Inspection instrument state monitoring system

Abstract

The invention discloses an inspection instrument state monitoring system, which belongs to the technical field of medical inspection equipment, and comprises an equipment sensing layer, a data transmission layer, a data processing layer, an application display layer and a power supply module, the equipment sensing layer comprehensively acquires operation parameters, environment parameters and fault information of the inspection instrument through various sensors and universal interfaces; the data transmission layer adopts a wired + wireless dual-mode design to realize stable data transmission and breakpoint resume; the data processing layer realizes accurate identification and pre-judgment through an anomaly identification module and a fault pre-judgment module, and realizes centralized data management in combination with local and cloud storage; the application display layer realizes data visualization, remote monitoring and graded early warning through an upper computer and a mobile phone APP; the power module is equipped with a standby power supply to deal with temporary power failure. According to the method, the defects of low efficiency, early warning lagging and high operation and maintenance cost of traditional manual inspection are overcome, operation is easy and convenient, the cost is moderate, adaptability is high, real-time monitoring, accurate early warning and preventive operation and maintenance of the inspection instrument can be achieved, it is guaranteed that inspection work is carried out in order, and the primary medical inspection quality and efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of medical testing equipment technology, specifically relating to a testing instrument status monitoring system, which is particularly suitable for primary healthcare institutions such as hospitals. It can monitor the operating status of various instruments used in clinical testing in real time, provide early warning of anomalies, trace data, and manage operation and maintenance, ensuring the orderly conduct of testing work and improving the quality and efficiency of primary healthcare testing. Background Technology

[0002] As a core institution for primary healthcare services, the county people's hospital undertakes testing-related work such as screening for common diseases, health checkups, and clinical diagnosis for the local population. It uses a wide variety of testing instruments, including blood routine analyzers, biochemical analyzers, urine analyzers, coagulation analyzers, and enzyme-linked immunosorbent assay (ELISA) readers. These instruments are the core support for the accurate acquisition of clinical test data, and their operational stability and reliability directly affect the accuracy of test results, thereby influencing the scientific nature of clinical diagnosis and treatment decisions.

[0003] Currently, the county hospital mainly relies on manual inspection to monitor the status of its testing instruments. Medical staff need to manually record and observe the operating parameters and working status of various instruments at regular intervals, and then investigate and deal with any abnormalities found.

[0004] This traditional monitoring method has many drawbacks: First, manual inspection is inefficient. The workload of medical staff in the laboratory departments of primary hospitals is heavy, making it difficult to achieve real-time and comprehensive monitoring of all instruments. Blind spots in inspection are likely to occur, causing minor abnormalities of instruments to go undetected in time, which can then develop into serious malfunctions, causing interruptions in laboratory work and delaying the diagnosis and treatment of patients. Secondly, the abnormal warning is delayed. After the instrument malfunctions, it can only be detected when medical staff discover it during their rounds or when the test results show obvious deviations. It is impossible to predict potential malfunctions in advance, which not only affects the efficiency of testing, but may also lead to distorted test results and cause medical risks. Third, data management is chaotic. Manually recorded instrument operation data and fault information are scattered and lack a unified storage, statistics and analysis platform. It is difficult to trace the historical trajectory of instrument operation, and it is inconvenient to summarize the wear and tear patterns and high-incidence points of the instrument, which is not conducive to the precise operation and maintenance of the instrument and the extension of its lifespan. Fourth, the operation and maintenance costs are high. When an instrument fails suddenly, it is often necessary to contact the manufacturer's professionals for repair. However, grassroots hospitals face difficulties in transportation and communication, resulting in long repair cycles and a lack of preventive operation and maintenance guidance based on operational data. This leads to a persistently high instrument failure rate and increased operation and maintenance costs.

[0005] In the existing technology, there are some monitoring systems for the status of laboratory instruments. However, these systems are mostly designed for large tertiary hospitals, with complex structures and high costs. They require professional technicians to operate and maintain them, making them difficult to adapt to the human, material, and financial resources of primary healthcare institutions such as county hospitals. At the same time, existing systems mostly focus on monitoring single instruments and cannot achieve unified access and centralized management of various laboratory instruments. They also lack adaptability to the laboratory work processes and operation and maintenance models of primary hospitals. The warning threshold settings do not conform to the actual operation of instruments at the primary level, resulting in a high rate of false alarms and missed alarms, and poor practicality.

