates to a human-computer interaction technology for biological detection equipment. Specifically, it relates to a multi-level error information display and feedback system and method with interactive traceability function for biological detection equipment. It is applicable to various biological sample detection equipment, such as gene sequencers, enzyme-linked immunosorbent assay (ELISA) readers, PCR instruments, etc., and is especially suitable for high-precision detection scenarios such as clinical diagnosis and drug development that require strict traceability of error causes and processing. Background Technology

[0002] Biological detection generally involves a complex process, including sample processing, parameter setting, detection execution, and result output. During this process, issues such as sample contamination, abnormal parameters, or equipment malfunctions can generate numerous error messages. Current error message processing systems for biological detection equipment have several shortcomings, including difficulties in error tracing, lack of multi-level error correlation, poor interactivity, and lack of process logging.

[0003] Error tracing is difficult: Error messages only show the current error status, such as "detection failed", and lack records of the causes and consequences of the error, including parameter adjustment records before the error occurred, changes in device sensor data, etc. Users need to manually check the log files, which are mostly in plain text format, resulting in low tracing efficiency.

[0004] Multi-level error correlation is missing: In the same detection process, low-level errors (such as insufficient sample size) may lead to high-level errors (such as invalid test results), but the interface does not show the causal relationship between errors, such as "insufficient sample size → unbalanced reaction solution ratio → invalid test results", making it difficult for users to locate the root cause.

[0005] Poor interactivity: Error messages are displayed statically as text and code, and users cannot directly operate and trace related data through the interface. For example, they cannot directly click on the error message to view the corresponding sample image, sensor curve, and other data information. Instead, they need to switch to multiple system modules to query, which is cumbersome.

[0006] No record of the process: Error handling steps (such as parameter adjustment, device restart) are not associated with error information and stored. During subsequent review or debriefing, the compliance of the handling cannot be verified, which does not meet the traceability requirements of clinical testing, and makes it impossible for maintenance engineers to associate traceability issues.

[0007] These problems result in error location taking 30-60 minutes, with a root cause identification rate of less than 50%, seriously affecting the optimization of the detection process and the reliability of the detection results. Summary of the Invention

[0008] The purpose of this invention is to solve the problems of difficulty in tracing error information in existing biological detection equipment, lack of multi-level error association, poor interactivity, and lack of processing records. It proposes a multi-level error information display and feedback system and method with interactive traceability function for biological detection equipment, so as to realize interactive traceability and full-process management of error information, and improve error handling efficiency and traceability accuracy.

[0009] The technical solution of this invention is:

[0010] This invention provides a method for displaying and providing feedback on multi-level error information with interactive traceability for biological detection equipment, comprising the following steps:

[0011] (1) The error information collected in real time is encoded according to the rule of "error level-error type-time stamp"; the error level is divided into the following 4 levels:

[0012] Level 1, resulting in termination of the testing process and invalidation of the data;

[0013] Level 2: The testing process continues, but the data is unreliable.

[0014] Level 3: The test data is valid but requires attention.

[0015] Level 4, does not affect the status notification of the detection;

[0016] (2) Based on the equipment operation data within 30 minutes before the error occurred, construct a traceability link including error code, preceding event, and associated data. The traceability link supports time axis indexing and fast node query.

[0017] For multi-level errors that are related, their relationships are displayed in the form of a directed graph; for high-throughput detection, a sample-error mapping is performed.

[0018] (3) Trigger differentiated feedback based on the error level, and provide standardized processing flow guidance for different error levels to guide users through each step of the operation, while automatically recording the operation time, operator, and operation result; the differentiated feedback includes:

[0019] A Level 1 error is indicated by a flashing red indicator light, a continuous high-frequency beep, and a full-screen pop-up window on the interface containing the source tracing entry point.

[0020] A Level 2 error is indicated by a solid yellow indicator light, a single beep, and a pop-up window on the side of the interface containing the processing steps.

