Laboratory equipment monitoring method and device

Abstract

This application relates to the field of equipment management technology, and in particular to a method and apparatus for monitoring equipment in a laboratory. The method includes: acquiring images of the actual laboratory scene; determining equipment information for at least one piece of testing equipment based on the images; displaying the equipment information for each piece of testing equipment while simultaneously determining whether each piece of testing equipment meets preset abnormality conditions based on the equipment information; wherein, if the preset abnormality conditions are met, an equipment abnormality alarm is triggered for the user. This solves the problem that related technologies can only detect equipment abnormalities through manual inventory and lack real-time alarm functionality, thus making it difficult to detect equipment abnormalities technically.

Description

Technical Field

[0001] This application relates to the field of equipment management technology, and in particular to a method and apparatus for monitoring equipment in a laboratory. Background Technology

[0002] In scientific research activities, laboratory equipment is an important material basis for conducting experimental research. Its asset supervision plays a vital role in ensuring the normal conduct of experiments, improving equipment utilization, reducing management costs, and preventing equipment loss or damage. With the development of science and technology, the number of laboratory equipment is constantly increasing and the types are becoming more and more complex, which also increases the difficulty of supervising equipment assets. Traditional laboratory equipment asset supervision methods are no longer able to meet the needs of modern laboratory management.

[0003] Early manual registration and inventory methods not only consumed a lot of manpower and time, but were also prone to omissions and errors, resulting in inaccurate equipment information. With the advancement of technology, some monitoring methods based on automation technology have emerged, but these methods still have many limitations in practical applications.

[0004] In the field of laboratory equipment asset supervision, the mainstream solutions and related technologies currently include the following:

[0005] 1. Manual registration and inventory: Laboratory management personnel regularly register the equipment manually, recording information such as the quantity, location, and status of the equipment, and conduct manual inventory checks.

[0006] 2. Barcode recognition technology: A unique barcode label is affixed to each piece of laboratory equipment. Managers use barcode scanners to scan the barcodes on the equipment to obtain equipment information and complete operations such as registration and inventory.

[0007] The most common similar solution is manual barcode recognition technology, which relies on laboratory administrators to manually inventory equipment regularly (e.g., weekly / monthly). The system reads the labels or barcodes on the equipment using a barcode scanner, records the current location and status of the equipment, and enters the inventory data into an Excel spreadsheet or laboratory asset management system to store the static information of the equipment. When the equipment information changes, the ledger needs to be updated manually.

[0008] However, the above-mentioned solutions in related technologies can only detect equipment abnormalities (such as missing or misplaced items) through manual inventory, and there is no real-time alarm function, resulting in a lag in the detection of abnormalities. Summary of the Invention

[0009] This application provides a laboratory equipment monitoring method and apparatus to solve the problems that related technologies can only detect equipment abnormalities through manual inventory and lack real-time alarm functions, thus making it difficult to detect equipment abnormalities technically.

[0010] The first aspect of this application provides a laboratory equipment monitoring method, comprising the following steps: acquiring actual scene images of the laboratory; determining equipment information of at least one test device based on the actual scene images; while displaying the equipment information of each test device, determining whether each test device meets preset abnormal conditions based on the equipment information, wherein if the preset abnormal conditions are met, an equipment abnormality alarm is triggered for the user.

[0011] Through the above-mentioned technical means, the embodiments of this application can collect laboratory scene images and extract various information of the test equipment from them. While visually displaying the equipment information, the equipment information is compared with preset abnormal conditions. The equipment that meets the abnormal conditions will trigger an alarm in real time. This can realize the integrated and automated management of test equipment information collection, display and abnormal warning, and greatly improve the intelligence level and risk response efficiency of laboratory equipment operation and maintenance.

[0012] Optionally, in one embodiment of this application, determining the equipment information of at least one test device based on the actual scene image includes: inputting the actual scene image into a pre-trained YOLO model, extracting at least one device feature, and identifying at least one of the category, location coordinates, appearance status, and basic information of each test device according to the at least one device feature; and generating the equipment information of the at least one test device according to the category, location coordinates, appearance status, and basic information of each test device.

[0013] Through the above-mentioned technical means, the embodiments of this application can accurately extract core features such as the category, location coordinates, appearance status and basic information of the test equipment by inputting actual laboratory scene images into a pre-trained YOLO model, and efficiently generate structured equipment information. This not only enables automated identification and information collection of test equipment, replacing the inefficient mode of manual inspection, but also improves the accuracy and real-time nature of equipment information, providing reliable data support for subsequent equipment anomaly monitoring and intelligent management.