[0006] Therefore, in response to the actual needs of county-level people's hospitals for monitoring laboratory instruments, it is urgent to develop a laboratory instrument status monitoring system that is structurally sound, cost-effective, easy to operate, suitable for grassroots healthcare, and capable of comprehensive monitoring, early warning, and precise operation and maintenance. This system would address the shortcomings of existing manual inspections and traditional monitoring systems, ensure the stable operation of laboratory instruments, improve the quality and efficiency of grassroots medical testing, and reduce maintenance costs. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide a monitoring system for the status of laboratory instruments, suitable for application scenarios in primary healthcare institutions such as county-level hospitals. This system solves problems such as low efficiency, delayed anomaly warnings, chaotic data management, and high maintenance costs associated with traditional manual inspections, as well as the poor adaptability, high cost, and insufficient practicality of existing monitoring systems. The system enables real-time monitoring, accurate early warning, centralized management, and preventative maintenance of the operating status of laboratory instruments, ensuring the orderly conduct of laboratory work, improving laboratory quality and efficiency, and reducing medical risks and maintenance costs.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A testing instrument status monitoring system includes an equipment sensing layer, a data transmission layer, a data processing layer, an application display layer, and a power module. The layers are electrically connected in sequence, and the power module provides stable power to the entire system. The device sensing layer is used to collect operating status data, environmental parameter data, and fault-related data of various testing instruments. It includes multiple sensor units, instrument data acquisition interfaces, and fault detection units. The sensor units include temperature sensors, humidity sensors, vibration sensors, current sensors, and voltage sensors. The instrument data acquisition interface adopts a universal interface design, is compatible with commonly used hospital testing instruments, and can be directly connected to the instrument's built-in controller. The fault detection unit is used to detect abnormal signals during instrument operation and generate corresponding fault codes. The data transmission layer is used to filter and reduce noise in various types of data collected by the device sensing layer before stably transmitting them to the data processing layer. It includes a data preprocessing module, a wired transmission module, and a wireless transmission module. The data preprocessing module uses a filtering algorithm to denoise the raw data and standardizes its format. The wired transmission module uses an Ethernet interface. The wireless transmission module adopts a dual-mode design (WiFi and Bluetooth) and supports data resume functionality. The data processing layer is used to store, analyze, and process the transmitted data to achieve anomaly identification, fault prediction, and early warning. It includes a data storage module, a data parsing module, an anomaly identification module, a fault prediction module, and an early warning control module. The data storage module uses a combination of local storage and cloud backup. The data parsing module parses the standardized data and extracts key information. The anomaly identification module presets normal operating parameter thresholds and environmental parameter thresholds for various testing instruments, and compares the parsed data with the preset thresholds in real time to identify abnormal data. The fault prediction module establishes a fault prediction model based on machine learning algorithms, combined with historical instrument operating data and fault data. The early warning control module generates corresponding early warning signals based on the anomaly identification results and fault prediction results. The application presentation layer is used to realize the visualization of data, operation control, information query, and command issuance. It includes a host computer monitoring terminal, a mobile monitoring terminal, and an early warning unit. The host computer monitoring terminal adopts a touch operation interface and supports data query, historical trajectory tracing, parameter setting, and operation and maintenance record entry functions. The mobile monitoring terminal adopts a mobile phone APP and supports remote viewing of instrument status and receiving early warning information. The early warning unit includes a sound early warning device, a light early warning device, and an SMS early warning module. The power module includes an AC power interface, a DC voltage regulator module, and a backup power module. The AC power interface is used to connect to mains power, the DC voltage regulator module is used to convert the mains power into a stable DC voltage required by the system, and the backup power module is used to supply power to the system when the mains power is interrupted.

[0009] Furthermore, the universal interface includes an RS232 interface, a USB interface, and an Ethernet interface, which are compatible with testing instruments from different manufacturers and of different models.

[0010] Furthermore, the data preprocessing module employs a Kalman filter algorithm to remove random noise and environmental interference during the sensor data acquisition process, thereby improving the accuracy of data acquisition.

[0011] Furthermore, the machine learning algorithm in the fault prediction module adopts the decision tree algorithm, combines historical fault data and operation data of hospital testing instruments for model training, and regularly optimizes the model based on the updates of instrument operation data.

[0012] Furthermore, the application presentation layer also includes a permission management module, which divides the permissions of medical staff, operation and maintenance personnel, and administrators according to the job settings of the laboratory department of the primary hospital, so as to realize hierarchical operation management.

[0013] Furthermore, it also includes an operation and maintenance management module, which is electrically connected to the data processing layer to record the instrument's operation and maintenance information, generate operation and maintenance reminders, and realize preventive operation and maintenance.

[0014] Furthermore, the wireless transmission module supports dual-mode communication of WiFi 6 and Bluetooth 5.2.

[0015] Furthermore, the temperature sensor has a measurement range of 0-50℃ and an accuracy of ±0.1℃; the humidity sensor has a measurement range of 20%-90%RH and an accuracy of ±1%RH; the vibration sensor has a measurement range of 0-10mm / s and an accuracy of ±0.01mm / s; the current sensor has a measurement range of 0-10A and an accuracy of ±0.01A; and the voltage sensor has a measurement range of 0-220V and an accuracy of ±0.1V.

