Engineering supervision data management system based on Internet of Things

Abstract

A project supervision data management system based on the Internet of Things relates to the field of project supervision of the Internet of Things, and comprises a plurality of edge computing devices used for collecting real-time parameter data in construction, obtaining project health indexes and sending alarm prompt information; the anomaly detection and alarm module is in communication connection with the edge computing equipment and is used for receiving the real-time parameter data and the alarm prompt information to obtain an anomaly detection result; the dynamic task management module is in communication connection with the edge computing device and the anomaly detection and alarm module, and is used for receiving the real-time parameter data and the anomaly detection and anomaly detection result, obtaining an offline task instruction and sending the offline task instruction to the edge computing device; and the block chain distributed storage module is in communication connection with the edge computing device and the anomaly detection and alarm module, and is used for receiving the real-time parameter data and the anomaly detection result, and performing encryption storage. According to the invention, the data real-time performance of project supervision can be improved.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things engineering supervision, and in particular to an engineering supervision data management system based on the Internet of Things. Background Art

[0002] With the continuous expansion of construction projects and the rapid development of construction technology, project supervision, as a key component in ensuring project quality and safety, faces increasing demands for data management. Project supervision data includes various parameters and indicators during the construction process, inspection records, and safety assessment data. The timely collection, effective management, and secure storage of this data are crucial to project quality.

[0003] Currently, project supervision data management primarily relies on a combination of manual inspections and fixed monitoring points. Supervisors regularly patrol the construction site, collecting relevant data using portable monitoring equipment. Fixed monitoring devices are also installed at key construction locations. This data is uploaded to a central management system for storage and analysis. The central management system organizes and categorizes the data, generates supervision reports, and performs simple early warnings based on preset thresholds.

[0004] However, at construction sites, due to complex environmental conditions, network communications are often unstable, and centralized data processing methods can easily lead to data transmission delays and slow system responses, making the data for project supervision insufficiently real-time. Summary of the Invention

[0005] This application provides an Internet of Things-based engineering supervision data management system for improving the real-time performance of engineering supervision data.

[0006] In the first aspect, the present application provides an Internet of Things-based engineering supervision data management system, including: multiple edge computing devices, each edge computing device is used to collect real-time parameter data during the corresponding construction process and process it to obtain an engineering health index, and send an alarm prompt information when it is determined that the engineering health index does not meet the preset safety conditions; an anomaly detection and alarm module, which is communicated with each edge computing device, is used to receive the real-time parameter data and alarm prompt information sent by each edge computing device and process it to obtain an anomaly detection result; a dynamic task management module, which is communicated with each edge computing device and the anomaly detection and alarm module, is used to receive the real-time parameter data uploaded by each edge computing device and the anomaly detection results uploaded by the anomaly detection and alarm module and process them to obtain offline task instructions, and send offline task instructions to each edge computing device when it is determined that the communication signal strength does not meet the preset strength conditions; a blockchain distributed storage module, which is communicated with each edge computing device and the anomaly detection and alarm module, is used to receive the real-time parameter data sent by each edge computing device and the anomaly detection results sent by the anomaly detection and alarm module and encrypt and store them.

[0007] In the above embodiment, the engineering supervision data management system is equipped with multiple edge computing devices that work together with an anomaly detection alarm module, a dynamic task management module, and a blockchain distributed storage module, thereby realizing the collection, processing, and storage of real-time parameter data during the construction process, improving the real-time nature of engineering supervision data, and ensuring construction safety through health index calculation and alarm mechanisms.

[0008] In combination with some embodiments of the first aspect, in some embodiments, each edge computing device includes: an acquisition unit, a health index calculation unit, an offline task processing unit, and an equipment operation control unit, wherein the acquisition unit is used to collect real-time parameter data of the corresponding construction process in real time; the health index calculation unit is used to cache real-time parameter data and calculate the project health index through a preset formula; the offline task processing unit is used to parse offline task instructions to obtain an offline task list, task execution priority, resource allocation strategy, task execution time window and equipment control instructions, and send the task execution time window and equipment control instructions to the construction equipment in the corresponding construction process, and execute the local computing tasks in the offline task list according to the task execution priority and resource allocation strategy when it is determined that the communication signal strength is lower than the preset strength threshold; the equipment operation control unit is used to trigger the construction equipment in the corresponding construction process to perform construction operations according to the equipment control instructions within the task execution time window, and control the construction equipment in the corresponding construction process to interrupt the construction operation when it is detected that the project health index is lower than the preset safety threshold.

[0009] In the above embodiment, the engineering supervision data management system integrates the acquisition unit, health index calculation unit, offline task processing unit and equipment operation control unit in the edge computing device, realizes localized data processing and equipment control, effectively reduces the network communication load, and improves the system response speed.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the offline task processing unit also includes a task feedback subunit and a communication coordination subunit, wherein the task feedback subunit is used to obtain the execution results of the construction operation from the construction equipment in the corresponding construction process; the communication coordination subunit is used to monitor the communication signal strength in real time, and encrypt and cache the execution results when the communication signal strength is continuously lower than the preset strength threshold during the first current monitoring period, and synchronously send the execution results to the blockchain distributed storage module and the dynamic task management module when the communication signal strength is continuously higher than the preset strength threshold for a first preset time period during the first subsequent monitoring period.

[0011] In the above embodiment, the engineering supervision data management system is provided with a task feedback subunit and a communication coordination subunit in the offline task processing unit, which optimizes the data transmission problem under the condition of unstable communication signal and ensures the reliable storage and synchronization of construction operation execution results.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the preset formula is: Among them, the EHI is the engineering health index, the W e q is the equipment health sub-index weight factor of the construction equipment in the construction process corresponding to each edge computing device, and W ve q is the equipment vibration weight factor of the construction equipment, and k ve q is the equipment vibration nonlinear adjustment coefficient of the construction equipment, Vibration_Equipment is the equipment vibration parameter of the construction equipment, and W ie q is the equipment inclination weight factor of the construction equipment, Inclination_Equipment is the equipment inclination parameter of the construction equipment, Max_Inclination_Equipment is the maximum inclination threshold of the construction equipment, and W g s is the ground and structure health sub-index weight factor during the construction process corresponding to each edge computing device, and the W vg s is the ground vibration weight factor corresponding to the construction process of each edge computing device, and k vgs is the nonlinear adjustment coefficient of the ground vibration during the construction process corresponding to each edge computing device, the Vibration_Ground is the ground vibration parameter during the construction process corresponding to each edge computing device, and the W ig s is the structural inclination weight factor of the building structure during the construction process corresponding to each edge computing device, Inclination_Structure is the structural inclination parameter of the building structure, and Max_Inclination_Structure is the maximum structural inclination threshold of the building structure.

[0013] In the above embodiment, the engineering supervision data management system introduces an engineering health index calculation formula that takes equipment vibration and inclination into consideration, thereby achieving a comprehensive assessment of the safety status of construction equipment and building structures and providing a more accurate basis for safety warnings.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the dynamic task management module includes: a task processing unit and a communication coordination unit, wherein the task processing unit is used to process real-time parameter data and anomaly detection results to obtain offline task instructions; the communication coordination unit is used to encapsulate and process the offline task list, task execution priority, resource allocation strategy, task execution time window and device control instructions to obtain offline task instructions, and monitor the communication signal strength in real time, and send the offline task instruction to each edge computing device when the communication signal strength is continuously lower than the preset strength threshold during the second current monitoring period, and send the offline task instruction to the blockchain distributed storage module when the communication signal strength is continuously higher than the preset strength threshold for a second preset time period during the second subsequent monitoring period.

[0015] In the above embodiment, the engineering supervision data management system realizes the intelligent generation and allocation of offline tasks through the cooperation of the task processing unit and the communication coordination unit, ensuring the continuous operation of the system when the communication signal is unstable.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the task processing unit obtains an offline task list, task execution priority, resource allocation strategy, task execution time window and equipment control instructions according to the real-time parameter data and the anomaly detection results in the following manner: marking the real-time parameter data that has not been fully uploaded to the blockchain distributed storage module in each edge computing device, and generating a data retransmission task, wherein the offline task list includes the data retransmission task; generating an equipment maintenance task according to the equipment anomaly type in the anomaly detection result and associating it with the abnormal construction equipment in the corresponding construction process, wherein the offline task list includes the equipment maintenance task; generating a routine inspection task that matches the current construction stage according to the construction progress in the real-time parameter data, wherein the offline task list includes the routine inspection task; assigning a unique task ID to the equipment maintenance task, the data retransmission task and the routine inspection task respectively, and binding all the unique task IDs to the unique device ID of the associated construction equipment, Generate a task and equipment mapping relationship table, wherein the associated construction equipment includes abnormal construction equipment, and the offline task list includes a task and equipment mapping relationship table; divide the equipment maintenance task into a first-level execution priority, divide the data retransmission task into a second-level execution priority, and divide the routine inspection task into a third-level execution priority according to the equipment anomaly level in the anomaly detection result, wherein the task execution priority includes a first-level execution priority, a second-level execution priority, and a third-level execution priority, the first-level execution priority is higher than the second-level execution priority, and the second-level execution priority is higher than the third-level execution priority; determine the resource allocation strategy for the equipment maintenance task, the data retransmission task, and the routine inspection task according to the task execution priority; generate a time interval according to the construction progress, allowing the construction equipment in the corresponding construction process to perform construction operations when the communication signal strength is lower than the preset strength threshold, as a task execution time window; generate equipment control instructions according to the equipment anomaly type, equipment anomaly level and offline task list.

