Space grid structure defect intelligent identification and repair method and system

Abstract

The invention discloses a space grid structure defect intelligent identification and repair method and system, and relates to the technical field of electrical digital data process.The method comprises the steps that a space grid structure is divided into a plurality of monitoring sub-areas, and the geometric dimension, section defects, the corrosion state, the buckling sensitive characteristic, the service environment and the connection state of a rod piece are monitored in real time; constructing characteristic parameters such as stability degradation, internal force disturbance, service environment corrosion, stress ratio and local section influence on the basis of the acquired data, calculating a structural stability coefficient, a reinforcing internal force disturbance coefficient and a service environment corrosion influence coefficient, and performing evaluation; according to the evaluation result, stability regulation and control, reinforcement process optimization and durability repair strategies are triggered in a graded mode, accurate recognition of structural defects, low-disturbance control of the reinforcement process and collaborative treatment of environmental corrosion risks are achieved, and therefore the overall safety, stability and service durability of the space grid structure are improved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to a method and system for intelligent identification and repair of defects in spatial grid structures. Background Technology

[0002] Large-span spatial grid structures are widely used in roof or canopy structures of stadiums, transportation hubs, industrial plants, and public facilities due to their advantages such as good load-bearing performance, high material utilization, and flexible design. These structures typically consist of multiple hollow metal members assembled using bolts, fasteners, or prefabricated nodes. The structural system is highly dependent on the stability of the members and the reliability of the node connections.

[0003] During long-term service, factors such as high humidity, temperature cycling, condensation retention, and corrosive media can cause some members in a spatial grid structure to experience problems such as wall thinning, local cross-sectional defects, loose connections, and redistribution of internal forces. These changes in state are usually generated in the form of electrical signals such as strain, voltage, displacement, and environmental parameters. Effective analysis and evaluation require sensor acquisition, analog-to-digital conversion, and electrical-digital data processing, which can lead to stability degradation or even local instability risks in the members.

[0004] Especially in prefabricated node structures, the changes in the degree of closure of fasteners and the preload of screws often manifest as dynamic fluctuations in connection state parameters and internal force response parameters. During reinforcement or maintenance, if there is a lack of real-time processing and logical judgment mechanisms for relevant digital monitoring data, new internal force disturbances can easily be introduced into the structure, further amplifying local stress concentration or stiffness abrupt change effects.

[0005] In existing technologies, safety assessments of spatial grid structures are mostly focused on overall stress analysis or judgment based on single test results. These assessments are typically based on human experience or static calculations and lack a digital data processing flow based on multi-source monitoring data. Furthermore, there is a lack of refined stability identification, threshold determination, and dynamic control methods for local monitoring sub-regions. At the same time, existing reinforcement methods mostly adopt one-time reinforcement or experience-based pre-tightening, without digitally quantifying and analyzing real-time monitoring data during the reinforcement process. This makes it difficult to assess the disturbance impact of the reinforcement operation itself on the internal force state of the original structure.

[0006] Furthermore, for space grid structures operating in high humidity or corrosive environments, existing technologies often treat corrosion as an independent problem, lacking a technical approach to unify and comprehensively evaluate environmental monitoring data, structural response data, and reinforcement process data through digital data processing. This can easily lead to the structure meeting stability requirements in the short term, but with insufficient long-term durability and service safety. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent identification and repair of defects in spatial grid structures, thereby solving the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a method for intelligent identification and repair of defects in spatial grid structures, comprising the following steps: Step 1: Divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and monitor the members in each monitoring sub-area in real time. Collect the following data: member outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. Step 2: By uniformly processing the collected data, the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter, and local section influence parameter are obtained respectively. Step 3: By extracting the stability degradation factor, local section influence parameter and stress ratio parameter of the monitored sub-region, calculate the structural stability coefficient JWX and compare it with the structural stability threshold Jth to determine whether the structural stability of the current monitored sub-region is qualified. If it is not qualified, the first strategy is adopted. Step 4: Verify and quantify the actual reinforcement process of the members within the monitoring sub-region; extract the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation, and internal force disturbance factor; calculate the reinforcement internal force disturbance coefficient GRX; compare it with the reinforcement internal force disturbance threshold Gth; determine whether the current reinforcement internal force disturbance state of the monitoring sub-region is qualified; if not, apply the second strategy. Step 5: By extracting the service environment corrosion factor, stress ratio parameter, corrosion depth of the member and wall thickness of the member, calculate the service environment corrosion influence coefficient FHY, and compare it with the service environment corrosion influence threshold Fth to determine whether the comprehensive influence of the service environment of the member in the monitoring sub-region on the structural durability is within an acceptable range; if not, the third strategy is adopted.

[0009] Preferably, step one includes: S11. By dividing the large-span spatial grid structure roof into N monitoring sub-regions with a uniform number of structural members according to the geometric structure distribution and the stress function of the members; S12. Within each monitoring sub-region, monitor the geometric dimensions and cross-sectional state of all members to be evaluated within the region. By installing a portable structural dimension scanning device on the outer surface of a typical member within the monitoring sub-region, scan the outer contour of the member and collect the outer diameter D. Install an ultrasonic thickness gauge on the outer surface of the member to detect the wall thickness and collect the wall thickness t. By arranging a scanning path along the axial direction of the member, detect the continuity of the member's cross-section, identify and record the location and length of cross-section weakening, and collect the length Ld of local cross-section defects. Combine the node positions at both ends of the member with the scanning trajectory information to determine the axial dimension of the member and collect the total length L of the member. S13. Monitor the corrosion status of the rods in each monitoring sub-region of the spatial grid structure. By installing corrosion depth detection probes on the outer surface of the rods in the monitoring sub-region, detect the thinning of the metal surface and collect the corrosion depth dc of the rods. S14. Monitor the buckling sensitivity of members in each monitoring sub-region of the spatial grid structure. By installing strain gauges at the middle position of the members in the monitoring sub-region, the axial strain changes of the members under service conditions are collected in real time. Micro-deformation sensors are installed to collect the local displacement and flexural response of the members. A buckling sensitivity index R reflecting the stability characteristics of the members is formed. S15. Monitor the service environment status of each monitoring area of ​​the spatial grid structure, install environmental sensing units at typical nodes in the monitoring sub-area, collect environmental humidity and condensate data in real time, and obtain the surface humidity H of the rod and the condensate accumulation time Tw; collect environmental temperature data synchronously at the same or adjacent locations to obtain the regional environmental temperature T. S16. Monitor the installation status of the rods in each monitoring area of ​​the spatial grid structure. Install a closure detection sensor at the fastener position in the monitoring sub-area to collect the closure degree C of the fastener; install a preload sensor at the bolt position to collect the bolt preload P.

