A historical and cultural building data monitoring and safety evaluation method and system

Abstract

The present application relates to the technical field of building data monitoring, in particular to a historical and cultural building data monitoring and safety evaluation method and system, which divides historical and cultural building monitoring zones and arranges distributed Internet of Things monitoring networks in the monitoring zones; collects and encrypts transmission of multi-source time sequence monitoring data in full dimensions; carries out hierarchical preprocessing and adaptive fusion of multi-source time sequence monitoring data, and constructs a hierarchical safety evaluation index system; improves fuzzy comprehensive safety quantitative evaluation by using improved analytic hierarchy process, and carries out graded early warning and protective treatment based on evaluation results and early warning grades, which avoids the problems of traditional monitoring, such as one-size-fits-all, localization and damage to cultural relics, by differentiating six-zone differentiated non-destructive arrangement, distinguishing building structure importance, matching monitoring equipment and collection frequency, and realizing full quantitative rating by using adaptive AHP improvement algorithm combined with fuzzy comprehensive evaluation, so as to eliminate artificial subjective errors, and the evaluation results are scientific, accurate and highly targeted.

Description

Technical Field

[0001] This invention relates to the field of building data monitoring technology, and in particular to a method and system for monitoring and assessing the safety of historical and cultural buildings. Background Technology

[0002] Historical and cultural buildings are irreplaceable cultural relics that carry the historical context, traditional architectural techniques, and folk culture of a region, and are the core carriers of cultural heritage protection. my country currently has a large number of ancient residences, ancestral halls, temples, city walls, and other historical buildings, most of which are wooden, brick-and-stone, or brick-and-wood mixed structures. Having endured years of natural erosion, structural aging, human disturbance, and the impact of extreme weather, they commonly suffer from safety problems such as foundation settlement, wall cracking, component aging, and weathering damage. These conditions make them highly susceptible to structural instability, collapse, and other safety accidents, causing irreparable damage to cultural relics.

[0003] Currently, the industry's safety monitoring and assessment of historical and cultural buildings suffers from several core technological shortcomings: First, the monitoring methods are rudimentary, mostly relying on manual periodic inspections and single-point sampling tests. These methods have limited monitoring dimensions and coverage, failing to capture hidden and transient structural hazards and environmental disturbance risks, and are extremely inefficient. Second, existing monitoring equipment is mostly general-purpose equipment for modern buildings, not designed for the non-destructive protection of ancient buildings and cultural relics. This can easily cause secondary damage to the ancient buildings themselves, and the lack of standardized monitoring based on building zones and component importance results in poor data specificity. Third, there is a lack of temporal prediction capabilities; only post-event hazard detection is possible, unable to predict the evolution trend of damage, making preventative protection difficult.

[0004] Based on this, the present invention proposes a method and system for monitoring and assessing the safety of historical and cultural buildings. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for monitoring and assessing the safety of historical and cultural buildings, aiming to solve the problems of crude monitoring, weak predictive ability, and poor adaptability to protection in traditional building safety monitoring and assessment technologies.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for monitoring and assessing the safety of historical and cultural buildings, comprising the following steps: Historical and cultural buildings are divided into monitoring zones, and a distributed Internet of Things (IoT) monitoring network is deployed in the monitoring zones. Based on the distributed IoT monitoring network, multi-source time-series monitoring data is collected and transmitted in all dimensions with encryption. The multi-source time-series monitoring data is subjected to hierarchical preprocessing and adaptive fusion, and a hierarchical security assessment index system is constructed. An improved analytic hierarchy process is used to enhance the fuzzy comprehensive security quantitative assessment, and graded early warning and protection measures are implemented based on the assessment results and early warning levels.

[0007] The specific method for dividing the historical and cultural building monitoring zones and deploying non-destructive differentiated sensor networks in the monitoring zones is as follows: Historical and cultural buildings are divided into six independent monitoring zones: foundation settlement monitoring zone, main load-bearing structure zone, auxiliary component zone, facade protection zone, indoor material aging monitoring zone, and external environmental disturbance zone. Based on the structural characteristics of each monitoring zone and the requirements for cultural relic protection, high-precision monitoring sensors are deployed in a differentiated manner to construct a distributed Internet of Things monitoring network.

