An ipv6-based rural building full-process compliance verification and hidden danger tracing system and method

Abstract

The application discloses a kind of rural building whole-process compliance verification and hidden danger tracing method and system based on IPv6.The method comprises the following steps: allocating global unique IPv6 address for monitoring node based on DHCPv6, establishing digital identity mapping; construction process characteristics are encoded using IPv6 flow label field, and multi-dimensional time sequence data stream is generated; compliance verification rule base is constructed based on national construction standard, and each construction stage data is automatically compared and scored; if abnormality is detected, according to the address and process mapping relationship, a hidden danger propagation path graph is constructed using a graph depth-first search algorithm, and a traceability report is generated; hierarchical early warning is pushed using IPv6 multicast mechanism and response is recorded; finally, the building safety comprehensive evaluation value is calculated by using the analytic hierarchy process weighting method.The application breaks the data island, realizes the whole-process real-time active compliance verification, hidden danger accurate tracing and differentiated early warning.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things and construction engineering quality supervision technology, specifically to a system and method for compliance verification and hidden danger traceability of rural construction projects based on IPv6. Background Technology

[0002] Rural buildings constitute the main form of rural housing and supporting facilities in my country, and are numerous and scattered. Due to the lack of professional and technical personnel to supervise the entire process, rural buildings commonly suffer from problems such as illegal construction, material skimping, and unauthorized design changes, leading to the long-term accumulation of hidden quality hazards and seriously threatening the lives and property of rural residents.

[0003] In terms of equipment identification and management, existing rural building monitoring systems mostly rely on IPv4 addresses or private LAN addresses for equipment management. Due to the limited IPv4 address space, when a large number of sensor nodes, camera equipment and data acquisition terminals are deployed at the construction site, problems such as equipment address conflicts and address reuse frequently occur. It is impossible to achieve a globally unique and accurate identification of each monitoring point, making it difficult to accurately locate the data source node during post-event tracing, and the tracing chain is at risk of being broken.

[0004] Regarding data connectivity across the entire process, existing systems generally suffer from data silos. Construction permit approval data is managed by the planning department, geological survey data is archived by the surveying unit, construction process data is recorded by the construction company, and final acceptance data is retained by the quality supervision department. There is a lack of unified data identification and sharing mechanisms across these stages. When a construction quality incident occurs, supervisors must review files from multiple departments one by one, which is time-consuming and labor-intensive, and is highly susceptible to failure in completing the full attribution of responsibility due to missing files or inconsistent information.

[0005] Regarding real-time compliance verification, current management of self-built houses in rural areas relies primarily on post-construction inspections, lacking a proactive compliance verification mechanism based on real-time sensor data during construction. Key quality issues such as abnormal foundation settlement and wall verticality deviations are often only discovered after construction is completed or even after occupancy, at which point rectification costs are extremely high, and some structural defects are even irreparable. Existing systems fail to compare the quantitative indicators of national construction standards and specifications with construction process data in real time; compliance judgments mainly rely on human experience, resulting in low standardization and a high rate of misjudgment.

[0006] In existing technologies, Chinese invention patent CN117057611A proposes a method and system for identifying safety risks in self-built houses. This method uses drones and multi-source sensors to collect house information and construct a 3D model, then combines machine learning to classify risk levels. However, this method has the following drawbacks: First, it primarily focuses on risk identification and monitoring after the house is built, lacking a real-time proactive compliance verification mechanism based on national standards and specifications throughout the entire construction process, resulting in delayed detection of potential hazards. Second, due to the lack of a unified underlying network identification system, data from the application, surveying, and construction stages are isolated, making it difficult to accurately trace responsibility across stages when building quality hazards occur.

[0007] On the other hand, the IPv6 protocol possesses a massive address space and rich message characteristics. Existing technologies, such as Chinese invention patent CN119788435A, propose a security audit and compliance inspection method based on IPv6 networks, which assesses compliance by analyzing network activity event logs. However, this method has the following drawbacks: it is limited to the field of IT network security event auditing and has not yet applied IPv6's DHCPv6 address semantic allocation, flow label encoding, and multicast mechanisms to the identity mapping of physical nodes in building engineering and the semantic identification of construction procedures; furthermore, the existing early warning mechanisms of rural building management systems often use network-wide broadcasts or single notifications, which can easily cause redundant interference from low-level information to the regulatory platform.

