A visual quality control method and system combining task allocation and progress tracking

Abstract

The application discloses a visual quality management and control method and system, which is based on cloud native four-layer architecture operation: the system receives a building machinery entrusted detection task, constructs a five-dimensional scheduling model integrating network signal-to-noise ratio and terminal edge computing power to perform weighted order distribution. The business application layer executes a board flow based on transaction read-write lock according to terminal data; the terminal performs ROI adaptive downsampling compression transmission on the photo under weak network. The system tracks the hidden danger information, closes the loop, calibrates the Z-axis elevation when the report is issued, and performs bit XOR fusion on the elevation, terminal MAC address, CPU clock jitter and photo bottom layer data to generate a multi-source joint hash value encryption storage. The application significantly improves the scheduling accuracy and data tamper-proofing and tracing ability in a restricted environment.

Description

Technical Field

[0001] This invention relates to the fields of safety management of construction lifting machinery and computer data processing technology, specifically to a visual quality control method and system that combines task allocation and progress tracking. Background Technology

[0002] With the acceleration of urbanization, construction lifting machinery (such as tower cranes and construction hoists) is increasingly widely used in engineering construction. However, the existing inspection and testing management model for construction lifting machinery still suffers from many underlying technical deficiencies during its digital transformation. Existing automatic dispatch systems (such as those incorporating conventional multi-objective optimization or genetic algorithms) are mostly based on pure distance and idle status, failing to consider the extreme communication environment of construction sites, where high-altitude work surfaces are often characterized by "weak networks and high latency." When the edge computing power of the assigned terminal is insufficient for local image preprocessing, it can easily lead to congestion in the uploading of on-site multimedia detection data and dispatch failures.

[0003] Traditional Kanban status transitions only operate at the front-end UI level. With frequent network outages at construction sites, status transition instructions are easily lost or data is read incorrectly, leading to fragmented progress control.

[0004] Conventional evidence preservation methods typically extract a single image hash value from on-site photos. However, equipment such as tower cranes has a wide height range and is prone to GPS positioning drift at high altitudes. Simple image hashing cannot atomically bind multimedia streams with the physical and three-dimensional spatial attributes of the detection terminal. The authenticity of the underlying data in the detection report is highly vulnerable to attacks using fake GPS locations or simulator check-ins. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a visualized quality control method that combines task allocation and progress tracking. The method is based on a four-layer architecture comprising an IT infrastructure layer, an information resource layer, an application support layer, and a business application layer. The method includes the following steps: Intelligent allocation steps for construction machinery inspection tasks: The system receives and parses the commissioned inspection task information, extracts task features including project address and equipment type; calls the AI ​​intelligent scheduling service of the application support layer, obtains the network signal-to-noise ratio features of the target construction site's high-altitude work surface and the preprocessing computing power features of candidate terminal edge images in real time, and constructs a five-dimensional scheduling model together with the pre-stored personnel geographical distribution, equipment load and real-time geographical location vectors, automatically generates the optimal task allocation scheme under the constraints of the network, and pushes the task to the corresponding terminal; The spatiotemporal trajectory and transaction read-write lock progress tracking steps are as follows: Real-time reception of dynamic progress data uploaded by the terminal; The business application layer synchronizes the progress data to the map visualization interface, supporting viewing of single-task and multi-task progress details; The status transition of the progress data is executed based on the asynchronous message queue and transaction read-write lock mechanism of the distributed database to drive automatic early warning and cross-lane transition of abnormal task cards; The steps for closed-loop multi-source joint hash quality control of hidden dangers are as follows: Information on hidden dangers entered by terminals is received in real time, a structured list of hidden dangers is generated and displayed on a multi-dimensional large screen; the rectification process of hidden dangers is tracked, and closed-loop control of "problem discovery - recording - rectification - review - closure" is executed. Furthermore, in response to the report generation command, the underlying physical MAC address of the shooting terminal, the CPU microsecond-level clock jump frequency, and the Z-axis elevation data of the Global Positioning System are simultaneously captured. These data are then XORed with the binary stream of the on-site inspection photos to generate a multi-source heterogeneous joint hash value for encrypted storage, ensuring the authenticity and traceability of the report data.

