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

Abstract

The invention relates to the technical field of steel structure assessment, and discloses a steel structure connection risk assessment system and method based on industrial data analysis, and the system comprises a multi-dimensional collection module and an intelligent analysis module. According to the steel structure connection risk assessment system and method based on industrial data analysis, management data of all components of a steel structure, inspection data of all joints and sensing data of all environmental factors are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent analysis module analyzes the assemblability of each component and generates a performance index; the weight distribution proportion can be adjusted according to actual engineering requirements, multi-dimensional analysis is flexible and high in applicability, and an intelligent analysis module analyzes the erosion effect of environmental factors on the performance of the steel structure, generates attenuation coefficients, sets threshold values in a fixed range and evaluates whether the steel structure has potential risks or not; and targeted suggestions are provided, preventive maintenance is realized, and the intelligent evaluation precision is high.

Description

Technical Field

[0001] The present invention relates to the technical field of steel structure assessment, and in particular to a steel structure connection risk assessment system and method based on industrial data analysis. Background Art

[0002] Steel structures are engineering structures constructed from steel sections and steel plates welded, bolted, or riveted together. They primarily consist of components such as steel beams, steel columns, and steel trusses. They excel in mechanized installation, facilitating factory fabrication and on-site assembly, significantly shortening construction schedules. The strength of steel structure joints is directly related to the stability and safety of the entire structure. Joint defects, such as poor weld quality or loose bolts, can cause localized deformation, damage, or even collapse when subjected to stress, endangering life and property. Stable connections ensure that steel structures function properly during operation. For structures subject to vibration or dynamic loads, such as bridges and crane beams in industrial plants, reliable connections ensure optimal performance under repeated loads, preventing premature failure due to connection problems that could affect normal operation. Good connections reduce stress concentration and fatigue damage, thereby extending the service life of steel structures. Conversely, problems with joints can accelerate corrosion, wear, and fatigue damage, reducing overall durability and increasing maintenance costs and frequency. In the construction industry, relevant design specifications and construction standards have clear requirements for the quality and safety of steel structure connections. Conducting connection risk assessments is a necessary means to ensure that steel structure projects comply with these specifications and standards, and is also an important basis for project quality acceptance.

[0003] At present, the traditional steel structure connection hazard assessment system based on industrial data analysis relies on a general algorithm model. However, the connection forms and usage environments of different steel structures are different, making it difficult to ensure the efficiency and reliability of hazard assessment through real-time, quantitative and intelligent means. The assessment results have certain deviations and often ignore the corrosive effects of factors such as vibration, oxidizing gas concentration, and ultraviolet radiation intensity on steel structures, making it impossible to provide strong data support for engineering safety management. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a steel structure connection risk assessment system and method based on industrial data analysis, which has the advantages of flexible and strong applicability of multi-dimensional analysis and high accuracy of intelligent assessment. It solves the problems of poor flexibility and applicability of traditional steel structure connection risk assessment systems based on industrial data analysis and ignoring the corrosive effects of environmental factors.

[0006] (2) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a steel structure connection risk 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 sensor data unit. The component data unit is connected to a database via a network to collect a component data set, and the component data set includes management data of all components of the steel structure. The joint data unit is connected to a detection device via a network to collect a joint data set, and the joint data set includes inspection data of all joints. The sensor data unit is connected to a sensor device via a network to collect an environmental data set, and the environmental data set includes sensor data of all environmental factors.

[0009] The intelligent analysis module consists of a performance evaluation unit, a stability evaluation unit and a risk assessment unit. The performance evaluation unit analyzes the assembly of each component based on the component data set and generates a corresponding performance index Xnzs. The stability evaluation unit analyzes the safety of each joint based on the component data set and the joint data set and generates a corresponding stability coefficient Wdx. The risk assessment unit is provided with a monitoring period Z of a fixed duration, and then combined with the environmental data set, analyzes the corrosive effect of environmental factors on the performance of the steel structure and generates a corresponding attenuation coefficient Sjx. The risk assessment unit is provided with a fixed range of index threshold ZSY, stability threshold WDY and attenuation threshold SJY, and then combined with the performance index Xnzs, stability coefficient Wdx and attenuation coefficient Sjx, evaluates whether the steel structure has any 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 Represents the management data of the first to nth components. The component management data includes hardness, density, surface roughness, thermal expansion coefficient, strength, number of bolts and number of welds. s represents the service life of a single component.

