New energy field operation full-process safety monitoring system and method fused with Internet of Things perception

Abstract

The invention discloses a new energy field operation full-process safety monitoring system and method fused with Internet of Things perception. The system comprises a perception layer, an edge layer and a cloud layer. The sensing layer, the edge layer and the cloud layer are in communication connection in sequence; the sensing layer comprises a personnel identification unit, a tool identification unit, an environment and meteorological monitoring unit and a video image identification unit; the edge layer comprises an edge controller, and the edge controller comprises a data access module, a risk scoring module, a four-state consistency interlocking module, a falling and weather composite linkage module and an interlocking execution module; the cloud layer comprises an operation process engine, a dynamic risk learning model, a digital twin tool management module, an isolation point intelligent confirmation module and a block chain evidence obtaining module. And synchronous sensing and dynamic interlocking control of personnel, tools, environments and operation processes are realized.

Description

Technical Field

[0001] This invention belongs to the field of new energy safety management technology, and relates to a safety monitoring system and method for the entire process of new energy field operations that integrates Internet of Things (IoT) sensing. Background Technology

[0002] As a crucial component of integrated wind, solar, and energy storage energy systems, new energy power plants are characterized by high-frequency, high-risk, and highly environmentally dependent on on-site operations. On-site operations encompass various types, including tower maintenance, nacelle maintenance, blade hoisting, energy storage compartment operation, and electrical isolation and power transmission. These operations generally involve hazardous conditions such as high altitude, confined spaces, live equipment, and heavy loads, placing extremely high demands on personnel qualifications, equipment condition, weather conditions, and the coordination of work processes. With the expansion of new energy power plants and the increasing intelligence of equipment, traditional safety supervision methods relying on manual inspections and experience-based management are no longer adequate to meet the complexity and immediacy requirements of on-site operations.

[0003] Currently, safety management systems for new energy operations generally suffer from the following problems: First, data collection methods are limited, failing to achieve multi-dimensional collaborative perception of personnel, tools, and the environment; second, existing safety control systems largely rely on manual approval and review, lacking real-time monitoring and automatic interlocking mechanisms for the entire operation process; third, while some systems possess remote monitoring capabilities, they lack a unified data loop, hindering predictive risk analysis and tamper-proof record preservation of work data; fourth, when communication is interrupted or the network malfunctions, the system often loses its real-time protection capabilities, resulting in delayed safety interlocking and management gaps. Consequently, safety incidents at new energy field operations remain frequent, particularly in extreme weather conditions or densely populated operational scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a new energy field operation safety monitoring system and method that integrates Internet of Things (IoT) sensing. This system is a new energy field operation safety monitoring platform that integrates IoT multi-source sensing, edge real-time control and cloud intelligent management to achieve synchronous sensing and dynamic interlocking control of personnel, tools, environment and operation process.

[0005] To achieve the above objectives, the present invention employs the following technical solution: A full-process safety monitoring system for new energy field operations that integrates Internet of Things (IoT) sensing, comprising a sensing layer, an edge layer, and a cloud layer; Communication connections are established sequentially between the perception layer, edge layer, and cloud layer. The perception layer includes a personnel identification unit, a tool identification unit, an environmental and meteorological monitoring unit, and a video image recognition unit; it is used to collect personnel identity and position posture, tool status, environmental parameters, and isolation point image information. The edge layer includes an edge controller, which comprises a data access module, a risk scoring module, a four-state consistency interlocking module, a fall and weather composite linkage module, and an interlocking execution module. It is used to receive data from the perception layer, perform multi-source data fusion calculation and risk assessment, and output interlocking control signals. The cloud layer includes a workflow engine, a dynamic risk learning model, a digital twin tool management module, an isolation point intelligent confirmation module, and a blockchain evidence collection module; it is used for workflow orchestration, risk trend prediction, digital twin management, and data evidence collection.

[0006] Optionally, the personnel identification unit includes UWB tags, inertial measurement units, and RFID name tags; the tool identification unit includes electronic padlocks and rigging strain sensors; and the environmental and meteorological monitoring unit includes temperature and humidity sensors, wind speed and direction sensors, electric field strength monitoring devices, and gas detection modules.

[0007] Optionally, the four-state consistency interlocking module is connected to the data interfaces of personnel qualification status, tool status, environmental condition status, and work process status respectively; the four-state consistency interlocking module outputs a work permission signal when personnel qualification status, tool status, environmental condition status, and work process status are all in a safe state, otherwise it outputs an interlocking signal.