[0008] In summary, existing technologies generally suffer from the following problems: 1) lack of a unified digital identity mapping across the entire process, resulting in data silos and broken traceability chains; 2) lack of real-time proactive compliance verification based on national standards and specifications, leading to delayed compliance judgments; 3) lack of precise graph algorithm tracing based on process logic when potential hazards occur, making it difficult to define responsibilities; 4) lack of differentiated targeted early warning push based on hazard levels, resulting in redundant and simplistic early warning mechanisms.

[0009] Therefore, how to utilize the massive address space and flow label characteristics of the IPv6 protocol to build a system and method that can implement full-process digital supervision, real-time compliance verification and accurate hazard tracing of rural buildings has become a key issue that urgently needs to be addressed in the field of rural construction quality management. Summary of the Invention

[0010] To address the aforementioned shortcomings in existing technologies, the present invention aims to provide a system and method for compliance verification and hazard tracing of rural construction projects based on IPv6, thereby solving the problems of data silos, delayed compliance verification, difficulty in hazard tracing, and single and redundant early warning mechanisms caused by the lack of unified identification in rural construction supervision.

[0011] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for full-process compliance verification and hazard tracing of rural buildings based on IPv6 is provided, including the following steps: S101. Based on the DHCPv6 protocol, assign a globally unique IPv6 address to each monitoring node at the construction site, bind the IPv6 address with the building site number and geographical coordinates, establish a digital identity mapping relationship, and generate a node identity identification table. S102. Collect foundation settlement sensor data, load-bearing structure strain data, wall verticality data and construction image data at the construction site. Use the IPv6 message flow label field to encode the construction process type, construction personnel identification and timestamp to generate a multi-dimensional time-series data stream with process identification. S103. Based on national construction standards and specifications, a compliance verification rule base is constructed. Data at each stage of application, surveying, foundation construction, main construction and completion acceptance are automatically compared item by item. A weighted scoring method is used to calculate the compliance score at each stage and output the full-process compliance score and anomaly marking table. S104. If a compliance anomaly is detected, the corresponding construction node identifier and construction timestamp are extracted from the anomaly marker table based on the mapping relationship between IPv6 address and process code. The corresponding responsible entity information is obtained by querying the node identity identifier table. A hazard propagation path graph is constructed using a graph depth-first search algorithm. The responsibility contribution of each upstream node to the current anomaly is calculated, and a hazard tracing report is generated. S105. Statistically analyze the completeness of information acquisition for each tracing path node in the hazard tracing report, and calculate the hazard tracing completeness score based on the percentage of complete nodes; continuously monitor the online status, data packet reporting success rate, and sensor calibration status of each sensing layer node by the edge layer, and calculate the sensor real-time status score by weighting. S106. Using the IPv6 multicast mechanism, hierarchical early warning information is synchronously pushed to the monitoring platform, construction unit terminal and homeowner terminal according to the level of hidden danger, and the corresponding response and disposal process is executed, and the disposal results are written into the full process archive. S107. The weighting coefficients are calculated using the combined full-process compliance score, the hazard tracing integrity score, and the sensor real-time status score, and the weighted output of the building safety comprehensive assessment value is calculated using the analytic hierarchy process.

[0012] Preferably, the method of step S101 specifically includes the following sub-steps: The homestead number, latitude and longitude coordinates, and construction permit number of the target building are obtained from the rural homestead management system. The above information is hashed to obtain a 64-bit unique building identifier. Using the DHCPv6 stateful allocation mode, the unique building identifier is embedded in the interface identifier field of the IPv6 address, generating semantically meaningful IPv6 addresses for the foundation monitoring node, load-bearing structure monitoring node, and construction camera node respectively. A four-element mapping relationship is established between IPv6 addresses and node physical location, node type, and construction stage. This relationship is written into a node identity table, which is stored using a distributed hash table and supports multi-dimensional retrieval by building identifier, construction stage, and time range.

