Gas sampling equipment internet of things collaborative management method

Abstract

The application discloses a kind of gas sampling equipment internet of things collaborative management methods, it is related to gas sampling equipment management field, including to the transmission link of full coverage internet of things communication is built, the multiple data of real-time acquisition gas sampling equipment;The data collected are classified, integrated, cleaned, summarized and visualized statistics, then generate the online trend of gas sampling equipment, type proportion and fault distribution multiclass analysis chart, to manage the global operation situation of gas sampling equipment;Sampling index and the operating parameter of equipment are verified in real time.The application is built by using the setting mode of internet of things communication transmission link, interfaces gas sampling equipment and realizes the real-time upload and encrypted storage of multi-source data, so that the equipment working condition of vacuum box multiway gas sampling instrument, sampling data, field image and positioning information are integrated to be collected, to prevent the problems of multiway sampling data dispersion and manual record error-prone.

Description

Technical Field

[0001] This invention relates to the field of gas sampling equipment management, and in particular to an Internet of Things (IoT) collaborative management method for gas sampling equipment. Background Technology

[0002] In the field of environmental gas monitoring, gas sampling equipment is used to sample the gas in the monitored area. There are various types of gas sampling equipment, including vacuum box multi-channel gas samplers, which are commonly used portable sampling devices.

[0003] When sampling, vacuum chamber multi-channel gas samplers typically require the acquisition of multiple data streams. These samplers also require management. Current technologies for managing vacuum chamber multi-channel gas samplers usually require staff to record sampling parameters and equipment operating conditions for each channel. However, this method is prone to data omissions and confusion, and it lacks centralized control over data from multiple devices, resulting in low collaborative efficiency. Furthermore, equipment malfunctions and exceeding sampling limits rely on manual inspections, which are often inconvenient for real-time early warning and protection. Therefore, it is necessary to adopt an IoT-based collaborative management method for gas sampling devices that can integrate multi-channel data and provide real-time early warnings. Summary of the Invention

[0004] The purpose of this invention is to provide an IoT collaborative management method for gas sampling equipment to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a gas sampling device IoT collaborative management method, comprising the following specific steps: Step 1: Establish a transmission link for full-coverage IoT communication, then connect it to the gas sampling equipment to collect multiple data points from the gas sampling equipment in real time. During the data collection process, multi-source heterogeneous data is uploaded and encrypted for storage in real time. Step 2: Classify, integrate, clean, summarize, and visualize the collected data, and then generate various analysis charts on the online trend, type proportion, and fault distribution of gas sampling equipment to manage the overall operational status of gas sampling equipment. Step 3: Real-time verification of sampling indicators and equipment operating parameters, and real-time verification through a two-level threshold intelligent early warning and judgment mechanism based on gas sampling parameters and equipment operating conditions. Then, automatic identification of data exceeding the standard and abnormal equipment failures, and proactive push of early warning information. Step 4: Establish an electronic full lifecycle file for the gas sampling equipment, and then integrate the configuration information, measurement history, maintenance records, maintenance events and remote operation logs of the gas sampling equipment to enable full lifecycle traceability management of the gas sampling equipment; Step 5: Deploy a geographically distributed, visualized, and collaborative management architecture that links multiple terminals, and then enable the Web and mobile terminals to work together to complete on-site and remote collaborative supervision; Step Six: Adopt a full lifecycle operation and maintenance management model for gas acquisition equipment, and integrate remote command issuance and knowledge base support.

[0006] Preferably, the multiple data in step one include equipment hardware operating conditions, gas sampling and detection data, on-site images and positioning information, and operation records. The acquisition and IoT communication transmission of these multiple data in step one specifically includes: S1: Deploy an IoT network card and communication module in the gas sampling equipment, then bind the equipment with a unique number, and continuously collect the equipment's online status, fault codes, maintenance lock status, hardware load, sampling operation progress, and other basic operating data. S2: Then, images are acquired from the gas sampling equipment. The image acquisition includes on-site photos of the entire time period before, during and after the gas sampling operation. During the operation, operator information, high-precision geographical location and timestamp traceability data are bound together. S3: Real-time acquisition of gas concentration, sampling flow rate, ambient temperature, humidity and pressure parameters, as well as other detection indicators, and then synchronous recording of instantaneous sampling data and phased data throughout the entire process; S4: Set up a network adaptive caching mechanism. If the network is disconnected during use, the device data will be automatically stored offline. If the network is restored, the interruption will be resumed directly. Then, the number of the gas sampling device, the network card information and the sampling data will be bound and associated.

[0007] Preferably, the classification, integration, visualization, and statistical analysis of the collected data in step two specifically includes: S11: Receives monitoring data and operating data uploaded by all gas sampling devices in real time, and then classifies and archives them according to device model, usage area and operation and maintenance unit; S12: Automatically calculates the trend of the number of online devices over seven days, and then generates a dynamic trend chart based on the trend. S13: Collect statistics on the fault types, frequency, and timing of gas sampling equipment, and then generate a fault distribution bar chart to show the high-incidence types of defects in gas sampling equipment. S14: Divide devices into four management categories: online devices, faulty devices, maintenance devices, and offline devices. The summary information of the quantities is centrally displayed on the main interface of the cloud platform. Entering the corresponding category will allow users to retrieve the detailed list of the corresponding devices.

