Large-span hyperbolic space truss all-weather load monitoring and advanced early warning system and method

Abstract

The invention discloses an all-weather load monitoring and advanced early warning system and method for a large-span hyperbolic space truss. The system comprises a three-dimensional perception module, a digital twinning module, an external intelligence access module, a full-spectrum climate deduction module, a multi-load coupling analysis module, a real-time early warning module, a prediction early warning module, a block chain evidence storage module and an early warning response module. According to the invention, the structure state and environment are monitored in real time through a stereo sensor network, real-time early warning is realized, and a digital twin module for real-time data driving correction is constructed and utilized; future weather forecast is automatically accessed, and is converted into predictive wind, snow, rain and temperature loads based on professional models such as wind tunnel tests and thermodynamics; multi-load time sequence coupling simulation is carried out in the digital twin module, and future structural response is predicted; and comparing a prediction result with a threshold value to generate an advanced early warning. According to the invention, the conversion from passive monitoring to active early warning is realized, and the safety guarantee capability of a complex space structure in atrocious weather is obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of civil engineering structure monitoring, in particular to a large-span hyperbolic spatial truss full-weather load monitoring and early warning system and method. BACKGROUND

[0002] Large-span spatial steel structures, especially hyperbolic truss structures, are widely used in large public buildings such as stadiums, transportation hubs, and cultural centers due to their beautiful architectural expression. Such structures are highly sensitive to wind, snow, temperature, and other weather loads due to their large span and complex shape (spatial curves, inclined supports). Non-uniform snow load distribution, wind-induced vibration effects, temperature stresses caused by sudden cooling and heating, and the coupling of multiple loads are major challenges to the safe operation of such structures.

[0003] Traditional structure health monitoring systems mainly focus on monitoring and alarming the response of existing loads (such as real-time wind and measured strain). They only use real-time data for early warning, which belongs to the post-response or in-response mode. The limitations are that they cannot predict the structural risks that may be caused by extreme or complex weather events in the future; they usually monitor a certain load effect independently, making it difficult to simulate the complex coupling of wind, snow, rain, temperature, and other weather factors in time series; the monitoring data and structural mechanics models are often separate, making it difficult to use real-time data to dynamically correct the calculation model, resulting in limited prediction accuracy; traditional alarms usually only indicate threshold exceedance, lack specific risk description, impact period, and actionable prevention measures, which is not conducive to precise decision-making and intervention by management departments.

[0004] Therefore, there is an urgent need for an intelligent system that integrates real-time monitoring, structural digital models, future weather information, and professional structural analysis to achieve a shift from passive response to proactive early warning, thereby providing advanced and reliable decision support for the safe operation of large-span complex spatial structures. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a large-span hyperbolic spatial truss full-weather load monitoring and early warning system and method. The system and method can achieve real-time data monitoring and early warning and accurate prediction of structural response under future complex weather loads, and generate early warning with clear risk description and prevention measures, thereby significantly improving the forward-looking and scientific nature of large-span spatial steel structure safety management.

[0006] To achieve the above purpose, the present application adopts the following technical solutions: In one aspect of the present application, a large-span hyperbolic spatial truss full-weather load monitoring and early warning system is provided, which comprises a stereoscopic perception module, a digital twin module, an external intelligence access module, a full-spectrum weather deduction module, a multi-load coupling analysis module, a real-time warning module, a prediction warning module, a blockchain storage module and a warning response module.

[0007] The stereoscopic perception module is connected with the digital twin module and the real-time warning module, the digital twin module is connected with the multi-load coupling analysis module, the external intelligence access module is connected with the full-spectrum weather deduction module, the full-spectrum weather deduction module is connected with the multi-load coupling analysis module, the multi-load coupling analysis module is connected with the real-time warning module and the prediction warning module, the real-time warning module and the prediction warning module are connected with the blockchain storage module at the same time, and the blockchain storage module is connected with the warning response module.