[0010] Preferably, step two includes: S21. Based on the local section defect length Ld, the corrosion depth dc, the total length L, and the outer diameter D of the member, the geometric slenderness characteristics of the member are processed using the slenderness ratio calculation method to obtain the slenderness ratio parameter s. Based on the slenderness ratio parameter s, combined with the local section defect length Ld and corrosion depth dc, a stability degradation evaluation function is used to obtain a stability degradation factor characterizing the degree of local stability attenuation of the member. ; S22. Based on the screw preload P and the fastener closure degree C, the loading state of the fastener is processed using an assembly connection state mapping method. Combined with the local displacement and deflection response data of the member, the local bending deformation of the member is extracted. Based on the local bending deformation of the rod By combining the screw preload P and the fastener closure degree C, and processing them through an internal force disturbance evaluation function, an internal force disturbance factor reflecting the influence of reinforcement or assembly status on the internal force of the member is obtained. ; S23. Based on the ambient temperature T, the surface humidity H of the member, and the condensate accumulation time Tw, the temperature change amplitude is calculated using the environmental load normalization method to obtain the regional temperature change parameters. Furthermore, the surface exposure conditions of the rods were comprehensively assessed by combining humidity and condensation accumulation time to form a characterization quantity of regional environmental corrosion. An environmental corrosion impact assessment function was then applied to obtain the service environment corrosion factor. ; S24. Based on the buckling sensitivity index R, the actual working stress of the member under its current service condition is calculated using the stress-strain conversion method. ; and by combining the standard strength parameters of the member material design, the nominal bearing stress of the member is obtained. Then, by processing the stress ratio, the stress ratio parameter used for load-bearing safety assessment is obtained. ; S25. Based on the corrosion depth dc and wall thickness t of the member, the effective bearing capacity of the local section of the member is calculated using the section effectiveness reduction method. This yields the local section influence parameter, which reflects the degree of influence of local section defects on the member's bearing performance. .

[0011] Preferably, step three includes: S31. By extracting stability degradation factors from the monitoring sub-regions Local section influence parameters and stress ratio parameter After dimensionless processing, the structural stability coefficient JWX is calculated and obtained.

[0012] Preferably, step three further includes: S32. By setting a pre-defined structural stability threshold Jth, and comparing the structural stability coefficient JWX with the structural stability threshold Jth, the first evaluation result is obtained, including: When the structural stability coefficient JWX ≥ the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is qualified, and monitoring will continue. When the structural stability coefficient JWX < the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is unqualified, and there is a risk of stability degradation caused by cross-section weakening or stress state. This triggers the first warning instruction and generates the first strategy: re-classify the closure degree C of the fasteners in the current monitoring sub-region, and improve the synergistic constraint ability of the reinforcement components on the axial and bending stiffness of the members by reducing the closure gap in segments, thereby suppressing the evolution of local instability into overall instability; simultaneously adjust the screw preload P to make the constraint force provided by the reinforcement device evenly distributed along the axial direction of the members, thereby reducing the stability degradation caused by sudden changes in local stiffness; and re-optimize the reinforcement length and layout sections in the axial direction of the members according to the length Ld and location of the local cross-section defect, so that the effective action area of ​​the reinforcement device covers the unfavorable stability section.

[0013] Preferably, step four includes: S41. After completing stability control and reinforcement adjustments, the actual reinforcement implementation process of the members within the monitored sub-region is inspected and quantitatively evaluated; the real-time local section defect length Ld, member outer diameter D, member wall thickness t, and member local bending deformation are extracted. and internal force disturbance factor After dimensionless processing, the internal force disturbance coefficient GRX of the reinforcement is calculated and obtained.

[0014] Preferably, step four further includes: S42. By setting a preset internal force disturbance threshold Gth, and comparing the internal force disturbance coefficient GRX with the internal force disturbance threshold Gth, the second evaluation results are obtained, including: When the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth, it indicates that the reinforcement internal force disturbance status of the current monitoring sub-region is qualified, and the current reinforcement sequence and loading method are maintained for continuous monitoring. When the internal force disturbance coefficient GRX is greater than the internal force disturbance threshold Gth, it indicates that the internal force disturbance state of the current monitored sub-region is unqualified. The additional stiffness or constraints introduced by the reinforcement interfere with the original internal force balance, and there is a risk of local stress concentration, secondary buckling, or transient instability during the construction stage caused by sudden stiffness changes or discontinuous loading. This triggers the second warning command and generates the second strategy: adopting a step-by-step rotational closure method with semi-circular fasteners, breaking down the one-time closure operation into multiple angle incremental closure steps, so that the reinforcement constraints are introduced step by step to avoid instantaneous stiffness changes; adopting a graded locking strategy for the screw preload P, dividing the target preload into several loading levels, applying them step by step in order from low to high, and measuring the local bending deformation of the member after each loading level is completed. Real-time monitoring is performed; during the progressive loading process, the closure degree C of the fasteners and the locking sequence are dynamically corrected to ensure that the internal force introduced by the reinforcement is smoothly transmitted along the axial direction of the member, reducing the peak value of internal force disturbance during the reinforcement process; after completing one round of progressive reinforcement, the reinforcement internal force disturbance coefficient GRX is recalculated; if the reinforcement internal force disturbance coefficient GRX > the reinforcement internal force disturbance threshold Gth, the low-disturbance progressive reinforcement scheme is repeated, with no more than 3 repetitions; if the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth cannot be achieved after 3 consecutive executions, the automatic reinforcement is paused, and the process transitions to the fine-grained loading control stage with manual intervention.

[0015] Preferably, step five includes: S51, By extracting corrosion factors in the service environment Stress ratio parameter The corrosion depth dc of the rod and the wall thickness t of the rod are dimensionlessly processed, and the corrosion influence coefficient FHY of the service environment is calculated.

[0016] Preferably, step five further includes: S52. By setting a preset service environment corrosion impact threshold Fth, and comparing the service environment corrosion impact coefficient FHY with the service environment corrosion impact threshold Fth, the third evaluation results are obtained, including: When the service environment corrosion influence coefficient FHY ≤ the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; continuous monitoring is required. When the service environment corrosion influence coefficient FHY > the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment on the structural durability of the members within the monitoring sub-region is not within an acceptable range; the corrosion influence poses a threat to the structural durability, with the risk of member section weakening and continuous decline in load-bearing capacity, triggering the third early warning instruction and generating the third strategy: Install water-absorbing resin pads on the inner wall of the reinforced members or in the vicinity of the defect to absorb condensate and reduce the residence time of local corrosive media, forming an adaptive anti-corrosion buffer layer; adaptively extend the reinforcement length of the reinforcement device, making... The anti-corrosion repair area and the structural reinforcement area form a synergistic coverage to improve overall durability. After the anti-corrosion repair is completed, environmental and component status data are re-collected and the service environment corrosion impact coefficient FHY is calculated. If the recalculation result still satisfies the service environment corrosion impact coefficient FHY ≤ service environment corrosion impact threshold Fth, the anti-corrosion repair strategy is repeated, with the number of repetitions not exceeding the preset number of 2. If it is still impossible to make the service environment corrosion impact coefficient FHY > service environment corrosion impact threshold Fth after 2 consecutive executions, it indicates that the artificial anti-corrosion assessment and long-term durability special treatment stage is to be entered.