[0013] Preferably, the method in step S102 specifically includes the following sub-steps: The 20-bit flow label field in the IPv6 packet header is divided into three subfields: the high 6 bits are used to encode the construction procedure type, supporting a total of 64 procedure categories; the middle 8 bits are used to encode the construction worker identification code; and the low 6 bits are used to encode the construction time period number for the day. Differential sampling methods are used to reduce data redundancy for foundation settlement data, strain data and verticality data. The sampled sensor values ​​are encapsulated with the corresponding IPv6 packets to form sensor data packets with process identifiers. The inter-frame difference algorithm is used to extract key frames from the construction image data. The hash value of the key frame image and the IPv6 flow tag are written into the time series database to form a multi-dimensional time series data stream covering the entire construction cycle.

[0014] Preferably, the method in step S103 specifically includes the following sub-steps: Quantitative indicators are extracted from national standards such as the "Code for Acceptance of Construction Quality of Masonry Structures" GB 50203 and the "Standard for Acceptance of Construction Quality of Building Foundation Engineering" GB 50202. The textual specifications are transformed into a set of calculable constraints, and a structured rule library containing the foundation bearing capacity threshold, wall verticality deviation threshold, and mortar strength grade lower limit is constructed. The actual measured values ​​at each construction stage are compared with the constraints of the rule base item by item. Indicators that exceed the threshold range are marked as abnormal, and the number and magnitude of abnormal items at each stage are counted. Differentiated indicator weights are set for each construction stage, and the compliance score for each stage is calculated using the following weighted formula: ; in For the first Compliance score during the construction phase For the first Phase 1 The weighting coefficient of each indicator For the first The individual score of each indicator, This represents the total number of indicators for this stage; the weighting coefficients are determined by constructing a judgment matrix using the analytic hierarchy process and performing a consistency check.

[0015] Preferably, in step S104, the step of constructing a hazard propagation path graph using a graph depth-first search algorithm and calculating the responsibility contribution of each upstream node to the current anomaly specifically includes: Using the IPv6 addresses of construction nodes as graph nodes and the sequential dependencies of construction procedures as directed edges, construct a directed acyclic graph of construction procedures. Starting from the anomaly marker node, a depth-first search algorithm is used to traverse backwards along the directed edges to identify the set of upstream construction nodes that have a causal relationship with the anomaly node. Let the abnormal node be The identified set of upstream nodes is ,node Along the directed edge to The process dependency weight is ,node The normalized anomaly magnitude of the corresponding indicator's measured value exceeding the rule base threshold is: ,node and The number of hops between the paths is Preset attenuation coefficient The nodes are calculated using the following formula. Contribution to responsibility for the current anomaly: in Based on the degree of responsibility and contribution Sort from largest to smallest to determine the root cause nodes and the order of contribution of each responsible entity; Based on the ranking of responsibility contribution, the construction node identifier, corresponding IPv6 address, construction timestamp and responsible entity information of each traceability path are extracted to generate a hidden danger traceability report containing a multi-level traceability chain. The report includes four parts: hidden danger type description, root cause node location, list of responsible entities and rectification suggestions.

[0016] Preferably, the method in step S105 specifically includes the following sub-steps: The completeness of obtaining the node identifier, IPv6 address, construction timestamp, and responsible entity information of each tracing path node in the hazard tracing report is statistically analyzed, and the proportion of the number of nodes with complete information to the total number of nodes in the tracing path is used as the hazard tracing completeness score. The edge layer continuously monitors the online status of each sensing layer node, the data packet reporting success rate, and whether the sensor's most recent calibration is qualified. The three indicators are weighted and summed according to preset weights to obtain the sensor's real-time status score.

[0017] Preferably, the method in step S106 specifically includes the following sub-steps: The comprehensive score for the potential hazard is the compliance score of the construction stage to which the anomaly belongs in step S103. Based on the comprehensive hazard assessment, the warning levels are divided into three levels: Level 1 warning is for a score below 60, Level 2 warning is for a score between 60 and 75, and Level 3 warning is for a score between 75 and 85. Each of the three warning levels is configured with an independent IPv6 multicast address range. The monitoring platform, construction unit terminal and homeowner terminal join the corresponding level multicast group through the MLD protocol. When the compliance verification engine outputs an anomaly flag, it sends a warning data packet to the corresponding multicast address based on the level of the potential hazard, thereby achieving differentiated targeted notification and avoiding redundant interference from low-level information to the regulatory platform. The early warning response results are transmitted back by each terminal via unicast and written into the full-process archive, forming a complete early warning-response closed-loop record.