[0017] In the above embodiment, the engineering supervision data management system realizes the orderly execution of offline tasks and improves the system operation efficiency through a systematic task priority division and resource allocation mechanism.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the task processing unit determines the resource allocation strategy of the equipment maintenance task, the data retransmission task, and the routine inspection task according to the task execution priority in the following manner: obtaining the performance status data of each edge computing device in real time; determining the load index of each edge computing device according to the performance status data; marking the edge computing device with a load index higher than the first preset load index as a performance overload device, and marking the edge computing device with a load index lower than the second preset load index as a performance idle device; determining the first complexity of the equipment maintenance task according to the device abnormality type and the performance idle device; determining the second complexity of the data retransmission task according to the amount of retransmission data in the data retransmission task and the preset communication bandwidth limit; Determine the third complexity of the routine inspection task based on the construction progress and the number of inspection items in the routine inspection task; determine the first computing resources required for the equipment maintenance task, the second computing resources required for the data retransmission task, and the third computing resources required for the routine inspection task based on the first complexity, the second complexity, and the third complexity; perform task dependency analysis on the equipment maintenance task, the data retransmission task, and the routine inspection task to determine the task dependency relationship; adjust the task execution priority to the target task execution priority based on the task dependency relationship to obtain a resource allocation strategy; based on the resource allocation strategy, allocate the equipment maintenance task, the data retransmission task, and the routine inspection task to the performance-idle equipment based on the first computing resources, the second computing resources, the third computing resources, and the target task execution priority.

[0019] In the above embodiment, the engineering supervision data management system achieves optimal allocation of computing resources and improves the overall performance of the system through real-time monitoring and load balancing of the performance status of edge computing devices.

[0020] In combination with some embodiments of the first aspect, in some embodiments, the task processing unit adjusts the task execution priority to the target task execution priority according to the task dependency in the following manner: when it is determined according to the dependency relationship that the equipment maintenance task depends on the data retransmission task, the data retransmission task is marked as the first predecessor task of the equipment maintenance task; when it is determined according to the dependency relationship that the routine inspection task depends on the equipment maintenance task, the equipment maintenance task is marked as the second predecessor task of the routine inspection task; when it is determined that the data retransmission task is the first predecessor task of the equipment maintenance task, the secondary execution priority of the data retransmission task is adjusted to the primary execution priority, and the execution order of the data retransmission task is set to take precedence over the equipment maintenance task; when it is determined that the equipment maintenance task is the second predecessor task of the routine inspection task, the third-level execution priority of the routine inspection task is adjusted to the second-level execution priority.

[0021] In the above embodiment, the engineering supervision data management system optimizes the task execution sequence and improves the task processing efficiency by establishing task dependency and a dynamic priority adjustment mechanism.

[0022] In combination with some embodiments of the first aspect, in some embodiments, the task processing unit allocates equipment maintenance tasks, data retransmission tasks, and routine inspection tasks to performance idle devices according to the first computing resources, the second computing resources, the third computing resources, and the target task execution priority in the following manner: traverse all performance idle devices to obtain all remaining computing resources of all performance idle devices; sort the first computing resources, the second computing resources, and the third computing resources according to the target task execution priority to generate a resource to-be-allocated queue; match the resource to-be-allocated queue with all remaining computing resources to obtain a resource matching result; when it is determined according to the resource matching result that the first remaining computing resource of the first performance idle device matches the first computing resource, allocate the equipment maintenance task to the first performance idle device, wherein all performance idle devices include the first performance idle device, and the all remaining computing resources include the first remaining computing resource; when it is determined according to the resource matching result that there is a When the second remaining computing resources of the second performance idle device match the second computing resources, the data supplementary transmission task is assigned to the second performance idle device, wherein all performance idle devices include the second performance idle device, and the all remaining computing resources include the second remaining computing resources; or, when it is determined according to the resource matching result that the second computing resources exceed the third remaining computing resources of a single performance idle device, the data supplementary transmission task is split into multiple supplementary transmission sub-tasks, and the multiple supplementary transmission sub-tasks are assigned to multiple third performance idle devices, wherein all performance idle devices include multiple third performance idle devices, and the all remaining computing resources include the third remaining computing resources; when it is determined according to the resource matching result that the fourth remaining computing resources of the fourth performance idle device match the third computing resources, the routine inspection task is assigned to the fourth performance idle device, wherein all performance idle devices include the fourth performance idle device, and the all remaining computing resources include the fourth remaining computing resources.

[0023] In the above embodiment, the engineering supervision data management system achieves optimal utilization of computing resources and improves system processing capabilities through an intelligent task allocation algorithm.

[0024] In combination with some embodiments of the first aspect, in some embodiments, the blockchain distributed storage module includes: an encryption unit and a distributed storage unit, wherein the encryption unit is used to receive real-time parameter data sent by each edge computing device, the anomaly detection results sent by the anomaly detection and alarm module, and the offline task instructions, resource matching results, task dependencies, target task execution priority and execution results sent by the dynamic task management module when it is determined that the communication signal strength is continuously higher than the preset strength threshold within a second preset time period, and hash encryption is performed on the real-time parameter data, anomaly detection results, offline task instructions, resource matching results, task dependencies, target task execution priority and execution results to obtain a hash encrypted data packet; the distributed storage unit is used to split the hash encrypted data packet into multiple hash encrypted data blocks according to a preset sharding method, and distribute the multiple hash encrypted data blocks to multiple nodes in the blockchain network for distributed storage.

[0025] In the above embodiment, the engineering supervision data management system realizes encrypted storage and distributed management of data through blockchain technology, ensuring the security and traceability of data.

[0026] In a second aspect, an embodiment of the present application provides a computer program code including instructions, which runs on an engineering supervision data management system.

[0027] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which are executed on an engineering supervision data management system.

[0028] It is understandable that the computer program code provided in the second aspect and the computer storage medium provided in the third aspect are both applicable to the engineering supervision data management system provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods, and will not be repeated here.

[0029] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Due to the use of multiple edge computing devices for real-time parameter collection and health index calculation, as well as the collaborative working mechanism of the anomaly detection and alarm module, dynamic task management module and blockchain distributed storage module, real-time monitoring and rapid response during the construction process are achieved, effectively solving the problems of data transmission delay and low processing efficiency in existing technologies, and thus realizing real-time collection, processing and storage of engineering supervision data, improving the efficiency of construction safety supervision and reducing construction risks.

[0030] 2. Due to the adoption of the hierarchical processing mechanism of the task processing unit in the dynamic task management module and the real-time monitoring mechanism of the communication coordination unit, the intelligent generation, priority division and resource allocation of offline tasks are realized, which effectively solves the problems of task processing confusion and resource waste in the case of communication interruption in the existing technology, and thus realizes the continuous and reliable operation of the system under unstable communication conditions, thereby improving the fault tolerance and availability of the system.

[0031] 3. Due to the adoption of a resource allocation strategy based on performance status monitoring and load balancing, as well as a task complexity assessment and dependency analysis mechanism, dynamic optimization allocation of computing resources is achieved, effectively solving the problems of unbalanced computing resource utilization and low task execution efficiency in existing technologies, thereby achieving an improvement in the overall system performance and optimization of task processing efficiency, and enhancing the scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a module architecture diagram of the engineering supervision data management system in the embodiment of the present application; Figure 2 This is another module architecture diagram of the engineering supervision data management system in the embodiment of the present application; Figure 3 This is a flowchart of a task processing unit determining a resource allocation strategy in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a physical device of the engineering supervision data management system in the embodiment of the present application. DETAILED DESCRIPTION

[0033] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0034] In the following, the terms "preset" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the indicated technical features. Therefore, the features defined as "preset" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "multiple" means two or more.

[0035] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0036] A large-scale construction project involved the construction of multiple high-rise buildings, with dozens of large-scale construction equipment, including tower cranes and excavators, operating simultaneously on site. Due to the complex construction site environment and the widespread distribution of equipment, traditional manual inspection methods struggled to promptly detect equipment anomalies and safety hazards. Furthermore, the massive amount of monitoring data generated during construction required real-time processing and analysis. However, due to network limitations, data uploads were often delayed or lost, impacting the real-time and accuracy of project supervision.

[0037] In related technologies, the collection and management of engineering supervision data can be achieved by combining fixed monitoring points with manual inspections. The following describes application scenarios using data management systems in related technologies.

[0038] At a construction site, supervisors collect data through a combination of regular inspections and fixed monitoring points. Fixed monitoring points transmit data to a central server via a wired network, while mobile monitoring equipment requires manual download and upload of data at regular intervals. Network failures prevent timely data upload, resulting in monitoring blind spots. Furthermore, the central server utilizes a centralized processing method, which can lead to system responsiveness delays when data volumes surge, impacting construction progress.

[0039] The IoT-based engineering supervision data management system in the embodiments of this application achieves comprehensive equipment monitoring and rapid response through the distributed deployment of edge computing devices and real-time data processing, which not only improves the real-time performance of data collection but also enhances the system's fault tolerance. The following describes the application scenarios of the engineering supervision data management system in this application.

[0040] With this solution, every piece of construction equipment on the site is equipped with an edge computing device, which can collect real-time data on parameters such as vibration and tilt. Even when the network is unstable, the edge device can independently calculate the project health index and immediately take safety measures if an anomaly is detected. Furthermore, the system ensures reliable data storage through blockchain technology and the orderly execution of various monitoring tasks through dynamic task management.

[0041] It can be seen that while realizing real-time data collection, it can also effectively solve the problem of data loss caused by unstable network communication, thereby realizing the intelligence and automation of engineering supervision.

[0042] For ease of understanding, the following describes the engineering supervision data management system provided by this implementation in combination with the above scenarios. Figure 1 , which is a module architecture diagram of the engineering supervision data management system in the embodiment of this application.

[0043] See also Figure 1 , Figure 1The engineering supervision data management system in the embodiment of the present application is shown, including: Multiple edge computing devices, each edge computing device is used to collect real-time parameter data during the corresponding construction process and process it to obtain the project health index, and send an alarm prompt message when it is determined that the project health index does not meet the preset safety conditions.

[0044] Among them, the edge computing device is an on-site intelligent terminal with data processing capabilities. The real-time parameter data includes operating status data such as equipment vibration, inclination, temperature, displacement, etc. The engineering health index is a comprehensive numerical indicator reflecting the safety status of construction equipment and environment. The preset safety conditions are the safety threshold ranges based on historical data and industry standards. The alarm prompt information is a standardized alarm data packet containing information such as abnormality type, location, and time.

