Steel Structure Wind Resistance Performance Detection Method and System

Abstract

The present invention relates to the technical field of wind resistance performance detection of steel structures, and specifically, to a method and system for detecting the wind resistance performance of steel structures. It includes the following steps: constructing a geometric model of the steel structure, calculating the geometric instability angle between the steel structure surfaces by the stereographic projection method, and identifying the instability regions under different wind load conditions; arranging sensors in the instability regions, collecting and normalizing the dynamic response data to construct the combined time series data of the instability regions; setting a reference entropy value and an instability entropy value threshold, calculating the sample entropy of the combined time series data of the instability regions, and determining the instability regions; using a combined risk assessment formula to calculate the instability risk index, and evaluating the risk level of the instability regions based on the instability risk index. The method and system for detecting the wind resistance performance of steel structures combine the stereographic projection method with the entropy theory, identify the instability risk regions under different wind load conditions, define and calculate the comprehensive instability risk index, and realize the multi-dimensional quantification and early warning of the structural instability risk.

Description

Technical Field

[0001] The present invention relates to the technical field of wind resistance performance detection of steel structures, and specifically, to a method and system for detecting the wind resistance performance of steel structures. Background Technique

[0002] The method and system for detecting the wind resistance performance of steel structures aim to improve the accuracy of stability assessment of steel structures under complex wind load conditions. By accurately identifying potential instability regions and monitoring real-time dynamic responses, the instability risk of the structure under extreme wind loads is controlled, and efficient detection of the wind resistance performance of steel structures is achieved.

[0003] Existing methods and systems for detecting the wind resistance performance of steel structures usually have difficulty in comprehensively capturing the mutual influence between geometric instability and dynamic response of the structure. Moreover, due to the subtle changes in the spatial geometric relationship of steel structures that are difficult to detect and the diverse changes in the internal response data of steel structures, problems such as insufficient accuracy of instability risk assessment, serious false alarms and missed alarms, and inability to timely identify key instability regions will occur. Therefore, a method and system for detecting the wind resistance performance of steel structures are provided. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for detecting the wind resistance performance of steel structures to solve the problems of insufficient accuracy of instability risk assessment, serious false alarms and missed alarms, and inability to timely identify key instability regions caused by the subtle changes in the spatial geometric relationship of steel structures that are difficult to detect and the diverse changes in the internal response data of steel structures as mentioned in the above background technique.

[0005] To achieve the above purpose, the present invention aims to provide a method for detecting the wind resistance performance of steel structures, including:

[0006] S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by the stereographic projection method, and identify the instability regions under different wind load conditions;

[0007] S2. Arrange sensors in the instability regions, collect and normalize the dynamic response data under wind load to construct the combined time series data of the instability regions;

[0008] S3. Set the reference entropy value and the instability entropy value threshold, calculate the sample entropy of the combined time series data of the instability regions to determine the instability regions;

[0009] S4. Use the combined risk assessment formula to calculate the instability risk index by fusing the geometric instability angle and the sample entropy of the instability regions.

[0010] The stereographic projection method is used to analyze the geometric relationship between the steel structure surfaces and determine the instability regions;

[0011] In S1, a geometric model of the steel structure is constructed. The geometric instability angle between the steel structure surfaces is calculated by the stereographic projection method, and the instability regions under different wind load conditions are identified. The specific method steps are as follows:

[0012] S1.1. Define each plane in the steel structure as , the node positions in the steel structure are , and the distance between adjacent planes is defined as ;

[0013] ;

[0014] Among them, is the normal vector of plane ; is the component of plane on the axis; is the component of plane on the axis; is the component of plane on the axis; is the number of the node in the steel structure;

[0015] S1.2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle;

[0016] S1.3. The region where the geometric instability angle is less than the critical instability angle is identified as the instability region.