[0006] Furthermore, the AI ​​intelligent scheduling service of the application support layer specifically includes: The historical detection efficiency of each detection personnel, the current number of allocated devices, the real-time GPS coordinates of the terminal, the network signal-to-noise ratio characteristics, and the computing power characteristics of the terminal edge image preprocessing are obtained to construct a five-dimensional feature matrix. Calculate the cosine similarity between the five-dimensional feature matrix and the feature vector of the task to be assigned, and assign dynamic compensation weights to high-computing-power terminals for weak network signal-to-noise ratio, and output a sorted list of recommended executors to generate the optimal task allocation scheme.

[0007] Furthermore, the dashboard constructed by the business application layer includes: lanes to be inspected, lanes under inspection, lanes for rectification and review, lane 5 to be reviewed, and lanes for archived reports; When a network connection interruption is detected at the detection site, the state transition instruction is written to the persistent asynchronous queue on the terminal. After the network is restored, the server node bandwidth is dynamically allocated and the state field of the corresponding task card is locked until the strong consistency of the distributed database data is confirmed, triggering the unlocking of the next swimlane.

[0008] Furthermore, in the aforementioned hidden danger closure visualization quality control step, the specific methods of multi-source joint hashing include: Image feature fingerprints are extracted from the on-site inspection photos, and the hexadecimal string of the image feature fingerprints is concatenated in memory with the terminal physical MAC address, the clock jump frequency, and the Z-axis elevation data according to preset rules. The concatenated data segments are subjected to avalanche obfuscation using a bitwise XOR algorithm to generate the multi-source heterogeneous joint hash value, and this hash value is then linked to the corresponding detection report full-process node on the blockchain.

[0009] Furthermore, in the spatiotemporal trajectory and transaction lock progress tracking step, when uploading on-site detection photos under the weak network environment, the local compression mechanism of the terminal with edge image preprocessing computing power characteristics is triggered: A lightweight target detection model is used to identify mechanical hazard areas in the on-site inspection photos to delineate regions of interest (ROIs). The ROIs are then encoded losslessly while maintaining their original resolution, and adaptive downsampling compression is performed on the background area. The compressed background area and the lossless ROIs are then packetized and encrypted for transmission to reduce channel occupancy in weak network environments.

[0010] Furthermore, when capturing the Z-axis elevation data of the Global Positioning System, in order to eliminate positioning drift caused by high-altitude multipath effects, the following elevation calibration steps are performed: The system acquires real-time air pressure values ​​collected by the terminal's built-in air pressure sensor and calculates the relative air pressure height by combining it with the reference air pressure data of the target construction site. The relative air pressure height is then fused with the initial Z-axis elevation output by the Global Positioning System using Kalman filtering. The calibrated Z-axis elevation data is then used in subsequent multi-source joint hash XOR fusion calculations.

[0011] Furthermore, in the real-time progress tracking step of the spatiotemporal trajectory: a tiered intelligent early warning threshold is set for different detection types. When the time taken for the inspection personnel to enter and exit the construction site or the overall inspection task time triggers the threshold, an abnormal alarm message is pushed to the manager.

[0012] Furthermore, the IT infrastructure layer adopts a cloud-native architecture, supports multi-environment deployment and elastic scaling, and the information resource layer is equipped with an operation log traceability module to record all operation behaviors of all projects, tasks and files in a streamlined log format.

[0013] This invention also provides a visual quality control system that combines task allocation and progress tracking. The system is used to implement the methods described above, and includes: The basic support module is used to establish a four-layer architecture that includes the IT infrastructure layer, information resource layer, application support layer, and business application layer. The task allocation module is used to receive tasks, parse and extract task features, construct a five-dimensional scheduling model that includes network signal-to-noise ratio and terminal edge computing power, and generate the optimal allocation scheme to push to the terminal. The progress tracking module is used to receive progress data containing spatiotemporal trajectories, trigger the local compression mechanism of the terminal with edge image preprocessing computing power, and execute state transitions and map visualization based on distributed database read-write locks. The quality control module is used to perform closed-loop tracking of potential hazards, perform elevation calibration in conjunction with the built-in barometric pressure sensor, and perform multi-source heterogeneous joint hash encryption processing based on bit XOR fusion to link report approval when generating test reports; The terminal interaction module is used to receive tasks, manage local persistent asynchronous queues, collect underlying hardware clock and Z-axis elevation data, and input potential hazard information.