[0045] Specifically, each edge computing device uses an industrial-grade embedded computer as its core processor and is equipped with a variety of sensor interfaces and communication modules. These sensor interfaces include analog inputs (4-20mA / 0-5V), digital inputs (24V), and RS485 interfaces for connecting to various field sensors. The communication modules support multiple communication methods, including 4G / 5G, WiFi, and Ethernet, for reliable data transmission. The devices utilize a real-time operating system, feature local data storage, and are equipped with a UPS to ensure continuous operation.

[0046] In practice, edge computing devices may face challenges such as inclement weather, unstable power supplies, and communication signal interference. To address these challenges, the system employs multiple protection measures: IP65 protection and above ensures water and dust resistance; dual power supply solutions mitigate the impact of power outages; multi-channel communication backup ensures reliable data transmission; and, in the event of communication interruptions, local storage preserves historical data for at least a preset period, such as 30 days.

[0047] Figure 1 The engineering supervision data management system also includes an anomaly detection and alarm module, which is connected to each edge computing device through a bus and is used to receive real-time parameter data and alarm prompt information sent by each edge computing device and process them to obtain anomaly detection results.

[0048] Among them, anomaly detection is a data anomaly pattern recognition process based on machine learning algorithms, the alarm prompt information is a standardized alarm data packet, and the anomaly detection result is an analysis report containing information such as anomaly type, severity, and processing suggestions.

[0049] Specifically, the anomaly detection and alerting module is hosted by high-performance servers and utilizes a distributed computing architecture. It comprises a data preprocessing unit, a model training unit, an anomaly identification unit, and an alert management unit. The data preprocessing unit is responsible for data cleaning, standardization, and feature extraction; the model training unit continuously optimizes the detection model based on historical data; the anomaly identification unit analyzes input data streams in real time; and the alert management unit is responsible for alert level classification and notification distribution. The module utilizes a microservices architecture, with each functional unit deployed independently and data exchanged through message queues.

[0050] In specific applications, problems such as false alarms and missed alarms may occur. The system improves detection accuracy by establishing a multi-level alarm confirmation mechanism, incorporating an expert knowledge base to assist in judgment, and setting adaptive alarm thresholds. Furthermore, the system supports dynamic configuration of alarm rules, allowing for flexible adjustment of detection strategies based on the characteristics of different project phases.

[0051] Figure 1 The engineering supervision data management system also includes a dynamic task management module, which is communicated with each edge computing device through a bus, and is also communicated with the anomaly detection and alarm module. It is used to receive the real-time parameter data uploaded by each edge computing device and the anomaly detection results uploaded by the anomaly detection and alarm module and process them to obtain offline task instructions, and send offline task instructions to each edge computing device when it is determined that the communication signal strength does not meet the preset strength conditions.

[0052] Among them, the offline task instructions are a standardized instruction set that includes information such as task type, execution time, and resource requirements. The preset strength condition is the minimum signal strength threshold required to ensure stable communication. The communication signal strength is a real-time indicator value reflecting the current network quality. The real-time parameter data is the original monitoring data collected from the edge device. The anomaly detection result is the abnormal status judgment information obtained through analysis.

[0053] Specifically, the dynamic task management module adopts a distributed task scheduling architecture, consisting of four core components: a task generator, a scheduler, a resource monitor, and a task executor. The task generator automatically generates tasks using a rules engine based on real-time data and detection results. The scheduler manages tasks using a priority queue and supports dynamic adjustment of task priorities. The resource monitor tracks indicators such as computing resource utilization and communication status of each edge device in real time. The task executor is responsible for task dispatch and execution status tracking. The module adopts a master-slave architecture, with the master node responsible for global scheduling and slave nodes deployed on each edge device to perform specific tasks. The system supports dynamic splitting and merging of tasks and can automatically adjust task granularity based on network conditions.

[0054] In actual operation, problems such as task conflicts, resource competition, and communication interruptions may arise. The system ensures reliable task execution by implementing task dependency management, resource reservation mechanisms, and task rollback mechanisms. When a decrease in communication quality is detected, the system automatically postpones non-urgent tasks to prioritize the completion of critical tasks. A manual intervention interface is also provided, allowing administrators to adjust task priorities and execution strategies based on actual conditions.

[0055] Figure 1 The engineering supervision data management system also includes a blockchain distributed storage module, which is connected to each edge computing device through a bus and is also connected to the anomaly detection and alarm module. It is used to receive real-time parameter data sent by each edge computing device and anomaly detection results sent by the anomaly detection and alarm module and encrypt and store them.

[0056] Among them, encrypted storage is a data security storage process that uses an asymmetric encryption algorithm, the blockchain network is a distributed ledger system composed of multiple storage nodes, the real-time parameter data is the original monitoring data, the anomaly detection result is the anomaly judgment information after analysis, and the storage node is a network node with data storage and verification functions.

[0057] Specifically, the blockchain distributed storage module utilizes a consortium chain architecture, comprising three layers: a data preprocessing layer, a blockchain core layer, and a storage layer. The data preprocessing layer is responsible for data format conversion, privacy information desensitization, and data sharding. The blockchain core layer implements the consensus mechanism (using the PBFT algorithm), smart contract execution, and node management. The storage layer utilizes a distributed file system, supporting distributed data storage and rapid retrieval. Each storage node is equipped with a high-performance server, using a solid-state drive array for storage services, and inter-node communication is achieved through a private network. The system supports incremental data backup and rapid recovery, achieving decentralized and tamper-resistant data storage.

[0058] During operation, problems such as insufficient storage capacity, node failures, and network latency may arise. The system ensures data availability and integrity through dynamic storage expansion, multi-copy backup strategies, and data sharding. When a node failure is detected, the system automatically initiates data migration to ensure uninterrupted service. Furthermore, data cleanup strategies are provided to automatically archive or delete expired data based on its importance and retention period. The system also supports tiered access permissions, ensuring data security while providing a flexible query interface.

[0059] In the above embodiment, distributed data processing is achieved through the collaborative work of multiple edge computing devices. In actual applications, the number and distribution of edge computing devices can be flexibly adjusted according to the project scale and site conditions. The following supplements the scenario of this embodiment.

[0060] In the later stages of the project, the system implemented more intelligent task scheduling and resource allocation based on accumulated historical data. For example, the system could predict equipment maintenance needs and schedule repair tasks in advance. It could also automatically increase local computing resources in a certain area when it detected frequent network signal instability. For large data synchronization tasks, the system would automatically select periods of low network load to execute them, ensuring data integrity while improving system efficiency.

[0061] In combination with the above scenarios, the following is a more detailed description of the engineering supervision data management system provided by this implementation. Figure 2 , which is another module architecture diagram of the engineering supervision data management system in the embodiment of this application.

[0062] It should be noted that Figure 2 Only one edge computing device is shown in the figure. Since the edge computing devices have the same structure, this edge computing device can be used to refer to other edge computing devices. That is, the figure should not limit the number of edge computing devices.

[0063] based on Figure 2 As shown, in some embodiments, each edge computing device includes: an acquisition unit, a health index calculation unit, an offline task processing unit, and an equipment operation control unit, wherein the acquisition unit is used to collect real-time parameter data of the corresponding construction process in real time; the health index calculation unit is used to cache the real-time parameter data and calculate the project health index through a preset formula; the offline task processing unit is used to parse the offline task instructions to obtain an offline task list, task execution priority, resource allocation strategy, task execution time window and equipment control instructions, and send the task execution time window and equipment control instructions to the construction equipment in the corresponding construction process, and execute the local computing task in the offline task list according to the task execution priority and resource allocation strategy when it is determined that the communication signal strength is lower than the preset strength threshold; the equipment operation control unit is used to trigger the construction equipment in the corresponding construction process to perform construction operations according to the equipment control instructions within the task execution time window, and control the construction equipment in the corresponding construction process to interrupt the construction operation when it is detected that the project health index is lower than the preset safety threshold.

[0064] Among them, the acquisition unit is a data acquisition device with multiple sensor interfaces, the real-time parameter data includes equipment operating parameters and environmental monitoring data, the health index calculation unit is a processing module that performs health status assessment, the offline task processing unit is a control module that manages local task execution, the equipment operation control unit is an execution module that executes equipment control instructions, the task execution time window is the time range allowed for task execution, the preset strength threshold is the minimum signal strength value to ensure communication quality, and the preset safety threshold is the critical value to ensure safe operation of the equipment.

[0065] Specifically, the acquisition unit adopts a modular design, including an analog acquisition module (16-bit AD conversion), a digital acquisition module, a sensor interface module (supports RS485 / 232 / CAN) and a data preprocessing module. The health index calculation unit uses an ARM processor, equipped with a real-time operating system, equipped with 256MB cache space, and supports floating-point operation acceleration. The offline task processing unit adopts a dual-core processor architecture, with one core focusing on task parsing and scheduling, and the other core responsible for resource monitoring and task execution. The equipment operation control unit adopts a real-time controller with multiple digital and analog output interfaces, supports PID control algorithm, and has a response time of less than 10ms. The units are interconnected through a high-speed bus, and a redundant design is used to improve reliability. The system is also equipped with a local solid-state hard drive that can store 7 days of operating data and supports power-off protection and data recovery.

[0066] In practical applications, the system may face issues such as inaccurate data collection, computational delays, and task conflicts. To address these issues, the data acquisition unit implements automatic calibration and data validity verification; the health index calculation unit supports load adaptation and dynamically adjusts the sampling frequency; the offline task processing unit implements dynamic task priority adjustment and resource contention arbitration; and the device operation control unit incorporates a fail-safe mechanism to ensure safe device shutdown in the event of an abnormality. The system supports remote maintenance and firmware upgrades, and functional parameters can be flexibly configured according to site needs.

