Steel structure connection danger assessment system and method based on industrial data analysis

Abstract

The application relates to the technical field of steel structure evaluation, and discloses a steel structure connection danger evaluation system and method based on industrial data analysis, which comprises a multidimensional acquisition module and an intelligent analysis module. The steel structure connection danger evaluation system and method based on industrial data analysis acquires management data of all components of a steel structure, inspection data of all joints and sensing data of all environmental factors through the multidimensional acquisition module, and classifies and forms a data set; the intelligent analysis module analyzes the assembly of each component, generates a performance index, analyzes the safety of each joint, generates a stability coefficient, and can adjust the weight distribution proportion according to actual engineering requirements; the multidimensional analysis is flexible and has strong applicability; the intelligent analysis module analyzes the erosion effect of environmental factors on the performance of the steel structure, generates a decay coefficient, sets a fixed range threshold, evaluates whether the steel structure has hidden dangers, provides targeted suggestions, realizes preventive maintenance, and has high intelligent evaluation precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel structure evaluation, in particular to a steel structure connection danger evaluation system and method based on industrial data analysis. BACKGROUND

[0002] Steel structure is an engineering structure type made of steel sections and steel plates through welding, bolting or riveting, mainly composed of steel beams, steel columns, steel trusses and other components. It is outstanding in installation mechanization, easy to manufacture in factories and assemble on site, which can greatly shorten the construction period. The stability and safety of the entire structure are directly related to the firmness of the steel structure connection. If there are hidden dangers in the connection, such as poor weld quality and loose bolts, it may lead to local deformation, damage or even collapse of the structure under stress, endangering the safety of personnel and property. Stable connection can ensure the normal support function of steel structure during use. For structures with vibration load or dynamic action, such as bridge, crane beam in industrial plant, reliable connection can ensure the structure to maintain good working performance under repeated load, avoid premature failure of the structure due to connection problems, and affect normal use. Good connection can reduce stress concentration and fatigue damage of the structure, thereby prolonging the service life of the steel structure. On the contrary, problems at the connection site may accelerate the corrosion, wear and fatigue failure of the structure, reduce the overall durability of the structure, and increase the maintenance cost and frequency. In the field of building engineering, relevant design specifications and construction standards have clear requirements for the connection quality and safety of steel structure. Connection danger evaluation is a necessary means to ensure that steel structure engineering meets these specifications and standards, and is an important basis for engineering quality acceptance.

[0003] At present, the traditional steel structure connection danger evaluation system based on industrial data analysis relies on general algorithm model. However, the connection form and use environment of different steel structures are not the same, it is difficult to ensure the efficiency and reliability of danger evaluation through real-time, quantization and intelligent means, the evaluation results have certain deviation, often ignoring the erosion effect of vibration, oxidizing gas concentration, ultraviolet radiation intensity and other factors on steel structure, and cannot provide strong data support for engineering safety management. SUMMARY

[0004] (I) Technical problems solved

[0005] In view of the shortcomings of the prior art, the present application provides a steel structure connection danger evaluation system and method based on industrial data analysis, which has the advantages of multi-dimensional analysis, flexible applicability, intelligent evaluation, high precision, etc., and solves the problems of poor flexible applicability and ignoring the erosion effect of environmental factors of the traditional steel structure connection danger evaluation system based on industrial data analysis.

[0006] (II) Technical solutions

[0007] To achieve the above object, the present application provides the following technical solutions: a steel structure connection danger assessment system based on industrial data analysis, comprising a multi-dimensional acquisition module and an intelligent analysis module;

[0008] The multi-dimensional acquisition module is composed of a component data unit, a joint data unit and a sensing data unit, the component data unit acquires a component data set by connecting a database through a network, the component data set comprises management data of all components of the steel structure, the joint data unit acquires a joint data set by connecting a detection device through a network, the joint data set comprises inspection data of all joints, and the sensing data unit acquires an environmental data set by connecting a sensing device through a network, the environmental data set comprises sensing data of all environmental factors.