[0014] Optionally, in one embodiment of this application, determining whether each test device meets the preset abnormal conditions based on the device information includes: retrieving target information of each test device from a preset database; and determining whether each test device meets the preset abnormal conditions based on the target information and the device information.

[0015] Through the above-mentioned technical means, the embodiments of this application can retrieve the target benchmark information of the test equipment from the preset database and accurately compare it with the real-time collected equipment information, thereby realizing the automated judgment of abnormal equipment status. This not only avoids the subjectivity and lag of manual inspection, but also can quickly identify abnormal situations such as equipment performance deviation and maintenance overdue based on preset rules, thereby significantly improving the accuracy and response efficiency of laboratory equipment management.

[0016] Optionally, in one embodiment of this application, determining whether each test device meets a preset abnormal condition based on the target information and the device information includes: obtaining the placement area and / or return time of any test device according to the target information and the device information; and determining that any test device is abnormal if the placement area is not within a preset area and / or the return time is not within a preset time period.

[0017] Through the above-mentioned technical means, the embodiments of this application can accurately compare the real-time collection of the placement area and return time information of the test equipment with the target area and target return time period stored in the preset database. This can quickly identify abnormal situations such as misplacement of equipment and return timeout, thereby realizing automated and standardized abnormal judgment of equipment position and return time, effectively avoiding the oversight of manual management, and improving the standardization and management efficiency of daily operation and maintenance of laboratory equipment.

[0018] Optionally, in one embodiment of this application, the step of providing an alarm for device malfunction to the user includes: matching the alarm action of any of the test devices according to the placement area and / or return time; and executing the alarm action to provide an alarm for device malfunction to the user.

[0019] Through the above-mentioned technical means, the embodiments of this application can accurately match and execute corresponding alarm actions based on different abnormality types such as abnormal equipment placement area and timeout of return time. This enables differentiated early warning of abnormal equipment conditions, effectively improves the pertinence and timeliness of alarms, helps managers quickly locate abnormal problems and take disposal measures, and reduces the risk of laboratory equipment management.

[0020] Optionally, in one embodiment of this application, after matching the alarm action of any of the test devices, the method further includes: identifying the actual type of any of the test devices; and correcting the alarm action according to the weight corresponding to the actual type.

[0021] Through the above-mentioned technical means, the embodiments of this application can, after matching basic alarm actions for equipment anomalies, identify the actual type of equipment and call the corresponding weight level to modify the alarm actions, thereby realizing hierarchical early warning based on equipment importance. This allows high-precision metering and safety management equipment with high weight to trigger higher-level alarm measures, avoiding the interference of conventional equipment alarms with the risk handling of core equipment, and significantly improving the accuracy of anomaly alarms and the rationality of the priority of laboratory equipment risk management.

[0022] A second aspect of this application provides a laboratory equipment monitoring device, comprising: a data acquisition module for acquiring images of an actual laboratory scene; a determination module for determining equipment information of at least one experimental device based on the actual scene images; and a monitoring module for displaying the equipment information of each experimental device while determining whether each experimental device meets preset abnormal conditions based on the equipment information, wherein if the preset abnormal conditions are met, an equipment abnormality alarm is triggered for the user.

[0023] Through the above-mentioned technical means, the embodiments of this application can collect laboratory scene images and extract various information of the test equipment from them. While visually displaying the equipment information, the equipment information is compared with preset abnormal conditions. The equipment that meets the abnormal conditions will trigger an alarm in real time. This can realize the integrated and automated management of test equipment information collection, display and abnormal warning, and greatly improve the intelligence level and risk response efficiency of laboratory equipment operation and maintenance.

[0024] Optionally, in one embodiment of this application, the determining module includes: an identification unit, configured to input the actual scene image into a pre-trained YOLO model, extract at least one device feature, and identify at least one of the category, location coordinates, appearance status, and basic information of each test device based on the at least one device feature; and a generation unit, configured to generate device information of the at least one test device based on the category, location coordinates, appearance status, and basic information of each test device.

[0025] Through the above-mentioned technical means, the embodiments of this application can accurately extract core features such as the category, location coordinates, appearance status and basic information of the test equipment by inputting actual laboratory scene images into a pre-trained YOLO model, and efficiently generate structured equipment information. This not only enables automated identification and information collection of test equipment, replacing the inefficient mode of manual inspection, but also improves the accuracy and real-time nature of equipment information, providing reliable data support for subsequent equipment anomaly monitoring and intelligent management.