[0017] Preferably, the intelligent identification and repair system for defects in spatial grid structures includes: The structural state perception and acquisition module is used to divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and to monitor the members in each monitoring sub-area in real time, collecting the member's outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. The structural multi-factor feature construction module is used to process the collected data in a unified manner and obtain the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter and local section influence parameter respectively. The structural stability assessment module is used to extract the stability degradation factor, local section influence parameters and stress ratio parameters of the monitored sub-region, calculate the structural stability coefficient JWX, and compare it with the structural stability threshold Jth to determine whether the structural stability of the members in the current monitored sub-region is qualified. If it is not qualified, the first strategy is given. The reinforcement implementation internal force disturbance assessment module is used to verify and quantify the actual reinforcement implementation process of the members in the monitored sub-region; it extracts the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation and internal force disturbance factor, calculates the reinforcement internal force disturbance coefficient GRX, and compares it with the reinforcement internal force disturbance threshold Gth to determine whether the reinforcement internal force disturbance state of the current monitored sub-region is qualified. If it is not qualified, a second strategy is given. The service environment corrosion impact assessment module is used to extract service environment corrosion factors, stress ratio parameters, member corrosion depth and member wall thickness, calculate the service environment corrosion impact coefficient FHY, and compare it with the service environment corrosion impact threshold Fth to determine whether the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; if not, a third strategy is given.

[0018] This invention provides a method and system for intelligent identification and repair of defects in spatial grid structures. It has the following beneficial effects: (1) The intelligent identification and repair method and system for defects in spatial grid structures synchronously collects information on the geometric dimensions, cross-sectional defects, corrosion status, buckling sensitivity, assembly stress state, and service environment conditions of members within the monitoring sub-region scale. The physical quantities are output as electrical signals and subjected to analog-to-digital conversion and electrical digital data acquisition. In step two, the obtained multi-source digital monitoring data undergoes unified electrical digital data processing and feature calculation, transforming the original monitoring data into stability degradation factors, internal force disturbance factors, service environment corrosion factors, stress ratio parameters, and local cross-sectional influence parameters. This achieves direct conversion from multi-source original monitoring data to readily available data. This paper presents a system for constructing a standardized digital characteristic parameter system for structural safety assessment. By performing dimensionless processing, parameter mapping, and logical correlation analysis on monitoring data from different sources and with different dimensions, it avoids the problem in existing technologies that rely on single detection quantities or isolated data to make judgments, which makes it difficult to reflect the true stress state and degradation degree of the members. This ensures that all subsequent stability assessments, reinforcement implementation inspections, and service environment durability analyses are based on characteristic parameters with clear sources, clear physical meanings, and interrelationships formed by electro-digital data processing, thereby significantly improving the scientificity, consistency, and reliability of spatial grid structure defect identification and repair decisions.

[0019] (2) This intelligent identification and repair method and system for defects in spatial grid structures divides a large-span spatial grid structure roof into multiple monitoring sub-regions. Within each sub-region, the geometric state, stress characteristics, and service environment of the members are simultaneously collected and processed. This constructs stability degradation factors, local section influence parameters, and stress ratio parameters, and further calculates the structural stability coefficient JWX, thereby achieving a quantitative and regionalized assessment of the structural stability of the members within the monitoring sub-regions. Compared with existing assessment methods that rely on an overall model or a single detection index, this invention can accurately locate the specific sub-regions where stability degradation occurs, avoiding the "average assessment" of the overall structure from masking local high-risk problems, and improving the targeting and reliability of defect identification.

[0020] (3) The intelligent identification and repair method and system for defects in the spatial grid structure does not directly assume the reliability of the reinforcement effect after completing the stability control. Instead, it constructs the reinforcement internal force disturbance coefficient GRX to verify and quantify the actual reinforcement implementation process of the members in the monitoring sub-region, and explicitly includes the internal force disturbance introduced during the reinforcement process into the safety judgment range. By comparing GRX with the reinforcement internal force disturbance threshold Gth, and triggering a progressive closure and graded pre-tightening strategy when it is unqualified, the risk of local stress concentration, secondary buckling or transient instability during the construction stage caused by one-time loading, sudden stiffness change or discontinuous loading is effectively avoided, and the reinforcement behavior is transformed from experience operation into an assessable, adjustable and traceable safe process.

[0021] (4) The intelligent identification and repair method and system for defects in the spatial grid structure, by constructing the service environment corrosion influence coefficient FHY, couples and evaluates the environmental temperature and humidity conditions, the effect of condensation, the corrosion depth of the members, the wall thickness change and the stress level, so as to realize the quantitative judgment of the long-term durability risk of the members in the monitoring sub-area. When the corrosion influence of the service environment exceeds the acceptable range, the system automatically generates a coordinated adjustment strategy for corrosion prevention and structural reinforcement, so that the corrosion repair area and the structural reinforcement area form a consistent coverage, thereby avoiding the problem of "short-term structural stability but insufficient long-term durability" in the existing technology, and significantly reducing the risk of cross-sectional weakening and load-bearing capacity reduction caused by continuous corrosion development. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of the intelligent identification and repair method for spatial grid structure defects of the present invention; Figure 2 This is a flowchart of the intelligent identification and repair system for spatial grid structure defects of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 Please see Figure 1 This invention provides a method for intelligent identification and repair of defects in spatial grid structures, comprising the following steps: Step 1: Divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and monitor the members in each monitoring sub-area in real time. Collect the following data: member outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. Step 2: By uniformly processing the collected data, the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter, and local section influence parameter are obtained respectively. Step 3: By extracting the stability degradation factor, local section influence parameter and stress ratio parameter of the monitored sub-region, calculate the structural stability coefficient JWX and compare it with the structural stability threshold Jth to determine whether the structural stability of the current monitored sub-region is qualified. If it is not qualified, the first strategy is adopted. Step 4: Verify and quantify the actual reinforcement process of the members within the monitoring sub-region; extract the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation, and internal force disturbance factor; calculate the reinforcement internal force disturbance coefficient GRX; compare it with the reinforcement internal force disturbance threshold Gth; determine whether the current reinforcement internal force disturbance state of the monitoring sub-region is qualified; if not, apply the second strategy. Step 5: By extracting the service environment corrosion factor, stress ratio parameter, corrosion depth of the member and wall thickness of the member, calculate the service environment corrosion influence coefficient FHY, and compare it with the service environment corrosion influence threshold Fth to determine whether the comprehensive influence of the service environment of the member in the monitoring sub-region on the structural durability is within an acceptable range; if not, the third strategy is adopted.

[0025] In this embodiment, the large-span spatial grid structure roof is divided into multiple monitoring sub-regions. Within each monitoring sub-region, electrical signals are collected, analog-to-digital conversion is performed, and electrical digital data processing is conducted on data related to the structural status of the members, the reinforcement process status, and the service environment status. This process sequentially constructs structural stability coefficients, reinforcement internal force disturbance coefficients, and service environment corrosion influence coefficients, forming a phased closed-loop evaluation mechanism based on digital characteristic parameter calculations and threshold determination: "stability assessment—reinforcement process inspection—durability impact determination." By digitally processing, dimensionlessly calculating, and logically analyzing multi-source monitoring data, the determination of structural safety status is no longer limited to a single point in time or a single indicator, but rather covers the entire service life of the structure and the reinforcement implementation process based on continuous electrical digital data processing results. This method can identify potential risks caused by cross-sectional weakening, abnormal stress, or environmental corrosion at an early stage, and achieve targeted control and verification through a hierarchical strategy based on digital criteria. It avoids the introduction of new internal force disturbances or durability hazards due to parameter mutations or data discontinuities during the reinforcement process, thereby significantly improving the digitalization, precision, and overall safety and reliability of spatial grid structure defect identification and repair decisions.

