A digital risk research management method and system for high-risk scenarios

Abstract

The application discloses a kind of digital risk research management methods and systems for high-risk scene, it is related to industrial manufacturing technical field, the method includes: in each target equipment in high-risk place, real-time monitoring device is installed, and based on the functional attribute of each target equipment, the corresponding data type label is configured to the real-time monitoring data obtained by the real-time monitoring device corresponding to each target equipment;When the data type label of real-time monitoring data is state class signal, then based on the preset determination rule and the state corresponding to real-time monitoring data, fault determination is carried out;When the data type label of real-time monitoring data is numerical value class signal, then based on the preset multiple different alarm levels and corresponding alarm trigger threshold, fault determination is carried out.The application can realize the collection of the key parameters of high-risk equipment, improve equipment fault, environmental anomaly prediction accuracy, convert sudden failure repair to planned maintenance, realize safety management front.

Description

Technical Field

[0001] This application relates to the field of industrial manufacturing technology, specifically to a digital risk pre-research management method and system for high-risk scenarios. Background Technology

[0002] Currently, in industrial manufacturing enterprises, the safety risk management of critical equipment and locations relies heavily on traditional manual maintenance and inspection methods. This approach not only consumes a large amount of manpower but also suffers from monitoring blind spots and lags, making it difficult to capture instantaneous abnormal fluctuations in key parameters such as temperature, pressure, and current during equipment operation. This results in insufficient predictability for sudden failures and potential safety accidents. While basic online monitoring systems are widely used in the industry, their data types are limited, sampling frequency is low, and analysis methods are simplistic, failing to achieve accurate risk warnings and tracing. Furthermore, based on adjustments to actual production conditions, there is a lack of effective simulation and assessment methods for changes in safety risks of high-risk equipment and locations, causing the optimization of control measures to often lag behind the occurrence of accidents.

[0003] Therefore, in order to address the above issues and meet practical needs, a digital risk pre-research and management technology for high-risk scenarios is proposed. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this application is to provide a digital risk pre-research and management method and system for high-risk scenarios, which can collect key parameters of high-risk equipment, improve the accuracy of equipment failure and environmental anomaly prediction, transform sudden failure repair into planned maintenance, and realize proactive safety management.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows: Firstly, this application provides a digital risk pre-research and management method for high-risk scenarios, the method comprising the following steps: Real-time monitoring devices are installed on each target device in high-risk locations, and based on the functional attributes of each target device, corresponding data type tags are configured for the real-time monitoring data obtained by the real-time monitoring device corresponding to each target device. When the data type label of the real-time monitoring data is a status signal, a fault determination is made based on the preset judgment rules and the status corresponding to the real-time monitoring data. When the data type label of the real-time monitoring data is a numerical signal, fault determination is performed based on multiple preset alarm levels and corresponding alarm trigger thresholds.

[0006] Based on the above technical solution, the method further includes the following steps: Once the fault determination is completed, a fault warning message is sent to the staff corresponding to the target device.

[0007] Based on the above technical solution, the method further includes the following steps: The filter media usage cycle of the target equipment used for dust removal is analyzed and obtained, and maintenance reminder information is pushed to the staff corresponding to the target equipment based on the filter media usage cycle.

[0008] Based on the above technical solution, the method further includes the following steps: A testing and calibration cycle is set for the target device, which is a combustible gas probe, and maintenance reminders are pushed to the staff corresponding to the target device at regular intervals based on the testing and calibration cycle.

[0009] Based on the above technical solution, the method further includes the following steps: The failure frequency of each target device is statistically analyzed and pushed to the staff corresponding to the target device.

[0010] Secondly, this application provides a digital risk pre-research management system for high-risk scenarios, the system comprising: The type label configuration module is used to configure corresponding data type labels for the real-time monitoring data obtained by the preset real-time monitoring device corresponding to each target device based on the functional attributes of each target device in the high-risk location. The fault determination module is used to determine faults based on preset determination rules and the corresponding status of the real-time monitoring data when the data type label of the real-time monitoring data is a status signal. The fault determination module is also used to determine faults based on multiple preset alarm levels and corresponding alarm trigger thresholds when the data type label of the real-time monitoring data is a numerical signal.

[0011] Based on the above technical solution, the system further includes: The fault warning module is used to push fault warning information to the staff corresponding to the target equipment after the fault determination is completed.