[0016] Furthermore, the backup power module uses a lithium battery pack, which can automatically switch power supply when the mains power is interrupted, maintaining normal system operation for 4-6 hours.

[0017] Furthermore, the warning unit emits sound and light prompts of different intensities according to the warning level. Level 1 warning corresponds to yellow light and low-pitched sound, Level 2 warning corresponds to orange light and rapid sound, and Level 3 warning corresponds to red light, sharp sound, and SMS warning.

[0018] Compared with the prior art, the present invention has the following advantages: Compared with the prior art, the present invention has the following beneficial effects: 1. High adaptability, well-suited to the application scenarios of county-level people's hospitals: This system adopts a universal interface design, which is compatible with various commonly used testing instruments in county-level people's hospitals. It does not require large-scale modification of existing instruments, has a moderate cost, is easy to operate, and does not require maintenance by professional technicians. It is suitable for the human, material, and financial resources of grassroots hospitals. At the same time, the system adopts a wired + wireless dual-mode transmission design, which flexibly adapts to the layout characteristics of the laboratory department. The backup power module can cope with temporary power outage scenarios, further improving the applicability of the system in grassroots hospitals.

[0019] 2. Achieve comprehensive and real-time monitoring, overcoming the drawbacks of manual inspection: This system, through multiple sensor units in the equipment perception layer and instrument data acquisition interfaces, can comprehensively collect the operating parameters, environmental parameters, working status, and fault-related data of the testing instruments, enabling real-time and all-round monitoring of various instruments. It eliminates the need for regular manual inspections, effectively reducing blind spots in inspections, lowering the workload of medical staff, and preventing abnormalities from going undetected due to human negligence.

[0020] 3. Precise early warning and fault prediction to reduce medical risks: This system, through the anomaly identification module and fault prediction module in the data processing layer, can compare operating data with preset thresholds in real time to identify abnormal data. At the same time, it can predict potential faults based on historical data and issue graded early warnings according to the severity of the anomalies. It can remind relevant personnel through various means such as sound, light, and SMS to achieve "early detection, early warning, and early handling", avoid test interruptions and distorted test results caused by instrument failure, reduce medical risks, and ensure the orderly conduct of test work.

[0021] 4. Centralized Data Management for Precise Operation and Maintenance: This system adopts a combination of local storage and cloud backup to centrally store and manage instrument operation data, fault data, and maintenance data. It supports data query and historical trajectory tracing, making it convenient for medical staff and maintenance personnel to summarize instrument wear patterns and high-incidence faults. At the same time, the maintenance management module can generate maintenance reminders to guide maintenance personnel to perform regular calibration and maintenance, realize preventive maintenance, extend the service life of instruments, reduce maintenance costs, and meet the low-cost maintenance needs of primary hospitals.

[0022] 5. Simple operation, practical functions, and easy to promote: The application display layer of this system adopts a design that combines a touch-screen host computer terminal with a mobile APP. The operation interface is simple and easy to use, which is adapted to the operating habits of primary healthcare workers. Healthcare workers and maintenance personnel can check the instrument status, receive early warning information, and handle faults anytime and anywhere. The overall system structure is reasonable, the cost is moderate, and the functions are practical. There is no need for complex hardware investment and technical support, making it easy to promote and apply in county people's hospitals and other primary healthcare institutions. Attached Figure Description