[0018] Preferably, the method in step S107 specifically includes the following sub-steps: A three-tiered assessment hierarchy is constructed: the target layer is the comprehensive assessment value of building safety; the criteria layer includes compliance score, hazard traceability integrity score, and sensor real-time status score; and the indicator layer consists of the specific scoring items under each criteria layer. The criteria layer is constructed by expert judgment, and the pairwise comparison judgment matrix is ​​constructed. The weight vector of each criterion is calculated by the square root method, and the consistency ratio is checked to ensure that the consistency ratio is less than the preset threshold. The comprehensive safety assessment value of a building is calculated using the following formula: ; in, , , The weighting coefficients for compliance score, traceability integrity score, and sensor status score are respectively, satisfying... ; , , All scores are normalized to a range of 0 to 100; a mandatory rectification process is triggered when the evaluation score is below 60.

[0019] It also provides a rural building full-process compliance verification and hidden danger traceability system based on IPv6, including: The sensing layer includes foundation settlement sensing nodes, load-bearing structure strain monitoring nodes, construction image nodes, and verticality and tilt angle nodes deployed at the construction site; each node transmits data through an IPv6 network. The edge layer includes an IPv6 gateway and edge computing nodes; the IPv6 gateway is used to allocate IPv6 addresses to the perception layer nodes and aggregate data; the edge computing nodes are used to preprocess and differentially sample the aggregated data. The processing layer includes a compliance verification engine, a hidden danger tracking module, an early warning push module, and a comprehensive assessment module; The compliance verification engine is used to compare data at each construction stage and output compliance scores and anomaly markers based on a compliance verification rule library built on national construction standards and specifications. The hazard tracing module is used to query the responsible entity information based on the mapping relationship between IPv6 address and process code when an anomaly is detected, construct a hazard propagation path graph using a graph depth-first search algorithm, and generate a hazard tracing report. The early warning push module is used to push graded early warning information to application layer terminals based on the level of potential risks using the IPv6 multicast mechanism. The comprehensive evaluation module is used to combine the compliance score of the entire process, the integrity score of hazard tracing, and the real-time status score of sensors using the analytic hierarchy process to output a comprehensive building safety evaluation value. The application layer includes a monitoring platform, a construction unit terminal, and a homeowner terminal. The monitoring platform, construction unit terminal, and homeowner terminal join the multicast group corresponding to the warning level through the MLD protocol, receive the warning information sent by the warning push module, and send the response results back to the full-process archive of the processing layer.

[0020] The beneficial effects of this invention are as follows: 1. This invention uses the DHCPv6 protocol to perform hash calculations on the homestead land number and embeds an IPv6 interface identifier to establish a "one house, one file, one address" mapping, realizing the unified association of data throughout the entire process of application, surveying, construction, and acceptance, laying a solid foundation for accurate traceability.

[0021] 2. This invention transforms national standards such as GB 50203 and GB 50202 into a computable structured rule base, and performs real-time comparison and weighted scoring of the actual data collected throughout the construction process, which significantly advances the discovery of hidden dangers and reduces rectification costs.

[0022] 3. This invention innovatively utilizes IPv6 flow tag fields to semantically encode process types, personnel, and time periods, and employs a graph depth-first search algorithm to traverse the directed acyclic graph of the construction process in reverse to calculate the responsibility contribution, thereby achieving accurate root cause localization and clear identification of responsible parties in a multi-level traceability chain.

[0023] 4. This invention utilizes the IPv6 multicast mechanism (MLD protocol) to configure independent multicast address ranges for the three-level early warning levels, thereby realizing differentiated targeted notifications based on the level of potential risks. This avoids redundant interference from low-level information to the monitoring platform and forms a closed-loop record of early warning and response. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of the present invention; Figure 2This is a schematic diagram illustrating IPv6 address allocation and node identity mapping according to the present invention; Figure 3 This is a schematic diagram of the compliance verification rule base structure of the present invention; Figure 4 This is a path diagram for tracing the potential risks of this invention; Figure 5 This is a diagram of the overall system architecture of the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0026] Example 1: A method for full-process compliance verification and hidden danger tracing of rural buildings based on IPv6 Taking the entire lifecycle construction process of a self-built house in a rural area as an example, the specific implementation steps of the method of this invention are as follows: Figure 1 The overall flowchart shows the specific components, including: 1. Establish digital identity mapping and generate multidimensional data streams. The homestead number, latitude and longitude coordinates, and construction permit number of the target building are obtained from the rural homestead management system, and a 64-bit unique building identifier is generated through hash calculation. For example... Figure 2 As shown in the IPv6 address allocation and node identity mapping diagram, a DHCPv6 stateful allocation mode is adopted. The identifier is embedded in the interface identifier field of the IPv6 address, generating globally unique semantic IPv6 addresses for monitoring nodes deployed on-site, such as those for foundation settlement, load-bearing structure strain, wall verticality, and construction images. A "one room, one file, one address" mapping relationship is established and written to a distributed hash table. During construction, each node continuously collects data, which is then encoded using the 20-bit flow label field in the IPv6 packet header, divided into three subfields: the high 6 bits encode the work process type, the middle 8 bits encode the construction worker identifier, and the low 6 bits encode the time period number for the day. After differential sampling and inter-frame differential processing, the data is encapsulated into a multi-dimensional time-series data stream with work process identifiers and written to the time-series database.