[0014] The present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By constructing a five-dimensional scheduling model that integrates network signal-to-noise ratio and edge computing power, the system can adaptively and accurately allocate tower crane detection tasks in complex electromagnetic and high-altitude weak network environments to terminals with strong local image compression computing power, significantly reducing the packet loss rate and network overhead of large-volume multimedia data transmission on site.

[0016] By decentralizing the workflow of the business dashboard to the underlying distributed database's transaction read-write locks and asynchronous message queues, absolute consistency of instructions is ensured even under conditions of frequent network outages and weak network conditions at the construction site.

[0017] Abandoning the single, easily forged image hash, it obtains microsecond-level CPU fluctuations and underlying physical MAC addresses. The system calculates the three-dimensional elevation of the axis and uses a bitwise XOR algorithm for multi-source heterogeneous fusion. This mechanism atomically binds the detection data with the "physical fingerprint of specific device hardware" and "unalterable spatial height," completely preventing the possibility of simulator check-in and detection report data tampering. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0019] Figure 1 A four-layer overall architecture diagram of a visual quality control system provided in this embodiment of the invention; Figure 2 This is a main flowchart of a visual quality control method provided in an embodiment of the present invention; Figure 3 The algorithm logic diagram of the AI ​​intelligent scheduling model based on the five-dimensional feature matrix provided in the embodiments of the present invention; Figure 4 This diagram illustrates the automatic flow and feedback mechanism of the five-lane swimming state provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the embodiments of this invention are described in detail below in conjunction with specific business scenarios. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] This invention provides a visual quality control method and system that combines task allocation and progress tracking. For example... Figure 1 As shown, in a preferred embodiment of the present invention, the system operates stably based on a four-layer architecture comprising an IT infrastructure layer, an information resource layer, an application support layer, and a business application layer. Specifically, the IT infrastructure layer introduces a cloud-native microservice architecture, supporting elastic dynamic scaling of container nodes; the information resource layer is configured with a data backup and operation log traceability module based on a distributed architecture (such as a MySQL master-slave cluster and MinIO object storage); the application support layer abandons conventional general-purpose distribution algorithms and integrates a deeply customized AI intelligent scheduling service for constrained physical environments and a multi-source heterogeneous image fingerprint extraction engine; and the business application layer provides a dynamic dashboard, a large-screen display module, and a map visualization interface.

[0022] Specifically, such as Figure 2 and Figure 3 As shown, this embodiment uses the statutory or commissioned inspection and testing scenario of construction lifting machinery (such as tower cranes, construction hoists, etc.) as an example to illustrate the method. When the task allocation module at the front end of the system receives the commissioned testing task information, the system automatically parses and extracts basic task features including project address, equipment type, testing type, construction period requirements, and qualification tags of testing personnel. At this time, in order to effectively solve the strong electromagnetic interference, weak network limitations, and terminal computing power bottlenecks that often accompany high-altitude work surfaces at construction sites, the application support layer calls the upgraded AI intelligent scheduling service.

[0023] The AI-powered intelligent scheduling service not only acquires the geographical distribution of each inspection personnel, current equipment load, historical inspection efficiency, and GPS location vectors transmitted in real-time from the terminals, but more importantly, it uses underlying network probes to obtain the network signal-to-noise ratio (SNR) characteristics of the high-altitude work surface at the target inspection site over historical periods. Simultaneously, it sends instructions to each candidate inspection terminal requesting feedback on its edge image preprocessing computing power characteristics (such as the number of available parallel computing threads for the terminal's CPU / GPU). The system integrates these parameters to construct a highly industry-specific "five-dimensional feature matrix." When the scheduling engine calculates the cosine similarity between this matrix and the tasks to be assigned, if the target construction site is determined to be in a weak network environment with an SNR below a preset threshold, the scheduling model triggers a dynamic compensation mechanism. This assigns extremely high weight scores to terminals with high edge computing power (capable of performing high-compression preprocessing of multimedia inspection data locally), thereby generating the optimal task allocation scheme under limited network physical constraints and pushing it to the terminals of the target inspection personnel.