[0023] Figure 1 This is a diagram showing the overall architecture of the instrument status monitoring system of the present invention. Figure 2 This is a flowchart of the fault prediction and graded early warning system of the present invention; Figure 3 This is a schematic diagram showing the adaptation of the sensing layer of the device and the testing instrument of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] The present invention proposes a testing instrument status monitoring system, which includes an equipment sensing layer, a data transmission layer, a data processing layer, an application display layer, and a power module. The layers are electrically connected in sequence, and the power module provides stable power supply to the entire system. The device sensing layer is used to collect operating status data, environmental parameter data, and fault-related data of various testing instruments. It includes multiple sensor units, an instrument data acquisition interface, and a fault detection unit. The sensor units include temperature sensors, humidity sensors, vibration sensors, current sensors, and voltage sensors, used to collect data on ambient temperature, ambient humidity, instrument vibration amplitude, operating current, and operating voltage during instrument operation. The instrument data acquisition interface adopts a universal interface design, compatible with commonly used testing instruments in county hospitals such as blood routine analyzers, biochemical analyzers, and urine analyzers. It can directly connect to the instrument's built-in controller to collect data on instrument operating parameters, operating status (standby, running, fault), testing progress, and remaining consumables. The fault detection unit is used to detect abnormal signals during instrument operation, including short-circuit signals, abnormal component wear signals, and signals indicating excessive operating parameters, and generates corresponding fault codes. The data transmission layer is used to filter and reduce noise in various types of data collected by the device sensing layer before stably transmitting them to the data processing layer. It includes a data preprocessing module, a wired transmission module, and a wireless transmission module. The data preprocessing module uses a filtering algorithm to denoise the collected raw data, removing invalid data caused by environmental and equipment interference. It also standardizes the data format to ensure that all types of data are uniformly adapted to subsequent data processing. The wired transmission module uses an Ethernet interface for short-distance, highly stable data transmission, suitable for scenarios where laboratory instruments are centrally located. The wireless transmission module uses a dual-mode design (WiFi and Bluetooth) for long-distance data transmission in scenarios where wiring is inconvenient. It can flexibly adapt to the layout characteristics of the county hospital's laboratory and supports data interruption resumption to avoid data loss. The data processing layer, as the core of the system, is used to store, analyze, and process the transmitted data, enabling anomaly identification, fault prediction, and early warning. It includes a data storage module, a data parsing module, an anomaly identification module, a fault prediction module, and an early warning control module. The data storage module combines local storage with cloud backup. Local storage uses a large-capacity solid-state drive to store instrument operation data and fault data from the past 1-3 years for easy and quick retrieval. Cloud backup uses a low-cost cloud server, suitable for the budgets of primary hospitals, for long-term data storage and off-site backup to prevent data loss. The data parsing module analyzes the standardized data, extracting key instrument operation parameters, environmental parameter thresholds, and corresponding fault codes. The system includes information such as fault type; the anomaly identification module presets normal operating parameter thresholds and environmental parameter thresholds for various testing instruments (set according to the actual operating conditions of the testing instruments in the county people's hospital), compares the parsed data with the preset thresholds in real time, identifies abnormal data exceeding the thresholds, and marks the anomaly type; the fault prediction module, based on machine learning algorithms and combined with historical instrument operating data and fault data, establishes a fault prediction model, analyzes the identified abnormal data, and predicts the potential fault type, fault occurrence time, and fault severity; the early warning control module generates corresponding early warning signals based on the anomaly identification results and fault prediction results, controls the early warning unit to issue early warning prompts, and simultaneously transmits the early warning information and fault prediction information to the application display layer; The application presentation layer is used to realize data visualization, operation control, information query, and command issuance. It includes a host computer monitoring terminal, a mobile monitoring terminal, and an early warning unit. The host computer monitoring terminal is deployed at the nurse station in the laboratory department. It adopts a touch-screen interface, which is easy to operate and adapts to the operating habits of primary healthcare workers. It can display the operating status, environmental parameters, abnormal information, and fault prediction results of various laboratory instruments in real time. It supports data query, historical trajectory tracing, parameter setting (early warning threshold, collection frequency), and operation and maintenance record entry. The mobile monitoring terminal adopts a mobile APP, allowing medical staff and maintenance personnel to remotely view the instrument status and receive early warnings via their mobile phones. The system provides information on fault details and handling suggestions, facilitating real-time monitoring of instrument status when personnel are out on patrol or away from the department. The early warning unit includes a sound alarm, a light alarm, and an SMS alarm module. The sound and light alarms are installed in the laboratory and nurses' station, emitting different intensities of sound and light alerts based on the alarm level (Level 1: minor abnormality, yellow light + low-pitched sound; Level 2: serious abnormality, orange light + rapid sound; Level 3: potential / sudden fault, red light + sharp sound + SMS alarm). The SMS alarm module can send alarm and fault information to designated medical staff and maintenance personnel's mobile phones, ensuring timely communication of abnormal information. The power module includes an AC power interface, a DC voltage regulator module, and a backup power module. The AC power interface is used to connect to the mains power to supply power to all layers of the system. The DC voltage regulator module is used to convert the mains power into a stable DC voltage required by the system to ensure stable system operation. The backup power module uses a lithium battery pack, which automatically switches to backup power supply when the mains power is interrupted, ensuring that the system's data acquisition, transmission, and early warning functions are not interrupted, and adapting to temporary power outage scenarios that may occur in primary hospitals.

[0026] Furthermore, the universal interface includes an RS232 interface, a USB interface, and an Ethernet interface, which can be adapted to different manufacturers and models of testing instruments, eliminating the need for large-scale modifications to existing instruments and reducing the modification costs and implementation difficulties for primary hospitals.

[0027] Furthermore, the data preprocessing module employs a Kalman filter algorithm, which can effectively remove random noise and environmental interference during the sensor data acquisition process, improve the accuracy of data acquisition, and ensure the precision of subsequent anomaly identification and fault prediction.