[0011] Preferably, the expression of the joint data set is {J1, J2, J3, ..., Jm}, where J1 to Jm represent the inspection data of the first to mth joints, and the joint inspection data includes strength, number of cracks and rust 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 Indicates the environmental amplitude, ZP t Indicates the ambient vibration frequency, YH t Indicates the ambient oxidizing gas concentration, FS t represents the ambient ultraviolet radiation intensity, and t represents the time point for obtaining the environmental sensing data.

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

[0014] According to the component data set, the management data of the i-th component is extracted and the hardness of the i-th component is marked as yd i , mark the density of the i-th component as md i , the surface roughness of the i-th component is marked as cu i , the thermal expansion coefficient of the i-th component is marked as rp i , mark the usage time of the i-th component as i s ;

[0015]

[0016] In the formula, BYD represents the standard value for measuring the hardness of the component, α1 represents the weight for the ratio of hardness to the standard value, BMD represents the standard value for measuring the density of the component, α2 represents the weight for the ratio of density to the standard value, BCU represents the standard value for measuring the surface roughness of the component, α3 represents the weight for the ratio of surface roughness to the standard value, BRP represents the standard value for measuring the thermal expansion coefficient of the component, α4 represents the weight for the ratio of thermal expansion coefficient to the standard value, α5 represents the weight for the service life, α1, α2, α3, α4 and α5 are all constants, and 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 of the stability coefficient Wdx is as follows:

[0018] According to the joint data set, the inspection data of the k-th joint is extracted, where the k-th joint is formed by connecting the i-th parent material component and the l-th parent material component through the welding process;

[0019] According to the component dataset and joint dataset, the strength of the i-th component is marked as qd i , mark the number of bolts of the i-th component as ls i, mark the number of welds of the i-th component as hf i , mark the strength of the lth component as qd l , mark the number of bolts of the lth component as ls l , mark the number of welds of the lth component as hf l , mark the strength of the kth joint as qd k , mark the number of cracks at the kth joint as lw k , mark the corrosion area of ​​the kth joint as xm k ;

[0020]

[0021] In the formula, β1 represents the weight of the strength ratio of the k-th joint and the i-th parent member, β2 represents the weight of the strength ratio of the k-th joint and the l-th parent member, β3 represents the weight of the sum of the number of bolts of the i-th parent member and the l-th parent member, β4 represents the weight of the sum of the number of welds of the i-th parent member and the l-th parent member, β5 represents the weight of the number of cracks, β6 represents the weight of the rust area, β1, β2, β3, β4, β5 and β6 are all constants, and

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

[0023] Preferably, the calculation process of 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 represents the range of ambient temperature within the monitoring period Z, ω1 represents the weight for the range of ambient temperature, ω2 represents the weight for the ambient humidity, ω3 represents the weight for the ambient amplitude, ω4 represents the weight for the ambient frequency, ω5 represents the weight for the ambient oxidizing gas concentration, and ω6 represents the weight for the 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 It 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 assembly performance of the component is poor and there is a potential danger, and it is recommended to replace the component in time.

[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 hidden 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 hidden danger. It is recommended to spray the steel structure surface in time.

[0028] The steel structure connection risk assessment method based on industrial data analysis includes the following steps:

[0029] Step 1: Connect the database, detection devices, and sensor devices through the network to obtain management data of all steel structure components, inspection data of all joints, and sensor data of all environmental factors, and classify them into component data sets, joint data sets, and environmental data sets;

[0030] Step 2: Analyze the assembly performance of each component based on the component data set 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 data set to analyze the erosion effect of environmental factors on the performance of the steel structure and generate the corresponding attenuation coefficient Sjx;

[0033] Step 5: Set the fixed range index threshold ZSY, stability threshold WDY, and attenuation threshold SJY, and then combine the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx to assess whether the steel structure has hidden dangers and output corresponding management suggestions.

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

[0035] 1. The present invention connects the database, detection device and sensor device through a multi-dimensional acquisition module network to obtain the management data of all steel structure components, the inspection data of all joints and the sensor data of all environmental factors, and classifies them into component data sets, joint data sets and environmental data sets. The intelligent analysis module analyzes the assemblability of each component based on the component data sets and generates a corresponding performance index Xnzs, effectively reducing subjective judgment errors. The intelligent analysis module analyzes the safety of each joint based on the component data sets and joint data sets, and generates a corresponding stability coefficient Wdx, comprehensively evaluating the safety of the joints. The weight distribution ratio can be adjusted according to actual engineering needs, thereby enhancing the applicability of the system. The multi-dimensional analysis is flexible and highly applicable.