[0021] Level 3 errors are indicated by a blue indicator light and a notification bar at the bottom of the screen.

[0022] Level 4 errors are indicated by a green indicator light and a text message in the status bar.

[0023] (4) After the error is handled, the system automatically generates a traceability report, which includes an error information summary, a traceability link map, a causal relationship analysis, a detailed processing record, and result verification.

[0024] Furthermore, among the error levels, Level 1 is a fatal error, including sample contamination and core module failure; Level 2 is a serious error, including excessive temperature fluctuation and abnormal fluorescence signal; Level 3 is a minor error, including insufficient sample volume, insufficient consumables, and duplicate sample numbers; and Level 4 is a prompt message, including partial completion and successful data saving.

[0025] The error types include sample-related errors, which include sample contamination; the timestamp is specified to the year, month, day, hour, minute, and second.

[0026] Furthermore, in step (2), the traceability link is displayed in the form of a timeline, and the timeline nodes include event descriptions and related data entries; the preceding events include sample loading operation records, operator information, time, and sample chamber cleaning status records; the related data includes photos of the sample chamber interior when contamination occurs and air cleanliness sensor curves.

[0027] The sample-error mapping is based on the sample tray layout. The holes corresponding to the error samples are marked with error level colors. Clicking on a hole will expand the error association chain of that sample.

[0028] Furthermore, the traceability link supports "drill-down query" and "reverse tracing" functions; the drill-down query allows users to click on nodes on the timeline to view a comparison table of parameters before and after adjustment, as well as real-time curves including changes in temperature and humidity; the reverse tracing allows users to input an operation record to query the subsequent errors that the operation may cause, providing early warnings of potential risks.

[0029] Furthermore, in step (2), when there are related multi-level errors, the relationship is displayed in the form of a directed graph by drawing a causal relationship map, with arrows pointing from low-level errors to high-level errors, and the error level represented by the node size; users can add custom annotations on the relationship map, and the annotations are bound to the error information for later viewing.

[0030] This invention also provides a multi-level error information display and feedback system with interactive traceability for biological detection equipment, comprising:

[0031] A data collection module is used to collect equipment operation data in real time; the data collection module includes an image sensor, a multi-parameter sensor array, and a data storage unit.

[0032] A multi-level error classification module is used to classify the collected error information.

[0033] An interactive traceability module is used to query and track the cause of errors; the interactive traceability module includes a traceability panel, which contains a timeline, associated data entry points, and a reverse traceability button;

[0034] The associated data visualization module is used to visually display the associated data of errors and the processing flow;

[0035] The process recording module is used to generate traceability reports.

[0036] Furthermore, the multi-parameter sensor array includes a temperature sensor, a humidity sensor, a pressure sensor, and a fluorescence intensity detector; the multi-level error classification module includes an indicator light and a buzzer.

[0037] Furthermore, the data collection module packages the image features and sensor values ​​and sends them to the error classification engine of the multi-level error classification module. The engine matches the error type database, determines the error level, and generates an error code. At this time, the indicator light and buzzer are activated, and the traceability panel pop-up window appears.

[0038] The beneficial effects of this invention are:

[0039] (1) The system and method provided by the present invention adopt an interactive traceability mechanism and realize the linkage query of error information and prior data through visualization methods such as time axis and causal graph, which solves the inefficiency problem of traditional traceability relying on plain text logs; it can shorten the error cause location time to 5-10 minutes and increase the root cause identification rate from 50% to 90%, thereby greatly improving the error traceability efficiency.

[0040] (2) This invention adopts multi-level error association visualization, and for the first time displays the causal relationship between low-level errors and high-level errors in the form of a graph, which helps users locate the root cause rather than just deal with the surface error, thereby achieving an accuracy rate of 95% in identifying multi-level error associations, avoiding the recurrence of the same chain of errors, and enhancing the ability to identify associated errors.