[0008] The multi-source time-series monitoring data includes static structural data, dynamic environmental data, component appearance damage data, and instantaneous disturbance data. The static structural data includes foundation settlement, component tilt, structural deformation, crack width and extension length. The dynamic environmental data includes ambient temperature and humidity, diurnal temperature difference, wind speed and wind pressure, and rainwater moisture content. The component appearance damage data includes component damage, insect infestation, weathering, and detachment characteristics. The instantaneous disturbance data includes vibration from pedestrian traffic, equipment disturbance, and vibration from extreme weather impacts.

[0009] The specific method for performing hierarchical preprocessing and adaptive fusion of the multi-source time-series monitoring data and constructing a hierarchical security assessment index system is as follows: The multi-source time-series monitoring data is subjected to hierarchical preprocessing; An improved weighted fusion algorithm is used to associate and register structured numerical data with image visualization feature data; Based on the structural mechanics characteristics of ancient buildings, national standards for cultural relic protection, and the mechanism of disease evolution, a four-level hierarchical safety assessment index system was established.

[0010] The specific method for improving fuzzy comprehensive security quantification assessment using the improved analytic hierarchy process and implementing graded early warning and protection measures based on the assessment results is as follows: An improved analytic hierarchy process is used to dynamically and adaptively adjust the weight coefficients of evaluation indicators at each level, weakening the weights of indicators with poor adaptability and strengthening the weights of disease-sensitive indicators. The standardized monitoring dataset is imported into the fuzzy comprehensive evaluation model. The scores of individual indicators are calculated through the membership matrix, and the overall safety score of the building is obtained by combining dynamic weights and weighted summation, and the warning level is divided. Based on the assessment results and the classification levels, a corresponding four-level early warning mechanism and dedicated response strategies are matched.

[0011] The criteria for classifying early warning levels include: a comprehensive score of 90-100 indicates a safe level, with no structural abnormalities and no risk of disease development; a comprehensive score of 75-89 indicates a basically safe level, with a stable structure, minor controllable defects, and no immediate safety hazards; a comprehensive score of 60-74 indicates a hidden danger level, with obvious component defects or local structural deformation, and the defects have the risk of continued development; and a comprehensive score below 60 indicates a dangerous level, with structural instability, large-area cracking, and severe settlement problems, posing a significant safety risk.

[0012] Secondly, the present invention also provides a method and system for monitoring and assessing the security of historical and cultural buildings, which is applied to the method for monitoring and assessing the security of historical and cultural buildings as described in the first aspect above, including a zone monitoring module, an encrypted transmission module, a data preprocessing module, a security assessment module, a time-series disease prediction module, a graded early warning and disposal module, a source tracing and storage module, and a human-computer interaction module; The zoning monitoring module is used to divide ancient buildings into multiple areas, enabling multi-dimensional, differentiated, and real-time data collection. The encrypted transmission module is used to realize encrypted transmission of differentiated frequency data, data verification, and breakpoint resumption; The data preprocessing module is used for denoising, error correction, completion, normalization and fusion registration of multi-source data to generate a standardized monitoring dataset. The security assessment module is used to dynamically adjust weights and quantify security levels; The time-series defect prediction module is used to predict the evolution trend of component defects and structural safety. The tiered early warning and response module is used to match the early warning level, generate differentiated maintenance, repair, and emergency response plans, and complete the closed-loop management. The traceability storage module is used to archive all basic building information, monitoring data, assessment reports, early warning records, and disposal ledgers; The human-computer interaction module is used for real-time data display, remote parameter configuration, early warning message push, and on-site inspection data upload.