[0017] As a further improvement of this technical solution, in S1.2, based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle. The specific method steps are as follows:

[0018] S1.2.1. Define the wind load direction vector as:

[0019] ;

[0020] Among them, is the component of the wind force in the direction; is the component of the wind force in the direction; is the component of the wind force in the direction;

[0021] S1.2.2. Use the stereographic projection method to calculate the normal vector of plane The angle between a plane and the normal vector is :

[0022] ;

[0023] Among them, is the modulus of the normal vector of the plane ; is the modulus of the normal vector of the plane ;

[0024] S1.2.3. Set the critical buckling angle , and compare the geometric buckling angle with the critical buckling angle:

[0025] If , then there is a buckling risk between the plane and the plane ;

[0026] If , then the plane and the plane are relatively stable.

[0027] As a further improvement of this technical solution, in S2, sensors are arranged in the buckling area to collect and normalize the dynamic response data under the action of wind load to construct the time series data of the buckling area. The specific method steps are as follows:

[0028] S2.1. Set that the total number of obtained buckling areas is , and the th buckling area is: , ;

[0029] S2.2. Arrange sensors in the buckling area to measure the dynamic response data under the action of wind load. The dynamic response data includes stress, acceleration, and displacement;

[0030] Among them, is the stress sensor; is the acceleration sensor; is the displacement sensor;

[0031] S2.3. Set the sampling interval as , the total sampling time as , the total number of sampling times , use the sensor to collect the dynamic response data and normalize the dynamic response data.

[0032] As a further improvement of this technical solution, in S2.3, the sampling interval is set to , the total sampling time is , the total number of sampling times , use the sensor to collect dynamic response data, and normalize the dynamic response data. The specific method steps are as follows:

[0033] S2.3.1. The stress data of the stress sensor at time is , then the stress time series collected during the total sampling time is ;

[0034] The acceleration data of the acceleration sensor at time is , then the acceleration time series collected during the total sampling time is ;

[0035] The displacement data of the displacement sensor at time is , then the displacement time series collected during the total sampling time is ;

[0036] Among them, is the sampling time;

[0037] S2.3.2. Use the min-max normalization algorithm to normalize the stress data, acceleration data, and displacement data to the interval to obtain the normalized stress data , the normalized acceleration data and the normalized displacement data ;

[0038] S2.3.2. Combine the dynamic response data in the normalized instability region to form the combined time series data of the instability region :

[0039] ;

[0040] Among them, is the combined time series data of the instability region .

[0041] As a further improvement of this technical solution, in S3, a reference entropy value and an instability entropy value threshold are set, and the sample entropy of the combined time series data of the instability region is calculated to determine the instability region. The specific method steps are as follows:

[0042] S3.1. Set the reference entropy value and the instability entropy value threshold ;

[0043] S3.2. Calculate the sample entropy of the combined time series data of the instability region ; ; ;

[0044] S3.3. Compare the sample entropy , the reference entropy value and the instability entropy value threshold among the three, and evaluate the instability region risk according to the risk assessment criteria.

[0045] As a further improvement of this technical solution, in S3.2, to calculate the sample entropy of the combined time series data of the instability region ; ; , the specific method is as follows:

[0046] S3.2.1. Select the embedding dimension and the similarity tolerance ;

[0047] S3.2.2. According to the embedding dimension , extract the template vectors of length from the combined time series data :

[0048] The template vector of the stress data sequence is:

[0049] ;

[0050] The template vector of the acceleration data sequence is:

[0051] ;

[0052] The template vector of the displacement data sequence is:

[0053] ;

[0054] where is the time, ; is the template vector of the stress data sequence at time ; is the template vector of the acceleration data sequence at time The template vector of the acceleration data sequence; At time The template vector of the displacement data sequence;

[0055] S3.2.3. Calculate among all template vectors with length the proportion of similar template pairs that meet the similarity tolerance :

[0056] Extract from the combined time series data template vectors with length ;

[0057] For each pair of template vectors with length and , calculate and distance :

[0058] ;

[0059] If , then the template vectors and are considered similar;

[0060] Calculate the number of all similar template vector pairs as ;

[0061] Calculate the proportion of similar template pairs that meet the similarity tolerance :

[0062] ;

[0063] Then construct the proportion of similar template pairs that meet the similarity tolerance among template vectors with length , and this calculation method is the same as calculating ;

[0064] S3.2.3. Based on the proportion and , calculate the sample entropy :

[0065] ;

[0066] where is the sample entropy.