[0009] The intelligent analysis module is composed of a performance evaluation unit, a stability evaluation unit and a danger evaluation unit, the performance evaluation unit analyzes the assembly of each component according to the component data set and generates a corresponding performance index Xnzs, the stability evaluation unit analyzes the safety of each joint according to the component data set and the joint data set and generates a corresponding stability coefficient Wdx, the danger evaluation unit is provided with a fixed monitoring period Z, and in combination with the environmental data set, analyzes the erosion of environmental factors on the performance of the steel structure and generates a corresponding attenuation coefficient Sjx, and the danger evaluation unit is provided with index threshold ZSY, stability threshold WDY and attenuation threshold SJY in a fixed range, and in combination with the performance index Xnzs, the stability coefficient Wdx and the attenuation coefficient Sjx, evaluates whether the steel structure has hidden dangers and outputs corresponding management suggestions.

[0010] Preferably, the expression of the component data set is {G1 s , G2 s , G3 s ,..., Gn s}, G1 s to Gn s represent the management data of the first to the nth component, and the component management data comprises hardness, density, surface roughness, thermal expansion coefficient, strength, number of bolts and number of welds, and s represents the service life of a single component.

[0011] Preferably, the expression of the joint data set is {J1, J2, J3,..., Jm}, J1 to Jm represent the inspection data of the first to the mth joint, and the joint inspection data comprises strength, crack number and corrosion area.

[0012] Preferably, the expression of the environmental data set is {WD t , SD t , ZF t , ZP t , YHt FS t}, WD t Indicates ambient temperature, SD t Indicates ambient humidity, ZF t ZP represents the ambient amplitude. t Indicates the ambient vibration frequency, YH t FS represents the concentration of oxidizing gases in the environment. t The value represents the intensity of ambient ultraviolet radiation, and t represents the time point at which the environmental sensor data was acquired.

[0013] Preferably, the calculation process for the performance index Xnzs is as follows:

[0014] Based on the component dataset, extract the management data for the i-th component and label the hardness of the i-th component as yd. i Let the density of the i-th component be denoted as md. i The surface roughness of the i-th component is denoted as cu. i Let the coefficient of thermal expansion of the i-th component be denoted as rp. i The usage time of the i-th component is marked as i. s ;

[0015]

[0016] In the formula, BYD represents the standard value used to measure the hardness of a component, α1 represents the weight of the ratio of hardness to the standard value, BMD represents the standard value used to measure the density of a component, α2 represents the weight of the ratio of density to the standard value, BCU represents the standard value used to measure the surface roughness of a component, α3 represents the weight of the ratio of surface roughness to the standard value, BRP represents the standard value used to measure the coefficient of thermal expansion of a component, α4 represents the weight of the ratio of the coefficient of thermal expansion to the standard value, and α5 represents the weight of service life. α1, α2, α3, α4, and α5 are all constants. This indicates that the performance index Xnzs of the i-th component is calculated according to the weights α1, α2, α3, α4, and α5. i .

[0017] Preferably, the calculation process for the stability coefficient Wdx is as follows:

[0018] Based on the joint dataset, the inspection data of the k-th joint is extracted, where the k-th joint is formed by connecting the i-th base material component and the l-th base material component through a welding process;

[0019] Based on the component dataset and joint dataset, the strength of the i-th component is labeled as qd. i The number of bolts in the i-th component is marked as ls. iThe number of welds on the i-th component is marked as hf. i The strength of the l-th component is denoted as qd. l The number of bolts in the l-th component is marked as ls. l The number of welds on the l-th component is marked as hf. l The strength of the joint at the k-th point is denoted as qd. k The number of cracks at the k-th joint is marked as lw. k The corrosion area at the k-th joint is marked as xm. k ;

[0020]

[0021] In the formula, β1 represents the weight of the strength ratio between the k-th joint and the i-th base material component, β2 represents the weight of the strength ratio between the k-th joint and the l-th base material component, β3 represents the weight of the total number of bolts in the i-th and l-th base material components, β4 represents the weight of the total number of welds in the i-th and l-th base material components, β5 represents the weight of the number of cracks, and β6 represents the weight of the rust area. β1, β2, β3, β4, β5, and β6 are all constants.