[0067] based on Figure 2 As shown, in some embodiments, the offline task processing unit also includes a task feedback subunit and a communication coordination subunit, wherein the task feedback subunit is used to obtain the execution results of the construction operation from the construction equipment in the corresponding construction process; the communication coordination subunit is used to monitor the communication signal strength in real time, and encrypt and cache the execution results when the communication signal strength is continuously lower than the preset strength threshold during the first current monitoring period, and synchronously send the execution results to the blockchain distributed storage module and the dynamic task management module when the communication signal strength is continuously higher than the preset strength threshold within the first preset time period during the first subsequent monitoring period.

[0068] Among them, the task feedback subunit is a functional module responsible for collecting and processing the task execution status, the communication coordination subunit is a functional module for managing the communication status and data transmission, the execution result is a data packet containing information such as task completion status, resource consumption, and execution time, and the first current monitoring period and the first subsequent monitoring period are the communication status evaluation time intervals defined by the system.

[0069] Specifically, the task feedback subunit adopts an event-driven architecture and consists of three components: a data acquisition interface, a state analyzer, and a result packager. The data acquisition interface supports multiple protocols (such as Modbus and OPC UA) and can obtain device execution status in real time. The state analyzer evaluates task execution progress and quality in real time. The result packager encapsulates execution data into a standard format. The communication coordination subunit adopts a state machine design and includes a signal strength detector, a data cache manager, and a transmission controller. The system uses a sliding window algorithm to assess signal quality, supports data compression and breakpoint resuming, and employs the AES-256 encryption algorithm to protect cached data.

[0070] During operation, problems such as failure to obtain task status and communication status misjudgment may occur. The system improves reliability by implementing multiple retry mechanisms, signal strength smoothing, and data integrity verification. In the event of prolonged communication interruptions, the system automatically expands local storage space to ensure that important data is not lost.

[0071] In some embodiments, the preset formula is: Among them, the EHI is the engineering health index, the W e q is the equipment health sub-index weight factor of the construction equipment in the construction process corresponding to each edge computing device. is the equipment vibration weight factor of the construction equipment. The equipment vibration nonlinear adjustment coefficient of the construction equipment, the Vibration_Equipment is the equipment vibration parameter of the construction equipment, the W ie q is the equipment inclination weight factor of the construction equipment, Inclination_Equipment is the equipment inclination parameter of the construction equipment, Max_Inclination_Equipment is the maximum inclination threshold of the construction equipment, and W g s is the ground and structure health sub-index weight factor during the construction process corresponding to each edge computing device. The ground vibration weight factor corresponding to each edge computing device during the construction process is The nonlinear adjustment coefficient of the ground vibration during the construction process corresponding to each edge computing device, the Vibration_Ground is the ground vibration parameter during the construction process corresponding to each edge computing device, The Inclination_Structure parameter is the structural inclination parameter of the building structure, and the Max_Inclination_Structure parameter is the maximum structural inclination threshold of the building structure.

[0072] This formula constructs a mathematical model for comprehensively evaluating the health status of the project, and obtains the final health index by weighting the key parameters of the two dimensions of construction equipment and ground structure. In the formula, the health status assessment of each construction equipment includes the total equipment weight factor W e q, this factor reflects the importance of the equipment in the overall project. In the equipment status assessment, the vibration characteristics of the equipment are first considered, and the equipment vibration weight factor W is used to calculate the equipment vibration weight. e q is used to measure the importance of vibration impact, and the vibration nonlinear adjustment coefficient k is introduced ve q is used to adjust the response characteristics of the vibration parameter Vibration_Equipment. ve q×Vibration_Equipment 2 ) maps the vibration parameters to the standard range. This nonlinear mapping can better simulate the characteristics of the impact of vibration on equipment performance in actual engineering. At the same time, the tilt state of the equipment is calculated by the equipment tilt angle weight factor W ie The weighted value q is used to normalize the ratio of the actually measured equipment inclination parameter Inclination_Equipment to the maximum inclination value Max_Inclination_Equipment allowed by the equipment, which can intuitively reflect the degree of equipment tilt risk.

[0073] In the ground structure health status assessment part, the model introduces the ground structure health weight factor W g s, is used to balance the importance of ground and structural conditions in the overall assessment. Ground vibration is weighted by the ground vibration weighting factor W vg s is weighted and the ground vibration nonlinear adjustment coefficient k is used vg s is used to adjust the influence of ground vibration parameter Vibration_Ground. Similar to equipment vibration assessment, exponential function is also used for nonlinear mapping. For structural inclination assessment, the structural inclination weight factor W is used to calculate the influence of ground vibration parameter Vibration_Ground. igs weights the structural inclination parameter Inclination_Structure and performs a ratio operation with the maximum inclination value Max_Inclination_Structure allowed by the structure to achieve a standardized assessment of the degree of structural inclination.

[0074] In practical applications, for example, a tower crane operating status assessment at a high-rise construction site uses the following: when monitoring crane vibration parameters of 0.5g, a tilt angle of 2 degrees (maximum allowable 5 degrees), ground vibration of 0.3g, and a structural tilt angle of 1 degree (maximum allowable 3 degrees), the weighting factors are set to 0.5 and the nonlinear adjustment coefficient is set to 2.0. After calculation, the contribution value of the equipment vibration assessment item is approximately 0.195, indicating that the equipment vibration has reached a level that requires attention; the contribution value of the equipment tilt assessment item is 0.1, indicating that the tilt is within a controllable range; the contribution value of the ground vibration assessment item is approximately 0.127, indicating that the impact of ground vibration is relatively small; and the contribution value of the structural tilt assessment item is approximately 0.083, indicating that the structural tilt is acceptable. The final Engineering Health Index (EHI) is 0.505, which comprehensively reflects that the project is in a moderate health state.

[0075] The advantages of this mathematical model include: It uses exponential functions to achieve nonlinear mapping of vibration parameters, which is more consistent with engineering practice; normalization allows for unified calculation of parameters of different physical dimensions; the introduction of multiple weighting factors makes the model adaptable and adjustable; and the final output health index is standardized to a uniform range, facilitating status assessment and decision-making for project managers. The model can optimize assessment results by adjusting weighting factors and nonlinear adjustment coefficients to suit different project characteristics and safety requirements.

[0076] based on Figure 2 As shown, in some embodiments, the dynamic task management module includes: a task processing unit and a communication coordination unit, wherein the task processing unit is used to process real-time parameter data and anomaly detection results to obtain offline task instructions; the communication coordination unit is used to encapsulate and process the offline task list, task execution priority, resource allocation strategy, task execution time window and device control instructions to obtain offline task instructions, and monitor the communication signal strength in real time, and send the offline task instruction to each edge computing device when the communication signal strength is continuously lower than the preset strength threshold during the second current monitoring period, and send the offline task instruction to the blockchain distributed storage module when the communication signal strength is continuously higher than the preset strength threshold within the second preset time period during the second subsequent monitoring period.

[0077] Among them, the task processing unit is the core processing module responsible for task generation and management, the communication coordination unit is the functional module for managing communication status and data transmission, the offline task instruction is a standardized task execution instruction set, the task execution priority is a hierarchical division that defines the order of task execution, the resource allocation strategy is an allocation scheme that optimizes the use of computing resources, the task execution time window is the time range within which the system allows task execution, and the second current monitoring period and the second subsequent monitoring period are the communication status evaluation time intervals defined by the system.

[0078] Specifically, the task processing unit adopts a microservice architecture, which includes four core components: a data analysis engine, a task generator, a priority manager, and a resource scheduler. The data analysis engine uses a deep learning algorithm to process real-time data and supports multi-dimensional feature extraction; the task generator automatically generates tasks based on a preset rule base and machine learning model, and supports task template customization; the priority manager implements dynamic calculation of task priorities based on multiple factors, taking into account factors such as task urgency, resource consumption, and dependencies; the resource scheduler uses an improved load balancing algorithm to support dynamic migration of tasks and load distribution. The communication coordination unit adopts an asynchronous communication architecture and is equipped with a dedicated network processor to implement multi-channel parallel transmission and support multiple protocols such as TCP / UDP / MQTT. The system also implements data compression and encrypted transmission, uses SHA-256 for data integrity verification, and adopts a breakpoint resume mechanism to ensure data transmission reliability.

[0079] In actual operation, the system may encounter problems such as task backlogs, uneven resource allocation, and fluctuating communication quality. To address these issues, the task processing unit implements adaptive task priority adjustment, automatically lowering the priority of non-critical tasks under high load. The resource scheduler supports elastic scaling and dynamically adjusts computing resource allocation. The communication coordination unit implements network quality prediction and adaptive routing, dynamically adjusting transmission strategies based on network conditions. The system also provides a comprehensive fault recovery mechanism, supporting task retry and rollback, to ensure reliable operation under various abnormal circumstances.

[0080] In some embodiments, the task processing unit obtains an offline task list, task execution priority, resource allocation strategy, task execution time window and equipment control instructions based on the real-time parameter data and the anomaly detection results in the following manner: marking the real-time parameter data that has not been fully uploaded to the blockchain distributed storage module in each edge computing device, and generating a data retransmission task, wherein the offline task list includes the data retransmission task; generating an equipment maintenance task based on the equipment anomaly type in the anomaly detection result and the association with the abnormal construction equipment in the corresponding construction process, wherein the offline task list includes the equipment maintenance task; generating a routine inspection task that matches the current construction stage based on the construction progress in the real-time parameter data, wherein the offline task list includes the routine inspection task; assigning a unique task ID to the equipment maintenance task, the data retransmission task and the routine inspection task respectively, and binding all unique task IDs to the unique device ID of the associated construction equipment to generate a task and equipment A mapping relationship table, wherein the associated construction equipment includes abnormal construction equipment, and the offline task list includes a task and equipment mapping relationship table; according to the equipment abnormality level in the abnormality detection result, the equipment maintenance task is divided into a first-level execution priority, the data retransmission task is divided into a second-level execution priority, and the routine inspection task is divided into a third-level execution priority, wherein the task execution priority includes a first-level execution priority, a second-level execution priority, and a third-level execution priority, the first-level execution priority is higher than the second-level execution priority, and the second-level execution priority is higher than the third-level execution priority; the resource allocation strategy of the equipment maintenance task, the data retransmission task, and the routine inspection task is determined according to the task execution priority; according to the construction progress, a time interval is generated to allow the construction equipment in the corresponding construction process to perform construction operations when the communication signal strength is lower than the preset strength threshold, as a task execution time window; and the equipment control instruction is generated according to the equipment abnormality type, the equipment abnormality level and the offline task list.