[0008] Preferably, the dual-level threshold intelligent early warning and judgment mechanism in step three adopts a fully automatic threshold judgment method, specifically including: S21: Pre-built gas sampling safety threshold library and equipment operation safety threshold library in the cloud, including key limits for gas detection concentration, sampling flow rate, equipment operating voltage, hardware operating temperature and continuous operation time; S22: Then, the real-time collected field data is dynamically compared with the preset threshold library to automatically verify the parameters; S23: If the sampled parameter is detected to be lower than the lower threshold or higher than the upper threshold, it will be directly judged as an abnormality. When the hardware parameters of the gas sampling device exceed the safe range, it will be directly judged as a fault condition. S24: If an abnormal event is triggered, it will be highlighted and displayed as a pop-up notification on the management interface of the cloud platform, and the abnormal time, abnormal parameters and device location will be recorded simultaneously.

[0009] Preferably, the establishment of an electronic full lifecycle record for the gas sampling device in step four specifically includes: S41: The cloud platform distinguishes between instrument model management and instrument information management, and then uniformly enters the model parameters, hardware configuration, compatible communication protocols and controllable command parameters of various gas sampling devices; S42: Enables refined management and control of information for individual devices, supports online editing of basic data and modification of configuration parameters for gas sampling devices on the cloud platform, and synchronizes updates to cloud archive data after submission of edited content; S43: Record and manage gas sampling equipment to fully record the entire lifecycle of gas sampling equipment calibration, maintenance, shutdown, modification and inspection, so as to form a permanent operation and maintenance file for gas sampling equipment; S44: Supports remote control commands from administrators, and also supports location jumps for gas sampling devices with entered latitude and longitude information to verify the installation location of the gas sampling device.

[0010] Preferably, the geographic distribution visualization collaborative management architecture in step five combines electronic map positioning, data filtering and querying, batch data export, and multi-role permission allocation, specifically including: S51: By combining electronic maps, gas sampling devices across the entire area are marked with red dots, intuitively displaying the installation distribution area of ​​the geographic distribution visualization and collaborative management architecture; S52: Configure map switching modes, including standard map and simplified map multiple switching displays. At the same time, the location details of gas sampling equipment on the map can be viewed with one click. S53: Build a measurement history database, and support multiple conditions such as instrument model, sampling time and monitoring area for combined filtering, and customize filtering conditions to retrieve target historical records; S54: Then configure a hierarchical permission management system to set differentiated permissions accordingly. The hierarchical permission management system includes a supervision end, an operation and maintenance end, and a device management end. In this way, advanced control functions are automatically hidden when permissions are insufficient.

[0011] Preferably, the geographic distribution visualization collaborative management architecture in step five also includes historical measurement data tracing and batch export operations, specifically including: S511: A single historical measurement record is associated with instrument configuration parameters, data from the entire sampling process, and geographic location information. Then, the complete operation details can be viewed in the corresponding record to enable full traceability of the sampling operation. S512: Supports batch multi-selection of test data records, custom selection of environmental protection-specific data export templates, and integration, layout and format conversion of multiple sets of monitoring data; S513: After the data is packaged, it is downloaded to the local mobile terminal and then a standardized table file is generated.

[0012] Preferably, step six further includes a dual judgment mechanism of cycle threshold and frequency threshold in the operation and maintenance management process, specifically including: The system presets a periodic maintenance time threshold and a fault frequency threshold for the gas sampling equipment. When the continuous operating time of the gas sampling equipment reaches the maintenance cycle threshold, it is directly marked as a device to be maintained, and a maintenance reminder is pushed to it. When the number of monthly failures of a single gas sampling device exceeds the preset frequency threshold, it is directly identified as a high-frequency failure device and then a maintenance task is assigned to it first.

[0013] Preferably, the full lifecycle operation and maintenance management mode in step six integrates functions such as gas sampling equipment information editing, remote control, operation and maintenance scheduling, problem feedback, technical training, and system iteration. The knowledge base support process in step six specifically includes: S61: Build an integrated equipment operation and maintenance knowledge base system, and the knowledge base system is divided into common problems, instrument learning, information query and problem feedback. Then, the knowledge base system is configured with keyword search and dual filtering by instrument type, as well as common problems and instrument learning, to locate the technical information of the corresponding gas sampling equipment. The common problems integrate question and answer viewing, like evaluation and comment interaction functions. The instrument learning has built-in two types of materials: technical manuals and teaching videos. S62: The operation and maintenance knowledge base system provides a special problem feedback portal, where operation and maintenance personnel can submit information on gas sampling equipment malfunctions, on-site operation problems, and platform usage defects.

[0014] Preferably, it also includes a multi-channel sampling task timeliness control and IoT heartbeat monitoring management process, specifically including: S71: Preset the single-channel continuous sampling time threshold, the maximum buffer time threshold for offline field operations, and the IoT heartbeat reporting interval threshold for the gas sampling equipment; S72: Real-time monitoring of the sampling channel polling operation interval on the gas sampling equipment. When the automatic cleaning and continuous sampling time of a single channel exceeds the preset operation threshold, the corresponding channel is locked and a maintenance reminder is pushed. S73: Compare the IoT heartbeat feedback data of the sampler. When the gas sampling device exceeds the heartbeat threshold and fails to upload operating information, it is determined to be an offline abnormal device. Then, the offline area is accurately marked in combination with the field operation point. S74: Combine offline caching duration threshold control for on-site sampling operations without network access. Before reaching the critical threshold, remind operators to complete data retransmission and archiving.

