An edge-based internet-of-things intelligent city planning construction method

Abstract

The application provides a smart city planning construction method based on edge Internet of Things, applied to the technical field of city edge Internet of Things, construction planning data and basic geographic data of a target planning area are acquired, construction demand data in a construction stage is constructed, a first edge Internet of Things node deployed in a construction area is controlled to collect construction equipment and environment state, node description data is generated, construction demand data and node description data are matched and analyzed, a node correlation degree score is calculated, and the node is divided into network roles and configured with authority according to the score, forming a second edge Internet of Things node, in the construction process, control adjustment parameters are generated based on construction state deviation relations, dynamic control and task allocation adjustment are performed on the equipment, the matching of construction demand and node capability is realized, and construction efficiency, resource utilization and operation safety are improved.

Description

Technical Field

[0001] This application relates to the field of urban edge IoT technology, and in particular to a smart city planning and construction method based on edge IoT. Background Technology

[0002] With the continuous advancement of smart city construction, the scale of urban infrastructure projects continues to expand, and the complexity and diversity of construction tasks have increased significantly. At construction sites, a large number of edge IoT nodes are usually deployed to collect information such as the status of construction equipment, construction progress, and environmental constraints, and to assist in construction management through data analysis.

[0003] In existing smart city construction scenarios, edge IoT nodes typically have their functions and permissions determined during the deployment phase, and their network roles and control configurations remain fixed throughout the construction process to complete construction management.

[0004] However, in existing smart city construction scenarios, edge IoT nodes have fixed network roles and control permissions during the deployment phase, making it impossible to adjust them according to the task characteristics and changes in the construction site at different construction stages. This fixed deployment mode leads to the underutilization of node resources. Some nodes may be idle at certain stages, while they are unable to undertake critical control tasks at other stages. Therefore, this mode cannot flexibly match construction needs at different construction stages, resulting in low resource utilization during the construction process. Summary of the Invention

[0005] This application provides a smart city planning and construction method based on edge IoT. Its core lies in: evaluating and adjusting the network roles and control configurations of edge IoT nodes deployed within the target planning area to achieve adaptive reconfiguration of node functions corresponding to the construction phase. This allows node resources to be flexibly allocated and efficiently utilized according to the construction needs of different phases during construction. The method generates construction demand data corresponding to each construction phase by parsing construction planning data and basic geographic data, and then performs correlation analysis with the node description data of the edge IoT nodes. It calculates the correlation score of the nodes in the corresponding construction phase, determines the node network role type based on the correlation score, and adjusts the control configuration of the nodes to form different types of edge IoT nodes, thereby achieving control of construction equipment and task allocation.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] This application provides a smart city planning and construction method based on edge IoT, which may include:

[0008] Obtain the planning data set corresponding to the target planning area, perform semantic parsing on the planning data set, and construct the construction demand data corresponding to the construction stage. Multiple first edge IoT nodes are deployed inside the target planning area.

[0009] During construction, the first edge IoT node is controlled to acquire the status data of the corresponding construction equipment, and node description data of the first edge IoT node is formed based on the status data.

[0010] The construction demand data is correlated with the node description data of the first edge IoT node to obtain the correlation score of the first edge IoT node in the corresponding construction stage.

[0011] Based on the correlation score, the network role type of the first edge IoT node in the corresponding construction stage is determined. Based on the network role type, the control configuration of the first edge IoT node is adjusted to form the second edge IoT node.

[0012] Control the second edge IoT node to collect construction status data, and calculate the deviation relationship between the construction status data and the construction demand data;

[0013] Based on the deviation relationship, corresponding construction control adjustment parameters are generated, and based on the construction control adjustment parameters, the second edge IoT node is controlled to control the construction equipment and allocate tasks.

[0014] In some possible implementations, the network role type includes a high-association role type and a low-association role type. Determining the network role type of the first edge IoT node in the corresponding construction stage based on the association score includes:

[0015] The correlation score is compared with a preset node segmentation threshold to obtain the comparison result;

[0016] Based on the comparison results, if the correlation score is greater than or equal to the node classification threshold, the first edge IoT node will be classified as a high-correlation role type in the corresponding construction stage.

[0017] Based on the comparison results, if the correlation score is less than the node classification threshold, the first edge IoT node will be classified as a low-correlation role type in the corresponding construction stage.

[0018] In some possible implementations, the second edge IoT node includes a collaborative control node and a sensing node. The step of adjusting the control configuration of the first edge IoT node based on the network role type to form the second edge IoT node includes:

[0019] If the network role type of the first edge IoT node is a highly associated role type, then the control authority of the first edge IoT node is elevated, and the first edge IoT node is adjusted to a collaborative control node. The collaborative control node is used for edge computing and controlling construction equipment.

[0020] If the network role type of the first edge IoT node is a low-association role type, then the control authority of the first edge IoT node is reduced, and the first edge IoT node is adjusted to a sensing node, which is used to collect environmental or device status data.

[0021] Among some possible implementation methods, the following are also included:

[0022] In response to the switching of construction phases, the construction demand data corresponding to the construction phase is correlated with the node description data of the second edge IoT node to determine the network role type of the second edge IoT node in the corresponding construction phase.

[0023] Based on the network role type, the control configuration of the second edge IoT node is adjusted again.

[0024] In some possible implementations, the association analysis of the construction demand data and the node description data of the first edge IoT node to obtain the association score of the first edge IoT node under the corresponding construction stage includes:

[0025] The construction demand data is parsed to obtain construction demand information, and the node description data of the first edge IoT node is parsed to obtain node capability information.

[0026] The construction demand information and the node capability information are matched and analyzed to determine the corresponding matching score;

[0027] Based on the preset matching weight coefficient, the matching degree scores are weighted and summed to obtain the association degree score of the first edge IoT node in the corresponding construction stage.

[0028] In some possible implementations, the matching score includes a first matching score and a second matching score, and the step of performing matching analysis between the construction requirement information and the node capability information to determine the corresponding matching score includes:

[0029] The construction demand information and the node capability information are parsed to determine the preset demand conditions corresponding to the construction demand information;

[0030] The node capability information is compared with the preset requirement conditions item by item;

[0031] If the node capability information meets the preset requirement conditions, it is determined that the construction requirement information is in a matching state, and the first matching score is used as the matching score between the construction requirement information and the node capability information.

[0032] If the node capability information does not meet the preset requirement conditions, the construction requirement information is determined to be in an unmatched state, and the second matching score is used as the matching score between the construction requirement information and the node capability information.