[0024] After the task assignment is completed, the system enters the spatiotemporal trajectory and state transition tracking phase. For example... Figure 4 As shown, the business application layer of this invention constructs a five-lane visual dashboard including a lane to be inspected, a lane in progress during inspection, a lane for rectification and review, a fifth lane pending review, and a lane for archived reports. Throughout the entire action node from the inspector's entry to their departure, the terminal transmits spatiotemporal progress data back to the backend. To prevent frequent network outages during high-altitude inspection operations from causing the loss of dashboard status transition instructions or dirty data reads, this invention decentralizes the visual flow depth from the front end to the distributed database transaction read-write lock mechanism in the backend. When the terminal interaction module detects a sudden network connection interruption, the current state transition instruction (such as transitioning from "to be detected" to "in detection") is not directly discarded, but is pushed into the terminal's local persistent asynchronous message queue for storage. After the network is restored, the system dynamically allocates server node bandwidth to receive queue data based on the heartbeat packet keep-alive mechanism, and adds an exclusive lock to the status field of the task card at the database level. Only after confirming that the MySQL master-slave node data has achieved strong consistency is the lock released and the unlocking of the next lookaboard swimlane is triggered, thereby eliminating the potential for state inconsistency under extreme working conditions at the underlying technology level.

[0025] Furthermore, when potential hazards are detected at the construction site, this invention implements a rigorous closed-loop quality control system through a quality management module. After inspectors input hazard information, such as cracks in the tower crane structure, into the terminal, the system automatically generates a structured hazard list using task cards and displays the risk level of the construction site in yellow on a large screen for visual categorization. Internally, the system enforces a streamlined closed-loop control process of "problem discovery - recording - rectification - review - closure," with task cards moving between the rectification / review lane and the inspection lane.

[0026] Once all potential hazards are eliminated, and the system responds to the report generation command, it performs multi-source joint hash encryption to build a highly reliable anti-tampering barrier and combat location simulator attacks. The system forcibly calls the terminal's API hardware interface in the background to accurately capture the terminal's underlying physical MAC address, the terminal's CPU clock tick frequency at the moment the photo is taken (microseconds), and the GPS Z-axis elevation 3D data representing the device's climbing height. The application support layer first performs feature reduction on the on-site inspection photos to extract an image fingerprint hexadecimal string, then concatenates it with the aforementioned three physical / spatial data in memory according to a preset byte alignment rule. Next, a bitwise XOR logic algorithm is used to perform deep avalanche obfuscation calculation on the concatenated data segment, generating a unique multi-source heterogeneous joint hash value. This hash value atomically hard-binds multimedia data with the "physical characteristics of specific hardware devices" and "difficult-to-forge spatial height parameters," and along with the entire three-level approval process of the inspection report, it is either stored on the blockchain or encrypted, thoroughly ensuring the report's authenticity and traceability.

[0027] To more intuitively illustrate the collaborative mechanism of the underlying technical features of this invention in actual inspection workflows, a complete application scenario example for construction hoisting machinery inspection is provided below: In a commissioned task for the periodic inspection of a QTZ125 tower crane at a construction site, the system received the task and assessed that the tower crane's operating height reached 80 meters, often accompanied by signal attenuation and a weak network environment. After calculating the five-dimensional matrix of the scheduling model, it not only considered the inspector Mr. Li's qualifications for inspecting heavy tower cranes and the spatial distance, but also identified that the flagship inspection terminal Mr. Li was using possessed extremely high local GPU edge processing computing power, capable of handling high-compression ratio image processing under weak network conditions. The system then issued the optimal allocation instruction to Mr. Li.

[0028] Engineer Li arrived at the construction site and began on-site testing. During the climb of the tower crane, a brief network outage occurred due to the shielding caused by the steel structure. Engineer Li's "Start Testing" check-in action on the terminal was safely pushed into the local persistent asynchronous queue. Ten minutes later, the network was restored, the terminal pushed the queue information to the server, and the underlying database triggered a transaction read-write lock. After ensuring data consistency between the master and slave nodes, the card on the management dashboard smoothly progressed to the "Testing in progress" lane.