[0041] (3) The present invention records the entire process, binds the error handling steps with traceability information, automatically generates compliance reports, and achieves a 100% record completeness rate of the error handling process, ensuring compliance and traceability, and meeting the strict traceability requirements in fields such as clinical diagnosis, drug development, and biological testing.

[0042] (4) Based on traditional multimodal feedback, this invention combines multimodal feedback with interaction to give feedback information interactive attributes (such as clicking a pop-up window to enter traceability), thereby improving the depth and efficiency of information transmission; at the same time, it optimizes the convenience of user operation, and the interactive traceability function reduces the data query steps by 60%, so that users can complete the entire process traceability without switching system modules. Attached Figure Description

[0043] Figure 1 This is a system architecture diagram of the present invention, showing the connection relationships between the multi-level error classification module, the interactive traceability engine, the associated data visualization module, etc.

[0044] Figure 2 This is a system workflow diagram, showing the complete process from error occurrence to traceability report generation;

[0045] Figure 3 A schematic diagram of an interactive traceability panel interface and an example of building a reverse traceability link;

[0046] Figure 4 This diagram illustrates the display interface and multi-level error causal relationship graph in a high-throughput detection device, showing the association path from low-level errors to high-level errors.

[0047] Figure 5 A schematic diagram of the error handling process is provided, including step guidance and an operation log column. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] To further understand the present invention, it will be further described in conjunction with the accompanying drawings and embodiments.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment provides a multi-level error information display and feedback system with interactive traceability for biological detection equipment. The system includes a data collection module, a multi-level error classification module, an interactive traceability module, a related data visualization module, and a processing record module.

[0052] The data collection module is used to collect equipment operation data in real time, including an image sensor, a multi-parameter sensor array, and a data storage unit; the multi-parameter sensor array includes a temperature sensor, a humidity sensor, a pressure sensor, and a fluorescence intensity detector.

[0053] The multi-level error classification module is used to classify the collected error information, including indicator lights and buzzers.

[0054] The interactive traceability module is used to query and track the cause of errors, including a source tracing panel; the source tracing panel includes a timeline, related data entry points, and a reverse tracing button;

[0055] The associated data visualization module is used to visually display the associated data of errors and the processing flow;

[0056] The process recording module is used to generate traceability reports.

[0057] The system configuration is as follows:

[0058] The hardware module (including image sensor and data storage module) and software system of this invention are integrated into the main control system of the biological detection equipment. They are connected to the equipment's sensors (temperature, humidity, fluorescence detection, etc.) and operation recording system (user login, parameter adjustment) through API interface, and the equipment operation data is collected and stored in real time (sampling frequency 1 time / second).

[0059] This system adopts a "hardware embedded integration + software modular deployment" architecture, forming a deep collaboration with the main control system of the biological detection equipment.

[0060] Hardware module integration includes:

[0061] (1) Core processing unit: Equipped with an ARM Cortex-A76 architecture processor (2.8 GHz), integrating a Neon vector engine, supporting real-time parallel data processing (can parse 100,000+ sensor data per second), serving as the computing core of the multi-level error classification and tracing engine.

[0062] (2) Sensing layer devices:

[0063] Industrial-grade image sensor (5-megapixel resolution, 30 fps): Deployed inside the sample chamber and on the work surface to collect images of the sample status (such as liquid level and contamination traces), supporting H.265 encoded compressed storage.

[0064] Multi-parameter sensor array: including temperature and humidity sensor (accuracy ±0.1℃ / ±2% RH), pressure sensor (range 0-500 g, accuracy 0.1 g), fluorescence intensity detector (detection range 400-700 nm, resolution 1 nm), connected to the main control unit via I²C bus, sampling frequency 1 time / second.

[0065] Feedback devices: integrated RGB three-color LED indicator (brightness adjustable), piezoelectric buzzer (frequency adjustable from 2-4 kHz), and 10.1-inch capacitive touch screen (resolution 1920×1200, supports 10-point touch) to achieve multimodal interactive feedback.