[0033] In some possible implementations, the weighted summation of the matching scores based on preset matching weight coefficients to obtain the association score of the first edge IoT node at the corresponding construction stage includes:

[0034] Obtain the preset matching weight coefficients for the corresponding construction stage;

[0035] Based on the preset matching weight coefficient, the matching degree scores are weighted and summed to obtain the association degree score of the first edge IoT node in the corresponding construction stage.

[0036] In some possible implementations, the planning dataset includes basic geographic data and construction planning data. The step of semantically parsing the planning dataset to construct construction demand data corresponding to the construction phase includes:

[0037] The construction process information and construction task arrangement information are parsed from the construction planning data. According to the construction sequence or time relationship, the construction process information and construction task arrangement information are merged into the corresponding construction stage to form the first set of construction tasks corresponding to the construction stage.

[0038] The spatial range information and environmental constraint information corresponding to the target construction area are parsed from the basic geographic data, and the spatial range information and environmental constraint information are associated with the first set of construction tasks under the corresponding construction stage to obtain the second set of construction tasks.

[0039] Based on the spatial range information and environmental constraint information corresponding to the second set of construction tasks, multiple construction requirement information is generated.

[0040] The various construction requirement information is summarized to form construction requirement data corresponding to the construction stage.

[0041] In some possible implementations, the control of the first edge IoT node to acquire the status data of the corresponding construction equipment, and the formation of node description data of the first edge IoT node based on the status data, including:

[0042] Control the first edge IoT node to obtain the status data of the corresponding construction equipment;

[0043] Extract the executable capability information and performance parameters of the first edge IoT node from the status data;

[0044] The executable capability information and performance parameters are processed to form node capability information corresponding to the construction requirements information;

[0045] The state characteristics of the first edge IoT node are obtained, and the node description data is generated by combining the node capability information and the state characteristics.

[0046] Among some possible implementation methods, the following are also included:

[0047] During at least one construction phase, detect the operating load and construction progress deviation of the second edge IoT node;

[0048] If the operating load exceeds a preset load threshold or the construction progress deviation exceeds a preset deviation threshold, then a reassessment and control configuration adjustment of the network role type of the second edge IoT node under the current construction stage will be triggered.

[0049] As can be seen from the above technical solution, this application has the following beneficial effects:

[0050] 1. This application achieves a quantitative assessment of node adaptability through dynamic correlation analysis of construction demand data and edge IoT node capability information, thereby solving the technical problem of static binding of node resources and stage tasks, improving resource utilization, and ensuring that key tasks obtain optimal node matching.

[0051] 2. This application classifies and configures network roles and permissions for edge IoT nodes based on correlation scores, realizing a division of labor mechanism in which highly correlated nodes focus on key control tasks and low-correlation nodes perform auxiliary data collection tasks. This overcomes the rigidity of collaboration caused by fixed roles and optimizes task execution efficiency and overall collaboration capabilities.

[0052] 3. This application achieves dynamic control of construction equipment and tasks by real-time perception of construction status deviation and generation of adjustment parameters, thereby effectively coping with uncertainties at the construction site, improving the accuracy and safety of the construction process, and reducing schedule delays and resource waste. Attached Figure Description

[0053] The present application will be further described below with reference to the accompanying drawings.

[0054] Figure 1 A flowchart of the first smart city planning and construction method based on edge IoT provided for this application;

[0055] Figure 2A flowchart of the second smart city planning and construction method based on edge IoT provided in this application;

[0056] Figure 3 A flowchart of the third smart city planning and construction method based on edge IoT provided for this application;

[0057] Figure 4 A flowchart of the fourth smart city planning and construction method based on edge IoT provided in this application. Detailed Implementation

[0058] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0059] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0060] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0061] Edge IoT nodes are IoT devices widely used in smart city construction management. They are used to collect various information from the construction site and manage construction equipment, tasks, and environmental constraints. Each edge IoT node typically consists of a sensor module, a communication module, a processing module, a storage module, and a control unit. The sensor module acquires the operating status of construction equipment, construction progress information, and on-site environmental data, including temperature, humidity, vibration, load, and spatial location information. The communication module supports bidirectional information transmission between nodes and between nodes and the management system, uploading collected data to the edge computing platform or cloud via wired or wireless networks, while receiving scheduling instructions from the management system. The processing module analyzes, extracts features from, and fuses the collected data to generate data that provides a basis for subsequent construction task matching, deviation analysis, and control decisions. The storage module saves the data collected by the node, processing results, and historical status information to ensure basic detection and control capabilities are maintained even in the event of network interruption or delay. The control unit executes equipment control, task allocation, and collaborative scheduling operations based on instructions from the upper-level system or local analysis results, realizing the control and management of construction equipment.

[0062] Research has revealed that in existing smart city construction management, edge IoT nodes typically have their functions and control permissions determined during the deployment phase, and their network roles remain fixed throughout the construction cycle. This fixed configuration model exposes certain limitations in actual construction. Because construction tasks are complex, diverse, and phased, the requirements for node control capabilities, data collection scope, and collaborative effects vary across different phases. Nodes with fixed functions cannot dynamically respond to task changes. Furthermore, the construction site environment and equipment status change continuously over time; for example, some nodes may be in a low-load state in one phase, while becoming critical control nodes in another, but fixed deployments cannot adjust node roles. The fixed network roles of nodes limit the full utilization of node resources; some nodes may remain idle or inefficient for extended periods, unable to support construction tasks. Therefore, this fixed configuration leads to low resource utilization in multi-phase construction management.

[0063] To address the aforementioned issues, this application provides a smart city planning and construction method based on edge IoT. Please refer to [link / reference]. Figure 1 .

[0064] S101, Obtain the planning data set corresponding to the target planning area, and construct the construction demand data corresponding to the construction phase.

[0065] Obtain the planning data set corresponding to the target planning area. This data set includes, but is not limited to, construction planning data and basic geographic data. Parse the planning data set, converting the construction planning data into a data structure usable for construction task matching, and extract the tasks, time nodes, and spatial constraints corresponding to each construction stage. Merge the construction procedure information and construction task arrangement information into the corresponding construction stages according to construction sequence or time relationship, forming a first set of construction tasks corresponding to each stage. Associate the spatial scope information and environmental constraint information parsed from the basic geographic data with the first set of construction tasks, forming a second set of construction tasks. Based on the spatial scope and environmental constraints corresponding to the second set of construction tasks, generate multiple construction demand information sets, and summarize these requirements to form the construction demand data corresponding to each construction stage.

[0066] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to some terms is given first.