[0008] Optionally, the fall and meteorological composite linkage module is connected to the inertial measurement unit in the personnel identification unit, and the fall and meteorological composite linkage module is connected to the wind speed and direction sensor and gas detection module in the environment and meteorological monitoring unit; when the fall and meteorological composite linkage module receives abnormal data of personnel vertical velocity and acceleration and simultaneously receives abnormal data of wind speed or gas concentration, it outputs a composite interlocking action signal.

[0009] Optionally, the isolation point intelligent confirmation module connects to a mobile terminal equipped with an NFC tag and a camera; the isolation point intelligent confirmation module receives isolation point images and NFC scan data, and compares the isolation point images with a standard template.

[0010] Optionally, the blockchain forensics module adopts a consortium blockchain architecture. The blockchain forensics module contains block records, which include block number, preceding hash, timestamp, object identifier, operation type, digital signature, and state digest.

[0011] A method for full-process safety monitoring of new energy field operations integrating IoT sensing includes the following steps: The personnel identification unit verifies the entry of workers, and the RFID work badge verifies their identity information, qualification level and health status. The tool identification unit verifies the isolation point, electronic padlock, and tool status; The edge controller integrates personnel data, tool data, environmental data, and work process data to calculate a risk score. The edge controller triggers an alarm or interlock control when the risk score reaches a threshold. The isolation point intelligent confirmation module performs image recognition and NFC verification on the reset action; The blockchain evidence collection module writes the operation data and reset confirmation information into the blockchain for evidence storage.

[0012] Optionally, the steps for calculating the risk score include: obtaining the risk weights for personnel, tools, environment, and work process; multiplying the personnel's actions and postures, tool status, environmental parameters, and work process stage indicators by their corresponding weights and then summing them to obtain the comprehensive risk value.

[0013] Optionally, the steps for verifying the status of tools include: the digital twin tool management module calculates the tool fatigue index; the fatigue index is calculated based on the number of strain samplings recorded by the tool within the statistical period, the actual strain value of the tool, and the maximum allowable strain value; the digital twin tool management module sets the tool status to disabled when the fatigue index is greater than or equal to 1.

[0014] Optionally, the steps to trigger the interlock control include: when the edge controller detects that the vertical speed of the personnel is greater than the set value and the vertical acceleration is less than the set negative value, and at the same time detects that the wind speed transient exceeds the set value or the oxygen concentration drops beyond the set value, the composite interlock action is executed; the composite interlock action includes cutting off the equipment power supply, locking the access control, and activating the alarm.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a three-layer collaborative architecture of perception, edge, and cloud, and utilizes multi-source IoT fusion technology to break down information silos among personnel, tools, environment, and processes. Its core feature is the edge-side four-state consistency interlocking mechanism, which ensures independent millisecond-level automatic locking and on-site protection in the event of communication anomalies or sudden risks, solving the problems of lag and management gaps in traditional manual supervision. Simultaneously, it combines a cloud-based Transformer-based dynamic risk learning model and blockchain evidence collection technology, elevating safety management from reactive post-event handling to pre-event short-term trend prediction. Furthermore, it utilizes digital twins and hash-based evidence storage to ensure the authenticity and immutability of data throughout the entire operation process, thereby achieving closed-loop intelligent control and inherent safety enhancement of new energy on-site operations from access and execution to reset. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system modules of the new energy field operation full-process safety monitoring platform integrating Internet of Things sensing according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the overall process of a safety monitoring method for new energy field operations according to Embodiment 2 of the present invention. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] The following disclosure provides many different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, examples of various specific processes and materials are provided in this invention, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0019] Example 1 This embodiment provides a full-process safety monitoring platform for new energy on-site operations that integrates IoT sensing. Addressing the challenges of numerous work sites, complex types of hazardous operations, frequent weather changes, scattered operational information, and lagging manual supervision in new energy power plant on-site operations, this platform constructs a full-process safety monitoring system centered on four core elements: people, materials, environment, and process.

[0020] The platform's overall architecture comprises three collaborative layers: the perception layer, the edge layer, and the cloud layer. It adopts an integrated model of real-time on-site perception, rapid edge decision-making, and intelligent cloud control, achieving a closed-loop safety management system covering the entire process from work access, execution monitoring, risk interlocking, anomaly handling, to reset confirmation.