[0008] The stereoscopic perception module is used for collecting stress data, physical state data and climate environment data of the large-span hyperbolic spatial truss structure in real time; the digital twin module is used for constructing and driving a virtual model of the large-span hyperbolic spatial truss structure to realize state visualization and mechanical simulation; the external intelligence access module is used for automatically obtaining future weather and official disaster warning information; the full-spectrum weather deduction module is embedded with structure response parameters based on wind tunnel test, thermodynamics and hydrology model and is used for converting external intelligence into various predictive weather loads; the multi-load coupling analysis module is used for calling the digital twin module to perform time-series coupling simulation calculation; the real-time warning module is used for collecting real-time data of the large-span hyperbolic spatial truss structure through the stereoscopic perception module, comparing the real-time data with threshold values, generating and publishing real-time warning information; the prediction warning module is used for generating and publishing early warning information with preventive measure suggestions based on simulation results; the blockchain storage module is used for credibly storing warning key data; and the warning response module is used for tracking warning response, recording disposal and effect feedback after warning publication.

[0009] Further, in the above system, the full-spectrum weather deduction module integrates a finite element analysis kernel and can perform nonlinear time-history analysis to simulate complex responses of the large-span hyperbolic spatial truss structure under coupling action of extreme weather loads.

[0010] In another aspect of the present application, a large-span hyperbolic spatial truss full-weather load monitoring and early warning method based on the above system is provided, which comprises the following steps: S1: according to the hyperbolic, spatial and inclined support characteristics of the large-span hyperbolic spatial truss structure, a stereoscopic sensor network covering key nodes of inner and outer arc trusses and support steel frames is constructed, stress, displacement, temperature, wind force, roof snow thickness and rainwater depth data of the large-span hyperbolic spatial truss structure are collected in real time, and the data collected by the stereoscopic sensor network are finally converged to the stereoscopic perception module.

[0011] S2: Based on the collected real-time data, drive the digital twin module, and visualize the real-time deformation, internal force state and temperature field distribution of the large-span hyperbolic space truss structure.

[0012] S3: The external intelligence access module automatically accesses the future weather forecast information, which includes wind speed and direction, snowfall, rainfall and duration, maximum and minimum temperature.

[0013] S4: The full-spectrum climate deduction module converts the future weather forecast information and the wind tunnel test modal data, thermal physical parameters and material temperature variation coefficient based on the pre-stored system into predictive loads acting on the digital twin module, including wind load, non-uniform snow load, water load and temperature load.

[0014] S5: In the digital twin module, the multi-load coupling analysis module simulates the influence of the time sequence coupling of multiple predictive loads on the large-span hyperbolic space truss structure, and outputs the future stress, displacement and temperature stress prediction sequence of the key parts.

[0015] S6: The prediction and early warning module compares the future stress, displacement and temperature stress prediction sequence with the preset threshold value, and if it exceeds the limit, it generates and publishes advance warning information with specific risk description and prevention measures suggestion; The real-time early warning module compares the real-time data of the large-span hyperbolic space truss structure collected by the stereoscopic perception module with the preset threshold value, and generates and publishes real-time warning information.

[0016] S7: The blockchain storage module generates the corresponding data digest (hash value) of the advance warning information and the key real-time and predictive data that triggered the warning, and stores the hash value in association with the timestamp and warning identifier in the blockchain network to realize the tamper-proof storage of the monitoring and warning core data source; The warning response module tracks the stored warning information for warning response, and records the disposal and effect feedback after the warning is published.

[0017] Further, in step S1 of the above method, the layout strategy of the stereoscopic sensor network comprises: Wireless displacement sensors are arranged at the middle and quarter-span outer sides of the top chord and bottom chord of the inner and outer arc trusses with a span of 81-86 meters, and at the outer sides of the connection between the top chord and the intermediate chord of the inner and outer arc trusses and the vertical columns and inclined columns of the support steel frame on both sides; A GPS base station is arranged within 20m of the ground near the large-span hyperbolic space truss structure.

[0018] Wireless stress sensors are arranged at the butt joint welds between the vertical columns of the two-side support steel frame and the upper chords of the inner arc truss and the middle chords of the inner arc truss, at the butt joint welds between the inclined columns of the two-side support steel frame and the upper chords of the outer arc truss and the middle chords of the outer arc truss, at the connection nodes between the upper chords of the inner arc truss and the inclined web members connected to the upper chords, at the middle portions of the upper chords of the outer arc truss and the connection nodes between the middle portions of the upper chords and the inclined web members connected to the middle portions.

[0019] A micro-meteorological station is arranged at the windward part of the roof of the large-span hyperbolic spatial truss structure, two snow accumulation sensors are arranged at the roof ends of the inner arc truss and the outer arc truss, and multiple water accumulation sensors are arranged in the roof gutter on the side of the outer arc truss.