[0026] Optionally, in one embodiment of this application, the monitoring module includes: a retrieval unit, configured to retrieve target information of each test device from a preset database; and a judgment unit, configured to judge whether each test device meets preset abnormal conditions based on the target information and the device information.

[0027] Through the above-mentioned technical means, the embodiments of this application can retrieve the target benchmark information of the test equipment from the preset database and accurately compare it with the real-time collected equipment information, thereby realizing the automated judgment of abnormal equipment status. This not only avoids the subjectivity and lag of manual inspection, but also can quickly identify abnormal situations such as equipment performance deviation and maintenance overdue based on preset rules, thereby significantly improving the accuracy and response efficiency of laboratory equipment management.

[0028] Optionally, in one embodiment of this application, the judgment unit is configured to: obtain the placement area and / or return time of any test device based on the target information and the device information; and determine that any test device is abnormal if the placement area is not within a preset area and / or the return time is not within a preset time period.

[0029] Through the above-mentioned technical means, the embodiments of this application can accurately compare the real-time collection of the placement area and return time information of the test equipment with the target area and target return time period stored in the preset database. This can quickly identify abnormal situations such as misplacement of equipment and return timeout, thereby realizing automated and standardized abnormal judgment of equipment position and return time, effectively avoiding the oversight of manual management, and improving the standardization and management efficiency of daily operation and maintenance of laboratory equipment.

[0030] Optionally, in one embodiment of this application, the monitoring module further includes: a matching unit, configured to match the alarm action of any of the test devices according to the placement area and / or return time; and an alarm unit, configured to execute the alarm action to alert the user of device malfunction.

[0031] Through the above-mentioned technical means, the embodiments of this application can accurately match and execute corresponding alarm actions based on different abnormality types such as abnormal equipment placement area and timeout of return time. This enables differentiated early warning of abnormal equipment conditions, effectively improves the pertinence and timeliness of alarms, helps managers quickly locate abnormal problems and take disposal measures, and reduces the risk of laboratory equipment management.

[0032] Optionally, in one embodiment of this application, it further includes: an identification module, configured to identify the actual type of any test device after matching the alarm action of any test device; and a correction module, configured to correct the alarm action according to the weight corresponding to the actual type.

[0033] Through the above-mentioned technical means, the embodiments of this application can, after matching basic alarm actions for equipment anomalies, identify the actual type of equipment and call the corresponding weight level to modify the alarm actions, thereby realizing hierarchical early warning based on equipment importance. This allows high-precision metering and safety management equipment with high weight to trigger higher-level alarm measures, avoiding the interference of conventional equipment alarms with the risk handling of core equipment, and significantly improving the accuracy of anomaly alarms and the rationality of the priority of laboratory equipment risk management.

[0034] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the laboratory equipment monitoring method as described in the above embodiments.

[0035] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described laboratory equipment monitoring method.

[0036] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described laboratory equipment monitoring method.

[0037] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0038] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a laboratory equipment monitoring method according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a laboratory equipment monitoring device according to an embodiment of this application; Figure 3 This is a frame diagram of a monitoring device according to a specific embodiment of this application; Figure 4 This is a flowchart of a regulatory process according to a specific embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0039] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0040] The following describes a laboratory equipment monitoring method and apparatus according to embodiments of this application with reference to the accompanying drawings. Addressing the problem mentioned in the background section that related technologies can only detect equipment anomalies through manual inventory and lack real-time alarm functions, thus making it difficult to detect equipment anomalies technically, this application provides a laboratory equipment monitoring method. In this method, features of real-time acquired scene images can be extracted and analyzed based on a pre-trained neural network model to determine the actual information of the equipment. This information is then compared with target information in an equipment information database to determine whether the equipment is abnormal. This method enables real-time monitoring of laboratory equipment assets, timely grasp of equipment location, status, and other information, and can use machine vision verification to replace manual verification, improving the efficiency of laboratory equipment asset monitoring and reducing time and labor costs. Therefore, it solves the problems of related technologies that can only detect equipment anomalies through manual inventory and lack real-time alarm functions, thus making it difficult to detect equipment anomalies technically.