[0026] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, step one includes: S11. By dividing the large-span spatial grid structure roof into N monitoring sub-regions with a uniform number of structural members according to the geometric structure distribution and the stress function of the members; S12. Within each monitoring sub-region, monitor the geometric dimensions and cross-sectional state of all members to be evaluated within the region. By installing a portable structural dimension scanning device on the outer surface of a typical member within the monitoring sub-region, scan the outer contour of the member and collect the outer diameter D. Install an ultrasonic thickness gauge on the outer surface of the member to detect the wall thickness and collect the wall thickness t. By arranging a scanning path along the axial direction of the member, detect the continuity of the member's cross-section, identify and record the location and length of cross-section weakening, and collect the length Ld of local cross-section defects. Combine the node positions at both ends of the member with the scanning trajectory information to determine the axial dimension of the member and collect the total length L of the member. S13. Monitor the corrosion status of the rods in each monitoring sub-region of the spatial grid structure. By installing corrosion depth detection probes on the outer surface of the rods in the monitoring sub-region, detect the thinning of the metal surface and collect the corrosion depth dc of the rods. S14. Monitor the buckling sensitivity of members in each monitoring sub-region of the spatial grid structure. By installing strain gauges at the middle position of the members in the monitoring sub-region, the axial strain changes of the members under service conditions are collected in real time. Micro-deformation sensors are installed to collect the local displacement and flexural response of the members. A buckling sensitivity index R reflecting the stability characteristics of the members is formed. S15. Monitor the service environment status of each monitoring area of ​​the spatial grid structure, install environmental sensing units at typical nodes in the monitoring sub-area, collect environmental humidity and condensate data in real time, and obtain the surface humidity H of the rod and the condensate accumulation time Tw; collect environmental temperature data synchronously at the same or adjacent locations to obtain the regional environmental temperature T. S16. Monitor the installation status of the rods in each monitoring area of ​​the spatial grid structure. Install a closure detection sensor at the fastener position in the monitoring sub-area to collect the closure degree C of the fastener; install a preload sensor at the bolt position to collect the bolt preload P.

[0027] In this embodiment, the large-span spatial grid structure roof is divided into multiple monitoring sub-regions according to the geometric distribution and stress function of the members. Within each sub-region, the geometric dimensions, cross-sectional defects, corrosion status, buckling sensitivity, service environment, and assembly connection status of the members are collected simultaneously. This achieves a complete characterization of the structure's multi-dimensional basic state of "geometry-mechanics-environment-connection," ensuring consistency and comparability of various raw data in terms of spatial location, stress attributes, and service conditions. This provides a reliable data foundation for the accurate construction of subsequent stability degradation factors, internal force disturbance factors, and environmental corrosion factors, effectively avoiding evaluation distortion caused by single-point monitoring or single-parameter acquisition, and improving the overall accuracy and pertinence of spatial grid structure defect identification and repair decisions.

[0028] Example 3 This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically, step two includes: S21. Based on the local section defect length Ld, the corrosion depth dc, the total length L, and the outer diameter D of the member, the geometric slenderness characteristics of the member are processed using the slenderness ratio calculation method to obtain the slenderness ratio parameter s. Based on the slenderness ratio parameter s, combined with the local section defect length Ld and corrosion depth dc, a stability degradation evaluation function is used to obtain a stability degradation factor characterizing the degree of local stability attenuation of the member. ; S22. Based on the screw preload P and the fastener closure degree C, the loading state of the fastener is processed using an assembly connection state mapping method. Combined with the local displacement and deflection response data of the member, the local bending deformation of the member is extracted. Based on the local bending deformation of the rod By combining the screw preload P and the fastener closure degree C, and processing them through an internal force disturbance evaluation function, an internal force disturbance factor reflecting the influence of reinforcement or assembly status on the internal force of the member is obtained. ; S23. Based on the ambient temperature T, the surface humidity H of the member, and the condensate accumulation time Tw, the temperature change amplitude is calculated using the environmental load normalization method to obtain the regional temperature change parameters. Furthermore, the surface exposure conditions of the rods were comprehensively assessed by combining humidity and condensation accumulation time to form a characterization quantity of regional environmental corrosion. An environmental corrosion impact assessment function was then applied to obtain the service environment corrosion factor. ; S24. Based on the buckling sensitivity index R, the actual working stress of the member under its current service condition is calculated using the stress-strain conversion method. ; and by combining the standard strength parameters of the member material design, the nominal bearing stress of the member is obtained. Then, by processing the stress ratio, the stress ratio parameter used for load-bearing safety assessment is obtained. ; S25. Based on the corrosion depth dc and wall thickness t of the member, the effective bearing capacity of the local section of the member is calculated using the section effectiveness reduction method. This yields the local section influence parameter, which reflects the degree of influence of local section defects on the member's bearing performance. .

[0029] In this embodiment, by performing unified physical modeling and parameterization on the collected geometric, connection, environmental, and stress data in step two, the original monitoring data is transformed into evaluation factors with clear engineering significance, such as stability degradation factors, internal force disturbance factors, service environment corrosion factors, stress ratio parameters, and local section influence parameters. This allows the geometric slenderness characteristics, assembly connection status, environmental corrosion effects, and actual stress levels of the members to be comprehensively and quantitatively characterized within the same analytical framework. This avoids the problems of difficulty in unifying multi-source data and fragmented evaluation dimensions in traditional methods. It provides comparable and logically consistent basic parameters for subsequent structural stability assessment, reinforcement process inspection, and durability determination, improving the scientificity and reliability of spatial grid structure defect identification and repair decisions.

[0030] Example 4 This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, step three includes: S31. By extracting stability degradation factors from the monitoring sub-regions Local section influence parameters and stress ratio parameter After dimensionless processing, the structural stability coefficient JWX is calculated using the following formula:

[0031] In the formula, the stability degradation factor The stability degradation factor characterizes the degree of reduction in the inherent stability bearing capacity of a member due to factors such as its slender geometric characteristics, local cross-sectional weakening, and corrosion. The smaller the value, the weaker the member's resistance to instability, and the worse the overall stability. Local section influence parameters. This parameter reflects the degree to which local section defects weaken the effective load-bearing section of a member, and embodies the level of integrity of the effective section under axial force and bending. Local section influence parameters. The smaller the value, the more severe the section weakening and the lower the contribution to stability; stress ratio parameter This characterizes the degree to which the current actual working stress of a member is utilized relative to its nominal bearing capacity, and is used to describe the stress margin state of the member; the formula uses... This indicates that as the actual stress level approaches the material's bearing limit, the structural stability margin continuously decreases; therefore, the structural stability coefficient JWX is determined by the stability degradation factor. Influence parameters of local cross sections Multiplication describes the "stable load-bearing capacity" of the members at the structural and cross-sectional levels, and then through... The stress state is corrected to comprehensively reflect the remaining stability level of the members in the monitoring sub-region under the combined action of geometry, material, cross-sectional integrity and actual force. The larger the structural stability coefficient JWX value, the more sufficient the structural stability of the member and the lower the risk of instability. The smaller the JWX value, the more significant the stability degradation, and the more likely the member is to experience local or overall instability under the existing stress state.