[0022] This indicates that the stability coefficient Wdx of the joint at position k is calculated according to the weights β1, β2, β3, β4, β5, and β6. k .

[0023] Preferably, the calculation process for the attenuation coefficient Sjx is as follows:

[0024] Sjx=ω1(maxWD t -minWD t )+ω2SD t +ω3ZF t +ω4ZP t +ω5YH t +ω6FS t

[0025] In the formula, maxWD t -minWD t Let ω1 represent the range of ambient temperature within the monitoring period Z, ω2 represent the weight of ambient humidity, ω3 represent the weight of ambient amplitude, ω4 represent the weight of ambient frequency, ω5 represent the weight of ambient oxidizing gas concentration, and ω6 represent the weight of ambient ultraviolet radiation intensity. ω1, ω2, ω3, ω4, ω5, and ω6 are all constants, and ω1 + ω2 + ω3 + ω4 + ω5 + ω6 = 1, ω1(maxWD) t-minWD t )+ω2SD t +ω3ZF t +ω4ZP t +ω5YH t +ω6FS t This indicates that the attenuation coefficient of the steel structure performance is calculated according to the weights of ω1, ω2, ω3, ω4, ω5, and ω6.

[0026] Preferably, when the performance index Xnzs is lower than the index threshold ZSY, it indicates that the component has poor assemblability and poses a potential danger, and it is recommended to replace the component in a timely manner.

[0027] Preferably, when the stability coefficient Wdx is lower than the stability threshold WDY, it indicates that the welding quality of the joint is poor and there is a potential danger. It is recommended to repair and fix it in time. When the attenuation coefficient Sjx exceeds the attenuation threshold SJY, it indicates that the environmental erosion is strong and there is a potential danger. It is recommended to spray the steel structure surface in time.

[0028] A method for assessing the hazards of steel structure connections based on industrial data analysis includes the following steps:

[0029] Step 1: Connect the database, detection device, and sensing device via the network to obtain management data of all steel structure components, inspection data of all joints, and sensing data of all environmental factors, and classify them into component datasets, joint datasets, and environmental datasets.

[0030] Step 2: Based on the component dataset, analyze the assemblability of each component and generate the corresponding performance index Xnzs;

[0031] Step 3: Based on the component dataset and joint dataset, analyze the safety of each joint and generate the corresponding stability coefficient Wdx;

[0032] Step 4: Set a fixed monitoring period Z, and then combine it with the environmental dataset to analyze the corrosive effect of environmental factors on the performance of steel structures, and generate the corresponding attenuation coefficient Sjx;

[0033] Step 5: Set fixed ranges for the index threshold ZSY, stability threshold WDY, and attenuation threshold SJY. Then, combine these with the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx to assess whether there are any potential hazards in the steel structure and output corresponding management recommendations.

[0034] Compared with existing technologies, this invention provides a steel structure connection hazard assessment system and method based on industrial data analysis, which has the following beneficial effects:

[0035] 1. This invention uses a multi-dimensional acquisition module network to connect a database, detection device, and sensing device to acquire management data of all steel structure components, inspection data of all joints, and sensing data of all environmental factors. These data are then categorized into component datasets, joint datasets, and environmental datasets. The intelligent analysis module analyzes the assemblability of each component based on the component dataset and generates a corresponding performance index Xnzs, effectively reducing subjective judgment errors. The intelligent analysis module also analyzes the safety of each joint based on the component and joint datasets and generates a corresponding stability coefficient Wdx, comprehensively evaluating the safety of the joints. The weight allocation ratio can be adjusted according to actual engineering needs, enhancing the applicability of the system. The multi-dimensional analysis is flexible and highly applicable.