[0041] In recent years, with the rapid development of deep learning technology, significant breakthroughs have been achieved in the field of object detection. The YOLO (You Only Look Once) object detection algorithm has emerged as a result. YOLO is a single-stage object detection algorithm based on convolutional neural networks. It breaks away from the traditional two-stage detection algorithm that first extracts candidate regions and then classifies and regresses them. It can directly predict the category and location of objects on a single image, greatly improving detection speed. In practical applications, the YOLO algorithm has been continuously iterated and optimized, maintaining high-speed detection while gradually improving detection accuracy. It not only performs excellently in general object detection but also demonstrates strong application potential in specialized fields such as industrial quality inspection and security monitoring.

[0042] Compared with traditional target recognition methods based on template matching or feature engineering, the YOLO algorithm can automatically learn the feature representation of the target and has better recognition capabilities in complex scenes, lighting changes, target occlusion, etc., providing strong technical support for achieving high-precision and real-time target detection tasks.

[0043] Compared to traditional manual supervision, machine vision-based supervision solutions use fixed cameras to capture images of the test bench area and employ machine learning to identify equipment and determine its location. This significantly improves supervision efficiency, eliminating the need for manual inventory of each device and enabling 24 / 7 monitoring. Furthermore, it avoids errors caused by visual fatigue and subjective judgment during manual identification, processing image information rapidly with high accuracy—5-8 frames per second—far exceeding the speed of manual inspections. This allows for efficient and precise supervision of laboratory equipment assets. Traditional solutions cannot simultaneously meet the requirements of real-time, accurate, and intelligent supervision. Solutions based on the YOLO object detection algorithm overcome these limitations, capturing real-time images of equipment through laboratory cameras and combining this with deep learning models to achieve high-precision identification and positioning, providing a new approach for dynamic, full-scene supervision of laboratory equipment assets.

[0044] Specifically, Figure 1 This is a schematic flowchart of a laboratory equipment monitoring method provided in an embodiment of this application.

[0045] like Figure 1 As shown, the equipment supervision method for this laboratory includes the following steps: In step S101, images of the actual scene in the laboratory are acquired.

[0046] In actual implementation, embodiments of this application can deploy high-definition cameras at key locations in the laboratory, thereby acquiring real-time images of the actual scene based on the cameras.

[0047] For example, this application embodiment can select a Hikvision DS-2CD3T47WD-L high-definition surveillance camera, which has a resolution of up to 4 megapixels, ensuring clear capture of laboratory equipment details, and can also clearly image small equipment (such as metallurgical microscopes); its frame rate can reach up to 25fps, which can meet the needs of real-time acquisition of dynamic scenes in the laboratory, such as the rapid movement of equipment and the instantaneous actions of personnel operating equipment, all of which can be recorded smoothly; it also supports infrared night vision with an effective distance of up to 30 meters, which can ensure that image information can still be collected normally in the low-light test environment or when there is no one on duty at night; the waterproof and dustproof rating is IP67, which is suitable for installation in various laboratory environments, including areas with some dust or possible contact with water splashes.

[0048] According to the embodiments of this application, the equipment can be installed at key locations such as the entrance and exit of the laboratory, main passage, and concentrated equipment placement area, based on the layout of the laboratory and the distribution of equipment, to ensure no blind spots in monitoring and to fully cover the area where the equipment assets are located.

[0049] In step S102, based on the actual scene image, the equipment information of at least one test device is determined.

[0050] The equipment information of the test equipment may include, but is not limited to: basic identity information, operating status information, technical parameter information, operation and maintenance management information, etc., without specific limitations.

[0051] Basic identification information may include: equipment name, model and specifications, manufacturer, serial number, manufacturing date, equipment asset number, installation location, etc.

[0052] Operating status information may include: current operating mode (standby / run / calibration), real-time operating parameters (such as the sampling flow rate of the exhaust gas analyzer and the speed of the engine test bench), fault codes, and whether it is in an alarm state.

[0053] Technical parameters may include: rated power, measurement range, accuracy class, applicable test types (such as engine emission testing / component durability testing), calibration validity period, etc.

[0054] Operation and maintenance management information may include: the time of the most recent calibration, the cumulative runtime, the maintenance record number, the responsible person, and the next scheduled maintenance time.

[0055] Optionally, in one embodiment of this application, determining the equipment information of at least one test device based on an actual scene image includes: inputting the actual scene image into a pre-trained YOLO model, extracting at least one device feature, and identifying at least one of the category, location coordinates, appearance status, and basic information of each test device based on the at least one device feature; and generating the equipment information of at least one test device based on the category, location coordinates, appearance status, and basic information of each test device.