[0067] The planning dataset is a summary dataset describing all planning-related information within the target construction area, including construction procedures, task arrangements, construction phase divisions, spatial scope, and environmental constraints. It provides the raw data foundation for the division of construction phase tasks and the generation of construction requirements.

[0068] Construction planning data refers to the construction process information and task arrangement information of a construction project. For example, the planning information of each stage of process, task arrangement and time node, including the construction process list, task arrangement table, construction stage division information and task priority, is used to determine the time sequence of construction tasks and the stage division.

[0069] Basic geographic data refers to spatial range information and environmental constraint information of the target planning area, such as construction area boundaries, road layout, building distribution, topography and on-site environmental restrictions (such as noise, vibration and load restrictions), which are used to provide spatial and environmental constraint references for construction tasks.

[0070] The first set of construction tasks reflects the specific tasks to be performed in each construction stage and their sequence, serving as the basis for the association between tasks and spatial constraints in subsequent stages.

[0071] The second set of construction tasks is formed by combining spatial range information and environmental constraint information from the analysis of basic geographic data, based on the first set of construction tasks. Each task object includes a task identifier, the construction stage to which it belongs, spatial range information, environmental constraint information, and execution order, providing spatial and environmental constraint information for construction scheduling.

[0072] The construction requirements information describes the task requirements, control capability requirements, data acquisition requirements, and spatial constraints at the corresponding construction stage, including task identifier, task type, execution time window, spatial location, node control capability requirements, data acquisition requirements, and environmental constraint information.

[0073] Construction demand data refers to the aggregated collection of all construction demand information throughout the entire construction cycle.

[0074] In some possible implementation methods, for construction planning data, each task record in the construction planning data is parsed to obtain the corresponding construction procedure information and construction task arrangement information, extracting task identifiers, procedure names, task content descriptions, and corresponding time nodes. The task identifier is used to uniquely identify the construction task; the procedure name is used to determine the stage attribute of the construction task (e.g., "foundation treatment," "road paving," etc.); the task content description includes specific construction operation requirements, such as ("excavate 20 cubic meters of soil," "lay a 5 cm thick asphalt layer," etc.); the time node includes the planned start and end times of the task, used to determine the execution order of the construction task in the construction stage. Based on the stage division information of the construction procedure, the parsed construction tasks are categorized according to construction sequence or time sequence, and tasks within the same construction stage are grouped together to form the first construction task set corresponding to the construction stage. The construction tasks within the first construction task set are arranged according to the planned execution order. Each construction task object in this set is standardized and structured, and the task identifier, the construction stage to which it belongs, the procedure type, the execution order, the planned time node, and the task priority are organized into a unified data structure representation (e.g., JSON format). The process type can be mapped to a pre-defined type mapping table by process name, and the task priority can be determined based on stage importance, whether it is on the critical path, or other preset rules. Through this structured processing, each task object contains complete stage attributes, execution order, and time constraint information, thus forming a complete set of first construction tasks. It should be noted that the above type mapping table and other preset rules can be set by those skilled in the art according to actual conditions, and no specific limitations are made here.

[0075] For the basic geographic data, the spatial extent and environmental constraints of the target construction area are analyzed. Spatial extent information includes the construction area boundaries, road layout, building and facility distribution, and topography, used to define the executable space for each construction task. Environmental constraints include noise limits, vibration limits, load limits, and temporary land occupation constraints, used to standardize construction operation conditions and data acquisition and equipment control requirements. The analyzed spatial extent and environmental constraints are then associated with each construction task in the first set of construction tasks to form a second set of construction tasks. Each task object in the set includes a task identifier, its construction stage, process type, execution sequence, planned time node, task priority, and corresponding spatial extent and environmental constraints.

[0076] Construction requirement information is generated based on the second set of construction tasks. For each construction task, corresponding construction requirement information is generated, including task identifier, task type, execution time window, spatial location, node control capability requirements, data acquisition requirements, and environmental constraints. The task type is determined based on the work process type, the node control capability requirements can be set according to the operational complexity required by the construction task, and the data acquisition requirements include specific indicators for monitoring construction progress, equipment status, and environmental conditions. Each piece of construction requirement information adopts a unified structured format, and all construction requirement information is summarized to form the construction requirement data corresponding to the construction stage.

[0077] S102, control the first edge IoT node to obtain the corresponding status data and form node description data.

[0078] After the construction phase begins, data acquisition commands are sent to multiple first-edge IoT nodes deployed within the target planning area. These nodes acquire status data of the corresponding construction equipment. The acquired status data is parsed to extract the nodes' executable capabilities and performance parameters. This information is then processed to form node capability information that matches the construction requirements and is represented in a structured format. Finally, the node capability information is integrated with the node's status characteristics (such as task load, online status, and fault warnings) to generate complete node description data.

[0079] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to some terms and related technologies is given first.

[0080] The first edge IoT node is an edge computing and communication unit deployed on-site in the target planning area, close to the construction equipment and working environment. It is used to realize the status perception of construction equipment, environmental data acquisition, preliminary data processing, and data interaction with the cloud platform. The first edge IoT node can be implemented using industrial IoT terminals with edge computing capabilities, embedded edge computing gateways, or integrated field control terminals. It typically includes a processor module, a communication module, and various interface modules for connecting with construction equipment and sensor devices.

[0081] In its implementation, the first edge IoT node can directly connect to the control unit or actuator of construction equipment, such as communicating with the control system of excavators, bulldozers, road rollers, concrete mixing equipment, or cranes to obtain equipment operating status parameters, including operating mode, current load, execution speed, and working duration. The first edge IoT node can also connect to various sensor components deployed on the construction site, such as displacement sensors, pressure sensors, vibration sensors, temperature and humidity sensors, or noise sensors, to collect construction environment status data and detection data during equipment operation. Simultaneously, the first edge IoT node can connect to environmental monitoring devices or auxiliary detection equipment, such as on-site environmental monitoring terminals, video acquisition devices, or positioning modules, to acquire information on environmental changes within the construction area, the spatial status of the work area, or changes in equipment position. Through these multiple interface methods, the first edge IoT node can achieve a comprehensive perception capability of equipment status, environmental conditions, and operational processes at the construction site.

[0082] In the initial construction phase, the first edge IoT node is in a unified basic node state. Its functional permissions and control capabilities have not yet been differentiated according to the specific construction phase, and it only has basic data collection, status reporting, and preliminary processing capabilities. As the construction phase progresses, the first edge IoT node will be further adjusted into a second edge IoT node with different network role types based on the results of subsequent correlation analysis, in order to meet the dynamic needs of control capabilities and data processing capabilities during the construction phase.