[0032] In this embodiment, by introducing stability degradation factors, local section influence parameters, and stress ratio parameters, and constructing a structural stability coefficient JWX based on dimensionless unified processing, the structural stability assessment results comprehensively reflect the coupling effect of material and component degradation degree, local geometric weakening effect, and actual stress state. This avoids the misjudgment problem caused by relying solely on a single parameter or limit value, thereby achieving a quantitative and continuous characterization of the overall structural stability level of the monitored sub-region. This provides a more physically consistent and engineering-applicable criterion basis for subsequent adjustment strategy formulation and reinforcement inspection.

[0033] Example 5 This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically, step three also includes: S32. By setting a pre-defined structural stability threshold Jth, and comparing the structural stability coefficient JWX with the structural stability threshold Jth, the first evaluation result is obtained, including: When the structural stability coefficient JWX ≥ the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is qualified, and monitoring will continue. When the structural stability coefficient JWX < the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is unqualified, and there is a risk of stability degradation caused by cross-section weakening or stress state. This triggers the first warning instruction and generates the first strategy: re-classify the closure degree C of the fasteners in the current monitoring sub-region, and improve the synergistic constraint ability of the reinforcement components on the axial and bending stiffness of the members by reducing the closure gap in segments, thereby suppressing the evolution of local instability into overall instability; simultaneously adjust the screw preload P to make the constraint force provided by the reinforcement device evenly distributed along the axial direction of the members, thereby reducing the stability degradation caused by sudden changes in local stiffness; and re-optimize the reinforcement length and layout sections in the axial direction of the members according to the length Ld and location of the local cross-section defect, so that the effective action area of ​​the reinforcement device covers the unfavorable stability section.

[0034] The structural stability threshold Jth is obtained by statistically analyzing a large amount of monitoring data from large-span spatial grid structures under normal service and stability degradation conditions. This extracts the distribution characteristics of structural stability coefficients in different monitoring sub-regions, identifying typical numerical ranges when structural stability is within a safe range and close to instability. Combining this with the engineering experience of structural engineering professionals regarding member stability, cross-sectional weakening effects, and changes in stress state, a reasonable threshold is determined to distinguish between a structural stability qualification state and a state with stability degradation risk. Referring to the requirements for stability safety margins in current spatial structure design codes, steel structure stability-related technical standards, and engineering reinforcement design guidelines, the threshold is verified and corrected to effectively reflect the overall stability level of members in the monitoring sub-region.

[0035] In this embodiment, by comparing the structural stability coefficient JWX with the preset structural stability threshold Jth, the stability state of the rod structure in the monitored sub-region is automatically determined. When the stability is deemed insufficient, a targeted adjustment strategy is generated based on multi-dimensional constraint parameters such as the degree of closure of the fasteners, the preload of the screws, and the layout of the reinforcement length. This transforms the reinforcement effect from local stiffness compensation to the synergistic constraint of axial and bending stiffness, thereby effectively suppressing the evolution of local instability into overall instability and improving the pertinence, uniformity, and overall stability assurance effect of the space grid structure defect repair measures.

[0036] Example 6 This embodiment is an explanation based on Embodiment 5. Please refer to it. Figure 1 Specifically, step four includes: S41. After completing stability control and reinforcement adjustments, the actual reinforcement implementation process of the members within the monitored sub-region is inspected and quantitatively evaluated; the real-time local section defect length Ld, member outer diameter D, member wall thickness t, and member local bending deformation are extracted. and internal force disturbance factor After dimensionless processing, the internal force disturbance coefficient GRX of the reinforcement is calculated and obtained, as shown in the following formula:

[0037] In the formula, the reinforcement internal force disturbance coefficient GRX is used to characterize the degree of internal force disturbance in the monitored sub-region caused by the combined effects of cross-sectional defects, geometric deformation, and internal force redistribution after reinforcement measures are completed. The reinforcement internal force disturbance coefficient GRX is calculated by using the internal force disturbance factor... The ratio of the length Ld of the local cross-section defect to the outer diameter D of the member, and the amount of local bending deformation of the member. The coupling calculation is performed relative to the member wall thickness t to comprehensively reflect the influence of local stiffness discontinuity, geometric deviation amplification, and changes in internal force transmission path on the member stress state during the reinforcement process. The larger the reinforcement internal force disturbance coefficient GRX value, the more obvious the internal force disturbance introduced during the reinforcement process, and the higher the risk of the member stress state deviating from the original design conditions.

[0038] In this embodiment, after stability control and reinforcement adjustment are completed, a reinforcement internal force disturbance coefficient GRX, calculated based on the coupling of internal force disturbance factor, cross-sectional defect scale, and local bending response, is introduced to quantitatively verify the actual reinforcement implementation process of the members. This can effectively reveal the degree of disturbance of the reinforcement measures on the stress state of the members, avoiding the need to rely solely on theoretical design parameters for post-event judgment. This method can promptly identify abnormal changes in internal forces caused by cross-sectional weakening, reinforcement stiffness mismatch, or uneven distribution of preload, providing objective quantitative basis for reinforcement effect verification and subsequent adjustments, thereby improving the reliability and safety controllability of structural reinforcement implementation.

[0039] Example 7 This embodiment is an explanation based on Embodiment 6. Please refer to it. Figure 1 Specifically, step four also includes: S42. By setting a preset internal force disturbance threshold Gth, and comparing the internal force disturbance coefficient GRX with the internal force disturbance threshold Gth, the second evaluation results are obtained, including: When the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth, it indicates that the reinforcement internal force disturbance status of the current monitoring sub-region is qualified, and the current reinforcement sequence and loading method are maintained for continuous monitoring. When the internal force disturbance coefficient GRX is greater than the internal force disturbance threshold Gth, it indicates that the internal force disturbance state of the current monitored sub-region is unqualified. The additional stiffness or constraints introduced by the reinforcement interfere with the original internal force balance, and there is a risk of local stress concentration, secondary buckling, or transient instability during the construction stage caused by sudden stiffness changes or discontinuous loading. This triggers the second warning command and generates the second strategy: adopting a step-by-step rotational closure method with semi-circular fasteners, breaking down the one-time closure operation into multiple angle incremental closure steps, so that the reinforcement constraints are introduced step by step to avoid instantaneous stiffness changes; adopting a graded locking strategy for the screw preload P, dividing the target preload into several loading levels, applying them step by step in order from low to high, and measuring the local bending deformation of the member after each loading level is completed. Real-time monitoring is performed; during the progressive loading process, the closure degree C of the fasteners and the locking sequence are dynamically corrected to ensure that the internal force introduced by the reinforcement is smoothly transmitted along the axial direction of the member, reducing the peak value of internal force disturbance during the reinforcement process; after completing one round of progressive reinforcement, the reinforcement internal force disturbance coefficient GRX is recalculated; if the reinforcement internal force disturbance coefficient GRX > the reinforcement internal force disturbance threshold Gth, the low-disturbance progressive reinforcement scheme is repeated, with no more than 3 repetitions; if the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth cannot be achieved after 3 consecutive executions, the automatic reinforcement is paused, and the process transitions to the fine-grained loading control stage with manual intervention.