[0036] 2. This invention uses an intelligent analysis module to set a fixed monitoring period Z, and then combines it with environmental datasets to analyze the corrosive effect of environmental factors on the performance of steel structures, generating a corresponding attenuation coefficient Sjx. This quantifies the corrosive effect of environmental factors on the structure, making it flexibly applicable to different application scenarios. The intelligent analysis module has fixed ranges for the index threshold ZSY, stability threshold WDY, and attenuation threshold SJY. Combined with the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx, it assesses whether there are any potential hazards in the steel structure. When the performance index Xnzs is lower than the index threshold ZSY, it indicates poor assemblability of the components and potential hazards, suggesting timely replacement of the components. When the stability coefficient Wdx is lower than the stability threshold WDY, it indicates poor welding quality of the joints and potential hazards, suggesting timely repair and fixation. When the attenuation coefficient Sjx exceeds the attenuation threshold SJY, it indicates strong environmental erosion and potential hazards, suggesting timely spraying of the steel structure surface. Based on the assessment results, targeted suggestions are provided to achieve preventive maintenance, detect degradation problems such as material corrosion and crack propagation, and environmental erosion problems, intervene in a timely manner, delay structural aging, and achieve high accuracy in intelligent assessment. Attached Figure Description

[0037] Fig. 1 This is the flowchart for this system;

[0038] Fig. 2 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0039] 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.

[0040] Traditional steel structure connection hazard assessment systems based on industrial data analysis rely on general algorithm models. However, different steel structures have varying connection types and operating environments, making it difficult to ensure the efficiency and reliability of hazard assessments through real-time, quantitative, and intelligent methods. The assessment results often contain biases, neglecting the corrosive effects of factors such as vibration, oxidizing gas concentration, and ultraviolet radiation intensity on steel structures, thus failing to provide strong data support for engineering safety management. Therefore, this paper presents a steel structure connection hazard assessment system and method based on industrial data analysis. Please refer to [link to relevant documentation]. Figs. 1-2 A steel structure connection hazard assessment system based on industrial data analysis, including a multi-dimensional acquisition module and an intelligent analysis module;

[0041] The multi-dimensional acquisition module consists of component data units, joint data units, and sensor data units. It can manage data in a unified manner, avoiding the information silo problem caused by a single data source, and thus more comprehensively identify potential risks, such as the compound problem caused by the superposition of environmental erosion and joint defects.

[0042] The component data unit collects component datasets via a network connection to a database. These datasets include management data for all steel structure components, and the expression for the component dataset is {G1}. S G2 S G3 s ..., Gn s}, G1 S To Gn s This represents the management data for the first to nth components. The component management data includes hardness, density, surface roughness, coefficient of thermal expansion, strength, number of bolts, and number of welds. s represents the service life of a single component. Specifically, component hardness is an indicator that measures the material's ability to resist local deformation, especially plastic deformation, indentation, or scratches. Component strength refers to the component's ability to resist damage when subjected to external forces. It is an important indicator for measuring the safety and reliability of components. There is a significant difference between the two.

[0043] The joint data unit collects joint datasets through a network connection detection device. The joint datasets include inspection data for all joints. The expression for the joint datasets is {J1, J2, J3, ..., Jm}, where J1 to Jm represent the inspection data for the first to the mth joints. The joint inspection data includes strength, number of cracks, and corrosion area.

[0044] The sensing data unit collects environmental datasets via a network connection to sensing devices. The environmental dataset includes sensor data for all environmental factors, and its expression is {WD}. t SD t ZF t ZP t YH tFS t}, WD t Indicates ambient temperature, SD t Indicates ambient humidity, ZF t ZP represents the ambient amplitude. t Indicates the ambient vibration frequency, YH t FS represents the concentration of oxidizing gases in the environment. t The value represents the intensity of ambient ultraviolet radiation, and t represents the time point at which the environmental sensor data was acquired.