[0027] 2. Compliance verification and precise hazard identification during the foundation construction phase During the foundation construction phase, the foundation settlement sensor nodes collect settlement data every 10 minutes. The time-series data stream is read, such as... Figure 3As shown in the schematic diagram of the compliance verification rule base structure, quantitative indicators are extracted based on national standards such as GB 50202 and transformed into a set of constraints. The actual sampled values ​​are automatically compared item by item with the allowable settlement rate threshold, and a weighted scoring method is used to calculate the compliance score for that stage. When three consecutive samples exceed the threshold, an anomaly is automatically marked. The process code and timestamp are extracted based on the IPv6 address of the anomaly node, and the responsible party is obtained by querying the node identification table. Subsequently, as... Figure 4 As shown in the hazard tracing path diagram, starting from the abnormal node, the graph depth-first search algorithm is used to traverse the directed acyclic graph along the construction process in reverse, calculate the responsibility contribution of the upstream node to the current abnormality, locate the root cause node, and generate a hazard tracing report containing a multi-level tracing chain.

[0028] 3. Compliance verification and differentiated grading early warning during the main construction phase During the main construction phase, wall verticality monitoring nodes collect vertical deviation data in real time and compare it against the verticality deviation threshold specified in GB 50203. When a node detects a deviation exceeding the specified range, abnormal node information is extracted to construct a hazard propagation path diagram, tracing back to the upstream load-bearing structure strain anomaly node that caused the deviation. The warning level is determined based on a comprehensive hazard score: if the score is 65, a data packet is sent to the configured secondary warning IPv6 multicast address range; if the score is 80, a targeted notification is sent to the tertiary warning multicast address. The monitoring platform, construction unit terminal, and homeowner terminal join the corresponding level multicast group via the MLD protocol to receive warnings and unicast back the response and handling results, writing them into the full-process archive to form a closed-loop record.

[0029] 4. Comprehensive safety assessment during the completion and acceptance phase During the completion and acceptance phase, a comprehensive safety assessment is conducted based on data from the entire process. A three-tiered assessment structure is constructed. A pairwise comparison matrix is ​​built at the criterion level through expert judgment. The weight vector is calculated using the square root method. After consistency ratio verification, the weight coefficients for compliance score, traceability integrity score, and sensor status score are determined as α=0.5, β=0.3, and γ=0.2, respectively. Each score is normalized to the 0-100 range and then input into the comprehensive assessment formula to calculate the comprehensive building safety assessment value H. If H ≥ 85, "Compliance Passed" is output; if 60 ≤ H < 85, "Rectification Required and Re-inspection Required" is output; if H < 60, a mandatory rectification process is triggered and a Level 1 warning is issued, achieving closed-loop supervision throughout the entire process.

[0030] Example 2: IPv6-based rural building end-to-end compliance verification and hidden danger traceability system To support the effective implementation of the above methods, this invention provides a rural construction full-process compliance verification and hidden danger traceability system based on IPv6, such as... Figure 5As shown in the overall system architecture diagram, the system is deployed at the construction site and in the cloud, and is divided into four layers: perception layer, edge layer, processing layer, and application layer. 1. Perception layer The perception layer includes foundation settlement sensing nodes, load-bearing structure strain monitoring nodes, wall verticality and tilt angle nodes, and construction image nodes deployed at the construction site. Each node has a built-in IPv6 communication protocol stack, supporting communication with the edge layer gateway via wireless or wired IPv6 networks, and is responsible for real-time collection of multi-dimensional physical parameters and image data from the construction site.