[0013] This invention discloses a method for monitoring and assessing the safety of historical and cultural buildings. The method involves dividing the historical and cultural buildings into monitoring zones and deploying a distributed Internet of Things (IoT) monitoring network within each zone. Based on this network, multi-source time-series monitoring data is collected and transmitted with encryption. The data undergoes hierarchical preprocessing and adaptive fusion, and a hierarchical safety assessment index system is constructed. An improved analytic hierarchy process (AHP) is used to enhance fuzzy comprehensive safety quantification assessment. Based on the assessment results and warning levels, graded early warning and protective measures are implemented. This method utilizes a six-zone differentiated non-destructive deployment scheme to distinguish between structurally important buildings. To ensure that monitoring equipment and acquisition frequencies are matched with the characteristics of the damage, the traditional monitoring methods are avoided due to their one-size-fits-all approach, localization, and potential damage to cultural relics. Through five refined processing steps—layered noise reduction, error correction, completion, normalization, and fusion registration—the problems of fragmented, time-series misaligned, and large errors in multi-source heterogeneous data are thoroughly solved. An improved AHP algorithm that is adaptive to materials and grades is adopted, combined with fuzzy comprehensive evaluation to achieve full-quantitative rating, eliminating subjective human error. The assessment results are scientific, accurate, and highly targeted, matching the maintenance, repair, and emergency needs of buildings in different safety states, forming a complete closed-loop management system, and significantly improving the implementation and effectiveness of ancient building safety protection. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0015] Figure 1 This is a flowchart of a method for monitoring and assessing the safety of historical and cultural buildings provided by the present invention.

[0016] Figure 2 This is a flowchart of step S1 of a method for monitoring and assessing the safety of historical and cultural buildings provided by the present invention.

[0017] Figure 3 This is a flowchart of step S3 of a method for monitoring and assessing the safety of historical and cultural buildings provided by the present invention.

[0018] Figure 4 This is a flowchart of step S4 of a method for monitoring and assessing the safety of historical and cultural buildings provided by the present invention.

[0019] Figure 5 This is a schematic diagram of a historical and cultural building data monitoring and safety assessment system provided by the present invention.

[0020] In the diagram: 1-Partition monitoring module, 2-Encrypted transmission module, 3-Data preprocessing module, 4-Security assessment module, 5-Time-series disease prediction module, 6-Graded early warning and response module, 7-Source traceability and storage module, 8-Human-computer interaction module. Detailed Implementation

[0021] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0022] Please see Figures 1-4 In a first aspect, the present invention provides a method for monitoring and assessing the safety of historical and cultural buildings, comprising the following steps: S1 divides the historical and cultural buildings into monitoring zones and deploys a distributed Internet of Things (IoT) monitoring network in the monitoring zones; Specific methods: S11 divides historical and cultural buildings into six independent monitoring zones: foundation settlement monitoring zone, main load-bearing structure zone, auxiliary component zone, facade protection zone, indoor material aging monitoring zone, and external environmental disturbance zone. In this embodiment of the invention, based on the cultural relic protection level, building structure, main load-bearing system, functional importance of components, and high-incidence areas of defects of historical and cultural buildings, the buildings are divided into six independent monitoring zones: foundation settlement monitoring zone, main load-bearing structure zone, auxiliary component zone, facade protection zone, indoor material aging monitoring zone, and external environmental disturbance zone. S12, based on the structural characteristics of each monitoring zone and the requirements for cultural relic protection, deploys differentiated high-precision monitoring sensors to construct a distributed Internet of Things monitoring network.

[0023] In this embodiment of the invention, considering the structural characteristics of each zone and the requirements for cultural relic protection, a non-drilling, non-destructive bonding method is adopted to deploy high-precision monitoring sensors in a differentiated manner, constructing a distributed Internet of Things monitoring network with layered encryption, redundancy in key areas, and regular coverage in secondary areas. High-precision fiber optic settlement sensors with a sampling accuracy of ≤0.01mm are deployed in the foundation settlement monitoring area; tilt sensors and micro-deformation sensors are deployed in the main load-bearing beams, columns, and walls; temperature and humidity sensors and high-definition macro image acquisition equipment are deployed in wooden components and antique-style finishes; and high-sensitivity vibration sensors and wind speed and pressure sensors are deployed in areas with street-facing and densely populated external environmental disturbances. All sensors are installed using a non-destructive method of silicone bonding and external bracket fixation, eliminating operations such as drilling and cutting that could damage the cultural relic. Dual-sensor redundancy is used in key diseased areas to improve monitoring reliability.