[0028] Furthermore, the machine learning algorithm in the fault prediction module adopts the decision tree algorithm, which combines historical fault data and operation data of the county people's hospital's laboratory instruments for model training. The model has a simple structure, high computational efficiency, and does not require professional computing power support. It is suitable for the hardware conditions of grassroots hospitals. At the same time, the model can be optimized regularly according to the updates of instrument operation data to improve the accuracy of fault prediction.

[0029] Furthermore, the application presentation layer also includes a permission management module, which divides different operation permissions according to the job settings of the laboratory department of the primary hospital. Medical staff can only view the instrument status, receive early warning information, and query test-related data. Maintenance personnel can view fault details, enter maintenance records, and adjust early warning thresholds. Administrators can perform system parameter settings, user management, data backup and recovery, etc., to ensure the security and standardization of system operation.

[0030] Furthermore, the system also includes an operation and maintenance management module, which is electrically connected to the data processing layer. This module is used to record the instrument's operation and maintenance information, including fault handling time, fault handling method, replaced parts, operation and maintenance personnel, and operation and maintenance cycle. At the same time, it can generate operation and maintenance reminders based on the instrument's operating data and fault data, reminding operation and maintenance personnel to perform regular calibration and maintenance, thereby achieving preventive operation and maintenance, extending the instrument's service life, and reducing operation and maintenance costs.

[0031] Furthermore, the wireless transmission module supports dual-mode communication of WiFi 6 and Bluetooth 5.2, with fast transmission speed, high stability, and a transmission distance of up to 50 meters, which can cover the entire area of ​​the county people's hospital's laboratory department. At the same time, it has low power consumption, reducing the system's power consumption.

[0032] Furthermore, the temperature sensor has a measurement range of 0-50℃ and an accuracy of ±0.1℃; the humidity sensor has a measurement range of 20%-90%RH and an accuracy of ±1%RH; the vibration sensor has a measurement range of 0-10mm / s and an accuracy of ±0.01mm / s; the current sensor has a measurement range of 0-10A and an accuracy of ±0.01A; and the voltage sensor has a measurement range of 0-220V and an accuracy of ±0.1V. These features enable the accurate acquisition of operating parameters and environmental parameters of various instruments, meeting the accuracy requirements of monitoring instruments at the grassroots level.

[0033] The present invention will be further described in detail below with reference to specific embodiments, so that those skilled in the art can understand it. Example

[0034] like Figure 1-3 As shown, this embodiment provides a monitoring system for the status of a testing instrument, suitable for the laboratory department of a county people's hospital. It includes an equipment sensing layer, a data transmission layer, a data processing layer, an application display layer, and a power module. The layers are electrically connected in sequence by wires. The power module provides a stable DC power supply to the entire system to ensure continuous and stable operation.

[0035] In this embodiment, the device sensing layer includes multiple sensor units, an instrument data acquisition interface, and a fault detection unit. The sensor units are low-cost, high-precision sensors adapted to the usage scenarios of primary hospitals. Among them, the temperature sensor is the DS18B20 model, with a measurement range of 0-50℃ and an accuracy of ±0.1℃. It is installed near the core components of the testing instrument to collect the ambient temperature and instrument surface temperature during instrument operation. The humidity sensor is the DHT11 model, with a measurement range of 20%-90%RH and an accuracy of ±1%RH. It is installed in various areas of the laboratory to collect ambient humidity data. The vibration sensor selected is model ADXL345, with a measurement range of 0-10mm / s and an accuracy of ±0.01mm / s. It is attached to the instrument housing and used to collect the vibration amplitude during instrument operation to determine whether there are any abnormalities such as looseness or wear of instrument components. The current sensor selected is model ACS712, with a measurement range of 0-10A and an accuracy of ±0.01A. It is connected in series with the instrument's power supply line and used to collect the instrument's operating current. The voltage sensor selected is model ZMPT101B, with a measurement range of 0-220V and an accuracy of ±0.1V. It is connected in parallel with the instrument's power supply line and used to collect the instrument's operating voltage.

[0036] The instrument's data acquisition interface adopts a combination of RS232, USB, and Ethernet interfaces, allowing direct connection to the built-in controllers of commonly used laboratory instruments in county hospitals, such as blood routine analyzers (e.g., Mindray BC-5150), biochemical analyzers (e.g., Beckman AU5800), and urine analyzers (e.g., URIT-180). No instrument modification is required; the instrument can collect data on operating parameters (e.g., detection speed, reagent usage), operating status (standby, running, fault), testing progress, and remaining consumables (reagents, sample cups). The fault detection unit uses an STM32 microcontroller as its control core, connected to the instrument's power supply and signal lines. It detects abnormal signals such as short circuits, abnormal component wear (e.g., abnormal motor speed, sensor failure), and exceeding operating parameter limits, generating corresponding fault codes (e.g., short circuit fault code E001, temperature exceedance fault code E002) for easy subsequent fault identification and handling.