[0040] The method for obtaining the internal force disturbance threshold Gth during reinforcement is as follows: Data on internal force changes during the reinforcement process of various typical spatial grid structures are collected and statistically analyzed. The distribution range of the internal force disturbance coefficient is compared between states where internal force balance is maintained and states with significant internal force disturbance. The boundary values ​​between the acceptable and risky ranges of the reinforcement process's impact on the original stress system are extracted. Combined with structural reinforcement construction experience and the judgments of professional technicians regarding stiffness abrupt changes, loading sequence, and internal force transmission characteristics, a threshold is determined to determine whether the internal force disturbance during the reinforcement process is within a controllable range. Referring to structural reinforcement construction technical specifications, recommended parameters for prefabricated connection structures, and construction safety control requirements, this threshold is reasonably modified to effectively constrain unfavorable internal force disturbances during the reinforcement process.

[0041] In this embodiment, by comparing the internal force disturbance coefficient GRX with the internal force disturbance threshold Gth, and introducing a progressive reinforcement strategy combining step-by-step closure, graded pre-tightening, and dynamic correction when the threshold is exceeded, the peak internal force disturbance caused by abrupt stiffness changes or discontinuous loading during the reinforcement process can be effectively suppressed, avoiding local stress concentration, secondary buckling, and transient instability during the construction stage. At the same time, through iterative evaluation and upper limit control of the number of iterations, the reinforcement process can be quantified and the risk can be adaptively converged, thereby improving the controllability and reliability of the reinforcement construction while ensuring structural safety.

[0042] Example 8 This embodiment is an explanation based on Embodiment 7. Please refer to it. Figure 1 Specifically, step five includes: S51, By extracting corrosion factors in the service environment Stress ratio parameter The corrosion depth dc of the rod and the wall thickness t of the rod are dimensionlessly processed, and the corrosion influence coefficient FHY of the service environment is calculated as follows:

[0043] In the formula, the service environment corrosion influence coefficient FHY is used to quantitatively characterize the comprehensive influence of corrosion factors on the durability and load-bearing capacity of the member under the coupled action of service environment conditions and stress state; the service environment corrosion influence coefficient FHY coefficient is obtained by considering the service environment corrosion factors. The ratio of corrosion depth dc of the member to the wall thickness t and the stress ratio parameter Product coupling is performed to reflect the amplification effect between corrosion development caused by environmental humidity, temperature and condensation conditions and the actual stress level of the members; the larger the service environment corrosion influence coefficient FHY value, the more significant the impact of corrosion on the effective cross section and load-bearing safety reserve of the members, and the stronger the adverse effect of the service environment on the structural durability.

[0044] In this embodiment, by performing dimensionless coupling calculations of the service environment corrosion effect factor, the corrosion depth to wall thickness ratio, and the stress ratio parameter, the service environment corrosion influence coefficient FHY is constructed. This allows for a unified quantitative characterization of environmental corrosion conditions, cross-sectional degradation, and actual stress state, enabling a comprehensive assessment of the corrosion-stress coupling effect of members under real service conditions. This avoids relying solely on a single corrosion index or nominal environmental conditions for judgment, thereby improving the accuracy of corrosion risk identification and the targeted nature of subsequent maintenance decisions.

[0045] Example 9 This embodiment is an explanation based on Embodiment 8. Please refer to it. Figure 1 Specifically, step five also includes: S52. By setting a preset service environment corrosion impact threshold Fth, and comparing the service environment corrosion impact coefficient FHY with the service environment corrosion impact threshold Fth, the third evaluation results are obtained, including: When the service environment corrosion influence coefficient FHY ≤ the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; continuous monitoring is required. When the service environment corrosion influence coefficient FHY > the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment on the structural durability of the members within the monitoring sub-region is not within an acceptable range; the corrosion influence poses a threat to the structural durability, with the risk of member section weakening and continuous decline in load-bearing capacity, triggering the third early warning instruction and generating the third strategy: Install water-absorbing resin pads on the inner wall of the reinforced members or in the vicinity of the defect to absorb condensate and reduce the residence time of local corrosive media, forming an adaptive anti-corrosion buffer layer; adaptively extend the reinforcement length of the reinforcement device, making... The anti-corrosion repair area and the structural reinforcement area form a synergistic coverage to improve overall durability. After the anti-corrosion repair is completed, environmental and component status data are re-collected and the service environment corrosion impact coefficient FHY is calculated. If the recalculation result still satisfies the service environment corrosion impact coefficient FHY ≤ service environment corrosion impact threshold Fth, the anti-corrosion repair strategy is repeated, with the number of repetitions not exceeding the preset number of 2. If it is still impossible to make the service environment corrosion impact coefficient FHY > service environment corrosion impact threshold Fth after 2 consecutive executions, it indicates that the artificial anti-corrosion assessment and long-term durability special treatment stage is to be entered.

[0046] The method for obtaining the service environment corrosion impact threshold Fth is as follows: Through statistical analysis of long-term operational data of spatial grid structures under different service environment conditions, the distribution range of the service environment corrosion impact coefficient under low corrosion impact and significant corrosion conditions is extracted. This identifies the characteristic numerical range when the environmental impact on structural durability changes from acceptable to unacceptable. Combining material durability analysis experience and the engineering judgments of professional technicians on corrosion development rate, cross-sectional weakening trend, and load-bearing capacity attenuation law, a threshold is determined to distinguish between a structural durability safety state and a corrosion risk state. Referring to steel structure corrosion protection design specifications, environmental corrosion level classification standards, and equipment operation and maintenance management guidelines, this threshold is verified and adjusted to accurately reflect the comprehensive impact level of the service environment on structural durability.

[0047] In this embodiment, by comparing the service environment corrosion impact coefficient FHY with the preset service environment corrosion impact threshold Fth, the durability risk of the members in the monitoring sub-area is classified and determined. When the corrosion impact exceeds the limit, a coordinated control strategy integrating water absorption and slow release, anti-corrosion repair and structural reinforcement is triggered. This allows the control of environmental corrosive media, the scope of anti-corrosion coverage and structural stress reinforcement to form a closed loop adjustment, avoiding the separation of corrosion control and structural reinforcement. This effectively inhibits the continuous weakening of the cross section induced by corrosion, delays the process of load-bearing capacity attenuation, and improves the overall durability and long-term safety and reliability of the spatial grid structure in complex service environments.

[0048] Example 10 For an intelligent identification and repair system for defects in spatial grid structures, please refer to... Figure 2Specifically, including: The structural state perception and acquisition module is used to divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and to monitor the members in each monitoring sub-area in real time, collecting the member's outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. The structural multi-factor feature construction module is used to process the collected data in a unified manner and obtain the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter and local section influence parameter respectively. The structural stability assessment module is used to extract the stability degradation factor, local section influence parameters and stress ratio parameters of the monitored sub-region, calculate the structural stability coefficient JWX, and compare it with the structural stability threshold Jth to determine whether the structural stability of the members in the current monitored sub-region is qualified. If it is not qualified, the first strategy is given. The reinforcement implementation internal force disturbance assessment module is used to verify and quantify the actual reinforcement implementation process of the members in the monitored sub-region; it extracts the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation and internal force disturbance factor, calculates the reinforcement internal force disturbance coefficient GRX, and compares it with the reinforcement internal force disturbance threshold Gth to determine whether the reinforcement internal force disturbance state of the current monitored sub-region is qualified. If it is not qualified, a second strategy is given. The service environment corrosion impact assessment module is used to extract service environment corrosion factors, stress ratio parameters, member corrosion depth and member wall thickness, calculate the service environment corrosion impact coefficient FHY, and compare it with the service environment corrosion impact threshold Fth to determine whether the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; if not, a third strategy is given.