[0066] (3) Storage module:

[0067] It uses 256 GB industrial-grade eMMC flash memory (read / write speed 500 MB / s) + 1TB SATA solid-state drive to store real-time running data (retaining the most recent 72 hours) and historical logs (retention period of 1 year), and supports power loss data protection.

[0068] (4) Communication interface:

[0069] It is equipped with a gigabit Ethernet port (supporting IEEE 802.3af PoE), a USB 3.0 interface (for connecting an external traceability report printer), and an RS485 bus (for connecting to the device's main control system), and supports MQTT protocol communication with the Laboratory Management System (LIMS).

[0070] The software system includes:

[0071] (1) Operating system: an embedded system based on Linux 5.4 kernel, ensuring task scheduling delay ≤ 1 ms to meet the real-time requirements of error detection.

[0072] (2) Core software modules:

[0073] Data acquisition service (developed in C language): Directly connects to sensor hardware through device driver, uses a circular buffer (8 MB in size) to temporarily store raw data, and generates a data checksum (SHA-256) every 30 seconds to prevent tampering.

[0074] Multi-level error classification engine (developed with Python + TensorFlow): Deploy a lightweight error recognition model (model size ≤ 50MB), load it into memory to run, and achieve millisecond-level determination of error level and type.

[0075] Interactive traceability engine (developed in C++): Based on a graph database (Neo4j embedded version), it builds a traceability link and supports timeline indexing and fast node query (response time ≤ 500 ms).

[0076] Visualization rendering module (developed with Qt 6): It uses OpenGL to accelerate the drawing of causal relationship graphs, supports graph scaling (1:10 to 10:1), node dragging and other interactive operations, and maintains a frame rate of over 60 fps.

[0077] (3) Interface adaptation:

[0078] With the device control system: Data interaction is achieved through a RESTful API (HTTPS encrypted). The API interface includes 12 functional endpoints such as error message push (POST / api / error) and device status query (GET / api / status).

[0079] Operation logging system: Integrates with LDAP user authentication interface to record operator employee number, operation time, operation content (such as parameter values ​​before / after adjustment), and the log format conforms to the HL7 FHIR standard.

[0080] Example 2

[0081] like Figure 2 As shown, this embodiment provides a method for displaying and providing feedback on multi-level error information with interactive traceability for biological detection equipment, including the following steps:

[0082] (1) Multi-level error classification and coding mechanism:

[0083] Error messages are categorized into four levels based on severity:

[0084] Level 1 (Fatal Error): Causes the detection process to terminate and the data to become invalid (e.g., sample contamination, core module failure).

[0085] Level 2 (Critical Error): The testing process continues but the data is unreliable (e.g., excessive temperature fluctuations, abnormal fluorescence signals).

[0086] Level 3 (Minor Error): The test data is valid but requires attention (e.g., insufficient consumables, duplicate sample numbers).

[0087] Level 4 (Prompt Message): Status notifications that do not affect the detection (such as phased completion, data saved successfully).

[0088] Errors are encoded using a three-dimensional encoding system: "Error Level - Error Type - Timestamp". This encoding contains the core attributes of the error, facilitating rapid identification. For example, in the error encoding "E1-SP-E1-SP-202310011430", "E1" represents a Level 1 error, "SP" represents a sample-related error, followed by the occurrence time: October 1, 2025, at 14:30.

[0089] (2) Design of an interactive traceability engine

[0090] like Figure 3 As shown in (a), this is a schematic diagram of the interactive traceability panel interface, which includes the timeline at the top, the associated data entry in the middle dashed box, and the reverse traceability button at the bottom.

[0091] like Figure 3 (b) shows an example of constructing a reverse tracing link:

[0092] The system automatically records equipment status data (sensor readings, parameter adjustment records, sample information) within 30 minutes prior to the error, constructing a traceability chain of "error node → preceding event → related data". For example:

[0093] When a Level 1 "Sample Contamination" error (E1-SP) occurs, the traceability chain includes:

[0094] Preliminary events: Sample loading operation record (operator, time), sample chamber cleaning status record.