[0031] 2. Edge layer The edge layer comprises an IPv6 gateway and edge computing nodes. The IPv6 gateway, serving as the core on-site access device, uses DHCPv6 stateful allocation to assign semantically encoded IPv6 addresses with embedded building unique identifiers to each node in the sensing layer, and aggregates data streams from each node. The edge computing nodes preprocess the aggregated data, performing differential sampling on sensor data such as foundation settlement, strain, and verticality to reduce data redundancy, and extracting keyframes from construction image data using an inter-frame differential algorithm. The processed data, along with IPv6 stream tags, is then encapsulated and reported to the processing layer.

[0032] 3. Processing layer The processing layer, the core brain of the system, is deployed in the cloud and includes a compliance verification engine, a potential hazard tracing module, an early warning push module, and a comprehensive assessment module. Compliance Verification Engine: Built-in compliance verification rule library based on national standards such as GB 50203 and GB 50202. This engine receives time-series data streams reported from the edge layer, extracts process, personnel, and time information from the stream label field, compares the data of each construction stage item by item, calculates the compliance score using a weighted formula, and outputs an anomaly flag table.

[0033] Hazard Tracing Module: Upon receiving the anomaly marker table, this module queries the responsible entity information based on the mapping relationship between IPv6 addresses and process codes. Starting from the anomaly node, it uses a graph depth-first search algorithm to traverse the directed acyclic graph of the construction process in reverse, calculates the responsibility contribution of each upstream node, and generates a hazard tracing report containing root cause location and a responsibility list.

[0034] Early warning push module: Based on the comprehensive risk assessment, three early warning levels are defined, and each level is configured with an independent IPv6 multicast address range. When an anomaly is detected, this module sends an early warning data packet to the corresponding multicast address to achieve differentiated targeted notification, and receives the handling results transmitted back by unicast from each terminal.

[0035] Comprehensive Assessment Module: During the completion and acceptance phase or routine inspections, this module uses the analytic hierarchy process to construct a judgment matrix, comprehensively calculates and outputs a comprehensive building safety assessment value by weighting the compliance score, the completeness score of hazard tracing, and the real-time status score of sensors, and triggers the corresponding rectification process.

[0036] 4. Application Layer The application layer includes a monitoring platform, construction unit terminals, and homeowner terminals. Each terminal device acts as an IPv6 network node, dynamically joining the corresponding IPv6 multicast group for the appropriate alert level via the MLD (Multicast Listener Discovery) protocol. When the alert push module sends a multicast alert, the corresponding terminal can accurately receive the alert information and execute the response and handling process. After the handling is completed, each terminal transmits the results back to the full-process archive in the processing layer via IPv6 unicast, completely breaking down data silos and forming a closed-loop management system covering "perception-verification-tracing-early warning-response".

[0037] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0038] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for full-process compliance verification and hidden danger tracing of rural buildings based on IPv6, characterized in that, Includes the following steps: S101. Based on the DHCPv6 protocol, assign a globally unique IPv6 address to each monitoring node at the construction site, bind the IPv6 address with the building site number and geographical coordinates, establish a digital identity mapping relationship, and generate a node identity identification table. S102. Collect foundation settlement sensor data, load-bearing structure strain data, wall verticality data and construction image data at the construction site. Use the IPv6 message flow label field to encode the construction process type, construction personnel identification and timestamp to generate a multi-dimensional time-series data stream with process identification. S103. Based on national construction standards and specifications, a compliance verification rule base is constructed. The data of each construction stage in the multi-dimensional time-series data stream with process identification is automatically compared item by item. The weighted scoring method is used to calculate the compliance score of each stage and output the full-process compliance score and anomaly mark table. S104. If a compliance anomaly is detected, the corresponding construction node identifier and construction timestamp are extracted from the anomaly marker table based on the mapping relationship between IPv6 address and process code. The corresponding responsible entity information is obtained by querying the node identity identifier table. A hazard propagation path graph is constructed using a graph depth-first search algorithm. The responsibility contribution of each upstream node to the current anomaly is calculated, and a hazard tracing report is generated. S105. Statistically analyze the completeness of information acquisition for each tracing path node in the hazard tracing report, and calculate the hazard tracing completeness score based on the percentage of complete nodes. The edge layer continuously monitors the online status, data packet reporting success rate, and sensor calibration status of each sensing layer node, and calculates the real-time status score of the sensor by weighting. S106. Determine the level of hidden danger based on the hidden danger source tracing report and anomaly marking table, use IPv6 multicast mechanism to synchronously push graded early warning information to the supervision platform, construction unit terminal and homeowner terminal, execute the corresponding response and disposal process, and write the disposal results into the full process file. S107. Combining the overall compliance score, the hazard tracing integrity score, and the sensor real-time status score, the weighting coefficients are calculated using the analytic hierarchy process (AHP) and the weighted output is the comprehensive building safety assessment value.