[0021] Structurally, this platform achieves the following functions through a hierarchical deployment of multi-source sensing devices, edge control units, and a cloud-based management and control center: 1. The perception layer is deployed at the new energy power station site and includes personnel identification unit, tool identification unit, environmental and meteorological monitoring unit, and video image recognition unit, which are used to collect personnel identity, location and posture, tool status, environmental parameters and isolation point image information in real time.

[0022] 2. The edge layer uses the edge controller as the core node and has built-in multi-source data fusion module, risk scoring module, four-state consistency interlocking module, fall and meteorological composite linkage module, and object caching module to achieve millisecond-level on-site risk assessment and automatic interlocking control.

[0023] 3. Deployed in the cloud layer at the operation and maintenance center or main control server, it is equipped with a work process engine, dynamic risk learning model, digital twin tool management module, isolation point intelligent confirmation module, blockchain evidence collection module and visual management interface to realize work process management, risk prediction, life tracking, reset verification and data anti-tampering evidence collection.

[0024] This platform uses a four-state consistency interlocking mechanism to jointly assess the status of personnel, tools, environment, and processes, ensuring that work permit conditions are met synchronously. It uses a dynamic risk learning model based on multimodal time-series data to predict short-term risk trends. It uses a combined fall and weather linkage mechanism to achieve rapid braking under abnormal transient conditions. It uses intelligent confirmation of isolation points and blockchain evidence collection to achieve credible verification and full-process traceability in the work completion stage, thereby significantly improving the operational safety level and management transparency of new energy power plants.

[0025] like Figure 1 As shown, the new energy field operation safety monitoring platform integrating IoT sensing of the present invention adopts a three-layer distributed structure. The sensing layer serves as the data source, realizing real-time monitoring of personnel, tools, environment, and work behavior; the edge layer serves as the decision-making center, realizing multi-source data fusion calculation, risk assessment, and on-site automatic interlocking control; the cloud layer serves as the intelligent management center, completing work process orchestration, risk trend prediction, digital twin management, evidence collection, and visual monitoring. Each layer achieves data interaction and policy synchronization through encrypted communication channels. When communication is abnormal, the edge layer can still operate independently and ensure the continued effectiveness of safety interlocking.

[0026] To enable those skilled in the art to more clearly understand the structural composition and implementation principle of the present invention, the following description is provided in conjunction with the appendix. Figure 1 This paper further explains the various functional modules of the new energy on-site operation full-process safety monitoring platform integrating IoT sensing described in this invention. The overview section of Example 1 introduced the overall architecture and collaborative logic of the platform. Next, the structure, function, and interconnection of the main modules will be described in detail from three levels: the sensing layer, the edge layer, and the cloud layer, so as to fully reveal the technical process and system collaborative mechanism of the platform to achieve full-process on-site safety monitoring.

[0027] I. Perception Layer The perception layer, serving as the foundation for platform data acquisition in this invention, mainly consists of personnel identification units, tool identification units, environmental and meteorological monitoring units, and video image recognition units. This layer is distributed and deployed in the operating areas of wind farms, photovoltaic power stations, and energy storage stations, and interacts with edge controllers via wireless communication.

[0028] 1. Personnel identification unit The personnel identification unit includes UWB tags, an inertial measurement unit (IMU), and an RFID work badge. The UWB tag acquires the personnel's position coordinates in three-dimensional space with a positioning accuracy of at least 0.3 meters and establishes an encrypted communication link with the edge controller. The IMU monitors the personnel's posture changes, acceleration, and angular velocity in real time, with an output frequency of 200 Hz, supporting the identification of fall actions, climbing postures, and abnormal static states. The RFID work badge embeds a security chip that stores personnel identification information, work qualification codes, health status summaries, and validity periods.

[0029] Upon entry, personnel verify their identity by swiping their work badges. The system then compares the validity period of their qualifications and their training status, and automatically generates virtual fence permissions upon successful verification.

[0030] 2. Tool and Equipment Identification Unit The tool identification unit includes an electronic padlock and a rigging strain sensor. The electronic padlock monitors the opening and closing status of electrical isolation points and mechanical connection points, and has built-in voltage sensing and temperature detection modules to determine if the lock is in an abnormal state. The strain sensor is attached or embedded in the lifting rigging and connectors, outputting strain data in real time and calculating the fatigue index, with a sampling accuracy of no less than 0.5%. When the fatigue index exceeds a set threshold, the tool is automatically marked as disabled, and a replacement prompt is generated in the work order.