[0024] S2 performs full-dimensional collection and encrypted transmission of multi-source time-series monitoring data based on the distributed IoT monitoring network; In this embodiment of the invention, based on a deployed distributed monitoring network, differentiated acquisition frequencies are set to collect static structural data, dynamic environmental data, component appearance defect data, and instantaneous disturbance data 24 hours a day. Among them, static structural data includes foundation settlement, component tilt, structural deformation, crack width and extension length; dynamic environmental data includes ambient temperature and humidity, diurnal temperature difference, wind speed and wind pressure, and rainwater moisture content; component appearance defect data includes component damage, insect infestation, weathering, and detachment characteristics; and instantaneous disturbance data includes vibration from pedestrian traffic, equipment disturbance, and vibration from extreme weather impacts. All collected raw time-series data are transmitted to a cloud database through an encrypted 5G IoT link, and data verification, breakpoint resumption, and raw data backup are completed simultaneously. Under normal weather conditions, the static structural data acquisition frequency is 10 minutes / time, the dynamic environmental data acquisition frequency is 5 minutes / time, and the image defect acquisition frequency is 1 hour / time. Under extreme high temperature, rainstorm, strong wind, and snowstorm conditions, the high-frequency acquisition mode is automatically triggered, and the structural and environmental data acquisition frequency is increased to 1 minute / time. Vibration disturbance data is acquired in real time at high frequency, realizing instantaneous capture of hidden dangers under extreme working conditions.

[0025] S3 performs hierarchical preprocessing and adaptive fusion on the multi-source time-series monitoring data, and constructs a hierarchical security assessment index system; Specific methods: S31 performs hierarchical preprocessing on the multi-source time-series monitoring data; In this embodiment of the invention, a layered and refined preprocessing is performed, sequentially completing high-frequency noise removal, abnormal data identification and removal, intelligent completion of time-series missing data, and multi-dimensional data dimension normalization. Specifically, a wavelet soft threshold denoising algorithm is used to filter high-frequency noise generated by equipment jitter and environmental electromagnetic interference; abnormal and abrupt data exceeding a reasonable threshold are identified and removed based on the 3σ criterion; for short-term data gaps, a dual completion method of time-series linear interpolation and adjacent monitoring node correlation data is used to repair data gaps; and an extreme value normalization algorithm is used to uniformly map data of different dimensions such as settlement, deformation, temperature and humidity, and image features to the 0-1 standard range, completing accurate fusion and registration of multi-source data.

[0026] S32 employs an improved weighted fusion algorithm to correlate and register structured numerical data with image visualization feature data; In this embodiment of the invention, an improved weighted fusion algorithm is used to associate and register structured numerical data and image visualization feature data, eliminating the problems of temporal misalignment and dimensional differences in multi-source data, and generating a standardized ancient building monitoring dataset that is temporally continuous, dimensionally unified, and has controllable errors.

[0027] Based on the structural mechanics characteristics of ancient buildings, national standards for cultural relic protection, and the mechanism of disease evolution, S33 establishes a four-level hierarchical safety assessment index system.

[0028] In this embodiment of the invention, based on the structural mechanics characteristics of ancient buildings, national standards for cultural relic protection, and the mechanism of disease evolution, a four-level hierarchical safety assessment index system is established. This system includes four primary indicators: structural safety, environmental adaptability, component integrity, and external force tolerance; twelve secondary indicators such as foundation stability, structural deformation, temperature and humidity adaptability, component aging, and load disturbance; and twenty-two tertiary quantitative indicators such as settlement rate, tilt angle, crack opening and closing degree, temperature and humidity fluctuation difference, and component damage rate. For the three mainstream ancient building materials—wood, brick and stone, and brick and wood composite—normal thresholds, warning thresholds, danger thresholds, and tolerance ranges are set for each indicator.

