On-site intelligent inspection and operation and maintenance system

Abstract

The invention relates to the technical field of industrial safety inspection, and discloses an on-site intelligent inspection and operation and maintenance system, which adopts a three-layer architecture of a front-end intelligent wearing sensing layer, a middle-end data transmission layer and a rear-end intelligent operation and maintenance platform layer, the information interaction module, the AI analysis module, the acousto-optic early warning module, the safety protection module and the image acquisition module are integrated, and on-site data acquisition, abnormity identification and personnel safety protection can be completed; the middle-end transmission layer comprises a communication module, an edge computing gateway and a data caching module, and realizes real-time secure transmission and offline supplementary transmission of data; and the rear-end platform layer is provided with a problem visualization module, a rectification task management module, a solution guidance module and an item selling tracking module, so that problem visualization presentation, intelligent order sending, guidance issuing and closed-loop item selling are realized. By constructing a complete closed loop of collection-analysis-early warning-rectification-item cancellation, the intelligence and safety of inspection operation and maintenance are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of industrial safety inspection technology, specifically to a method and system for on-site intelligent inspection and maintenance. Background Technology

[0002] In industrial sectors such as power, chemical, and construction, on-site inspections are a core component in ensuring stable equipment operation and preventing safety accidents. According to relevant statistics, the root cause of most on-site safety accidents lies in inadequate inspections and the failure to promptly correct non-standard operations. The limitations of traditional inspection models have become a prominent pain point in industry safety management.

[0003] Traditional inspections rely heavily on manual records and paper ledgers, which not only suffer from data entry delays (with an average delay of over 2 hours in problem feedback) and easy information omissions, but also make it difficult to achieve real-time control over the operational behavior of inspection personnel, resulting in the inability to promptly stop violations. Furthermore, the existing maintenance process suffers from disconnects between the "problem discovery-rectification-closure" stages, leading to untimely communication of rectification instructions, lack of progress tracking, and difficulty in verifying rectification results, further amplifying safety risks.

[0004] Currently, most intelligent inspection technologies in the industry focus on optimizing a single function. Some devices only have positioning or image acquisition capabilities and cannot complete the entire chain response from anomaly identification to early warning intervention. Furthermore, the linkage between front-end inspection devices and back-end operation and maintenance systems is poor, and the back-end platforms are mostly static data statistics tools, making it difficult to obtain real-time on-site dynamics and issue accurate rectification guidance, thus failing to form a closed-loop management system. Summary of the Invention

[0005] To address the technical problems of lag and fragmentation in traditional inspections, this invention provides an intelligent on-site inspection and maintenance system that enables real-time linkage between front-end equipment and back-end maintenance systems, achieving closed-loop management of the entire process from "problem identification to early warning, rectification, and item clearance," thereby improving the safety of on-site operations and the level of intelligence in maintenance management.

[0006] In a first aspect, the present invention provides an intelligent on-site inspection and maintenance system, comprising: The system consists of a front-end intelligent wearable sensing layer, a mid-end data transmission layer, and a back-end intelligent operation and maintenance platform layer, among which: The front-end smart wearable sensing layer is made of waterproof and wear-resistant material and integrates an information interaction integration module, an AI analysis module, an audio-visual early warning module, a safety protection module and an image acquisition module, which are used for real-time collection of on-site inspection data, anomaly identification and personnel safety protection early warning. The mid-level data transmission layer includes a communication module, an edge computing gateway, and a data cache, which are used to transmit the data, analysis results, and early warning information collected by the front-end smart wearable sensing layer to the back-end smart operation and maintenance platform layer in real time. The backend intelligent operation and maintenance platform layer includes: a problem visualization display module, a rectification task management module, and a solution guidance and item tracking module, which are used to realize the visualization of problems found in inspections, intelligent dispatch of rectification tasks, distribution of solution guidance, and tracking and closed-loop item tracking of the entire rectification process.

[0007] The on-site intelligent inspection and maintenance system provided in this invention employs a three-tier architecture, enabling end-to-end control of on-site inspections from front-end data collection to back-end maintenance. The front-end integrates multiple modules to simultaneously complete data collection, anomaly identification, and personnel protection, breaking the limitations of traditional single-function inspection equipment. The mid-level transmission layer ensures real-time and secure data flow, preventing data loss or leakage. The back-end platform achieves integrated management of problem visualization, intelligent task dispatch, guidance issuance, and item tracking, solving the pain points of delayed problem discovery and disconnected rectification in traditional models. The overall architecture forms a closed loop of "collection-analysis-early warning-rectification-item closing," improving the timeliness of problem discovery, reducing the incidence of non-standard operations, and increasing the completion rate of maintenance and rectification, significantly enhancing the intelligence and security of inspection and maintenance.

[0008] In one optional implementation, the information interaction integration module includes: Intercom unit, used to enable voice communication; The gyroscope unit is used to monitor the posture of the inspection personnel; Altimeter unit, used to monitor the height of the space where inspection personnel are located; Network data transmission unit, used to support multimodal communication; The positioning unit uses a composite positioning technology combining satellite and ground augmentation to acquire and upload the three-dimensional spatial position and movement trajectory of inspection personnel in real time, and is equipped with an electronic fence function. The identity recognition unit is used to identify and verify the identity of the wearer and retrieve their corresponding backend permission information; The interactive display screen is used to receive backend instructions, display on-site early warning information, present rectification guidance plans, and provide feedback on task processing progress. It also supports simple touch operation to achieve two-way information interaction.

[0009] The information interaction integration module provided in this invention establishes a multi-dimensional information interaction and status monitoring system for front-end inspections. The intercom unit ensures real-time on-site communication; the gyroscope and altimeter units enable precise monitoring of personnel posture and spatial height, providing data support for safety protection; composite positioning technology combined with electronic fences enables three-dimensional position tracking and boundary crossing warnings, ensuring inspection personnel are on duty; the identity recognition unit enables precise matching of personnel permissions, preventing unauthorized operations; and the interactive display screen establishes a two-way interaction channel between the front and back ends, allowing instructions, warnings, and guidance information to reach the site directly. This module integrates multiple functions, eliminating the information silos problem in traditional inspections, improving the collaboration and standardization of on-site operations, and significantly increasing operational response efficiency.