[0015] The technical effects and advantages of this invention are as follows: (1) By utilizing the setting method of building an Internet of Things communication transmission link, the present invention connects to the gas sampling equipment and realizes real-time uploading and encrypted storage of multi-source data, thereby enabling the integrated collection of equipment status, sampling data, on-site images and positioning information of the vacuum box multi-channel gas sampler, which helps to prevent the problem of scattered multi-channel sampling data and easy errors in manual recording, and thus helps to ensure the uniqueness and integrity of data in the scenario of mixed deployment of multi-gas sampling equipment; (2) By setting up a dual-level threshold intelligent early warning and judgment mechanism, the present invention configures sampling parameters and equipment operating thresholds and verifies them in real time. This is conducive to automatically identifying excessive data and single-channel abnormal faults, quickly pushing early warning information and locking abnormal records without manual intervention, which helps to eliminate the use of invalid sampling data and thus improves the accuracy and reliability of gas monitoring results. (3) By deploying a geographic distribution visualization control architecture with multi-terminal linkage, this invention integrates electronic map positioning and multi-condition data filtering functions, which is conducive to the accurate marking and centralized supervision of the location of portable sampling devices in the whole area. At the same time, it is conducive to quickly retrieving historical data of multiple gas sampling devices by numbering and sampling area, which is conducive to ensuring data continuity in the field under weak network environment and further meeting the collaborative needs. (4) By establishing an electronic archive for the entire life cycle of the equipment, this invention integrates the configuration information, measurement history, maintenance events and remote operation logs of the gas sampling equipment, which makes the entire process of the gas sampling equipment from commissioning to scrapping traceable. At the same time, with the multi-role differentiated permission control, it helps to prevent equipment miscontrol or data leakage caused by unauthorized operation, thus improving security. (5) By adding a triple judgment mechanism of offline operation threshold, channel polling threshold and IoT heartbeat monitoring threshold, this invention can automatically control the continuous sampling load of multiple channels, which is conducive to quickly identifying equipment offline anomalies and reminding offline data to be retransmitted. This makes it easier to adapt to the complex needs of portable field operation scenarios and reduce the cost of manual inspection. (6) By integrating remote command issuance, intelligent operation and maintenance scheduling and knowledge base support, this invention supports online modification of parameters such as channel sampling interval and automatic cleaning cycle, which is conducive to automatically allocating operation and maintenance tasks based on fault frequency and maintenance cycle threshold, realizing the optimized allocation of operation and maintenance resources for multiple devices, which helps to reduce the overall operation and maintenance cost. At the same time, it provides operators with exclusive technical information and problem feedback channels, further reducing labor intensity. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the IoT collaborative management method for gas sampling equipment according to the present invention; Figure 2 This is a schematic diagram of the dual-level threshold intelligent early warning judgment process of the present invention; Figure 3 This is a schematic diagram of the IoT heartbeat and multi-channel sampling timeliness threshold determination process of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides, for example Figures 1-3 The method for collaborative management of gas sampling devices via the Internet of Things (IoT) includes the following specific steps: Step 1: Establish a full-coverage IoT communication transmission link, then connect it to the gas sampling equipment to collect multiple data points from the gas sampling equipment in real time. During data collection, multi-source heterogeneous data is uploaded and encrypted for storage in real time. The IoT communication transmission link adopts an architecture that integrates the gas sampling equipment with an IoT network card, a 4G / 5G / Wi-Fi multi-mode communication module, and a cloud server. The IoT network card is an industrial-grade low-power LoRa gateway network card, which supports a wide operating temperature range of -40℃ to 85℃, adapting to harsh outdoor environments. The communication module integrates a TCP / IP protocol stack to achieve data... According to encrypted transmission, when setting up the IoT communication transmission link, the unique QR code on the gas sampling device is first scanned using a handheld terminal to bind the device number to the network card MAC address. Then, communication parameters are configured to ensure a stable communication link between the sampler and the cloud-based instrument-to-cloud platform. The multi-source heterogeneous data specifically includes three categories: device hardware operating condition data, gas sampling and detection data, and traceability data. Device hardware operating condition data includes the sampler's motherboard voltage (12V±0.5V), channel solenoid valve switching status, vacuum pump operating power (50W±5W), and continuous operation time, etc. Gas sampling and detection data refers to SO2, NO... X Gas concentration unit is mg / m³, sampling flow rate is 0.1-1L / min, ambient temperature and humidity range is -20℃-60℃ and 10%-90RH, pressure is 86-106kPa, traceability data refers to on-site photos with a resolution of 1920×1080 pixels, positioning information uses GPS and Beidou dual-mode positioning, timestamp is accurate to the second, operator ID is bound to system account, data storage uses distributed database, archived in three-level directory by device number, channel number and collection time, and supports millions of data concurrent read and write; Step 2: Classify, integrate, clean, summarize, and visualize the collected data, and then generate various analysis charts on the online trend, type proportion, and fault distribution of gas sampling equipment to manage the overall operational status of gas sampling equipment. Step 3: Real-time verification of sampling indicators and equipment operating parameters is performed using a dual-level threshold intelligent early warning and judgment mechanism based on gas sampling parameters and equipment operating conditions. Exceeding limits and equipment malfunctions are automatically identified, and early warning information is proactively pushed out. The dual-level thresholds include gas sampling safety thresholds and equipment operating safety thresholds, both preset and stored in a threshold library through a visual configuration interface on the cloud platform. The gas sampling safety thresholds refer to the "GB3095-2012 Ambient Air Quality Standard," while the equipment operating safety thresholds are set according to the technical manual of the gas sampling equipment. Thresholds can be configured by zone. Batch configuration of domains and device types is possible, as well as individual adjustments for single devices and channels. Adjustments are synchronized to the sampler's local cache in real time, ensuring threshold verification continues even during network outages. Threshold determination relies on the threshold calculation sublayer of the cloud management platform, operating fully automatically without manual intervention. The specific execution logic is as follows: the sampling device collects data every 10 seconds and uploads it to the platform layer via an encrypted tunnel; the platform layer's data processing sublayer parses the device number and channel number from the data; the threshold calculation sublayer retrieves the preset threshold corresponding to that channel from the cache; and the real-time data is compared with the threshold, for example, if the channel sampling concentration is 0.18 mg / m³. 3 Exceeding the upper limit threshold by 0.15 mg / m³ 3 The system detected an abnormality in the sampling; after triggering an alert, a pop-up window appeared on the application layer web client with a red background, displaying the device number, corresponding channel, sampling exceeding the standard, and concentration of 0.18 mg / m³. 3 The system can simultaneously send messages to the handheld terminals of maintenance personnel, including the time and location of the warnings. The warning log is automatically archived to the historical warning table of the cluster's warning management system, and can be filtered and queried by anomaly type and time. Step 4: Establish an electronic full lifecycle file for the gas sampling equipment, and then integrate the configuration information, measurement history, maintenance records, maintenance events and remote operation logs of the gas sampling equipment to enable full lifecycle traceability management of the gas sampling equipment; Step 5: Deploy a multi-terminal, geographically distributed, visual, and collaborative management architecture. This architecture enables collaborative monitoring between web and mobile devices, facilitating both on-site and remote monitoring. The geographically distributed, visual, and collaborative management system utilizes an electronic map service with map software API interfaces, supporting adaptive loading on both web and mobile devices. The map zoom levels range from 5 to 20, with level 5 displaying provincial-level regional distribution and level 20 displaying the 10-meter radius around a specific sampling point. The size of the red markers on the devices dynamically adjusts based on their online status. Clicking on a point displays an information card showing the device number, model, current online status, and the operational status of the four channels, indicated by green and red (green for normal, red for abnormal). The system also includes the most recent sampling time and concentration data. Clicking on a point allows users to view details and navigate to the device management page. Step Six: A full lifecycle operation and maintenance management model is adopted for the gas sampling equipment, integrating remote command issuance and knowledge base support. The full lifecycle archive uses a structured data format, including a basic information module, channel configuration module, operation record module, maintenance module, and remote operation module. Basic information includes equipment number, model, manufacturing date, calibration certificate number, and installation location (latitude and longitude). The channel configuration module includes the sampling interval for four channels, automatic cleaning duration, and flow calibration value. The operation record module includes daily runtime, sampling frequency, and channel start / stop records. The maintenance module includes fault codes, repaired parts, maintenance personnel, and calibration time. The remote operation module includes command issuance time, parameter modification records, and operator account. The archive data supports timeline backtracking, allowing viewing of equipment status changes over any period. All modification records are retained in the operation log and cannot be deleted, meeting environmental data traceability and auditing requirements. The knowledge base function is integrated into external handheld terminals and the cloud. The terminal management platform can be accessed via the terminal system menu to the knowledge base or the platform auxiliary functions to the knowledge base. Keyword search: Enter keywords such as channel cleaning failure, and the system will perform fuzzy matching of related questions and materials. It supports filtering by categories such as common problems, instrument learning, and information query. Instrument learning materials can be viewed by clicking on the manual and zooming in with two fingers. Page navigation and bookmarking functions are supported. MP4 videos can be played in landscape or portrait mode and support speed adjustment and pause. Problem feedback: On the problem feedback page, select the equipment failure type, fill in the problem description, select the associated equipment number and channel number, and click submit. The feedback information is uploaded to the cloud management platform backend. The technical staff response time is ≤24 hours, and the processing result is notified via terminal message push. Steps one to six realize the full-domain IoT collaborative management and control of gas sampling equipment, significantly improving the efficiency of environmental monitoring, reducing the labor intensity of sampling personnel and the overall management and control cost, and adapting to the exclusive needs of multi-point and multi-channel parallel sampling in the field.