[0031] 3. Environmental and Meteorological Monitoring Unit The environmental and meteorological monitoring unit includes temperature and humidity sensors, wind speed and direction sensors, an electric field strength monitoring device, and oxygen and carbon dioxide gas detection modules. Wind speed and direction are sampled at a frequency of 1 Hz, and the electric field monitoring range is 0 to 2 kV / m. When the oxygen concentration is below 19.5% or the carbon dioxide concentration is above 0.5%, the system automatically identifies it as a confined space risk and triggers an operational warning. The data from this unit is used for real-time risk scoring and also serves as input parameters for a dynamic risk learning model.

[0032] 4. Video Image Recognition Unit The video image recognition unit consists of a high-definition camera and an edge recognition module. It is used to identify the status of disconnect switches, the presence of padlocks, PPE equipment worn by personnel (such as safety helmets and safety belts), and unauthorized personnel within the work area. The recognition results are output in structured data format, labeled with recognition time, confidence level, and location index. The edge controller determines the validity of the results based on the confidence threshold.

[0033] II. Edge Layer The edge layer is the decision-making and control center on site, consisting of an edge controller and multiple internal functional modules, including a data access module, a time synchronization module, a risk scoring module, a four-state consistency interlocking module, a fall and weather composite linkage module, an object caching module, and an interlocking execution module.

[0034] 1. Data access and time synchronization module This module is responsible for receiving multi-channel data from the perception layer and unifying its timestamps. Time synchronization is based on GPS timing signals or NTP network timing mechanisms to ensure that various types of data are fused under the same time base.

[0035] 2. Risk Scoring Module The risk scoring module calculates the overall risk value using the following formula:

[0036] Wherein, H(t) represents personnel-level risk, T(t) represents tool-level risk, E(t) represents environmental-level risk, and P(t) represents work process-level risk. The weights w1 to w4 are learned from historical samples. When R(t) reaches the warning threshold, an audio-visual alert is triggered; when R(t) reaches the shutdown threshold, a safety interlock is executed.

[0037] 3. Four-state consistency interlocking module This module performs parallel verification of four statuses: personnel qualifications, tool status, environmental conditions, and work process. The work process can only proceed if all four statuses are in a safe state. If any status fails to meet the preset conditions, the edge controller immediately outputs an interlock signal, locking the access control, disengaging the electronic padlock, and stopping the equipment, and records the triggering reason in the event log.

[0038] 4. Fall and Weather Synergy Module When a person's vertical velocity is detected to be greater than 2.0 meters per second and vertical acceleration is less than -5.0 meters per square second, and the wind speed change exceeds 5 meters per second or the oxygen concentration drops by more than 3%, the edge controller executes a compound interlocking action, immediately disconnects the power supply to the lifting platform, locks the access control, activates the audible and visual alarm, and stores the event fragment in the local cache for evidence retrieval.

[0039] 5. Object caching and network outage fault tolerance module The object caching module stores critical personnel objects, work ticket objects, and rule packages to ensure continuous operation even during network outages. After communication is restored, the system replays event logs in timestamp order and synchronizes them to the cloud database to prevent information loss.

[0040] 6. Interlocking Execution Module This module is responsible for translating the logical decisions of the edge controller into concrete actions, including access control relay outputs, padlock control signals, alarm light control signals, and wireless broadcast commands. The action execution delay is no more than 100 milliseconds, ensuring timely on-site response.

[0041] III. Cloud Layer The cloud layer is deployed in the operations and maintenance center or main control platform, and is responsible for overall data management, intelligent analysis and visualization. It mainly includes the following modules: 1. Job Workflow Engine This module maintains a template library for different job types and defines job steps, isolation lists, and interlocking conditions. After a job ticket is generated, the process engine sequentially verifies the approval status of each step and synchronizes the process progress with the edge layer to prevent cross-step operations.

[0042] 2. Dynamic Risk Learning Model The dynamic risk learning model employs a dual-stream Transformer architecture: one stream processes personnel actions and location features, while the other processes environment and tool features. The fusion layer outputs risk predictions for the next 10, 30, and 60 seconds. When the predicted value exceeds the threshold, an early warning or shutdown command is issued to achieve short-term risk trend prediction and prevention.