2. The method according to claim 1, characterized in that, The method of step S101 specifically includes the following sub-steps: The homestead number, latitude and longitude coordinates, and construction permit number of the target building are obtained from the rural homestead management system. The above information is hashed to obtain a 64-bit unique building identifier. Using the DHCPv6 stateful allocation mode, the building's unique identifier is embedded in the interface identifier field of the IPv6 address, generating semantically meaningful IPv6 addresses for the foundation monitoring node, load-bearing structure monitoring node, and construction camera node respectively. A four-element mapping relationship is established between IPv6 addresses and node physical location, node type, and construction stage. This relationship is written into a node identity table, which is stored using a distributed hash table and supports multi-dimensional retrieval by building identifier, construction stage, and time range.

3. The method according to claim 1, characterized in that, In step S102, the encoding of the construction process type, construction personnel identification, and timestamp using the IPv6 packet flow label field specifically includes: The 20-bit flow label field in the IPv6 packet header is divided into three subfields: the high 6 bits are used to encode the construction procedure type, supporting a total of 64 procedure categories; the middle 8 bits are used to encode the construction worker identification code; and the low 6 bits are used to encode the construction time period number for the day. Differential sampling methods are used to reduce data redundancy for foundation settlement data, strain data and verticality data. The sampled sensor values ​​are encapsulated with the corresponding IPv6 packets to form sensor data packets with process identifiers. The inter-frame difference algorithm is used to extract key frames from the construction image data. The hash value of the key frame image and the IPv6 flow tag are written into the time series database to form a multi-dimensional time series data stream covering the entire construction cycle.

4. The method according to claim 1, characterized in that, The method in step S103 specifically includes the following sub-steps: Quantitative indicators are extracted from national standards such as the "Code for Acceptance of Construction Quality of Masonry Structures" GB 50203 and the "Standard for Acceptance of Construction Quality of Building Foundation Engineering" GB 50202. The textual specifications are transformed into a set of calculable constraints, and a structured rule library containing the foundation bearing capacity threshold, wall verticality deviation threshold, and mortar strength grade lower limit is constructed. The actual measured values ​​at each construction stage are compared with the constraints of the rule base item by item. Indicators that exceed the threshold range are marked as abnormal, and the number and magnitude of abnormal items at each stage are counted. Differentiated indicator weights are set for each construction stage, and the compliance score for each stage is calculated using the following weighted formula: ； in For the first Compliance score during the construction phase For the first Phase 1 The weighting coefficient of each indicator For the first The individual score of each indicator, This represents the total number of indicators for this stage; the weighting coefficients are determined by constructing a judgment matrix using the analytic hierarchy process and performing a consistency check.

5. The method according to claim 1, characterized in that, In step S104, the step of constructing a hazard propagation path graph using a graph depth-first search algorithm and calculating the responsibility contribution of each upstream node to the current anomaly specifically includes: Using the IPv6 addresses of construction nodes as graph nodes and the sequential dependencies of construction procedures as directed edges, construct a directed acyclic graph of construction procedures. Starting from the anomaly marker node, a depth-first search algorithm is used to traverse backwards along the directed edges to identify the set of upstream construction nodes that have a causal relationship with the anomaly node. Let the abnormal node be The identified set of upstream nodes is ,node Along the directed edge to The process dependency weight is ,node The normalized anomaly magnitude of the corresponding indicator's measured value exceeding the rule base threshold is: ,node and The number of hops between the paths is Preset attenuation coefficient The nodes are calculated using the following formula. Contribution to responsibility for the current anomaly: ; in Based on the degree of responsibility and contribution Sort from largest to smallest to determine the root cause nodes and the order of contribution of each responsible entity; Based on the ranking of responsibility contribution, the construction node identifier, corresponding IPv6 address, construction timestamp and responsible entity information of each traceability path are extracted to generate a hidden danger traceability report containing a multi-level traceability chain. The report includes four parts: hidden danger type description, root cause node location, list of responsible entities and rectification suggestions.