[0083] Status data refers to the real-time information collected by the first edge IoT node on the construction equipment and the node itself, including equipment operating parameters, task execution status, sensor detection data, and the node's own performance parameters, which are used to reflect the operating status of the construction equipment and the available resources of the node.

[0084] Executable capability information refers to the specific types of construction operations that the first edge IoT node can perform, the upper limit of its operational capabilities (such as load capacity, operation speed, etc.), and performance parameters refer to the node's current available computing power.

[0085] Node capability information refers to structured data that integrates executable capability information with performance parameters, used to represent the executable capabilities of the first edge IoT node in a specific construction stage and corresponding to construction requirements.

[0086] State characteristics refer to the real-time state information of the first edge IoT node during construction.

[0087] Node description data refers to a complete data object formed by integrating node capability information and status characteristics. Each node description data includes node identifier, executable capabilities, performance parameters, status characteristics, and information matching construction requirements, which is used for construction requirement correlation analysis, network role allocation, and control strategy formulation.

[0088] In some possible implementation methods, the first edge IoT node is not deployed in a fixed manner, but is flexibly deployed and dynamically configured according to the type of construction task, the division of construction stages, and the on-site operation requirements within the target planning area.

[0089] Specifically, before construction begins, the deployment location, number, and coverage of the first edge IoT nodes are planned based on the construction process distribution, key work areas, and construction equipment layout scheme determined in the construction planning data. This ensures that the first edge IoT nodes can spatially cover the main construction work areas and functionally meet the data collection and control requirements of the corresponding construction tasks.

[0090] For example, in construction areas involving concentrated operations of large machinery and equipment, such as earthwork excavation areas, road paving areas, or structural construction areas, a first edge IoT node can be deployed near the corresponding work area to form a close connection with the excavating, compacting, or pouring equipment. Through this proximity deployment, the first edge IoT node can acquire the real-time operating status and operational parameters of the construction equipment with low communication latency and complete preliminary data processing on the site, thereby improving response speed and control accuracy during construction.

[0091] In some possible implementation methods, after the construction phase begins, data acquisition commands are sent to multiple first-edge IoT nodes deployed within the target planning area to control each node to acquire status data of the corresponding construction equipment and itself. After acquiring the status data, the operating parameters of the equipment and the task execution status are analyzed to identify the types of construction operations that the first-edge IoT nodes can currently perform, as well as the capability range of each operation. Examples include operation categories such as excavation, pouring, laying, or monitoring, along with their load capacity, operating speed, and other limitations. Simultaneously, the node's own performance parameters are analyzed to obtain information such as currently available computing power, memory capacity, storage capacity, and network bandwidth.

[0092] After parsing, the executable capability information and performance parameters are organized according to a unified data structure to form node capability information corresponding to the construction requirements information. During the process of organizing the executable capability information and performance parameters of the first edge IoT node into node capability information, the various types of data obtained from parsing are classified and standardized. For executable operation types, the construction operation types that the first edge IoT node can perform are mapped according to the construction task classification standard. For example, "excavation" or "shoveling and transporting earth" is classified as "earthwork operation," and "pouring" or "concrete paving" is classified as "structural construction." For operation capability limits, such as load capacity, operating speed, or coverage area, the units or measurement systems used in the construction task are uniformly adopted to ensure direct correspondence with the operation requirements defined in the construction requirements information. Node performance parameters, including currently available computing power, memory capacity, storage capacity, and network bandwidth, are also organized according to unified fields so that they can directly reflect the resource status that the node can call when executing a task. This allows the node capability information to be directly used to determine whether the node can undertake the corresponding construction task, and ensures information structure and comparability. Node capability information typically includes fields such as operation type, capability range, and available resources, and is represented in a standardized format. Integrating node capability information with node status characteristics generates node description data. During this integration process, each node description data object contains a node identifier, status characteristics, and node capability information corresponding to construction requirements, thus forming complete node description data.

[0093] S103, perform correlation analysis between construction demand data and node description data to calculate the correlation score of the first edge IoT node in the corresponding construction stage. Please refer to [link / reference]. Figure 2 .

[0094] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to some terms is given first.

[0095] Preset requirement conditions refer to a set of quantifiable parameters generated based on the node capability requirements, task execution constraints, and environmental constraints described in the construction requirement information. These parameters are used to determine whether the first edge IoT node can meet the construction requirement information. Each preset requirement condition can contain multiple parameter items, such as operating accuracy, operating speed, load capacity, data acquisition frequency, or environmental adaptability. The item-by-item comparison between the node capability information and the preset requirement conditions forms the basis of the matching analysis.

[0096] The preset matching weight coefficient refers to the weight value pre-set for each construction requirement, which reflects the relative importance or priority of the construction requirement in the overall task of the current construction phase. The higher the weight coefficient, the greater the contribution of the construction requirement to the construction phase goal, and the more significant the impact of its matching on the overall correlation score of the first edge IoT node.

[0097] The first matching score refers to the quantitative score assigned to a construction requirement information when the node capability information meets or exceeds the preset requirement conditions in the matching analysis of a single construction requirement information and node capability information. It is used to characterize the degree to which the node fully meets the requirement.

[0098] The second matching score refers to the quantitative score assigned to a construction requirement when the node's capability information fails to meet the preset requirement conditions in the matching analysis of a single construction requirement and node capability information. It is used to characterize the node's failure to meet or partial mismatch with the requirement. It is usually set to 0, representing a no-match state.

[0099] It should be noted that the first matching score, the second matching score, and the preset matching weight coefficient mentioned above can be set by those skilled in the art according to the actual situation, and are not limited here.

[0100] S201, parse the construction requirement data and node description data respectively to obtain construction requirement information and node capability information. Extract the construction requirement information set from the construction requirement data corresponding to the construction stage. Each construction requirement information is an atomic task unit, including task identifier, task type, execution time window, spatial location, node control capability requirements, data acquisition requirements, and environmental constraints. It also includes a task weight coefficient to characterize the importance of the task in the current construction stage. Simultaneously, parse the node capability information and status characteristics of each node from the node description data. Node capability information includes the types of construction operations the node can execute and their upper limits, currently available computing resources, storage, and network bandwidth, etc. Status characteristics include the node's online status, current load, and potential fault information, etc.

[0101] S202, based on construction demand information, determine preset demand conditions and perform matching analysis with node capability information to obtain a matching score. Matching analysis is performed on each piece of construction demand information and a single node capability piece of information. In this process, quantifiable preset demand conditions are generated based on the node capability requirements and environmental constraints in the construction demand information. For example, the "high-precision compaction" task can be interpreted as a compaction degree greater than 96%. Each parameter in the node capability information is compared with the preset demand conditions item by item. If the node capability information meets or exceeds all preset conditions, the construction demand information is determined to be in a matched state; otherwise, it is determined to be in a mismatched state. A first matching score is assigned to the construction demand information in a matched state, and a second matching score is assigned to the mismatched state, thus obtaining the matching score between a single piece of construction demand information and node capability information.