[0056] In actual implementation, the embodiments of this application can input the collected scene images into a pre-trained YOLO model via wired transmission or Wi-Fi. The model is then used to extract and analyze the features of the images, thereby accurately locating the laboratory equipment in the scene and identifying its category. Information such as the location coordinates and appearance status of the equipment can be generated simultaneously.

[0057] Through the above-mentioned technical means, the embodiments of this application can accurately extract core features such as the category, location coordinates, appearance status and basic information of the test equipment by inputting actual laboratory scene images into a pre-trained YOLO model, and efficiently generate structured equipment information. This not only enables automated identification and information collection of test equipment, replacing the inefficient mode of manual inspection, but also improves the accuracy and real-time nature of equipment information, providing reliable data support for subsequent equipment anomaly monitoring and intelligent management.

[0058] In step S103, while displaying the equipment information of each test device, it is determined whether each test device meets the preset abnormal conditions based on the equipment information. If the preset abnormal conditions are met, an equipment abnormality alarm is issued to the user.

[0059] For example, in a materials mechanical properties testing laboratory setting, this embodiment of the application can deploy a high-definition industrial camera to collect images of the test area, and use an edge computing terminal equipped with a lightweight YOLOv5 model and an OCR recognition module to extract information such as the model, calibration validity period, and real-time test load of equipment such as universal testing machines and hardness testers, and display it on the monitoring platform. At the same time, this information is compared with preset abnormal conditions (such as calibration expiration or load fluctuation exceeding the threshold). For equipment that meets the abnormal conditions, the platform information module is immediately highlighted in yellow and an SMS alarm is sent to the management personnel, realizing the automated collection, visualization, and real-time early warning of abnormalities of test equipment information.

[0060] Optionally, in one embodiment of this application, determining whether each test device meets the preset abnormal conditions based on the device information includes: retrieving the target information of each test device from a preset database; and determining whether each test device meets the preset abnormal conditions based on the target information and the device information.

[0061] In actual implementation, the embodiments of this application can simultaneously link the equipment information database to retrieve the basic equipment files, compare the target information in the basic equipment files with the equipment information, and thus determine whether each test equipment meets the preset abnormal conditions.

[0062] In conjunction with the requirements for anomaly detection in testing equipment, target information can be understood as benchmark parameters and standard thresholds pre-stored in a database for comparison with equipment information. Its content must correspond to the equipment's operating characteristics and management requirements, and can specifically include the following categories: Performance threshold information: the range of key parameters for normal operation of the equipment, such as the target temperature and humidity fluctuation threshold of the constant temperature and humidity chamber (±2℃ / ±5% RH), the load measurement accuracy threshold of the universal testing machine (≤±0.5%), and the sampling flow rate threshold of the exhaust gas analyzer (5-10L / min), etc.

[0063] Operation and maintenance management benchmark information: management standards for the entire life cycle of equipment, such as calibration cycle (12 months / time), cumulative running time threshold (8000h requires shutdown for maintenance), maintenance interval (filter element replacement every 3 months), factory rated parameters (rated power, measurement range), etc.

[0064] Status determination benchmark information: The status definition of normal operation of the equipment, such as the allowed working modes (standby / run / calibration), prohibited abnormal status codes (such as E01 sensor failure, E02 data transmission interruption), and the self-test pass standard before startup.

[0065] Safety and compliance benchmark information: safety thresholds and compliance requirements for equipment operation, such as the extreme operating temperature of high and low temperature test chambers (-40℃~150℃), the maximum test pressure threshold of pressure testing machines (≤100MPa), environmental safety standards for equipment operation (no open flame, ventilation volume ≥500m³ / h), and the appropriate placement area for the equipment.

[0066] Through the above-mentioned technical means, the embodiments of this application can retrieve the target benchmark information of the test equipment from the preset database and accurately compare it with the real-time collected equipment information, thereby realizing the automated judgment of abnormal equipment status. This not only avoids the subjectivity and lag of manual inspection, but also can quickly identify abnormal situations such as equipment performance deviation and maintenance overdue based on preset rules, thereby significantly improving the accuracy and response efficiency of laboratory equipment management.

[0067] Optionally, in one embodiment of this application, determining whether each test device meets the preset abnormal conditions based on target information and device information includes: obtaining the placement area and / or return time of any test device according to the target information and device information; and determining that any test device is abnormal if the placement area is not within the preset area and / or the return time is not within the preset time period.