[0010] In one alternative implementation, the AI ​​analysis module incorporates an edge computing chip and is configured to perform the following image analysis process: By acquiring the real-time video stream provided by the image acquisition module, keyframe images are extracted from the video stream at a preset frame rate and preprocessed. Based on multi-threaded or parallel computing, the following analysis is performed synchronously on the same batch of preprocessed keyframe images: The first analysis task is to use the non-standard operation identification model to identify whether there is at least one of the following behaviors: failure to wear protective equipment as required, illegal crossing of guardrails, or unauthorized operation of unauthorized equipment. The second analysis task is to call upon the on-site hazard detection model to identify whether there is an abnormal state, such as flame, smoke, liquid leakage, equipment casing damage, or abnormal instrument pointer. Decisions are made based on the analysis results: if the confidence level of the hazard detected by the second analysis task exceeds the first threshold, the highest level of emergency response is triggered; if the first analysis task detects a violation, an alarm with associated location and evidence is triggered; otherwise, a regular status report is sent.

[0011] The AI ​​analysis module provided in this invention, relying on edge computing chips and parallel analysis mechanisms, achieves rapid and accurate identification of on-site anomalies. By extracting and preprocessing keyframes, the validity of the analyzed data is ensured; dual-task parallel analysis can simultaneously identify non-standard operations and on-site hazards, covering multiple types of safety risks; differentiated decision-making logic can trigger corresponding responses based on the hazard level, achieving both the highest-level handling of emergency situations and accurate alerts and evidence preservation for general violations. Compared to traditional single-task identification devices, it significantly shortens anomaly response time and avoids safety accidents caused by missed or misjudged detections.

[0012] In one optional implementation, the non-standard operation identification model and the on-site hazard detection model are integrated into the same AI analysis framework. This framework shares the same backbone feature extraction network. The features extracted by the backbone feature extraction network are processed by the feature enhancement module and then input into the dedicated non-standard operation identification head and the on-site hazard detection head respectively, so as to output the detection results in parallel.

[0013] The AI ​​analysis framework provided in this invention achieves efficient collaboration between two types of detection models through a shared backbone feature extraction network. The shared network reduces redundant computational consumption in feature extraction, improving operational efficiency in edge computing scenarios; the feature enhancement module strengthens key image features, improving recognition accuracy; and the dedicated recognition head ensures targeted detection of non-standard operations and hazards, enabling parallel output of both types of results. This framework reduces hardware computational requirements and saves device power consumption while improving overall recognition efficiency and ensuring dual-task recognition accuracy. It solves the problems of insufficient computational power and slow response in traditional multi-model deployments, and is suitable for the lightweight and low-power requirements of front-end wearable devices.

[0014] In one optional implementation, the security protection module includes: The land fall protection unit, which connects the gyroscope unit and the image acquisition module, is configured to: when the data collected by the gyroscope determines that the human body's tilting acceleration and tilt angle exceed a preset threshold, and the image data collected by the image acquisition module assists in confirmation, trigger the airbag to inflate and deploy within a set time. The water rescue unit, including a water immersion detection circuit, a water immersion locking circuit, a pressure sensor, and an airbag assembly, is configured as follows: When the water resistance of the water immersion detection circuit is less than the first preset resistance value, the pressure sensor is activated to verify the water depth. When the water depth is greater than the preset water depth threshold, the water immersion locking circuit is closed, the airbag assembly is activated to inflate and deploy, and the water surface positioning beacon is automatically activated. If the water resistance is greater than or equal to the first preset resistance value, the system directly returns to the normal monitoring state.

[0015] The safety protection module provided in this invention constructs an intelligent safety protection system for inspection personnel in both land and water scenarios. The land-based fall protection unit achieves dual verification of fall protection triggers through gyroscope attitude monitoring combined with image-assisted confirmation, completing airbag inflation within 0.25 seconds to accurately buffer impact injuries, avoid false triggers, and ensure timely protection. The water-based rescue unit accurately identifies real-life water-fall scenarios through resistance detection and water depth verification; airbag deployment maintains the person's floating state, and positioning beacons enable rapid location of the person in the water. This module overcomes the shortcomings of traditional protective equipment, which offers single-scenario protection and is prone to false triggers, significantly improving the accuracy of protection triggers and greatly reducing the risk of injury or death in scenarios such as falls from heights and falls into water, forming a closed-loop safety system of "monitoring-judgment-protection-location."

[0016] In one optional implementation, the audible and visual warning module includes a buzzer and an LED warning light, and drives the buzzer and LED warning light to emit different modes of audible and visual alarm signals according to different triggering scenarios.

[0017] The audible and visual early warning module of this invention achieves graded alerts for on-site anomalies through differentiated audible and visual alarm modes. Different buzzer frequencies and LED flashing patterns are configured for different scenarios such as non-standard operations, equipment malfunctions, and hazardous areas, allowing inspection personnel to quickly distinguish the warning level. The high-volume buzzer and long-distance visual warning light ensure effective transmission of warning information in complex industrial environments. This module overcomes the shortcomings of traditional single-warning methods that are easily overlooked, achieving linkage between local warnings and real-time personnel perception. Simultaneously, it can transmit warning information to surrounding personnel, preventing the spread of danger and improving the timeliness of risk prevention and control in on-site operations.

[0018] In one optional implementation, the communication module supports multiple wireless communication standards and automatically switches according to signal quality; An edge computing gateway, deployed on the field side, is configured to preprocess and encrypt the data transmitted by the front-end smart wearable sensing layer. The preprocessing includes image compression and key early warning information filtering. The data buffer is used to automatically buffer the data to be transmitted by the front-end smart wearable sensing layer when the network signal of the communication module is interrupted, and to automatically complete the data retransmission after the network signal is restored.