[0043] 3. Digital Twin Tool Management Module This module establishes a digital twin of the tool, monitoring its usage frequency, strain curve, calibration cycle, and fatigue index. The fatigue index calculation formula is:

[0044] in, : The fatigue index represents the fatigue of tools and equipment. It is a dimensionless quantity used to characterize the degree of fatigue of tools and equipment during the current cumulative use process. It is a key indicator for assessing the remaining life of tools and equipment. : Indicates the number of strain samplings recorded by the tool within the statistical period, used to calculate the average fatigue level; : indicates the first The actual strain value of the tool during the sampling is expressed in megapascals (MPa), reflecting the stress state of the tool under a specific operating load. This indicates the maximum permissible strain value determined during the design or calibration phase of the tool, expressed in megapascals (MPa), and serves as a benchmark for assessing fatigue safety margin.

[0045] When the fatigue index F ≥ 1, it indicates that the cumulative strain of the tool has approached or exceeded its design safety limit. The system determines that the tool has entered the fatigue failure range, automatically sets its status to disabled, and generates a replacement prompt on the work order interface. This parameter model can be calibrated according to the material properties and verification standards of different types of tools to ensure the accuracy and universality of the safety assessment results.

[0046] When F≥1 or the cumulative number of uses reaches the limit, the system automatically sets the tool status to disabled and prompts for replacement on the work ticket interface.

[0047] 4. Intelligent Confirmation Module for Isolation Points Each isolation point is equipped with an NFC tag and a camera. When resetting, staff scan the NFC with a mobile terminal and upload the image. The cloud-based CV model identifies the handle position, padlock status, and tag number of the isolation switch and compares them with the template. If the identification result matches the standard template, the reset is confirmed to be complete. Otherwise, a reset abnormal event is generated and the work ticket is prevented from closing.

[0048] 5. Blockchain Evidence Collection Module This module adopts a consortium blockchain architecture. The block record includes the block number, preceding hash, timestamp, object identifier, operation type, digital signature, and state digest. A new block is generated whenever a work ticket is issued, personnel enter the site, tools are distributed, isolation is executed, a risk event occurs, or a reset is completed and accepted, thus achieving full-process data tamper-proofing and accountability traceability.

[0049] 6. Visual interface and data linkage The cloud-based system provides a large-scale dashboard and mobile application for operational status, displaying risk heat maps, personnel distribution, fence status, and tool lifespan information. The system supports data interface integration with existing platforms such as SCADA, CMMS, and EMS, ensuring integrated operation and maintenance management.

[0050] The perception layer and edge layer communicate using a hybrid approach of LoRa, ZigBee, and Wi-Fi, while the edge layer and cloud layer communicate using TLS encryption. All communication data includes timestamps and signature fields to prevent forgery and replay attacks. The edge controller operates independently even when offline, ensuring the continued effectiveness of critical security interlocks. All event data and state changes are synchronized to the cloud, generating hash digests and writing them into the forensic chain to create a traceable record.

[0051] like Figure 1 As shown, the new energy on-site operation safety monitoring platform integrating IoT sensing of the present invention realizes full-process supervision of operation through a multi-layer structure.

[0052] The perception layer acquires real-time on-site data, the edge layer completes fusion computing and risk interlocking, and the cloud layer realizes process orchestration, predictive analysis, and evidence collection and auditing. The closed-loop flow of information between the three layers enables the platform to form a complete safety loop in the stages of operation access, execution monitoring, anomaly handling, and reset confirmation, thereby significantly reducing the risk of safety accidents at new energy power plants.

[0053] In summary, the IoT-integrated new energy field operation full-process safety monitoring platform of the present invention, through the collaborative design of the sensing layer, edge layer and cloud layer, constructs an intelligent safety supervision system for the complex operating environment of new energy power stations, and realizes dynamic safety control of the entire process from operation access to reset confirmation.

[0054] This invention combines multi-source sensing technology with edge computing to propose a four-state consistency interlocking control mechanism for the first time. This mechanism jointly determines the status of personnel qualifications, tools, environmental conditions, and work processes, achieving proactive risk prevention and automated on-site protection. The edge controller has the ability to operate independently without network access, maintaining basic safety functions even in communication anomalies, ensuring the system retains its safety interlocking characteristics even in extreme environments.

[0055] The cloud layer introduces a dynamic risk learning model based on a dual-stream Transformer architecture. By integrating the temporal characteristics of personnel actions, tool status, and environmental weather, it achieves multi-time-domain risk trend prediction for the next 10, 30, and 60 seconds, providing an early warning mechanism for on-site safety. Combined with a digital twin tool management module, this invention can dynamically track tool lifespan and fatigue levels, enabling preventative maintenance and refined management.