[0068] Taking small testing devices such as electronic balances and portable pH meters as examples, this application embodiment can calculate the actual placement area and return time of the aforementioned small testing devices based on real-time acquired images using the YOLO algorithm. At the same time, it retrieves the preset placement area and return time threshold corresponding to the device from the preset database. After comparison, it is determined that electronic balances placed in non-preset areas and portable pH meters whose return time exceeds the threshold both meet the abnormal conditions. Subsequently, the platform pop-up alarm and administrator prompt can be triggered to realize the automated abnormal determination of the location and return time of small laboratory devices.

[0069] Through the above-mentioned technical means, the embodiments of this application can accurately compare the real-time collection of the placement area and return time information of the test equipment with the target area and target return time period stored in the preset database. This can quickly identify abnormal situations such as misplacement of equipment and return timeout, thereby realizing automated and standardized abnormal judgment of equipment position and return time, effectively avoiding the oversight of manual management, and improving the standardization and management efficiency of daily operation and maintenance of laboratory equipment.

[0070] Optionally, in one embodiment of this application, providing a device malfunction alarm to the user includes: matching the alarm action of any test device based on the placement area and / or return time; and executing the alarm action to provide a device malfunction alarm to the user.

[0071] For example, a smart camera is deployed above the designated placement area for small testing equipment such as electronic balances and portable pH meters in a physicochemical laboratory. The system uses visual recognition to extract the actual placement area of ​​the equipment and the return time after use. It then retrieves the designated placement area (electronic balance → area A, pH meter → area B) and the return time threshold (≤30 minutes) from a pre-set database. After comparison, it determines that the electronic balance placed in area C is an "area abnormality" and the pH meter that took 45 minutes to return is an "time abnormality." Subsequently, the system matches the corresponding alarm action according to different abnormality types—for the electronic balance with an area abnormality, the test area's audible and visual alarm is triggered to sound continuously and the equipment location map on the management platform flashes red; for the pH meter that has exceeded the return time, the administrator's mobile APP pushes an overtime reminder and the platform pops up a window displaying the information of the last user of the equipment. Finally, the system executes the above differentiated alarm actions to achieve accurate reminders and traceability management of equipment abnormalities.

[0072] Through the above-mentioned technical means, the embodiments of this application can accurately match and execute corresponding alarm actions based on different abnormality types such as abnormal equipment placement area and timeout of return time. This enables differentiated early warning of abnormal equipment conditions, effectively improves the pertinence and timeliness of alarms, helps managers quickly locate abnormal problems and take disposal measures, and reduces the risk of laboratory equipment management.

[0073] Optionally, in one embodiment of this application, after matching the alarm action of any test device, the method further includes: identifying the actual type of any test device; and correcting the alarm action according to the weight corresponding to the actual type.

[0074] For example, the actual placement area and return time of electronic balances (high-precision measuring devices), portable pH meters (routine testing devices), and pH calibration solution storage devices (safety control devices) are collected. Preset placement areas (balance → area A, pH meter → area B, calibration solution storage device → area D) and return time thresholds (≤30 minutes) are retrieved from a preset database. After comparison, it is determined whether the device should be placed in area C. The system identifies three devices: an electronic balance in the area (area anomaly), a pH meter that took 45 minutes to return to its original position (time anomaly), and a pH calibration solution storage device that took 35 minutes to return to its original position (time anomaly). The system first matches basic alarm actions based on the anomaly type: area anomalies trigger an audible and visual alarm and a red highlight on the platform; time anomalies trigger a push notification on the mobile app. Then, it identifies the actual type of each device and retrieves the preset weight levels (safety control devices weight 1.0 > high-precision metrology devices weight 0.8 > routine testing devices weight 0.5). Based on the weights, the system corrects the alarm actions: for the pH calibration solution storage device (weight 1.0), in addition to the basic app push notification, a dedicated SMS notification to the safety administrator and temporary access control lock for the laboratory are added; for the electronic balance (weight 0.8), in addition to the basic audible and visual alarm, a phone reminder to the person in charge of the device is added; for the portable pH meter (weight 0.5), the basic app push action is maintained without additional additions. Finally, the corrected tiered alarm actions are executed, achieving accurate risk warnings based on the importance of the devices.

[0075] Through the above-mentioned technical means, the embodiments of this application can, after matching basic alarm actions for equipment anomalies, identify the actual type of equipment and call the corresponding weight level to modify the alarm actions, thereby realizing hierarchical early warning based on equipment importance. This allows high-precision metering and safety management equipment with high weight to trigger higher-level alarm measures, avoiding the interference of conventional equipment alarms with the risk handling of core equipment, and significantly improving the accuracy of anomaly alarms and the rationality of the priority of laboratory equipment risk management.