[0019] The mid-range data transmission layer provided in this invention provides stable, secure, and uninterrupted transmission assurance for front-end and back-end data flow. The multi-standard communication module can automatically switch according to signal quality, ensuring data transmission rates of ≥10Mbps and latency of ≤100ms in remote areas. The edge computing gateway's preprocessing function compresses redundant data and filters key information, reducing bandwidth consumption, while the AES-256 encryption algorithm ensures data transmission security. The data buffer caches data during network outages and automatically retransmits it after network recovery, ensuring no data loss. This transmission layer solves the pain points of unstable signals, data leakage, and data loss during network outages in traditional transmission, enabling real-time secure data flow and providing reliable data support for timely response and closed-loop management of the back-end platform, thus improving data transmission reliability.

[0020] In one optional implementation, the backend intelligent operation and maintenance platform layer includes: The problem visualization module is used to display the location, trajectory, and problem warning points of inspection personnel on an electronic map in real time, and generate inspection statistics dashboards; The rectification task management module is used to automatically dispatch rectification tasks to the responsible person's terminal based on the type, level, and location of the early warning information, combined with the skill matching degree and location proximity of the operation and maintenance personnel, through an intelligent dispatching algorithm. The problem-solving guidance module is used to build a standardized guidance knowledge base categorized by industry and problem type, and to automatically push matching problem-solving guidance solutions to the responsible person's terminal. The item tracking module is used to track the status of rectification tasks, receive and verify the rectification completion evidence and overdue reminder statistics uploaded by the responsible person, and realize closed-loop management of tasks.

[0021] The backend intelligent operation and maintenance platform layer provided in this embodiment of the invention achieves closed-loop management of inspection issues through multi-module collaboration. The issue visualization module intuitively presents personnel location, issue location, and inspection data, facilitating overall control by management personnel; the intelligent dispatching of the rectification task management module enables precise task allocation; the standardized knowledge base of the solution guidance module lowers the on-site operation threshold, allowing even novices to handle issues in a standardized manner; and the item tracking module tracks rectification status, verifies evidence, and provides overdue reminders, preventing rectification from becoming a mere formality. This platform layer integrates the functions of the entire operation and maintenance process, eliminating the inefficiencies of traditional manual dispatching, offline guidance, and ledger tracking, ensuring the standardization and traceability of the operation and maintenance process.

[0022] In one optional implementation, the intelligent task dispatching algorithm calculates a task dispatching priority score for candidate responsible persons based on a comprehensive scoring model, and assigns tasks according to this score; the comprehensive scoring model is: W=α·S k +β·L o +γ·E m , Where W is the overall score, S k To assess the skill fit of the candidate, L o E represents the proximity of the candidate responsible person to the location where the task occurred. m For task urgency, α, β, and γ are preset weighting coefficients; skill matching degree S k Calculated based on the difference between the candidate's skill level and the skill level required for the task; Location proximity L o The task urgency E is calculated based on the straight-line distance d between the candidate responsible person and the location where the task occurs; m Dynamically determined based on the event type corresponding to the task; The task allocation process of the intelligent task dispatch algorithm includes: determining the geographical screening range of the initial candidates based on the urgency of the task; calculating and sorting the comprehensive score W of the candidates within the range; assigning the task to the candidate with the highest score, and automatically delegating the task to the next candidate if the candidate fails to respond within a preset time.

[0023] The intelligent task dispatching algorithm provided in this invention achieves accurate and efficient allocation of rectification tasks through a quantitative scoring model. The comprehensive scoring model integrates skill matching, location proximity, and task urgency, and the quantitative indicators eliminate subjective biases inherent in manual task dispatching. Differentiated geographical filtering avoids resource waste from large-scale filtering. An automatic postponement mechanism solves the problem of task shelving due to unresponsive responsible parties. Adaptable to the operational needs of multiple industries, it ensures both the scientific and fair nature of task dispatching while improving the efficiency and accuracy of rectification task handling.

[0024] In one optional implementation, the backend intelligent operation and maintenance platform layer is further configured to: when receiving an emergency distress signal or security protection trigger signal triggered by the frontend intelligent wearable sensing layer, display a prominent alarm on the platform interface and initiate an emergency linkage process.

[0025] The emergency response linkage function provided in this invention establishes a rapid response rescue link for sudden on-site emergencies. Upon receiving an emergency call or safety protection trigger signal, the platform prominently alerts management personnel immediately. The emergency response linkage process quickly activates the rescue plan, achieving seamless integration of "on-site triggering - background alarm - rescue dispatch." This function addresses the pain points of delayed emergency signal transmission and slow rescue response in traditional models, opening up information channels between on-site protection and background rescue, significantly improving the survival probability of inspection personnel in extreme situations, perfecting the system's safety assurance system, and achieving an upgrade from passive protection to proactive rescue. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a structural diagram of an on-site intelligent inspection and maintenance system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a smart wearable device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the analysis process of the AI ​​analysis module with built-in edge computing chip according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the data flow of a three-layer framework provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0030] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0031] Currently, most inspection technologies in the industry focus on optimizing a single function. For example, some intelligent inspection equipment only has positioning or image acquisition functions and cannot achieve full-process management of "problem identification-early warning-rectification-item clearance". Existing operation and maintenance systems are mostly back-end data statistics platforms, which have poor linkage with front-end inspection equipment and are difficult to obtain real-time on-site dynamics and guide problem solving.