[0012] Based on the above technical solution, the system further includes: The fault early warning module is used to analyze and obtain the filter media usage cycle of the target equipment used for dust removal, and based on the filter media usage cycle, push maintenance reminder information to the staff corresponding to the target equipment.

[0013] Based on the above technical solution, the system further includes: The maintenance reminder module is used to set a detection and calibration cycle for the target equipment whose real-time monitoring device is a combustible gas probe, and to periodically push maintenance reminder information to the staff corresponding to the target equipment based on the detection and calibration cycle.

[0014] Based on the above technical solution, the system further includes: The frequency statistics module is used to count the fault frequency of each target device and push the data to the staff corresponding to the target device.

[0015] Compared with the prior art, the advantages of this application are: This application enables the collection of key parameters of high-risk equipment, improves the accuracy of equipment failure and environmental anomaly prediction, transforms emergency failure repair into planned maintenance, and realizes proactive safety management.

[0016] This application utilizes data simulation to conduct pre-emptive drills in a virtual space, thereby optimizing control measures in advance, enhancing the inherent level of safety, and forming a full-process risk control model encompassing "perception, analysis, decision-making, handling, and assessment." Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of a digital risk pre-research and management method for high-risk scenarios, as exemplified in this application. Figure 2 This is a structural block diagram of a digital risk pre-research management system for high-risk scenarios, as described in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0021] This application provides a digital risk pre-research and management method and system for high-risk scenarios, which can collect key parameters of high-risk equipment, improve the accuracy of equipment failure and environmental anomaly prediction, transform sudden failure repair into planned maintenance, and realize proactive safety management.

[0022] To achieve the aforementioned technical effects, the overall concept of this application is as follows: A digital risk pre-research and management method for high-risk scenarios, comprising the following steps: S1. Install real-time monitoring devices on each target device in high-risk locations, and configure corresponding data type tags for the real-time monitoring data obtained by the real-time monitoring device corresponding to each target device based on the functional attributes of each target device. S2. When the data type label of the real-time monitoring data is a status signal, the fault is determined based on the preset judgment rules and the status corresponding to the real-time monitoring data. S3. When the data type label of the real-time monitoring data is a numerical signal, the fault determination is performed based on multiple preset alarm levels and corresponding alarm trigger thresholds.

[0023] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0024] Firstly, see [the following] Figure 1 As shown in the figure, this application provides a digital risk pre-research and management method for high-risk scenarios, which includes the following steps: S1. Install real-time monitoring devices on each target device in high-risk locations, and configure corresponding data type tags for the real-time monitoring data obtained by the real-time monitoring device corresponding to each target device based on the functional attributes of each target device. S2. When the data type label of the real-time monitoring data is a status signal, the fault is determined based on the preset judgment rules and the status corresponding to the real-time monitoring data. S3. When the data type label of the real-time monitoring data is a numerical signal, the fault determination is performed based on multiple preset alarm levels and corresponding alarm trigger thresholds.

[0025] It should be noted that the existing patent, with publication number CN112488483B, entitled "An EHS Transparent Management System and Method Based on AI Technology," uses video surveillance to monitor in real time whether personnel activities in the production area are following standardized procedures. The monitoring server has preset parameter values ​​and uses real-time video from the application to identify and detect the workers' attire and behavior. The client allows managers to understand the monitoring situation in real time and provides warnings for dangerous behaviors that do not meet the requirements. This solves the problem in existing technologies where on-duty personnel in factory workshops have low safety awareness, employees' non-standard production operations, and insufficient preventive measures in the factory, which easily leads to safety accidents.

[0026] The technical flaw of this existing patent is: Video surveillance equipment is used to monitor, identify, and issue early warnings for unsafe human behavior. However, there is a lack of real-time monitoring of unsafe equipment conditions, the delivery of early warning information, and the use of data for preliminary research to provide improvement solutions and achieve proactive safety management.

[0027] The system can issue early warnings for individual unsafe behavior cases. However, it lacks closed-loop management of the entire process of unsafe behavior cases, and it lacks data statistics and dashboard displays for unsafe behavior cases of specific time periods and specific types.

[0028] In this embodiment, key parameters of high-risk equipment can be collected, improving the accuracy of equipment failure and environmental anomaly prediction, transforming emergency failure repair into planned maintenance, and realizing proactive safety management.