[0045] The intelligent analysis module consists of a performance evaluation unit, a stability evaluation unit, and a hazard evaluation unit. The performance evaluation unit analyzes the assemblability of each component based on the component dataset and generates the corresponding performance index Xnzs. Its calculation process is as follows:

[0046] Based on the component dataset, extract the management data for the i-th component and label the hardness of the i-th component as yd. i Let the density of the i-th component be denoted as md. i The surface roughness of the i-th component is denoted as cu. i Let the coefficient of thermal expansion of the i-th component be denoted as rp. i The usage time of the i-th component is marked as i. s ;

[0047]

[0048] In the formula, BYD represents the standard value used to measure the hardness of a component, α1 represents the weight of the ratio of hardness to the standard value, BMD represents the standard value used to measure the density of a component, α2 represents the weight of the ratio of density to the standard value, BCU represents the standard value used to measure the surface roughness of a component, α3 represents the weight of the ratio of surface roughness to the standard value, BRP represents the standard value used to measure the coefficient of thermal expansion of a component, α4 represents the weight of the ratio of the coefficient of thermal expansion to the standard value, and α5 represents the weight of service life. α1, α2, α3, α4, and α5 are all constants. This indicates that the performance index Xnzs of the i-th component is calculated according to the weights α1, α2, α3, α4, and α5. i This quantifies the assembly performance of each component, effectively reducing subjective judgment errors.

[0049] The stability assessment unit analyzes the safety of each joint based on the component dataset and joint dataset, and generates the corresponding stability coefficient Wdx. The calculation process is as follows:

[0050] Based on the joint dataset, the inspection data of the k-th joint is extracted, where the k-th joint is formed by connecting the i-th base material component and the l-th base material component through a welding process;

[0051] Based on the component dataset and joint dataset, the strength of the i-th component is labeled as qd. i The number of bolts in the i-th component is marked as ls. i The number of welds on the i-th component is marked as hf. i The strength of the l-th component is denoted as qd. l The number of bolts in the l-th component is marked as ls. l The number of welds on the l-th component is marked as hf. l The strength of the joint at the k-th point is denoted as qd. k The number of cracks at the k-th joint is marked as lw. k The corrosion area at the k-th joint is marked as xm. k ;

[0052]

[0053] In the formula, β1 represents the weight of the strength ratio between the k-th joint and the i-th base material component, β2 represents the weight of the strength ratio between the k-th joint and the l-th base material component, β3 represents the weight of the total number of bolts in the i-th and l-th base material components, β4 represents the weight of the total number of welds in the i-th and l-th base material components, β5 represents the weight of the number of cracks, and β6 represents the weight of the rust area. β1, β2, β3, β4, β5, and β6 are all constants. This indicates that the stability coefficient Wdx of the joint at position k is calculated according to the weights β1, β2, β3, β4, β5, and β6. k The weight allocation ratio can be adjusted according to actual engineering needs to enhance the applicability of the system, comprehensively evaluate the safety of the joint, and provide flexible and highly applicable multi-dimensional analysis.

[0054] The hazard assessment unit is set with a fixed monitoring period Z. Combined with the environmental dataset, the corrosive effect of environmental factors on the performance of the steel structure is analyzed, and the corresponding attenuation coefficient Sjx is generated. The calculation process is as follows:

[0055] Sjx=ω1(maxWD t -minWD t )+ω2SD t +ω3ZF t +ω4ZP t +ω5YH t +ω6FS t

[0056] In the formula, maxWD t -minWD tLet ω1 represent the range of ambient temperature within the monitoring period Z, ω2 represent the weight of ambient humidity, ω3 represent the weight of ambient amplitude, ω4 represent the weight of ambient frequency, ω5 represent the weight of ambient oxidizing gas concentration, and ω6 represent the weight of ambient ultraviolet radiation intensity. ω1, ω2, ω3, ω4, ω5, and ω6 are all constants, and ω1 + ω2 + ω3 + ω4 + ω5 + ω6 = 1, ω1(maxWD) t -minWD t )+ω2SD t +ω3ZF t +ω4ZP t +ω5YH t +ω6FS t This means that the attenuation coefficient of steel structure performance is calculated according to the weights of ω1, ω2, ω3, ω4, ω5 and ω6, which quantifies the corrosive effect of environmental factors on the structure and is flexibly applicable to different application scenarios, providing reliable data support for steel structure maintenance. For example, in coastal high humidity environments, the weight ratio of environmental humidity and ultraviolet radiation intensity is increased, while in inland dry environments, the weight ratio of extreme temperature difference is increased.