[0081] The task processing unit is the core functional module in the system responsible for task generation and management. Real-time parameter data is the raw monitoring data collected from edge devices. Anomaly detection results are the system's abnormality judgment information on the equipment status. The offline task list is a collection of tasks to be executed. Task execution priority is a hierarchical division that defines the order in which tasks are executed. The resource allocation strategy is a scheduling plan for system resources. The task execution time window is the time range within which tasks are allowed to be executed. Equipment control instructions are operating commands sent to construction equipment. Data retransmission tasks are tasks that process incomplete uploaded data. Equipment maintenance tasks are maintenance tasks that handle equipment anomalies. Routine inspection tasks are routine inspection tasks performed according to plan. The unique task ID is a task identifier assigned by the system. The unique device ID is a unique identifier for the device. The equipment anomaly level is a classification of the severity of the anomaly (levels 1-3).

[0082] Specifically, this step is executed after the system receives real-time parameter data and anomaly detection results. First, the system scans the data upload status of each edge device, compares the local data with the uploaded data, marks any data that has not been uploaded, and generates a re-upload task. The system sets a data volume threshold for each re-upload task (typically 100MB). Tasks exceeding the threshold are automatically split. The system then analyzes the anomaly detection results and generates maintenance tasks based on a pre-defined library of anomaly types (including mechanical, electrical, and communication failures). Each maintenance task includes detailed information such as the fault description, required tools, and estimated repair time. Next, the system matches the current construction progress (based on a Gantt chart) to the pre-defined inspection plan and generates routine inspection tasks. The system maintains a globally unique ID generator (using a snowflake algorithm) to assign unique identifiers to all tasks and devices and constructs a many-to-many mapping table. Tasks are prioritized based on the device anomaly level (Level 1: Urgent, Level 2: Important, and Level 3: Normal) and resource requirements are calculated. Finally, the system generates a task execution window and detailed device control instructions based on the construction progress and communication status.

[0083] During actual execution, the system may encounter issues such as incomplete data, task conflicts, and insufficient resources. To address data incompleteness, the system implements data integrity verification and breakpoint resuming mechanisms. Task conflicts are resolved through task dependency analysis and dynamic priority adjustment. To address insufficient resources, the system supports task splitting and dynamic resource scheduling. Furthermore, the system implements task execution monitoring and exception recovery mechanisms to ensure reliable task execution.

[0084] In some embodiments, the task processing unit determines the resource allocation strategy for equipment maintenance tasks, data retransmission tasks, and routine inspection tasks according to the task execution priority in the following manner. Figure 3 , which is a flow chart of a task processing unit determining a resource allocation strategy in an embodiment of the present application.

[0085] S301. Obtain the performance status data of each edge computing device in real time.

[0086] Performance status data refers to a collection of operating status indicators for edge computing devices, including real-time monitoring data such as CPU utilization, memory utilization, storage space utilization, and network bandwidth utilization. Real-time acquisition means the system continuously collects data according to a preset sampling period. Edge computing devices are smart terminal devices with local computing capabilities deployed at construction sites.

[0087] Specifically, this step is executed continuously after the system is started and is used to monitor the operating status of edge devices in real time. The system collects device status data through the performance monitoring agent, including detailed indicators such as processor load (user state, system state, IO wait time ratio), memory usage (used memory, available memory, cache size, swap area usage), storage status (disk read and write rate, IOPS, remaining space), network status (packet receiving and sending rate, bandwidth utilization, network latency). The sampling period can be dynamically adjusted according to the importance of the device. The sampling interval for key devices can reach 100ms, and the sampling interval for ordinary devices is 1s.

[0088] In some embodiments, real-time acquisition of performance status data can be achieved through a variety of methods: Optionally, the system can deploy a lightweight monitoring agent on the edge device. The agent obtains the underlying performance data through the performance counter interface provided by the operating system. After local aggregation processing, the agent transmits the data to the management module using a custom binary protocol, while also achieving local caching and breakpoint resuming of the data. Optionally, the system can adopt a distributed monitoring architecture based on the SNMP protocol, define monitoring indicators by configuring the MIB library, achieve standardized monitoring of device performance, and support threshold alarms and trend analysis. It is understandable that other methods can also be used to achieve real-time monitoring of device performance status, which are not limited here.

[0089] S302. Determine the load index of each edge computing device based on the performance status data.

[0090] Among them, the load index refers to a comprehensive evaluation indicator that reflects the overall load level of the equipment, the performance status data represents the real-time value of each performance indicator of the equipment, and the determination process represents the calculation process of converting multi-dimensional performance indicators into a single evaluation value.

[0091] Specifically, this step is executed immediately after obtaining the performance status data and is used to evaluate the actual load level of the device. The system first standardizes various performance indicators to eliminate dimensional effects. Then, based on the preset weight coefficients, indicators such as CPU utilization (weight 0.4), memory utilization (weight 0.3), storage I / O load (weight 0.2), and network load (weight 0.1) are weighted and calculated. At the same time, considering the historical load trend of the device, an exponential smoothing algorithm is used to smooth instantaneous fluctuations. The system will also normalize the final load index based on the differences in the hardware configuration of the device, so that the load indexes of devices with different performance are comparable.

[0092] In some embodiments, the load index can be calculated using a variety of methods: Alternatively, the system can employ a fuzzy logic-based load assessment method, mapping each performance indicator to a fuzzy set and calculating a composite load index using fuzzy inference rules. This method can better handle the uncertainty of performance indicators. Alternatively, the system can employ machine learning methods to train a load prediction model based on historical operating data. This model can simultaneously consider current status and historical trends, providing a more accurate load assessment. It is understood that other methods can also be employed to calculate and assess the load index, and these are not intended to be limiting herein.

[0093] S303. Mark the edge computing device whose load index is higher than the first preset load index as a performance overload device, and mark the edge computing device whose load index is lower than the second preset load index as a performance idle device.

[0094] Among them, the first preset load index refers to the overload warning threshold defined by the system, the second preset load index represents the judgment threshold of low load of the equipment, the performance overload device refers to the equipment whose current load exceeds the normal operating range, the performance idle device refers to the equipment with low resource utilization, and the marking process refers to the operation of recording the equipment status information in the system status table.

[0095] Specifically, this step is performed after the load index is calculated, and is used to identify the uneven distribution of resources in the system. The system first reads the preset load threshold parameters in the configuration library, where the first preset load index is usually set to 0.85, indicating that the device is in a high-load state; the second preset load index is usually set to 0.3, indicating that the device is in a resource-idle state. The system compares the current load index with these two thresholds and updates the device tag in the status database. At the same time, the system records the timestamp and duration of the device entering the overload or idle state for subsequent load balancing decisions. For devices that are continuously in an overloaded state, the system will increase its load alarm level.

[0096] In some embodiments, device status marking can be implemented in a variety of ways: Optionally, the system employs a dynamic threshold adjustment mechanism to adaptively adjust the overload and idle thresholds based on historical load distribution. By calculating the statistical characteristics of the load over the past 24 hours and combining the device type and importance, a personalized load threshold is dynamically determined. Optionally, the system employs a multi-level threshold mechanism to set multiple intermediate states between overload and idle, enabling more fine-grained load classification. It is understood that other methods can also be used to implement dynamic marking of device status, which are not limited here.

[0097] S304: Determine the first complexity of the equipment maintenance task based on the equipment anomaly type and the number of idle devices. Determine the second complexity of the data retransmission task based on the amount of retransmission data and the preset communication bandwidth limit. Determine the third complexity of the routine inspection task based on the construction progress and the number of inspection items in the routine inspection task.

[0098] Among them, the equipment abnormality type refers to the classification of equipment failure or abnormal status detected by the system, the first complexity represents the difficulty assessment value of the equipment maintenance task, the amount of retransmitted data refers to the size of historical data that needs to be retransmitted, the second complexity represents the resource consumption assessment value of the data retransmission task, the number of inspection items refers to the number of inspection contents that need to be completed in the routine inspection task, and the third complexity represents the workload assessment value of the routine inspection task.

[0099] Specifically, this step is performed after obtaining the task information and is used to evaluate the complexity of different types of tasks. For equipment maintenance tasks, the system calculates the first complexity based on factors such as the severity of the abnormality type (level 1-5), the repair difficulty coefficient (0.5-2.0), and the required professional skills requirements (level 1-3). For data retransmission tasks, the system calculates the second complexity by considering factors such as the total amount of data, transmission bandwidth limit (usually 60% of the peak bandwidth), and data priority. For routine inspection tasks, the system calculates the third complexity based on factors such as the number of inspection items, the standard execution time of each item, and mutual dependencies. The system will also consider the time urgency of the task and dynamically adjust the complexity.

[0100] In some embodiments, task complexity assessment can be achieved through a variety of methods: Optionally, the system employs statistical learning methods based on historical data, analyzing historical execution records of similar tasks to establish a mapping relationship between task characteristics and complexity, thereby achieving automatic complexity assessment and prediction. Optionally, the system employs expert system methods, establishing a rule base and knowledge base, and integrating the experience of experts in multiple fields to achieve intelligent judgment of task complexity. It is understood that other methods can also be used to achieve scientific assessment of task complexity, which are not limited here.

[0101] S305. Determine the first computing resources required for the equipment maintenance task, the second computing resources required for the data retransmission task, and the third computing resources required for the routine inspection task based on the first complexity, the second complexity, and the third complexity.