6. The method according to claim 1, characterized in that, The method in step S105 specifically includes the following sub-steps: The completeness of obtaining the node identifier, IPv6 address, construction timestamp, and responsible entity information of each tracing path node in the hazard tracing report is statistically analyzed, and the proportion of the number of nodes with complete information to the total number of nodes in the tracing path is used as the hazard tracing completeness score. The edge layer continuously monitors the online status of each sensing layer node, the data packet reporting success rate, and whether the sensor's most recent calibration is qualified. The three indicators are weighted and summed according to preset weights to obtain the sensor's real-time status score.

7. The method according to claim 1, characterized in that, The method in step S106 specifically includes the following sub-steps: Based on the comprehensive hazard score, the warning level is divided into three levels: Level 1 warning is for a score below 60 points, Level 2 warning is for a score between 60 and 75 points, and Level 3 warning is for a score between 75 and 85 points. Each of the three warning levels is configured with an independent IPv6 multicast address range. The monitoring platform, construction unit terminal and homeowner terminal join the corresponding level multicast group through the MLD protocol. When the compliance verification engine outputs an anomaly flag, it sends a warning data packet to the corresponding multicast address based on the level of the potential hazard, thereby achieving differentiated targeted notification. The early warning response results are transmitted back by each terminal via unicast and written into the full-process archive, forming a complete early warning-response closed-loop record.

8. The method according to claim 1, characterized in that, The method in step S107 specifically includes the following sub-steps: A three-tiered assessment hierarchy is constructed: the target layer is the comprehensive assessment value of building safety; the criteria layer includes compliance score, hazard traceability integrity score, and sensor real-time status score; and the indicator layer consists of the specific scoring items under each criteria layer. The criteria layer is constructed by expert judgment, and the pairwise comparison judgment matrix is ​​constructed. The weight vector of each criterion is calculated by the square root method, and the consistency ratio is checked to ensure that the consistency ratio is less than the preset threshold. The comprehensive safety assessment value of a building is calculated using the following formula: ； in, , , The weighting coefficients for compliance score, traceability integrity score, and sensor status score are respectively, satisfying... ; , , All scores are normalized to a range of 0 to 100; a mandatory rectification process is triggered when the evaluation score is below 60.

9. A rural building compliance verification and hidden danger traceability system based on IPv6, characterized in that, include: The sensing layer includes foundation settlement sensing nodes, load-bearing structure strain monitoring nodes, construction image nodes, and verticality and tilt angle nodes deployed at the construction site. Each node transmits data via an IPv6 network; The edge layer includes an IPv6 gateway and edge computing nodes; the IPv6 gateway is used to allocate IPv6 addresses to the perception layer nodes and aggregate data. The edge computing nodes are used to preprocess and differentially sample the aggregated data; The processing layer includes a compliance verification engine, a hidden danger tracking module, an early warning push module, and a comprehensive assessment module; The compliance verification engine is used to compare data at each construction stage and output compliance scores and anomaly markers based on a compliance verification rule library built on national construction standards and specifications. The hazard tracing module is used to query the responsible entity information based on the mapping relationship between IPv6 address and process code when an anomaly is detected, construct a hazard propagation path graph using a graph depth-first search algorithm, and generate a hazard tracing report. The early warning push module is used to push graded early warning information to application layer terminals based on the level of potential risks using the IPv6 multicast mechanism. The comprehensive evaluation module is used to combine the compliance score of the entire process, the integrity score of hazard tracing, and the real-time status score of sensors using the analytic hierarchy process to output a comprehensive building safety evaluation value. The application layer includes a monitoring platform, a construction unit terminal, and a homeowner terminal. The monitoring platform, construction unit terminal, and homeowner terminal join the multicast group corresponding to the warning level through the MLD protocol, receive the warning information sent by the warning push module, and send the response results back to the full-process archive of the processing layer.

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