[0037] The data transmission layer includes a data preprocessing module, a wired transmission module, and a wireless transmission module. The data preprocessing module uses an STM32F103 microcontroller with a built-in Kalman filter algorithm to denoise the raw data collected by the device sensing layer, removing invalid data caused by environmental interference (such as electromagnetic interference) and device interference. It also converts data of different formats into a standardized JSON format to ensure consistent data compatibility for subsequent data processing. The wired transmission module uses an RJ45 Ethernet interface to connect to the laboratory's local area network for short-range, highly stable data transmission, suitable for areas with concentrated instrument placement. The wireless transmission module uses an ESP32 chip, supporting dual-mode communication of WiFi 6 and Bluetooth 5.2. It offers fast transmission speeds, high stability, and a transmission distance of up to 50 meters, covering the entire laboratory area. This is ideal for data transmission in scenarios where cabling is inconvenient, and it also supports data interruption resumption. When the network is interrupted, it can continue transmitting unfinished data upon reconnection, preventing data loss.

[0038] The data processing layer includes a data storage module, a data parsing module, an anomaly detection module, a fault prediction module, and an early warning and control module. The data storage module combines local storage with cloud backup. Local storage uses a 1TB high-capacity solid-state drive, installed in the host computer monitoring terminal, to store the instrument's operating data, fault data, and early warning information for the past year, facilitating quick retrieval. Cloud backup uses Alibaba Cloud's low-cost, lightweight application server, suitable for the budget of primary hospitals, for long-term data storage and off-site backup, preventing data loss due to local storage device failure. The data parsing module, developed using Python and running on the cloud server, parses standardized data, extracting key instrument operating parameters, environmental parameter thresholds, and fault codes corresponding to fault types (e.g., fault code E001 corresponds to a short circuit, E002 corresponds to excessive temperature). The anomaly detection module... The system presets normal operating parameter thresholds and environmental parameter thresholds for various testing instruments (set according to the actual operating conditions of the county hospital's testing instruments, such as a normal operating temperature of 10-30℃ and a normal operating current of 0.5-2A for a blood routine analyzer). It then compares the parsed data with these preset thresholds in real time, identifying abnormal data exceeding the thresholds and marking the abnormality type (e.g., abnormal temperature, abnormal current). The fault prediction module, based on a decision tree algorithm, trains a model using historical instrument operating data and fault data. It analyzes the identified abnormal data to predict potential fault types (e.g., persistently exceeding the temperature threshold indicates a heat dissipation component failure), fault occurrence time, and fault severity (e.g., minor or severe abnormality). The early warning control module generates corresponding early warning signals based on the abnormality identification and fault prediction results, controls the early warning unit to issue warning prompts, and simultaneously transmits the warning information and fault prediction information to the application display layer.

[0039] The application presentation layer includes a host computer monitoring terminal, a mobile monitoring terminal, and an early warning unit. The host computer monitoring terminal uses a 15-inch touchscreen industrial tablet PC, deployed at the nurse station in the laboratory department. It uses a Windows operating system and has dedicated monitoring software installed. The user interface is simple and easy to use, adapted to the operating habits of primary healthcare workers. It can display the real-time operating status (standby, running, fault), environmental parameters (temperature, humidity), abnormal information, and fault prediction results of various testing instruments. It supports data query (by instrument model and time range), historical trajectory tracing (viewing changes in instrument operating data within a certain time period), parameter settings (adjusting early warning thresholds and data acquisition frequency), and operation and maintenance record entry. The mobile monitoring terminal uses a mobile APP, supporting Android and iOS systems. Healthcare workers and maintenance personnel can download and install the APP on their mobile phones, register and log in to remotely view instrument status and receive early warnings. Information allows users to view fault details and handling suggestions (e.g., fault code E002 corresponds to excessive temperature; the handling suggestion is to check the cooling fan and clean the cooling holes), facilitating real-time monitoring of instrument status during field inspections or when not in the department. The early warning unit includes an audible alarm, a visual alarm, and an SMS alarm module. The audible alarm uses an active buzzer, and the visual alarm uses LED tri-color warning lights (yellow, orange, and red), installed in the laboratory and nurses' station. Different intensities of audible and visual cues are emitted according to the alarm level (Level 1: minor abnormality, yellow light + low-pitched sound, every 5 seconds; Level 2: serious abnormality, orange light + rapid sound, every 1 second; Level 3: potential / sudden fault, red light + sharp sound, continuous ringing). Simultaneously, the SMS alarm module sends alarm and fault information to designated medical staff and maintenance personnel via GSM to ensure timely communication of abnormal information.