[0019] Furthermore, the data in step one includes equipment hardware operating conditions, gas sampling and detection data, on-site images and positioning information, and operation records. The specific data acquisition and IoT communication transmission in step one include: S1: Deploy an IoT network card and communication module in the gas sampling equipment, then bind the equipment with a unique number, and continuously collect the equipment's online status, fault codes, maintenance lock status, hardware load, sampling operation progress, and other basic operating data. S2: Then, image acquisition is performed on the gas sampling equipment. Image acquisition includes on-site photos of the entire time period before, during and after the gas sampling operation. During operation, the operator information, high-precision geographical location and timestamp traceability data are bound. When performing image acquisition, a handheld terminal is used. The on-site handheld terminal's camera acquisition component directly captures on-site photos of the entire time period before, during and after the gas sampling operation. S3: Real-time acquisition of gas concentration, sampling flow rate, ambient temperature, humidity and pressure parameters, as well as other detection indicators, and then synchronous recording of instantaneous sampling data and phased data throughout the entire process; S4: Set up a network adaptive caching mechanism. If the network is disconnected during use, the device data will be automatically stored offline. If the network is restored, the interruption will be resumed directly. Then, the number of the gas sampling device, the network card information and the sampling data will be bound and associated. Through the above steps, it is beneficial to ensure the comprehensiveness, uniqueness and continuity of multi-channel data acquisition, solve the problems of data loss and data confusion of multiple devices in the field with weak network environment, realize the accurate association between sampling data and traceability information, and provide a complete and reliable data source for subsequent analysis and early warning.