[0095] Related data: Image sensor data (photos of the sample chamber interior) at the time of pollution occurrence, and air cleanliness sensor curves.

[0096] Interactive traceability operation:

[0097] Clicking on the error code (such as E1-SP-202310011430) will bring up the "Tracing Panel," which displays the tracing chain in a timeline format. The timeline nodes include event descriptions and related data entries.

[0098] Supports "drill-down query": Click the "Parameter Adjustment" node on the timeline to view a comparison table of parameters before and after adjustment; click the "Sensor Data" node to view real-time curves, such as temperature and humidity change curves.

[0099] Provides a "reverse traceability" function: by entering an operation record, such as "14:25 Adjust sample size", you can query the subsequent errors that the operation may cause and provide early warning of potential risks.

[0100] (3) Visual display of multi-level error association

[0101] (3.1) Causal relationship diagram:

[0102] like Figure 4 As shown, for multi-level errors that are related (such as level 3 "insufficient sample size" → level 2 "reaction solution ratio imbalance" → level 1 "invalid test result"), the interface displays the relationship in the form of a directed graph (arrows point from lower-level errors to higher-level errors), and the node size represents the error level (level 1 nodes are the largest).

[0103] (3.2) Sample-Error Mapping:

[0104] In high-throughput testing equipment, the interface is based on a sample tray layout. The wells corresponding to erroneous samples are marked with error level colors. Clicking on a well can expand the error chain of that sample (e.g., "Sample A3 → Insufficient sample quantity → Abnormal reaction → Invalid result").

[0105] (3.3) Dynamic annotation function:

[0106] Users can add custom annotations to the correlation graph (such as "Insufficient sample size here is due to operational error"). The annotations are linked to the error information and stored for later viewing.

[0107] (3.4) Error handling process recording and feedback mechanism

[0108] Handling Step Guidance and Recording: For different levels of errors, the system provides standardized handling process guidance (such as "Stop → Clean → Calibrate → Restart" for level 1 errors). After each step is completed by the user (click "Confirm Cleaning Complete"), the system automatically records the operation time, operator, and operation result (such as "Negative Contamination Detection after Cleaning").

[0109] Multimodal feedback adaptation:

[0110] Trigger differentiated feedback based on error level:

[0111] Level 1 error: red flashing indicator light + high-frequency beep (continues until processing begins) + full-screen pop-up window (including the source tracing entry point).

[0112] Level 2 error: Yellow constant indicator light + single beep + pop-up window on the side of the interface (including troubleshooting steps).

[0113] Level 3 error: Blue indicator light + bottom prompt bar.

[0114] Level 4 error: Green indicator light + status bar text prompt.

[0115] (4) After error handling is completed, the system automatically generates a traceability report containing "error information → traceability link → processing record → result verification" (see Figure 5 It supports PDF export and electronic signatures, meeting clinical compliance requirements.

[0116] Application Example 1

[0117] Taking a clinical real-time quantitative PCR instrument (model: CFX96) as an example, the actual operation process of the method of the present invention is described in detail:

[0118] 1. System initialization and parameter configuration

[0119] After the device is powered on, the system automatically performs a hardware self-test: the image sensor captures a baseline image of the sample chamber for subsequent contamination comparison, the sensor array warms up for 3 minutes to ensure data stability, and the software module is loaded and verified to check file integrity.

[0120] Administrators configure system parameters via touchscreen:

[0121] Data retention period: 30 days by default (can be extended to 90 days).

[0122] Error warning thresholds: For example, when the remaining consumables are ≤20%, the level 3 error trigger threshold is set; for example, the level 2 error temperature fluctuation threshold is ±1℃.