[0036] 2. The present invention sets a fixed-duration monitoring cycle Z through the intelligent analysis module. Combined with the environmental data set, it analyzes the erosion effect of environmental factors on the performance of the steel structure and generates a corresponding attenuation coefficient Sjx to quantify the erosion effect of environmental factors on the structure, making it flexibly applicable to different application scenarios. The intelligent analysis module is set with a fixed range of 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 the steel structure has hidden dangers. When the performance index Xnzs is lower than the index threshold ZSY, it indicates that the assembly of the component is poor and there is a hidden danger. It is recommended to replace the component in time. 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 hidden 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 effect is strong and there is a hidden danger. It is recommended to spray the steel structure surface in time. Targeted suggestions are provided based on the evaluation results to achieve preventive maintenance, detect degradation problems such as material corrosion and crack propagation and environmental erosion problems, intervene in time, and delay structural aging. The intelligent evaluation has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the flow chart of this system;

[0038] Figure 2 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] The traditional steel structure connection risk assessment system based on industrial data analysis relies on a general algorithm model. However, the connection forms and usage environments of different steel structures are different. It is difficult to ensure the efficiency and reliability of risk assessment through real-time, quantitative and intelligent means. The assessment results have certain deviations and often ignore the corrosive effects of factors such as vibration, oxidizing gas concentration, and ultraviolet radiation intensity on steel structures. It cannot provide strong data support for engineering safety management. Therefore, a steel structure connection risk assessment system and method based on industrial data analysis is provided. Please refer to Figure 1-Figure 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, consisting of component data units, joint data units, and sensor data units, manages data in a unified manner, avoiding information silos caused by a single data source. This allows for more comprehensive identification of potential risks, such as compound problems caused by the combination of environmental corrosion and joint defects.

[0042] The component data unit collects component data sets through the network connection database. The component data sets include the management data of all components of the steel structure. The expression of the component data set is {G1 S , G2 S 、G3 s ,...,Gn s}, G1 S To Gn s Represents the management data of the first to nth components. Component management data includes hardness, density, surface roughness, thermal expansion coefficient, 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 scratching. Component strength refers to the component's ability to resist damage when subjected to external forces. It is an important indicator for measuring component safety and reliability. There is a significant difference between the two.

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

[0044] The sensor data unit collects environmental data sets through network connection sensor devices. The environmental data sets include sensor data of all environmental factors. The expression of environmental data sets is {WD t , SD t 、ZF t , ZP t 、YH t, FS t}, WD t Indicates ambient temperature, SD t Indicates ambient humidity, ZF t Indicates the environmental amplitude, ZP t Indicates the ambient vibration frequency, YH t Indicates the ambient oxidizing gas concentration, FS t represents the ambient ultraviolet radiation intensity, and t represents the time point at which the environmental sensor data is obtained;

[0045] The intelligent analysis module consists of a performance evaluation unit, a stability evaluation unit, and a risk assessment unit. The performance evaluation unit analyzes the assembly of each component based on the component data set and generates a corresponding performance index Xnzs. The calculation process is as follows:

[0046] According to the component data set, the management data of the i-th component is extracted and the hardness of the i-th component is marked as yd i , mark the density of the i-th component as md i , the surface roughness of the i-th component is marked as cu i , the thermal expansion coefficient of the i-th component is marked as rp i , mark the usage time of the i-th component as i s ;

[0047]

[0048] In the formula, BYD represents the standard value for measuring the hardness of the component, α1 represents the weight for the ratio of hardness to the standard value, BMD represents the standard value for measuring the density of the component, α2 represents the weight for the ratio of density to the standard value, BCU represents the standard value for measuring the surface roughness of the component, α3 represents the weight for the ratio of surface roughness to the standard value, BRP represents the standard value for measuring the thermal expansion coefficient of the component, α4 represents the weight for the ratio of thermal expansion coefficient to the standard value, α5 represents the weight for the service life, α1, α2, α3, α4 and α5 are all constants, and Indicates that the performance index Xnzs of the i-th component is calculated according to the weights α1, α2, α3, α4 and α5 i , quantify the assembly performance of each component and effectively reduce subjective judgment errors;

[0049] 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 Wdx. The calculation process is as follows:

[0050] According to the joint data set, the inspection data of the k-th joint is extracted, where the k-th joint is formed by connecting the i-th parent material component and the l-th parent material component through the welding process;