[0020] Specifically, step two, which involves classifying, integrating, and visualizing the collected data, includes: S11: Receives monitoring data and operating data uploaded by all gas sampling devices in real time, and then classifies and archives them according to device model, usage area and operation and maintenance unit; S12: Automatically calculates the trend of the number of online devices over seven days, and then generates a dynamic trend chart based on the trend. S13: Collect statistics on the fault types, frequency, and timing of gas sampling equipment, and then generate a fault distribution bar chart to show the high-incidence types of defects in gas sampling equipment. S14: The system categorizes devices into four main management categories: online devices, faulty devices, maintenance devices, and offline devices. The system centrally displays the summarized quantity information on the main interface of the cloud platform. By entering the corresponding category, users can retrieve the detailed list of the corresponding devices. Through these steps, the system facilitates macro-level control and refined analysis of the operational status of all sampling devices. It intuitively presents the online status of devices and the types of high-incidence faults, solving the problems of low efficiency and unintuitive data in traditional manual statistics. This provides data support for regulatory decision-making and operation and maintenance scheduling.

[0021] Furthermore, the two-level threshold intelligent early warning and judgment mechanism in step three adopts a fully automatic threshold judgment method, specifically including: S21: Pre-built gas sampling safety threshold library and equipment operation safety threshold library in the cloud, including key limits for gas detection concentration, sampling flow rate, equipment operating voltage, hardware operating temperature and continuous operation time; S22: Then, the real-time collected field data is dynamically compared with the preset threshold library to automatically verify the parameters; S23: If the sampled parameter is detected to be lower than the lower threshold or higher than the upper threshold, it will be directly judged as an abnormality. When the hardware parameters of the gas sampling device exceed the safe range, it will be directly judged as a fault condition. S24: If an abnormal event is triggered, it will be highlighted and a pop-up notification will be displayed on the management interface of the cloud platform. The abnormal time, abnormal parameters and device location will be recorded simultaneously. Through the above steps, it is beneficial to achieve accurate and real-time verification of multi-channel sampling data and equipment operating conditions. Excessive data and faults can be quickly identified without manual intervention, eliminating invalid data flow, greatly improving the accuracy of monitoring results and the reliability of equipment operation, and reducing the cost of manual verification.

[0022] Furthermore, step four, establishing an electronic lifecycle record for the gas sampling equipment, specifically includes: S41: The cloud platform distinguishes between instrument model management and instrument information management, and then uniformly inputs the model parameters, hardware configuration, compatible communication protocols, and controllable command parameters of various gas sampling devices. The cloud platform and cloud management platform adopt a four-layer architecture: perception layer, transmission layer, platform layer, and application layer, as follows: Perception layer: Composed of the gas sampling device's built-in IoT network card, sensor module, positioning module, and camera module, responsible for collecting multi-source heterogeneous data and converting it into standard digital signals. The sensor module includes a concentration sensor, temperature and humidity sensor, and pressure sensor; the positioning module includes GPS and BeiDou; Transmission layer: The cloud platform and cloud management platform adopt a four-layer architecture: perception layer, transmission layer, platform layer, and application layer. Transmission Layer: Employs a redundant design with multiple communication methods including 4G / 5G / Wi-Fi / LoRa, achieving data transmission through industrial-grade routers and VPN encrypted tunnels. It supports breakpoint resumption and flow control, ensuring data stability in weak network environments. Platform Layer: Includes a data storage sublayer, a data processing sublayer, and a threshold calculation sublayer. The data processing sublayer deploys a distributed computing framework to perform data cleaning, classification, integration, and statistical analysis, such as seven-day online trends and fault distribution statistics. The threshold calculation sublayer incorporates a threshold comparison algorithm, receiving data from the transmission layer in real time and automatically comparing it with a preset threshold library. Application Layer: Provides Web... The platform features a visual interface for both mobile and desktop devices, comprising five main functional modules: device monitoring, operation and maintenance management, data statistics, threshold configuration, and a knowledge base. It supports multi-role permission isolation and operation log auditing, adapting to diverse use cases such as enterprise operation and maintenance and device management. The platform hardware is deployed on cloud servers, supporting elastic scaling and capable of supporting simultaneous access for over 100,000 sampling devices. The cloud management platform supports web access. Data statistics viewing includes: on the device monitoring page, users can switch between viewing a seven-day online trend chart, a device type percentage chart, and a fault distribution chart. Charts can be exported as PNG format, and statistical data can be filtered by week / month / quarter. Data originates from real-time analysis results from the platform's distributed computing framework. Threshold configuration involves accessing system settings and threshold management, selecting the corresponding device, and setting sampling parameters for each of the four channels. With operating parameter thresholds, after entering the upper and lower limits and clicking save, the threshold data is synchronously stored in the cluster and cache. The system prompts that the threshold configuration is successful and will be synchronized to all associated devices. Operation and maintenance scheduling: On the operation and maintenance management page, the platform layer automatically generates pending work orders (including device number, fault type, location, and priority). The administrator clicks to assign and selects operation and maintenance personnel (filtered by region and skill tags). The work order is pushed to the operation and maintenance personnel through the handheld terminal. After the operation and maintenance personnel complete the repair, they upload on-site photos and repair records, click submit, and the work order status is updated to completed. The platform automatically records the closed-loop time and archives it to the full lifecycle archive. Architecture adaptation operation: When a new sampling device is connected, the platform layer automatically detects the device model and communication protocol and adapts to the access requirements through an elastic expansion mechanism, without the need for manual adjustment of the underlying architecture configuration. S42: Enables refined management and control of information for individual devices, supports online editing of basic data and modification of configuration parameters for gas sampling devices on the cloud platform, and synchronizes updates to cloud archive data after submission of edited content; S43: Record and manage gas sampling equipment to fully record the entire lifecycle of gas sampling equipment calibration, maintenance, shutdown, modification and inspection, so as to form a permanent operation and maintenance file for gas sampling equipment; S44: Supports remote control commands from management personnel. It also supports location jump for gas sampling equipment with entered latitude and longitude information to verify the installation location of the gas sampling equipment. Through the above steps, it is conducive to realizing full-process traceability management of equipment from commissioning to scrapping. It supports remote and precise control of multi-channel parameters, solves the problems of scattered file management, inconvenient parameter adjustment, and difficulty in verifying equipment location in traditional systems, and meets the management needs of portable equipment in the field.