[0032] This invention provides an embodiment of an intelligent on-site inspection and maintenance system. Figure 1 This is a schematic diagram of the structural composition of the on-site intelligent inspection and maintenance system according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: a front-end smart wearable sensing layer 10, a mid-end data transmission layer 20, and a back-end smart operation and maintenance platform layer 30, wherein: The front-end smart wearable sensing layer 10 is made of waterproof and wear-resistant material and integrates an information interaction integration module 11, an AI analysis module 12, an audio-visual early warning module 13, a safety protection module 14, and an image acquisition module 15, which are used for real-time collection of on-site inspection data, anomaly identification, and personnel safety protection early warning.

[0033] Specifically, the front-end smart wearable sensing layer 10, as the core carrier for on-site data collection and early warning, adopts lightweight, waterproof, and wear-resistant materials (such as Oxford cloth with a TPU coating). In one example, such as... Figure 2 As shown, this is a smart wearable vest that integrates the aforementioned core modules, ensuring that inspection personnel are comfortable to wear and that it does not interfere with their work. Among them: The information interaction integration module 11 of this invention establishes a multi-dimensional information interaction and status monitoring system for front-end inspection. In one example, such as... Figure 2 As shown, it is installed on the abdomen of the smart wearable device and includes: The intercom unit is used to enable voice communication, thereby ensuring real-time communication within the target factory area being inspected.

[0034] The gyroscope unit is used to monitor human posture. By monitoring human posture in the construction environment, when a person accelerates and falls, the gyroscope unit identifies the posture and provides a bursting signal to the airbag assembly, enabling the airbag to wrap around the upper body and provide protection.

[0035] A network data transmission unit is used to support multimodal communication; for example, it is compatible with 4G, 5G and WIFI functions, and provides data transmission capabilities for wearable devices and central control units.

[0036] The positioning unit employs a composite positioning technology combining satellite and ground-based augmentation to acquire and upload the three-dimensional spatial position and movement trajectory of inspection personnel in real time, and is equipped with an electronic fence function. In one example, "GPS + Beidou dual-mode positioning" technology is used, with a positioning accuracy of ≤1 meter, a data acquisition frequency of 1 time / 30 seconds, and real-time recording of the inspection personnel's position and trajectory. The built-in electronic fence function can preset the boundaries of the inspection area and dangerous areas. When personnel deviate from the preset inspection route or enter a dangerous area, an early warning is immediately triggered, and data output is provided: position coordinates, movement speed, and trajectory record. Combined with an altimeter, it provides the real-time height of personnel, enabling precise positioning within the spatial structure. The data is then uploaded to the backend in real time via a transmission module.

[0037] The identity recognition unit is used to identify and verify the identity of the wearer and retrieve their corresponding backend permission information. For example, employees in the factory area are equipped with identity IC cards. When using the wearable device, the IC card is inserted into the identity recognition unit to match the personnel information with the backend information, read information such as the personnel's restricted activity area, and upload the data information corresponding to the personnel's use of the wearable device.

[0038] The interactive display screen receives backend commands, displays on-site early warning information, presents rectification guidance plans, and provides feedback on task processing progress. It also supports simple touch operation for two-way information interaction, establishing a seamless communication channel between the front-end and back-end, allowing commands, warnings, and guidance information to reach the site directly. This module integrates multiple functions, eliminating information silos in traditional inspections, improving the collaboration and standardization of on-site operations, and enhancing operational response efficiency.

[0039] The AI ​​analysis module 12 of this embodiment of the invention has a built-in edge computing chip (computing power ≥ 2 TOPS), such as Figure 3 As shown, it is configured to perform the following image analysis process: 1. By acquiring the real-time video stream provided by the image acquisition module (dual cameras), keyframe images are extracted from the video stream at a preset frame rate and preprocessed (e.g., downsampling, normalization, and enhancement). Based on multi-threaded or parallel computing, the following analysis is performed synchronously on the same batch of keyframe images after preprocessing: The first analysis task is to use the non-standard operation identification model to identify whether there is any behavior such as failure to wear protective equipment as required (e.g., failure to use safety ropes / protective gloves / work clothes correctly), unauthorized crossing of guardrails, or unauthorized operation of unauthorized equipment. The second analysis task is to call upon the on-site hazard detection model to identify any abnormal conditions, such as flames, smoke, liquid leaks, damaged equipment casings, or abnormal instrument pointers.

[0040] In this embodiment of the invention, the non-standard operation identification model and the on-site hazard detection model are integrated into the same AI analysis framework. This framework shares the same backbone feature extraction network. The features extracted by the backbone feature extraction network are processed by the feature enhancement module and then input into the dedicated non-standard operation identification head and the on-site hazard detection head respectively, so as to output the detection results in parallel.

[0041] 2. Execute decisions based on the analysis results. If the confidence level of the hazard detected by the second analysis task exceeds the first threshold, trigger the highest level of emergency response; if the first analysis task detects violations, trigger an alarm with associated location and evidence; otherwise, send a regular status report. For example, match corresponding handling strategies for different risk levels: High risk (thunderstorm state, confidence level > 90%): Triggers "discovery + Level 3 alarm + high-definition feedback"; Medium risk (confidence level 70%-90%): Trigger "12B alarm + feedback"; Low risk (gusts): Only record "Cycle Status Report (1 time / second)"; It also supports "real-time data transmission (200ms@52)" to ensure that the backend / cloud can obtain on-site data in a timely manner.

[0042] The AI ​​analysis module described above achieves a recognition accuracy of ≥90% in the model analysis process. Figure 3 The time baseline in the diagram indicates the following key nodes: T0: frame capture start point; T+30ms: preprocessing completed; T+90ms: dual-channel inference ends; T+100ms: alarm command issued. From image acquisition to result output, the time is ≤1 second to ensure real-time performance. Through the design of "parallel detection + decision fusion", it can simultaneously identify personnel protection compliance and on-site hazards, and dynamically adjust the response strategy according to the risk level, taking into account both detection efficiency and accuracy. It is suitable for intelligent inspection and safety monitoring in industrial scenarios such as power and chemical industries.

[0043] The audible and visual warning module 13 in this embodiment of the invention includes a buzzer and an LED warning light, and drives the buzzer and LED warning light to emit different modes of audible and visual alarm signals according to different triggering scenarios.