[0029] Furthermore, the method also includes the following steps: Once the fault determination is completed, a fault warning message is sent to the staff corresponding to the target device.

[0030] Furthermore, the method also includes the following steps: The filter media usage cycle of the target equipment used for dust removal is analyzed and obtained, and maintenance reminder information is pushed to the staff corresponding to the target equipment based on the filter media usage cycle.

[0031] Furthermore, the method also includes the following steps: A testing and calibration cycle is set for the target device, which is a combustible gas probe, and maintenance reminders are pushed to the staff corresponding to the target device at regular intervals based on the testing and calibration cycle.

[0032] Furthermore, the method also includes the following steps: The failure frequency of each target device is statistically analyzed and pushed to the staff corresponding to the target device.

[0033] The technical solution based on the embodiments of this application, in specific implementation, includes the following three core components: establishing a data model, risk warning and control, and risk pre-research and data twinning, as detailed below: Phase 1: Establishing the Data Model 1.1 Sensor Deployment and Data Acquisition: By modifying the hardware of high-risk equipment on site, adding sensors, switches, transformers, industrial control computers, flow meters, etc., and upgrading the software, the key parameters of high-risk equipment can be collected in milliseconds and entered into the industrial Internet of Things (IoT) platform.

[0034] For visual monitoring systems in key locations such as manufacturing plants, important traffic sections in industrial parks, and repair bays in stores, resources are integrated and systems are optimized and connected to an industrial Internet of Things (IoT) platform. This enables video capture and source tracing of abnormal on-site environments and personnel violations.

[0035] 1.2 Data Model Building: Based on an Industrial Internet of Things (IoT) system, a device data model is built. First, device type labels are defined. A labeling system is established based on device functions and attributes. By filtering devices with different label attributes, device classification and management can be achieved. Next, the labels and attributes of the data collected by the devices are labeled, including signal name, attribute description, unit, location address, industrial data type, acquisition method, and acquisition frequency. By filtering data with different attributes, upper-level data requests can access device data.

[0036] Based on an Industrial Internet of Things (IoT) system, AI vision algorithms are deployed. These algorithms analyze personnel violations and environmental anomalies, such as people not using pedestrian walkways, people making or receiving phone calls in violation of regulations, people not wearing safety helmets, and fire thermal imaging. AI algorithms are used to identify and record these violations, including time, location, screenshots, and videos. Data is then filtered based on alarm time, location, violation type, and statistical area to generate necessary data reports.

[0037] Phase Two: Risk Warning and Control 2.1 Risk Warning: After completing the data model construction in section 1.2, alarm rules are set for the signals of devices connected to the Industrial Internet of Things (IoT) system. First, select the target device and signal attributes from the attribute set, and then define the alarm triggering conditions according to requirements. A specific example is as follows: ① Status signals (such as safety light curtains, visual AI algorithm signals, etc.): When the condition value is 0, it is not triggered; when the condition value is 1, the safety light curtain is triggered; when the condition value is 2, the safety light is faulty.

[0038] ② Monitoring numerical information: (e.g., VOCs online monitoring indicators, non-methane total hydrocarbons, etc.): Set the alarm trigger condition to trigger an alarm when the collected value is >= 25.

[0039] Alarm levels are defined, including alarm, general, severe, and critical, with textual descriptions of the alarm status to convey critical information to recipients via alert text. Specific alarm push notification formats are also defined, including alert message templates, sending types, maximum resend counts, recipients, and responsible personnel. This establishes a complete set of signal alert push rules, enabling timely and accurate warnings of environmental and equipment anomalies.

[0040] 2.2 Work Order Dispatch and Processing: When equipment monitoring indicators trigger alarm rules, an anomaly warning is simultaneously pushed to the designated roles receiving the warning information via the Industrial Internet of Things (IoT) system and mobile client. This prompts personnel to arrive on-site immediately and, according to the digital workflow rules (L3~L4), confirm and restore the abnormal equipment and environment. Then, a work order is closed through the Industrial Internet of Things (IoT) platform or mobile client. Work order closure must record the responsible person, problem type, problem assessment, and problem status. By dispatching work tasks through the system, a closed-loop management system is achieved for high-risk equipment and location anomalies, ensuring the traceability of problem handling solutions.