[0023] Furthermore, the geographic distribution visualization and collaborative management architecture in step five combines electronic map positioning, data filtering and querying, batch data export, and multi-role permission allocation, specifically including: S51: By combining electronic maps, gas sampling devices across the entire area are marked with red dots, intuitively displaying the installation distribution area of ​​the geographic distribution visualization and collaborative management architecture; S52: Configure map switching modes, including standard map and simplified map multiple switching displays. At the same time, the location details of gas sampling equipment on the map can be viewed with one click. S53: Build a measurement history database, and support multiple conditions such as instrument model, sampling time and monitoring area for combined filtering, and customize filtering conditions to retrieve target historical records; S54: Then configure a hierarchical permission management system to set different permissions accordingly. The hierarchical permission management system includes a supervision end, an operation and maintenance end, and an equipment management end. In addition, advanced control functions are automatically hidden for those with insufficient permissions. Through the above steps, it is beneficial to realize the visualization of equipment location, rapid traceability of historical data, and hierarchical collaborative supervision, solve the problems of scattered management of multi-point equipment, cumbersome data query, and ambiguous permission control, meet the differentiated needs of enterprises and equipment management parties, and improve the efficiency of collaborative supervision.

[0024] Specifically, the geographic distribution visualization and collaborative management architecture in step five also includes historical measurement data tracing and batch export operations, specifically including: S511: A single historical measurement record is associated with instrument configuration parameters, data from the entire sampling process, and geographic location information. Then, the complete operation details can be viewed in the corresponding record to enable full traceability of the sampling operation. S512: Supports batch multi-selection of test data records, custom selection of environmental protection-specific data export templates, and integration, layout and format conversion of multiple sets of monitoring data; S513: After the data is packaged, it is downloaded to the local mobile terminal and then a standardized table file is generated. Through the above steps, it is beneficial to achieve full traceability of the sampling operation, meet the requirements of environmental data compliance reporting, ledger archiving and auditing, solve the problems of inconsistent traditional data export formats and chaotic archiving, and improve the efficiency of data reporting and review.

[0025] Furthermore, step six also includes a dual judgment mechanism for the operation and maintenance management process, incorporating both periodic and frequency thresholds, specifically including: The system presets a periodic maintenance time threshold and a fault frequency threshold for the gas sampling equipment. When the continuous operating time of the gas sampling equipment reaches the maintenance cycle threshold, it is directly marked as a device to be maintained, and a maintenance reminder is pushed to it. When the monthly failure count of a single gas sampling device exceeds the preset frequency threshold, it is directly identified as a high-frequency failure device and prioritized for maintenance tasks. The preset thresholds are configured as follows: maintenance cycle threshold is 30 days, and failure frequency threshold is 3 times / month. The judgment logic is as follows: the cloud management platform's data processing sublayer automatically counts the device's runtime and failure count every morning at midnight, and stores the results in the cluster; when the device's runtime reaches 30 days, the application layer marks the device as pending maintenance and pushes a reminder that the device has reached its maintenance cycle, requesting calibration to the administrator terminal; when the monthly failure count of a single channel is 4 times, the system determines it as a high-frequency failure channel, sets the work order priority to urgent, and the platform automatically selects experienced maintenance personnel in the corresponding region and assigns work orders; after receiving the task, the maintenance personnel view the channel's historical failure records through the terminal, which can be retrieved from the full lifecycle archive, then formulate a maintenance plan, upload on-site photos and maintenance records after maintenance is completed, click submit, the work order status is updated to completed, and the platform layer automatically resets the failure frequency statistics.

[0026] Furthermore, the full lifecycle operation and maintenance management model in step six integrates functions such as gas sampling equipment information editing, remote control, operation and maintenance scheduling, problem feedback, technical training, and system iteration. The knowledge base support process in step six specifically includes: S61: Build an integrated equipment operation and maintenance knowledge base system, which is divided into common problems, instrument learning, information query and problem feedback. The knowledge base system is configured with keyword search and dual filtering by instrument type, as well as common problems and instrument learning, to locate the technical information of the corresponding gas sampling equipment. Common problems integrate question and answer viewing, likes and comments, and interactive comment functions. Instrument learning has built-in technical manuals and teaching videos. S62: The operation and maintenance knowledge base system provides a dedicated feedback portal for issues. Operation and maintenance personnel can submit information on gas sampling equipment malfunctions, on-site operational problems, and platform usage defects. Through the above steps, it is beneficial to provide operators with accurate and convenient technical support, realize the sharing of operation and maintenance experience and rapid troubleshooting, reduce the skill threshold and labor intensity of operators, and solve the problems of inconvenient access to technical data and untimely problem feedback during field operations.