[0020] An array of temperature sensors is arranged on the surface of the structure, fourteen temperature sensors are arranged on the inner arc truss and the outer arc truss, and the temperature sensors are arranged in a quincunx net shape.

[0021] All sensor data is aggregated to the stereoscopic perception module through the Internet of Things gateway.

[0022] Further, in step S2 of the above method, the real-time collected stress data is compared with the theoretical stress calculated by the digital twin module to realize accurate diagnosis and model correction of the health state of the large-span hyperbolic spatial truss structure; and the temperature field distribution of the large-span hyperbolic spatial truss structure is monitored to evaluate the influence of temperature stress.

[0023] Further, in step S4 of the above method, the derivation of the predictive load includes: The wind load is derived from the future wind speed forecast based on the wind tunnel test database; The non-uniform snow load is derived from the snowfall forecast based on the wind-snow coupling distribution coefficient; The water accumulation load is derived from the rainfall forecast based on the roof drainage model; The temperature load is derived from the air temperature forecast based on the heat conduction model.

[0024] Further, in step S5 of the above method, when performing simulation calculation in the digital twin module, the time series combination of multiple predictive loads is considered to simulate the coupling influence of complex climate processes on the large-span hyperbolic spatial truss structure, wherein the complex climate processes include: high temperature first, then heavy rain, and then rapid cooling, heavy rain and rapid cooling, wind and rain, wind and cooling followed by snow, wind and snow followed by warming and snow melting, etc.

[0025] Further, in step S6 of the above method, the early warning information includes: Warning level: divided into four levels of attention, preparation, action and emergency according to the severity of the risk; Risk description: specifically describes the expected structural response problem; Influence period: the effective time range of the early warning; Precaution: targeted treatment recommendations.

[0026] Compared with the prior art, the present application has the following beneficial effects: 1) The present application has the ability of early warning, by accessing future weather forecasts, the monitoring is extended from "now" to "future", realizing the early perception and warning of the risk of large-span hyperbolic space truss structure, and winning valuable time for taking preventive measures.

[0027] 2) The present application has the function of full climate load coupling analysis, which integrates wind, snow, rain, temperature and other climate factors into a unified time sequence simulation framework, can simulate the complex coupling process in the real world, and the evaluation result is more comprehensive and more accurate.

[0028] 3) The present application has the function of virtual-real interaction and model evolution, based on digital twinning technology, realizes real-time interaction and dynamic correction of physical structure and virtual model, ensures high fidelity of analysis model, and improves the credibility of prediction; the generated early warning information not only contains risk level, but also specifically describes risk type, affected part and period, and gives targeted precaution suggestions (such as evacuating XX area within the next 6 hours, starting snow melting equipment, etc.), greatly improving the practical value of early warning.

[0029] 4) The present application introduces blockchain storage technology, ensures the authenticity and non-tamperability of monitoring data, early warning logic and release record, and enhances the public credibility of the entire system in responsibility tracing and auditing.

[0030] 5) The sensor distribution strategy and load deduction model of the present application are optimized for large-span hyperbolic space truss and other complex structures with characteristics such as large-span hyperbolic, inclined support, etc., solving the difficult problems in the monitoring of such structures. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the specific embodiments of the present application, the following will briefly introduce the drawings needed to be used in the specific embodiments. In the drawings, each element or part is not necessarily drawn according to the actual proportion.

[0032] Figure 1 It is the overall architecture diagram of the system of the present application.

[0033] Figure 2 It is the general flow chart of the method of the present application.

[0034] Figure 3 It is the distribution diagram of wireless displacement sensor on large-span hyperbolic space truss in the present application (only one side of outer arc truss is shown).

[0035] Figure 4Distribution diagram of wireless stress sensor on large-span hyperbolic space truss joint in the application (only one side of the outer arc truss is shown).

[0036] Figure 5 Distribution diagram of snow sensor, water sensor, and micro-meteorological station on large-span hyperbolic space truss joint in the application.