[0067] As a further improvement of this technical solution, in S3.3, the sample entropy ​, reference entropy value and the threshold value of instability entropy The three are compared, and the risk of the instability area is evaluated according to the risk assessment criteria. The specific method is as follows:

[0068] The risk assessment criteria are:

[0069] Normal state: If , then the response complexity of the instability area is low, and the structure is in a safe and stable state;

[0070] Warning state: If , then the structure area is in a warning state, the response complexity is relatively high, and the monitoring frequency is increased;

[0071] Instability state: If , then the structure area may enter the instability state, and an alarm should be triggered immediately;

[0072] Based on the above risk assessment criteria, a risk assessment report is generated and the instability area is marked.

[0073] As a further improvement of this technical solution, in S4, a combined risk assessment formula is used to calculate the instability risk index of the geometric instability angle and sample entropy fusion of the instability area. The specific method is as follows:

[0074] Define the combined risk assessment formula as:

[0075] ;

[0076] Among them, is the instability risk index;

[0077] Compare the calculated instability risk index with the safety risk index :

[0078] When, the risk is low;

[0079] When, the risk is high.

[0080] On the other hand, the present invention provides a steel structure wind resistance performance detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steel structure wind resistance performance detection method described in any one of the above.

[0081] Compared with the prior art, the beneficial effects of the present invention:

[0082] 1. In the steel structure wind resistance performance detection method and system, based on the stereographic projection method to calculate the geometric instability angle between steel structure surfaces and combined with the entropy theory for quantitative analysis of the complexity of regional dynamic response, it is possible to accurately identify the regions with higher instability risks under different wind load conditions and evaluate the instability of the regions with higher instability risks.

[0083] 2. In the steel structure wind resistance performance detection method and system, by combining the geometric instability angle with the sample entropy of regional dynamic response, calculating the comprehensive instability risk index, and realizing multi-dimensional quantification and early warning of the structural instability risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is the overall method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] Embodiment 1:

[0087] Please refer to Figure 1 As shown, this embodiment provides a steel structure wind resistance performance detection method, including the following steps:

[0088] S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by the stereographic projection method, and identify the instability regions under different wind load conditions;

[0089] The stereographic projection method is used to analyze the geometric relationship between the steel structure surfaces and determine the instability regions;

[0090] In step S1 of this embodiment, a geometric model of the steel structure is constructed, the geometric instability angle between the steel structure surfaces is calculated by the stereographic projection method, and the instability regions under different wind load conditions are identified. The specific method steps are as follows:

[0091] S1.1. Define each plane in the steel structure as , the node position in the steel structure as , and define the distance between adjacent planes as ;

[0092] ;

[0093] Among them, is the normal vector of plane ; is the plane In the component of the axis; is the plane In the component of the axis; is the plane In the component of the axis; is the number of the node in the steel structure;

[0094] S1.2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure planes, set the critical instability angle, and compare the geometric instability angle with the critical instability angle;

[0095] S1.3. The area where the geometric instability angle is less than the critical instability angle is identified as the instability area.

[0096] In this embodiment S1.2, based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure planes, set the critical instability angle, and compare the geometric instability angle with the critical instability angle. The specific method steps are as follows:

[0097] S1.2.1. Define the wind load direction vector as:

[0098] ;

[0099] Among them, is the component of the wind force in the direction; is the component of the wind force in the direction; is the component of the wind force in the direction;

[0100] S1.2.2. Use the stereographic projection method to calculate the angle between the normal vector of the plane and the normal vector of the plane :

[0101] ;

[0102] Among them, is the modulus of the normal vector of the plane ; is the modulus of the normal vector of the plane ;

[0103] S1.2.3. Set the critical instability angle , and compare the geometric instability angle with the critical instability angle:

[0104] If , then there is a risk of instability between surface and surface ;

[0105] If , then the surface and the surface are relatively stable.