[0076] The laboratory equipment monitoring method proposed in this application can extract and analyze the features of real-time acquired scene images based on a pre-trained neural network model to determine the actual information of the equipment. This information is then compared with target information in the equipment information database to determine whether the equipment is abnormal. This method enables real-time monitoring of laboratory equipment assets, allowing for timely access to information such as the equipment's location and status. Furthermore, it can replace manual verification with machine vision verification, improving the efficiency of laboratory equipment asset monitoring and reducing time and labor costs. This solves the problems of related technologies that can only detect equipment anomalies through manual inventory and lack real-time alarm functions, thus making it difficult to detect equipment anomalies technically.

[0077] Next, refer to the appendix. Figure 2 This application describes a laboratory equipment monitoring device proposed according to an embodiment.

[0078] Figure 2This is a block diagram of a laboratory equipment monitoring device according to an embodiment of this application.

[0079] like Figure 2 As shown, the equipment monitoring device 10 of the laboratory includes: a data acquisition module 100, a determination module 200, and a monitoring module 300.

[0080] The acquisition module 100 is used to acquire images of the actual scene in the laboratory.

[0081] The determination module 200 is used to determine the equipment information of at least one test device based on the actual scene image.

[0082] The monitoring module 300 is used to display the equipment information of each test device and determine whether each test device meets the preset abnormal conditions based on the equipment information. If the preset abnormal conditions are met, an equipment abnormality alarm will be issued to the user.

[0083] Optionally, in one embodiment of this application, the determining module 200 includes: an identification unit and a generation unit; wherein, the identification unit is used to input the actual scene image into a pre-trained YOLO model, extract at least one device feature, and identify at least one of the category, location coordinates, appearance status, and basic information of each test device based on the at least one device feature; the generation unit is used to generate device information of at least one test device based on at least one of the category, location coordinates, appearance status, and basic information of each test device.

[0084] Optionally, in one embodiment of this application, the monitoring module 300 includes: a retrieval unit and a judgment unit; wherein, the retrieval unit is used to retrieve target information of each test device from a preset database; and the judgment unit is used to judge whether each test device meets preset abnormal conditions based on the target information and device information.

[0085] Optionally, in one embodiment of this application, the determination unit is used to: obtain the placement area and / or return time of any test equipment based on the target information and equipment information; and determine that any test equipment is abnormal if the placement area is not within the preset area and / or the return time is not within the preset time period.

[0086] Optionally, in one embodiment of this application, the monitoring module 300 further includes: a matching unit and an alarm unit; wherein, the matching unit is used to match the alarm action of any test device according to the placement area and / or return time; the alarm unit is used to execute the alarm action to alert the user of equipment abnormality.

[0087] Optionally, in one embodiment of this application, the laboratory equipment monitoring device 10 further includes: an identification module and a correction module; wherein, the identification module is used to identify the actual type of any test equipment after matching the alarm action of any test equipment; and the correction module is used to correct the alarm action according to the weight corresponding to the actual type.

[0088] In other words, such as Figure 3 As shown, the equipment monitoring device of this application can be composed of an image acquisition module, a data transmission module, a YOLO target detection module, an equipment information database, an equipment information matching module, and a real-time monitoring module.

[0089] It may include: the main body of the device being tested, the components of the testing system, and the code of the identification and processing module.

[0090] The components of the monitoring device may include high-definition cameras, network transmission equipment (Ethernet devices, wireless routers, etc.), computers, and monitoring terminals (display devices, tablet computers, etc.). The identification and processing module code includes the system running code and the feature value dataset of the detected device.

[0091] The relationships between the various components are as follows: The image acquisition module is the system's visual input, acquiring raw images of the laboratory scene through deployed cameras to provide material sources for subsequent detection; the equipment information database stores static equipment files (name, model, etc.), providing a basic data pool for equipment information matching, and simultaneously receiving dynamic status updates; the data transmission module connects the image acquisition module and the YOLO target detection module, used for transmitting raw images; connects the YOLO target detection module and the equipment information matching module, used for transmitting detection results; connects the equipment information matching module and the real-time monitoring module, used for transmitting integrated information; and ensures real-time and stable data flow through networks such as Ethernet / Wi-Fi, with each module working collaboratively; the YOLO target detection module runs the YOLO model based on computer hardware, performing "recognition operations" (location, classification) on the devices in the acquired images, and outputting dynamic equipment information.