[0102] S203, after completing all individual matching analyses, further calculate the correlation score of the first edge IoT node for all construction requirement information in the current construction phase. Obtain the matching weight coefficient for each construction requirement information, which reflects the relative importance of the task in the current phase. Multiply the matching score of the first edge IoT node for each construction requirement information by the corresponding matching weight coefficient and sum them to obtain the correlation score of the node in the current construction phase. This score can quantify the overall adaptability of the node to the current construction phase; the higher the score, the more high-weight requirements the node can meet, and the stronger its correlation with the overall goal of the construction phase.

[0103] In some possible implementation methods, construction requirement data is parsed in a structured data format (such as JSON, XML, or database tables) to extract task identifiers, task types, execution time windows, spatial locations, node control capability requirements, data acquisition requirements, and environmental constraints from each construction requirement. Simultaneously, the pre-set matching weight coefficients for the corresponding construction task are read. Correspondingly, node description data can also be processed in the same way, integrating node capability information (including executable operation types, operational capability limits, available computing resources, storage capacity, and network bandwidth) and status characteristics (such as online status, current load, and fault information) into a unified data structure.

[0104] The system extracts node capability requirements and environmental constraints from construction demand information and transforms this information into quantifiable parameters. For example, environmental constraints (such as noise limits of less than 70 decibels and work areas of less than 500 square meters) are converted into comparable numerical ranges. For complex tasks, multiple conditions can be combined into complete preset requirements based on AND, AND, and OR relationships. Each parameter item corresponds to a node capability field through a parameter mapping table. By transforming textual task descriptions into strict numerical conditions, node capability information can be directly compared item by item, thereby achieving matching analysis.

[0105] Each construction requirement is iterated through, and its parameters are compared item by item with the corresponding node capability information field. The node capability value is then compared with preset requirement conditions to generate a binary matching result. If all parameters meet or exceed the preset conditions, the node is considered a match and assigned a first matching score (e.g., 10); otherwise, it is considered an unmatched node and assigned a second matching score (e.g., 0). In implementation, multiple construction requirement and node capability information matrices can be processed through loop iteration or vectorized operations to improve computational efficiency. This process can also incorporate node status characteristics to determine node availability. For example, if an online node is offline or overloaded, the matching score can be automatically reduced to an unmatched state to reflect the actual available resources.

[0106] Obtain the preset matching weight coefficient for each construction requirement information. This weight coefficient represents the relative importance or priority of the task in the current construction phase. Multiply the matching score of the first edge IoT node with each construction requirement information and its corresponding matching weight coefficient to form the weighted matching value of a single task. This step can be understood as constructing a weighted value matrix, where rows represent the first edge IoT nodes, columns represent construction requirement information, and each matrix element is the weighted matching score of the node on the corresponding task. After the matrix is ​​constructed, sum all the weighted matching values ​​of each node to obtain the relevance score of the node in the current construction phase.

[0107] In some possible implementation methods, the preset requirement conditions can be determined in practical applications as follows: First, identify key capability indicators (such as accuracy, efficiency, frequency, etc.) based on the construction task type and task description document; second, set operational boundary conditions (such as permissible noise levels, work area, and temporary land occupation restrictions) based on environmental constraints and spatial conditions; third, map these indicators to node capability information fields to form a quantifiable set of conditions; fourth, the condition parameters can be adjusted by introducing task importance or safety level, for example, critical path tasks require more stringent capability indicators. This method ensures that the preset requirement conditions corresponding to each construction requirement information are both comparable and reflect the actual requirements of the construction phase.

[0108] In some possible implementation methods, the correlation score of the first edge IoT node under the corresponding construction stage can also be calculated by an item-by-item comparison method based on interval mapping.

[0109] The specific process is as follows: the construction demand data is parsed and converted into multiple preset demand conditions. Each preset demand condition corresponds to a specific demand parameter name and the target value range of the demand parameter in the current construction stage. The target value range is used to limit the minimum and maximum demand thresholds allowed to meet the construction requirements.

[0110] The node description data of the first edge IoT node is parsed to obtain node capability information, and the node capability parameter values ​​corresponding to the preset requirements are extracted. The node capability parameter values ​​are used to characterize the actual capability level of the first edge IoT node in the corresponding capability dimension.

[0111] The node capability parameter values ​​are compared and analyzed item by item with the corresponding preset requirements.

[0112] Determine whether the node capability parameter value falls within the target value range of the corresponding requirement parameter;

[0113] When the node capability parameter value is within the target value range, it is determined that the node capability parameter is in a matching state with the corresponding preset requirement condition, and the node capability information meets the preset requirement condition. Thus, it is determined that the construction requirement information is in a matching state, and the first matching degree score is used as the matching degree score between the construction requirement information and the node capability information.

[0114] When the node capability parameter value does not fall within the target value range, it is determined that the node capability information does not meet the preset requirement conditions, thus classifying the construction requirement information as unmatched. The second matching score is then used as the matching score between the construction requirement information and the node capability information. The first and second matching scores are pre-set discrete values ​​used to distinguish between the satisfied and unsatisfied states of the node capability information and the construction requirement information under the preset requirement conditions, giving the matching result a clear binary judgment characteristic.

[0115] The matching scores corresponding to each preset requirement condition are normalized to ensure that the matching scores obtained under different requirement conditions are within a uniform numerical range. Based on this, the normalized matching scores are accumulated or averaged to obtain the overall matching level of the first edge IoT node under the corresponding construction stage.

[0116] The overall matching level is used as the correlation score of the first edge IoT node in the corresponding construction stage. The correlation score is used to characterize the comprehensive adaptability between the node capability information of the first edge IoT node and the construction demand information of the current construction stage, and serves as the basis for subsequent node network role determination and control configuration adjustment.

[0117] For example, in a certain construction phase, the construction demand data includes a construction demand for "road base course paving construction." After structured parsing, this construction demand information yields a task identifier of 01, a task type of road base course construction, an execution time window from 8:00 to 12:00 daily, and a spatial location of construction unit A within the target planning area. It also includes node control capability requirements and environmental constraints. The node control capability requirements include: supporting continuous paving operations, a minimum working width of no less than 3 meters, and a unit time work efficiency no less than a set threshold. The environmental constraints include: an allowable maximum noise level of 70 decibels and a single operation coverage area not exceeding 500 square meters. Based on the above information, this construction demand information is converted into corresponding preset demand conditions, where each demand condition is represented by a parameter name and a target value range.