[0029] During the inspection, Engineer Li discovered a potential hazard of loose bolts on the slewing mechanism. He entered the hazard list and sent it to the construction team for rectification. After the construction team rectified the issue, Engineer Li reviewed it on-site and clicked "Confirm Item Closure." Once the hazard was completely cleared, the system automatically generated an inspection report. During the generation phase, the system not only extracted the image fingerprints from the before-and-after photos taken by Engineer Li, but also instantly captured the MAC address of Engineer Li's terminal, the CPU's microsecond clock tick at that time, and the Z-axis elevation at an altitude of 80 meters. These four sets of data underwent a bitwise XOR avalanche fusion in the system memory, generating an irreversible joint hash string, which was then archived in the "Archived Reports Swimlane" along with the report. Through this mechanism, the system completely closed the entire inspection process, while delivering an electronic inspection archive with tamper-proof evidence and underlying technical backing.

[0030] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A visual quality control method combining task allocation and progress tracking, characterized in that, The method is based on a four-layer architecture comprising an IT infrastructure layer, an information resource layer, an application support layer, and a business application layer. The method includes the following steps: Intelligent allocation steps for construction machinery inspection tasks: The system receives and parses the commissioned inspection task information of construction lifting machinery and extracts task features; The AI ​​intelligent scheduling service of the application support layer is invoked to obtain the network signal-to-noise ratio characteristics of the high-altitude operation surface of the target construction site and the edge image preprocessing computing power characteristics of the candidate terminals in real time. The network signal-to-noise ratio characteristics and the edge image preprocessing computing power characteristics are combined with the pre-stored personnel geographical distribution, equipment load and real-time geographical location vector to jointly construct a five-dimensional scheduling model. The optimal task allocation scheme under the constraints of the limited network is automatically generated and the task is pushed to the terminal of the corresponding inspection personnel to realize one-click task allocation and reception. Spatiotemporal trajectory and transaction lock progress tracking steps: Real-time reception of dynamic progress data uploaded by the inspection personnel through the terminal, the progress data including departure time, arrival time, departure time, task duration, entry and exit time, on-site inspection photos and global positioning system positioning data; The business application layer synchronizes the progress data to the map visualization interface for spatiotemporal trajectory display, and the status transition of the progress data is executed based on the asynchronous message queue and transaction read-write lock mechanism of the distributed database to drive the automatic warning and Kanban flow of abnormal task cards. The steps of closed-loop multi-source joint hash quality control for hidden dangers are as follows: the system receives hidden danger information entered by the inspection personnel through the terminal in real time, and automatically links the corresponding task cards, target equipment and personnel to generate a structured list of hidden dangers and displays it on a multi-dimensional visualization screen. Based on a pre-set control signal tracking process for potential hazard rectification, a closed-loop management system is implemented, encompassing problem discovery, recording, rectification, review, and event closure. Furthermore, in response to a report generation command, the system simultaneously captures the underlying physical MAC address of the camera terminal, the CPU's microsecond-level clock tick frequency, and the global positioning system data. The axis elevation data is XORed with the binary stream of the on-site inspection photos to generate a multi-source heterogeneous joint hash value for encrypted storage, and linked to the entire process nodes of report generation, review, and approval.

2. The visual quality control method according to claim 1, characterized in that, The AI ​​intelligent scheduling service in the application support layer specifically includes: The historical detection efficiency of each detection personnel, the current number of allocated devices, the real-time GPS coordinates of the terminal, the network signal-to-noise ratio characteristics, and the computing power characteristics of the terminal edge image preprocessing are obtained to construct a five-dimensional feature matrix. Calculate the cosine similarity between the five-dimensional feature matrix and the feature vector of the task to be assigned, and assign dynamic compensation weights to high-computing-power terminals for weak network signal-to-noise ratio. Output a sorted list of recommended executors to generate the optimal task allocation scheme.