[0123] Report generation template: Select a PDF template that meets CLIA certification requirements. The template includes an electronic signature field, a timestamp encryption area, etc.

[0124] 2. Error detection and graded response (taking sample contamination as an example)

[0125] 2.1 Error Trigger:

[0126] (1) When the PCR instrument ran for 45 cycles (16:42), the image sensor captured green fluorescence residue in the B4 well of the sample compartment three times in a row (normally there should be no fluorescence), and the fluorescence intensity detector read 23000 RFU (threshold ≤ 5000 RFU).

[0127] (2) The data acquisition service packages the image features and sensor values ​​and sends them to the error classification engine. The engine matches the "sample contamination" feature library and determines it as a level 1 error (fatal error), generating the code "E1-SP-202310151642" (E1=level 1, SP=sample contamination, timestamp accurate to the second).

[0128] 2.2 Multimodal Feedback Startup:

[0129] (1) The sample compartment indicator light turns red and flashes (frequency 2 Hz), and the buzzer emits a 4 kHz high-frequency prompt tone (lasts for 5 seconds, with a 2-second interval).

[0130] (2) A full-screen pop-up window appears on the touch screen (occupying 80% of the display area). The top displays the error code and level, the middle displays a real-time pollution image thumbnail, and the bottom has three operation buttons: "View Source Tracing", "Processing Guide", and "Pause Equipment".

[0131] 3. Interactive traceability operation process

[0132] 3.1 Construction of the traceability chain:

[0133] (1) After clicking “View Source”, the system calls the interactive traceability engine to automatically extract the relevant data from the 30 minutes before the error occurred (16:12-16:42) and construct a timeline:

[0134] 16:12: Operator Li, a technician, logs into the system (employee number L2023) and loads the B4 well sample (sample number Sample-20231015-084).

[0135] 16:15: Sample volume test showed 9.8 μL (standard 10 μL ± 0.5 μL, qualified), and the image sensor recorded the liquid level position.

[0136] 16:20: The equipment door was opened (Operation log: Technician Li, reason "replenishing reaction solution"), lasting 15 seconds.

[0137] 16:30: The temperature sensor recorded that the temperature of the sample chamber fluctuated to 40℃ (set value 37℃, deviation 3℃) for 2 minutes.

[0138] Timeline nodes are color-coded: green (normal operation), yellow (abnormal parameters), and red (error nodes).

[0139] 3.2 Drill-down query

[0140] (1) Click the “Equipment door open” node at 16:20 to expand the detailed record: including the opening time (15 seconds), the ambient temperature and humidity at that time (25℃, 60% RH), and the screenshot of the operator’s electronic signature.

[0141] (2) Click the “Temperature Fluctuation” node at 16:30 to bring up the temperature curve chart (horizontal axis time, vertical axis temperature). Abnormal intervals are marked with red shading and the curve analysis conclusion is attached: “Temperature imbalance may be caused by opening the equipment door.”

[0142] 4. Multi-level error association and handling log

[0143] 4.1 Causal Relationship Diagram Display:

[0144] (1) The system automatically identifies the association error: Level 3 "Abnormal opening of equipment door" → Level 2 "Excessive temperature fluctuation" → Level 1 "Sample contamination", in the graph:

[0145] Level 3 nodes (blue triangles) are labeled "Door opening time 15 seconds > safety threshold 10 seconds".

[0146] Level 2 nodes (yellow squares) are labeled "Temperature deviation 3℃ > threshold 1℃".

[0147] Level 1 nodes (red circles) are marked with "23,000 RFU of fluorescent residue".

[0148] (2) Arrows indicate causal weights (e.g., “Equipment door opening → temperature fluctuation” weight 0.92), allowing users to click the arrows to view the correlation basis (e.g., 92% of temperature fluctuations in historical data were caused by opening the door).