[0029] S4 employs an improved analytic hierarchy process to enhance fuzzy comprehensive security quantification assessment, and implements tiered early warning and protection measures based on the assessment results and early warning levels.

[0030] Specific methods: S41 uses an improved analytic hierarchy process to dynamically and adaptively correct the weight coefficients of evaluation indicators at each level, weakening the weights of indicators with poor adaptability and strengthening the weights of disease-sensitive indicators. In this embodiment of the invention, an improved analytic hierarchy process (AHP) is adopted. Based on the construction materials, repair period, and cultural relic protection level of the historical and cultural building to be tested, the weight coefficients of the evaluation indicators at each level are dynamically and adaptively adjusted, weakening the weight of indicators with poor adaptability and strengthening the weight of indicators sensitive to defects.

[0031] The specific rules for dynamic weight adaptive adjustment include: For ancient buildings with pure wooden structures, the weights of indicators such as temperature and humidity fluctuations, insect infestation of components, and wood aging are increased, while the weight of indicators such as foundation settlement is decreased. For ancient buildings with brick and stone structures, the weights of indicators such as settlement rate, wall cracks, and weathering damage are increased, while the weights of indicators sensitive to temperature and humidity are decreased. For ancient buildings under provincial or higher-level key protection, the weights of structural safety indicators are increased overall to enhance the rigor of the overall safety assessment. For ordinary historical buildings, the weights of various indicators are balanced to meet the needs of routine safety management.

[0032] S42 imports the standardized monitoring dataset into the fuzzy comprehensive evaluation model, calculates the score of each indicator through the membership matrix, and obtains the overall safety score of the building by combining dynamic weights and weighted summation, and classifies the warning level. In this embodiment of the invention, a standardized monitoring dataset is imported into a fuzzy comprehensive evaluation model. The score of each individual indicator is calculated through a membership matrix, and the overall safety score of the building is obtained by combining dynamic weights and weighted summation. The model accurately classifies four safety levels: safe, basically safe, hidden danger, and dangerous. At the same time, an LSTM time-series prediction model is trained based on long-term historical monitoring data to predict the development trend of component defects and the evolution law of structural safety in future cycles.

[0033] The criteria for classifying early warning levels are as follows: a comprehensive score of 90-100 indicates a safe level, with no structural abnormalities and no risk of disease development; a comprehensive score of 75-89 indicates a basically safe level, with a stable structure, minor and controllable defects, and no immediate safety hazards; a comprehensive score of 60-74 indicates a hidden danger level, with obvious component defects or local structural deformation, and the defects have the risk of continued development; and a comprehensive score below 60 indicates a dangerous level, with structural instability, large-area cracking, and severe settlement problems, posing a significant safety risk.

[0034] S43 is based on the assessment results and the classification levels, matching a corresponding four-level early warning mechanism and dedicated handling strategies.

[0035] In this implementation plan, based on the assessed building safety level, defect prediction results, and early warning level, a corresponding four-level early warning mechanism and dedicated handling strategies are matched; the green safety level maintains routine monitoring and quarterly regular inspections; the yellow basic safety level optimizes monitoring frequency, increases inspection frequency in key areas, and outputs preventive maintenance plans; the orange hidden danger level triggers two-way audio-visual early warnings on the cloud and terminals, pinpoints the precise location of defects, and generates localized minimally invasive repair plans; the red danger level triggers emergency early warnings, and pushes out plans for personnel evacuation, temporary reinforcement, closed management, and special repairs; all handling records, assessment results, and monitoring data are archived in real time, forming a closed-loop safety management process.