[0051] According to the component dataset and joint dataset, the strength of the i-th component is marked as qd i , mark the number of bolts of the i-th component as ls i , mark the number of welds of the i-th component as hf i , mark the strength of the lth component as qd l , mark the number of bolts of the lth component as ls l , mark the number of welds of the lth component as hf l , mark the strength of the kth joint as qd k , mark the number of cracks at the kth joint as lw k , mark the corrosion area of ​​the kth joint as xm k ;

[0052]

[0053] In the formula, β1 represents the weight of the strength ratio of the k-th joint and the i-th parent member, β2 represents the weight of the strength ratio of the k-th joint and the l-th parent member, β3 represents the weight of the sum of the number of bolts of the i-th parent member and the l-th parent member, β4 represents the weight of the sum of the number of welds of the i-th parent member and the l-th parent member, β5 represents the weight of the number of cracks, β6 represents the weight of the rust area, β1, β2, β3, β4, β5 and β6 are all constants, and Indicates that the stability coefficient Wdx of the kth joint is calculated according to the weights of β1, β2, β3, β4, β5 and β6 k , the weight distribution ratio can be adjusted according to actual engineering needs, the applicability of the system can be enhanced, the safety of the joint can be comprehensively evaluated, and the multi-dimensional analysis is flexible and applicable;

[0054] The hazard assessment unit is set with a fixed monitoring period Z. Combined with the environmental data set, it analyzes the erosion effect of environmental factors on the performance of the steel structure and generates the corresponding attenuation coefficient Sjx. 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 trepresents the range of ambient temperature within the monitoring period Z, ω1 represents the weight for the range of ambient temperature, ω2 represents the weight for the ambient humidity, ω3 represents the weight for the ambient amplitude, ω4 represents the weight for the ambient frequency, ω5 represents the weight for the ambient oxidizing gas concentration, and ω6 represents the weight for the 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 According to the weights of ω1, ω2, ω3, ω4, ω5 and ω6, the attenuation coefficient of the steel structure performance is calculated, and the erosion effect of environmental factors on the structure is quantified. It is flexibly applicable to different application scenarios and provides reliable data support for the maintenance of steel structures. 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 temperature extremes is increased;

[0057] The hazard assessment unit is equipped with a fixed range of index threshold ZSY, stability threshold WDY and attenuation threshold SJY, and then combines the performance index Xnzs, stability coefficient Wdx and attenuation coefficient Sjx to assess whether there are hidden dangers in the steel structure. When the performance index Xnzs is lower than the index threshold ZSY, it indicates that the assembly of the component is poor and there are hidden dangers. It is recommended to replace the component in time. 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 are hidden dangers. 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 are hidden dangers. It is recommended to spray the steel structure surface in time. Targeted suggestions are provided based on the assessment results to achieve preventive maintenance, detect degradation problems such as material corrosion and crack extension and environmental erosion problems, intervene in time, and delay structural aging. The intelligent assessment has high accuracy.

[0058] The steel structure connection risk assessment method based on industrial data analysis includes the following steps:

[0059] Step 1: Connect the database, detection devices, and sensor devices through the network to obtain management data of all steel structure components, inspection data of all joints, and sensor data of all environmental factors, and classify them into component data sets, joint data sets, and environmental data sets;

[0060] Step 2: Analyze the assembly performance of each component based on the component data set 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 data set to analyze the erosion effect of environmental factors on the performance of the steel structure and generate the corresponding attenuation coefficient Sjx;

[0063] Step 5: Set the fixed range index threshold ZSY, stability threshold WDY, and attenuation threshold SJY, and then combine the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx to assess whether the steel structure has hidden dangers and output corresponding management suggestions.

[0064] Example 1:

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

[0066]

[0067] Among them, BYD=100 represents the standard value for measuring the hardness of the component, α1=0.3 represents the weight for the ratio of hardness to the standard value, BMD=8 represents the standard value for measuring the density of the component, α2=0.2 represents the weight for the ratio of density to the standard value, BCU=0.8 represents the standard value for measuring the surface roughness of the component, α3=0.2 represents the weight for the ratio of surface roughness to the standard value, BRP=2 represents the standard value for measuring the thermal expansion coefficient of the component, α4=0.2 represents the weight for the ratio of thermal expansion coefficient to the standard value, α5=0.1 represents the weight for the service life, α1, α2, α3, α4 and α5 are all constants, and 0.3+0.2+0.2+0.2+0.1=1. According to the weights of α1, α2, α3, α4 and α5, the performance index Xnzs of the angle steel is calculated. i It is 0.4575, and the index threshold ZSY is set to 0.5~3.0. After judgment, 0.4575 is lower than the index threshold ZSY, which means that the assembly performance of the angle steel is poor and there is a hidden danger. It is recommended to replace the component in time.