[0037] In the figure: 101 - stereoscopic perception module, 102 - digital twin module, 103 - external intelligence access module, 104 - full-spectrum climate deduction module, 105 - multi-load coupling analysis module, 106 - real-time early warning module, 107 - prediction early warning module, 108 - blockchain storage module, 109 - early warning response module. 201 - inner arc truss, 202 - outer arc truss, 203 - vertical column, 204 - inclined column. 205 - wireless displacement sensor, 206 - wireless stress sensor, 207 - snow sensor, 208 - water sensor, 209 - micro-meteorological station. DETAILED DESCRIPTION

[0038] The embodiments of the technical solutions of the application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the application, and therefore only serve as examples, and cannot limit the protection scope of the application. It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should be understood as the usual meaning understood by the skilled person in the field to which the application belongs. Embodiment 1

[0039] As shown in Figure 1 , the embodiment provides a large-span hyperbolic space truss full-weather load monitoring and early warning system, which includes a stereoscopic perception module 101, a digital twin module 102, an external intelligence access module 103, a full-spectrum climate deduction module 104, a multi-load coupling analysis module 105, a real-time early warning module 106, a prediction early warning module 107, a blockchain storage module 108, and a early warning response module 109.

[0040] The stereoscopic perception module 101 is connected with the digital twin module 102 and the real-time early warning module 106, respectively, the digital twin module 102 is connected with the multi-load coupling analysis module 105, the external intelligence access module 103 is connected with the full-spectrum climate deduction module 104, the full-spectrum climate deduction module 104 is connected with the multi-load coupling analysis module 105, the multi-load coupling analysis module 105 is connected with the real-time early warning module 106 and the prediction early warning module 107, respectively, the real-time early warning module 106 and the prediction early warning module 107 are connected with the blockchain storage module 108 at the same time, and the blockchain storage module 108 is connected with the early warning response module 109.

[0041] The stereoscopic perception module 101 is used for real-time collection of stress data, physical state data and climate environment data of the large-span hyperbolic space truss structure; the digital twin module 102 is used for construction and driving of a virtual model of the large-span hyperbolic space truss structure, and realizes state visualization and mechanical simulation; the external intelligence access module 103 is used for automatic acquisition of future weather and official disaster warning information; the full-spectrum climate deduction module 104 is embedded with structure response parameters based on wind tunnel test, thermodynamics and hydrological model, and is used for conversion of the external intelligence into various predictive climate loads; the full-spectrum climate deduction module 104 is integrated with a finite element analysis kernel, and can perform nonlinear time-history analysis to simulate complex responses of the large-span hyperbolic space truss structure under coupling action of extreme climate loads; the multi-load coupling analysis module 105 is used for calling the digital twin module 102 to perform time-series coupling simulation calculation; the real-time warning module 106 is used for collecting real-time data of the large-span hyperbolic space truss structure through the stereoscopic perception module 101, comparing with a threshold value, and generating and publishing real-time warning information; the prediction and warning module 107 is used for generating and publishing advanced warning information with preventive measure suggestions based on simulation results; the blockchain storage module 108 is used for credible storage of warning key data; and the warning response module 109 is used for tracking warning responses, and recording disposal and effect feedback situations after the warning is published. Embodiment 2

[0042] As shown in Figures 1 to 5 The embodiment provides a large-span hyperbolic space truss full-climate load monitoring and advanced warning method based on the system in embodiment 1, and the method comprises the following steps: S1: according to the hyperbolic, space and inclined support characteristics of the large-span hyperbolic space truss structure, a stereoscopic sensor network covering key nodes of inner and outer arc trusses 201 and 202 and a support steel frame is constructed, real-time stress, displacement, temperature, wind force, roof snow thickness and rainwater depth data of the large-span hyperbolic space truss structure are collected, and finally the data collected by the stereoscopic sensor network is converged to the stereoscopic perception module 101.

[0043] The large-span hyperbolic spatial truss structure comprises a left side supporting steel frame, a right side supporting steel frame, an outer truss 202 and an inner truss 201; the outer truss 202 is fixedly installed between the outer side end of the left side supporting steel frame and the outer side end of the right side supporting steel frame, and the inner truss 201 is fixedly installed between the inner side end of the left side supporting steel frame and the inner side end of the right side supporting steel frame; the inner truss 201 is in an inwardly curved arc shape as a whole in the horizontal direction, and the vertical section height of the inner truss 201 gradually decreases from the two ends to the middle; the inner truss 201 comprises upper chord members, lower chord members and intermediate chord members, the upper chord members and the lower chord members are arranged in an inwardly curved arc shape, and the lower chord members are further arranged in an upwardly curved arc shape; the outer truss 202 is in an inwardly curved arc shape as a whole in the horizontal direction, and the vertical section height of the outer truss 202 gradually decreases from the two ends to the middle; the outer truss 202 comprises upper chord members, lower chord members and intermediate chord members, the upper chord members and the lower chord members are arranged in an inwardly curved arc shape, and the lower chord members are further arranged in an upwardly curved arc shape.