[0041] Phase Three: Risk Pre-analysis and Data Twin: 3.1 Risk Preliminary Study: Based on the data model established in section 1.2, the collected data is intelligently analyzed and assessed through the Industrial Internet of Things (IoT) platform to provide predictive maintenance solutions, fault maintenance solutions, energy consumption and cost improvement solutions, etc. 1. Predictive maintenance solution: ① Analyze and assess the usage cycle data of filter media in dust removal equipment, and combine this with on-site production variables to accurately predict the next replacement time. Send reminder messages to guide technicians in establishing adequate spare parts inventory and reducing inventory costs. Timely and accurate filter media replacement ensures that the equipment's dust removal efficiency meets requirements.

[0042] ②Based on the detection and calibration time data of monitoring equipment such as combustible gas probes, set detection and calibration cycle thresholds, push reminder messages, and guide technicians to complete equipment detection and calibration regularly to ensure the accuracy and reliability of the equipment.

[0043] 2. Fault maintenance solutions: ① For high-frequency and long-term abnormal locations, conduct data trend analysis and judgment, match mature solutions based on the work order task database, and guide technicians to carry out equipment maintenance and inspection in advance to ensure that equipment risks are controllable.

[0044] ② By using VOCs monitoring data, combined with on-site equipment status and production variables, and based on the work order database, mature solutions are matched to guide technical personnel to conduct pre-management and ensure that VOCs emissions meet national emission standards.

[0045] 3. Energy consumption and cost improvement plans: ① By analyzing the RTO temperature curve, the heating time can be accurately determined, guiding technicians to dynamically adjust the start-up time and reduce overall energy consumption.

[0046] ② By analyzing the temperature and pressure data of the RTO combustion chamber, we can determine and match the optimal parameter adjustment scheme to improve the air-fuel ratio of the equipment and reduce natural gas consumption.

[0047] 3.2 Data Twin Applications: By utilizing the equipment database and inputting production variables and key parameters, the system simulates the operation of equipment under different working conditions. Combined with the feedback values ​​of equipment parameters under virtual operation, risk assessment is conducted. Based on the data model and work order database, mature optimization solutions are matched. This not only enables the prediction of equipment compliance under different conditions but also allows for the optimization of safety risk control measures in advance, truly achieving proactive management.

[0048] 3.3 Integrated Management Dashboard: For high-risk equipment and locations already connected to the Industrial Internet of Things (IoT) platform, on-site inspection QR codes are set up. Daily inspections are confirmed by scanning the codes using mobile devices. Management information such as inspection time, location, inspector, and reported issues is collected and compiled into a management dashboard. Inspection tasks are assigned to different roles according to the safety risk classification and control mechanism.

[0049] In summary, the technical solution of this application can achieve millisecond-level acquisition of key parameters such as temperature, pressure, and current of high-risk equipment, improving the accuracy of equipment failure and environmental anomaly prediction, transforming emergency fault repair into planned maintenance, and realizing proactive safety management. Furthermore, it can utilize data simulation to rehearse in a virtual space, optimizing control measures in advance, enhancing the inherent level of safety, and forming a full-process risk management model of "perception, analysis, decision-making, handling, and evaluation."

[0050] Furthermore, the technical advantages of the technical solution in this application are as follows: The document, comprising three core sections—data modeling, risk warning and control, and risk pre-research and data twinning—explains the digital risk pre-research management process and methods for high-risk equipment and locations. It addresses the problems of traditional monitoring systems, such as limited data types, low sampling frequency, single analysis methods, lack of data analysis and pre-research, inability to perform data simulation, and inability to provide optimization solutions.

[0051] It supports millisecond-level acquisition and modeling of environmental visual data, equipment electrical signals, and other data, replacing traditional manual safety inspections with digital inspection methods, reducing labor costs, improving the accuracy of equipment and environmental risk prediction, and enabling rapid emergency response to abnormal operating conditions.

[0052] By utilizing digital models for data analysis and solution matching, multi-dimensional composite analysis methods can be used to intervene in advance for abnormal equipment parameters, provide predictive maintenance, reduce the rate of sudden equipment failures, and achieve proactive safety management.

[0053] By using data simulation, equipment operation can be rehearsed in a virtual space to identify and optimize safety risk management plans in advance, thereby reducing the accident rate.