[0049] In this embodiment, the operation monitoring and safety assessment functions of the large-span spatial grid structure roof are divided into a structural state perception and acquisition module, a structural multi-factor feature construction module, a structural stability assessment module, a reinforcement implementation internal force disturbance assessment module, and a service environment corrosion impact assessment module. This decouples the perception, feature extraction, and assessment of the structural geometric state, stress state, and environmental effects, forming a clear functional division. This improves the overall data processing consistency and the scalability of the assessment logic, avoids functional overlap between different assessment objectives, and facilitates independent upgrades or replacements of individual modules according to actual engineering needs. This enhances the system's engineering adaptability and stability in the long-term operation monitoring and reinforcement assessment of complex large-span structures.

[0050] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0051] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A method for intelligent identification and repair of defects in spatial grid structures, characterized in that, Includes the following steps: Step 1: Divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and monitor the members in each monitoring sub-area in real time. Collect the following data: member outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. Step 2: By uniformly processing the collected data, the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter, and local section influence parameter are obtained respectively. Step 3: By extracting the stability degradation factor, local section influence parameter and stress ratio parameter of the monitored sub-region, calculate the structural stability coefficient JWX and compare it with the structural stability threshold Jth to determine whether the structural stability of the current monitored sub-region is qualified. If it is not qualified, the first strategy is adopted. Step 4: Verify and quantify the actual reinforcement process of the members within the monitoring sub-region; extract the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation, and internal force disturbance factor; calculate the reinforcement internal force disturbance coefficient GRX; compare it with the reinforcement internal force disturbance threshold Gth; determine whether the current reinforcement internal force disturbance state of the monitoring sub-region is qualified; if not, apply the second strategy. Step 5: By extracting the service environment corrosion factor, stress ratio parameter, corrosion depth of the member and wall thickness of the member, calculate the service environment corrosion influence coefficient FHY, and compare it with the service environment corrosion influence threshold Fth to determine whether the comprehensive influence of the service environment of the member in the monitoring sub-region on the structural durability is within an acceptable range; if not, the third strategy is adopted.

2. The intelligent identification and repair method for defects in spatial grid structures according to claim 1, characterized in that, Step one includes: S11. By dividing the large-span spatial grid structure roof into N monitoring sub-regions with a uniform number of structural members according to the geometric structure distribution and the stress function of the members; S12. Within each monitoring sub-region, monitor the geometric dimensions and cross-sectional state of all members to be evaluated within the region. By installing a portable structural dimension scanning device on the outer surface of a typical member within the monitoring sub-region, scan the outer contour of the member and collect the outer diameter D. Install an ultrasonic thickness gauge on the outer surface of the member to detect the wall thickness and collect the wall thickness t. By arranging a scanning path along the axial direction of the member, detect the continuity of the member's cross-section, identify and record the location and length of cross-section weakening, and collect the length Ld of local cross-section defects. Combine the node positions at both ends of the member with the scanning trajectory information to determine the axial dimension of the member and collect the total length L of the member. S13. Monitor the corrosion status of the rods in each monitoring sub-region of the spatial grid structure. By installing corrosion depth detection probes on the outer surface of the rods in the monitoring sub-region, detect the thinning of the metal surface and collect the corrosion depth dc of the rods. S14. Monitor the buckling sensitivity of members in each monitoring sub-region of the spatial grid structure. By installing strain gauges at the middle position of the members in the monitoring sub-region, the axial strain changes of the members under service conditions are collected in real time. Micro-deformation sensors are installed to collect the local displacement and flexural response of the members. A buckling sensitivity index R reflecting the stability characteristics of the members is formed. S15. Monitor the service environment status of each monitoring area of ​​the spatial grid structure, install environmental sensing units at typical nodes in the monitoring sub-area, collect environmental humidity and condensate data in real time, and obtain the surface humidity H of the rod and the condensate accumulation time Tw; collect environmental temperature data synchronously at the same or adjacent locations to obtain the regional environmental temperature T. S16. Monitor the installation status of the rods in each monitoring area of ​​the spatial grid structure. Install a closure detection sensor at the fastener position in the monitoring sub-area to collect the closure degree C of the fastener; install a preload sensor at the bolt position to collect the bolt preload P.

3. The intelligent identification and repair method for defects in spatial grid structures according to claim 2, characterized in that, Step two includes: S21. Based on the local section defect length Ld, the corrosion depth dc, the total length L, and the outer diameter D of the member, the geometric slenderness characteristics of the member are processed using the slenderness ratio calculation method to obtain the slenderness ratio parameter s. Based on the slenderness ratio parameter s, combined with the local section defect length Ld and corrosion depth dc, a stability degradation evaluation function is used to obtain a stability degradation factor characterizing the degree of local stability attenuation of the member. ; S22. Based on the screw preload P and the fastener closure degree C, the loading state of the fastener is processed using an assembly connection state mapping method. Combined with the local displacement and deflection response data of the member, the local bending deformation of the member is extracted. Based on the local bending deformation of the rod By combining the screw preload P and the fastener closure degree C, and processing them through an internal force disturbance evaluation function, an internal force disturbance factor reflecting the influence of reinforcement or assembly status on the internal force of the member is obtained. ; S23. Based on the ambient temperature T, the surface humidity H of the member, and the condensate accumulation time Tw, the temperature change amplitude is calculated using the environmental load normalization method to obtain the regional temperature change parameters. Furthermore, the surface exposure conditions of the rods were comprehensively assessed by combining humidity and condensation accumulation time to form a characterization quantity of regional environmental corrosion. An environmental corrosion impact assessment function was then applied to obtain the service environment corrosion factor. ; S24. Based on the buckling sensitivity index R, the actual working stress of the member under its current service condition is calculated using the stress-strain conversion method. ; and by combining the standard strength parameters of the member material design, the nominal bearing stress of the member is obtained. Then, by processing the stress ratio, the stress ratio parameter used for load-bearing safety assessment is obtained. ; S25. Based on the corrosion depth dc and wall thickness t of the member, the effective bearing capacity of the local section of the member is calculated using the section effectiveness reduction method. This yields the local section influence parameter, which reflects the degree of influence of local section defects on the member's bearing performance. .

4. The intelligent identification and repair method for defects in spatial grid structures according to claim 3, characterized in that, Step three includes: S31. By extracting stability degradation factors from the monitoring sub-regions Local section influence parameters and stress ratio parameter After dimensionless processing, the structural stability coefficient JWX is calculated and obtained.