[0044] In one example, a buzzer (volume ≥ 85dB) and an LED warning light (flashing red light, visibility ≥ 50 meters) are used. In another example, such as... Figure 2 As shown, it is installed on the shoulder of the smart wearable vest; the warning trigger scenarios are: when the AI ​​analysis module identifies non-standard operation or equipment malfunction, when the positioning module detects deviation from the route / entry into a dangerous area; and emergency alarms in dangerous environments, such as accidental falls or people actively calling for help in emergency situations. Warning levels: Level 1 warning (non-standard operation): intermittent buzzer sound + slow flashing LED light; Level 2 warning (equipment malfunction / dangerous area): continuous buzzer sound + rapid flashing LED light.

[0045] The safety protection module 14 in this embodiment of the invention includes: a land fall protection unit and a water rescue unit, wherein: The land-based anti-fall unit, which connects the gyroscope unit and the image acquisition module, is configured to: when the data collected by the gyroscope determines that the human body's tilting acceleration and tilt angle exceed a preset threshold, and the image data collected by the image acquisition module confirms this, trigger the airbag to inflate and deploy within a set time.

[0046] In one example, a power maintenance worker slipped and fell while inspecting equipment on a high-altitude inspection platform at a substation due to slippery ground. The gyroscope unit in his wearable smart device collected real-time 9-axis posture data of the human body at a frequency of 200Hz. After analysis, the module determined that the fall acceleration reached 6g and the tilt angle exceeded 70°, both exceeding preset thresholds (acceleration 5g, tilt angle 60°). The device then automatically activated the image acquisition module to visually confirm the human posture and surrounding environment. After algorithm analysis, the confidence level reached 92% (preset threshold 85%). A trigger command was quickly transmitted to the airbag controller of the ground-based fall protection unit. Within 0.25 seconds, the controller initiated a CO2 refrigerant release procedure, and the safety airbag rapidly inflated and deployed, enveloping the worker's upper body. The internal pressure stabilized at 4kPa, successfully cushioning the impact between the worker and the ground / equipment, preventing severe injuries to the head, torso, and other critical areas. Simultaneously, the device uploaded an emergency signal to the backend maintenance platform, allowing management personnel to immediately activate the rescue plan, further ensuring the worker's safety.

[0047] This invention combines gyroscope attitude data with visual confirmation from an image acquisition module to achieve dual verification of fall protection triggering. This avoids gyroscope mis-triggers (such as when a person bends over to work) and ensures effective response in emergency situations, increasing the accuracy of protective triggering to over 98%. The entire process from abnormal posture detection to airbag deployment takes ≤0.5 seconds, far faster than human reaction speed. It can form a protective barrier at the critical moment of a person falling / collapsing, significantly reducing the risk of injury or death in scenarios such as falls from heights and falls.

[0048] The water rescue unit includes a water immersion detection circuit, a water immersion locking circuit, a pressure sensor, and an airbag assembly. It is configured to: activate the pressure sensor to verify water depth when the water resistance of the water immersion detection circuit is less than a first preset resistance value; close the water immersion locking circuit when the water depth is greater than a preset water depth threshold, activate the airbag assembly to inflate and deploy, and automatically activate the water surface positioning beacon; if the water resistance is greater than or equal to the first preset resistance value, it directly returns to the normal monitoring state. It should be noted that the water immersion detection circuit, water immersion locking circuit, and pressure sensor use existing, relatively mature products. The airbag assembly includes a safety airbag, a nitrogen tank, and a corresponding control module. In one example, the safety airbag inflates and deploys into a rescue arm airbag within 0.3 seconds, helping the inspection personnel maintain an upper body floating posture and avoid the risk of drowning.

[0049] In one example, a water conservancy project inspector slipped and fell into the reservoir while inspecting equipment around the reservoir area. The electrodes of the water immersion detection circuit in his wearable smart inspection device quickly came into contact with the water, triggering a resistance test. The measured water resistance was 35kΩ, less than the first preset resistance value of 50kΩ. The device immediately triggered a pressure sensor to verify the water depth. The detection data showed a depth of 30cm, exceeding the preset water depth threshold of 20cm. The device then quickly closed the water immersion locking circuit, activated the nitrogen tank, and the airbag assembly inflated and deployed into a life-saving arm airbag within 0.3 seconds, helping the inspector maintain a floating position. Simultaneously, the device automatically activated a water surface positioning beacon, continuously sending out positioning signals. The backend maintenance platform obtained the personnel's location in real time through the positioning beacon, quickly dispatched nearby rescue forces, and completed the rescue within 15 minutes, successfully ensuring the safety of the inspector. If the equipment is splashed with rainwater due to the operator working in the rain, and the water immersion detection circuit measures the resistance of the rainwater to be 60kΩ (≥50kΩ), the equipment will directly return to the normal monitoring state and will not falsely trigger the airbag.

[0050] This invention employs a dual judgment mechanism of water resistance detection and water depth verification. This mechanism can accurately identify the actual scenario of a person falling into the water, while also eliminating interference from non-dangerous situations such as splashing water during rain or equipment getting wet. This reduces the false trigger rate of the rescue unit to below 1%, ensuring the stability of the equipment. Furthermore, the entire process from detecting a person falling into the water to the deployment of the airbag takes only 0.5 seconds, quickly helping the person maintain a floating state and buying precious time for rescue. Simultaneously, the synchronously activated positioning beacon can accurately locate the person in the water, solving the pain points of "difficult positioning and slow search and rescue" in water rescue.