[0054] Secondly, see Figure 2 As shown in the figure, this application embodiment provides a digital risk pre-research and management system for high-risk scenarios. The system includes: The type label configuration module is used to configure corresponding data type labels for the real-time monitoring data obtained by the preset real-time monitoring device corresponding to each target device based on the functional attributes of each target device in the high-risk location. The fault determination module is used to determine faults based on preset determination rules and the corresponding status of the real-time monitoring data when the data type label of the real-time monitoring data is a status signal. The fault determination module is also used to determine faults based on multiple preset alarm levels and corresponding alarm trigger thresholds when the data type label of the real-time monitoring data is a numerical signal.

[0055] In this embodiment, key parameters of high-risk equipment can be collected, improving the accuracy of equipment failure and environmental anomaly prediction, transforming emergency failure repair into planned maintenance, and realizing proactive safety management.

[0056] Furthermore, the system also includes: The fault warning module is used to push fault warning information to the staff corresponding to the target equipment after the fault determination is completed.

[0057] Furthermore, the system also includes: The fault early warning module is used to analyze and obtain the filter media usage cycle of the target equipment used for dust removal, and based on the filter media usage cycle, push maintenance reminder information to the staff corresponding to the target equipment.

[0058] Furthermore, the system also includes: The maintenance reminder module is used to set a detection and calibration cycle for the target equipment whose real-time monitoring device is a combustible gas probe, and to periodically push maintenance reminder information to the staff corresponding to the target equipment based on the detection and calibration cycle.

[0059] Furthermore, the system also includes: The frequency statistics module is used to count the fault frequency of each target device and push the data to the staff corresponding to the target device.

[0060] The technical solution based on the embodiments of this application, in specific implementation, includes the following three core components: establishing a data model, risk warning and control, and risk pre-research and data twinning, as detailed below: Phase 1: Establishing the Data Model 1.1 Sensor Deployment and Data Acquisition: By modifying the hardware of high-risk equipment on site, adding sensors, switches, transformers, industrial control computers, flow meters, etc., and upgrading the software, the key parameters of high-risk equipment can be collected in milliseconds and entered into the industrial Internet of Things (IoT) platform.

[0061] For visual monitoring systems in key locations such as manufacturing plants, important traffic sections in industrial parks, and repair bays in stores, resources are integrated and systems are optimized and connected to an industrial Internet of Things (IoT) platform. This enables video capture and source tracing of abnormal on-site environments and personnel violations.

[0062] 1.2 Data Model Building: Based on an Industrial Internet of Things (IoT) system, a device data model is built. First, device type labels are defined. A labeling system is established based on device functions and attributes. By filtering devices with different label attributes, device classification and management can be achieved. Next, the labels and attributes of the data collected by the devices are labeled, including signal name, attribute description, unit, location address, industrial data type, acquisition method, and acquisition frequency. By filtering data with different attributes, upper-level data requests can access device data.

[0063] Based on an Industrial Internet of Things (IoT) system, AI vision algorithms are deployed. These algorithms analyze personnel violations and environmental anomalies, such as people not using pedestrian walkways, people making or receiving phone calls in violation of regulations, people not wearing safety helmets, and fire thermal imaging. AI algorithms are used to identify and record these violations, including time, location, screenshots, and videos. Data is then filtered based on alarm time, location, violation type, and statistical area to generate necessary data reports.

[0064] Phase Two: Risk Warning and Control 2.1 Risk Warning: After completing the data model construction in section 1.2, alarm rules are set for the signals of devices connected to the Industrial Internet of Things (IoT) system. First, select the target device and signal attributes from the attribute set, and then define the alarm triggering conditions according to requirements. A specific example is as follows: ① Status signals (such as safety light curtains, visual AI algorithm signals, etc.): When the condition value is 0, it is not triggered; when the condition value is 1, the safety light curtain is triggered; when the condition value is 2, the safety light is faulty.

[0065] ② Monitoring numerical information: (e.g., VOCs online monitoring indicators, non-methane total hydrocarbons, etc.): Set the alarm trigger condition to trigger an alarm when the collected value is >= 25.

[0066] Alarm levels are defined, including alarm, general, severe, and critical, with textual descriptions of the alarm status to convey critical information to recipients via alert text. Specific alarm push notification formats are also defined, including alert message templates, sending types, maximum resend counts, recipients, and responsible personnel. This establishes a complete set of signal alert push rules, enabling timely and accurate warnings of environmental and equipment anomalies.