[0106] S2. Arrange sensors in the unstable area, collect and normalize the dynamic response data under wind load to construct the combined time series data of the unstable area;

[0107] In step S2 of this embodiment, sensors are arranged in the unstable area, and the dynamic response data under wind load are collected and normalized to construct the time series data of the unstable area. The specific method steps are as follows:

[0108] S2.1. Set that the total number of unstable areas obtained is , and the th unstable area is: , ;

[0109] S2.2. Arrange sensors in the unstable area to measure the dynamic response data under wind load. The dynamic response data include stress, acceleration and displacement;

[0110] Among them, is a stress sensor; is an acceleration sensor; is a displacement sensor;

[0111] S2.3. Set the sampling interval as , the total sampling time as , the total number of sampling times , use the sensor to collect the dynamic response data and normalize the dynamic response data.

[0112] In step S2.3 of this embodiment, set the sampling interval as , the total sampling time as , the total number of sampling times , use the sensor to collect the dynamic response data and normalize the dynamic response data. The specific method steps are as follows:

[0113] S2.3.1. If the stress data of the stress sensor at time is , then within the total sampling time The collected stress time series is ;

[0114] Acceleration sensor At time The acceleration data is , then in the total sampling time The collected acceleration time series is ;

[0115] Displacement sensor At time The displacement data is , then in the total sampling time The collected displacement time series is ;

[0116] Among them, Is the sampling time;

[0117] S2.3.2. Use the min-max normalization algorithm to normalize the stress data, acceleration data, and displacement data to the Interval to obtain the normalized stress data , normalized acceleration data And normalized displacement data ;

[0118] In this embodiment, use the min-max normalization algorithm to normalize the stress data, acceleration data, and displacement data to the Interval to obtain the normalized stress data , normalized acceleration data And normalized displacement data , and the normalization formula is as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] Among them, And Are the minimum and maximum values of the stress data respectively; And Are the minimum and maximum values of the acceleration data respectively; And Are the minimum and maximum values of the displacement data respectively;

[0123] S2.3.2. Combine the dynamic response data of the normalized instability region Into the combined time series data of the instability region ​ :

[0124] ;

[0125] Among them, is the combined time series data of the instability region .

[0126] S3. Set the reference entropy value and the instability entropy value threshold, and calculate the sample entropy of the combined time series data of the instability region to determine the instability region;

[0127] In step S3 of this embodiment, set the reference entropy value and the instability entropy value threshold, calculate the sample entropy of the combined time series data of the instability region to determine the instability region, and the specific method steps are as follows:

[0128] S3.1. Set the reference entropy value and the instability entropy value threshold ;

[0129] In this embodiment, the reference entropy value represents the typical sample entropy value of the structural region under normal and low wind loadings. The reference entropy value can be calculated from the sample entropy measured from experimental data or historical data under low wind load conditions and used as a reference value for the pre-instability state;

[0130] The instability entropy value threshold represents the critical value of the sample entropy at which the structure may enter the instability state; when the sample entropy value exceeds this threshold, the dynamic response of the region is considered to be in a high-risk instability state. The instability entropy value threshold is usually set through experimental tests or simulation, reflecting the upper limit of the response complexity of the structure under extreme wind loadings;

[0131] S3.2. Calculate the sample entropy of the combined time series data of the instability region ; ;

[0132] S3.3. Compare the sample entropy , the reference entropy value and the instability entropy value threshold among the three, and evaluate the risk of the instability region according to the risk assessment criteria.

[0133] In step S3.2 of this embodiment, calculate the sample entropy of the combined time series data of the instability region , and the specific method is as follows: ;

[0134] S3.2.1. Select the embedding dimension and the similarity tolerance ;

[0135] In this embodiment, the embedding dimension is the dimension for segmenting the time series;

[0136] The similarity tolerance is set to a certain proportion of the standard deviation of the time series, and 0.25 times the standard deviation is selected;

[0137] S3.2.2. Extract template vectors of length from the combined time series data according to the embedding dimension :

[0138] The template vector of the stress data series is:

[0139] ;

[0140] The template vector of the acceleration data series is:

[0141] ;

[0142] The template vector of the displacement data series is:

[0143] ;

[0144] where is the moment, ; is the template vector of the stress data series at the moment ; is the template vector of the acceleration data series at the moment ; is the template vector of the displacement data series at the moment ;

[0145] S3.2.3. Calculate the proportion of similar template pairs that meet the similarity tolerance among all template vectors of length :