[0056] In the post-operation phase, this invention employs an intelligent confirmation mechanism at isolation points to perform dual image and NFC comparisons on the reset status, ensuring the authenticity and reliability of the reset operation. Furthermore, a consortium blockchain evidence collection module enables tamper-proofing and accountability for data throughout the entire operation process. All critical data generated during each operation stage is stored using hash signatures, giving the system forensic-level data credibility.

[0057] Compared to traditional operational safety monitoring methods, this invention achieves an innovative closed loop at the technical level, integrating multi-dimensional perception fusion, risk prediction and control, and reliable data traceability. At the system architecture level, it realizes a safety control model that combines edge autonomy and cloud collaboration. At the management level, it achieves a digital safety supervision effect with full-process transparency and traceable responsibility. This platform demonstrates excellent adaptability and promotional value in high-risk scenarios such as hoisting, maintenance, high-altitude operations, and energy storage compartment operations at new energy power plants.

[0058] Therefore, this invention not only improves the accuracy and response speed of safety management in new energy field operations, but also significantly enhances the intelligence level of risk identification, decision-making interlocking, and event tracing, and has significant engineering application significance and creativity.

[0059] Example 2 like Figure 2 As shown, this embodiment is based on the IoT-integrated on-site safety monitoring platform for new energy operations described in Embodiment 1. It presents a safety monitoring method for the on-site operation process of new energy power plants, with a core process of sensing, interlocking, prediction, reset, and verification. This method, through multi-source data fusion and dynamic risk learning, achieves risk identification, interlocking control, anomaly handling, and reset verification throughout the entire on-site operation process, ensuring personnel safety, tool reliability, and process traceability in new energy operations.

[0060] S1: Work access and personnel identity verification.

[0061] Before the work begins, the system first verifies the entry of on-site personnel through a personnel identification unit. Each person wears a smart identification device that includes a UWB positioning module, an IMU sensor, and an RFID work badge. Upon entry, the RFID work badge automatically verifies identity information, qualification level, and health status. The system then compares this information with the work permit permissions and validity period recorded in the cloud database to determine whether entry into the work area is permitted.

[0062] Verified personnel information is synchronized to the edge controller, which automatically generates a virtual fence based on the number of personnel and their job information, and performs real-time detection and alarm for unauthorized personnel entering the premises.

[0063] S2: Work preparation and tool and equipment status verification.

[0064] Before the operation begins, the system verifies the status of the isolation points, electronic padlocks, and tools according to the work order list. The edge controller receives the opening and closing signals, voltage status, and temperature information from the electronic padlocks to confirm whether the isolation points are in a safe separation state.

[0065] Meanwhile, rigging strain sensors measure the strain data of lifting or supporting tools in real time, and the cloud-based digital twin module calculates the fatigue index F. When F ≥ 1 or the calibration period exceeds the preset cycle, the tool is automatically marked as disabled, and the system generates a replacement prompt in the work interface.

[0066] Once all tools, isolation points, and safety conditions meet the operational standards, the system automatically generates a work permit signal, allowing the work ticket to proceed to the execution phase.

[0067] S3: Operation execution and process risk monitoring.

[0068] During the operation process, the edge controller fuses the personnel IMU data, UWB position information, meteorological parameters and the strain state of tools with a millisecond-level cycle, and calculates the risk score value R(t) in real time:

[0069] where H(t) is the personnel movement and posture characteristics, T(t) is the tool state, E(t) is the environmental parameter, P(t) is the operation process stage index, and w1 to w4 are weight coefficients.

[0070] When R(t) reaches the warning threshold the system triggers an audible and visual alarm; when R(t) reaches the shutdown threshold the interlock control module immediately issues a locking signal to cut off the power of the lifting platform, lock the access control and broadcast an emergency notice.

[0071] In addition, the cloud dynamic risk learning model synchronously performs short-term trend prediction on the input data, and预判 the risk evolution within the next 10 seconds, 30 seconds and 60 seconds. When the predicted risk exceeds the warning threshold, the cloud sends an early braking instruction to the edge node to achieve active prevention and control prior to the accident.

[0072] S4: Abnormal detection and emergency interlock disposal.