[0068] Example 2:

[0069] In this experiment, the rust area was selected to be 2mm 2The joint area is taken as the experimental object. The joint area is formed by the parent material component A and the parent material component B through the welding process. After testing, the strength of the parent material component A is 100MPa, the number of bolts of the parent material component A is 10, and the number of welds of the parent material component A is 5. The strength of the parent material component B is 110MPa, the number of bolts of the parent material component B is 12, and the number of welds of the parent material component B is 6. The strength of the joint area is 120MPa, and the number of cracks in the joint area is 3. The calculation process of the stability coefficient Wdx of the joint area is as follows:

[0070]

[0071] Among them, β1=0.4 represents the weight for the strength ratio of the joint area and the parent member A, β2=0.3 represents the weight for the strength ratio of the joint area and the parent member B, β3=0.1 represents the weight for the total number of bolts of the parent member A and the parent member B, β4=0.1 represents the weight for the total number of welds of the parent member A and the parent member B, β5=0.05 represents the weight for the number of cracks, β6=0.05 represents the weight for 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. According to the weights of β1, β2, β3, β4, β5 and β6, the stability coefficient Wdx of the joint area is calculated. k It is 3.86, and the stable threshold WDY is set to 3.5~5.5. After judgment, 3.86 is included in the stable threshold WDY, which means that the welding quality of the joint is good and there is no hidden danger.

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

Claims

1. A steel structure connection risk assessment system based on industrial data analysis, characterized by: Including multi-dimensional acquisition module and intelligent analysis module; The multi-dimensional acquisition module is composed of a component data unit, a joint data unit and a sensor data unit. The component data unit is connected to a database via a network to collect a component data set, and the component data set includes management data of all components of the steel structure. The joint data unit is connected to a detection device via a network to collect a joint data set, and the joint data set includes inspection data of all joints. The sensor data unit is connected to a sensor device via a network to collect an environmental data set, and the environmental data set includes sensor data of all environmental factors. The intelligent analysis module consists of a performance evaluation unit, a stability evaluation unit and a risk assessment unit. The performance evaluation unit analyzes the assembly of each component based on the component data set and generates a corresponding performance index Xnzs. The stability evaluation unit analyzes the safety of each joint based on the component data set and the joint data set and generates a corresponding stability coefficient Wdx. The risk assessment unit is provided with a monitoring period Z of a fixed duration, and then combined with the environmental data set, analyzes the corrosive effect of environmental factors on the performance of the steel structure and generates a corresponding attenuation coefficient Sjx. The risk assessment unit is provided with a fixed range of index threshold ZSY, stability threshold WDY and attenuation threshold SJY, and then combined with the performance index Xnzs, stability coefficient Wdx and attenuation coefficient Sjx, evaluates whether the steel structure has any hidden dangers and outputs corresponding management suggestions.

2. The steel structure connection risk assessment system based on industrial data analysis according to claim 1 is characterized in that: The expression of the component data set is {G1 s , G2 s 、G3 s ,...,Gn s }, G1 s To Gn s Represents the management data of the first to nth components. The component management data includes hardness, density, surface roughness, thermal expansion coefficient, strength, number of bolts and number of welds. s represents the service life of a single component.

3. The steel structure connection risk assessment system based on industrial data analysis according to claim 2, characterized in that: The expression of the joint data set is {J1, J2, J3, ..., Jm}, where J1 to Jm represent the inspection data of the first to mth joints, and the joint inspection data includes strength, number of cracks, and rust area.

4. The steel structure connection risk assessment system based on industrial data analysis according to claim 3 is characterized in that: The expression of the environmental data set is {WD t , SD t 、ZF t , ZP t 、YH t , FS t }, WD t Indicates ambient temperature, SD t Indicates ambient humidity, ZF t Indicates the environmental amplitude, ZP t Indicates the ambient vibration frequency, YH t Indicates the ambient oxidizing gas concentration, FS t represents the ambient ultraviolet radiation intensity, and t represents the time point for obtaining the environmental sensing data.