[0040] The power supply module includes an AC power interface, a DC voltage regulator module, and a backup power module. The AC power interface is used to connect to the mains power (220V, 50Hz) to power all layers of the system. The DC voltage regulator module uses the LM2596 model to convert the mains power into the stable DC voltage (5V, 12V) required by the system, ensuring stable system operation. The backup power module uses a 12V, 10Ah lithium battery pack, which is connected to the DC voltage regulator module. When the mains power is interrupted, it automatically switches to backup power supply, which can maintain the normal operation of the system for 4-6 hours, ensuring that the system's data acquisition, transmission, and early warning functions are uninterrupted, and is suitable for temporary power outage scenarios that may occur in primary hospitals.

[0041] In this embodiment, the application presentation layer also includes a permission management module. Based on the job settings of the laboratory department of the county people's hospital, three levels of operation permissions are divided: medical staff permissions, maintenance personnel permissions, and administrator permissions. Medical staff can only view instrument status, receive early warning information, and query test-related data, but cannot set parameters or enter maintenance records. Maintenance personnel can view fault details, enter maintenance records (such as fault handling time, handling method, and replacement parts), and adjust early warning thresholds, but cannot set system parameters or manage users. Administrators can perform system parameter settings (such as data acquisition frequency and cloud backup cycle), user management (adding, deleting, and modifying users), data backup and recovery, etc., to ensure the security and standardization of system operation.

[0042] In this embodiment, the system also includes an operation and maintenance management module, which is electrically connected to the early warning control module of the data processing layer. This module is used to record the instrument's operation and maintenance information, including fault handling time, fault handling method, replaced parts, operation and maintenance personnel, and operation and maintenance cycle. At the same time, it can generate operation and maintenance reminders based on the instrument's operating data and fault data (such as reminding to perform calibration after the instrument has been running continuously for 3 months; reminding to replenish consumables when the remaining consumables are less than 10%), reminding operation and maintenance personnel to perform regular calibration and maintenance, thereby achieving preventive operation and maintenance, extending the instrument's service life, and reducing operation and maintenance costs.

[0043] The working process of this embodiment is as follows: 1. Data Acquisition: The sensor unit of the equipment sensing layer collects real-time data on the ambient temperature, ambient humidity, vibration amplitude, operating current, and operating voltage of the testing instrument. The instrument data acquisition interface collects real-time data on the instrument's operating parameters, working status, testing progress, and remaining consumables. The fault detection unit detects abnormal signals during the instrument's operation and generates fault codes. 2. Data transmission: The data preprocessing module performs noise reduction and standardization on the collected raw data. The processed data stream is stably transmitted to the data processing layer through a wired or wireless transmission module. 3. Data Processing: The data parsing module parses the transmitted data and extracts key information; the anomaly identification module compares the parsed data with preset thresholds in real time to identify abnormal data and mark the anomaly type; the fault prediction module predicts the potential fault type, occurrence time and severity based on historical data and abnormal data; the early warning control module generates graded early warning signals based on the anomaly identification results and fault prediction results. 4. Early Warning and Display: The early warning unit issues corresponding sound, light, and SMS warning prompts based on the early warning signal; the host computer monitoring terminal and mobile monitoring terminal in the application display layer display the instrument's operating status, environmental parameters, abnormal information, and fault prediction results in real time, and medical staff and maintenance personnel can view relevant information through the terminal; 5. Operation and Maintenance: After receiving the early warning information, the operation and maintenance personnel can view the fault details and handling suggestions through the terminal, and promptly check and repair the instrument. After the repair is completed, the operation and maintenance record is entered into the operation and maintenance management module. At the same time, the operation and maintenance management module generates operation and maintenance reminders based on the instrument operation data to guide the operation and maintenance personnel to perform regular calibration and maintenance. 6. Data storage and backup: The data storage module of the data processing layer stores various types of data locally, and at the same time regularly uploads the data to the cloud server for backup to ensure data security and facilitate subsequent query and traceability.

[0044] The system in this embodiment is adapted to the application scenario of county people's hospitals. It has a reasonable structure, moderate cost, simple operation, and practical functions. It can effectively solve the drawbacks of traditional manual inspection, realize real-time monitoring, accurate early warning, centralized management and preventive operation and maintenance of the status of testing instruments, ensure the orderly conduct of testing work, improve testing quality and efficiency, reduce medical risks and operation and maintenance costs, and is easy to promote and apply in primary medical institutions.