[0102] Among them, the first computing resource refers to the total amount of computing resources such as CPU, memory, storage, etc. required to complete the equipment maintenance task; the second computing resource refers to the network bandwidth and storage resources required to perform the data retransmission task; the third computing resource refers to the system resource quota required to complete the routine inspection task; the resource determination process refers to the process of calculating the required resource amount based on the complexity of the task.

[0103] Specifically, this step is performed after obtaining the task complexity assessment, and is used to accurately calculate the resource requirements of various tasks. The system adopts a multi-dimensional resource evaluation model. For the first computing resource, based on the complexity coefficient of the equipment maintenance task (usually 0.5-2.0), combined with the benchmark resource configuration (number of CPU cores × complexity coefficient, memory capacity × complexity coefficient), the specific resource requirements are calculated. For the second computing resource, the system adopts an adaptive bandwidth allocation algorithm based on the data volume and network conditions of the data retransmission task, and dynamically calculates the allocatable network resources and storage space while ensuring the normal operation of basic services. For the third computing resource, the system calculates the overall resource requirements based on the number of items and standard resource consumption quotas of routine inspection tasks, combined with the concurrent execution factor (1.2-1.5). At the same time, the system will consider the resource utilization efficiency factor (usually 0.8-0.9) and reserve a certain amount of resource redundancy to cope with load fluctuations.

[0104] In some embodiments, resource requirements can be calculated using a variety of methods: Alternatively, the system can employ machine learning methods to analyze historical task execution data and establish a regression model that correlates task characteristics with resource consumption. This model dynamically predicts resource requirements by factoring in multiple characteristics, such as task type, execution environment, and time constraints. Alternatively, the system can employ a resource estimation method based on queue theory to establish a queuing model for task execution and, in conjunction with quality of service requirements, calculate the minimum resource configuration required to meet performance objectives. It is understood that other methods can also be employed to accurately calculate resource requirements, and these are not intended to be limiting herein.

[0105] S306: Perform task dependency analysis on the equipment maintenance task, data retransmission task, and routine inspection task to determine task dependency relationships.

[0106] Among them, task dependency analysis refers to the process of determining the execution order and association relationship between tasks. Task dependency represents the precedence, post-order or parallel execution relationship between multiple tasks. Task execution priority refers to the priority ranking of tasks when competing for resources.

[0107] Specifically, this step is performed after determining the resource requirements, and is used to establish a dependency graph for task execution. The system first constructs a task dependency matrix, and the matrix element values ​​represent the dependency strength between tasks (between 0 and 1). For equipment maintenance tasks, the system analyzes the physical connection relationship and functional dependency relationship of the equipment to determine the maintenance order. For data retransmission tasks, the system establishes a priority sequence for data retransmission based on the timing relationship and business relevance of the data. For routine inspection tasks, the system constructs a dependency tree for the inspection process based on the logical relationship and importance of the inspection items. The system uses an improved critical path algorithm to identify key nodes and bottleneck links in the task chain, and calculates the earliest start time and latest completion time of each task. At the same time, the system will take into account resource competition and improve overall execution efficiency through task decomposition and parallel optimization.

[0108] In some embodiments, task dependency analysis can be implemented in a variety of ways: Optionally, the system employs a task scheduling algorithm based on graph theory, representing task dependencies as a directed acyclic graph. By traversing and topologically sorting the graph, the system determines the optimal execution order of tasks and identifies sets of tasks that can be executed in parallel. Optionally, the system employs an intelligent planning approach, establishing a state-space model of task execution and combining it with a heuristic search algorithm to find the optimal execution solution that satisfies dependency constraints. It is understood that other approaches to task dependency analysis can also be employed, and these are not limited here.

[0109] S307: Adjust the task execution priority to the target task execution priority according to the task dependency relationship to obtain a resource allocation strategy.

[0110] Among them, the target task execution priority refers to the final task priority value after dependency analysis and adjustment. The resource allocation strategy represents the specific plan for the system to allocate computing resources to each task. Task execution priority adjustment refers to the process of optimizing the initial priority based on the dependency relationship. The priority value range is usually 1-100, and the larger the value, the higher the priority.

[0111] Specifically, this step is performed after completing task dependency analysis and is used to optimize the task execution order and resource allocation plan. The system first calculates a basic priority score based on task urgency (weight 0.4), business importance (weight 0.3), resource requirements (weight 0.2), and execution complexity (weight 0.1). It then adjusts priorities based on task dependencies: for tasks on the critical path, their priorities are increased (increased by 10-20 points); for nodes with multiple dependent tasks, the priorities are dynamically adjusted based on the completion status of the dependent tasks; for tasks that can be executed in parallel, the system balances priorities to improve resource utilization efficiency. The system also considers factors such as task time window constraints (whether there is a latest completion time requirement), resource contention (current system load), and historical execution data (average execution time and success rate of similar tasks). Based on the adjusted priorities, the system generates a detailed resource allocation strategy, including computing resource quotas, execution time windows, load balancing strategies, and more.

[0112] In some embodiments, priority adjustment and resource allocation can be achieved in a variety of ways: Optionally, the system adopts an adaptive priority adjustment algorithm to dynamically adjust task priorities by monitoring the task execution status and system resource usage in real time. The algorithm first establishes a task feature vector, which includes dimensions such as time urgency, resource requirements, and dependent task completion, and then uses a weighted scoring model to calculate the adjustment coefficient. Finally, the priority is fine-tuned in real time according to the current state of the system; Optionally, the system adopts a resource allocation method based on reinforcement learning to learn the optimal resource allocation strategy by establishing a reward model for task scheduling. This method models the task scheduling problem as a Markov decision process, and optimizes the scheduling strategy through continuous interaction with the environment to maximize resource utilization and task completion efficiency. It is understandable that other methods can also be used to achieve dynamic adjustment of task priorities and intelligent allocation of resources, which are not limited here.

[0113] In some embodiments, the task processing unit adjusts the task execution priority to the target task execution priority according to the task dependency in the following manner: when it is determined according to the dependency relationship that the equipment maintenance task is dependent on the data retransmission task, the data retransmission task is marked as the first predecessor task of the equipment maintenance task; when it is determined according to the dependency relationship that the routine inspection task is dependent on the equipment maintenance task, the equipment maintenance task is marked as the second predecessor task of the routine inspection task; when it is determined that the data retransmission task is the first predecessor task of the equipment maintenance task, the secondary execution priority of the data retransmission task is adjusted to the primary execution priority, and the execution order of the data retransmission task is set to take precedence over the equipment maintenance task; when it is determined that the equipment maintenance task is the second predecessor task of the routine inspection task, the third-level execution priority of the routine inspection task is adjusted to the second-level execution priority.

[0114] Dependencies are constraints on the execution order between tasks. A first predecessor task is a task that must be completed before the main task. A second predecessor task is a task that must be completed before the routine inspection task. The target task's execution priority is the final priority adjusted after dependency analysis. The execution order is the order in which tasks are actually executed. Task dependencies are data structures that describe the relationships between tasks.

[0115] Specifically, this step is performed after the initial task priority division is completed. The system first constructs a task dependency graph and uses a directed acyclic graph (DAG) structure to store the dependencies between tasks. For equipment maintenance tasks, the system analyzes the data support required. If it finds that the associated data has not been fully uploaded, it automatically establishes a dependency with the data retransmission task. The system uses an adjacency matrix to store dependencies, and the matrix element values ​​represent the dependency strength (0-1). When it is determined that there is a dependency, the system automatically adjusts the task priority: the data retransmission task as the predecessor task is promoted to the first level priority and placed before the equipment maintenance task that depends on it in the task scheduling queue; similarly, for routine inspection tasks that depend on equipment maintenance, the system raises its priority from level three to level two to ensure that the task execution order meets the dependency constraints. The system also maintains a priority adjustment log to record the reasons and results of each adjustment.

[0116] During actual execution, problems such as circular dependencies and priority conflicts may arise. The system addresses these issues through the following mechanisms: For circular dependencies, the system implements a loop detection algorithm that immediately issues an alarm and requires manual intervention upon detection. For priority conflicts, the system employs a multi-level priority queue, resolving conflicts by subdividing priorities within the same level. Furthermore, the system implements a dynamic dependency update mechanism that adjusts dependencies in real time based on task execution status.

[0117] S308. Based on the resource allocation strategy, the equipment maintenance task, the data retransmission task, and the routine inspection task are allocated to the performance-idle equipment according to the first computing resource, the second computing resource, the third computing resource, and the target task execution priority.

[0118] Among them, the resource allocation strategy represents the specific plan and rules for the system to schedule tasks and allocate resources; the first computing resource refers to the high-performance computing resources required to process equipment maintenance tasks, including key indicators such as CPU occupancy and memory capacity; the second computing resource is used to represent the data transmission and storage resources required for data retransmission tasks, mainly involving I / O performance and network bandwidth; the third computing resource represents the basic computing resources required for routine inspection tasks; the target task execution priority refers to the final task priority sequence after dependency analysis and adjustment; performance idle devices refer to edge computing devices with low current load and allocable resources; equipment maintenance tasks refer to high-priority tasks that require fault diagnosis and repair; data retransmission tasks are used to represent medium-priority tasks for processing data transmission and storage; routine inspection tasks refer to regular inspection tasks executed as planned.

[0119] This step is performed after determining the task priority and completing the resource demand analysis. Specifically, the system first collects the resource status information of all edge devices in real time through the resource monitoring module, including multi-dimensional indicators such as CPU usage, memory usage, disk I / O, and network bandwidth. Then, the system sorts the resource requirements according to the execution priority of the target task and builds a resource allocation queue. For equipment maintenance tasks, the system gives priority to allocating high-performance equipment that meets the first computing resource requirements to ensure that the task has sufficient computing power; for data retransmission tasks, the system analyzes the size of the data. When the resource requirements of a single task exceed the processing capacity of a single device, task slicing technology is used to split it into multiple subtasks suitable for parallel processing; for routine inspection tasks, the system ensures the resource requirements of high-priority tasks and reasonably utilizes the remaining computing resources. During the entire allocation process, the system continuously monitors resource usage, dynamically adjusts the resource allocation strategy through the load balancing algorithm, and maintains a detailed resource allocation log.