[0149] 4.2 Standardized Processing and Recording:

[0150] (1) Click “Processing Guide”, and the system will load the Level 1 error handling process:

[0151] Step 1 (completed at 16:45): Click “Confirm Shutdown”, the equipment will automatically stop running, and the shutdown time will be recorded.

[0152] Step 2 (completed at 16:50): Remove the sample from well B4, take a picture of the contaminated sample, automatically upload it to the system, and record the sample disposal method as autoclaving.

[0153] Step 3 (completed at 17:05): Clean the sample chamber with 75% alcohol, take a picture after cleaning with the image sensor, and compare it with the baseline image to confirm that the contamination has been removed.

[0154] Step 4 (completed at 17:10): Calibrate the temperature sensor (current deviation is 0.2℃, which is acceptable), and record the calibration personnel as Engineer Wang (employee number W2019).

[0155] (2) Each step requires the operator to click “Confirm Completion” and write an electronic signature (supports pressure-sensitive pen input). The system will automatically generate an operation log with a timestamp.

[0156] 5. Traceability Report Generation and Export

[0157] (1) After processing, the system generates a PDF traceability report within 10 seconds, which contains 5 core modules:

[0158] Error message summary: code, level, time of occurrence, brief description.

[0159] Source tracing map: timeline screenshot + key node data table.

[0160] Causal relationship analysis: graph screenshot + basis for weight calculation.

[0161] Processing record details: step number, operation content, time, signature.

[0162] Verification results: Equipment status after processing (e.g., temperature deviation of 0.2℃), re-detection data (new sample in well B4 showed no fluorescent contamination).

[0163] (2) The report is encrypted with AES-256 and supports export via USB interface or direct upload to LIMS system. A unique verification code is automatically generated during export (for subsequent tamper verification).

[0164] Application Example 2

[0165] This application example includes the following process:

[0166] (1) During the operation of the PCR instrument, at 14:30, "invalid test result" (level 1 error, code E1-RE-202310011430) was detected. The system triggered a red flashing indicator light and a high-frequency beep, and a full-screen pop-up window appeared on the interface.

[0167] A pop-up window displays the error message, code, and a "View Traceability Link" button. After clicking, the user enters the traceability panel.

[0168] (2) Time axis display on the traceability panel: 14:20 Sample loading (operator: Dr. Zhang) → 14:22 Sample volume detection (A3 sample volume is only 8 μL, standard is 10 μL) → 14:25 Addition of reaction solution → 14:30 Invalid test result.

[0169] (3) Click the “14:22 Sample Quantity Detection” node to view the image of sample A3 (showing that the liquid level is below the standard line) and sensor data (weight detection 8 μL) to confirm that the sample quantity is insufficient.

[0170] The causal relationship diagram shows: Level 3 "Insufficient sample size (A3)" → Level 2 "Imbalance in reaction solution ratio" → Level 1 "Invalid test results", with arrows clearly indicating the correlation path.

[0171] Users can annotate on the graph that "insufficient sample volume is caused by pipette calibration deviation," and the annotation will be automatically saved.

[0172] (4) The system provides the following processing instructions: “1. Stop the machine and remove sample A3 → 2. Prepare the sample again (ensure 10 μL) → 3. Calibrate the pipette → 4. Restart the detection.”

[0173] After the user completes each step and clicks "confirm," the system records information such as "Sample replacement completed at 14:35" and "Pipette calibration passed at 14:40."

[0174] (5) After the test is completed, the system generates a traceability report, which includes error codes, traceability link screenshots, processing records, and operator signature column. Users can export the report as a PDF archive for clinical review.