[0057] The hazard assessment unit is equipped with fixed-range index thresholds ZSY, stability threshold WDY, and attenuation threshold SJY. Combined with the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx, it assesses whether the steel structure has any potential hazards. When the performance index Xnzs is lower than the index threshold ZSY, it indicates poor assemblability of the components, posing a hazard, and timely replacement of the components is recommended. When the stability coefficient Wdx is lower than the stability threshold WDY, it indicates poor welding quality of the joints, posing a hazard, and timely repair and fixation are recommended. When the attenuation coefficient Sjx exceeds the attenuation threshold SJY, it indicates strong environmental corrosion, posing a hazard, and timely spraying of the steel structure surface is recommended. Based on the assessment results, targeted recommendations are provided to achieve preventative maintenance, detect material corrosion, crack propagation, and other degradation problems, as well as environmental corrosion problems, and intervene in a timely manner to delay structural aging. The intelligent assessment has high accuracy.

[0058] A method for assessing the hazards of steel structure connections based on industrial data analysis includes the following steps:

[0059] Step 1: Connect the database, detection device, and sensing device via the network to obtain management data of all steel structure components, inspection data of all joints, and sensing data of all environmental factors, and classify them into component datasets, joint datasets, and environmental datasets.

[0060] Step 2: Based on the component dataset, analyze the assemblability of each component and generate the corresponding performance index Xnzs;

[0061] Step 3: Based on the component dataset and joint dataset, analyze the safety of each joint and generate the corresponding stability coefficient Wdx;

[0062] Step 4: Set a fixed monitoring period Z, and then combine it with the environmental dataset to analyze the corrosive effect of environmental factors on the performance of steel structures, and generate the corresponding attenuation coefficient Sjx;

[0063] Step 5: Set fixed ranges for the index threshold ZSY, stability threshold WDY, and attenuation threshold SJY. Then, combine these with the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx to assess whether there are any potential hazards in the steel structure and output corresponding management recommendations.

[0064] Example 1:

[0065] In this experiment, angle steel components were selected as the experimental object. The angle steel was tested and found to have a hardness of 80, a density of 7.5, a surface roughness of 1.2, a coefficient of thermal expansion of 2.3, and a service life of 5 years. The calculation process for the performance index Xnzs of this angle steel is as follows:

[0066]

[0067] Where BYD = 100 represents the standard value used to measure the hardness of the component, α1 = 0.3 represents the weight of the ratio of hardness to the standard value, BMD = 8 represents the standard value used to measure the density of the component, α2 = 0.2 represents the weight of the ratio of density to the standard value, BCU = 0.8 represents the standard value used to measure the surface roughness of the component, α3 = 0.2 represents the weight of the ratio of surface roughness to the standard value, BRP = 2 represents the standard value used to measure the coefficient of thermal expansion of the component, α4 = 0.2 represents the weight of the ratio of the coefficient of thermal expansion to the standard value, and α5 = 0.1 represents the weight of service life. α1, α2, α3, α4, and α5 are all constants, and 0.3 + 0.2 + 0.2 + 0.2 + 0.1 = 1. Based on the weights of α1, α2, α3, α4, and α5, the performance index Xnzs of the angle steel is calculated. i The value is 0.4575. The index threshold ZSY is set to 0.5 to 3.0. It is determined that 0.4575 is lower than the index threshold ZSY, which indicates that the angle steel has poor assemblability and poses a potential danger. It is recommended to replace the component in time.