[0120] In some embodiments, dynamic resource allocation and task scheduling can be achieved in a variety of ways: Optionally, the system can adopt a weight-based resource allocation method, first calculate the resource weight coefficient of each task, then build a resource pool based on the device resource status, then use a multi-objective optimization algorithm to solve the optimal allocation plan, and finally execute resource allocation and start the task; Optionally, the system can implement a scheduling strategy based on a priority queue, first establish a multi-level task waiting queue, then use a real-time scheduling algorithm to dynamically allocate computing resources, then adjust the task execution order through a feedback control mechanism, and finally achieve parallel processing and load balancing of tasks. It is understandable that other resource scheduling and task allocation methods can also be used to achieve efficient operation of the system, which is not limited here.

[0121] In some embodiments, the task processing unit allocates equipment maintenance tasks, data retransmission tasks, and routine inspection tasks to performance idle devices according to the first computing resources, the second computing resources, the third computing resources, and the target task execution priority in the following manner: traverse all performance idle devices to obtain all remaining computing resources of all performance idle devices; sort the first computing resources, the second computing resources, and the third computing resources according to the target task execution priority to generate a resource to-be-allocated queue; match the resource to-be-allocated queue with all remaining computing resources to obtain a resource matching result; when it is determined according to the resource matching result that the first remaining computing resource of the first performance idle device matches the first computing resource, allocate the equipment maintenance task to the first performance idle device, wherein all performance idle devices include the first performance idle device, and the all remaining computing resources include the first remaining computing resource; when it is determined according to the resource matching result that the second performance idle device exists When the second remaining computing resources of the device match the second computing resources, the data supplementary transmission task is assigned to the second performance idle device, wherein all performance idle devices include the second performance idle device, and the all remaining computing resources include the second remaining computing resources; or, when it is determined according to the resource matching result that the second computing resources exceed the third remaining computing resources of a single performance idle device, the data supplementary transmission task is split into multiple supplementary transmission sub-tasks, and the multiple supplementary transmission sub-tasks are assigned to multiple third performance idle devices, wherein all performance idle devices include multiple third performance idle devices, and the all remaining computing resources include the third remaining computing resources; when it is determined according to the resource matching result that the fourth remaining computing resources of the fourth performance idle device match the third computing resources, the routine inspection task is assigned to the fourth performance idle device, wherein all performance idle devices include the fourth performance idle device, and the all remaining computing resources include the fourth remaining computing resources.

[0122] Idle devices are edge computing devices with low current loads. Remaining computing resources are the currently available computing power of the devices. The resource pending allocation queue is an ordered list of pending resources. The resource matching result is the resource allocation plan. Supplementary data transmission subtasks are small tasks resulting from splitting large supplementary data transmission tasks. The first, second, third, and fourth idle devices are different idle computing nodes. The first, second, third, and fourth remaining computing resources are the available resources of the corresponding devices.

[0123] Specifically, this step is performed after determining the execution priority of the target task. The system first obtains the resource status of all idle devices through the resource monitoring module, including indicators such as CPU usage, memory usage, storage space, and network bandwidth. For each idle device, the system calculates its comprehensive resource capability value (obtained through weighted calculation). Then, the system sorts the computing resource requirements to be allocated by priority and builds a resource allocation queue. The system uses an optimal matching algorithm to match resource requirements with device capabilities, giving priority to meeting the resource requirements of high-priority tasks. For data retransmission tasks, the system will judge the scale of the task. When the resource requirements of a single task exceed the capabilities of a single device, the system will automatically split the task into multiple subtasks. The data volume of each subtask is determined according to the processing capacity of the target device. The system also implements a load balancing mechanism to ensure the balance of resource allocation.

[0124] During the resource allocation process, problems such as resource fragmentation, device failures, and task execution failures may occur. The system addresses resource fragmentation through the following methods: To address resource fragmentation, a resource consolidation mechanism is implemented, allowing the combination of multiple small resource pools. To address device failures, the system supports task migration and fault recovery, dynamically migrating tasks to other available devices. To address task failures, the system implements a task retry mechanism and supports resource reallocation for failed tasks. Furthermore, the system maintains a detailed resource allocation log, supporting rollback and adjustment of resource allocations.

[0125] based on Figure 2 As shown, in some embodiments, the blockchain distributed storage module includes: an encryption unit and a distributed storage unit, wherein the encryption unit is used to receive real-time parameter data sent by each edge computing device, the anomaly detection results sent by the anomaly detection and alarm module, and the offline task instructions, resource matching results, task dependencies, target task execution priority and execution results sent by the dynamic task management module when it is determined that the communication signal strength is continuously higher than the preset strength threshold within a second preset time period, and hash encryption processing is performed on the real-time parameter data, anomaly detection results, offline task instructions, resource matching results, task dependencies, target task execution priority and execution results to obtain a hash encrypted data packet; the distributed storage unit is used to split the hash encrypted data packet into multiple hash encrypted data blocks according to a preset sharding method, and distribute the multiple hash encrypted data blocks to multiple nodes in the blockchain network for distributed storage.

[0126] Among them, the encryption unit is a functional module that realizes data encryption and security processing, the distributed storage unit is a functional module that manages distributed data storage, the hash encrypted data packet is a standardized data packet that has been encrypted, the hash encrypted data block is the storage unit after the data packet is fragmented, the preset fragmentation method is the system-defined data fragmentation strategy, and the blockchain network node is the network node that participates in data storage and verification.

[0127] Specifically, the encryption unit adopts a multi-layer encryption architecture, including a data preprocessor, an encryption processor, and an integrity verifier. The data preprocessor supports multiple data format conversions and normalization processing; the encryption processor adopts the national secret algorithm SM2 / SM3 / SM4, combined with asymmetric encryption and homomorphic encryption technology, to support fine-grained access control; the integrity verifier uses the Merkle tree structure to implement data integrity verification. The distributed storage unit adopts a distributed architecture and implements a distributed storage system based on IPFS, including a data shard manager, a node manager, and a storage coordinator. The data shard manager supports configurable sharding strategies and implements data redundancy backup based on Reed-Solomon encoding; the node manager is responsible for node status monitoring and fault detection, and supports dynamic joining and exiting of nodes; the storage coordinator implements data load balancing and dynamic migration, and supports hot data caching and intelligent prefetching. The system adopts an improved PBFT consensus mechanism, supports fast transaction confirmation, and the TPS can reach 1000+.

[0128] In practical applications, the system may face performance bottlenecks, insufficient storage space, node failures, and other issues. To address these issues, the encryption unit implements parallel encryption processing and hardware acceleration, significantly improving encryption efficiency. The distributed storage unit supports dynamic expansion of storage space and implements tiered data storage based on access frequency. The system also provides a comprehensive disaster recovery mechanism, enabling rapid data recovery and verification. Furthermore, fine-grained permission management and audit tracking ensure the security and traceability of data access. In the event of a node failure, the system automatically initiates data migration and recovery processes to ensure service continuity.

[0129] In the embodiments of this application, due to the use of edge computing devices for localized data processing, real-time health index calculation and anomaly detection, combined with blockchain technology for secure data storage, and a dynamic task management module for optimized resource allocation, real-time collection, processing and storage of engineering supervision data are achieved, effectively optimizing the problems of data transmission delay, low processing efficiency and poor data security in traditional supervision systems, thereby achieving intelligent, automated and reliable engineering supervision, significantly improving the level of construction safety management, and providing strong guarantees for engineering quality control. At the same time, the system's distributed architecture and dynamic task management mechanism ensure stable operation in complex construction environments, providing innovative solutions for the supervision and management of large-scale engineering projects.

[0130] The following describes the engineering supervision data management system in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , is a schematic diagram of the physical device structure of the engineering supervision data management system in an embodiment of the present application.

[0131] It should be noted that Figure 4 The structure of the engineering supervision data management system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0132] like Figure 4 As shown, the construction supervision data management system includes a CPU 401, which can perform various appropriate actions and processes based on programs stored in a ROM 402 or programs loaded from a storage unit 408 into a RAM 403, such as executing the construction supervision data management system described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An I / O interface 405 is also connected to the bus 404.

[0133] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a push button switch, and the like; an output section 407 including a liquid crystal display (LCD), an audio output device, an indicator light, and the like; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is installed in the drive 410 as needed, so that a computer program read therefrom can be installed into the storage section 408 as needed.

[0134] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from a removable medium 411. When the computer program is executed by the CPU 401, the various functions defined in the present invention are performed.

[0135] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, program segment, or a portion of code, and the above-mentioned module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings.

[0136] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0137] As used in the above embodiments, the term “when…” may be interpreted to mean “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted to mean “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

Claims

1. An engineering supervision data management system based on the Internet of Things, characterized in that: include: Multiple edge computing devices, each of which is used to collect real-time parameter data during a corresponding construction process and process it to obtain a project health index, and to send an alarm prompt message when it is determined that the project health index does not meet a preset safety condition; An anomaly detection and alarm module, which is in communication with each of the edge computing devices and is used to receive the real-time parameter data and the alarm prompt information sent by each of the edge computing devices and process them to obtain an anomaly detection result; a dynamic task management module, communicatively connected to each of the edge computing devices and the anomaly detection and alarm module, configured to receive the real-time parameter data uploaded by each of the edge computing devices and the anomaly detection results uploaded by the anomaly detection and alarm module, process the data to obtain an offline task instruction, and send the offline task instruction to each of the edge computing devices if it is determined that the communication signal strength does not meet a preset strength condition; The blockchain distributed storage module is communicatively connected to each of the edge computing devices and the anomaly detection and alarm module, and is used to receive the real-time parameter data sent by each of the edge computing devices and the anomaly detection results sent by the anomaly detection and alarm module and to perform encrypted storage.