[0027] Furthermore, it also includes timeliness control for multi-channel sampling tasks and IoT heartbeat monitoring management processes, specifically including: S71: Preset the single-channel continuous sampling time threshold, the maximum buffer time threshold for offline field operations, and the IoT heartbeat reporting interval threshold for the gas sampling equipment; S72: Real-time monitoring of the sampling channel polling operation interval on the gas sampling equipment. When the automatic cleaning and continuous sampling time of a single channel exceeds the preset operation threshold, the corresponding channel is locked and a maintenance reminder is pushed. S73: Compare the IoT heartbeat feedback data of the sampler. When the gas sampling device exceeds the heartbeat threshold and fails to upload operating information, it is determined to be an offline abnormal device. Then, the offline area is accurately marked in combination with the field operation point. S74: Combines offline caching duration threshold management for on-site sampling operations without network access. Before reaching the critical threshold, it reminds operators to complete data retransmission and archiving. The preset threshold configurations are as follows: single-channel continuous sampling duration threshold is set to 24 hours, maximum cache duration threshold for field offline operations is 72 hours, and IoT heartbeat reporting interval threshold is 5 minutes. The judgment logic is as follows: The cloud management platform's transmission layer monitors the channel sampling duration and heartbeat signal in real time. If the heartbeat signal is not reported on time, it first attempts to connect again via LoRa communication; if there is still no response, it is determined to be offline. If continuous sampling of a channel is detected for 24 hours, the application layer locks the channel and pushes a notification that the channel has exceeded the continuous sampling threshold, requiring a 30-minute shutdown and return to the operating terminal. Unlocking requires... Manual channel restart; if the device fails to upload operating status information for more than 5 minutes beyond the heartbeat threshold, the application layer marks the device location as a flashing red dot on the electronic map and pushes a reminder that the device is offline and asks for network status verification; when the offline cache time reaches 60 hours, which is 80% of the threshold of 72 hours, the system pushes a message to the operator's terminal that the offline data is about to reach the cache limit and requests that the data be re-uploaded as soon as possible. After the re-upload is completed, the cached data is synchronized to the cluster to ensure the integrity and continuity of multi-channel gas sampling data. Through the above steps, it is beneficial to realize the automatic management of multi-channel sampling load and the rapid identification of device offline status, ensure data integrity in the field with weak network environment, avoid equipment damage caused by channel overload operation, and reduce the frequency of manual inspection and the risk of data loss.

[0028] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative management method for gas sampling equipment via the Internet of Things, characterized in that: The specific steps include the following: Step 1: Establish a transmission link for full-coverage IoT communication, then connect it to the gas sampling equipment to collect multiple data points from the gas sampling equipment in real time. During the data collection process, multi-source heterogeneous data is uploaded and encrypted for storage in real time. Step 2: Classify, integrate, clean, summarize, and visualize the collected data, and then generate various analysis charts on the online trend, type proportion, and fault distribution of gas sampling equipment to manage the overall operational status of gas sampling equipment. Step 3: Real-time verification of sampling indicators and equipment operating parameters, and real-time verification through a two-level threshold intelligent early warning and judgment mechanism based on gas sampling parameters and equipment operating conditions. Then, automatic identification of data exceeding the standard and abnormal equipment failures, and proactive push of early warning information. Step 4: Establish an electronic full lifecycle file for the gas sampling equipment, and then integrate the configuration information, measurement history, maintenance records, maintenance events and remote operation logs of the gas sampling equipment to enable full lifecycle traceability management of the gas sampling equipment; Step 5: Deploy a geographically distributed, visualized, and collaborative management architecture that links multiple terminals, and then enable the Web and mobile terminals to work together to complete on-site and remote collaborative supervision; Step Six: Adopt a full lifecycle operation and maintenance management model for gas acquisition equipment, and integrate remote command issuance and knowledge base support.

2. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: The data in step one includes equipment hardware operating conditions, gas sampling and detection data, on-site images and positioning information, and operation records. The data acquisition and IoT communication transmission in step one specifically includes: S1: Deploy an IoT network card and communication module in the gas sampling equipment, then bind the equipment with a unique number, and continuously collect the equipment's online status, fault codes, maintenance lock status, hardware load, sampling operation progress, and other basic operating data. S2: Then, images are acquired from the gas sampling equipment. The image acquisition includes on-site photos of the entire time period before, during and after the gas sampling operation. During operation, operator information, high-precision geographical location and timestamp traceability data are bound together. S3: Real-time acquisition of gas concentration, sampling flow rate, ambient temperature, humidity and pressure parameters, as well as other detection indicators, and then synchronous recording of instantaneous sampling data and phased data throughout the entire process; S4: Set up a network adaptive caching mechanism. If the network is disconnected during use, the device data will be automatically stored offline. If the network is restored, the interruption will be resumed directly. Then, the number of the gas sampling device, the network card information and the sampling data will be bound and associated.

3. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: Step two, which involves classifying, integrating, and visualizing the collected data, specifically includes: S11: Receives monitoring data and operating data uploaded by all gas sampling devices in real time, and then classifies and archives them according to device model, usage area and operation and maintenance unit; S12: Automatically calculates the trend of the number of online devices over seven days, and then generates a dynamic trend chart based on the trend. S13: Collect statistics on the fault types, frequency, and timing of gas sampling equipment, and then generate a fault distribution bar chart to show the high-incidence types of defects in gas sampling equipment. S14: Divide devices into four management categories: online devices, faulty devices, maintenance devices, and offline devices. The summary information of the quantities is centrally displayed on the main interface of the cloud platform. Entering the corresponding category will allow users to retrieve the detailed list of the corresponding devices.

4. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: The dual-level threshold intelligent early warning and judgment mechanism in step three adopts a fully automatic threshold judgment method, specifically including: S21: Pre-built gas sampling safety threshold library and equipment operation safety threshold library in the cloud, including key limits for gas detection concentration, sampling flow rate, equipment operating voltage, hardware operating temperature and continuous operation time; S22: Then, the real-time collected field data is dynamically compared with the preset threshold library to automatically verify the parameters; S23: If the sampled parameter is detected to be lower than the lower threshold or higher than the upper threshold, it will be directly judged as an abnormality. When the hardware parameters of the gas sampling device exceed the safe range, it will be directly judged as a fault condition. S24: If an abnormal event is triggered, it will be highlighted and displayed as a pop-up notification on the management interface of the cloud platform, and the abnormal time, abnormal parameters and device location will be recorded simultaneously.

5. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: The establishment of an electronic full lifecycle record for the gas sampling equipment in step four specifically includes: S41: The cloud platform distinguishes between instrument model management and instrument information management, and then uniformly enters the model parameters, hardware configuration, compatible communication protocols and controllable command parameters of various gas sampling devices; S42: Enables refined management and control of information for individual devices, supports online editing of basic data and modification of configuration parameters for gas sampling devices on the cloud platform, and synchronizes updates to cloud archive data after submission of edited content; S43: Record and manage gas sampling equipment to fully record the entire lifecycle of gas sampling equipment calibration, maintenance, shutdown, modification and inspection, so as to form a permanent operation and maintenance file for gas sampling equipment; S44: Supports remote control commands from administrators, and also supports location jumps for gas sampling devices with entered latitude and longitude information to verify the installation location of the gas sampling device.

6. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: The geographic distribution visualization and collaborative management architecture in step five combines electronic map positioning, data filtering and querying, batch data export, and multi-role permission allocation, specifically including: S51: By combining electronic maps, gas sampling devices across the entire area are marked with red dots, intuitively displaying the installation distribution area of ​​the geographic distribution visualization and collaborative management architecture; S52: Configure map switching modes, including standard map and simplified map multiple switching displays. At the same time, the location details of gas sampling equipment on the map can be viewed with one click. S53: Build a measurement history database, and support multiple conditions such as instrument model, sampling time and monitoring area for combined filtering, and customize filtering conditions to retrieve target historical records; S54: Then configure a hierarchical permission management system to set differentiated permissions accordingly. The hierarchical permission management system includes a supervision end, an operation and maintenance end, and a device management end. In this way, advanced control functions are automatically hidden when permissions are insufficient.

7. The IoT collaborative management method for gas sampling equipment according to claim 6, characterized in that: The geographic distribution visualization and collaborative management architecture in step five also includes historical measurement data tracing and batch export operations, specifically including: S511: A single historical measurement record is associated with instrument configuration parameters, data from the entire sampling process, and geographic location information. Then, the complete operation details can be viewed in the corresponding record to enable full traceability of the sampling operation. S512: Supports batch multi-selection of test data records, custom selection of environmental protection-specific data export templates, and integration, layout and format conversion of multiple sets of monitoring data; S513: After the data is packaged, it is downloaded to the local mobile terminal and then a standardized table file is generated.

8. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: Step six also includes a dual judgment mechanism for operation and maintenance management processes, comprising both cycle thresholds and frequency thresholds, specifically including: The system presets a periodic maintenance time threshold and a fault frequency threshold for the gas sampling equipment. When the continuous operating time of the gas sampling equipment reaches the maintenance cycle threshold, it is directly marked as a device to be maintained, and a maintenance reminder is pushed to it. When the number of monthly failures of a single gas sampling device exceeds the preset frequency threshold, it is directly identified as a high-frequency failure device and then a maintenance task is assigned to it first.

9. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: The full lifecycle operation and maintenance management mode in step six integrates functions such as gas sampling equipment information editing, remote control, operation and maintenance scheduling, problem feedback, technical training, and system iteration. The knowledge base support process in step six specifically includes: S61: Build an integrated equipment operation and maintenance knowledge base system, and the knowledge base system is divided into common problems, instrument learning, information query and problem feedback. Then, the knowledge base system is configured with keyword search and dual filtering by instrument type, as well as common problems and instrument learning, to locate the technical information of the corresponding gas sampling equipment. The common problems integrate question and answer viewing, like evaluation and comment interaction functions. The instrument learning has built-in two types of materials: technical manuals and teaching videos. S62: The operation and maintenance knowledge base system provides a special problem feedback portal, where operation and maintenance personnel can submit information on gas sampling equipment malfunctions, on-site operation problems, and platform usage defects.

10. The IoT collaborative management method for gas sampling equipment according to claim 1, characterized in that: It also includes timeliness control for multi-channel sampling tasks and IoT heartbeat monitoring management processes, specifically including: S71: Preset the single-channel continuous sampling time threshold, the maximum buffer time threshold for offline field operations, and the IoT heartbeat reporting interval threshold for the gas sampling equipment; S72: Real-time monitoring of the sampling channel polling operation interval on the gas sampling equipment. When the automatic cleaning and continuous sampling time of a single channel exceeds the preset operation threshold, the corresponding channel is locked and a maintenance reminder is pushed. S73: Compare the IoT heartbeat feedback data of the sampler. When the gas sampling device exceeds the heartbeat threshold and fails to upload operating information, it is determined to be an offline abnormal device. Then, the offline area is accurately marked in combination with the field operation point. S74: Combine offline caching duration threshold control for on-site sampling operations without network access. Before reaching the critical threshold, remind operators to complete data retransmission and archiving.

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