[0044] The layout strategy of the three-dimensional sensor network comprises: Wireless displacement sensors 205 are arranged at the middle and quarter-span outer sides of the upper chords and lower chords of the inner and outer arc trusses 201 and 202, and at the outer sides of the connection positions of the upper chords and intermediate chords of the inner and outer arc trusses 201 and 202 with the vertical columns 203 and inclined columns 204 of the two side supporting steel frames respectively; a GPS base station is arranged on the ground within 20 m of the large-span hyperbolic spatial truss structure.

[0045] Wireless stress sensors 206 are arranged at the butt joint welds of the upper chords and intermediate chords of the inner arc truss 201 at the two ends of the vertical columns 203 of the two side supporting steel frames, at the butt joint welds of the upper chords and intermediate chords of the outer arc truss 202 at the two ends of the inclined columns 204 of the two side supporting steel frames, at the connection nodes of the inclined web members connected with the upper chords and the lower chords at the middle of the upper chords of the inner arc truss 201, and at the connection nodes of the inclined web members connected with the middle of the upper chords and the lower chords of the outer arc truss 202.

[0046] A micro-weather station 209 is arranged at the windward part of the structure roof, two snow sensors 207 are arranged at the two ends of the roofs of the inner and outer arc trusses 201 and 202, and a plurality of water sensors 208 are arranged in the roof gutter on one side of the outer arc truss 202.

[0047] Temperature sensor arrays are arranged on the surfaces of the structure, fourteen temperature sensors are arranged on the inner arc truss 201 and the outer arc truss 202 respectively, and the temperature sensors are arranged in a quincunx net shape.

[0048] All sensor data is gathered to the three-dimensional perception module 101 through the Internet of Things gateway.

[0049] S2: Based on the collected real-time data, the digital twin module 102 is driven to visually present the real-time deformation, internal force state and temperature field distribution of the long-span hyperbolic space truss structure.

[0050] In this step, the real-time collected stress data is compared with the theoretical stress calculated by the digital twin module 102 to realize accurate diagnosis and model correction of the health state of the long-span hyperbolic space truss structure; at the same time, the temperature field distribution of the long-span hyperbolic space truss structure is monitored to evaluate the influence of temperature stress.

[0051] S3: The external intelligence access module 103 automatically accesses future weather forecast information, which includes wind speed and direction, snowfall, rainfall and duration, maximum and minimum temperature.

[0052] S4: The full-spectrum climate deduction module 104 converts the future weather forecast information and the wind tunnel test modal data, thermal physical parameters and material temperature variation coefficient pre-stored in the system into predictive loads acting on the digital twin module 102, including wind load, non-uniform snow load, water accumulation load and temperature load.

[0053] In this step, the derivation of the predictive load includes: The wind load is derived from the future wind speed forecast based on the wind tunnel test database; The non-uniform snow load is derived from the snowfall forecast based on the wind-snow coupling distribution coefficient; The water accumulation load is derived from the rainfall forecast based on the roof drainage model; The temperature load is derived from the air temperature forecast based on the heat conduction model.

[0054] S5: In the digital twin module 102, the time sequence coupling effect of multiple predictive loads on the long-span hyperbolic space truss structure is simulated and calculated by the multi-load coupling analysis module 105, and the future stress, displacement and temperature stress prediction sequence of the key parts are output.

[0055] In this step, when simulating and calculating in the digital twin module 102, the time sequence combination of multiple predictive loads is considered to simulate the coupling effect of complex climate processes on the long-span hyperbolic space truss structure; wherein, the complex climate processes include: high temperature first, then heavy rain, and then rapid cooling, heavy rain after a storm, rapid cooling, wind and rain, rapid warming, heavy snow after strong wind, wind and snow, and then warming and snow melting, etc.

[0056] S6: The prediction and early warning module 107 compares the future stress, displacement and temperature stress prediction sequence with the preset threshold value, and if it is out of limit, generates and publishes an advanced warning information with specific risk description and prevention measures suggestion; the real-time early warning module 106 compares the real-time data of the long-span hyperbolic spatial truss structure collected by the stereoscopic perception module 101 with the preset threshold value, and generates and publishes real-time warning information.