[0045] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A monitoring system for the status of an inspection instrument, characterized in that: It includes a device sensing layer, a data transmission layer, a data processing layer, an application display layer, and a power module. Each layer is electrically connected in sequence, and the power module provides stable power to the entire system. The device sensing layer is used to collect operating status data, environmental parameter data, and fault-related data of various testing instruments. It includes multiple sensor units, instrument data acquisition interfaces, and fault detection units. The sensor units include temperature sensors, humidity sensors, vibration sensors, current sensors, and voltage sensors. The instrument data acquisition interface adopts a universal interface design, is compatible with commonly used hospital testing instruments, and can be directly connected to the instrument's built-in controller. The fault detection unit is used to detect abnormal signals during instrument operation and generate corresponding fault codes. The data transmission layer is used to filter and reduce noise in various types of data collected by the device sensing layer and then stably transmit them to the data processing layer. It includes a data preprocessing module, a wired transmission module, and a wireless transmission module. The data preprocessing module uses a filtering algorithm to denoise the raw data and performs format standardization on the data. The wired transmission module uses an Ethernet interface; the wireless transmission module adopts a dual-mode design of WiFi and Bluetooth, and supports data interruption resumption. The data processing layer is used to store, analyze, and process the transmitted data to achieve anomaly identification, fault prediction, and early warning. It includes a data storage module, a data parsing module, an anomaly identification module, a fault prediction module, and an early warning control module. The data storage module uses a combination of local storage and cloud backup. The data parsing module parses the standardized data and extracts key information. The anomaly identification module presets normal operating parameter thresholds and environmental parameter thresholds for various testing instruments, and compares the parsed data with the preset thresholds in real time to identify abnormal data. The fault prediction module is based on machine learning algorithms and combines historical instrument operation data and fault data to establish a fault prediction model. The early warning control module generates a corresponding early warning signal based on the anomaly identification result and the fault prediction result; The application presentation layer is used to realize the visualization of data, operation control, information query, and command issuance. It includes a host computer monitoring terminal, a mobile monitoring terminal, and an early warning unit. The host computer monitoring terminal adopts a touch operation interface and supports data query, historical trajectory tracing, parameter setting, and operation and maintenance record entry functions. The mobile monitoring terminal adopts a mobile phone APP and supports remote viewing of instrument status and receiving early warning information. The early warning unit includes a sound early warning device, a light early warning device, and an SMS early warning module. The power module includes an AC power interface, a DC voltage regulator module, and a backup power module. The AC power interface is used to connect to mains power, the DC voltage regulator module is used to convert the mains power into a stable DC voltage required by the system, and the backup power module is used to supply power to the system when the mains power is interrupted.

2. The testing instrument status monitoring system according to claim 1, characterized in that: The universal interfaces include RS232, USB, and Ethernet interfaces, which are compatible with testing instruments from different manufacturers and models.

3. The testing instrument status monitoring system according to claim 1, characterized in that: The data preprocessing module employs a Kalman filter algorithm to remove random noise and environmental interference during the sensor data acquisition process, thereby improving the accuracy of data acquisition.

4. The testing instrument status monitoring system according to claim 1, characterized in that: The machine learning algorithm in the fault prediction module adopts the decision tree algorithm, which combines historical fault data and operation data of hospital testing instruments for model training, and optimizes the model regularly based on the update of instrument operation data.

5. The testing instrument status monitoring system according to claim 1, characterized in that: The application presentation layer also includes a permission management module, which divides permissions for medical staff, maintenance personnel, and administrators according to the job settings of the laboratory department of a primary hospital, so as to realize hierarchical operation management.

6. The testing instrument status monitoring system according to claim 1, characterized in that: It also includes an operation and maintenance management module, which is electrically connected to the data processing layer to record the instrument's operation and maintenance information, generate operation and maintenance reminders, and realize preventive operation and maintenance.

7. The testing instrument status monitoring system according to claim 1, characterized in that: The wireless transmission module supports dual-mode communication of WiFi 6 and Bluetooth 5.

2.

8. The testing instrument status monitoring system according to claim 1, characterized in that: The temperature sensor has a measurement range of 0-50℃ and an accuracy of ±0.1℃; the humidity sensor has a measurement range of 20%-90%RH and an accuracy of ±1%RH; the vibration sensor has a measurement range of 0-10mm / s and an accuracy of ±0.01mm / s; the current sensor has a measurement range of 0-10A and an accuracy of ±0.01A; and the voltage sensor has a measurement range of 0-220V and an accuracy of ±0.1V.

9. The testing instrument status monitoring system according to claim 1, characterized in that: The backup power module uses a lithium battery pack, which can automatically switch power supply when the mains power is interrupted, maintaining normal system operation for 4-6 hours.

10. The testing instrument status monitoring system according to claim 1, characterized in that: The warning unit emits sound and light alerts of different intensities according to the warning level. Level 1 warning corresponds to yellow light and low-pitched alert, Level 2 warning corresponds to orange light and rapid alert, and Level 3 warning corresponds to red light, sharp alert, and SMS warning.

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