[0073] During the operation, when the edge controller detects that both the falling conditions (vertical speed > 2.0 m / s and vertical acceleration < -5.0 m / s²) and meteorological conditions (wind speed sudden change > 5 m / s or oxygen concentration decrease > 3%) are satisfied, the composite interlock mechanism is triggered.

[0074] The system immediately executes multi-channel linkage measures, including: 1. Cut off the power supply of the lifting equipment.

[0075] 2. Lock the operation access control and start an audible and visual alarm.

[0076] 3. Automatically save the IMU curve and video frames from 5 seconds before to 10 seconds after the incident.

[0077] 4. Encrypt and cache the event data and upload it to the cloud blockchain forensics module.

[0078] This process is fully automated and cannot be intervened or deleted manually, ensuring the integrity and authenticity of the accident record.

[0079] S5: Operation completion and reset confirmation.

[0080] After the operation is completed, the personnel perform the isolation point reset operation. The system uses the isolation point intelligent confirmation module to perform image recognition and NFC verification of the reset action. The isolation point image captured by the camera is compared with the cloud template to determine whether the padlock position, handle direction and tag number are consistent with the standard template.

[0081] If the comparison results are consistent, the system confirms that the reset was successful; if there is inconsistency or the image confidence level is lower than the threshold, a reset exception event is generated, preventing the work ticket from being closed and requiring a review.

[0082] The operation data and reset confirmation information are hashed and encrypted before being written into the blockchain evidence storage module to form the final evidence block, which records the operator, timestamp, status summary and digital signature, enabling full traceability of the operation process.

[0083] S6: Results archiving and security assessment.

[0084] All data, status changes, and event records from all operational stages are aggregated into a cloud database. The blockchain module generates a unique operational process fingerprint for subsequent auditing and accountability. The cloud platform generates a comprehensive safety assessment report based on historical risk scores, operational duration, and alarm frequency, providing management with quantitative safety indicators and optimization suggestions.

[0085] The new energy field operation safety monitoring method described in this embodiment achieves automatic monitoring, intelligent early warning and reliable recording of the entire process through the fusion perception of four-dimensional data of personnel, tools, environment and process and edge-cloud collaborative control.

[0086] Compared with traditional safety management methods that rely on manual inspections and post-event reporting, this invention can respond to abnormal states within milliseconds and achieve minute-level early warnings through a dynamic risk prediction model. At the same time, the blockchain evidence storage and intelligent reset confirmation mechanism make the operation closed loop verifiable and traceable, significantly improving the inherent safety level of new energy power stations.

[0087] This invention achieves real-time monitoring and intelligent control of on-site operations at new energy power plants by constructing a three-layer collaborative architecture consisting of a perception layer, an edge layer, and a cloud layer. Structurally, the platform forms a closed-loop perception system that integrates multi-source information; functionally, it enables fully automated safety supervision of the entire process, from operation access, execution monitoring, anomaly handling to reset confirmation, significantly improving the safety protection level of new energy operations.

[0088] By setting up a four-state consistency interlocking module, this invention realizes the joint determination of personnel qualification status, tool status, environmental condition status and work process status, ensuring that the system immediately triggers power-off, interlocking and alarm operations when any safety condition is abnormal; at the same time, a dynamic risk learning model is used to predict the short-term trend of work data, so that risk warning is transformed from passive response to active prevention and control, effectively reducing sudden accidents.

[0089] Furthermore, this invention introduces an intelligent isolation point confirmation module and a blockchain evidence collection module, enabling the authenticity of the operation reset to be verified and the entire process of data traceable, ensuring the standardization of safe operation and the tamper-proof nature of records. This platform demonstrates excellent adaptability and engineering application value in high-altitude, energized, heavy-load, and confined space operations at new energy power plants, significantly improving the safety and intelligent management level of on-site operations in new energy.

[0090] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0091] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

[0095] It should be understood that the above description is for illustrative purposes and not for limitation. Many embodiments and applications beyond the provided examples will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined by reference to the above description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.

Claims

1. A full-process safety monitoring system for new energy field operations integrating Internet of Things (IoT) sensing, characterized in that, This includes the perception layer, edge layer, and cloud layer; Communication connections are established sequentially between the perception layer, edge layer, and cloud layer. The perception layer includes a personnel identification unit, a tool identification unit, an environmental and meteorological monitoring unit, and a video image recognition unit; it is used to collect personnel identity and position posture, tool status, environmental parameters, and isolation point image information. The edge layer includes an edge controller, which comprises a data access module, a risk scoring module, a four-state consistency interlocking module, a fall and weather composite linkage module, and an interlocking execution module. It is used to receive data from the perception layer, perform multi-source data fusion calculation and risk assessment, and output interlocking control signals. The cloud layer includes a workflow engine, a dynamic risk learning model, a digital twin tool management module, an isolation point intelligent confirmation module, and a blockchain evidence collection module; Used for workflow orchestration, risk trend prediction, digital twin management, and data forensics.