[0118] The node description data of a specific first edge IoT node is parsed to extract its corresponding node capability information and status characteristics. The node capability information shows that the construction equipment controlled by this first edge IoT node supports road paving operations, with a maximum coverage width of 3.5 meters, and the unit time operation efficiency is within a preset range. The node status characteristics show that this first edge IoT node is currently online, its task load is below the load threshold, and no fault warning information has been detected.

[0119] During the matching analysis, each preset requirement condition formed in the construction requirement information is compared with the node capability parameters of the first edge IoT node. For the working width requirement condition, the node capability parameter value of 3.5 meters is compared with the target value range, and it is determined that the node capability parameter value is within the target value range. For the working efficiency requirement condition, the node capability parameter value is compared with the corresponding threshold, and it is determined that the requirement is met. For environmental constraints such as noise and working range, numerical range judgments are also performed, and it is confirmed that the node capability information meets the corresponding preset requirement conditions. Since all preset requirement conditions are met, the construction requirement information is determined to be in a matching state, and a first matching degree score is assigned to the matching result between the construction requirement information and the first edge IoT node, thus obtaining the corresponding matching degree score.

[0120] The preset matching weight coefficient corresponding to the construction requirement information is read, and the matching score is multiplied by the matching weight coefficient to obtain the weighted matching value of the first edge IoT node under the construction requirement information. After completing the matching analysis of all construction requirement information in the current construction stage, the weighted matching values ​​corresponding to the first edge IoT node are summed to obtain the correlation score of the first edge IoT node in the current construction stage.

[0121] S104, Based on the correlation score, determine the network role type of the first edge IoT node in the corresponding construction stage, and form the second edge IoT node. Please refer to [link / reference]. Figure 3 .

[0122] After completing the correlation analysis between the first-level edge IoT nodes and the construction demand data during the construction phase, each first-level edge IoT node receives a correlation score, which quantifies the node's adaptability to the current construction phase tasks. Based on the correlation scores of the first-level edge IoT nodes, they are classified into different network role types, and the node control configuration is adjusted to form second-level edge IoT nodes with clear functional positioning, thereby optimizing task execution efficiency and resource allocation during the construction phase.

[0123] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to some terms is given first.

[0124] Network role types are used to define the functions and permissions of nodes during the construction phase, including high-association role types and low-association role types.

[0125] Highly associated role type refers to a node role type whose association score is greater than or equal to the preset node division threshold.

[0126] Low-association role type refers to the node role type whose association score is less than the preset node classification threshold.

[0127] The second edge IoT node refers to the first edge IoT node after adjustment based on the node network role type, including collaborative control nodes and sensing nodes, which are used to perform construction tasks or collect environmental and equipment data.

[0128] In some possible implementation methods, a preset node partitioning threshold is needed to allocate functions and classify network roles for the first edge IoT nodes. This threshold can be predetermined based on the criticality of the tasks during the construction phase, the overall distribution of nodes, and the construction management strategy. Specific methods include statistically analyzing the correlation score distribution of all first edge IoT nodes, pre-determining the proportion that should be classified as high-correlation role types (e.g., 30% to 50% of the total number of nodes), and then sorting the nodes according to their correlation scores from highest to lowest. The score corresponding to the lowest-scoring high-correlation role type is the node partitioning threshold.

[0129] In the actual partitioning process, the following logical judgment is performed on each first edge IoT node: the node's correlation score is compared with a preset node partitioning threshold. If the node score is greater than or equal to the threshold, the node is classified as a high-correlation role type, whose functional permissions include participating in critical task execution, undertaking edge computing tasks, and playing a control and scheduling role in the construction network; if the node score is lower than the threshold, the node is classified as a low-correlation role type, whose main responsibilities are environmental monitoring, equipment status acquisition, or auxiliary detection, and it does not participate in critical task control. Through this partitioning, the node's functions and permissions directly correspond to its adaptability, ensuring that critical tasks can be completed by high-capability nodes first, while low-capability nodes will not affect the overall construction efficiency by undertaking complex tasks.

[0130] In some possible implementations, for the first edge IoT node classified as a highly relevant role type, its computing power, memory capacity, storage capacity, network bandwidth, and online status are checked to ensure that the node has the resources required to perform edge computing and task control. Utilizing a layered edge computing architecture, high-weight tasks and critical path tasks are assigned to nodes with sufficient resources and high reliability. By issuing control configuration commands, the node's permission level is elevated, including allowing it to perform the following functions: real-time operational control of construction equipment, such as starting, stopping, or adjusting the operating parameters of construction machinery; task allocation and coordination for low-relevance role types or other collaborative control nodes; local data processing and analysis capabilities to calculate the deviation between construction status data and construction requirements, generating optimized control parameters; and priority management of critical tasks, enabling priority scheduling of highly important tasks. Upon receiving the configuration command, the first edge IoT node activates its operation permissions and computing task allocation module, and simultaneously updates its role identifier to that of a collaborative control node in the second edge IoT node, so that it automatically assumes edge computing and control functions in subsequent task execution.

[0131] For first-edge IoT nodes classified as low-association roles, their control permissions are reduced, allowing them to focus on data acquisition and status monitoring tasks. Specific implementation methods include: disabling or restricting their direct operation permissions on construction equipment to prevent accidental operation of non-critical nodes from affecting construction safety; configuring node acquisition strategies, including acquisition frequency, acquisition parameters (such as temperature, vibration, construction progress, noise, load status, etc.), and data upload strategies, to periodically or on-demand send the acquired data to collaborative control nodes or the cloud; and monitoring the node's own status, such as online status, remaining computing resources, and network connection quality, to ensure the stable execution of acquisition tasks.

[0132] For example, node configuration can be completed through the following steps: reading the network role type and resource status of each first edge IoT node; selecting the corresponding configuration template (collaborative control template or perception template) according to the network role type; distributing the corresponding template to the first edge IoT node, activating the corresponding function, and updating the node permission table; finally, the node generates a role identifier locally and starts the corresponding function, while simultaneously reporting a successful configuration status and node capability indicators. The entire process ensures that the node's function matches its correlation score, guaranteeing that highly correlated role types undertake critical tasks while enabling low-correlation role types to efficiently complete auxiliary data collection.