[0175] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, alterations, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for displaying and providing feedback on multi-level error information with interactive traceability for biological detection equipment, characterized in that, Includes the following steps: (1) The error information collected in real time is encoded according to the "error level-error type-time stamp" rule; the error level is divided into the following 4 levels: Level 1, resulting in termination of the testing process and invalidation of the data; Level 2: The testing process continues, but the data is unreliable. Level 3: The test data is valid but requires attention. Level 4, does not affect the status notification for detection; (2) Based on the equipment operation data within 30 minutes before the error occurred, construct a traceability link including error code, preceding event, and associated data. The traceability link supports time axis indexing and fast node query. For multi-level errors that are related, their relationships are displayed in the form of a directed graph; for high-throughput detection, a sample-error mapping is performed. (3) Trigger differentiated feedback based on the error level, and provide standardized processing flow guidance for different error levels to guide users through each step of the operation, while automatically recording the operation time, operator, and operation result; the differentiated feedback includes: A Level 1 error is indicated by a flashing red indicator light, a continuous high-frequency beep, and a full-screen pop-up window on the interface containing the source tracing entry point. A Level 2 error is indicated by a solid yellow indicator light, a single beep, and a pop-up window on the side of the interface containing the processing steps. Level 3 errors are indicated by a blue indicator light and a notification bar at the bottom of the screen. Level 4 errors are indicated by a green indicator light and a text message in the status bar. (4) After the error is handled, the system automatically generates a traceability report, which includes an error information summary, a traceability link map, a causal relationship analysis, a detailed processing record, and result verification.

2. The method according to claim 1, characterized in that, The error levels are as follows: Level 1 is a fatal error, including sample contamination and core module failure; Level 2 is a serious error, including excessive temperature fluctuation and abnormal fluorescence signal; Level 3 is a minor error, including insufficient sample volume, insufficient consumables, and duplicate sample numbers; Level 4 is a prompt message, including partial completion and successful data saving. The error types include sample-related errors, which include sample contamination; the timestamp is specified to the year, month, day, hour, minute, and second.

3. The method according to claim 1, characterized in that, In step (2), the traceability link is displayed in the form of a timeline, and the timeline nodes include event descriptions and related data entry points; the preceding events include sample loading operation records, operator information, time, and sample chamber cleaning status records; the related data include photos of the sample chamber interior when contamination occurs and air cleanliness sensor curves; The sample-error mapping is based on the sample tray layout. The holes corresponding to the error samples are marked with error level colors. Clicking on a hole will expand the error association chain of that sample.

4. The method according to claim 3, characterized in that, The traceability link supports "drill-down query" and "reverse tracing" functions; the drill-down query allows users to click on nodes on the timeline to view a comparison table of parameters before and after the adjustment, as well as real-time curves including changes in temperature and humidity; the reverse tracing allows users to input an operation record to query the subsequent errors that the operation may cause and provide early warnings of potential risks.

5. The method according to claim 1, characterized in that, In step (2), when there are related multi-level errors, the relationship is displayed in the form of a directed graph by drawing a causal relationship map, with arrows pointing from low-level errors to high-level errors, and the error level is represented by the node size; Users can add custom annotations to the associated graph, and these annotations are linked to and stored with error messages for later viewing.

6. A multi-level error information display and feedback system with interactive traceability for biological detection equipment, characterized in that, include: A data collection module is used to collect equipment operation data in real time; the data collection module includes an image sensor, a multi-parameter sensor array, and a data storage unit. A multi-level error classification module is used to classify the collected error information. An interactive traceability module is available for querying and tracking the cause of errors. The interactive traceability module includes a traceability panel, which contains a timeline, associated data entry points, and a reverse traceability button. The associated data visualization module is used to visually display the associated data of errors and the processing flow; The process recording module is used to generate traceability reports.

7. The system according to claim 6, characterized in that, The multi-parameter sensor array includes a temperature sensor, a humidity sensor, a pressure sensor, and a fluorescence intensity detector; the multi-level error classification module includes an indicator light and a buzzer.

8. The system according to claim 7, characterized in that, The data collection module packages image features and sensor values ​​and sends them to the error classification engine of the multi-level error classification module. The engine matches the error type database, determines the error level, and generates an error code. At this time, the indicator light and buzzer will start, and the traceability panel pop-up window will appear.

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