[0036] Please see Figure 5 Secondly, the present invention also provides a method and system for monitoring and assessing the security of historical and cultural building data, which is applied to the method for monitoring and assessing the security of historical and cultural building data as described in the first aspect above, including a zone monitoring module 1, an encrypted transmission module 2, a data preprocessing module 3, a security assessment module 4, a time-series disease prediction module 5, a graded early warning and disposal module 6, a traceability and storage module 7, and a human-computer interaction module 8. The zoning monitoring module 1 is used to divide ancient buildings into multiple areas, enabling multi-dimensional, differentiated, and all-time data collection; The encrypted transmission module 2 is used to realize encrypted transmission of differentiated frequency data, data verification, and breakpoint resume transmission; The data preprocessing module 3 is used for denoising, error correction, completion, normalization and fusion registration of multi-source data to generate a standardized monitoring dataset; The security assessment module 4 is used to dynamically adjust weights and quantify security levels; The time-series defect prediction module 5 is used to predict the evolution trend of component defects and structural safety. The graded early warning and response module 6 is used to match the early warning level, generate differentiated maintenance, repair, and emergency response plans, and complete the closed-loop management. The traceability storage module 7 is used to archive all building basic information, monitoring data, assessment reports, early warning records, and disposal ledgers; The human-computer interaction module 8 is used for real-time data display, remote parameter configuration, early warning message push, and on-site inspection data upload.

[0037] In this implementation scheme, the zoning monitoring module 1 is used to accurately divide the ancient building into multiple areas and deploy differentiated non-destructive sensors to achieve multi-dimensional, differentiated, and all-time data collection. The encrypted transmission module 2 is used to achieve differentiated frequency data collection, encrypted transmission, data verification, and breakpoint resumption to ensure the security and integrity of data transmission. The data preprocessing module 3 is used to complete multi-source data denoising, error correction, completion, normalization, and fusion registration to generate a standardized monitoring dataset. The safety assessment module 4 incorporates a multi-material adaptation assessment index system and an improved AHP-fuzzy comprehensive evaluation algorithm to dynamically adjust weights and quantify safety levels. The time-series disease prediction module 5 mines the patterns in time-series data based on the LSTM model to predict the evolution trend of component diseases and structural safety. The graded early warning and disposal module 6 matches four early warning levels and automatically generates differentiated maintenance, repair, and emergency disposal plans to complete the closed-loop management. The traceability and storage module 7 is used to archive all building basic information, monitoring data, assessment reports, early warning records, and disposal ledgers, supporting full data traceability and statistical analysis. The human-computer interaction module 8 includes a PC-based management backend and a mobile app for real-time data display, remote parameter configuration, early warning message push, and on-site inspection data upload.

[0038] The above-disclosed embodiments are merely preferred embodiments of a method and system for monitoring and assessing the data of historical and cultural buildings, and should not be construed as limiting the scope of the claims of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments, and equivalent variations made in accordance with the claims of this application, still fall within the scope of this application.

Claims

1. A method for monitoring and assessing the safety of historical and cultural buildings, characterized in that, Includes the following steps: Historical and cultural buildings are divided into monitoring zones, and a distributed Internet of Things (IoT) monitoring network is deployed in the monitoring zones. Based on the distributed IoT monitoring network, multi-source time-series monitoring data is collected and transmitted in all dimensions with encryption. The multi-source time-series monitoring data is subjected to hierarchical preprocessing and adaptive fusion, and a hierarchical security assessment index system is constructed. An improved analytic hierarchy process is used to enhance the fuzzy comprehensive security quantitative assessment, and graded early warning and protection measures are implemented based on the assessment results and early warning levels.

2. The method for monitoring and assessing the safety of historical and cultural buildings as described in claim 1, characterized in that, The specific method for dividing the historical and cultural building monitoring zones and deploying lossless differentiated sensor networks in the monitoring zones is as follows: Historical and cultural buildings are divided into six independent monitoring zones: foundation settlement monitoring zone, main load-bearing structure zone, auxiliary component zone, facade protection zone, indoor material aging monitoring zone, and external environmental disturbance zone. Based on the structural characteristics of each monitoring zone and the requirements for cultural relic protection, high-precision monitoring sensors are deployed in a differentiated manner to construct a distributed Internet of Things monitoring network.