5. The steel structure connection risk assessment system based on industrial data analysis according to claim 4 is characterized in that: The performance index Xnzs calculation process is as follows: According to the component data set, the management data of the i-th component is extracted and the hardness of the i-th component is marked as yd i , mark the density of the i-th component as md i , the surface roughness of the i-th component is marked as cu i , the thermal expansion coefficient of the i-th component is marked as rp i , mark the usage time of the i-th component as i s ; In the formula, BYD represents the standard value for measuring the hardness of a component, α1 represents the weight for the ratio of hardness to the standard value, BMD represents the standard value for measuring the density of a component, α2 represents the weight for the ratio of density to the standard value, BCU represents the standard value for measuring the surface roughness of a component, α3 represents the weight for the ratio of surface roughness to the standard value, BRP represents the standard value for measuring the coefficient of thermal expansion of a component, α4 represents the weight for the ratio of the coefficient of thermal expansion to the standard value, α5 represents the weight for the service life, α1, α2, α3, α4 and α5 are all constants, and α1+α2+α3+α4+α5=1. Indicates that the performance index Xnzs of the i-th component is calculated according to the weights α1, α2, α3, α4 and α5 i .

6. The steel structure connection risk assessment system based on industrial data analysis according to claim 5, characterized in that: The calculation process of the stability coefficient Wdx is as follows: According to the joint data set, the inspection data of the k-th joint is extracted, where the k-th joint is formed by connecting the i-th parent material component and the l-th parent material component through the welding process; According to the component dataset and joint dataset, the strength of the i-th component is marked as qd i , mark the number of bolts of the i-th component as ls i , mark the number of welds of the i-th component as hf i , mark the strength of the lth component as qd l , mark the number of bolts of the lth component as ls l , mark the number of welds of the lth component as hf l , mark the strength of the kth joint as qd k , mark the number of cracks at the kth joint as lw k , mark the corrosion area of ​​the kth joint as xm k ; In the formula, β1 represents the weight of the strength ratio of the k-th joint and the i-th parent member, β2 represents the weight of the strength ratio of the k-th joint and the l-th parent member, β3 represents the weight of the total number of bolts of the i-th parent member and the l-th parent member, β4 represents the weight of the total number of welds of the i-th parent member and the l-th parent member, 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, and β1+β2+β3+β4+β5+β6=1. Indicates that the stability coefficient Wdx of the kth joint is calculated according to the weights of β1, β2, β3, β4, β5 and β6 k .

7. The steel structure connection risk assessment system based on industrial data analysis according to claim 6, characterized in that: The calculation process of the attenuation coefficient Sjx is as follows: Sjx=ω1(maxWD t -minWD t )+ω2SD t +ω3ZF t +ω4ZP t +ω5YH t +ω6FS t In the formula, maxWD t -minWD t represents the range of ambient temperature within the monitoring period Z, ω1 represents the weight for the range of ambient temperature, ω2 represents the weight for the ambient humidity, ω3 represents the weight for the ambient amplitude, ω4 represents the weight for the ambient frequency, ω5 represents the weight for the ambient oxidizing gas concentration, and ω6 represents the weight for the 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 It indicates that the attenuation coefficient of the steel structure performance is calculated according to the weights of ω1, ω2, ω3, ω4, ω5 and ω6.

8. The steel structure connection risk assessment system based on industrial data analysis according to claim 7, characterized in that: When the performance index Xnzs is lower than the index threshold ZSY, it indicates that the assembly performance of the component is poor and there is a potential danger, and it is recommended to replace the component in time.

9. The steel structure connection risk assessment system based on industrial data analysis according to claim 8, characterized in that: 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 are hidden dangers. 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 are hidden dangers. It is recommended to spray the steel structure surface in time.

10. A steel structure connection risk assessment method based on industrial data analysis, applied to a steel structure connection risk assessment system based on industrial data analysis as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Connect the database, detection devices, and sensor devices through the network to obtain management data of all steel structure components, inspection data of all joints, and sensor data of all environmental factors, and classify them into component data sets, joint data sets, and environmental data sets; Step 2: Analyze the assembly performance of each component based on the component data set and generate the corresponding performance index Xnzs; Step 3: Based on the component dataset and joint dataset, analyze the safety of each joint and generate the corresponding stability coefficient Wdx; Step 4: Set a fixed monitoring period Z, and then combine it with the environmental data set to analyze the erosion effect of environmental factors on the performance of the steel structure and generate the corresponding attenuation coefficient Sjx; Step 5: Set the fixed range index threshold ZSY, stability threshold WDY, and attenuation threshold SJY, and then combine the performance index Xnzs, stability coefficient Wdx, and attenuation coefficient Sjx to assess whether the steel structure has hidden dangers and output corresponding management suggestions.

Images