[0051] The image acquisition module 15 in this embodiment of the invention is used to capture images / videos of the on-site environment and equipment status of the inspection personnel according to a preset triggering method; for example, it is equipped with a 20-megapixel high-definition wide-angle camera (120° field of view) and an infrared camera, which supports 1080P video recording and real-time capture, and is installed on the chest of the wearer to ensure that the shooting angle covers the inspection work area; it supports automatic focus and low-light shooting (minimum illumination 0.1Lux), which is suitable for low-light scenes such as night and basement; the triggering method is: manual triggering (the inspection personnel press the button on the side of the wearer) or automatic triggering (when the positioning module detects that the personnel have entered the critical equipment area, the shooting is automatically started).

[0052] In this embodiment of the invention, the mid-level data transmission layer 20 is used to transmit the data, analysis results, and early warning information collected by the front-end smart wearable sensing layer 10 to the back-end smart operation and maintenance platform layer 30 in real time; in one embodiment, the mid-level data transmission layer 20 includes: The communication module supports multiple wireless communication standards and automatically switches according to signal quality; for example, it adopts 5G / 4G dual-mode wireless communication and supports automatic switching to 4G network in remote areas (such as mountain power stations), with a data transmission rate of ≥10Mbps and a latency of ≤100ms.

[0053] An edge computing gateway, deployed on-site (e.g., in a park or enterprise), is configured to preprocess and encrypt the data transmitted by the front-end smart wearable sensing layer (e.g., using the AES-256 encryption algorithm). The preprocessing includes image compression and key early warning information filtering. The data buffer is used to automatically buffer the data to be transmitted by the front-end smart wearable sensing layer (e.g., buffer capacity ≥ 10GB) when the network signal of the communication module is interrupted, and automatically complete the data retransmission after the network signal is restored to ensure that the data is not lost.

[0054] The backend intelligent operation and maintenance platform layer 30 in this embodiment of the invention is used to realize the visualization of problems found during inspections, intelligent dispatch of rectification tasks, distribution of solution guidance, and tracking and closed-loop completion of the entire rectification process. Other layers interact with the backend intelligent operation and maintenance platform layer. For example... Figure 4As shown, in one embodiment, the backend intelligent operation and maintenance platform layer 30 includes: The problem visualization module 31 is used to display the location, trajectory and problem warning points of the inspection personnel on the electronic map in real time, and generate inspection statistics dashboard.

[0055] For example, the electronic map displays the real-time location and movement trajectory of all inspection personnel, marking those deviating from the route (red markers) and those performing normal inspections (green markers); the problem alert list sorts and displays problems discovered by the front end according to "urgency" (high / medium / low), including problem type (non-standard operation / equipment malfunction), location, on-site images, and alert level; the statistical dashboard displays key indicators such as the daily inspection coverage area, number of problems found (categorized by type), non-standard operation incidence rate, and alert handling rate in real time. It also supports querying historical inspection data and problem records by time (day / week / year), region, and problem type, generating data reports (such as Excel and PDF formats); and provides data visualization charts (line charts, bar charts) to show problem trends and changes in non-standard operation patterns, providing a basis for optimizing inspection plans.

[0056] The rectification task management module 32 is used to automatically dispatch rectification tasks to the responsible person's terminal based on the type, level, and location of the early warning information, combined with the skill matching degree and location proximity of the operation and maintenance personnel, through an intelligent dispatching algorithm.

[0057] Specifically, the intelligent task dispatching algorithm of this invention calculates a task dispatching priority score for candidate responsible persons based on a comprehensive scoring model, and allocates tasks according to this score; the comprehensive scoring model is as follows: W=α·S k +β·L o +γ·E m , Where W is the overall score, S k To assess the skill fit of the candidate, L o E represents the proximity of the candidate responsible person to the location where the task occurred. m The urgency of the task is represented by α, β, and γ, which are preset weighting coefficients.

[0058] By presetting the weight coefficients α, β, and γ, different dimensions can be emphasized according to the enterprise's operation and maintenance strategy. For example, setting the skill matching degree weight α to the highest level (such as 0.4) can prioritize ensuring the professionalism of task handling; if the emergency task response is strengthened, the task urgency weight γ can be increased to ensure that high-risk tasks receive the best resource allocation first, thereby achieving the rational allocation of resources and improving the timely response rate of rectification tasks.

[0059] Skill matching S kThe skill level of the candidate is calculated based on the difference between the candidate's skill level and the skill level required for the task. For example, suppose a pipeline weld leak occurs in a chemical industrial park, and the rectification task requires maintenance personnel with advanced skill level 3 to handle it. The system selects 3 candidates: Candidate A has advanced skill level 3, with a skill difference of |3-3|=0, and a perfect skill match, meaning they fully meet the task's skill requirements; Candidate B has intermediate skill level 2, with a skill difference of |2-3|=1, and a good skill match, possessing basic handling capabilities but slightly lacking in professional expertise; Candidate C has basic skill level 1, with a skill difference of |1-3|=2, and a low skill match, unable to independently complete the high-difficulty welding and leak-sealing operation.

[0060] Location proximity L o The distance d between the candidate responsible person and the location where the task occurred is calculated; if the location proximity formula is set as L... o =1 / (1+0.01d) (d is in meters), when the task occurs at a pipeline leak point in a chemical industrial park, candidate A is 500 meters away from the incident point, L o =1 / (1+0.01×500)=0.167; Candidate B is 800 meters away, L o =1 / (1+0.01×800)=0.111; Candidate C is 1000 meters away, L o =1 / (1+0.01×1000)=0.1, which shows that candidate A has the highest location proximity and can arrive at the scene the fastest.

[0061] Urgency level E m The event type is dynamically determined based on the task, such as scenarios that could easily lead to major safety accidents, like fires or large-scale equipment leaks. m =1.0; General risk scenarios such as failure to wear protective equipment properly, abnormal instrument readings, etc., E m =0.7; Low-risk scenarios such as equipment surface stains and slight deviations from inspection routes, E m =0.3. If a fire breaks out in a certain park (E m =1.0), which will receive higher weight in the comprehensive score calculation and will be given priority in matching responsible persons who meet the skill standards and are located nearby.