5. The intelligent identification and repair method for defects in spatial grid structures according to claim 4, characterized in that, Step three also includes: S32. By setting a pre-defined structural stability threshold Jth, and comparing the structural stability coefficient JWX with the structural stability threshold Jth, the first evaluation result is obtained, including: When the structural stability coefficient JWX ≥ the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is qualified, and monitoring will continue. When the structural stability coefficient JWX < the structural stability threshold Jth, it indicates that the structural stability of the members in the current monitoring sub-region is unqualified, and there is a risk of stability degradation caused by cross-section weakening or stress state. This triggers the first warning instruction and generates the first strategy: re-classify the closure degree C of the fasteners in the current monitoring sub-region, and improve the synergistic constraint ability of the reinforcement components on the axial and bending stiffness of the members by reducing the closure gap in segments, thereby suppressing the evolution of local instability into overall instability; simultaneously adjust the screw preload P to make the constraint force provided by the reinforcement device evenly distributed along the axial direction of the members, thereby reducing the stability degradation caused by sudden changes in local stiffness; and re-optimize the reinforcement length and layout sections in the axial direction of the members according to the length Ld and location of the local cross-section defect, so that the effective action area of ​​the reinforcement device covers the unfavorable stability section.

6. The intelligent identification and repair method for defects in spatial grid structures according to claim 5, characterized in that, Step four includes: S41. After completing stability control and reinforcement adjustments, the actual reinforcement implementation process of the members within the monitored sub-region is inspected and quantitatively evaluated; the real-time local section defect length Ld, member outer diameter D, member wall thickness t, and member local bending deformation are extracted. and internal force disturbance factor After dimensionless processing, the internal force disturbance coefficient GRX of the reinforcement is calculated and obtained.

7. The intelligent identification and repair method for defects in spatial grid structures according to claim 6, characterized in that, Step four also includes: S42. By setting a preset internal force disturbance threshold Gth, and comparing the internal force disturbance coefficient GRX with the internal force disturbance threshold Gth, the second evaluation results are obtained, including: When the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth, it indicates that the reinforcement internal force disturbance status of the current monitoring sub-region is qualified, and the current reinforcement sequence and loading method are maintained for continuous monitoring. When the internal force disturbance coefficient GRX is greater than the internal force disturbance threshold Gth, it indicates that the internal force disturbance state of the current monitored sub-region is unqualified. The additional stiffness or constraints introduced by the reinforcement interfere with the original internal force balance, and there is a risk of local stress concentration, secondary buckling, or transient instability during the construction stage caused by sudden stiffness changes or discontinuous loading. This triggers the second warning command and generates the second strategy: adopting a step-by-step rotational closure method with semi-circular fasteners, breaking down the one-time closure operation into multiple angle incremental closure steps, so that the reinforcement constraints are introduced step by step to avoid instantaneous stiffness changes; adopting a graded locking strategy for the screw preload P, dividing the target preload into several loading levels, applying them step by step in order from low to high, and measuring the local bending deformation of the member after each loading level is completed. Real-time monitoring is performed; during the progressive loading process, the closure degree C of the fasteners and the locking sequence are dynamically corrected to ensure that the internal force introduced by the reinforcement is smoothly transmitted along the axial direction of the member, reducing the peak value of internal force disturbance during the reinforcement process; after completing one round of progressive reinforcement, the reinforcement internal force disturbance coefficient GRX is recalculated; if the reinforcement internal force disturbance coefficient GRX > the reinforcement internal force disturbance threshold Gth, the low-disturbance progressive reinforcement scheme is repeated, with no more than 3 repetitions; if the reinforcement internal force disturbance coefficient GRX ≤ reinforcement internal force disturbance threshold Gth cannot be achieved after 3 consecutive executions, the automatic reinforcement is paused, and the process transitions to the fine-grained loading control stage with manual intervention.

8. The intelligent identification and repair method for defects in spatial grid structures according to claim 7, characterized in that, Step five includes: S51, By extracting corrosion factors in the service environment Stress ratio parameter The corrosion depth dc of the rod and the wall thickness t of the rod are dimensionlessly processed, and the corrosion influence coefficient FHY of the service environment is calculated.

9. The intelligent identification and repair method for defects in spatial grid structures according to claim 8, characterized in that, Step five also includes: S52. By setting a preset service environment corrosion impact threshold Fth, and comparing the service environment corrosion impact coefficient FHY with the service environment corrosion impact threshold Fth, the third evaluation results are obtained, including: When the service environment corrosion influence coefficient FHY ≤ the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; continuous monitoring is required. When the service environment corrosion influence coefficient FHY > the service environment corrosion influence threshold Fth, it indicates that the comprehensive impact of the service environment on the structural durability of the members within the monitoring sub-region is not within an acceptable range; the corrosion influence poses a threat to the structural durability, with the risk of member section weakening and continuous decline in load-bearing capacity, triggering the third early warning instruction and generating the third strategy: Install water-absorbing resin pads on the inner wall of the reinforced members or in the vicinity of the defect to absorb condensate and reduce the residence time of local corrosive media, forming an adaptive anti-corrosion buffer layer; adaptively extend the reinforcement length of the reinforcement device, making... The anti-corrosion repair area and the structural reinforcement area form a synergistic coverage to improve overall durability. After the anti-corrosion repair is completed, environmental and component status data are re-collected and the service environment corrosion impact coefficient FHY is calculated. If the recalculation result still satisfies the service environment corrosion impact coefficient FHY ≤ service environment corrosion impact threshold Fth, the anti-corrosion repair strategy is repeated, with the number of repetitions not exceeding the preset number of 2. If it is still impossible to make the service environment corrosion impact coefficient FHY > service environment corrosion impact threshold Fth after 2 consecutive executions, it indicates that the artificial anti-corrosion assessment and long-term durability special treatment stage is to be entered.

10. A system for intelligent identification and repair of defects in spatial grid structures, applied to the method for intelligent identification and repair of defects in spatial grid structures as described in any one of claims 1 to 9, characterized in that, include: The structural state perception and acquisition module is used to divide the large-span spatial grid structure roof into multiple monitoring sub-areas, and to monitor the members in each monitoring sub-area in real time, collecting the member's outer diameter, wall thickness, defect length, corrosion depth, buckling sensitivity index, ambient humidity and temperature, condensate accumulation time, fastener closure degree and screw preload. The structural multi-factor feature construction module is used to process the collected data in a unified manner and obtain the stability degradation factor, internal force disturbance factor, service environment corrosion factor, stress ratio parameter and local section influence parameter respectively. The structural stability assessment module is used to extract the stability degradation factor, local section influence parameters and stress ratio parameters of the monitored sub-region, calculate the structural stability coefficient JWX, and compare it with the structural stability threshold Jth to determine whether the structural stability of the members in the current monitored sub-region is qualified. If it is not qualified, the first strategy is given. The reinforcement implementation internal force disturbance assessment module is used to verify and quantify the actual reinforcement implementation process of the members in the monitored sub-region; it extracts the real-time local section defect length, member outer diameter, member wall thickness, member local bending deformation and internal force disturbance factor, calculates the reinforcement internal force disturbance coefficient GRX, and compares it with the reinforcement internal force disturbance threshold Gth to determine whether the reinforcement internal force disturbance state of the current monitored sub-region is qualified. If it is not qualified, a second strategy is given. The service environment corrosion impact assessment module is used to extract service environment corrosion factors, stress ratio parameters, member corrosion depth and member wall thickness, calculate the service environment corrosion impact coefficient FHY, and compare it with the service environment corrosion impact threshold Fth to determine whether the comprehensive impact of the service environment of the members in the monitoring sub-region on the structural durability is within an acceptable range; if not, a third strategy is given.

Images