[0068] Example 2:

[0069] In this experiment, a rust area of ​​2 mm was selected. 2The joint area was used as the experimental object. This joint area was formed by welding base component A and base component B. Testing showed that base component A had a strength of 100 MPa, 10 bolts, and 5 welds. Base component B had a strength of 110 MPa, 12 bolts, and 6 welds. The joint area had a strength of 120 MPa and 3 cracks. The calculation process for the stability coefficient Wdx of this joint area is as follows:

[0070]

[0071] Wherein, β1 = 0.4 represents the weight of the strength ratio between the joint area and the base material A, β2 = 0.3 represents the weight of the strength ratio between the joint area and the base material B, β3 = 0.1 represents the weight of the total number of bolts in base material A and base material B, β4 = 0.1 represents the weight of the total number of welds in base material A and base material B, β5 = 0.05 represents the weight of the number of cracks, and β6 = 0.05 represents the weight of the rust area. β1, β2, β3, β4, β5, and β6 are all constants, and 0.4 + 0.3 + 0.1 + 0.1 + 0.05 + 0.05 = 1. Based on the weights of β1, β2, β3, β4, β5, and β6, the stability coefficient Wdx of the joint area is calculated. k The value is 3.86. The stability threshold WDY is set to 3.5-5.5. It is determined that 3.86 is included in the stability threshold WDY, indicating that the welding quality of the joint is good and there is no potential danger.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A steel structure connection hazard assessment system based on industrial data analysis, characterized in that: Includes a multi-dimensional data acquisition module and an intelligent analysis module; The multi-dimensional acquisition module consists of a component data unit, a joint data unit, and a sensor data unit. The component data unit collects component datasets via a network connection to a database. The component datasets include management data for all steel structure components, and the expression for the component datasets is as follows: , to Indicates the first to the second The component management data includes hardness, density, surface roughness, coefficient of thermal expansion, strength, number of bolts, and number of welds. Indicates the usage duration of a single component; The connector data unit collects connector datasets via a network-connected detection device. The connector datasets include inspection data for all connectors, and the expression for the connector datasets is: , to Indicates the first to the second Inspection data for the joints, including strength, number of cracks, and area of ​​corrosion; The sensing data unit collects environmental datasets via a network connection to the sensing device. The environmental datasets include sensing data for all environmental factors, and the expression for the environmental datasets is: , Indicates ambient temperature. Indicates ambient humidity. Indicates the amplitude of environmental vibration. Indicates the environmental vibration frequency. Indicates the concentration of oxidizing gases in the environment. Indicates the intensity of ambient ultraviolet radiation. Indicates the time point at which environmental sensor data was acquired; The intelligent analysis module consists of a performance evaluation unit, a stability evaluation unit, and a hazard evaluation unit. The performance evaluation unit analyzes the assemblability of each component based on the component dataset and generates a corresponding performance index. The calculation process is as follows: Based on the component dataset, extract the first... The management data of the first component, and the first component's data. The hardness of each component is marked as , will the The density of each component is marked as , will the The surface roughness of each component is marked as follows: , will the The coefficient of thermal expansion of each component is marked as follows: , will the The usage time of each component is marked as ; In the formula, This represents the standard value used to measure the hardness of a component. This indicates the weight given to the ratio of hardness to the standard value. This represents the standard value used to measure the density of a component. This indicates the weight given to the ratio of density to the standard value. This represents a standard value used to measure the surface roughness of a component. This indicates the weight given to the ratio of surface roughness to a standard value. This represents the standard value used to measure the coefficient of thermal expansion of a component. This indicates the weighting of the ratio of the coefficient of thermal expansion to the standard value. This indicates the weight based on usage duration. , , , and All are constants, and , Indicates according to , , , and Weights, calculated to obtain the first Performance index of individual components ; The stability assessment unit analyzes the safety of each joint based on the component dataset and the joint dataset, and generates the corresponding stability coefficient. The calculation process is as follows: Based on the connector dataset, extract the first... Inspection data for the joint, including the first The connector is made by the first The first parent material component and the first The components are made by connecting the base materials together using a welding process; Based on the component dataset and the joint dataset, the first The strength of each component is marked as , will the The number of bolts on each component is marked as follows: , will the The number of welds on each component is marked as follows: , will the The strength of each component is marked as , will the The number of bolts on each component is marked as follows: , will the The number of welds on each component is marked as follows: , will the The strength marking of the joint is as follows , will the The number of cracks at the joint is marked as , will the The rusted area of ​​the joint is marked as ; In the formula, Indicates that for the first The joint and the first The weight of the strength ratio of each parent material component Indicates that for the first The joint and the first The weight of the strength ratio of each parent material component Indicates that for the first The first parent material component and the first The weight of the total number of bolts in each base material component. Indicates that for the first The first parent material component and the first The weight of the total number of welds in each base material component. This indicates the weight assigned to the number of cracks. This indicates the weight given to the area of ​​rust. , , , , and All are constants, and , Indicates according to , , , , and Weights, calculated to obtain the first Stability coefficient of the joint ; The hazard assessment unit is equipped with a monitoring cycle of fixed duration. Then, by combining environmental datasets, the corrosive effects of environmental factors on the performance of steel structures are analyzed, and corresponding attenuation coefficients are generated. The calculation process is as follows: In the formula, Indicates the monitoring period The extreme temperature difference in the environment This indicates the weight given to extreme differences in ambient temperature. This indicates the weight given to ambient humidity. This indicates the weight for the environmental amplitude. This indicates the weighting relative to the environmental vibration frequency. This indicates the concentration of oxidizing gases in the environment. This indicates the weight given to the intensity of environmental ultraviolet radiation. , , , , and All are constants, and , Indicates according to , , , , and The weights are used to calculate the attenuation coefficient of the steel structure's performance. The hazard assessment unit is equipped with an index threshold within a fixed range. Stability threshold and attenuation threshold Combined with performance index Stability coefficient and attenuation coefficient Assess whether there are any potential hazards in the steel structure and provide corresponding management recommendations.