[0062] The model integrates three core dimensions: skill matching, location proximity, and task urgency. It ensures that the dispatched personnel have the professional ability to handle the task, while also taking into account response time and task urgency, thus avoiding problems such as skill mismatch leading to handling errors or delays in rescue due to excessive distance.

[0063] The task allocation process of the intelligent task dispatch algorithm includes: determining the geographical screening range of the initial candidates based on the urgency of the task; calculating and sorting the comprehensive score W of the candidates within the range; assigning the task to the candidate with the highest score, and automatically reassigning the task to the next candidate if the candidate fails to respond within a preset time.

[0064] In one example, a pipeline in a chemical industrial park suddenly leaked (a high-urgency fire / leakage incident). The front-end smart wearable device uploaded the problem information to the back-end intelligent operation and maintenance platform, triggering the rectification task allocation process: 1. Determine the geographical screening scope: The platform determines that the task is an emergency event and filters maintenance personnel within a 1km radius according to the rules (e.g., emergency events are filtered within a 1km radius, and regular events are filtered within a 5km radius). Initially, 3 candidates with equipment leak plugging qualifications are identified. 2. Based on the above comprehensive scoring model, calculate the comprehensive score ranking: the final ranking is A > C > B. The platform will prioritize assigning the task to candidate A. If A does not respond within 30 seconds, the system will automatically postpone the task to candidate C with the second highest score, ensuring that the task is assigned within 1 minute.

[0065] This invention combines urgency to define geographical scope, avoiding resource waste caused by large-scale screening. At the same time, it uses a comprehensive scoring system based on multiple dimensions such as skills, location, and urgency to ensure that the assigned task is to the optimal matching personnel. Compared with traditional manual task assignment, this greatly shortens the task assignment response time. Furthermore, by setting a preset response time threshold and implementing automatic priority assignment, it solves the industry pain point of "tasks being shelved due to the responsible person being unreachable or unresponsive," ensuring that urgent tasks are taken over within the golden handling time and improving the timely response rate of rectification tasks.

[0066] The solution guidance module 33 is used to build a standardized guidance knowledge base categorized by industry and problem type, and to automatically push matching solution guidance solutions to the responsible person's terminal.

[0067] For example, a guidance knowledge base is built according to industry (power, chemical, construction) and problem type, including text descriptions, step-by-step illustrations, and operation videos; Example: Guidance content for "pipeline leakage problem": Close the upstream valve of the leak point; wear chemical protective clothing and a protective mask; use a special leak-sealing tool to seal the leak; take a picture after sealing and upload it for verification; When a problem is generated, the platform automatically matches the corresponding solution guidance in the knowledge base and pushes it to the responsible person's smart wearable interactive display screen and mobile APP to ensure that on-site personnel can quickly obtain the operation guide; It also supports online communication functions: allowing on-site personnel to initiate online consultations through smart wearables or mobile APPs and communicate with back-end technical experts in real time (text, images, video calls) to solve complex problems.

[0068] The item tracking module 34 is used to track the status of rectification tasks, receive and verify the rectification completion evidence and overdue reminder statistics uploaded by the responsible person, and realize closed-loop management of tasks.

[0069] Specifically, the tracking of rectification task status is as follows: the platform displays the status of each rectification task in real time: pending assignment, assigned, in progress, pending verification, and completed; the person in charge must upload the rectification images or videos before the task deadline, and the platform will automatically push them to the administrator for verification. Verification of rectification completion evidence: If the administrator verifies that the rectification is qualified, the platform will automatically update the task status to "completed" and record the completion time and verification personnel information; if the rectification is unqualified, the administrator will mark the problem (such as "incomplete plugging") and provide feedback to the responsible person, requiring rectification to be carried out again; Overdue reminder statistics: For tasks that are not completed by the deadline, the platform will trigger an SMS reminder every 30 minutes; a monthly rectification task completion rate report will be generated, which includes the number of overdue tasks and an analysis of the reasons for uncompleted tasks.

[0070] In one embodiment, the backend intelligent operation and maintenance platform layer is also used to: when receiving an emergency distress signal or safety protection trigger signal triggered by the frontend intelligent wearable sensing layer, highlight an alarm on the platform interface and initiate an emergency linkage process. This emergency linkage function establishes a rapid response rescue link for sudden on-site emergencies. When an emergency distress signal or safety protection trigger signal is received, the platform's highlighted alarm can immediately remind management personnel, and the emergency linkage process can quickly activate the rescue plan, achieving seamless connection between "on-site triggering - backend alarming - rescue dispatching". This function solves the pain points of delayed emergency signal transmission and slow rescue response in the traditional mode, opens up the information channel between on-site protection and backend rescue, can shorten the emergency rescue response time to within 15 minutes, significantly improve the survival probability of inspection personnel in extreme situations, improve the system's safety assurance system, and achieve an upgrade from passive protection to proactive rescue.

[0071] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A field intelligent inspection and maintenance system, characterized in that, include: The system consists of a front-end intelligent wearable sensing layer, a mid-end data transmission layer, and a back-end intelligent operation and maintenance platform layer, among which: The front-end smart wearable sensing layer is made of waterproof and wear-resistant material and integrates an information interaction integration module, an AI analysis module, an audio-visual early warning module, a safety protection module and an image acquisition module, which are used for real-time collection of on-site inspection data, anomaly identification and personnel safety protection early warning. The mid-level data transmission layer includes a communication module, an edge computing gateway, and a data cache, which are used to transmit the data, analysis results, and early warning information collected by the front-end smart wearable sensing layer to the back-end smart operation and maintenance platform layer in real time. The backend intelligent operation and maintenance platform layer includes: a problem visualization display module, a rectification task management module, and a solution guidance and item tracking module, which are used to realize the visualization of problems found in inspections, intelligent dispatch of rectification tasks, distribution of solution guidance, and tracking and closed-loop item tracking of the entire rectification process.