[0067] 2.2 Work Order Dispatch and Processing: When equipment monitoring indicators trigger alarm rules, an anomaly warning is simultaneously pushed to the designated roles receiving the warning information via the Industrial Internet of Things (IoT) system and mobile client. This prompts personnel to arrive on-site immediately and, according to the digital workflow rules (L3~L4), confirm and restore the abnormal equipment and environment. Then, a work order is closed through the Industrial Internet of Things (IoT) platform or mobile client. Work order closure must record the responsible person, problem type, problem assessment, and problem status. By dispatching work tasks through the system, a closed-loop management system is achieved for high-risk equipment and location anomalies, ensuring the traceability of problem handling solutions.

[0068] Phase Three: Risk Pre-analysis and Data Twin: 3.1 Risk Preliminary Study: Based on the data model established in section 1.2, the collected data is intelligently analyzed and assessed through the Industrial Internet of Things (IoT) platform to provide predictive maintenance solutions, fault maintenance solutions, energy consumption and cost improvement solutions, etc. 1. Predictive maintenance solution: ① Analyze and assess the usage cycle data of filter media in dust removal equipment, and combine this with on-site production variables to accurately predict the next replacement time. Send reminder messages to guide technicians in establishing adequate spare parts inventory and reducing inventory costs. Timely and accurate filter media replacement ensures that the equipment's dust removal efficiency meets requirements.

[0069] ②Based on the detection and calibration time data of monitoring equipment such as combustible gas probes, set detection and calibration cycle thresholds, push reminder messages, and guide technicians to complete equipment detection and calibration regularly to ensure the accuracy and reliability of the equipment.

[0070] 2. Fault maintenance solutions: ① For high-frequency and long-term abnormal locations, conduct data trend analysis and judgment, match mature solutions based on the work order task database, and guide technicians to carry out equipment maintenance and inspection in advance to ensure that equipment risks are controllable.

[0071] ② By using VOCs monitoring data, combined with on-site equipment status and production variables, and based on the work order database, mature solutions are matched to guide technical personnel to conduct pre-management and ensure that VOCs emissions meet national emission standards.

[0072] 3. Energy consumption and cost improvement plans: ① By analyzing the RTO temperature curve, the heating time can be accurately determined, guiding technicians to dynamically adjust the start-up time and reduce overall energy consumption.

[0073] ② By analyzing the temperature and pressure data of the RTO combustion chamber, we can determine and match the optimal parameter adjustment scheme to improve the air-fuel ratio of the equipment and reduce natural gas consumption.

[0074] 3.2 Data Twin Applications: By utilizing the equipment database and inputting production variables and key parameters, the system simulates the operation of equipment under different working conditions. Combined with the feedback values ​​of equipment parameters under virtual operation, risk assessment is conducted. Based on the data model and work order database, mature optimization solutions are matched. This not only enables the prediction of equipment compliance under different conditions but also allows for the optimization of safety risk control measures in advance, truly achieving proactive management.

[0075] 3.3 Integrated Management Dashboard: For high-risk equipment and locations already connected to the Industrial Internet of Things (IoT) platform, on-site inspection QR codes are set up. Daily inspections are confirmed by scanning the codes using mobile devices. Management information such as inspection time, location, inspector, and reported issues is collected and compiled into a management dashboard. Inspection tasks are assigned to different roles according to the safety risk classification and control mechanism.

[0076] In summary, the technical solution of this application can achieve millisecond-level acquisition of key parameters such as temperature, pressure, and current of high-risk equipment, improving the accuracy of equipment failure and environmental anomaly prediction, transforming emergency fault repair into planned maintenance, and realizing proactive safety management. Furthermore, it can utilize data simulation to rehearse in a virtual space, optimizing control measures in advance, enhancing the inherent level of safety, and forming a full-process risk management model of "perception, analysis, decision-making, handling, and evaluation."

[0077] Furthermore, the technical advantages of the technical solution in this application are as follows: The document, comprising three core sections—data modeling, risk warning and control, and risk pre-research and data twinning—explains the digital risk pre-research management process and methods for high-risk equipment and locations. It addresses the problems of traditional monitoring systems, such as limited data types, low sampling frequency, single analysis methods, lack of data analysis and pre-research, inability to perform data simulation, and inability to provide optimization solutions.