3. The visual quality control method according to claim 1, characterized in that, The dashboards built by the business application layer include: Lanes to be inspected, lanes under inspection, lanes requiring rectification and review, lane 5 pending review, and lanes with archived reports; When a network connection interruption is detected at the detection site, the state transition instruction is written to the persistent asynchronous queue on the terminal. After the network is restored, the server node bandwidth is dynamically allocated and the state field of the corresponding task card is locked until the strong consistency of the distributed database data is confirmed, triggering the unlocking of the next swimlane.

4. The visual quality control method according to claim 1, characterized in that, In the aforementioned hidden danger closure visualization quality control step, the specific methods of multi-source joint hashing include: Image feature fingerprints are extracted from the on-site inspection photos. The hexadecimal string of the image feature fingerprint is then combined with the terminal's physical MAC address, the clock jump frequency, and the... The axis elevation data is spliced ​​in memory according to a preset byte alignment rule; The concatenated data segments are subjected to avalanche obfuscation using a bitwise XOR algorithm to generate the multi-source heterogeneous joint hash value, and the hash value is then encrypted and stored by hash association with the corresponding detection report full-process node.

5. The visual quality control method according to claim 1, characterized in that, In the spatiotemporal trajectory and transaction lock progress tracking step, when uploading on-site detection photos in the weak network environment, the local compression mechanism of the terminal with edge image preprocessing computing power is triggered: A lightweight target detection model is used to identify mechanical hazard areas in the on-site inspection photos to delineate regions of high interest. The high-interest region is preserved with lossless encoding at its original resolution, while the background region is subjected to adaptive downsampling compression. The compressed background area and the lossless high interest area are packetized and encrypted for transmission to reduce channel occupancy in weak network environments.

6. The visual quality control method according to claim 4, characterized in that, In capturing the Global Positioning System When processing axis elevation data, to eliminate positioning drift caused by high-altitude multipath effects, the following elevation calibration steps should be performed: The system acquires real-time air pressure values ​​collected by the terminal's built-in air pressure sensor and calculates the relative air pressure height by combining this with the benchmark air pressure data of the target construction site. The relative air pressure altitude is compared with the output of the Global Positioning System. The initial elevation of the axis is fused using Kalman filtering, and the calibrated data is output. The axis elevation data is used in the bit XOR fusion.

7. The visual quality control method according to claim 1, characterized in that, In the spatiotemporal trajectory and transaction lock progress tracking steps: A tiered intelligent early warning threshold is set for different detection types and task priorities. When the time spent by the inspectors entering and leaving the construction site or the total time spent on the detection task triggers the tiered intelligent early warning threshold, an abnormal alarm message is pushed to the manager through system pop-ups, emails and SMS channels.

8. The visual quality control method according to claim 1, characterized in that, The IT infrastructure layer adopts a cloud-native architecture, supports multi-environment deployment and elastic scaling, and the information resource layer is equipped with an operation log traceability module to record all operation behaviors of all projects, tasks and files in a streamlined log format.

9. A visualized quality control system combining task allocation and progress tracking, characterized in that, The system is used to implement the method as described in any one of claims 1 to 8, the system comprising: The basic support module is used to establish a four-layer architecture that includes the IT infrastructure layer, information resource layer, application support layer, and business application layer. The task allocation module is used to receive entrusted detection tasks, parse and extract task features, construct a five-dimensional scheduling model that includes network signal-to-noise ratio and terminal edge computing power, and automatically generate the optimal task allocation scheme and push it to the terminal. The progress tracking module is used to receive progress data containing spatiotemporal trajectories uploaded by the terminal, trigger the local compression mechanism of the terminal with edge image preprocessing computing power, execute the state transition and map visualization display based on distributed database transaction read-write lock, and provide automatic warnings for timeouts, failure to upload photos, or abnormal positioning. The quality control module is used for hazard entry, classification and statistical display, and closed-loop tracking from problem discovery to elimination. It combines the built-in barometric pressure sensor to perform elevation calibration and performs multi-source heterogeneous joint hash encryption based on bit XOR fusion when generating test reports to link the report review and approval process. The terminal interaction module is used to detect when personnel receive tasks on the terminal side, manage local persistent asynchronous queues, and collect data from the underlying hardware clock. Axis elevation data and entry of potential hazard information.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1 to 8.

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