[0146] Extract template vectors of length from the combined time series data ;

[0147] For each pair of template vectors of length and , calculate the distance between and :

[0148] ;

[0149] If , the template vectors and are considered similar;

[0150] Calculate the number of all similar template vector pairs as ;

[0151] Calculate the proportion of similar template pairs that meet the similarity tolerance :

[0152] ;

[0153] Then, among the template vectors with a length of , calculate the proportion of similar template pairs that meet the similarity tolerance . This calculation method is the same as that for calculating ;

[0154] S3.2.3. Based on the proportion and , calculate the sample entropy :

[0155] ;

[0156] Among them, is the sample entropy.

[0157] In this embodiment, the sample entropy represents the dynamic response complexity of the instability region . The larger the sample entropy , the higher the complexity and instability of the structural region.

[0158] In this embodiment S3.3, compare the sample entropy , the reference entropy value and the instability entropy value threshold , and evaluate the instability region risk according to the risk assessment criterion. The specific method is as follows:

[0159] The risk assessment criterion is:

[0160] Normal state: If , the response complexity of the instability region is low, and the structure is in a safe and stable state;

[0161] Warning state: If , the structural region is in a warning state, the response complexity is relatively high, and the monitoring frequency is increased;

[0162] Instability state: If , the structural area may enter an unstable state, and a warning should be triggered immediately;

[0163] Based on the above risk assessment criteria, generate a risk assessment report and mark the unstable area.

[0164] S4. Use the combined risk assessment formula to calculate the instability risk index by fusing the geometric instability angle and sample entropy of the unstable area;

[0165] In this embodiment S4, use the combined risk assessment formula to calculate the instability risk index by fusing the geometric instability angle and sample entropy of the unstable area. The specific method is as follows:

[0166] Define the combined risk assessment formula as:

[0167] ;

[0168] Wherein, is the instability risk index;

[0169] Compare the calculated instability risk index with the safety risk index :

[0170] When, the risk is relatively low;

[0171] When, the risk is relatively high.

[0172] Embodiment 2:

[0173] This embodiment provides a steel structure wind resistance performance detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steel structure wind resistance performance detection method described in any one of the above.

[0174] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A method for testing the wind resistance of a steel structure, characterized in that: The following steps are involved: S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by stereographic projection method, and identify the instability area under different wind load conditions; S2. Arrange sensors in the unstable area to collect and normalize the dynamic response data under wind load to construct the combined time series data of the unstable area; S3, setting a baseline entropy value and an instability entropy value threshold, calculating the sample entropy of the combined time series data of the instability region to determine the instability region; S4, using the combined risk assessment formula to calculate the instability risk index of the geometric instability angle of the unstable area and the sample entropy fusion, and evaluate the risk level of the unstable area based on the instability risk index; in S3, the baseline entropy value and the instability entropy value threshold are set, and the sample entropy of the combined time series data of the unstable area is calculated. The specific method steps are as follows: S3.

1. Setting the baseline entropy value and the instability entropy threshold ; S3.

2. Calculation of unstable area Combined time series data The sample entropy of ; S3.

3. Sample entropy , Baseline entropy value and the instability entropy threshold The three are compared and the risk of unstable area is assessed according to the risk assessment criteria; S3.2, calculation of unstable area Combined time series data The sample entropy of , the specific method is as follows: S3.2.

1. Selecting the embedding dimension and similarity tolerance ; S3.2.

2. According to the embedding dimension , from the combined time series data The length extracted is The template vector is: The template vector for the stress data series is: ； The template vector of the acceleration data sequence is: ； The template vector of the displacement data sequence is: ； in, It's time. ; For the moment Template vector of the stress data sequence; For the moment Template vector of the acceleration data sequence; For the moment The template vector of the displacement data sequence; S3.2.

3. Calculate all lengths The template vector satisfies the similarity tolerance Similar template comparison : From combined time series data The length extracted is The template vector of For each pair of length Template vector and ,calculate and distance : ； like , then the template vector and resemblance; Calculate the number of all similar template vector pairs as ; Calculate the similarity tolerance Similar template comparison : ； The reconstructed length is The template vector satisfies the similarity tolerance Similar template comparison This calculation method is the same as that of same; S3.2.