2. The system according to claim 1, wherein: Each of the edge computing devices includes: a collection unit, a health index calculation unit, an offline task processing unit, and a device operation control unit, wherein: The acquisition unit is used to collect the real-time parameter data during the corresponding construction process in real time; The health index calculation unit is used to cache the real-time parameter data and calculate the project health index through a preset formula; the offline task processing unit is used to parse the offline task instructions to obtain an offline task list, task execution priority, resource allocation strategy, task execution time window and equipment control instructions, and send the task execution time window and the equipment control instructions to the corresponding construction equipment in the construction process, and execute the local computing task in the offline task list according to the task execution priority and the resource allocation strategy when it is determined that the communication signal strength is lower than a preset strength threshold; The equipment operation control unit is used to trigger the corresponding construction equipment in the construction process to perform construction operations according to the equipment control instructions within the task execution time window, and to control the corresponding construction equipment in the construction process to interrupt the construction operation when it is detected that the project health index is lower than a preset safety threshold.

3. The system according to claim 2, characterized in that The offline task processing unit further includes a task feedback subunit and a communication coordination subunit, wherein: The task feedback subunit is used to obtain the execution result of the construction operation from the construction equipment in the corresponding construction process; the communication coordination subunit is used to monitor the communication signal strength in real time, and encrypt and cache the execution result when it is monitored that the communication signal strength is continuously lower than the preset strength threshold during the first current monitoring period, and synchronously send the execution result to the blockchain distributed storage module and the dynamic task management module when it is monitored that the communication signal strength is continuously higher than the preset strength threshold for a first preset time period during the first subsequent monitoring period.

4. The system according to claim 3, characterized in that The preset formula is: Wherein, the EHI is the engineering health index, the W e q is the equipment health sub-index weight factor of the construction equipment in the construction process corresponding to each edge computing device, is the equipment vibration weight factor of the construction equipment, the equipment vibration nonlinear adjustment coefficient of the construction equipment, the Vibration_Equipment is the equipment vibration parameter of the construction equipment, is the equipment inclination weight factor of the construction equipment, Inclination_Equipment is the equipment inclination parameter of the construction equipment, Max_Inclination_Equipment is the maximum inclination threshold of the construction equipment, and W g s is the ground and structure health sub-index weight factor during the construction process corresponding to each edge computing device, The ground vibration weight factor corresponding to each edge computing device during the construction process, The nonlinear adjustment coefficient of the ground vibration during the construction process corresponding to each edge computing device, the Vibration_Ground is the ground vibration parameter during the construction process corresponding to each edge computing device, is a structural inclination weight factor of the building structure during the construction process corresponding to each edge computing device, the Inclination_Structure is a structural inclination parameter of the building structure, and the Max_Inclination_Structure is a structural maximum inclination threshold of the building structure.

5. The system according to claim 2, wherein: The dynamic task management module includes: a task processing unit and a communication coordination unit, wherein: The task processing unit is used to process the real-time parameter data and the abnormality detection result to obtain the offline task instruction; The communication coordination unit is used to encapsulate the offline task list, the task execution priority, the resource allocation strategy, the task execution time window and the device control instruction to obtain the offline task instruction, and monitor the communication signal strength in real time. If the communication signal strength is continuously lower than the preset strength threshold during the second current monitoring period, the offline task instruction is sent to each edge computing device; if the communication signal strength is continuously higher than the preset strength threshold for a second preset time period during the second subsequent monitoring period, the offline task instruction is sent to the blockchain distributed storage module.

6. The system according to claim 5, characterized in that The task processing unit obtains the offline task list, the task execution priority, the resource allocation strategy, the task execution time window, and the device control instruction according to the real-time parameter data and the anomaly detection result in the following manner: Marking the real-time parameter data that has not been completely uploaded to the blockchain distributed storage module in each of the edge computing devices, and generating a data retransmission task, wherein the offline task list includes the data retransmission task; Generate an equipment maintenance task according to the equipment abnormality type in the abnormality detection result and the corresponding abnormal construction equipment in the construction process, wherein the offline task list includes the equipment maintenance task; generating a routine inspection task matching the current construction stage according to the construction progress in the real-time parameter data, wherein the offline task list includes the routine inspection task; Assigning unique task IDs to the equipment maintenance task, the data retransmission task, and the routine inspection task, respectively, and binding all the unique task IDs to the unique device IDs of the associated construction equipment to generate a task-to-device mapping relationship table, wherein the associated construction equipment includes the abnormal construction equipment, and the offline task list includes the task-to-device mapping relationship table; According to the equipment abnormality level in the abnormality detection result, the equipment maintenance task is divided into a first-level execution priority, the data retransmission task is divided into a second-level execution priority, and the routine inspection task is divided into a third-level execution priority, wherein the task execution priority includes the first-level execution priority, the second-level execution priority, and the third-level execution priority, the first-level execution priority is higher than the second-level execution priority, and the second-level execution priority is higher than the third-level execution priority; Determining the resource allocation strategy for the equipment maintenance task, the data retransmission task, and the routine inspection task according to the task execution priority; Generating, according to the construction progress, a time interval allowing the construction equipment in the corresponding construction process to perform the construction operation when the communication signal strength is lower than a preset strength threshold, as the task execution time window; The device control instruction is generated according to the device abnormality type, the device abnormality level and the offline task list.

7. The system according to claim 6, characterized in that The task processing unit determines the resource allocation strategy for the equipment maintenance task, the data retransmission task, and the routine inspection task according to the task execution priority in the following manner: Acquire performance status data of each edge computing device in real time; Determine a load index of each edge computing device according to the performance status data; Marking the edge computing device whose load index is higher than a first preset load index as a performance overload device, and marking the edge computing device whose load index is lower than a second preset load index as a performance idle device; The first complexity of the equipment maintenance task is determined based on the equipment abnormality type and the performance idle equipment; the second complexity of the data retransmission task is determined based on the amount of retransmission data in the data retransmission task and the preset communication bandwidth limit; the third complexity of the routine inspection task is determined based on the construction progress and the number of inspection items in the routine inspection task; Determine, based on the first complexity, the second complexity, and the third complexity, a first computing resource required for the equipment maintenance task, a second computing resource required for the data retransmission task, and a third computing resource required for the routine inspection task; perform task dependency analysis on the equipment maintenance task, the data retransmission task, and the routine inspection task to determine a task dependency relationship; Adjusting the task execution priority to the target task execution priority according to the task dependency to obtain the resource allocation strategy; Based on the resource allocation strategy, the equipment maintenance task, the data retransmission task, and the routine inspection task are allocated to the performance-idle device according to the first computing resource, the second computing resource, the third computing resource, and the target task execution priority.

8. The system according to claim 7, characterized in that The task processing unit adjusts the task execution priority to the target task execution priority according to the task dependency relationship in the following manner: In a case where it is determined according to the dependency relationship that the equipment maintenance task is dependent on the data retransmission task, marking the data retransmission task as a first predecessor task of the equipment maintenance task; In a case where it is determined according to the dependency relationship that the routine inspection task is dependent on the equipment maintenance task, marking the equipment maintenance task as a second predecessor task of the routine inspection task; When it is determined that the data retransmission task is the first predecessor task of the equipment maintenance task, adjusting the second-level execution priority of the data retransmission task to the first-level execution priority, and setting the execution order of the data retransmission task to take precedence over the equipment maintenance task; When it is determined that the equipment maintenance task is the second predecessor task of the routine inspection task, the third-level execution priority of the routine inspection task is adjusted to the second-level execution priority.

9. The system according to claim 8, characterized in that The task processing unit allocates the equipment maintenance task, the data retransmission task, and the routine inspection task to the idle device in the following manner according to the first computing resource, the second computing resource, the third computing resource, and the target task execution priority: Traversing all the idle performance devices to obtain all remaining computing resources of all the idle performance devices; Sort the first computing resource, the second computing resource, and the third computing resource according to the target task execution priority to generate a resource to-be-allocated queue; Matching the resource to-be-allocated queue with all the remaining computing resources to obtain a resource matching result; if it is determined according to the resource matching result that the first remaining computing resources of a first performance idle device match the first computing resource, allocating the equipment maintenance task to the first performance idle device, wherein all the performance idle devices include the first performance idle device, and the all remaining computing resources include the first remaining computing resource; if it is determined according to the resource matching result that the second remaining computing resources of a second performance idle device match the second computing resource, allocating the data supplementary transmission task to the second performance idle device, wherein all the performance idle devices include the second performance idle device, and the all remaining computing resources include the second remaining computing resource; or if it is determined according to the resource matching result that the second computing resource exceeds the third remaining computing resource of a single performance idle device, splitting the data supplementary transmission task into multiple supplementary transmission subtasks, and allocating the multiple supplementary transmission subtasks to multiple third performance idle devices, wherein all the performance idle devices include multiple third performance idle devices, and the all remaining computing resources include the third remaining computing resource; When it is determined according to the resource matching result that the fourth remaining computing resources of the fourth performance idle device match the third computing resources, the routine inspection task is assigned to the fourth performance idle device, wherein all the performance idle devices include the fourth performance idle device, and all the remaining computing resources include the fourth remaining computing resources.

10. The system according to any one of claims 1 to 9, characterized in that: The blockchain distributed storage module includes: an encryption unit and a distributed storage unit, wherein: The encryption unit is used to receive the real-time parameter data sent by each of the edge computing devices, the anomaly detection result sent by the anomaly detection and alarm module, and the offline task instruction, resource matching result, task dependency, target task execution priority and execution result sent by the dynamic task management module when it is determined that the communication signal strength is continuously higher than the preset strength threshold within a second preset time period, and perform hash encryption processing on the real-time parameter data, the anomaly detection result, the offline task instruction, the resource matching result, the task dependency, the target task execution priority and the execution result to obtain a hash encrypted data packet; The distributed storage unit is used to split the hash-encrypted data packet into multiple hash-encrypted data blocks according to a preset fragmentation method, and distribute the multiple hash-encrypted data blocks to multiple nodes in the blockchain network for distributed storage.

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