[0078] It supports millisecond-level acquisition and modeling of environmental visual data, equipment electrical signals, and other data, replacing traditional manual safety inspections with digital inspection methods, reducing labor costs, improving the accuracy of equipment and environmental risk prediction, and enabling rapid emergency response to abnormal operating conditions.

[0079] By utilizing digital models for data analysis and solution matching, multi-dimensional composite analysis methods can be used to intervene in advance for abnormal equipment parameters, provide predictive maintenance, reduce the rate of sudden equipment failures, and achieve proactive safety management.

[0080] By using data simulation, equipment operation can be rehearsed in a virtual space to identify and optimize safety risk management plans in advance, thereby reducing the accident rate.

[0081] In summary, the digital risk pre-research and management system for high-risk scenarios provided in this application embodiment has the same technical principles as the digital risk pre-research and management method for high-risk scenarios provided in the first aspect in terms of technical issues, technical solutions, and technical effects, so it will not be described in detail here.

[0082] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the system or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0083] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A digital risk pre-research and management method for high-risk scenarios, characterized in that, The method includes the following steps: Real-time monitoring devices are installed on each target device in high-risk locations, and based on the functional attributes of each target device, corresponding data type tags are configured for the real-time monitoring data obtained by the real-time monitoring device corresponding to each target device. When the data type label of the real-time monitoring data is a status signal, a fault determination is made based on the preset judgment rules and the status corresponding to the real-time monitoring data. When the data type label of the real-time monitoring data is a numerical signal, fault determination is performed based on multiple preset alarm levels and corresponding alarm trigger thresholds.

2. The digital risk pre-research and management method for high-risk scenarios as described in claim 1, characterized in that, The method further includes the following steps: Once the fault determination is completed, a fault warning message is sent to the staff corresponding to the target device.

3. The digital risk pre-research and management method for high-risk scenarios as described in claim 1, characterized in that, The method further includes the following steps: The filter media usage cycle of the target equipment used for dust removal is analyzed and obtained, and maintenance reminder information is pushed to the staff corresponding to the target equipment based on the filter media usage cycle.

4. The digital risk pre-research and management method for high-risk scenarios as described in claim 1, characterized in that, The method further includes the following steps: A testing and calibration cycle is set for the target device, which is a combustible gas probe, and maintenance reminders are pushed to the staff corresponding to the target device at regular intervals based on the testing and calibration cycle.

5. The digital risk pre-research and management method for high-risk scenarios as described in claim 1, characterized in that, The method further includes the following steps: The failure frequency of each target device is statistically analyzed and pushed to the staff corresponding to the target device.

6. A digital risk pre-research management system for high-risk scenarios, characterized in that, The system includes: The type label configuration module is used to configure corresponding data type labels for the real-time monitoring data obtained by the preset real-time monitoring device corresponding to each target device based on the functional attributes of each target device in the high-risk location. The fault determination module is used to determine faults based on preset determination rules and the corresponding status of the real-time monitoring data when the data type label of the real-time monitoring data is a status signal. The fault determination module is also used to determine faults based on multiple preset alarm levels and corresponding alarm trigger thresholds when the data type label of the real-time monitoring data is a numerical signal.

7. The digital risk pre-research management system for high-risk scenarios as described in claim 6, characterized in that, The system also includes: The fault warning module is used to push fault warning information to the staff corresponding to the target equipment after the fault determination is completed.

8. The digital risk pre-research management system for high-risk scenarios as described in claim 7, characterized in that, The system also includes: The fault early warning module is used to analyze and obtain the filter media usage cycle of the target equipment used for dust removal, and based on the filter media usage cycle, push maintenance reminder information to the staff corresponding to the target equipment.

9. The digital risk pre-research management system for high-risk scenarios as described in claim 6, characterized in that, The system also includes: The maintenance reminder module is used to set a detection and calibration cycle for the target equipment whose real-time monitoring device is a combustible gas probe, and to periodically push maintenance reminder information to the staff corresponding to the target equipment based on the detection and calibration cycle.

10. The digital risk pre-research management system for high-risk scenarios as described in claim 6, characterized in that, The system also includes: The frequency statistics module is used to count the fault frequency of each target device and push the data to the staff corresponding to the target device.

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