[0092] like Figure 4As shown, after the device is powered on, the system automatically completes initialization. The image acquisition module acquires real-time scene images through high-definition cameras deployed at key locations in the laboratory. The image data is then transmitted to the YOLO target detection module via wired or Wi-Fi transmission through the data transmission module. This module runs a trained YOLO model to extract and analyze features from the images, accurately locates the laboratory equipment in the image, identifies its category, and simultaneously generates information such as equipment location coordinates and appearance status. These identification results are then sent back to the data transmission module. The data transmission module sends the identification information to the real-time monitoring module and simultaneously retrieves the equipment's basic file from the equipment information database. The real-time monitoring module integrates and displays the equipment's real-time location, category, basic information, and status on a graphical interface. Based on preset rules such as normal area range and return time, it automatically determines whether the equipment is abnormal. If it crosses boundaries or fails to return to its original position for an extended period, an alarm is immediately triggered. At the same time, the equipment information database updates the equipment's dynamic data in real time, forming a complete closed-loop process from image acquisition, intelligent identification, information matching to monitoring alarms.

[0093] It should be noted that the foregoing explanation of the laboratory equipment monitoring method embodiment also applies to the laboratory equipment monitoring device of this embodiment, and will not be repeated here.

[0094] The laboratory equipment monitoring device proposed in this application can extract and analyze the features of real-time acquired scene images based on a pre-trained neural network model to determine the actual information of the equipment. This information is then compared with target information in the equipment information database to determine whether the equipment is abnormal. This method enables real-time monitoring of laboratory equipment assets, allowing for timely monitoring of equipment location, status, and other information. Furthermore, it can replace manual verification with machine vision verification, improving the efficiency of laboratory equipment asset monitoring and reducing time and labor costs. This solves the problems of related technologies that can only detect equipment anomalies through manual inventory and lack real-time alarm functions, thus making it difficult to detect equipment anomalies technically.

[0095] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0096] When processor 502 executes the program, it implements the laboratory equipment monitoring method provided in the above embodiments.

[0097] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0098] The memory 501 is used to store computer programs that can run on the processor 502.

[0099] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0100] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0101] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0102] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0103] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described laboratory equipment monitoring method.

[0104] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described laboratory equipment monitoring method.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring laboratory equipment, characterized in that, Includes the following steps: Collect images of the actual laboratory scene; Based on the actual scene image, determine the equipment information of at least one test device; While displaying the equipment information of each test device, the system determines whether each test device meets preset abnormal conditions based on the equipment information. If the preset abnormal conditions are met, an equipment abnormality alarm is triggered for the user.

2. The method according to claim 1, characterized in that, The process of determining the equipment information of at least one test device based on the actual scene image includes: The actual scene image is input into a pre-trained YOLO model to extract at least one device feature, and at least one of the following is identified for each test device based on the at least one device feature: category, location coordinates, appearance status, and basic information. Equipment information for at least one test device is generated based on at least one of the following: category, location coordinates, appearance condition, and basic information of each test device.

3. The method according to claim 1, characterized in that, The step of determining whether each test device meets the preset abnormal conditions based on the device information includes: Retrieve the target information of each test device from the preset database; Based on the target information and the equipment information, it is determined whether each test device meets the preset abnormal conditions.

4. The method according to claim 3, characterized in that, The step of determining whether each test device meets the preset abnormal conditions based on the target information and the device information includes: Based on the target information and the equipment information, obtain the placement area and / or return time of any test equipment; If the placement area is not within the preset area and / or the return time is not within the preset time period, the test equipment is determined to be abnormal.

5. The method according to claim 4, characterized in that, The method of providing device malfunction alarms to users includes: The alarm action of any of the test devices is matched according to the placement area and / or return time. The alarm action is executed to alert the user of a device malfunction.

6. The method according to claim 5, characterized in that, After matching the alarm action of any of the aforementioned test devices, the following is also included: Identify the actual type of any of the test devices; The alarm action is adjusted according to the weight corresponding to the actual type.

7. A laboratory equipment monitoring device, characterized in that, include: The acquisition module is used to acquire images of the actual laboratory scene. The determination module is used to determine the equipment information of at least one test device based on the actual scene image; The monitoring module is used to display the equipment information of each test device and determine whether each test device meets the preset abnormal conditions based on the equipment information. If the preset abnormal conditions are met, an equipment abnormality alarm is issued to the user.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the laboratory equipment monitoring method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the laboratory equipment monitoring method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the laboratory equipment monitoring method as described in any one of claims 1-6.

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