2. The new energy field operation full-process safety monitoring system integrating IoT sensing as described in claim 1, characterized in that, The personnel identification unit includes UWB tags, inertial measurement units, and RFID name tags; the tool identification unit includes electronic padlocks and rigging strain sensors; and the environmental and meteorological monitoring unit includes temperature and humidity sensors, wind speed and direction sensors, electric field strength monitoring devices, and gas detection modules.

3. The new energy field operation full-process safety monitoring system integrating IoT sensing as described in claim 1, characterized in that, The four-state consistency interlocking module is connected to the data interfaces of personnel qualification status, tool status, environmental condition status, and work process status respectively. The four-state consistency interlocking module outputs a work permission signal when the personnel qualification status, tool status, environmental condition status, and work process status are all in a safe state; otherwise, it outputs an interlocking signal.

4. The new energy field operation full-process safety monitoring system integrating IoT sensing as described in claim 1, characterized in that, The fall and meteorological composite linkage module is connected to the inertial measurement unit in the personnel identification unit, and the fall and meteorological composite linkage module is connected to the wind speed and direction sensor and the gas detection module in the environmental and meteorological monitoring unit; when the fall and meteorological composite linkage module receives abnormal data of personnel vertical velocity and acceleration and simultaneously receives abnormal data of wind speed or gas concentration, it outputs a composite interlock action signal.

5. The new energy field operation full-process safety monitoring system integrating IoT sensing as described in claim 1, characterized in that, The isolation point intelligent confirmation module connects to a mobile terminal equipped with an NFC tag and a camera; The intelligent verification module for isolation points receives images of isolation points and NFC scan data, and compares the isolation point images with a standard template.

6. The new energy field operation full-process safety monitoring system integrating IoT sensing as described in claim 1, characterized in that, The blockchain forensics module adopts a consortium blockchain architecture. The blockchain forensics module contains block records, which include block number, preceding hash, timestamp, object identifier, operation type, digital signature and state digest.

7. A method for full-process safety monitoring of new energy field operations based on the system described in any one of claims 1-6, integrating Internet of Things (IoT) sensing, characterized in that... Includes the following steps: The personnel identification unit verifies the entry of workers, and the RFID work badge verifies their identity information, qualification level and health status. The tool identification unit verifies the isolation point, electronic padlock, and tool status; The edge controller integrates personnel data, tool data, environmental data, and work process data to calculate a risk score. The edge controller triggers an alarm or interlock control when the risk score reaches a threshold. The isolation point intelligent confirmation module performs image recognition and NFC verification on the reset action; The blockchain evidence collection module writes the operation data and reset confirmation information into the blockchain for evidence storage.

8. The method for full-process safety monitoring of new energy field operations integrating IoT sensing as described in claim 7, characterized in that, The steps for calculating the risk score include: obtaining the risk weights for personnel, tools, environment, and work process; multiplying the personnel's actions and postures, tool status, environmental parameters, and work process stage indicators by their corresponding weights and then summing them to obtain the comprehensive risk value.

9. The method for full-process safety monitoring of new energy field operations integrating IoT sensing as described in claim 7, characterized in that, The steps for verifying the status of tools and equipment include: the digital twin tool and equipment management module calculates the fatigue index of the tools and equipment; the calculation of the fatigue index is based on the number of strain samplings recorded by the tools and equipment within the statistical period, the actual strain value of the tools and equipment, and the maximum allowable strain value; the digital twin tool and equipment management module sets the status of the tools and equipment to disabled when the fatigue index is greater than or equal to 1.

10. The method for full-process safety monitoring of new energy field operations integrating IoT sensing as described in claim 7, characterized in that, The steps to trigger the interlock control include: when the edge controller detects that the vertical speed of the personnel is greater than the set value and the vertical acceleration is less than the set negative value, and at the same time detects that the wind speed transient exceeds the set value or the oxygen concentration drops beyond the set value, the compound interlock action is executed; the compound interlock action includes cutting off the equipment power supply, locking the access control and activating the alarm.

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