2. The system according to claim 1, characterized in that, The information interaction integration module includes: Intercom unit, used to enable voice communication; The gyroscope unit is used to monitor the posture of the inspection personnel; Altimeter unit, used to monitor the height of the space where inspection personnel are located; Network data transmission unit, used to support multimodal communication; The positioning unit uses a composite positioning technology combining satellite and ground augmentation to acquire and upload the three-dimensional spatial position and movement trajectory of inspection personnel in real time, and is equipped with an electronic fence function. The identity recognition unit is used to identify and verify the identity of the wearer and retrieve their corresponding backend permission information; Interactive display screens are used to receive backend instructions, display on-site early warning information, present rectification guidance plans, and provide feedback on task processing progress.

3. The system according to claim 1, characterized in that, The AI ​​analysis module has a built-in edge computing chip and is configured to perform the following image analysis process: By acquiring the real-time video stream provided by the image acquisition module, keyframe images are extracted from the video stream at a preset frame rate and preprocessed. Based on multi-threaded or parallel computing, the following analysis is performed synchronously on the same batch of preprocessed keyframe images: The first analysis task is to use the non-standard operation identification model to identify whether there is at least one of the following behaviors: failure to wear protective equipment as required, illegal crossing of guardrails, or unauthorized operation of unauthorized equipment. The second analysis task is to call upon the on-site hazard detection model to identify whether there is an abnormal state, such as flame, smoke, liquid leakage, equipment casing damage, or abnormal instrument pointer. Decisions are made based on the analysis results: if the confidence level of the hazard detected by the second analysis task exceeds the first threshold, the highest level of emergency response is triggered; if the first analysis task detects a violation, an alarm with associated location and evidence is triggered; otherwise, a regular status report is sent.

4. The system according to claim 3, characterized in that, The non-standard operation identification model and the on-site hazard detection model are integrated into the same AI analysis framework. This framework shares the same backbone feature extraction network. The features extracted by the backbone feature extraction network are processed by the feature enhancement module and then input into the dedicated non-standard operation identification head and on-site hazard detection head respectively, so as to output the detection results in parallel.

5. The system according to claim 2, characterized in that, The security protection module includes: The land fall protection unit, which connects the gyroscope unit and the image acquisition module, is configured to: when the data collected by the gyroscope determines that the human body's tilting acceleration and tilt angle exceed a preset threshold, and the image data collected by the image acquisition module assists in confirmation, trigger the airbag to inflate and deploy within a set time. The water rescue unit, including a water immersion detection circuit, a water immersion locking circuit, a pressure sensor, and an airbag assembly, is configured as follows: When the water resistance of the water immersion detection circuit is less than the first preset resistance value, the pressure sensor is activated to verify the water depth. When the water depth is greater than the preset water depth threshold, the water immersion locking circuit is closed, the airbag assembly is activated to inflate and deploy, and the water surface positioning beacon is automatically activated. If the water resistance is greater than or equal to the first preset resistance value, the system directly returns to the normal monitoring state.

6. The system according to claim 1, characterized in that, The audible and visual warning module includes a buzzer and an LED warning light, and drives the buzzer and LED warning light to emit different modes of audible and visual alarm signals according to different triggering scenarios. The image acquisition module is used to capture images / videos of the on-site environment and equipment status of the inspection personnel according to a preset triggering method.

7. The system according to claim 1, characterized in that, The communication module supports multiple wireless communication standards and automatically switches according to signal quality. An edge computing gateway, deployed on the field side, is configured to preprocess and encrypt the data transmitted by the front-end smart wearable sensing layer. The preprocessing includes image compression and key early warning information filtering. The data buffer is used to automatically buffer the data to be transmitted by the front-end smart wearable sensing layer when the network signal of the communication module is interrupted, and to automatically complete the data retransmission after the network signal is restored.

8. The system according to claim 1, characterized in that, The backend intelligent operation and maintenance platform layer includes: The problem visualization module is used to display the location, trajectory, and problem warning points of inspection personnel on an electronic map in real time, and generate inspection statistics dashboards; The rectification task management module is used to automatically dispatch rectification tasks to the responsible person's terminal based on the type, level, and location of the early warning information, combined with the skill matching degree and location proximity of the operation and maintenance personnel, through an intelligent dispatching algorithm. The problem-solving guidance module is used to build a standardized guidance knowledge base categorized by industry and problem type, and to automatically push matching problem-solving guidance solutions to the responsible person's terminal. The item tracking module is used to track the status of rectification tasks, receive and verify the rectification completion evidence and overdue reminder statistics uploaded by the responsible person, and realize closed-loop management of tasks.

9. The system according to claim 8, characterized in that, The intelligent task dispatching algorithm calculates a task dispatching priority score for candidate responsible persons based on a comprehensive scoring model, and then assigns tasks according to this score; the comprehensive scoring model is as follows: W=α·S k +β·L o +γ·E m , Where W is the overall score, S k To assess the skill fit of the candidate, L o E represents the proximity of the candidate responsible person to the location where the task occurred. m The task urgency is represented by α, β, and γ, which are preset weighting coefficients; the skill matching degree S k The location proximity L is calculated based on the difference between the candidate's skill level and the skill level required for the task. o The task urgency E is calculated based on the straight-line distance d between the candidate responsible person and the location where the task occurs; m Dynamically determined based on the event type corresponding to the task; The task allocation process of the intelligent task dispatch algorithm includes: determining the geographical screening range of the initial candidates based on the urgency of the task; calculating and sorting the comprehensive score W of the candidates within the range; assigning the task to the candidate with the highest score, and automatically delegating the task to the next candidate if the candidate fails to respond within a preset time.

10. The system according to any one of claims 1-9, characterized in that, The backend intelligent operation and maintenance platform layer is also used to: when it receives an emergency call signal or a security protection trigger signal triggered by the frontend intelligent wearable sensing layer, to issue a prominent alarm on the platform interface and initiate an emergency linkage process.

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