2. The steel structure connection hazard assessment system based on industrial data analysis according to claim 1, characterized in that: The performance index Below the exponential threshold When this occurs, it indicates that the component has poor assemblability and poses a potential safety hazard; it is recommended to replace the component promptly.

3. The steel structure connection hazard assessment system based on industrial data analysis according to claim 2, characterized in that: The stability coefficient Below the stability threshold When the welding quality of the joint is poor, it indicates a potential safety hazard, and timely repair and fixation are recommended. The attenuation coefficient... Exceeding the attenuation threshold When the condition is severe, it indicates strong environmental erosion and potential dangers; it is recommended to promptly spray the steel structure surface.

4. A method for assessing the hazard of steel structure connections based on industrial data analysis, applied to the steel structure connection hazard assessment system based on industrial data analysis as described in any one of claims 1-3, characterized in that, Includes the following steps: Step 1: Connect the database, detection device, and sensing device via the network to obtain management data of all steel structure components, inspection data of all joints, and sensing data of all environmental factors, and classify them into component datasets, joint datasets, and environmental datasets. Step 2: Based on the component dataset, analyze the assemblability of each component and generate the corresponding performance index. ; Step 3: Based on the component dataset and joint dataset, analyze the safety of each joint and generate the corresponding stability coefficient. ; Step 4: Set a fixed monitoring period Then, by combining environmental datasets, the corrosive effects of environmental factors on the performance of steel structures are analyzed, and corresponding attenuation coefficients are generated. ; Step 5: Set a fixed range of exponential thresholds Stability threshold and attenuation threshold Combined with performance index Stability coefficient and attenuation coefficient Assess whether there are hidden dangers in the steel structure. The system identifies the underlying issues and provides corresponding management recommendations.

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