[0057] In this step, the advanced warning information includes: Early warning level: divided into four levels of attention, preparation, action and emergency according to the risk severity; Risk description: specifically explains the expected structural response problem; Influence period: effective time range of the warning; Preventive measures: targeted treatment suggestions.

[0058] S7: The blockchain storage module 108 generates a corresponding data digest hash value for the advanced warning information and the key real-time and prediction data triggering the warning, and stores the hash value in association with the time stamp and the warning identifier in the blockchain network, so as to realize the tamper-proof storage of the monitoring and warning core data source; the warning response module 109 tracks the warning response of the stored warning information, and records the disposal and effect feedback after the warning is published.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A system for monitoring and providing early warning of all-weather loads on a large-span hyperbolic space truss, characterized in that: It includes a three-dimensional perception module (101), a digital twin module (102), an external intelligence access module (103), a full-spectrum climate simulation module (104), a multi-load coupling analysis module (105), a real-time early warning module (106), a prediction and early warning module (107), a blockchain evidence storage module (108), and an early warning response module (109). The three-dimensional perception module (101) is connected to the digital twin module (102) and the real-time early warning module (106) respectively. The digital twin module (102) is connected to the multi-load coupling analysis module (105). The external intelligence access module (103) is connected to the full-spectrum climate simulation module (104). The full-spectrum climate simulation module (104) is connected to the multi-load coupling analysis module (105). The multi-load coupling analysis module (105) is connected to the real-time early warning module (106) and the prediction early warning module (107) respectively. The real-time early warning module (106) and the prediction early warning module (107) are simultaneously connected to the blockchain evidence storage module (108). The blockchain evidence storage module (108) is connected to the early warning response module (109). The three-dimensional perception module (101) is used to collect stress data, physical state data and climate environment data of the large-span hyperbolic space truss structure in real time; the digital twin module (102) is used to construct and drive the virtual model of the large-span hyperbolic space truss structure to realize state visualization and mechanical simulation; the external intelligence access module (103) is used to automatically obtain future meteorological and official disaster warning information; the full-spectrum climate inference module (104) is embedded with structural response parameters based on wind tunnel tests, thermodynamics and hydrological models, and is used to convert external intelligence into various predictive climate loads; the multi-load coupling analysis module (105) is used to call the digital twin module (102) to perform time-series coupling simulation calculations; the real-time warning module (106) is used to collect real-time data of the large-span hyperbolic space truss structure through the three-dimensional perception module (101), compare it with the threshold, and generate and release real-time warning information; The prediction and early warning module (107) is used to generate and release advanced early warning information with preventive measures suggestions based on simulation results; the blockchain evidence storage module (108) is used to reliably store key early warning data. The early warning response module (109) is used to track early warning responses and record the handling and effect feedback after the early warning is issued.

2. The large-span hyperbolic space truss all-weather load monitoring and early warning system according to claim 1, characterized in that: The full-spectrum climate simulation module (104) integrates a finite element analysis kernel, which can perform nonlinear time history analysis to simulate the complex response of a large-span hyperbolic space truss structure under the coupled action of extreme climate loads.

3. A method for all-weather load monitoring and early warning of a large-span hyperbolic space truss based on the system described in claim 1, characterized in that, Includes the following steps: S1: Based on the hyperbolic, spatial and inclined support characteristics of the large-span hyperbolic space truss structure, a three-dimensional sensor network covering the key nodes of the inner and outer arc trusses (201, 202) and the supporting steel frame is constructed to collect stress, displacement, temperature, wind force, roof snow thickness and rainwater accumulation depth data of the large-span hyperbolic space truss structure in real time. The data collected by the three-dimensional sensor network is finally aggregated into the three-dimensional perception module (101). S2: Based on the collected real-time data, drive the digital twin module (102) to visualize the real-time deformation, internal force state and temperature field distribution of the large-span hyperbolic space truss structure; S3: The external intelligence access module (103) automatically accesses future weather forecast information, including wind speed and direction, snowfall, rainfall and duration, and maximum and minimum temperatures; S4: The full-spectrum climate simulation module (104) transforms future weather forecast information and wind tunnel test modal data, thermophysical parameters and material temperature change coefficients pre-stored in the system into predictive loads acting on the digital twin module (102). The predictive loads include wind load, non-uniform snow load, water accumulation load and temperature load. S5: In the digital twin module (102), the influence of the temporal coupling effect of various predictive loads on the large-span hyperbolic space truss structure is simulated and calculated through the multi-load coupling analysis module (105), and the future stress, displacement and temperature stress prediction sequence of key parts are output. S6: The prediction and early warning module (107) compares the predicted sequence of future stress, displacement and temperature stress with the preset threshold. If the threshold is exceeded, it generates and publishes an early warning message with a specific risk description and preventive measures. The real-time early warning module (106) compares the real-time data of the large-span hyperbolic space truss structure collected by the stereo perception module (101) with a preset threshold, and generates and publishes real-time early warning information. S7: The blockchain evidence storage module (108) generates a corresponding data summary from the early warning information and the key real-time and predicted data that trigger the warning, and stores the hash value, timestamp, and warning identifier in the blockchain network to achieve tamper-proof evidence storage of the core data source for monitoring and early warning. The early warning response module (109) tracks the early warning information stored in the early warning system and records the handling and feedback of the effects after the early warning is issued.

4. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 3, characterized in that: In step S1, the deployment strategy for the stereo sensor network includes: Wireless displacement sensors (205) are installed at the middle and outer sides of the upper and lower chords of the inner and outer arc trusses (201, 202) with spans of 81-86 meters, respectively, as well as at the outer sides of the connections between the upper and middle chords of the inner and outer arc trusses (201, 202) and the vertical columns (203) and inclined columns (204) of the supporting steel frame on both sides. A GPS base station is installed on the ground within 20m of the large-span hyperbolic space truss structure. Wireless stress sensors (206) are installed at the butt welds of the vertical columns 203 supporting the steel frame on both sides and the upper chords and middle chords at both ends of the inner arc truss (201), at the butt welds of the inclined columns (204) supporting the steel frame on both sides and the upper chords and middle chords at both ends of the outer arc truss (202), at the middle part of the upper chord of the inner arc truss (201), at the connection node between the diagonal web members connected to the upper chord and the lower chord, at the middle part of the upper chord of the outer arc truss (202), and at the connection node between the diagonal web members connected to the middle part of the upper chord and the lower chord. A micro weather station (209) is deployed on the windward side of the structural roof; two snow sensors (207) are installed at each end of the roof of the inner and outer arc trusses (201, 202); and multiple water sensors (208) are installed in the roof gutter on one side of the outer arc truss (202). A temperature sensor array is set on the surface of the structure, with fourteen temperature sensors set on the inner arc truss (201) and the outer arc truss (202) respectively. The temperature sensors are arranged in a plum blossom dot pattern. All sensor data is aggregated to the stereo sensing module (101) via an IoT gateway.

5. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 3, characterized in that: In step S2, the real-time collected stress data is compared with the theoretical stress calculated by the digital twin module (102) to achieve accurate diagnosis and model correction of the health status of the large-span hyperbolic space truss structure; at the same time, the temperature field distribution of the large-span hyperbolic space truss structure is monitored to assess the influence of temperature stress.

6. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 3, characterized in that: In step S4, the derivation of the predictive load includes: Wind loads are derived from future wind speed forecasts based on wind tunnel test databases; Non-uniform snow load is derived from snowfall forecast based on the wind-snow coupling distribution coefficient; Water accumulation load was derived from rainfall forecasts based on a roof drainage model; Temperature load is derived from temperature forecasts based on a heat conduction model.

7. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 3, characterized in that: In step S5, when performing simulation calculations in the digital twin module (102), the time series combination of multiple predictive loads is considered to simulate the coupling effect of complex climate processes on the large-span hyperbolic space truss structure.

8. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 3, characterized in that: In step S6, the advance warning information includes: Warning levels: Divided into four levels based on the severity of the risk: Attention, Preparedness, Action, and Emergency; Risk Description: Provide a detailed explanation of any anticipated structural response issues; Period of Impact: The effective time range of the warning; Preventive measures: Targeted treatment recommendations.

9. The method for monitoring and providing early warning of all-weather loads on large-span hyperbolic space trusses according to claim 7, characterized in that: Complex climate processes include: high temperatures followed by torrential rain and then rapid cooling; strong winds and torrential rain followed by rapid cooling; wind and rain accompanied by rapid warming; strong winds and cooling followed by heavy snow; and wind and snow accompanied by warming and snow melting.

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