[0133] Among some possible implementation methods, please refer to Figure 4When a construction phase transition condition is detected (e.g., a phase time node is reached or a key construction task is completed), the system acquires the construction requirement data corresponding to the new construction phase and sends a phase transition notification to the second edge IoT nodes within the target planning area. The second edge IoT nodes include collaborative control nodes and sensing nodes. Node description data includes the node's executable capability information, performance parameters, and status characteristics, reflecting the node's currently available resources and execution capabilities.

[0134] After the construction phase switch, the new construction demand data is correlated with the node description data of the second edge IoT node. The correlation score of the second edge IoT node is obtained according to the method in S103 above, and the control configuration of the second edge IoT node is adjusted again according to the method in S104 above.

[0135] S105 controls the second edge IoT node to control the construction equipment and assign tasks.

[0136] During construction, the control system continuously collects construction status data from the second edge IoT node. Based on the construction status data and the construction demand data corresponding to the current construction stage, the system calculates the construction status deviation relationship and generates construction control adjustment parameters. The construction control adjustment parameters are then sent to the corresponding second edge IoT node, which in turn controls the connected construction equipment according to the parameters and allocates, adjusts, or rearranges the construction tasks.

[0137] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to some terms is given first.

[0138] Construction status data is a set of data collected in real time or periodically by multiple second-edge IoT nodes during the construction process, used to comprehensively reflect the actual execution status of the construction site.

[0139] Construction status deviation relationship is used to characterize the degree of deviation or inconsistency between the actual construction execution status and the expected construction target. It can reflect whether the construction progress is lagging behind, whether the construction equipment capacity is insufficient, whether the equipment load is abnormal, or whether the construction environment has changed. By calculating the construction status deviation relationship, it is possible to determine whether the current construction control strategy still meets the construction requirements, thus providing a basis for determining whether control adjustments are needed.

[0140] Construction control adjustment parameters are a set of control instructions formed based on the deviation relationship of construction status, used to guide the second edge IoT node to adjust the construction process.

[0141] In some possible implementation methods, the calculation of construction status deviation relationships is based on a multi-dimensional parameter comparison between construction status data and construction demand data. Specifically, the construction status data is analyzed to extract construction status parameters that correspond one-to-one with key demand indicators in the construction demand data, including construction progress parameters, construction equipment operating parameters, construction equipment load parameters, and construction environment status parameters. These construction status parameters are then compared item by item with the construction demand parameters under the current construction stage. By calculating the difference, deviation ratio, or deviation level between each construction status parameter and its corresponding construction demand parameter, local deviation results are obtained for each parameter dimension. Based on this, the local deviation results are summarized to form a construction status deviation relationship that reflects the degree of difference between the overall construction execution status and the construction objectives.

[0142] In the process of forming the construction status deviation relationship, the deviation relationship can be represented in a vectorized way. For example, the construction status deviation relationship can be represented as a vector, where each dimension corresponds to the deviation value of a certain key parameter in the construction status data (such as construction progress, equipment load, work efficiency, environmental conditions, etc.). This deviation value can be calculated using the method described above. When the deviation result of a certain construction status parameter exceeds the preset allowable threshold, the construction status corresponding to that parameter is determined to be in an abnormal deviation state. When multiple parameter dimensions simultaneously show abnormal deviation states, it is determined that there is a significant deviation between the overall construction status under the current construction stage and the construction requirements, thereby triggering the subsequent construction control adjustment process.

[0143] In some possible implementation methods, decision analysis of construction control strategies is performed based on construction status deviation relationships. Specifically, construction status deviation relationships are mapped to a preset set of control strategy rules. This set of rules describes the correlation between different deviation types and corresponding control adjustment measures. When the construction status deviation relationship indicates that construction progress parameters deviate from the expected progress target, construction control adjustment parameters are generated to adjust the execution order or priority of construction tasks. When the construction status deviation relationship indicates that construction equipment operating parameters or construction equipment load parameters deviate from a preset range, construction control adjustment parameters are generated to adjust construction equipment operating parameters or reallocate construction tasks. When the construction status deviation relationship indicates that construction environment state parameters change, construction control adjustment parameters are generated to switch construction modes or adjust operation strategies.

[0144] During the generation of construction control adjustment parameters, a construction control adjustment priority mechanism can be used to sort and filter multiple construction control adjustment parameters. The construction control adjustment priority mechanism is determined based on the severity and scope of influence of each deviation dimension in the construction state deviation relationship, so that the construction control adjustment parameters corresponding to deviations that have a greater impact on construction safety or construction continuity are issued first, thereby ensuring the stability and controllability of the construction process.

[0145] After the construction control adjustment parameters are issued, the second edge IoT node executes the corresponding control operations based on the received parameters. Specifically, the second edge IoT node parses the construction control adjustment parameters, maps the equipment control parameters to the control interface of the construction equipment, and adjusts the operating status, operating intensity, or operation mode of the construction equipment. At the same time, based on the construction control adjustment parameters, the second edge IoT node updates the allocation strategy for construction tasks, and reallocates, adjusts, or rearranges the tasks to ensure that the actual execution capability of the construction equipment is consistent with the current construction requirements.

[0146] For example, in a certain construction phase, suppose the road base paving task is behind schedule, with the node deviation vector showing that the paved area per unit time is less than 15% of the target. Adjustment parameters are calculated through matrix mapping, instructing the relevant second-edge IoT nodes to increase the paving machinery's speed by 10% and simultaneously prioritize this task to ensure timely completion of paving in critical areas. After parsing the parameters, the nodes control the machinery to accelerate its operation and adjust the task scheduling order within the nodes, while also transferring some low-priority tasks to idle nodes, thereby optimizing the overall construction task execution efficiency and reducing the schedule deviation.

[0147] Among some possible implementation methods, please refer to Figure 4 During construction, the second edge IoT node continuously collects its own operational load information, including current task load, processor utilization, memory usage, network bandwidth usage, and the operating status of the controlled construction equipment. Simultaneously, it acquires construction progress data and calculates the deviation between the actual completion status of each task and the planned schedule within each construction phase, forming a construction progress deviation index. Both operational load and construction progress deviation are represented numerically or hierarchically to determine whether a node is overloaded or behind schedule.

[0148] When the node load exceeds the preset load threshold or the construction progress deviation exceeds the preset deviation threshold, a re-evaluation of the network role type of the node is triggered according to the methods described in S103 and S104.

[0149] For example, during the road paving construction phase, it was detected that a second edge IoT node (cooperative control node) was overloaded due to continuously processing multiple critical tasks, and the construction progress was behind schedule. After detecting the threshold exceeding the limit, the node's adaptability was reassessed, its role was adjusted to a sensing node, and some critical tasks were assigned to second edge IoT nodes with lower loads. At the same time, new control permissions and task scheduling schemes were issued.