3. Comparison based on similar templates and , calculate the sample entropy : ； in, is the sample entropy; In S4, a combined risk assessment formula is used to calculate the instability risk index of the geometric instability angle of the instability region and the sample entropy fusion, and the specific method is as follows: The combined risk assessment formula is defined as: ； in, is the instability risk index; The instability risk index is calculated and Security Risk Index Compare: When the risk is low; , the risk is higher.

2. The method for detecting wind resistance of steel structures according to claim 1, characterized in that: The stereographic projection method is used to analyze the geometric relationship between steel structure surfaces and determine the unstable area; In S1, a geometric model of the steel structure is constructed, and the geometric instability angles between the steel structure surfaces are calculated by the stereographic projection method to identify the instability areas under different wind load conditions. The specific method steps are as follows: S1.

1. Define each plane in the steel structure as , the node position in the steel structure is , define the distance between adjacent planes as ; ; in, For face The normal vector of For face exist The weight of the axis; For face exist The weight of the axis; For face exist The weight of the axis; is the number of the node in the steel structure; S1.

2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle; S1.

3. The area where the geometric instability angle is less than the critical instability angle is considered to be an unstable area.

3. The method for detecting wind resistance of steel structure according to claim 2, characterized in that: In S1.2, based on the geometric model of the steel structure, the geometric instability angle between the steel structure surfaces is calculated using the stereographic projection method, the critical instability angle is set, and the geometric instability angle is compared with the critical instability angle. The specific method steps are as follows: S1.2.

1. Define wind load direction vector for: ； in, For wind power Directional component; For wind power Directional component; For wind power Directional component; S1.2.

2. Calculation of surface using stereographic projection Normal vector With face Normal vector The angle between : ； in, For face Normal vector Model; For face Normal vector Model; S1.2.

3. Setting the critical instability angle , compare the geometric instability angle with the critical instability angle: like , then face With face There is a risk of instability; like , then face With face Relatively stable between.

4. The method for detecting wind resistance of steel structures according to claim 1, characterized in that: In S2, sensors are arranged in the unstable area to collect and normalize dynamic response data under wind load to construct time series data of the unstable area. The specific method steps are as follows: S2.

1. Set the number of unstable regions to be , The unstable regions are: , ; S2.

2. In the unstable area Internal sensor , used to measure the dynamic response data under wind load, including stress, acceleration and displacement; in, is a stress sensor; is an acceleration sensor; is a displacement sensor; S2.3, set the sampling interval to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized.

5. The method for detecting wind resistance of steel structure according to claim 4, characterized in that: In S2.3, the sampling interval is set to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized. The specific steps are as follows: S2.3.1 Stress sensor At the moment The stress data is , then the total sampling time The collected stress time series is ; Accelerometer At the moment The acceleration data is , then the total sampling time The collected acceleration time series is ; Displacement Sensors At the moment The displacement data is , then the total sampling time The collected displacement time series is ; in, is the sampling time; S2.3.2, use the minimum-maximum normalization algorithm to normalize the stress data, acceleration data and displacement data to interval, and obtain normalized stress data , normalized acceleration data and normalized displacement data ; S2.3.

2. Normalized unstable region The dynamic response data of the combination is the unstable area Combined time series data : ； in, The unstable region Combined time series data.

6. The method for detecting wind resistance of steel structures according to claim 1, characterized in that: In S3.3, the sample entropy , Baseline entropy value and the instability entropy threshold The three are compared, and the risk of unstable area is assessed according to the risk assessment criteria. The specific method is as follows: The risk assessment criteria are: Normal state: If , then the unstable region The response complexity is low and the structure is in a safe and stable state; Warning status: If , then the structural area is in a warning state, the response complexity is high, and the monitoring frequency is increased; Instability: If , then the structural area may enter an unstable state and an early warning should be triggered immediately; Based on the above risk assessment criteria, a risk assessment report is generated and the unstable areas are marked.

7. A steel structure wind resistance performance detection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the method for detecting wind resistance of a steel structure as described in any one of claims 1 to 5.

Images