3. The method for monitoring and assessing the safety of historical and cultural buildings as described in claim 1, characterized in that, The multi-source time-series monitoring data includes static structural data, dynamic environmental data, component appearance damage data, and instantaneous disturbance data. The static structural data includes foundation settlement, component tilt, structural deformation, crack width and extension length. The dynamic environmental data includes ambient temperature and humidity, diurnal temperature difference, wind speed and wind pressure, and rainwater moisture content. The component appearance damage data includes component damage, insect infestation, weathering, and detachment characteristics. The instantaneous disturbance data includes vibration from pedestrian traffic, equipment disturbance, and vibration from extreme weather impacts.

4. The method for monitoring and assessing the safety of historical and cultural buildings as described in claim 1, characterized in that, The specific method for performing hierarchical preprocessing and adaptive fusion of the multi-source time-series monitoring data, and constructing a hierarchical security assessment index system is as follows: The multi-source time-series monitoring data is subjected to hierarchical preprocessing; An improved weighted fusion algorithm is used to associate and register structured numerical data with image visualization feature data; Based on the structural mechanics characteristics of ancient buildings, national standards for cultural relic protection, and the mechanism of disease evolution, a four-level hierarchical safety assessment index system was established.

5. The method for monitoring and assessing the safety of historical and cultural buildings as described in claim 1, characterized in that, The specific method for improving fuzzy comprehensive security quantitative assessment using the improved analytic hierarchy process and implementing graded early warning and protection measures based on the assessment results is as follows: An improved analytic hierarchy process is used to dynamically and adaptively adjust the weight coefficients of evaluation indicators at each level, weakening the weights of indicators with poor adaptability and strengthening the weights of disease-sensitive indicators. The standardized monitoring dataset is imported into the fuzzy comprehensive evaluation model. The scores of individual indicators are calculated through the membership matrix, and the overall safety score of the building is obtained by combining dynamic weights and weighted summation, and the warning level is divided. Based on the assessment results and the classification levels, a corresponding four-level early warning mechanism and dedicated response strategies are matched.

6. The method for monitoring and assessing the safety of historical and cultural buildings as described in claim 5, characterized in that, The criteria for classifying early warning levels include: a comprehensive score of 90-100 indicates a safe level, with no structural abnormalities and no risk of disease development; a comprehensive score of 75-89 indicates a basically safe level, with a stable structure, minor controllable defects, and no immediate safety hazards; a comprehensive score of 60-74 indicates a hidden danger level, with obvious component defects or local structural deformation, and the defects have the risk of continued development; and a comprehensive score below 60 indicates a dangerous level, with structural instability, large-area cracking, severe settlement, and significant safety risks.

7. A method and system for monitoring and assessing the safety of historical and cultural buildings, applied to the method for monitoring and assessing the safety of historical and cultural buildings as described in any one of claims 1-6, characterized in that, It includes a zone monitoring module, an encrypted transmission module, a data preprocessing module, a security assessment module, a time-series disease prediction module, a graded early warning and response module, a source tracing and storage module, and a human-computer interaction module; The zoning monitoring module is used to divide ancient buildings into multiple areas, enabling multi-dimensional, differentiated, and real-time data collection. The encrypted transmission module is used to realize encrypted transmission of differentiated frequency data, data verification, and breakpoint resumption; The data preprocessing module is used for denoising, error correction, completion, normalization and fusion registration of multi-source data to generate a standardized monitoring dataset. The security assessment module is used to dynamically adjust weights and quantify security levels; The time-series defect prediction module is used to predict the evolution trend of component defects and structural safety. The tiered early warning and response module is used to match the early warning level, generate differentiated maintenance, repair, and emergency response plans, and complete the closed-loop management. The traceability storage module is used to archive all basic building information, monitoring data, assessment reports, early warning records, and disposal ledgers; The human-computer interaction module is used for real-time data display, remote parameter configuration, early warning message push, and on-site inspection data upload.

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