[0150] This application transforms urban planning semantics and construction task requirements into structured construction demand data, while simultaneously sensing and forming capability description data for edge nodes in real time. Then, by calculating the "association score" between node capabilities and stage tasks, the adaptability value of each node in the current stage is evaluated. Based on this score, nodes are dynamically divided into different network roles, and differentiated functional configurations and access controls are automatically applied. This achieves on-demand allocation of node resources according to changes in construction stage task requirements, ensuring that high-capability nodes prioritize critical tasks and general-purpose nodes efficiently execute auxiliary tasks. This significantly improves the comprehensive utilization rate of edge resources throughout the entire construction lifecycle. Furthermore, this method can automatically trigger role reassessment and adjustment during stage switching or operational anomalies, achieving efficient adaptation to diverse and dynamic needs in complex construction scenarios.

[0151] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A smart city planning and construction method based on edge IoT, characterized in that, The method includes: Obtain the planning data set corresponding to the target planning area, perform semantic parsing on the planning data set, and construct the construction demand data corresponding to the construction stage. Multiple first edge IoT nodes are deployed inside the target planning area. During construction, the first edge IoT node is controlled to acquire the status data of the corresponding construction equipment, and node description data of the first edge IoT node is formed based on the status data. The construction demand data is correlated with the node description data of the first edge IoT node to obtain the correlation score of the first edge IoT node in the corresponding construction stage. Based on the correlation score, the network role type of the first edge IoT node in the corresponding construction stage is determined, and the network role type includes high correlation role type and low correlation role type. The correlation score is compared with a preset node segmentation threshold to obtain the comparison result; Based on the comparison results, if the correlation score is greater than or equal to the node classification threshold, the first edge IoT node will be classified as a high-correlation role type in the corresponding construction stage. Based on the comparison results, if the correlation score is less than the node classification threshold, the first edge IoT node will be classified as a low-correlation role type in the corresponding construction stage. Based on the network role type, the control configuration of the first edge IoT node is adjusted to form a second edge IoT node, which includes a collaborative control node and a sensing node. If the network role type of the first edge IoT node is a highly associated role type, then the control authority of the first edge IoT node is elevated, and the first edge IoT node is adjusted to a collaborative control node. The collaborative control node is used for edge computing and controlling construction equipment. If the network role type of the first edge IoT node is a low-association role type, then reduce the control authority of the first edge IoT node and adjust the first edge IoT node to a sensing node, which is used to collect environmental or device status data. Control the second edge IoT node to collect construction status data, and calculate the deviation relationship between the construction status data and the construction demand data; Based on the deviation relationship, corresponding construction control adjustment parameters are generated, and based on the construction control adjustment parameters, the second edge IoT node is controlled to control the construction equipment and allocate tasks.

2. The method according to claim 1, characterized in that, Also includes: In response to the switching of construction phases, the construction demand data corresponding to the construction phase is correlated with the node description data of the second edge IoT node to determine the network role type of the second edge IoT node in the corresponding construction phase. Based on the network role type, the control configuration of the second edge IoT node is adjusted again.

3. The method according to claim 1, characterized in that, The step of performing correlation analysis between the construction demand data and the node description data of the first edge IoT node to obtain the correlation score of the first edge IoT node in the corresponding construction stage includes: The construction demand data is parsed to obtain construction demand information, and the node description data of the first edge IoT node is parsed to obtain node capability information. The construction demand information and the node capability information are matched and analyzed to determine the corresponding matching score; Based on the preset matching weight coefficient, the matching degree scores are weighted and summed to obtain the association degree score of the first edge IoT node in the corresponding construction stage.

4. The method according to claim 3, characterized in that, The matching score includes a first matching score and a second matching score. The process of matching the construction requirement information with the node capability information to determine the corresponding matching score includes: The construction demand information and the node capability information are parsed to determine the preset demand conditions corresponding to the construction demand information; The node capability information is compared with the preset requirement conditions item by item; If the node capability information meets the preset requirement conditions, it is determined that the construction requirement information is in a matching state, and the first matching score is used as the matching score between the construction requirement information and the node capability information. If the node capability information does not meet the preset requirement conditions, the construction requirement information is determined to be in an unmatched state, and the second matching score is used as the matching score between the construction requirement information and the node capability information.

5. The method according to claim 3, characterized in that, The matching degree scores are weighted and summed based on preset matching weight coefficients to obtain the association degree score of the first edge IoT node in the corresponding construction stage, including: Obtain the preset matching weight coefficient for the corresponding construction stage. The preset matching weight coefficient refers to the weight value set in advance for each construction requirement information, which is used to reflect the relative importance or priority of the construction requirement in the overall task of the current construction stage. Based on the preset matching weight coefficient, the matching degree scores are weighted and summed to obtain the association degree score of the first edge IoT node in the corresponding construction stage.

6. The method according to claim 1, characterized in that, The planning dataset includes basic geographic data and construction planning data. The semantic parsing of the planning dataset to construct construction demand data corresponding to the construction phase includes: The construction process information and construction task arrangement information are parsed from the construction planning data. According to the construction sequence or time relationship, the construction process information and construction task arrangement information are merged into the corresponding construction stage to form the first set of construction tasks corresponding to the construction stage. The spatial range information and environmental constraint information corresponding to the target construction area are parsed from the basic geographic data, and the spatial range information and environmental constraint information are associated with the first set of construction tasks under the corresponding construction stage to obtain the second set of construction tasks. Based on the spatial range information and environmental constraint information corresponding to the second set of construction tasks, multiple construction requirement information is generated. The various construction requirement information is summarized to form construction requirement data corresponding to the construction stage.

7. The method according to claim 1, characterized in that, The process involves controlling the first edge IoT node to acquire status data of the corresponding construction equipment, and forming node description data for the first edge IoT node based on the status data, including: Control the first edge IoT node to obtain the status data of the corresponding construction equipment; Extract the executable capability information and performance parameters of the first edge IoT node from the status data; The executable capability information and performance parameters are processed to form node capability information corresponding to the construction requirements information; The state characteristics of the first edge IoT node are obtained, and the node description data is generated by combining the node capability information and the state characteristics.

8. The method according to claim 1, characterized in that, Also includes: During at least one construction phase, detect the operating load and construction progress deviation of the second edge IoT node; If the operating load exceeds a preset load threshold or the construction progress deviation exceeds a preset deviation threshold, then a reassessment and control configuration adjustment of the network role type of the second edge IoT node under the current construction stage will be triggered.

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